Category: 3. Business

Introduction

Maintenance therapy is recommended for patients diagnosed with chronic obstructive pulmonary disease (COPD). These treatments include long-acting muscarinic antagonists (LAMAs) and long-acting beta₂-agonists (LABAs), with an inhaled corticosteroid (ICS) added according to the frequency of exacerbations.¹ Currently, several single-inhaler triple combinations of these treatment classes are available.

Besides the general recommendations for which inhaler combination to use, the question of when to initiate maintenance therapy with these inhalers has been put forward. Recent observational studies have investigated the comparative effectiveness of prompt versus delayed timing of initiating single-inhaler triple inhaler therapy after a COPD exacerbation.^2–6 These studies found that prompt initiation of single-inhaler triple therapy was associated with significant reductions in the rates of moderate and severe exacerbations, compared with delayed initiation. Such observational studies present major methodological challenges related to the definition of timing of initiation in relation to the timing of the outcome events, that can result in time-related biases.

We review these studies and discuss methodological aspects of their study design that can introduce bias in the results. We illustrate the biases using a general practice clinical database and present results of the analysis using an approach that avoids these biases.

The Published Studies

As the published observational studies used a similar design, we describe the first one in detail to explain the approach.² The Mannino study evaluated the impact of prompt versus delayed initiation of single-inhaler triple therapy (SITT) with fluticasone furoate, umeclidinium, and vilanterol, following a COPD exacerbation, using a US claims database. Patients with a COPD exacerbation between September 2017 and September 2019 were identified, with the first exacerbation occurring in that period taken as the index exacerbation. The index date was taken as the date of discharge for exacerbations requiring hospitalisation and the date of the physician visit for moderate exacerbations. The study cohort was formed exclusively from those who initiated a SITT within 6 months after the index date, with SITT timing classified as prompt (initiation within 30 days after the index date; N = 529) or delayed (initiation 31–180 days after the index date; N = 1,375). Patients were aged 40 years or more at the index date, had at least 12 months of continuous health insurance coverage before index (baseline), no exacerbation and no SITT prescription during this baseline period. The subjects needed at least 6 months of coverage after the index. Subjects were followed from the index date until the end of the observation period for the occurrence of COPD exacerbations and other outcomes. The technique of inverse probability of treatment weighting was used to adjust the rate ratio of these outcomes for differences in baseline characteristics between the prompt and delayed groups. Patients in the prompt initiation group had a 21% lower rate of COPD exacerbation (rate ratio 0.79; 95% CI: 0.65–0.94) and a 28% lower incidence of a first exacerbation (hazard ratio 0.72; 95% CI: 0.62–0.83) compared with delayed initiators.

Methodological Issues

A randomized trial of this question would enroll patients at the index exacerbation and randomly allocate them to either the prompt or delayed treatment strategy. The allocation of the two groups is thus known at the time of randomization (index date) with outcome events counted as of this time, and which can thus occur prior to treatment initiation. The published observational studies, on the other hand, had to peek into “future” to define the two treatment groups, such as treatment initiation within 30 days after the index (prompt) or at 31–180 days after the index (delayed). This use of “future” time creates several methodological challenges and can lead to potential biases in observational studies. We use the Mannino study described in detail above as an example to explain the methodological issues.²

The first methodological issue with the observational studies involves the outcome (exacerbations) allowed to occur prior to the initiation of triple therapy, which can introduce protopathic bias.⁷ Indeed, some physicians may wait for a second exacerbation, namely the first during follow-up, as an indication to initiate triple therapy, as per guidelines.¹ Thus, it may not be the treatment that led to the exacerbation, but the reverse. In particular, patients with early exacerbations will likely receive their triple inhaler after day 30, when the exacerbation ended, and thus more likely to be classified in the “delayed” treatment group, a bias compounded by the longer duration of this delayed period. This could explain the observational study’s reported median times to the first COPD exacerbation of 367 versus 200 days for the prompt and delayed initiation groups, respectively.² Moreover, the corresponding Kaplan–Meier curve shows that around 10% of the delayed group had their first exacerbation in the first 30 days, the period defining “prompt” treatment, and that 48% of that group had their first exacerbation before 6 months, the end of the “delayed” treatment period.

The second methodological issue relates to the cohort selection that imposes a period of continuous health insurance coverage after the index date, resulting in potential selection bias from immortal time.^8,9 Indeed, the cohort included only patients with at least 6 months of coverage after the index date, thus excluding patients who die in this 6-month period, even if they had initiated triple therapy and had exacerbations before death. By this criterion, patients must survive to initiate treatment, whether prompt or delayed, even if they had exacerbations before death. This imposition of 6 months of coverage after an index that inherently excludes deaths can result in selection bias that could favor one group over the other. The Mannino study reports that 25% of the original 668,011 subjects were excluded because they had less than 6 months of eligibility, with no information on mortality.²

Study Design to Avoid Bias

The study design that avoids these biases must attempt to emulate the randomized trial when using these observational data, while not looking into the future. The key with the randomized trial is that the timing of inhaler initiation, namely allocation to a prompt or delayed treatment regimen, is known at the time of randomisation, so that the two groups can be properly compared on the rate of exacerbation during a prespecified follow-up period after the index exacerbation. In this case, exacerbation events can precede the inhaler initiation but will follow the random treatment group assignment time. The difference in the rates between the two groups will provide the effect of prompt versus delayed treatment.

The observational study must thus also seek to allocate “exposure” (prompt or delayed inhaler initiation) at the index date to avoid the methodological biases raised above when this exposure allocation is based on looking in the future. To accomplish this, it is important to recognize that a subject who has not yet received the inhaler at the index date could, in fact, belong to both the prompt and delayed group at that time point and up until inhaler therapy is initiated. To resolve this dilemma, the concept of “cloning” is used in observational research to assign each patient to the two possible treatment strategies (prompt and delayed inhaler initiation) that the patient could belong to at the index date.^10–12 Thus, the patient is “cloned” so that their data are included twice in the analysis, though the follow-up will be censored according to the timing of the inhaler initiation and the outcome events, which could be counted in one, two, or none of the groups depending on where the clones are censored.

As an illustration for the outcome of time to first exacerbation, consider, for example, a patient who initiates triple therapy on day 15, thus within 30 days (Figure 1, subject 1). This patient will generate two clones, a “prompt” clone that will be considered as exposed to the prompt treatment strategy for the entire follow-up, whose follow-up will end at the time of the event, and a “delayed” clone that will be considered as exposed to the delayed treatment strategy and censored at 14 days, the day at which we still did not know the membership of its parent. The second example (subject 2) is a patient who initiates triple therapy on day 60 (during the 31–180-day period). This patient will generate two clones, the first is a “prompt” clone that will be censored at 30 days, the time they can no longer be prompt, and a “delayed” clone that will be classified as “delayed” exposure for the entire follow-up, whose follow-up will end at the time of the event (Figure 1, subject 2). The third example (subject 3) does not initiate triple therapy at all during follow-up. The “prompt” clone will be censored at 30 days and the delayed clone follow-up will end at the time of the event within the 31–180 period (Figure 1, subject 3). The fourth example (subject 4) is a patient who initiates triple therapy on day 35 (during the 31–180-day period), who has their first exacerbation on day 20. The two generated clones, one prompt and one delayed, will both have an outcome event at day 20, at which point their follow-up stops (Figure 1, subject 4). Figure 2 displays the corresponding cloning patterns illustrating the situation where the outcome involves the frequency of exacerbations over time.

Figure 1 Illustration of cloning approach for the analysis of time to first exacerbation.

Figure 2 Illustration of cloning approach for the analysis of the frequency of exacerbations.

This cloning approach addresses the bias resulting from peeking into the future to define exposure. Nonetheless, simply computing the corresponding cumulative incidences or rates of exacerbation for the two cloned treatment strategies will still produce bias from giving equal weights to the clones. This is addressed by accounting for the artificial censoring of the clones at specific times, which can be done using inverse probability of censoring weights (ICPW).¹¹

Illustration

We formed a cohort of patients with COPD from the Clinical Practice Research Datalink (CPRD), a primary care database from the United Kingdom (UK) that contains primary care medical records for over 50 million people enrolled in more than 1800 general practices. These data have shown to be of high quality, including for studies of COPD.^13–17

The study cohort included all patients with a diagnosis of COPD, treated with maintenance therapy, at or after age 40 who had a moderate or severe exacerbation of COPD after 15 September 2017, the year single-inhaler triple therapy became available in the UK. A moderate exacerbation was defined by a new prescription for prednisolone, while a severe exacerbation was defined as a hospitalization for COPD (ICD-10: J41, J42, J43, J44). The first such exacerbation defined the index date. All subjects had to have at least one year of medical history prior to the index date (baseline period). Patients receiving triple therapy, either in a single inhaler or multiple inhalers, in the year before the exacerbation defining cohort entry, were excluded. All subjects were followed for up to one year after the index date, with follow-up ending at death, 31 March 2021, or the end of the patient’s registration in the practice, whichever occurred first.

The covariates measured at baseline included age, sex, body mass index (BMI), smoking status and alcohol abuse. The severity of COPD was measured using the type of index exacerbation (moderate, severe), the number of COPD hospitalisations and the use of other respiratory drugs (SABA, SAMA, theophylline), during the one-year baseline period, as well as by the percent predicted FEV₁. A prescription for prednisolone, LABA, LAMA, ICS, and respiratory antibiotics in the month prior to the index date were also considered. Baseline co-morbidity in the one-year baseline period was measured using clinical diagnoses, hospitalizations, and prescriptions (Table 1).

Table 1 Baseline Characteristics of the Overall Study Cohort of 91,958 Subjects with an Index Exacerbation, Generating 91,958 Clones of Prompt and 91,958 Clones of Delayed Initiation of Single-Inhaler Triple Therapy, After Weighing by Inverse Probability of Censoring

Each cohort subject was cloned and assigned to both the prompt initiators arm (initiation within 30 days of the index date) and to the delayed initiators arm (initiation 31–180 days after the index date). Within each treatment group, clones were artificially censored at the time that the treatment they received was no longer compatible with their group membership. To account for the bias introduced by this artificial censoring mechanism, IPCW was estimated by pooled logistic regression, separately for prompt initiators at day 30, for delayed initiators at index date, between day 1 and day 30 and at day 180, as a function of the baseline covariates. IPCW estimation for prompt initiators, delayed initiators at index date and at day 180, was based on time updated values of the baseline covariates and the type of initial exacerbation (moderate or severe). For the delayed initiators, estimation of IPCW between day 1 and day 30 also included the time since cohort entry (linear, quadratic and cubic terms). Only prompt initiators starting treatment between day 16 and 30 were upweighted to replace clones censored on day 30. Similarly, only delayed initiators between day 152 and 180 were upweighted to replace clones censored on day 180. In addition to the artificial censoring associated with the cloning process, patients were also censored when they initiated triple therapy in multiple inhalers (same day prescription for LABA, LAMA and ICS). To account for this type of censoring another IPCW was estimated in the full cohort and before cloning, using pooled logistic regression and with the same set of variables. Final time-varying weights for each section of person-time were obtained from the cumulative product of all IPCWs.

For data analysis, we used the Cox proportional hazards model to compare the incidence of an exacerbation during the one-year follow-up, weighted for the inverse of the probability of censoring. The corresponding 95% confidence intervals (CI) for the hazard ratios were obtained using the non-parametric bootstrap method based on 1000 random samples. Similarly, weighted cumulative incidence curves were estimated over the one-year follow-up, as well as differences and ratios of cumulative incidence at 3-month time points during follow-up.

We also replicated the approach used in the Mannino study, as described above.² Briefly, the study cohort included exclusively the subjects who had a prompt (within 30 days after the index date) or delayed (31–180 days after the index date classification) treatment initiation, restricted to those who had no exacerbation prior to the index date and at least 6 months of coverage after the index date. Inverse probability of treatment weighting, with stabilised weights, was used to adjust the hazard ratio of exacerbation, using the bootstrap method based on 1000 random samples outcomes to estimate the corresponding confidence interval. Since our study cohort on which the cloning analysis was based included subjects with exacerbations prior to the index date, we repeated the analysis, but not restricted to those who had no exacerbation prior to the index date and at least 6 months of coverage after the index date. The study protocol was approved by CPRD’s Research Data Governance Committee (protocol # 23_002846) and the Research Ethics Board of the Jewish General Hospital (protocol # JGH-2024-3847), Montreal Canada.

Results

The overall study cohort included 91,958 eligible subjects who had an exacerbation after September 2017. There were 4,876 new-users of single-inhaler triple therapy within 180 days after the index COPD exacerbation. Of these, 1,394 were prompt initiators and 3,482 delayed initiators of single-inhaler triple therapy, with 87,082 who either received it after 180 days or not at all. The prompt initiators appear more severe, with lower FEV₁ percent predicted and more likely to have a hospitalised exacerbation as the index event (Table S1).

The cloning of these subjects generated 91,958 prompt initiator clones and the same number of delayed initiator clones, including 426 clones who initiated triple therapy in multiple inhalers on the index date and 419 clones who initiated single-inhaler triple therapy on the index date (Table 1).

Of the 91,532 clones assigned to the prompt initiation strategy, 18,748 had a moderate or severe exacerbation after the index date, during 86,431 person-months of follow-up, resulting in an unadjusted incidence rate of a first exacerbation of 0.217 per patient per month (Table 2). The corresponding incidence rate for clones assigned to the delayed initiation strategy was 0.130 per patient per month. After weighing for censoring, the incidence rates are 0.101 and 0.103 per person per month, respectively, for the clones assigned to the prompt and delayed initiation regimens (HR 0.98; 95% CI: 0.80–1.19). The adjusted cumulative incidence curves of a moderate or severe exacerbation, weighted for censoring, are displayed in Figure 3. For severe exacerbations, the weighted incidence rates are 0.011 and 0.008 per person per month for the clones assigned to the prompt and delayed initiation regimens, respectively (HR 1.26; 95% CI: 0.81–1.96), with the corresponding adjusted cumulative incidence curves displayed in Figure 4. For the cumulative incidence curves given in Figures 3 and 4 the estimates of the differences and ratios of these cumulative incidences between the prompt and delayed initiators at different time points in follow-up are displayed in Table 3.

Table 2 Hazard Ratio of a Moderate or Severe Exacerbation for Prompt versus Delayed Initiation of Single-Inhaler Triple Therapy, Estimated Using Cloning to Define the Treatment Strategy Over Time, Weighed by Inverse Probability of Censoring

Table 3 Difference and Ratio of the Cumulative Incidence of Exacerbation Over Follow-up Time Comparing Prompt versus Delayed Initiation of Single-Inhaler Triple Therapy, Estimated Using Cloning to Define the Treatment Strategy Over Time, Weighted by Inverse Probability of Censoring

Figure 3 Cumulative incidence of the first moderate or severe exacerbation, for the prompt and delayed initiators, using the cloning approach, weighted by inverse probability of censoring.

Figure 4 Cumulative incidence of the first severe exacerbation, for the prompt and delayed initiators, using the cloning approach, weighted by inverse probability of censoring.

For the replication of the Mannino study, the analysis was restricted to the 2,650 new-users of single-inhaler triple therapy within 6 months after the index COPD exacerbation, with no prior exacerbations and at least 6 months of follow-up. Of these, 809 were prompt initiators of single-inhaler triple therapy and 1,841 were delayed initiators, who were similar in terms of baseline clinical characteristics after weighing (Table 4). The hazard ratio of a first moderate or severe exacerbation was 0.73 (95% CI: 0.65–0.81), comparing prompt with delayed initiation, while it was 0.58 (95% CI: 0.46–0.74) for a severe exacerbation (Table 5). These results were similar after removing the restrictions of 6 months of coverage and no prior exacerbations (Table 5).

Table 4 Baseline Characteristics of the 809 Prompt and 1,841 Delayed Initiators of Single-Inhaler Triple Therapy, Identified from the Cohort of 91,958 Subjects with an Index Exacerbation, Crude and Weighted by Inverse Probability of Treatment, Used to Replicate the Approach of Mannino

Table 5 Hazard Ratio of Moderate or Severe Exacerbation for Prompt versus Delayed Initiation of Single-Inhaler Triple Therapy, Used to Replicate the Approach of Mannino, Estimated Using the Cox Proportional Hazards Model, Adjusted by Inverse Probability of Treatment Weighing

Discussion

In this large-scale real-world study, we found that prompt treatment with single-inhaler triple therapy after a COPD exacerbation was not more effective than delayed treatment on reducing the incidence of a subsequent exacerbation. We showed that the methods used by previous studies that suggested significant effectiveness with prompt therapy, were affected by major time-related biases that favored the prompt treatment group.^2,3,6 For example, our illustration showed that, using the corrected approach, the hazard ratio of a COPD exacerbation was 0.98 (95% CI: 0.80–1.19) with prompt versus delayed treatment, while the corresponding HR with the time-related biased method employed by the previous studies was a significant 0.73 (95% CI: 0.65–0.81).

The time-related biases affecting the previous studies first involved “peeking into the future” to define prompt and delayed treatment, with a return to time zero to start follow-up for outcome exacerbation events. This approach thus allowed treatment initiation occurring after outcome events, which introduces protopathic bias.⁷ Indeed, multiple exacerbations, the study outcome, are an indication to initiate triple therapy, as recommended by the GOLD guidelines.¹ The other source of bias, namely selection bias from immortal time, resulting from imposing 6 months of coverage after the index date, was present in two of the studies to date.^2,6 This criterion excludes subjects who died during this period, when they could have initiated triple therapy and had exacerbations, which could be differential in the two treatment groups. While other studies did not impose this 6-month condition, they did introduce protopathic bias.^3–5 Our bias analysis showed that this 6-month imposition did not affect the findings, implying that the major time-related bias in these studies is protopathic bias, a bias present in all studies.

This approach has also been used in several other observational studies have investigated the comparative effectiveness of prompt versus delayed initiation of triple therapy, though including triple therapy in multiple inhalers after a COPD exacerbation.^18–22 These studies also found that prompt initiation of triple therapy was associated with significant reductions in the rates of exacerbations, and related costs, compared with delayed initiation. These findings are thus also affected by the same protopathic bias.

The “cloning” approach that we used is specifically designed to avoid the protopathic bias resulting from peeking into the future to define the treatment strategy. This cloning approach emulates a randomized trial by allocating the treatment strategy, prompt or delayed initiation of triple therapy, as of the index date, thus not looking in the future. Cloning involves creating data replicates of each patient, one for each of the study treatment strategies (in this case, prompt and delayed initiation) that the patient could belong to at the index date.^10–12 The patient’s data are thus included twice in the analysis, censored by the timing of exposure and outcome events. While the same outcome events can be counted in multiple regimens in this approach, a censor-weighted data analysis and the bootstrap can account for the replicated data.

Our study has some limitations typical to observational studies. The inhaler information is based on written prescriptions and can thus introduce some exposure misclassification, including on the timing of the actual treatment initiation, which can lag behind the prescription date. Thus, the 30- and 180-day thresholds used to define prompt and delayed treatment will necessarily be affected by this misclassification, which should differentially affect the shorter prompt treatment period. Indeed, we can assume that some subjects with a prescription date just prior to 30 days will be misclassified as prompt initiators if they in fact initiate their inhaler after 30 days. The use of censoring weights, however, because they are calculated using the observed exposure timing, does not account for this misclassification. To account for such exposure measurement error would require validating exposure in a subset of the study population and incorporating sensitivity analyses alongside censoring weights. Also, the outcome of a moderate exacerbation is defined only based on a prescription for prednisolone which, while a common practice in the UK, could introduce some misclassification. Our study also has strengths, besides the cloning approach that avoids the time-related biases of previous studies. Indeed, this approach is not affected by confounding as the cloning results in the same patients being replicated, making the two comparison groups identical on all subject characteristics.

In conclusion, this large-scale real-world study found that prompt treatment with single-inhaler triple therapy after a COPD exacerbation was not more effective than delayed treatment on reducing the incidence of a subsequent exacerbation. We showed that the methods used by previous studies that suggested significant effectiveness with prompt therapy, were affected by time-related biases that favored the prompt treatment group. For example, using the corrected approach, we found no reduction in the risk of a COPD exacerbation with prompt versus delayed treatment, while the time-related biased method used in previous studies suggested a significant 27% reduction in the outcome event.

Data Sharing Statement

This study is based in part on data from the Clinical Practice Research Datalink obtained under license from the UK Medicines and Healthcare products Regulatory Agency. The data are provided by patients and collected by the UK National Health Service as part of their care and support. Because electronic health records are classified as “sensitive data” by the UK Data Protection Act, information governance restrictions (to protect patient confidentiality) prevent data sharing via public deposition. Data are available with approval through the individual constituent entities controlling access to the data. Specifically, the primary care data can be requested via application to the Clinical Practice Research Datalink (https://www.cprd.com).

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research was funded in part by grants from the Canadian Institutes of Health Research (CIHR) and the Canadian Foundation for Innovation (CFI). Pr. Suissa is the recipient of the Distinguished James McGill Professorship award.

Disclosure

SS attended, in the last three years, scientific advisory committee meetings or received speaking fees from AstraZeneca, Boehringer-Ingelheim, Novartis, and Panalgo. The authors report no other conflicts of interest in this work.

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And 24% of BNPL borrowers were late making a payment in 2024, a 6% increase from the prior year, according to the Federal Reserve. Among people who make $25,000 or less, the rate increased from 31% to 40%.

Notably, BNPL access “significantly reduces the sensitivity of spending relative to income”, according to a Harvard Business School report.

“This effect is concentrated among individuals likely to be liquidity constrained, specifically, lower-income users and users without credit cards,” the report notes.

Becca, a 26-year-old tech worker in New York who declined to use her last name, said she used BNPL for things like pricier beauty products – including a Chanel perfume. She said she might “spend like 80 bucks this month on it and make two separate $40 payments, and then next month, I pay off the rest”.

While the payment option has helped her, she is concerned about companies like DoorDash offering BNPL for minor purchases like a pizza delivery.

“It’s just encouraging poor spending behavior from young people,” Becca said. “All these items build up because you’re using it again and again and again. You don’t feel like you’re spending a lot of money.”

It may be some time before the economy feels the impact of the new credit score calculation, Rossman said. While Fico stated that it would make the new scores available in fall 2025, most lenders continue to use a credit score model from 2009, (despite Fico since releasing new versions).

“Change comes relatively slowly in the credit scoring world, so even if this becomes available in the fall, that doesn’t mean everybody is going to be using it right away,” said Rossman.

“It’s kind of like your phone. For instance, Apple has the iPhone 16, but a lot of people are still using the 15 or the 14 or even older models. Credit scoring works the same way.”

WATCH TIME: 4 minutes

Welcome to this special edition of Neurology News Network. I’m Marco Meglio.

Newly announced interim data from a phase 1 study showed that treatment with salanersen (Biogen), an investigational antisense oligonucleotide administered once a year, was safe and led to slowing of neurodegeneration in patients with spinal muscular atrophy (SMA) previously on gene therapy. Based on these findings, Biogen plans to test the therapy in phase 3 studies, the design of which is being discussed with the FDA. Building on the mechanism of action of nusinersen (Spinraza; Biogen), salanersen is designed to enhance potency and allow for once-yearly dosing, offering potential improvements in convenience and efficacy. Mechanistically, salanersen targets the SMN2 gene, modulating its splicing to increase production of functional survival motor neuron (SMN) protein, which is deficient in SMA.

Edgewise Therapeutics has announced positive topline data from the MESA open-label extension study, with results showing that treatment with investigational sevasemten led to notable improvements in North Star Ambulatory Assessment (NSAA) scores over an 18-month period in patients with Becker muscular dystrophy (BMD). In addition, the company shared encouraging data from its phase 2 LYNX and FOX trials of Duchenne muscular dystrophy (DMD), as well as completion of a Type C meeting with the FDA, paving a path for sevasemten to become the first approved therapy for BMD. MESA, an open-label extension, featured 99% of eligible participants from the previously completed ARCH, CANYON, GRAND CANYON, or DUNE trials. In the latest data update, results showed a 0.8-point increase in NSAA scores among participants from CANYON, the major phase 2 trial of sevasemten, after 18 months of treatment. More notably, those who switched from placebo to sevasemten demonstrated a 0.2-point improvement since their crossover.

In a new announcement from UCB, fenfluramine (Fintepla), an FDA-approved antiseizure medication, met its primary and secondary end points in the phase 3 GEMZ trial of patients with CDKL5 deficiency disorder (CDD). Based on these data, the company plans to submit an application for fenfluramine to become a potential treatment option for patients living with CDD. Between baseline and the titration plus maintenance phase, fenfluramine demonstrated statistically significant changes relative to placebo on the primary end point of percent change in countable motor seizure frequency. In this phase 3, double-blind, placebo-controlled, fixed-dose study, fenfluramine continued to show a safety profile that was consistent with its previous indications in Dravet syndrome (DS) and Lennox-Gastaut syndrome (LGS).

For more direct access to expert insight, head to NeurologyLive.com. This has been Neurology News Network. Thanks for watching.

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Roula Khalaf, Editor of the FT, selects her favourite stories in this weekly newsletter.

Dealmaking slumped in the second quarter to the lowest level in a decade, excluding the early months of the pandemic, as Donald Trump’s “liberation day” tariffs extended a run of uncertainty that has forced dealmakers to pull back from all but the largest takeovers.

The total number of deals announced in the three months to June 30 fell to about 10,900, according to data from the London Stock Exchange Group. Excluding the second quarter of 2022, when Covid-19 lockdowns upended global markets and just 10,600 deals were unveiled, the figure was the lowest since the start of 2015.

Dealmakers had initially expected that a more conservative White House would pull back on regulation and unleash a wave of takeovers.

Instead, companies and investors have had to navigate a more perilous geopolitical backdrop than anticipated, with the announcement of wide-ranging tariffs by the US on April 2 and conflicts in the Middle East driving volatility in markets.

“Following the initial exuberance of the first month or two, the attitude in the boardroom has been cautious,” said Lorenzo Corte, global head of transactions at the law firm Skadden. 

Despite the escalation of trade tensions since the start of the quarter in April, the LSEG data show that the value of transactions held steady from the first quarter of the year at $969bn, propped up by a handful of strategic megadeals.

Deals worth more than $10bn have risen by three-quarters this year, with top transactions in the second quarter including Cox’s $35bn takeover of Charter Communications, a $33bn take-private of Toyota Motor’s biggest subsidiary, and a consortium led by Abu Dhabi National Oil Company’s $24bn acquisition of Australia’s Santos.

“There’s pent up demand to do large strategic transactions,” said Jim Langston, partner at law firm Paul Weiss. “If companies are going to make a bet on M&A, they want it to be something that moves the needle, that the reward is worth the risk. ”

The uncertain outlook for economic growth, inflation and the dollar have also acted as a particular drag on the private equity industry, making it more difficult to value assets.

Global private-equity backed acquisitions slowed sharply between the first and second quarters of the year, from about 2,500 in the first three months to closer to 1,850 in the second. There were 1,250 fewer private equity deals struck in the first half of this year compared with the same period in 2024.

Dealmakers have focused on public company takeovers and the sale or carve-out of assets regarded as no longer core, according to Jens Welter, Citi’s head of North America investment banking coverage.

Such transactions include KKR’s £4.7bn acquisition of London-listed industrial group Spectris, and BP’s exploration of a sale for its lubricants arm Castrol. Welter said that dealmakers were adding in terms to contracts to help agree transactions in choppier markets.

“While we expect the take-private and corporate carve-out volumes to remain at record levels, transactions are highly structured involving rollovers and deferred mechanisms,” Welter said.

Some advisers remained optimistic that a stabilising geopolitical outlook would lead to a pick-up in activity in the second half of the year.

Oliver Smith, co-head of Davis Polk’s M&A practice, said the build-up in demand felt like the early days of the Covid-19 pandemic.

“People realised then that the sky wasn’t falling in and things picked up for a while,” said Smith. “It feels like that moment in time is coming once companies get used to the uncertainty.”

SHANGHAI — A giant 26-meter (85-foot) Lego figure named Dada welcomed visitors to the new Legoland resort in Shanghai.

The resort, which opened Saturday, is the first in China. It is one of 11 parks across the world and was built with 85 million Lego bricks.

Among the main attractions is Miniland, which replicates well-known sights from across the world using Lego bricks. It features landmarks across China like Beijing’s Temple of Heaven and Shanghai’s Bund waterfront. There’s also a boat tour through a historic Chinese water town built with Lego bricks.

“My first impression is it is a good recreation, like a real fairyland of Lego,” said Ji Yujia, a Lego fan who was there on opening day.

The resort was developed in conjunction with the Shanghai government by Merlin Entertainments and the LEGO Group.

Visitors were greeted by performances featuring Legoland characters. Tickets range from $44 (319 yuan) to $84 (599 yuan).

—-

Corrects to say that Legoland in Shanghai is not the largest in the world.

Introduction

Fractures take place in people of all age groups. The episode depends on the type of trauma, location, and associated injuries. The incidence of fractures ranges between 733 and 4017 per 100000 patient-years.^1–3 Traumatic fractures are the major cause of morbidity and mortality, and in one study, 23,917 individuals sustained 27,169 fractures, with 64.5% of the fractures occurring in women.¹ The epidemiological data for fractures and dislocations in Saudi Arabia are not available.^4,5 It is expected that the number of fractures and dislocations will increase due to population growth.

Figure 1 PRISMA flowchart Showing the Final Selection of Analyzed studies.

Figure 2 Comparison between AI Model and Clinicians for Accuracy, Sensitivity and Specificity.

The reported incidence of missed diagnosis of fractures or dislocations by plain radiographs ranges between 3% and 10%,^6–8 and this inversely affects the final outcome of the recovery. The majority of the errors take place in the emergency room, where the radiographs are wrongly elucidated as some injuries might be tenuous, and in the majority, conspicuous injuries are missed due to improper training with sub-standard techniques employed in radiological evaluation.⁹ This could be more common in the junior residents under training in the emergency room and orthopedics and traumatology. Unfortunately, this is not uncommon in trained radiologists as well. In the USA, radiologists were at the 6th position in malpractice claims,^10–14 even though they make up about 3.1% of the 892 million physicians.¹⁵ It becomes mandatory to find ways to reduce this discrepancy at both fronts at the training levels and the trained level, and one such tenet is to bring the utilization of AI in the field of diagnosis of fractures and dislocations.

AI, which is part of computer science, can perform tasks that are usually performed by humans to humans. AI requires a high level of input from different images and then can use different algorithms using machine learning, deep learning, and convolutional neural networks to extricate high-level information from the input of images.¹⁶ Recent studies have suggested convincing accuracy of diagnosis of fractures and dislocations using AI algorithms, and with the objective to assess the accuracy, sensitivity, and specificity of AI algorithms in the diagnosis of fractures using plain radiographs, this review was carried out.

Methods

We searched all related electronic databases for English language literature between January 2015 and July 2023, Pub Med, Scopus, Web of Science, Cochrane Central Ovid Medline, Ovid Embase, EBSCO Cumulative Index to Allied Health Literature, Web of Science, and Cochrane Central with keywords of Artificial Intelligence, fractures, dislocations, X-rays, radiographs, missed diagnosis. All articles that fulfilled the following inclusion criteria: primary research using validated AI algorithms for fracture detection and Only studies with a comparative study between AI algorithms and clinicians were included in the analysis. Only studies with a comparative study between AI algorithms and clinicians were included in the analysis. All other publications and data were excluded, including reports by letter to the editor, conference presentations, and systematic reviews. EndNoteTM 39 was used to tabulate the references and delete any duplicates.

Data Extraction

We extracted available information from included studies fitting our inclusion criteria. The data extracted included a number of patients/images studied, site of fractures analyzed, algorithms used, the accuracy of the report based on the algorithm, sensitivity and specificity, area under the curve (AUC), comparison between the algorithm, junior orthopedic resident, emergency physicians, and board certified radiologists.

Statistical Analysis

The diagnostic prediction of the fractures of different algorithms was analyzed using contingency tables for validation. Regression analysis was performed between the different sites of fractures and the influence of the algorithms. A p-value of <0.05 was accepted as statistically significant at a 95% confidence interval (CI). SPSS (Statistical Package for Social Sciences) Inc., which is a statistical software developed by IBM for data management, advanced analytics, multivariate analysis, and business intelligence version 29, was used.

Results

We identified 2049 studies retrieved in which 347 were duplicates, and 1651 publications were excluded due to inclusion and exclusion criteria. Fifty-one studies were reviewed in depth as they nearly fulfilled the inclusion criteria, and only 27 publications fulfilled our objectives to be analyzed in detail and were included in this study (Figure 1). Eighty-eight thousand, nine hundred and ninety-six images were analyzed for fractures (Table 1), which showed that the overall accuracy of the correct diagnosis was 90.35±6.88 (73.59–98) percent, sensitivity 90.08±8.2 (73.8–99) percent, specificity 90.16±7 (72–100) and AUC was 0.931±0.06 (0.72–0.994). The fractures analyzed were common fractures from the wrist, upper and lower limbs, and spine. All studies had internally and externally validated algorithms for Diffusion-convolutional neural networks (DCNN). The majority of the studies limited their analysis for diagnoses based on a single view of the radiograph.

Table 1 Characteristics of Studies, Number of Images Analyzed, Site of Fractures, Algorithms Used, Accuracy, Sensitivity, Specificity and Area Under Curve

Table 2 shows the analysis of 214950 images where a comparison was made between the AI algorithm versus a junior resident in training. The accuracy of the AI model was 94.24±4.19, and that of orthopedic resident was 85.18±7.01 (P value of <0.0001), with sensitivity 92.15±7.12 versus 86.38±7.6 (P<0.0001) and specificity of 93.77±4.03 versus 87.05±12.9 (P<0.0001). Yamada et al (2020) 40 compared the AI model versus orthopedic residents and board-certified radiologists and found the accuracy to be 98% versus 87% and 92% (P value of <0.0001). Figure 2 shows the comparison between the AI model and the clinician for accuracy, sensitivity, and specificity.

Table 2 Comparative Data Between the AI Models and Clinicians

Discussion

This review shows that accuracy in the diagnosis of fractures using AI algorithms surpasses that of the trained and trainee residents. Secondly, the use of AI helped the trainees and trained radiologists in improving the accuracy, sensitivity, and specificity of fracture diagnosis. In this study, the AI with different models showed that the overall accuracy of the correct diagnosis was 90.35±6.88%, sensitivity 90.08±8.2%, specificity 90.16±7 and AUC was 0.931±0.06. These results were based on plain radiographs and included all limb and vertebral fractures.

In the recent past, there has been a consequential increase in different AI models, particularly CNNs, in the arena of trauma and orthopedics. Individual models have conclusively shown that AI models are accurate in the diagnosis of fractures, which are better than junior residents and, if not better, but at par with the senior radiologist. One aspect that needs to be questioned is that most of the reported data comes from retrospective testing, and few only are based prospectively on clinical practice. The accuracy of diagnosis of fractures varied at different sites of fractures. Murphy et al (2022)⁴⁴ reported an analysis of hip fractures, comparing the AI model with two trained and expert clinicians, and found that the AI model was 19% more accurate than the physicians. Another report suggested that the sensitivity of the correct diagnosis increases by over 10%. Lindsey et al (2018)³³ reported that the physician’s average sensitivity in the diagnosis of fractures improved from 80.8% to 91.5% (95% CI, 89.3–92.9%), and specificity was 87.5% to 93.9% (95% CI, 92.9–94.9%) when they were aided with Deep convolutional neural network and added to this the physicians experienced a reduction in misreading around 47.0%. Duron et al (2021)⁴² further concurred after their review that emergency room physicians improved their results after AI assistance from 61.3% to 74.3% (up 13.0%), and the trained radiologists enhanced their diagnosis from 80.2% to 84.6% (up 4.3%). Distal radius fractures, which amount to over 20% of all fractures, were studied using an ensemble model of AI between three groups: AI, orthopedic surgeons, and radiologists, and it was reported statistically significant between the three groups. The accuracy, sensitivity, and specificity between the attending orthopedic surgeons and radiologists showed significant differences: 93.69%, 91.94%, and 95.44% compared to 92.53%, 90.44%, and 94.62%. When the physician’s groups were compared to the AI ensemble tool, it was a highly significant score of 97.75%, 97.13%, and 98.37% by the AI tool.⁴³

Missed extremity fracture diagnosis in trauma practice has always been an issue and is the second most injuries to be misdiagnosed.⁴⁵ The most common malpractice claims against radiologists involve inaccuracies in the reporting of extremity fractures.^10,46,47 Orthopaedic residents are not immune to making misinterpretations of radiographs in extremity fractures. One such study from the United Kingdom highlights that Senior Orthopaedic Residents on plain radiographs missed 4% of fractures, 7.8% made a wrong diagnosis, and 12.6%, a fracture was diagnosed when there was none.⁴⁸ Report indicates that over the years, the number of claims against orthopadicians has increased, but complaints have remained comparatively the same.⁴⁹ In the present belligerent and litigation-oriented society, it is imperative that junior orthopedic residents have all the help in making a correct fracture diagnosis and not miss even a meager injury. AI and its algorithms can never replace human doctors but can unquestionably enhance and complement in improving the accuracy of fracture diagnosis.³⁷ Moreover, adequate and timely training of trainee residents in radiographic interpretation is paramount. It was reported that junior residents till 3rd of training level are more vulnerable to making errors in radiographic interpretation.⁹

Our review has limitations due to the number of studies we have included in the analysis, as there are a number of publications that are increasing by the day, and it is possible that we have not included the most recent literature. Secondly, we could not add the data of comparative accuracy between the unaided and aided AI tools in the fracture diagnosis. Lastly, we are basing the conclusion on the retrospective studies, and there were no prospective studies to compare with. The strength of the study is we have compared a large dataset, which suggests that the different AI models are more accurate than the physicians.

In conclusion, this review highlights with unbiased evaluations recommend that the use of AI models can definitely help residents in training by increasing the accuracy of fracture diagnosis and reducing the errors in diagnosis of fractures. AI has developed cutting edge tools, which need to be further evaluated so that procurement authorities in hospitals could integrate AI into healthcare and help physicians at all levels to improve correctness in fracture diagnosis, to prevent complications of delayed diagnosis.

Disclosure

The authors report no conflicts of interest in this work.

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