Patient diversity and author representation in clinical studies supporting the Surviving Sepsis Campaign guidelines for management of sepsis and septic shock 2021: a systematic review of citations.

BACKGROUND: The generalizability of the Surviving Sepsis Campaign (SSC) guidelines to various patient populations and hospital settings has been debated. A quantitative assessment of the diversity and representation in the clinical evidence supporting the guidelines would help evaluate the generalizability of the recommendations and identify strategic research goals and priorities. In this study, we evaluated the diversity of patients in the original studies, in terms of sex, race/ethnicity, and geographical location. We also assessed diversity in sex and geographical representation among study first and last authors. METHODS: All clinical studies cited in support of the 2021 SSC adult guideline recommendations were identified. Original clinical studies were included, while editorials, reviews, non-clinical studies, and meta-analyses were excluded. For eligible studies, we recorded the proportion of male patients, percentage of each represented racial/ethnic subgroup (when available), and countries in which they were conducted. We also recorded the sex and location of the first and last authors. The World Bank classification was used to categorize countries. RESULTS: The SSC guidelines included six sections, with 85 recommendations based on 351 clinical studies. The proportion of male patients ranged from 47 to 62%. Most studies did not report the racial/ ethnic distribution of the included patients; when they did so, most were White patients (68-77%). Most studies were conducted in high-income countries (77-99%), which included Europe/Central Asia (33-66%) and North America (36-55%). Moreover, most first/last authors were males (55-93%) and from high-income countries (77-99%). CONCLUSIONS: To enhance the generalizability of the SCC guidelines, stakeholders should define strategies to enhance the diversity and representation in clinical studies. Though there was reasonable representation in sex among patients included in clinical studies, the evidence did not reflect diversity in the race/ethnicity and geographical locations. There was also lack of diversity among the first and last authors contributing to the evidence.

Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study.

PURPOSE: Chronic obstructive pulmonary disease (COPD) is one of the most common chronic illnesses in the world. Unfortunately, COPD is often difficult to diagnose early when interventions can alter the disease course, and it is underdiagnosed or only diagnosed too late for effective treatment. Currently, spirometry is the gold standard for diagnosing COPD but it can be challenging to obtain, especially in resource-poor countries. Chest X-rays (CXRs), however, are readily available and may have the potential as a screening tool to identify patients with COPD who should undergo further testing or intervention. In this study, we used three CXR datasets alongside their respective electronic health records (EHR) to develop and externally validate our models. METHOD: To leverage the performance of convolutional neural network models, we proposed two fusion schemes: (1) model-level fusion, using Bootstrap aggregating to aggregate predictions from two models, (2) data-level fusion, using CXR image data from different institutions or multi-modal data, CXR image data, and EHR data for model training. Fairness analysis was then performed to evaluate the models across different demographic groups. RESULTS: Our results demonstrate that DL models can detect COPD using CXRs with an area under the curve of over 0.75, which could facilitate patient screening for COPD, especially in low-resource regions where CXRs are more accessible than spirometry. CONCLUSIONS: By using a ubiquitous test, future research could build on this work to detect COPD in patients early who would not otherwise have been diagnosed or treated, altering the course of this highly morbid disease.

Emulating Target Trials Comparing Early and Delayed Intubation Strategies.

BACKGROUND: Whether intubation should be initiated early in the clinical course of critically ill patients remains a matter of debate. Results from prior observational studies are difficult to interpret because of avoidable flaws including immortal time bias, inappropriate eligibility criteria, and unrealistic treatment strategies. RESEARCH QUESTION: Do treatment strategies that intubate patients early in the critical care admission improve 30-day survival compared with strategies that delay intubation? STUDY DESIGN AND METHODS: We estimated the effect of strategies that require early intubation of critically ill patients compared with those that delay intubation. With data extracted from the Medical Information Mart for Intensive Care-IV database, we emulated three target trials, varying the flexibility of the treatment strategies and the baseline eligibility criteria. RESULTS: Under unrealistically strict treatment strategies with broad eligibility criteria, the 30-day mortality risk was 7.1 percentage points higher for intubating early compared with delaying intubation (95% CI, 6.2-7.9). Risk differences were 0.4 (95% CI, -0.1 to 0.9) and -0.9 (95% CI, -2.5 to 0.7) percentage points in subsequent target trial emulations that included more realistic treatment strategies and eligibility criteria. INTERPRETATION: When realistic treatment strategies and eligibility criteria are used, strategies that delay intubation result in similar 30-day mortality risks compared with those that intubate early. Delaying intubation ultimately avoids intubation in most patients.

The impact of commercial health datasets on medical research and health-care algorithms.

As the health-care industry emerges into a new era of digital health driven by cloud data storage, distributed computing, and machine learning, health-care data have become a premium commodity with value for private and public entities. Current frameworks of health data collection and distribution, whether from industry, academia, or government institutions, are imperfect and do not allow researchers to leverage the full potential of downstream analytical efforts. In this Health Policy paper, we review the current landscape of commercial health data vendors, with special emphasis on the sources of their data, challenges associated with data reproducibility and generalisability, and ethical considerations for data vending. We argue for sustainable approaches to curating open-source health data to enable global populations to be included in the biomedical research community. However, to fully implement these approaches, key stakeholders should come together to make health-care datasets increasingly accessible, inclusive, and representative, while balancing the privacy and rights of individuals whose data are being collected.