Baseline haemoglobin A1c and the risk of COVID-19 hospitalization among patients with diabetes in the INSIGHT Clinical Research Network.

AIMS: To examine the association between baseline glucose control and risk of COVID-19 hospitalization and in-hospital death among patients with diabetes. METHODS: We performed a retrospective cohort study of adult patients in the INSIGHT Clinical Research Network with a diabetes diagnosis and haemoglobin A1c (HbA1c) measurement in the year prior to an index date of March 15, 2020. Patients were divided into four exposure groups based on their most recent HbA1c measurement (in mmol/mol): 39-46 (5.7%-6.4%), 48-57 (6.5%-7.4%), 58-85 (7.5%-9.9%), and ≥86 (10%). Time to COVID-19 hospitalization was compared in the four groups in a propensity score-weighted Cox proportional hazards model adjusting for potential confounders. Patients were followed until June 15, 2020. In-hospital death was examined as a secondary outcome. RESULTS: Of 168,803 patients who met inclusion criteria; 50,016 patients had baseline HbA1c 39-46 (5.7%-6.4%); 54,729 had HbA1c 48-57 (6.5-7.4%); 47,640 had HbA1c 58-85 (7.5^%-9.9%) and 16,418 had HbA1c ≥86 (10%). Compared with patients with HbA1c 48-57 (6.5%-7.4%), the risk of hospitalization was incrementally greater for those with HbA1c 58-85 (7.5%-9.9%) (adjusted hazard ratio [aHR] 1.19, 95% confidence interval [CI] 1.06-1.34) and HbA1c ≥86 (10%) (aHR 1.40, 95% CI 1.19-1.64). The risk of COVID-19 in-hospital death was increased only in patients with HbA1c 58-85 (7.5%-9.9%) (aHR 1.29, 95% CI 1.06, 1.61). CONCLUSIONS: Diabetes patients with high baseline HbA1c had a greater risk of COVID-19 hospitalization, although association between HbA1c and in-hospital death was less consistent. Preventive efforts for COVID-19 should be focused on diabetes patients with poor glucose control.

Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health.

The coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.