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Vaccine boosters help keep cancer patients from being hospitalized or admitted to intensive care units due to COVID-19, according to a new study led by Cedars-Sinai investigators. Their findings, published in JAMA Oncology, offer real-world evidence to support vaccine recommendations for these patients.

Cancer patients are a vulnerable population. Their immune systems can be weakened by their disease and the treatments they receive, which is why major health organizations recommend that these patients be vaccinated against COVID-19. Our study supports these recommendations. We used real-world data across four major health systems in the U.S. to show that these booster vaccines reduce the risk of hospitalization and severe illness.”

Jane Figueiredo, PhD, director of Community Health and Population Research at Cedars-Sinai and senior author of the study

Investigators analyzed data on more than 161,000 patients treated for cancer during 2022 and 2023 at Cedars-Sinai, Kaiser Permanente Northern California, Northwell Health, and the Veterans Health Administration.

The data showed COVID-19 boosters reduced cancer patients’ hospitalizations and ICU admissions by 29% and prevented one hospitalization or ICU admission for every 150-166 boosted patients.

“The reduction in hospitalizations was significant, and the number of patients we needed to treat to see a benefit to the boosters is quite low,” said Figueiredo, who is also program leader of Cancer Prevention and Control at Cedars-Sinai. “This shows a great benefit to our cancer patients and should encourage patients to discuss vaccination with their healthcare providers.”

Figueiredo noted that among their sample, the percentage of patients receiving boosters was quite low. By Jan. 1, 2022, 68% had received a booster. And after updated boosters targeting more than one COVID-19 strain had become available, only 38% of patients had received one.

“Whether this is due to patient concerns about safety or provider uncertainty about whether to administer a vaccine during treatment is not clear,” Figueiredo said. “What is clear is that we need to advocate strongly for vulnerable groups, including cancer patients, to receive these vaccines.”

Figueiredo noted that the COVID-19 pandemic was the first opportunity for investigators to examine data on mRNA vaccines, and five years later there is still much to learn about the effectiveness of these vaccines in various populations.

“This is the largest study to date of COVID-19 booster effectiveness in cancer patients, a high-risk population of critical importance,” said Robert Figlin, MD, interim director of Cedars-Sinai Cancer. “It adds substantially to our understanding of the effectiveness of COVID-19 vaccines, and we will undertake additional studies as vaccine formulations change and new variants emerge so that we can make recommendations that best protect the health of our patients.”

Figueiredo’s team is preparing to publish vaccine effectiveness data on patients with autoimmune diseases and patients who have undergone solid organ transplants.

“There are several different groups whose immune systems have been affected in different ways, which gives us an opportunity to expand further our understanding of how these vaccines work,” Figueiredo said.

Several local factors – including the minimum temperature reached in autumn, the water level in lakes and ponds in winter, and the presence of mute swans (Cygnus olor) – could be key for predicting the potential of an outbreak of highly pathogenic avian flu (HPAI) occurring in Europe. The findings, published in Scientific Reports, are derived from a machine learning model trained on the characteristics of 21^st century European HPAI outbreaks and could help improve future monitoring programmes.

HPAI outbreaks are a serious concern for both animal and public health. A wave of HPAI outbreaks across the Northern Hemisphere during 2022 were associated with an increase in the number of avian influenza virus infections in mammals, which subsequently increased the likelihood of a spillover event to humans. To reduce the chances of such an event occurring, it is critical for scientists to understand the underlying factors which can increase the likelihood of an HPAI outbreak.

Joacim Rocklöv and colleagues trained a machine learning model on the characteristics of every HPAI outbreak reported in Europe between 2006 and 2021. The characteristics assessed were all identified as potential outbreak predictors and included: the seasonal temperature and precipitation conditions in the region; the local wild bird population; the local farmed poultry density; and the seasonal vegetation density and water level in the region. The authors then tested the accuracy of their model using the outbreak data for 2022 and 2023.

The authors found that the coldest recorded temperature in autumn had the greatest effect on the likelihood of an outbreak occurring. However, the actual effect varied considerably by region. In some areas, warmer minimum temperatures were associated with a higher outbreak likelihood, while in others, they were associated with a lower likelihood. Cold winter and spring temperatures were also both associated with an increase in the outbreak likelihood. However, a low vegetation density between October and December, and a lower-than-expected water level in lakes and ponds between January and March were both associated with a decrease in the outbreak likelihood. The authors also noted that the presence of a local population of mute swans was associated with an increase in the likelihood of an outbreak.

The authors suggest that the results could be used to help tailor regional HPAI surveillance programmes across Europe, increasing the likelihood of identifying an outbreak in its earliest stages.

In a proof-of-concept study published in the Journal of the American College of Surgeons, researchers investigated whether artificial intelligence (AI) models such as ChatGPT-4 can be used to accurately extract and classify diagnostic data from radiologic imaging reports of pancreatic cysts. The study, conducted by researchers at Memorial Sloan Kettering Cancer Center, compared the AI model’s performance to the benchmark of manual chart review by radiologists. The findings indicate that AI can generate and classify diagnostic variables from electronic medical data as accurately as the manual approach.

“ChatGPT-4 is a much more efficient approach, is cost effective, and allows researchers to focus on data analysis and quality assurance rather than the process of reviewing chart after chart,” said study coauthor Kevin C. Soares, MD, MS, a hepatopancreatobiliary cancer surgeon at Memorial Sloan Kettering Cancer Center in New York City. “Our study established that this AI approach was essentially equally as accurate as the manual approach, which is the gold standard.”

Study Methods and Rationale

In the study, researchers analyzed a dataset comprising 3,198 unique MRI and CT scans from 991 adult patients under long-term surveillance for pancreatic cysts between 2010 and 2024. ChatGPT-4 was used to extract nine radiographic elements relevant to pancreatic cyst progression, including cyst size, main pancreatic duct size, ductal dilation ≥ 5 mm, number of lesions, branch duct dilation, presence of solid components, calcifications, pancreatic atrophy, and signs of pancreatitis. The extracted data were compared against a manually annotated institutional cyst database that served as the reference standard.

Key Findings

ChatGPT-4 demonstrated high accuracy across all clinical variables analyzed. The model’s accuracy rate ranged from 97% in identifying the presence of solid components to 99% in detecting calcific lesions. Performance remained robust for other high-risk factors, with 92% accuracy for cyst size and 97% for main pancreatic duct size.

“AI can help us expand medical research and improve patient outcomes,” Dr. Soares said. “The question I get asked most often is, ‘What is the chance that this cyst is going to develop into cancer?’ We now have an efficient way to look at the MRI and CT scans of thousands of patients and give our patients a better answer. This approach goes a long way to reduce anxiety and help patients feel more confident about their treatment decisions.”

Despite these encouraging findings, the researchers caution that the study employed only one AI source, ChatGPT-4, and results are limited to the data that were used. AI can only work with the information provided to it. These limitations may reduce the broader applicability of the findings. Nevertheless, the researchers concluded: “[AI] can accurately extract and curate data from radiology reports for pancreatic cyst surveillance and can be reliably used to assemble longitudinal databases. Future application of this work may potentiate the development of artificial intelligence-based surveillance models.”

Disclosure: For full disclosures of the study authors, visit journals.lww.com/journalacs.

According to the US Centers for Disease Control and Prevention (CDC), a third of US teenagers are prediabetic, meaning that their blood sugar levels surpass normal (less than 5.6mmol/L) but are not yet classified as diabetes (over 6.9mmol/L) so lie in between 5.6mmol/L to 6.9mmol/L. Previous research from the CDC suggests that this rate has more than doubled since 1999, which is likely a consequence of lifestyle factors such as poor diet and a lack of physical exercise. Approximately 70% of people with prediabetes develop type 2 diabetes (T2D), so it is vital to minimise the factors associated with elevated glucose levels to prevent future diagnosis of T2D among this group. Dr Christopher Holliday, the CDC’s leading official in diabetes prevention, said the results of this research should serve as a “wake-up call”, as the risk of T2D poses a “significant threat” to young people’s health. GlobalData epidemiologists anticipate that if children and adolescents in the US continue to live unhealthy lifestyles, the prevalence of T2D and a cascade of other chronic illnesses will also increase and likely be diagnosed earlier in adulthood.

T2D is a chronic condition that occurs when the body does not produce, or cannot use, the hormone insulin responsible for regulating blood sugar levels. Insulin issues typically begin during puberty and, without treatment, can cause damage to blood vessels and nerves, which increases the risk of cardiovascular issues, kidney disease, neuropathy, retinopathy, and amputations—additionally, diabetes can weaken the immune system, increasing susceptibility to infections such as Covid-19. For this reason, diabetes is the seventh leading cause of death in the US.

As stated previously, being prediabetic, or diabetic, is associated with an inactive lifestyle and poor diet—the increase in the young prediabetic population suggests that this lifestyle is increasing across the US. Dr Holliday stated that “simple life changes like healthy eating and staying active can make a big difference in preventing or delaying T2D”. This emphasises the need for the promotion of a healthy lifestyle among children and adolescents in the US—health officials must focus on groups who are vulnerable to food insecurity and poor health literacy in a time where weight loss treatments are increasing in popularity to ensure that the fundamentals of a healthy lifestyle are not forgotten.

GlobalData epidemiologists currently expect that in 2025 there will be 18,000 diagnosed prevalent cases of T2D among 13–19 years, which is expected to remain stable at 18,000 cases in 2028—however, if the rate of prediabetes continues to increase it is likely that there will be an increase in the diagnosis of T2D in teenagers and young adults, in addition to the increase of other chronic conditions. But if the necessary lifestyle improvements are made, there should be a decrease in the diagnosed prevalent cases of T2D and related chronic conditions in the US.