Deo, Neha

Objective: Artificial intelligence and machine learning (AI/ML) are increasingly being applied to big clinical data to tackle research questions that cannot be answered with traditional statistical methods. The field is still in its nascent stages and there is a paucity of guidelines for conducting and reporting AI/ML research in hypertension. The objective was to apply the HUMANE checklist to survey the present landscape of AI/ML in hypertension to inform the development of hypertension-specific guidelines and recommendations. Design and method: The HUMANE checklist was developed by global clinical and AI/ML experts through the Delphi method. It assesses the quality of medical AI/ML articles based on whether they cover subjects expected in any peer-reviewed, clinical or AI/ML research publication. A cooping review was carried out to identify articles presenting original research in AI/ML and hypertension published in 2019–2021. Two independent reviewers applied the checklist to each article and in the case of discordance, the response was adjudicated by an AI/ML expert. Results were analysed to assess compliance with the survey (% of papers satisfying checklist requirements). Results: A total of 63 manuscripts was reviewed. A summary of results is shown in Figure 1. Highest compliance was seen for items relating to general article presentation, with compliance ranging from 68% to 98% (description of statistical analysis methods and background context, respectively). Lowest compliance was seen with checklist items relating to clinical research and AI/ML methods. 44% of reviewed articles described the demographics of their dataset and 48% stated their inclusion/exclusion criteria. Nonetheless, datasets were deemed appropriate for investigative aims in 93% of articles. 30% of manuscripts reported their calibration measures, while 73% stated their performance metrics. Internal validation was carried out in 75% of studies, but external validity was assessed in only 14% of cases. Algorithmic bias was addressed in 11% of papers. Conclusions: Application of AI/ML methods in hypertension research is growing, but the majority of current work has major shortfalls in reporting quality, model validation and algorithmic bias. Our study identifies areas of improvement to enable the full realisation of the potential of AI/ML in hypertension.

AbstractIntroductionCoronavirus disease 2019 (COVID‐19) is associated with high rates of morbidity and mortality. Primary hypothyroidism is a common comorbid condition, but little is known about its association with COVID‐19 severity and outcomes. This study aims to identify the frequency of hypothyroidism in hospitalized patients with COVID‐19 as well as describe the differences in outcomes between patients with and without pre‐existing hypothyroidism using an observational, multinational registry.MethodsIn an observational cohort study we enrolled patients 18 years or older, with laboratory‐confirmed severe acute respiratory syndrome coronavirus‐2 infection between March 2020 and February 2021. The primary outcomes were (1) the disease severity defined as per the World Health Organization Scale for Clinical Improvement, which is an ordinal outcome corresponding with the highest severity level recorded during a patient's index COVID‐19 hospitalization, (2) in‐hospital mortality and (3) hospital‐free days. Secondary outcomes were the rate of intensive care unit (ICU) admission and ICU mortality.ResultsAmong the 20,366 adult patients included in the study, pre‐existing hypothyroidism was identified in 1616 (7.9%). The median age for the Hypothyroidism group was 70 (interquartile range: 59–80) years, and 65% were female and 67% were White. The most common comorbidities were hypertension (68%), diabetes (42%), dyslipidemia (37%) and obesity (28%). After adjusting for age, body mass index, sex, admission date in the quarter year since March 2020, race, smoking history and other comorbid conditions (coronary artery disease, hypertension, diabetes and dyslipidemia), pre‐existing hypothyroidism was not associated with higher odds of severe disease using the World Health Organization disease severity index (odds ratio [OR]: 1.02; 95% confidence interval [CI]: 0.92, 1.13; p = .69), in‐hospital mortality (OR: 1.03; 95% CI: 0.92, 1.15; p = .58) or differences in hospital‐free days (estimated difference 0.01 days; 95% CI: −0.45, 0.47; p = .97). Pre‐existing hypothyroidism was not associated with ICU admission or ICU mortality in unadjusted as well as in adjusted analysis.ConclusionsIn an international registry, hypothyroidism was identified in around 1 of every 12 adult hospitalized patients with COVID‐19. Pre‐existing hypothyroidism in hospitalized patients with COVID‐19 was not associated with higher disease severity or increased risk of mortality or ICU admissions. However, more research on the possible effects of COVID‐19 on the thyroid gland and its function is needed in the future.