Yale Journal of Biology and Medicine最新文献

Ophthalmology stands at the vanguard of incorporating big data into medicine, as exemplified by the integration of The Intelligent Research in Sight (IRIS) Registry. This synergy cultivates patient-centered care, demonstrates real world efficacy and safety data for new therapies, and facilitates comprehensive population health insights. By evaluating the creation and utilization of the world's largest specialty clinical data registry, we underscore the transformative capacity of data-driven medical paradigms, current shortcomings, and future directions. We aim to provide a scaffold for other specialties to adopt big data integration into medicine.

Laron syndrome (LS) is a rare autosomal recessively segregating disorder of severe short stature. The condition is characterized by short limbs, delayed puberty, hypoglycemia in infancy, and obesity. Mutations in growth hormone receptor (GHR) have been implicated in LS; hence, it is also known as growth hormone insensitivity syndrome (MIM-262500). Here we represent a consanguineous Pakistani family in which three siblings were afflicted with LS. Patients had rather similar phenotypic presentations marked with short stature, delayed bone age, limited extension of elbows, truncal obesity, delayed puberty, childish appearance, and frontal bossing. They also had additional features such as hypo-muscularity, early fatigue, large ears, widely-spaced breasts, and attention deficit behavior, which are rarely reported in LS. The unusual combination of the features hindered a straightforward diagnosis and prompted us to first detect the regions of shared homozygosity and subsequently the disease-causing variant by next generation technologies, like SNP genotyping and exome sequencing. A homozygous pathogenic variant c.508G>C (p.(Asp170His)) in GHR was detected. The variant is known to be implicated in LS, supporting the molecular diagnosis of LS. Also, we present detailed clinical, hematological, and hormonal profiling of the siblings.

Diagnostic imaging reports are generally written with a target audience of other providers. As a result, the reports are written with medical jargon and technical detail to ensure accurate communication. With implementation of the 21st Century Cures Act, patients have greater and quicker access to their imaging reports, but these reports are still written above the comprehension level of the average patient. Consequently, many patients have requested reports to be conveyed in language accessible to them. Numerous studies have shown that improving patient understanding of their condition results in better outcomes, so driving comprehension of imaging reports is essential. Summary statements, second reports, and the inclusion of the radiologist's phone number have been proposed, but these solutions have implications for radiologist workflow. Artificial intelligence (AI) has the potential to simplify imaging reports without significant disruptions. Many AI technologies have been applied to radiology reports in the past for various clinical and research purposes, but patient focused solutions have largely been ignored. New natural language processing technologies and large language models (LLMs) have the potential to improve patient understanding of their imaging reports. However, LLMs are a nascent technology and significant research is required before LLM-driven report simplification is used in patient care.

Objectives: To evaluate the comparative effectiveness of treatments, a randomized clinical trial remains the gold standard but can be challenged by a high cost, a limited sample size, an inability to fully reflect the real world, and feasibility concerns. The objective is to showcase a big data approach that takes advantage of large electronic medical record (EMR) data to emulate clinical trials. To overcome the limitations of regression analysis, a deep learning-based analysis pipeline was developed. Study Design and Setting: Lumpectomy (breast-conserving surgery) and mastectomy are the two most commonly used surgical procedures for early-stage female breast cancer patients. An emulation trial was designed using the Surveillance, Epidemiology, and End Results (SEER)-Medicare data to evaluate their relative effectiveness in overall survival. The analysis pipeline consisted of a propensity score step, a weighted survival analysis step, and a bootstrap inference step. Results: A total of 65,997 subjects were enrolled in the emulated trial, with 50,704 and 15,293 in the lumpectomy and mastectomy arms, respectively. The two surgery procedures had comparable effects in terms of overall survival (survival year change = 0.08, 95% confidence interval (CI): -0.08, 0.25) for the elderly SEER-Medicare early-stage female breast cancer patients. Conclusion: This study demonstrated the power of "mining large EMR data + deep learning-based analysis," and the proposed analysis strategy and technique can be potentially broadly applicable. It provided convincing evidence of the comparative effectiveness of lumpectomy and mastectomy.

Background: Low-resolution administrative databases can give biased results, whereas high-resolution, time-stamped variables from clinical databases like MIMIC-IV might provide nuanced insights. We evaluated racial-ethnic disparities in life-sustaining ICU-treatments (Invasive Mechanical Ventilation (IMV), Renal Replacement Therapy (RRT), and Vasopressors (VP)) among patients with sepsis. Methods: In this observational retrospective cohort study, patients fulfilling sepsis-3 criteria were categorized by treatment assignment within the first 4 days. The outcomes were treatment allocations. The likelihood of receiving treatment was calculated by race-ethnicity (Racial-ethnic group (REG) or White group (WG)) using 5-fold sub-sampling nested logistic regression and XGBoost. Results: In 23,914 admissions, 82% were White, 42% were women. REG were less likely to receive IMV across all eligibility days (day 1 odds ratio (OR) 0.87, 95% confidence interval (CI) 0.83-0.94, day 4 OR 0.80, 95% CI 0.72 - 0.87). There were no differences in RRT (day 1 OR 1.00, 95% CI 0.96-1.09, day 4 OR 1.00, 95% CI 0.94-1.06). REG were also less likely to be treated with VP at days 1 to 3 (day 1 OR 0.87, 95% CI 0.76-0.94), but not at day 4 (OR 0.95, 95% CI 0.87-1.01). These findings remained robust when relaxing eligibility criteria for treatment allocation. Conclusion: Our findings reveal significant disparities in the use of invasive life-saving ICU treatments among septic patients from racial and ethnic minority backgrounds, particularly with respect to IMV and VP use. These disparities underscore not only the need to address inequality in critical care settings, but also highlight the importance of high-resolution data.

Background: Relatively little attention has been paid to the potential effects of rising temperatures on changes in human behavior that lead to health and social consequences, including aggression. This study investigated the association between ambient temperature and aggression using assault death data from Seoul, South Korea (1991-2020). Methods: We conducted a time-stratified case-crossover analysis based on conditional logistic regression to control for relevant covariates. The exposure-response curve was explored, and stratified analyses were conducted by season and sociodemographic characteristics. Results: The overall risk of assault deaths significantly increased by 1.4% per 1°C increase in ambient temperature. A positive curvilinear relationship was observed between ambient temperature and assault deaths, which flattened out at 23.6°C during the warm season. Furthermore, risk increases were higher in males, teenagers, and those with the least education. Conclusion: This study highlighted the importance of understanding the impact of rising temperatures on aggression in the context of climate change and public health.