UK factories staged a recovery in October after the reopening of Jaguar Land Rover operations and a pick-up in consumer spending, according to a closely watched survey of the manufacturing sector.
The S&P Global purchasing managers’ index (PMI) rose to a one-year high as business optimism improved and factory output expanded.
Jaguar Land Rover, Britain’s biggest carmaker, supported the recovery after it began reopening facilities hit by a cyber-attack that some experts estimated cost the UK economy about £1.9bn.
Manufacturers were able to shake off some of the uncertainty from Donald Trump’s tariffs. Consumers also increased spending on new cars, improving the outlook for the makers of vital industrial components.
S&P Global said the PMI rose to 49.7 in October from 46.2 in September, where a figure above 50 indicates expansion. A sub-index measuring factory output jumped sharply to 51.6 from 45.7 in September, signalling a return to growth.
Martin Beck, the chief economist at the consultants WPI Strategy, said there were reasons to be optimistic about a recovery gathering pace.
“Rising real wages should underpin domestic demand for goods, while government incentives for green technologies and battery production could boost investment,” he added.
“The recent depreciation of sterling against the dollar and euro also improves UK export competitiveness. And the government’s decision to increase the discount on electricity network charges for energy-intensive industries offers some relief on costs.”
However, Mike Thornton, the head of industrials at the accountants RSM UK, said: “While the uptick in manufacturing activity in October shows a reverse on the downward trend seen in August and September, only time will tell if this is a temporary rebound in output rather than a sustained recovery.
“Following Jaguar Land Rover’s phased production restart in October, it’s likely that this has created a ripple effect throughout the supply chain, particularly as the shutdown impacted more than 5,000 middle-market businesses.”
The UK’s manufacturing sector has suffered a succession of blows since the Covid pandemic. Industry bodies have complained that a steep rise in gas and electricity costs, in addition to rising wages and higher employment taxes, have crippled many businesses.
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The British Chambers of Commerce, the CBI and Make UK, the manufacturing lobby group, have called on the chancellor to give extra support to the manufacturing sector in the budget later this month.
Rob Dobson, a director at S&P Global Market Intelligence, said: “There are concerns the forthcoming budget will exacerbate the lingering challenges created by last year’s budget, especially in relation the impact of national minimum wage and employer national insurance on costs, demand and production.
“This means that business optimism remains below its long-run average despite rising to an eight-month high in October.
“Manufacturers seem to be stuck in a holding pattern until the domestic policy and geopolitical backdrops exhibit greater clarity.”
The biological characteristics of Bambusa oldhamii Munro
Clumping bamboo has sympodial rhizomes that are characterized by their rhizomes growing outwards from buds on a “mother” rhizome, eventually turning upwards to become new culms (Fig. 1a). The culm height of B. oldhamii ranges from 6 to 12 m, the culm diameter ranges from 3 to 9 cm, depending on the growth environment, the internode length ranges from 20 to 35 cm, and the wall thickness ranges from 4 to 12 mm, depending on growth conditions (Fig. 1b). There are multiple branches in each node of the middle and top culms in B. oldhamii, about 5 leaves in each little branch, and the leaf length ranges from 10–20 cm, and the leaf width ranges from 1.0 to 1.5 cm (Fig. 1c). The flowering period happens between May to November and includes bracts (3–5) and florets (5–9) (Fig. 1d). The shooting period is from May to November, the young edible shoots are sold as fresh vegetables on the market or marked as processed food, and the sheaths of the shoots are deciduous, leathery, dark brown spinous-hairy (Fig. 1e-g). The shoot of B. oldhamii had their best taste and maximum edible portion when they had not erupted out of the soil surface (Fig. 1h-j). When the shoot of B. oldhamii erupted out of the soil surface, the above-ground part of the shoot sheaths would be green with the sunlight and days (Fig. 1k). The old inedible shoots would be degraded or grown into young bamboo culms and then form a new bamboo (Fig. 1l).
Fig. 1
The biological phenotype and genome characteristics of B. oldhamii. a A clumping forest of B. oldhamii; b A culm internode of B. oldhamii; c A leaf branch of B. oldhamii; d The florets of B. oldhamii; e A edible shoot of B. oldhamii; f The shoots of B. oldhamii with shells off; g The off shells of a bamboo shoot; h Phenotype of the shoots under soil surface; i The phenotype of the edible shoot usually sold on market. j The longitudinal section of the edible shoot is usually sold on the market. k–l The shoot has erupted out of the soil surface within several days. m The genome circular graph of B. oldhamii from the inner to outer track represents the density of tRNA, snRNA, rRNA, miRNA, and Genes; the last outer track represents the length of chromosomes in different colors
To comprehend this bamboo species more deeply, the genetic genome information of B. oldhamii was explored. Compared with the internal standard tomato with a genome size of 800 Mb and a peak around 51.03 using flow cytometric fluorescent detection (Fig. S1a), the estimated genome size of B. oldhamii is 1.375 Gb with a peak around 87.77 (Fig. S1b). The Illumina sequencing method was used to estimate the genome size of B. oldhamii around 1.33 Gb based on the 27 K-mer length (Fig. S2). Based on the above genome size estimation results, we processed the whole nuclear genome sequence with PacBio HiFi (40X) and HiC (100X) sequencing projects. After the PacBio HiFi and Hi-C sequencing of B. oldhamii, about 97.23% of contig sequences were anchored on the pseudomolecule chromosomes, and a total of 35 pseudochromosomes were acquired based on synteny analysis with the relative species D. latiflorus. The final Pseudochromosome genome was evaluated with the Chromosome Hi-C signal heatmap, corresponding to the Hi-C assisted genome assembly thesis (Fig. S3), and the final genome BUSCO evaluation is 96.59%.
The final assembled genome size of B. oldhamii is 1.446 Gb, the N50 of all the 1851 scaffolds is 38,936,033 bp, and the GC contents are 45% (Table 1). After the gene structure annotation, a total of 88,140 genes were acquired. All 88,140 genes were annotated in Nr, Swissprot, KEGG, KOG, TrEMBL, Interpro, and GO databases with rates of 76.87%, 50.46%, 49.24%, 47.25%, 76.70%, 62.48%, and 55.74% respectively. Finally, about 78.11% (68,843) of genes acquired the functional annotations (Table 1) (Table S1). The noncoding RNA, including miRNA, tRNA, rRNA, and snRNA, was annotated at the rate of 0.003297%, 0.043679%, 0.356499%, and 0.008666% respectively, on the whole genome (Fig. 1m) (Table S2). In the genome annotation of B. oldhamii, a total of 779,839,625 bp repeat genome sequences were acquired and occupied 53.89% of the genome size (Table S3). Based on the transcription factor annotation, the large number of transcription factor families is bHLH, NAC, MYB, bZIP, C2H2, AP2/ERF-ERF, and AP2/ERF-AP2 (Table S4).
Table 1 Statistics of the assembly genome features and gene annotation in B. oldhamii
The basic genome analysis of B. oldhamii Munro
Through the identification and analysis of genome homologous genes and families can obtain single-copy gene families and multi-copy gene families. We statistics the families and genes in the following species: A. thaliana, A. trichopoda, B. oldhamii (subgenomes A, B, and C), D. latiflorus (subgenomes A, B, and C), O. latifolia, O. sativa, P. edulis (subgenomes C and D), P. trichocarpa, and R. guianensis (Table S5). The results of orthologs and family statistics showed that A. trichopoda has the greatest number of Single-copy orthologs, P. trichocarpa has the greatest number of Multi-copy orthologs, and O. latifolia has the most Unique paralogs (Fig. S4a) (Table S5). 5522 ortholog families exist in both species of B. oldhamii (subgenomes A, B, and C) and O. sativa (Fig. S4b), and 8250 ortholog families that exist in the species of B. oldhamii_A, P. edulis_C, D. latiflorus_A, and O.latifolia (Fig. S4c). As the Phylogenetic tree analysis results show that the species of A. trichopoda is the outer taxa, the other species in the phylogenetic tree are consistent with the phylogenetic positions of the APG IV system. A. thaliana and P. trichocarpa branched together, and the others belong to Poaceae including the bamboos and rice, moreover, the A sub-genome of D. latiflorus_A and B. oldhamii_A branched together, the B sub-genome of D. latiflorus_B and B. oldhamii_B branched together, and the C sub-genome of D. latiflorus_C and B. oldhamii_C branched together (Fig. S5).
Based on the time tree criterion (http://www.timetree.org/), the divergence time between O. sativa and A. thaliana was 130–200 million years ago (Mya), and between P. trichoparca and A. thaliana was 100–120 Mya. As the result shows that the divergence time between O. latifolia and R. guianensis is 47.5 (26.3—69.8) Mya, and the divergence time between O. sativa and bamboos is 100.7 (76.1–126.1) Mya (Fig. 2a). The results of 4DTv analysis showed that the last whole genome duplication of B. oldhamii_A, B. oldhamii_B, and B. oldhamii_C happened after the first divergence of O. sativa. (Fig. 2b). We do the gene family expansion and contraction between genomes and subgenomes (Fig. 2c) (Table S6). The results showed that A. thaliana has the most expansion gene families, and P. trichocarpa has the most contraction gene families, and then is the plant species A. trichopoda (Fig. 2c). The genes in the collinearity fragment have maintained a high degree of conservation throughout species evolution. The genome synteny results showed that there are more blocks between O. sativa and B. oldhamii than between B. oldhamii and D. latiflorus, but the mean block length was larger in B.oldhamii and D. latiflorus than in B. oldhamii and O.sativa (Fig. 2d-f) (Table S7).
Fig. 2
The phenotype and genome characteristics of B. oldhamii. a The estimation of divergence time among species. The number in blue color on the branches means the estimated divergence time (Mya), and the red nodes are tagged as the criteria divergence time (Mya). b The distribution of 4DTv distance (corrected for multiple substitutions). c The gene family expansion and contraction between A. thaliana, A. trichopoda, B. oldhamii (subgenomes A, B, and C), D. latiflorus (subgenomes A, B, and C), O.latifolia, O.sativa, P.edulis (subgenomes C and D), P. trichocarpa, and R. guianensis. d The synteny analysis between O. sativa, B. oldhamii_A, and D. latiflorus_A. e The synteny analysis between O.sativa, B.oldhamii_B, and D.latiflorus_B. (f) The synteny analysis between O. sativa, B. oldhamii_C, and D. latiflorus_C
We acquired the expansion and contraction genes in the three subgenomes of B. oldhamii, in B. oldhamii_A, there were 160 contraction genes and 888 expansion genes (Table S8), and the expansion genes were enriched in the pathway of Plant-pathogen interaction, sulfur relay system, homologous recombination, and valine leucine and isoleucine degradation and others (Fig. S6) (Table S9). In B. oldhamii_B, there are 163 contraction genes belong to 128 families and 669 expansion genes belong to 99 families (Table S10), and the expansion genes were enriched in the pathway of alpha-Linolenic acid metabolism, homologous recombination, glycerophospholipid metabolism, arginine biosynthesis, sesquiterpenoid and triterpenoid biosynthesis, diterpenoid biosynthesis, flavonoid biosynthesis and others (Fig. S7) (Table S11). In B. oldhamii_C, there are 167 contraction genes belonging to 182 families, and 372 expansion genes belong to 46 families (Table S12), and the expansion was enriched in the pathway of homologous recombination and basal transcription factors (Fig. S8) (Table S13).
Data mining of transcriptome and metabolome in multiple shoot developmental phases
Seven developed phases of the shoot in B. oldhamii were picked out searching for the bitter chemicals and key genes contributing to the taste transition from delicious taste to a little bitter taste with transcriptome and metabolome (Fig. 3a). DNBSEQ platforms were used to sequence the 21 shoot samples (7 phases with three biological replicates in each phase). The transcriptome data of each sample is about 6.45 Gb, the average mapping rate of the genome is 91.49%, and the average mapping rate of the gene sets is 65.69%. Based on the reference genome annotation, 88,140 genes were Known Genes, and 3,228 Novel Genes from the RNA-seq data (Table S14). A total of 53,252 genes were detected as expressed genes, 50,129 were known genes (49,365 DEG and 764 Non DEG), and 3,123 genes were novel (3121 DEG and 2 Non DEG) (Fig. 3b) (Table S15). A total of 42,887 new transcripts were detected, 39,659 new alternative splice isoforms distributed in 20,786 known protein-coding genes, and 3,228 were novel gene coding transcripts (Table S16). The FPKM distribution of the transcription factor families in Known Genes was analyzed (Fig. 3c). The results showed that the families of bHLH, MYB-related, bZIP, C3H, HB-, C2H2-, and TUB transcription factors might play crucial roles in shoot growth and development. There are large number of those transcription factors whose expressions were activated in certain shoot developing phases (FPKM > 10).
Fig. 3
Basic data mining of transcriptome and metabolome in multiple shoots developing phases of B. oldhamii. a The sectional shoot morphology of seven developing phases; b The FPKM distribution of expressed Known genes and Novel genes in the transcriptome of seven developing phases. c The FPKM distribution of transcription factors in the genome’s known genes is based on the transcriptome expression data. d The classification of all metabolites detected in the shoot of seven developing phases; e The classification of mined bitter metabolites and relative accumulated patterns in seven developing bamboo shoots of B. oldhamii
A total of 428 qualified metabolites were identified (Table S17). The class of phytochemical compounds includes 37 flavonoids, 29 terpenoids, and others (Fig. 3d). The class of compounds with biological roles includes 31 amino acids, peptides, and analogs, 16 benzene and derivatives, 11 carbohydrates, 11 purines and derivatives, 10 organic acids, and others (Fig. 3d). The class of Lipids includes 15 polyketides, 10 Fatty acyls, and others (Fig. 3d). In order to acquire the bitter metabolites, each metabolite was checked against bitterDB. Finally, 33 bitter metabolites were acquired in this research, major distributed in the families of Amino acids metabolites (L-Isoleucine, L-Leucine, L-Tryptophan, and L-Tyrosine), Flavonoids (Naringenin, Genistein, Genistin, Daidzin, Apigenin, Isorhamnetin, Kaempferol, and Hesperetin), Purines and derivatives (Caffeine, Pentoxifylline, Theobromine, and Adenosine), Terpenoids (Limonin, Andrographolide, Ginkgolide A, L-MENTHONE, Oleuropein, Nerol, Stevioside, Obacunone, Gentiopicrin) and others (Dicumarol, Arbutin, Amarogentin, and others) (Fig. 3e). The taste transition is complex; bitter taste is usually accompanied by astringent taste generation. Tannin is a criterion to measure astringent taste. In the correlation analysis, we added tannin as a measurement criterion for mining the accompanied bitter metabolites. The bitter metabolites positively correlated with the content of tannins were Flavonoids (Genistin, Daidzin, Kaempferol), Terpenoids (Oleuropein, Stevioside, Obacunone), Purine and derivatives (Theobromine), Coumarin and derivatives (Dicumarol), Benzene and derivatives (Salicylic acid), Carbohydrate (Arbutin), and Amarogentin. The negative correlation between bitter metabolites with tannin content was amino acids (L-Isoleucine, L-Leucine, L-Tyrosine), Terpenoids (L-menthone, Nerol, Gentiopicrin), and Sinapic acid (Fig. S9).
The differentially accumulated metabolites between phases were analyzed (Table S18). In the comparison of BoS2 vs. BoS1 (81 DAMs, 46 up DAMs, and 35 down DAMs), all the DAMs were significantly enriched in the pathway of flavone and flavonol biosynthesis, monoterpenoid biosynthesis, and tyrosine metabolism (Fig. S10a). In the comparison of BoS3 vs. BoS2 (49 DAMs, 25 up DAMs, and 24 down DAMs), all the DAMs were significantly enriched in the pathway of phenylalanine, tyrosine, and tryptophan biosynthesis (Fig. S10b). In the comparison of BoS4 vs. BoS3 (61 DAMs, 26 up DAMs, and 35 down DAMs), all the DAMs were significantly enriched in the pathway of Monoterpenoid biosynthesis, Metabolic pathways, and Biosynthesis of secondary metabolites (Fig. S10c). In the comparison of BoS5 vs. BoS4 (172 DAMs, 89 up DAMs, and 83 down DAMs), the total DAMs were significantly enriched in the pathway of aminoacyl-tRNA biosynthesis, metabolic pathway, biosynthesis of secondary metabolites, biosynthesis of amino acids, and phenylalanine metabolism (Fig. S10d). In the comparison of BoS6 vs. BoS5 (45 DAMs, 31 up DAMs, and 14 down DAMs), all the DAMs were enriched in the pathway of Stilbenoid, diarylheptanoid, and gingerol biosynthesis (Fig. S10e). In the comparison of BoS7 vs. BoS6 (23 DAMs, 14 up DAMs, and 9 down DAMs), all the DAMs were enriched in the pathway of flavone and flavonol biosynthesis (Fig. S10f). Finally, a total of 373 non-repeated differentially accumulated metabolites were acquired (Table S19).
Joint analysis between transcriptome and metabolome
From the combined analysis between metabolome and transcriptome, the DAMs and DEGs in each comparison were enriched with KEGG pathways. All the 81 DAMs and 858 DEGs in comparison of BoS2 vs. BoS1 enriched in the pathway of Biosynthesis of secondary metabolites, Glyoxylate and dicarboxylate metabolism, Glycolysis/Gluconeogenesis, and other pathways (Fig. S11) (Table S20). All the 49 DAMs and 439 DEGs in comparison of BoS3 vs. BoS2 enriched in the pathway of Biosynthesis of secondary metabolites, beta-alanine metabolism, Amino sugar and nucleotide sugar metabolism, Alanine, aspartate, and glutamate metabolism, Tryptophan metabolism, and other pathways (Fig. S12) (Table S21). All the 61 DAMs and 388 DEGs in comparison of BoS4 vs. BoS3 enriched in the pathway of Biosynthesis of secondary metabolites, Glutathione metabolism, Flavonoid biosynthesis, Plant hormone signal transduction, and other pathways (Fig. S13) (Table S22). All the 172 DAMs and 516 DEGs in the comparison of BoS5 vs. BoS4 enriched in the pathway of Biosynthesis of secondary metabolites, Isoflavonoid biosynthesis, Plant hormone signal transduction, and other pathways (Fig. S14) (Table S23). All the 45 DAMs and 337 DEGs in comparison of BoS6 vs. BoS5 enriched in the pathway of Biosynthesis of secondary metabolites, Pyrimidine metabolism, and other pathways (Fig. S15) (Table S24). All the 23 DAMs and 410 DEGs in comparison of BoS7 vs. BoS6 enriched in the pathway of Flavone and flavonol biosynthesis and Flavonoid biosynthesis (Fig. S16) (Table S25). From this joint comparison analysis, we find that the pathway of biosynthesis of secondary metabolites, amino acid metabolism, biosynthesis of flavonoid, flavone, flavonol and isoflavonoid, plant hormone signal transduction, and others play important roles in the flavor transition biological process.
The WGCNA analysis between the bitter metabolites and gene expression showed that the bitter metabolites, such as L-Isoleucine, L-Leucine, and L-Tyrosine, were positively correlated with the module of MEturquoise, while Salicylic acid, arbutin, dicumarol, genistin, stevioside, and amarogentin were negatively correlated with the module of MEturquoise (Fig. 4a). The correlated analysis between bitter metabolites and transcription factors showed that the family of bHLH and HB- (including HB-BELL, HB-HD-Zip, HB-KNOX, HB-WOX, and HB-others) has large genes correlated with bitter metabolites, indicating that bHLH and HB- transcription factor families might play pivotal roles in regulating the biosynthesis or metabolism of bitter metabolites (Fig. 4b). To mine out the key genes in bHLH transcription factors, we did the network analysis of the bHLH transcription factor existing in MEturquoise (weight > 0.3) and sorted with degree values, and the results showed that Bo00073447 (bHLH) was a hub gene in the bHLH Co-expression Network (Fig. 4c) (Table S26). To mine out the key genes in HB- transcription factors, we do the network analysis of HB- transcription factors existing in MEturquoise (weight > 0.3) and sorted by degree values, and we find some key genes, Bo00029783(HB-HD-ZIP), Bo00006584(HB-HD-ZIP), Bo00043187(HB-KNOX), and Bo00010600 (HB-WOX) play roles in the HB- Co-expression Network (Fig. 4d) (Table S27).
Fig. 4
Weighted correlation network analysis and gene co-expression network between bitter metabolites and transcription factor families. a Weighted correlation network analysis (WGCNA) between bitter metabolites and expressed genes (FPKM ≥ 5). b Chord Diagram of the correlated relationship between bitter metabolites and transcription factors. c Gene Co-expression Network of bHLH transcription factors existed in the MEturquois module (weight > 0.3). d Gene Co-expression Network of HB- transcription factors existed in the MEturquois module (weight > 0.3)
Model of bitter flavor formation in the shoot of B. oldhamii
From the above joint analysis between bitter metabolites and transcription factors, we found that the expression of bHLH and HB- transcription factors was relevant to the accumulation of bitter metabolites might play roles in the biosynthesis and metabolic pathways of bitter metabolites. In this research, combined with our previous research [15], the mined bitter chemicals were searched against the KEGG pathway database, the bitter metabolites related pathways in each class were simplified and merged with flavonoids, amino acids, terpenoids, purine, solanidine, and hydrogen cyanide pathways (Fig. S17). The flavonoid bitter metabolites (Naringenin, Genistein, Genistin, Daidzin, Apigenin, Kaempferol, and Hesperetin) and other researched metabolites (Salicylate, Salicin, Arbutin, Amygdalin [15], Sinapic acid) are merged in the flavonoid-related pathway originally synthesized from bitter amino acid L-Phenylalanine (Fig. S17a). The bitter amino acids (L-Isoleucine, L-Leucine, L-Tryptophan, and L-Tyrosine) and arginine [15] merged in the amino acid biosynthesis and metabolic pathways (Fig. S17b). The terpenoid metabolites (Limonin, Ginkgolide A, Oleuropein, Nerol, Stevioside, Menthone, and Gentiopicrin) were merged in the terpenoid pathway originating from terpenoid backbone biosynthesis (Fig. S17c). Purine bitter metabolites (Caffeine, Theobromine, and Adenosine) and hypoxanthine [15] were merged in the Purine metabolism pathway (Fig. S17d). Solanidine and Amygdalin were detected in our previous research [15] were merged in the abbreviated biosynthesis of various alkaloids pathway (Fig. S17e) and the abbreviated cyanoamino acid metabolism pathway (Fig. S17f). Amygdalin is a kind of cyanogenic glucoside, but the majority of academics regard taxiphyllin as the major cyanogenic glucoside that exists in the edible bamboo shoot tip [57] and is easily decomposed to hydrogen cyanide (HCN), which causes a bitter flavor. The bitter amino acids (phenylalanine, tyrosine, isoleucine, leucine, and valine) were substances for the biosynthesis of cyanogenic glucosides(Fig. S17f) in plants [12]. We consider that there are several kinds of cyanogenic glucosides that exist in the bamboo shoot, causing the bitter flavor transition, not just one kind of cyanogenic glucoside.
From the above analysis, we hypothesize a simple model about this research (Fig. 5a). In an appropriate environment, when the shoot of B. oldhamii grown out of the soil surface and accepted sunlight signals for days, the expression of bHLH and HB- transcription factors were activated or repressed which influence the genes’ expression in the related pathways (such as, Circadian rhythm; Plant hormone signal transduction; Phenylpropanoid biosynthesis; Flavonoid biosynthesis; Flavone and flavonol biosynthesis; and others) to determine the content accumulation variation of bitter metabolites (such as, flavonoids; amino acids; terpenoids; Purines and derivatives; Cyanogenic glycosides; and others), and finally causing the bitter flavor formation or transition in the tip shoot of B. oldhamii (Fig. 5a). There were exist diversity bamboo species in the world, and the bamboo forests have diverse usages, such as edible shoots collection, bamboo timber collection, city or road ornamental, germplasm preservation, soil improvement, nature reserve, and others. Multiple bamboo species and forests were used to produce edible shoots, which could be sold as fresh shoots or used to make processed food (Picked shoots, Salted shoots, Boiled shoots, Soused shoots, Canned shoots, Dried shoots) (Fig. 5b). The majority of bamboo forests were used for ecosystem balance and bamboo timber, such as bamboo weaving, bamboo charcoal, disposable tableware, bamboo fiber clothes, bamboo timber architecture, bamboo flooring, and bamboo crafts (Fig. 5c). This research was focused on the bamboo species of B. oldhamii, which is especially distributed in the southeast of China, and the shoots that emerged in summer are mainly used as an edible shoot locally. The hypothetical model about the bitter flavor formation or transition in the tip shoot is based on the Multi-omics data in B. oldhamii, which needs to be verified with deep research. Bamboo has a variety of species, and the shoot shape, color, taste, and properties of various bamboo species were different, and the edible portion or part was different depending on the bamboo species. Each bamboo species has its unique flavor and taste; the flavor formation or transition in the edible shoot of other bamboo species still needs further research.
Fig. 5
A hypothetical model of bitter metabolites formation, and the usages of edible bamboo shoot and timber in B. oldhamii. a The hypothetical model of bitter flavor metabolites formation in the shoot tip of B. oldhamii. b The traditional uses of edible bamboo shoots. c The traditional usages of bamboo timber
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Premier portfolio across the highest-return U.S. shale basins drives significant free cash flow and enhanced stockholder value
Pro forma second quarter of 2025 production totaled 526 MBoe/d
Pro forma full-year 2025 consensus free cash flow of more than $1.4 billion
Step-change in free cash flow supports sustained return of capital
Value-Driven Synergies
Proven management and a world-class technical team positioned to deliver identified and achievable annual synergies of approximately $200 million with upside potential
Synergies create potential for accelerated debt repayment and improved through-cycle returns
Value-Accretive Substance
Significant accretion on key per share financial metrics, before synergies
Free cash flow to be prioritized for debt reduction and sustainable quarterly fixed dividend of $0.20 per share
Committed to leading in sustainability and environmental stewardship while expanding our positive impact in the communities where we operate
Companies to host a live Q&A call today at 8:00 a.m. Mountain time/10:00 a.m. Eastern time
DENVER, Nov. 3, 2025 /PRNewswire/ — SM Energy Company (“SM Energy”) (NYSE: SM) and Civitas Resources, Inc. (“Civitas”) (NYSE: CIVI) today announced they have entered into a definitive merger agreement involving an all-stock transaction (the “Transaction”).
Under the terms of the Transaction, each common share of Civitas will be exchanged for 1.45 shares of SM Energy common stock. The combined company’s enterprise value of approximately $12.8 billion is inclusive of each company’s net debt.
The combined company will have a premier portfolio of approximately 823,000 net acres, with the Permian position being the cornerstone. Pro forma full-year 2025 consensus free cash flow generation of more than $1.4 billion enables sustained capital returns, and increased market capitalization enhances trading liquidity with broader investment appeal.
Transformational Combination Delivering Superior Value
Value-Enhancing Scale. The combined company will operate a premier asset portfolio consisting of approximately 823,000 net acres across the highest-return U.S. shale basins, immediately transformed into a top-10 U.S. independent oil-focused producer. We expect that this premier portfolio will deliver a step-change in free cash flow enabling sustained capital returns.
Synergy-Enhanced Free Cash Flow. Identified and achievable annual synergies totaling $200 million, with upside potential to $300 million, is expected to enhance stockholder value. Identified synergies include opportunities across the combined organization consisting of overhead and G&A, drilling and completion and operational costs, and cost of capital. These synergies are expected to accelerate deleveraging and support a sustainable returns strategy.
Proven Management. A trusted leadership team, supported by a combined world-class technical team, equipped with the processes and infrastructure to deliver a successful integration.
Significant Accretion on Key Financial Per Share Metrics, Before Synergies. The combination is expected to be immediately accretive to key per share financial metrics, including operating cash flow, debt-adjusted cash flow, free cash flow, and net asset value.
Financial Discipline. Free cash flow will be prioritized for debt reduction with path to 1.0x net leverage by YE 2027 at $65/Bbl WTI and $3.50/MMBtu Henry Hub with substantial liquidity and an improved credit profile.
Sustainable Quarterly Fixed Dividend Maintained at $0.20/Share. The combined company will deliver sustainable dividends, a program that SM Energy has grown on a per share basis by 33% since the program was introduced in 2022.
Advancing Our Collective Commitment to Sustainability and Stewardship. The combined company will uphold its long-standing focus on responsible operations, safety, and environmental excellence, while integrating best practices.
SM Energy Chief Executive Officer Herb Vogel comments: “This strategic combination creates a leading oil and gas company with enhanced scale, numerous value-adding synergies, and significant free cash flow, driving superior value to stockholders. Congratulations to the Civitas team on building a leading sustainable energy company in the Permian and DJ basins since its inception in 2021. Their operational excellence and talent are reflected in today’s transaction. Together, we look forward to unlocking stockholder value as a unified organization.”
SM Energy President and Chief Operating Officer Beth McDonald comments: “This merger combines two premier operators and establishes a company with transformative scale in the highest-return U.S. shale basins. By combining two complementary portfolios, we expect to unlock significant free cash flow to strengthen our balance sheet, accelerate stockholder returns, and position us for sustainable growth through every cycle.”
Civitas Interim Chief Executive Officer Wouter van Kempen comments: “Today marks a pivotal moment for Civitas and SM Energy as we announce a merger that unlocks new potential to deliver enhanced stockholder value and achieve outcomes beyond the reach of either company alone. By combining our strong technical teams and complementary assets, we gain scale, sharpen our competitive edge, and strengthen our ability to responsibly produce energy that contributes to energy security and prosperity. This merger positions us to lead with operational and environmental excellence, generate meaningful synergies, and accelerate value creation.”
“This transformative transaction will immediately create a leading independent E&P company, with a strong asset position across the premium oil oriented basins in the U.S.,” said Ben Dell from Kimmeridge. “The step-change in scale coupled with identified operational synergies should enhance long-term value to all shareholders for years to come.”
TRANSACTION DETAILS
Under the terms of the agreement, Civitas stockholders will receive 1.45 shares of SM Energy common stock at closing. After closing, the company will continue to trade as SM Energy (NYSE: SM). Upon completion of the Transaction, SM Energy stockholders will own approximately 48% of the combined company and Civitas stockholders will own approximately 52% on a fully diluted basis. At this exchange ratio, and the respective companies’ closing share prices on October 31, 2025, inclusive of net debt, the combined company would have an enterprise value of approximately $12.8 billion. SM Energy will issue approximately 126.3 million shares of common stock as consideration to the holders of Civitas common shares in accordance with the terms of the merger agreement.
GOVERNANCE AND LEADERSHIP
Following the merger, the Board of Directors will total 11 members and will be comprised of 6 representatives from SM Energy and 5 representatives from Civitas. Julio Quintana will serve as Non-Executive Chairman. The combined company will be headquartered in Denver, Colorado.
Herb Vogel will serve as Chief Executive Officer of the combined company, and the previously announced expected CEO transition to Beth McDonald remains on-track.
TIMING AND APPROVALS
The combination has been unanimously approved by the boards of directors of both companies. The Transaction is expected to close in the first quarter of 2026. The Transaction is subject to customary closing conditions, including approvals by SM Energy and Civitas stockholders and regulatory clearances.
ADVISORS
Evercore is serving as financial advisor and Gibson, Dunn & Crutcher LLP as legal advisor to SM Energy.
J.P. Morgan is serving as financial advisor and Kirkland & Ellis LLP as legal advisor to Civitas Resources.
CONFERENCE CALL AND ADDITIONAL MATERIALS
November 3, 2025 – Please join SM Energy and Civitas management at 8:00 a.m. Mountain time/10:00 a.m. Eastern time today for a joint conference call to discuss the Transaction.
The discussion will be accessible via:
An investor presentation regarding the Transaction can also be found at www.sm-energy.com and www.civitasresources.com.
SM Energy’s third quarter 2025 earnings pre-recorded webcast originally scheduled for November 4, 2025, and the live Q&A session originally scheduled for November 5, 2025, have been cancelled and replaced with today’s joint conference call.
ABOUT SM ENERGY
SM Energy Company is an independent energy company engaged in the acquisition, exploration, development, and production of crude oil, natural gas, and NGLs in the states of Texas and Utah. SM Energy routinely posts important information about the Company on its website. For more information about SM Energy, please visit its website at www.sm-energy.com.
ABOUT CIVITAS
Civitas Resources, Inc. is an independent exploration and production company focused on the acquisition, development, and production of crude oil and liquids-rich natural gas from its premier assets in the Permian Basin in Texas and New Mexico and the DJ Basin in Colorado. Civitas’ proven business model to maximize shareholder returns is focused on four key strategic pillars: generating significant free cash flow, maintaining a premier balance sheet, returning capital to shareholders, and demonstrating ESG leadership. For more information about Civitas, please visit www.civitasresources.com.
NOTICE REGARDING INFORMATION CONTAINED IN THIS RELEASE
FORWARD LOOKING STATEMENTS
This press release contains “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended. All statements, other than statements of historical fact, included in this press release that address events, or developments that SM Energy and Civitas expect, believe, or anticipate will or may occur in the future are forward-looking statements. The words “intend,” “expect,” and similar expressions are intended to identify forward-looking statements. Forward-looking statements in this press release include, but are not limited to, statements regarding the Transaction, pro forma descriptions of the combined company and its operations, integration and transition plans, synergies, opportunities and anticipated future performance. There are a number of risks and uncertainties that could cause actual results to differ materially from the forward-looking statements included in this communication. These include the expected timing and likelihood of completion of the Transaction, including the timing, receipt and terms and conditions of any required governmental and regulatory approvals of the Transaction that could reduce anticipated benefits or cause the parties to abandon the Transaction, the ability to successfully integrate the businesses, the occurrence of any event, change or other circumstances that could give rise to the termination of the merger agreement, the possibility that stockholders of SM Energy or Civitas may not approve the Transaction, the risk that the parties may not be able to satisfy the conditions to the Transaction in a timely manner or at all, risks related to disruption of management time from ongoing business operations due to the Transaction, the risk that any announcements relating to the Transaction could have adverse effects on the market price of SM Energy’s common stock or Civitas common stock, the risk that the Transaction and its announcement could have an adverse effect on the ability of SM Energy and Civitas to retain customers and retain and hire key personnel and maintain relationships with their suppliers and customers and on their operating results and businesses generally, the risk the pending Transaction could distract management of both entities and they will incur substantial costs, the risk that problems may arise in successfully integrating the businesses of the companies, which may result in the combined company not operating as effectively and efficiently as expected, the risk that the combined company may be unable to achieve synergies or it may take longer than expected to achieve those synergies and other important factors that could cause actual results to differ materially from those projected. All such factors are difficult to predict and are beyond SM Energy’s or Civitas’ control, including those detailed in SM Energy’s annual reports on Form 10-K, quarterly reports on Form 10-Q and current reports on Form 8-K that are available on its website at www.sm-energy.com/investors and on the SEC’s website at www.sec.gov, and those detailed in Civitas’ annual reports on Form 10-K, quarterly reports on Form 10-Q and current reports on Form 8-K that are available on Civitas’ website at ir.civitasresources.com/investor-relations and on the SEC’s website at www.sec.gov. All forward-looking statements are based on assumptions that SM Energy or Civitas believe to be reasonable but that may not prove to be accurate. Such forward-looking statements are based on assumptions and analyses made by SM Energy and Civitas in light of their perceptions of current conditions, expected future developments, and other factors that SM Energy and Civitas believe are appropriate under the circumstances. These statements are subject to a number of known and unknown risks and uncertainties. Forward-looking statements are not guarantees of future performance and actual events may be materially different from those expressed or implied in the forward-looking statements. The forward-looking statements in this press release speak as of the date of this press release.
SM ENERGY INVESTOR CONTACT
Patrick Lytle, [email protected], 303-864-2502
CIVITAS INVESTOR CONTACT
Brad Whitmarsh, [email protected], 832-736-8909
NO OFFER OR SOLICITATION
This communication is for informational purposes only and is not intended to, and shall not, constitute an offer to buy or sell or the solicitation of an offer to buy or sell any securities, or a solicitation of any vote or approval, nor shall there be any sale of securities in any jurisdiction in which such offer, solicitation or sale would be unlawful prior to registration or qualification under the securities laws of any such jurisdiction. No offering of securities shall be made, except by means of a prospectus meeting the requirements of Section 10 of the Securities Act of 1933, as amended.
ADDITIONAL INFORMATION AND WHERE TO FIND IT
In connection with the proposed Transaction, SM Energy intends to file with the SEC a registration statement on Form S-4 (the “Registration Statement”) that will include a joint proxy statement of SM Energy and Civitas and a prospectus of SM Energy (the “Joint Proxy Statement/Prospectus”). Each of SM Energy and Civitas may also file other relevant documents with the SEC regarding the proposed Transaction. This communication is not a substitute for the Joint Proxy Statement/Prospectus or Registration Statement or any other document that SM Energy or Civitas, as applicable, may file with the SEC in connection with the proposed Transaction. After the Registration Statement has been declared effective by the SEC, a definitive Joint Proxy Statement/Prospectus will be mailed to the stockholders of each of SM Energy and Civitas. BEFORE MAKING ANY VOTING OR INVESTMENT DECISION, INVESTORS AND SECURITY HOLDERS OF SM ENERGY AND CIVITAS ARE URGED TO READ THE REGISTRATION STATEMENT, THE JOINT PROXY STATEMENT/PROSPECTUS AND ANY OTHER RELEVANT DOCUMENTS THAT MAY BE FILED WITH THE SEC, AS WELL AS ANY AMENDMENTS OR SUPPLEMENTS TO THESE DOCUMENTS, CAREFULLY AND IN THEIR ENTIRETY IF AND WHEN THEY BECOME AVAILABLE BECAUSE THEY CONTAIN OR WILL CONTAIN IMPORTANT INFORMATION ABOUT SM ENERGY, CIVITAS, THE PROPOSED TRANSACTION AND RELATED MATTERS. Investors and security holders will be able to obtain free copies of the Registration Statement and the Joint Proxy Statement/Prospectus, as well as other filings containing important information about SM Energy, Civitas and the proposed Transaction, once such documents are filed with the SEC through the website maintained by the SEC at www.sec.gov. Copies of the documents filed with the SEC by SM Energy will be available free of charge on SM Energy’s website at www.sm-energy.com/investors. Copies of the documents filed with the SEC by Civitas will be available free of charge on Civitas’ website at ir.civitasresources.com/investor-relations. The information included on, or accessible through, SM Energy’s or Civitas’ website is not incorporated by reference into this communication.
PARTICIPANTS IN THE SOLICITATION
SM Energy, Civitas and certain of their respective directors and executive officers may be deemed to be participants in the solicitation of proxies in respect of the proposed Transaction. Information about the directors and executive officers of SM Energy, including a description of their direct or indirect interests, by security holdings or otherwise, is set forth in SM Energy’s proxy statement for its 2025 Annual Meeting of Stockholders, which was filed with the SEC on April 7, 2025 (and which is available at www.sec.gov/Archives/edgar/data/893538/000089353825000032/sm-20250404.htm) and a Form 8-K filed by SM Energy on September 8, 2025 (and which is available at www.sec.gov/Archives/edgar/data/893538/000089353825000116/sm-20250904.htm). Information about the directors and executive officers of Civitas, including a description of their direct or indirect interests, by security holdings or otherwise, is set forth in a Form 8-K filed by Civitas on August 6, 2025 (and which is available at www.sec.gov/Archives/edgar/data/1509589/000110465925074774/tm2522747d1_8k.htm), a Form 8-K filed by Civitas on May 7, 2025 (and which is available at www.sec.gov/Archives/edgar/data/1509589/000110465925045550/tm2514090d1_8k.htm), and Civitas’ proxy statement for its 2025 Annual Meeting of Stockholders, which was filed with the SEC on April 21, 2025 (and which is available at www.sec.gov/Archives/edgar/data/1509589/000155837025005077/civi-20241231xdef14a.htm). Other information regarding the participants in the proxy solicitations and a description of their direct and indirect interests, by security holdings or otherwise, will be contained in the Joint Proxy Statement/Prospectus and other relevant materials to be filed with the SEC regarding the proposed Transaction when such materials become available. Investors should read the Joint Proxy Statement/Prospectus carefully when it becomes available before making any voting or investment decisions. You may obtain free copies of these documents from SM Energy and Civitas using the sources indicated above.
Fastmarkets’ MB-MNO-0001 manganese ore high grade index tracks the spot prices of high-grade manganese ore in the CIF China market, with manganese content and other chemical specifications set to match the prevailing brands.
Fastmarkets’ MB-MNO-0005 manganese ore high grade port index tracks the spot prices of high-grade manganese ore in the FOT China market, with manganese content and other chemical specifications set to match the prevailing brands.
After a period of observation of spot market trends and developments, Fastmarkets has gathered sufficient data and background to recalibrate the base brands to reflect the higher liquidity and diversity of material in these markets.
This adjustment has been kept within the current parameters of the methodology, using the same chemical specifications, to maintain stability of the indices.
This adjustment in base brands has been applied from November 1. This change was previously announced on October 13.
Disclaimer: Fastmarkets does not endorse or vet base brands as superior to others. They are used solely as representative anchors for normalization purposes. Fastmarkets reserves the right to amend the base brand(s) and index specifications in response to fundamental changes in products and market behavior.
Fastmarkets specifications define only chemical and physical properties. Soft factors and base brands are not disclosed.
To provide feedback on this decision on the manganese ore high grade indices, please send feedback to pricing@fastmarkets.com and ores_alloys@fastmarkets.com. Please add the subject heading “FAO: Paul Lim/Janie Davies, re: manganese ore high grade indices.”
Please indicate if comments are confidential. Fastmarkets will consider all comments received and will make comments not marked as confidential available upon request.
To see all Fastmarkets pricing methodology and specification documents, go to the Fastmarkets methodology page.
Coronary heart disease is the leading cause of death worldwide, accounting for approximately 30% of all deaths.1 Percutaneous coronary intervention (PCI) can directly implant stents in the most severely narrowed coronary arteries to improve the degree of coronary artery stenosis. It has the characteristics of rapidly restoring myocardial blood and oxygen supply and thereby improving the clinical symptoms of patients, and has become the most effective means of treating coronary heart disease.2,3 However, in-stent restenosis (ISR) reduces the overall effectiveness of PCI. Although the incidence of ISR in bare-metal stents is 30% at 6 months, and that in drug-eluting stents (DES) drops to 7% at 4 years,4 ISR remains the main cause of long-term failure after PCI.5 A 10-year data from a DES randomized trial showed that ISR led to a target lesion revascularization rate of approximately 20% at 10 years.6 A study by Seiler et al demonstrated that at 1-year follow-up, the MACE rate after ISR treatment was 19.7%; 15.4% of patients experienced TVF (target vessel failure), with myocardial infarction (MI) and stent thrombosis occurring in 5.9% and 2.1% of patients, respectively.7 Another study has demonstrated that the rate of target vessel revascularization due to in-stent restenosis three years after drug-eluting stent implantation reached 5.2%.8 Therefore, early identification and control of related risk factors are key approaches to reducing the incidence of ISR and improving patient prognosis.
Fractional Flow Reserve (FFR) is an invasive diagnostic technique widely regarded as the “gold standard” for assessing the hemodynamic significance of coronary artery lesions. It effectively quantifies the extent of myocardial ischemia, offers clinical guidance on the necessity of stent implantation during PCI, and aids in predicting patient outcomes following PCI.9 Previous studies have shown that FFR has certain value in the diagnosis and prognosis assessment of ISR.10 However, its invasive nature, high cost, large radiation dose and adverse reactions have limited its clinical application. Therefore, a non-invasive CT-based fractional flow reserve (CT-FFR) has emerged. CT-FFR is obtained through computational fluid dynamics (CFD) simulation,11 and in recent years, CT-FFR software based on machine learning (ML) algorithms has gradually matured.12 Existing studies have demonstrated that CT-FFR has a good correlation with invasive FFR in identifying ISR (OR = 0.84).13
Inflammation plays a significant role in the occurrence and development of ISR. Pericoronary adipose tissue (PCAT) refers to the adipose tissue that surrounds the coronary arteries and exhibits a significant bidirectional interaction with the vascular wall.14 Fat attenuation index (FAI), a novel and non-invasive imaging biomarker based on coronary computed tomography angiography (CCTA), visualizes and quantifies the inflammation around coronary arteries by mapping the attenuation gradient of PCAT and tracking the changes in the size of local adipocytes and lipid content around coronary arteries.15 Previous studies have shown that FAI is associated with high-risk and vulnerable plaques and has high diagnostic value for coronary artery stenosis and myocardial ischemia, and is significantly superior to CCTA alone.16–18 Further studies have confirmed that FAI is significantly associated with adverse cardiac events and ISR, and has high predictive value.19,20 However, the predictive value of CT-FFR combined with FAI based on deep learning for ISR after PCI and its prognostic assessment have not been systematically verified.
To explore how the fractional flow reserve and fat attenuation index around the stent affect ISR, this study proposes the following verifiable hypothesis: The CT-FFR and FAI values derived from the CCTA, are correlated with the occurrence of ISR. Therefore, this study aims to investigate the correlation between CT-FFR and FAI values derived from the CCTA deep learning method and ISR following percutaneous coronary intervention, as well as to evaluate their predictive value for ISR by integrating the functional and inflammatory parameters around the stent.
Materials and Methods
Patient Population
This was a single-center retrospective study. A retrospective collection was made of patients with coronary heart disease who underwent coronary stent implantation at Linyi Central Hospital from January 2019 to December 2024 and were readmitted for treatment due to recurrent angina pectoris and other symptoms. All patients underwent coronary CT angiography (CCTA) and invasive coronary angiography (CAG) simultaneously. Inclusion criteria were: (1) Patients who underwent both CCTA and CAG after coronary stent implantation, with an interval of ≥ 90 days, The interval between CCTA and CAG is less than two weeks; (2) Good image quality, suitable for image analysis and measurement of FAI and CT-FFR values. Exclusion criteria were: (1) Patients with incomplete clinical data; (2) Patients who did not undergo CAG or the time interval between CAG and CCTA < 90 days; (3) Patients with stents located only in the left main trunk or branches, except for the three main arteries, or stents located within 10 mm of the proximal right coronary artery; (4) Patients with congenital coronary artery anomalies or congenital heart disease; (5) Patients who underwent coronary artery bypass grafting (CABG) surgery; (6) Patients with poor image quality due to various reasons, making assessment impossible. The clinical data of patients were collected from the hospital’s electronic medical record system, outpatient records, and telephone follow-up interviews. Record the patient’s age, gender, height, weight, BMI, hypertension, hyperlipidemia, diabetes, smoking, drinking and laboratory test data during the CCTA examination. Laboratory tests included lipid analysis, myocardial enzyme spectrum analysis, C-reactive protein and erythrocyte sedimentation rate analysis. If a patient had multiple laboratory tests, the results closest to CCTA were selected. Record whether the patient has achieved complete revascularization, the pharmacological therapy following revascularization, and the clinical presentation of patients, LV functions and comorbidities. Record the characteristics of the stent, including the vessel and segment where the stent is located, the diameter of the stent, the total length of the stent, the number of stents, and the overlapping situation.
CT Acquisition and Reconstruction
CCTA was performed with a third-generation dual-source CT device (Siemens SOMATOM Force). All patients signed informed consent forms. Scanning range: The upper boundary starts 2cm below the tracheal bifurcation, and the lower boundary ends above the diaphragm. The contrast dose tracking and triggering technique was used to select the ROI at the ascending aorta root, set the triggering threshold to 100 Hu, and delay 5 seconds for automatic scanning. Scanning parameters: Tube current modulation technology was adopted, and the tube voltage ranged from 70–120 kV. The interval is 0.5 mm, and the layer thickness is 0.75 mm. During the examination, the patient’s electrocardiogram, heart rate, blood pressure, clinical symptoms, and other relevant parameters are continuously monitored. The procedure will be terminated if any of the following abnormalities occur: arrhythmia, a sustained decrease in heart rate, a blood pressure drop exceeding 40 mmHg compared to baseline, acute chest pain, or new-onset ST-segment elevation or depression.
CCTA Image Analysis
All CCTA images were uploaded to the post-processing workstation (Syngo.Via, Siemens), and the best diastolic images were selected for image processing. Two radiologists with more than five years of experience in coronary CTA interpretation analyzed the CCTA measurements. The CCTA measurement parameters encompass the normal lumen diameter at the proximal stent segment, the minimum lumen diameter (MLD) within the stent, and the minimum lumen area (MLA) of the stent, all derived through standardized image post-processing techniques such as multiplanar reconstruction (MPR), curved planar reconstruction (CPR), maximum intensity projection (MIP), and volume rendering technique (VRT).
CT-FFR measurement: All patients’ CCTA data were uploaded to the artificial intelligence analysis software (Coronary Doc. Shukun Technology, China) in DICOM format. The software leverages artificial intelligence technology based on neural network models to learn the relationship between computational fluid dynamics (CFD) and anatomical structures, enabling the calculation of CT-FFR values for blood vessels with diameters exceeding 2 millimeters.21,22 CT-FFR measurement includes the CT-FFR value at the proximal edge of the stent (CT-FFRpro), the CT-FFR value at the minimum area of the stent (CT-FFRmin), the CT-FFR value at the distal edge of the stent (CT-FFRdis), the CT-FFR value 2 cm from the distal edge of the stent (CT-FFR2cm). Additionally, the ΔCT-FFR value was calculated as the difference between CT-FFRpro and CT-FFRdis. The study also recorded the rate of change of CT-FFR values relative to stent length, expressed as ΔCT-FFR per unit length (ΔCT-FFR/length).
FAI measurement: The fat area value range is from −190HU to −30HU. The radial distance of the outer wall of the coronary artery is manually modified to the length of the target vessel diameter. Based on the vessel level, the software can automatically calculate the FAI value within 40 mm of the proximal end of the three main coronary arteries of the patient. To minimize the influence of the aortic wall, the stent area placed within the 10 mm segment at the distal end of the right coronary artery was excluded in this study.15 Based on the vessel level, the FAI value of the target vessel where the stent is located is recorded. Based on the lesion level, the length range of the stent from the proximal to the distal end (including 5 mm within the proximal and distal edges) is manually defined, and the radial distance is manually adjusted to make the diameter equal to the diameter of the stent, for the measurement of lesion-specific FAI (Lesion-specific FAI, FAIlesion) around the stent.23 Two senior radiologists, each with more than five years of experience in cardiovascular imaging diagnosis, independently performed CT-FFR and FAI analyses in a double-blind fashion, without access to clinical data or ICA results. In the event of discrepancies, a senior physician was consulted to resolve differences through consensus evaluation.
ICA Acquisition
The examination was conducted using the Philips Azurion 7 M20 angiography machine. Conventional angiography of the left and right coronary arteries was performed in various positions. Two senior cardiologists made the diagnosis of in-stent restenosis (ISR) without knowledge of the CCTA results, and the results were recorded and stored in the hospital’s electronic medical record system. ISR was defined as a lumen diameter stenosis of ≥ 50% in the stent segment or its proximal or distal edge (a segment adjacent to the stent with a length of 5 mm).24 According to ICA measurements, all patients who underwent coronary stenting were divided into two groups: vessels with ISR (ISR group) and vessels without ISR (non-ISR group).
Statistical Analysis
SPSS 26.0 and R (4.2.1) software were used for statistical analysis. Continuous data conforming to a normal distribution are presented as the means ± standard deviations and were compared between groups with the independent sample t test. Continuous data that were not normally distributed are expressed as medians (interquartile intervals) and were compared between groups with the Wilcoxon rank-sum test. Categorical variables are expressed as frequencies and percentages and were compared with the chi-square test. Spearman correlation analysis was employed to explore the relationships between various risk factors and ISR. Following the collinearity analysis of the research parameters, both univariate and multivariate logistic regression analyses were performed to identify independent risk factors for ISR. Subsequently, nomogram models and various clinical models were developed. The predictive performance of these models was assessed using the area under the curve (AUC) of the receiver operating characteristic curve and the concordance index (C-index). Additionally, calibration curves were employed to evaluate the agreement between predicted probabilities and observed outcomes, while decision curve analysis was conducted to determine the clinical utility of the models. A P value <0.05 was considered statistically significant.
Results
Patient Characteristics and Clinical Outcomes
A total of 378 patients were ultimately included in this study. Among them, 120 cases were included in the ISR group (31.7%), 88 were male (73.3%), and the age was 65.05 ± 8.58 (years). A total of 258 cases were included in the non-ISR group (68.3%), with 176 males (68.2%) and an age of 63.36 ± 8.71 (years). The flow chart is shown in Figure 1. There were statistically significant differences between the two patient groups with respect to hyperlipidemia, lipoprotein(a), hydroxybutyrate dehydrogenase, troponin, NT-proBNP levels and ACEI/ARB. The results of the blood vessels where the stents were located showed that there were 218 cases (57.7%) of LAD, 69 cases (18.2%) of LCX, and 91 cases (24.1%) of RCA. The results of the stent segments showed that there were 143 cases (37.8%) in the proximal segment, 90 cases (23.8%) in the proximal and middle segments, 82 cases (21.7%) in the middle segment, and 63 cases (16.7%) in the distal segment. There were statistically significant differences in stent length, number of stents, stent diameter, minimum stent lumen area, and minimum lumen diameter between the two groups (P < 0.05). There was no statistically significant difference in the diameter of normal blood vessels in the proximal segment of the stent and the overlapping stents. There were statistically significant differences in CT-FFRpro, CT-FFRmin, CT-FFRdis, CT-FFR2cm, ΔCT-FFR, and ΔCT-FFR/length between the two groups (P < 0.05). The FAI values of the target vessels and the FAI values (FAIlesion) around the stents showed statistically significant differences between the two groups (P < 0.05). The patient and stent characteristics as well as relevant results of CCTA, CT-FFR and FAI measurements are displayed in Table 1.
Table 1 Comparison of General Data Between the Two Groups
Figure 1 Flow chart of inclusion and exclusion criteria for the study population.
Correlation Analysis of CT-FFR, FAI and ISR
According to the Spearman correlation analysis, CT-FFR2cm showed a moderate negative correlation with ISR (R = −0.60, P < 0.001). ΔCT-FFR demonstrated a moderate positive correlation with ISR (R = 0.684, P < 0.001). FAIlesion also exhibited a moderate positive association with ISR (R = 0.576, P < 0.001). Furthermore, ΔCT-FFR/length was moderately positively correlated with ISR (R = 0.635, P < 0.001) (Figure 2).
Figure 2 The correlation between various factors and in-stent restenosis after PCI.
Univariate and Multivariate Analyses for ISR
Univariate logistic regression analysis demonstrated that CT-FFRpro, CT-FFRmin, CT-FFRdis, CT-FFR2cm, ΔCT-FFR, FAI, FAIlesion, stent length, ΔCT-FFR/length, stents number, MLD and MLA, brain natriuretic peptide precursor, hyperlipidemia, ACEI/ARB and lipoprotein(a) were all associated with ISR. Following collinearity assessment, CT-FFRpro and CT-FFRdis were excluded, due to multicollinearity. The remaining variables were entered into a multivariate logistic regression model. The results showed that CT-FFR2cm, ΔCT-FFR, FAIlesion, ΔCT-FFR/length, hyperlipidemia and lipoprotein a were independent predictors of ISR (Table 2).
Table 2 Univariate and Multivariate Logistic Regression Analysis for ISR
Nomogram Construction and Prediction Performance of ISR Test Parameters
A nomogram model was developed based on the aforementioned independent influencing factors to predict the occurrence of in-stent restenosis (ISR) in patients who underwent percutaneous coronary intervention (PCI) (Figure 3). The model demonstrated high predictive accuracy, as evidenced by a C-index of 0.966. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of the predictive model. The results indicated that ΔCT-FFR exhibited the highest predictive value for ISR, with an AUC of 0.923 (95% CI: 0.889–0.957), outperforming both FAIlesion (AUC=0.857, 95% CI: 0.817–0.896) and clinical data (AUC=0.688, 95% CI: 0.628–0.748). This difference was statistically significant (P < 0.05) (Figure 4). Several clinical models were constructed, and calibration and decision curve analyses were conducted to further validate the ISR prediction model. Model 1 only includes ΔCT-FFR; Model 2 includes ΔCT-FFR and FAIlesion; Model 3 includes ΔCT-FFR, FAIlesion and clinical data. The ROC results showed that the area under the curve (AUC), cut-off value, sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of Model 1 for predicting ISR were 0.923 (95% CI: 0.889–0.957), 0.08, 0.965, 0.783, 0.907, 0.905, and 0.913, respectively. For Model 2, the corresponding values were 0.955 (95% CI: 0.930–0.981), 0.439, 0.957, 0.858, 0.926, 0.936, and 0.904, respectively. For Model 3, these values were 0.958 (95% CI: 0.932–0.984), 0.337, 0.942, 0.892, 0.926, 0.949, and 0.877, respectively. Model 3 had the highest predictive value (AUC: 0.958, 95% CI, 0.932–0.984). There was a statistically significant difference between Model 1 and Model 2 (AUC: 0.923 vs 0.955, P < 0.05). The predictive value of ΔCT-FFR in combination with FAI is significantly greater than that of either parameter alone. There was no statistically significant difference between Model 2 and Model 3 (AUC: 0.955 vs 0.958, P > 0.05) (Figure 5 and Table 3). The calibration curve results show that most of the data points in the ISR prediction model are close to the ideal line, indicating that the model has a high degree of calibration (Figure 6). The results of the decision curve analysis show that in ISR prediction, the net gain of the model in most threshold probability ranges is higher than that under the assumption that all patients are positive or negative, indicating that the model has high clinical practicability (Figure 7).
Table 3 The Results of the Work Characteristic Curves of ISR Predicted by Each Model
Figure 3 A nomogram model was developed to predict the occurrence of in-stent restenosis.
Figure 4 The receiver operating characteristic curves of each factor for predicting in-stent restenosis (ISR).
Figure 5 The receiver operating characteristic curves of each model for predicting in-stent restenosis.
Figure 6 Calibration curves of each model for the prediction of in-stent restenosis.
Figure 7 Decision curves of each model for the prediction of in-stem restenosis.
Discussion
To the best of our knowledge, this is the first study to investigate the correlation between CT-FFR combined with FAI and its potential in predicting in-stent restenosis (ISR) in patients with coronary heart disease following percutaneous coronary intervention (PCI). Our findings demonstrate that ΔCT-FFR, CT-FFR2cm, and peri-stent FAI derived from CCTA are moderately associated with ISR and may act as independent predictors for ISR after stent implantation. Notably, while the integration of imaging features and clinical data enhances the identification of ISR, imaging features exhibit greater predictive value compared to clinical data alone.
Percutaneous coronary intervention initiates two distinct pathological processes that ultimately lead to ISR. In the early phase, ISR is primarily attributed to excessive neointimal hyperplasia, whereas in the later phase, it is predominantly caused by neoatherosclerosis.25,26 Plaque rupture may further precipitate acute coronary syndrome and potentially result in major adverse cardiovascular events (MACE), including sudden cardiac death, thereby posing a significant threat to patient safety.27 The fractional flow reserve (FFR) measured after PCI reflects the hemodynamic alterations in the target vessel where the stent is deployed. A lower FFR value indicates a reduced maximum blood flow capacity in the stented vessel under hyperemic conditions compared to a normal vessel, which may result in impaired blood flow, promote platelet aggregation and vascular occlusion, and consequently increase the risk and severity of ISR.28 Conversely, a higher FFR level suggests favorable myocardial perfusion, adequate subendocardial oxygen supply, and diminished ischemia-induced inflammatory responses. These factors contribute to stable cellular metabolism, protection against ischemia-reperfusion injury, promotion of collateral circulation development, mitigation of adverse effects associated with vascular stenosis, and inhibition of ISR progression.29,30 Previous studies have indicated that the FFR can serve as a valuable tool for assessing prognosis following PCI, with a significant post-PCI improvement in FFR being linked to greater symptom relief and a reduced incidence of adverse cardiovascular events.9,31 Onuma et al32 were the first to demonstrate the feasibility of using computational fluid dynamics (CFD)-based CT-derived FFR in patients undergoing PCI with bioabsorbable stents. Andreini et al33 reported a case of severe ISR that was missed by coronary CT angiography but accurately detected by CT-FFR. In this case, CCTA did not detect significant stenosis within the RCA stent of the patient. On the contrary, CT-FFR analysis showed obvious distal stenosis of the RCA stent segment. Invasive coronary angiography confirmed severe ISR in RCA. Wang et al evaluated the predictive value of CT-FFR prior to PCI for target vessel failure (TVF) following stent implantation and found that CT-FFR, as an independent predictor of TVF, significantly improved risk reclassification compared with a clinical risk factor model.34 Tang et al13 were the first to investigate the predictive performance of machine learning (ML)-based CT-FFR for ISR. Their results indicated that CT-FFR achieved an accuracy rate of 85% in identifying ISR. During the follow-up period, statistically significant differences were observed between the ISR and non-ISR groups in terms of ΔCT-FFR and ΔCT-FFR/length. Moreover, ΔCT-FFR/length was identified as an independent predictor of ISR. In this study, statistically significant differences in CT-FFR2cm, ΔCT-FFR, and ΔCT-FFR/length were found at each measured location between the two groups (P < 0.05), and CT-FFR2cm, ΔCT-FFR, and ΔCT-FFR/length were all independently associated with ISR. These findings are largely consistent with those of previous studies. Furthermore, this study revealed that the predictive values of CT-FFR2cm, ΔCT-FFR, and ΔCT-FFR/length for ISR were significantly higher than those of traditional risk factors such as hyperlipidemia and lipoprotein(a) [Lp(a)]. Therefore, we believe that for patients undergoing follow-up after PCI, it is essential to measure CT-FFR values at multiple locations within the stent, with particular emphasis on the difference in CT-FFR between the proximal and distal ends. As an independent predictor of ISR, ΔCT-FFR demonstrates high predictive accuracy for ISR assessment, significantly outperforming conventional clinical data.
Previous studies have confirmed that the development of ISR is closely associated with inflammatory processes.35 Following stent implantation, vascular endothelial cells are damaged, and mechanical injury to the vascular wall triggers an inflammatory response. This inflammation promotes the formation of neointimal hyperplasia (NIH) and contributes to the development of neoatherosclerosis. Peri-coronary adipose tissue (PCAT) is capable of directly releasing substantial amounts of pro-inflammatory adipokines, cytokines, and chemokines, which contribute to endothelial dysfunction, inflammatory cell infiltration, and smooth muscle cell migration.36,37 Additionally, mediators secreted from the inflamed vascular wall, such as interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and plasminogen activator inhibitor-1 (PAI-1), can exert paracrine effects on PCAT. These mediators inhibit the proliferation and differentiation of human preadipocytes within PCAT, thereby suppressing lipid accumulation, reducing adipocyte numbers, and lowering overall lipid content.38,39 Nogic et al initially examined the association between the mean attenuation of lesion-specific pericoronary adipose tissue (PCATlesion) prior to stent implantation and the occurrence of stent failure following PCI. Their findings indicated that PCATlesion values were significantly elevated in patients who experienced stent failure compared to those who did not, and an increased PCATlesion was identified as an independent predictor of stent failure.23 The fat attenuation index, which is derived from CCTA, quantitatively assesses the CT attenuation gradient of PCAT, thereby reflecting the inflammatory activity of the coronary arteries and enabling non-invasive detection of coronary vascular inflammation. Adolf et al investigated the predictive significance of lesion-specific FAI (FAIlesion) prior to stent placement with respect to in-stent restenosis (ISR). They found a significant correlation between FAIlesion and ISR, with elevated FAIlesion levels serving as an independent predictor of stent restenosis.20 Qin et al first investigated the predictive value of peristent FAI for ISR and demonstrated that peristent FAI serves as an independent predictor of ISR, potentially functioning as a non-invasive biomarker for assessing the risk and severity of ISR following stent implantation.40 These findings align with our research results. In our study, the lesion-specific peristent FAI (FAIlesion) was significantly higher in the ISR group compared to the non-ISR group. As an independent predictor of ISR, FAIlesion exhibited a moderate correlation with the occurrence of ISR. Moreover, the predictive value of FAIlesion for ISR was found to be significantly greater than that of hyperlipidemia and lipoprotein(a) (Lp(a)). This study demonstrates that the FAI surrounding the stent, as a novel imaging biomarker of inflammation, holds significant clinical potential for the non-invasive assessment of ISR and may serve as a predictive tool for evaluating ISR risk following stent implantation. However, findings regarding the application value of FAI in ISR exhibit inconsistency. Another study evaluating the diagnostic value of radiomic features of pericoronary adipose tissue for in-stent restenosis reported no significant difference in peristent FAI between the ISR and non-ISR groups.41 Therefore, the clinical utility of FAI in ISR requires comprehensive validation through large-scale clinical trials.
Limitations of this study include the following: (1) This was a single-center retrospective study, and selection bias may have occurred during patient enrollment. (2) The subjective editing of the stent vessel lumen profile could potentially influence subsequent CT-FFR and FAI calculations. Therefore, larger-scale and more comprehensive studies are required to further validate the clinical application of these parameters in the context of ISR. (3) Previous research has indicated that early and late in-stent restenosis involve distinct pathophysiological mechanisms.42,43 However, this study did not differentiate between early and late ISR.
In conclusion, ΔCT-FFR and peri-stent FAI are independent predictors of in-stent restenosis following percutaneous coronary intervention, and demonstrate superior predictive performance for ISR compared to clinical characteristics. The combined application of these two parameters further enhances the predictive performance for ISR. The integration of CT-FFR and FAI techniques derived from CCTA enables a comprehensive and systematic evaluation of ISR through a “one-stop” assessment encompassing functional and inflammatory data. This non-invasive approach provides additional diagnostic value for ISR risk assessment: it not only helps reduce unnecessary invasive examinations in some patients and optimize the clinical management pathway for post-PCI ISR, but also lays a crucial foundation for individualized treatment decisions.
Data Sharing Statement
The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.
Ethics Statement
This study was approved by the ethic committee of Linyi Central Hospital (LCH-LW-2025115) and it was carried out following the guidelines of the Helsinki Declaration (World Medical Association Declaration of Helsinki). Written informed consent was obtained from the patients for their participation in this study.
Disclosure
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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To our knowledge, our pilot study is the first attempt to conduct Level 4 Group SSTP among South Korean families. We adapted the Group SSTP to South Korean families of children with DDs and evaluated the intervention’s feasibility and effectiveness on the child and family outcomes based on the Resiliency Model [22].
Participants expressed high satisfaction with the intervention, indicating its potential in South Korea. They valued learning practical parenting skills and sharing insights with peers, with some expressing a desire to maintain connections after the sessions. While remote sessions eased participation for some, others preferred in-person meetings, suggesting that future research could offer both options. Overall, the feedback confirmed the intervention’s feasibility.
However, attrition was a potential consideration in assessing the feasibility, as two parents withdrew early. One, a single mother of a child with significant behavioral challenges, discontinued after the first session due to difficulty managing her child during the session, noting the absence of caregiving support. Another mother missed several sessions due to personal reasons, without attributing her non-participation to dissatisfaction with the intervention or difficulties with the virtual format.
Compared to traditional in-person delivery, the virtual format improved accessibility, enabling parents from distant regions to participate in real time. Nevertheless, the absence of a controlled physical environment may have introduced distractions that affected retention. The challenges experienced by the caregiver may have resulted from the combination of the specific characteristics of this intervention with its online delivery format, rather than the modality alone. For example, the parent might have more readily participated in an online program that actively engaged the child, thereby reducing the cognitive and logistical demands of attending to both the session and the child simultaneously. This highlights the need to assess not only the feasibility of the delivery modality in isolation but also the feasibility of the entire intervention package within its implementation context. Future research should explore strategies such as providing additional support for childcare or incorporating content that actively engages children.
In terms of individual-level score trends, Subject 6 demonstrated the most positive changes in all outcomes, except for the child’s problem behavior, across the time points (T0–T2). The baseline scores for the outcomes were relatively unfavorable, and the mother was young and had a lower economic status. She expressed a desire to participate in an additional round of the intervention, as she found it particularly helpful in learning parenting strategies tailored to her child. This participant did not report prior intervention experience, which may help explain the notable improvements observed. On the other hand, Subject 2 exhibited the least favorable outcomes, including declines in parenting efficacy, positive parenting skills, and family QoL. Notably, this participant’s baseline scores were relatively high, and her economic status was comparatively more favorable than that of other participants. The differences between the two subjects may stem from disparities in opportunities to participate in various interventions, influenced by their differing socioeconomic statuses. These patterns, though based on a small sample, highlight the importance of considering caregiver background and tailoring program outreach and delivery to reach underserved populations who may benefit most. Thus, future studies should investigate caregivers’ backgrounds, including prior intervention experiences with various delivery modalities such as virtual formats, to better understand how these may influence participation and engagement. Moreover, as in other countries where SSTP is implemented nationally [26], adopting the program at a national level in South Korea may be particularly beneficial for underserved families who lack access to parenting resources.
Additionally, although Subject 2 reported a high level of satisfaction with the intervention and provided positive qualitative feedback, she rated the usability and usefulness of the mobile app the lowest among all participants and expressed difficulties with app usage. Conversely, Subject 7, who reported the second-highest ratings for the usability and usefulness of the mobile app, demonstrated the greatest reduction in the child’s behavioral problems. This may suggest that the mobile app functioned as a supplementary tool in delivering the intervention, and its seamless integration into the intervention process could enhance overall effectiveness.
In terms of group level, we observed that comparisons between T0 and immediately post-intervention (T1) showed no statistically significant differences. However, we found significant effects of the intervention on child and family outcomes, including children’s behavior problems, QoL, parenting stress, efficacy, positive parenting behavior, and the parent-child relationship, from pre-intervention to one month post-intervention (T2).
One possible explanation for these findings is the cumulative effects of the intervention. SSTP focuses on providing families with practical parenting strategies that align with their values and needs, aiming to empower them to manage challenges independently [23]. Even after the intervention, families continued to apply positive parenting skills, which may have contributed to the improved outcomes observed over time.
The main limitation of our study was its small sample size. Therefore, our findings should be interpreted with caution. The outcome data gathered only from participant reports may have had a self-report bias. Including other informants may provide a more balanced assessment of the intervention.
This multi-national population-based cohort study presents routinely collected data from Danish, English, and Swedish patient registries and electronic health records that were linked to national statistics in each country.
Data sources
We extracted individual participant data from the Danish National Patient Registry (DNPR), Hospital Episode Statistics Admitted Patient Care database (HES APC) for England, and from the Swedish National Patient Register (SNPR) [12,13,14]. For simplicity, the term registry will be used in the following for all three countries.
The DNPR includes data from all Danish hospitals, both public and private, covering inpatient care and, since 1995, also including outpatient care. It captures information such as diagnoses, surgical procedures, treatments, complications, and hospital admissions. The DNPR also records information on outpatient visits to specialist clinics and emergency department visits [12]. After identifying the Danish study population in the DNPR, it was linked to the Civil Registration System (CRS) by the unique 10-digit personal identification number given to all Danes at birth or immigration since 1968. This allows individual-level linkage of data between multiple registries. The CRS also contains information on date of birth, age, sex, and vital status [17].
HES APC is a dataset containing data on all remunerated activity within National Health Service hospitals (NHS) or NHS-funded care in England where the patient requires an inpatient stay in secondary care. This includes day-case procedures and provides data on primary and secondary clinical diagnoses. Data can be linked at a patient level to all other secondary care episodes within the NHS, in addition to national mortality data [14].
The SNPR contains information from inpatient care and, since 2001, also from outpatient care, thus including hospital admissions, emergency department visits, and specialist outpatient visits. It covers data from all Swedish hospitals, both public and private, and includes information on patient demographics, diagnoses, treatments, and procedures [13]. To ensure a high degree of completeness from the SNPR, the Swedish data extract did not include data from before 2001.
Information about the specific content of each national patient registry is provided in Table S1 (Additional file 1: Table S1) [12,13,14, 17,18,19,20,21]. Furthermore, population data for incidence calculation were extracted from national statistics in each country [18, 20, 21]. All individual-level data were provided in a de-identified format.
Participants
Based on the International Classification of Diseases, 10th Revision (ICD-10), all adults (≥ 18 years) with PHF (S.42.2*) were identified in the three national patient datasets [11,12,13].
The participants’ first fracture on each arm was included. In cases where laterality codes were missing, only the first fracture in one arm was included. Each index episode was analysed as an independent observation.
The exclusion criteria included the presence of bilateral PHF, any concurrent injury, and cancer registered at the same episode/index date as the fracture. This was to exclude polytrauma and pathological fractures. Specific details of the ICD-10 codes used for including and excluding patients, as well as recording comorbidities and SAEs, are found in Table S2 (Additional file 1: Table S2-S4).
Primary outcome
The primary outcome variables were the numbers of pre-defined surgical procedures for PHF. The Nordic Medico-Statistical Committee (NOMESCO) Classification codes (NCSP-codes) were used to identify surgical procedures linked to the ICD-10 code in Denmark and Sweden, while Operational Procedure Codes, 4th Edition (OPCS-4) were used to identify surgical procedures linked to the ICD-10 code in England [19, 22]. The predefined surgical procedures were: plate fixation, screw fixation, K-wire fixation, intramedullary nail (IM nail) fixation, external fixation, and arthroplasty. If patients had surgery within 30 days after the fracture index date, surgery was considered as initial treatment. If the index date of the surgical procedure was later than 30 days after the fracture, the initial treatment was categorised as non-operative, thus, they were excluded from the analysis. A complete coding list for the surgical procedures can be found in Table S2 (Additional file 1: Table S2). If more than one surgical procedure was performed on the same date and no data were available to classify which was the primary, the procedure was classified using a predefined hierarchy (Additional file 1: Table S5).
Secondary outcomes
Each specific surgical procedure was linked to the first episode of each SAE in a set of SAEs, occurring within 30 days after the index date of the surgical procedure. The SAEs were identified based on ICD-10 codes and included: stroke, respiratory tract infection, myocardial infarction, pulmonary embolus, urinary tract infection, and acute renal failure. In addition, mortality within 30 and 90 days was counted.
Covariates
To identify potential confounding, age, sex, and comorbidities were compared between surgical subgroups and between countries. Information on history of ischaemic heart disease (IHD), diabetes mellitus (DM), and chronic obstructive pulmonary disease (COPD) was extracted. The overall level of comorbidity was calculated as the CCI using the algorithm by Quan et al. [23]. In Denmark and Sweden, a one-year lookback period was applied when extracting past medical history and CCI variables, whereas in England, no time limit was applied to the lookback period due to the limitation of no outpatient and emergency department information within the English dataset.
Data processing
The primary investigators, responsible for the data in each participating country, cleaned and processed their national data. Data management flow charts for each country can be found in supplementary material (Additional file 2: Fig. S1a,b,c). Patient-level analyses were performed securely according to locally agreed data management procedures. Do-files were developed and shared among the three countries to enable reproducible analytical pipelines in each centre.
Statistical analysis
National baseline characteristics of patients divided by surgical subtype after isolated PHF were presented to identify potential differences in population profiles and sources of bias.
For each country, incidence rates (IR) per 100,000 person-years with 95% confidence intervals (95% CI) were calculated for all surgically managed PHFs and for each surgical subtype. This was done by using national population estimates. In addition, age- and sex-specific IRs were calculated. The annual surgical IRs for all three countries were plotted against the date of a Cochrane systematic review and a large clinical trial, to determine if there was an impact of trial recruitment or publication of high-quality evidence [9, 10].
SAEs, occurring within 30 days of each surgical procedure, were presented as cumulative incidence proportions (hereafter referred to as incidence) with 95% CI, assuming a normal approximation to the Poisson distribution for random count data.
Survival analysis using a Kaplan–Meier method was undertaken to show survival over the first post-operative year, as well as for the entire study period. Patients were censored at the date of death or end of follow-up, whichever came first.
Multivariable logistic regression analysis was undertaken to determine the impact of age (in 20-year age bands), sex, and overall comorbidity (using CCI categorised as (0–1), (2–3), (4 +)) upon the rise of SAEs within 30 days as well as 30-day, 90-day, and 1-year post-operative mortality.
Aggregated results from each country were compiled by the first author. The statistical package Stata (version 17, StataCorp, College Station, TX) was used for data cleaning, pre-processing, and statistical analyses.
The study was reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement [24].
