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ImportanceMetformin is often used as a first-line therapy for type 2 diabetes; however, frequent discontinuation with reduced kidney function and increased disease severity indicates that a comparison with any other group (eg, nonusers or insulin users) must address significant residual confounding concerns.ObjectivesTo examine the potential for residual confounding in a commonly used observational study design applied to metformin and to propose a more robust study design for future observational studies of metformin.Design, Setting, and ParticipantsThis retrospective cohort study with a prevalent user design was conducted using an administrative claims database for Medicare Advantage beneficiaries in the US. Participants were categorized into 2 distinct cohorts: 404 458 individuals with type 2 diabetes and 81 791 individuals with prediabetes. Clinical history was observed in 2018, and end points were observed in 2019. Statistical analyses were conducted between May and December 2021.ExposuresPrevalent use (recent prescription and history of use on at least 90 of the preceding 365 days) of metformin or insulin but not both at the start of the observation period.Main Outcomes and MeasuresTotal inpatient admission days in 2019 and total medical spending (excluding prescription drugs) in 2019. Each of these measures was treated as a binary outcome (0 vs >0 inpatient days and top 10% vs bottom 90% of medical spending).ResultsThe study included 404 458 adults with type 2 diabetes (mean [SD] age, 74.5 [7.5] years; 52.7% female). A strong metformin effect estimate was associated with reduced inpatient admissions (odds ratio, 0.60; 95% CI, 0.58-0.62) and reduced medical expenditures (odds ratio, 0.57; 95% CI, 0.55-0.60). However, implementation of additional robust design features (negative control outcomes and a complementary cohort) revealed that the estimated beneficial effect was attributable to residual confounding associated with individuals’ overall health, not metformin itself.Conclusions and RelevanceThese findings suggest that common observational study designs for studies of metformin in a type 2 diabetes population are at risk for consequential residual confounding. By performing 2 additional validation checks, the study design proposed here exposes residual confounding that nullifies the initially favorable claim derived from a common study design.

Objective: Despite technological and treatment advancements over the past two decades, cardiogenic shock (CS) mortality has remained between 40-60%. A number of factors can lead to delayed diagnosis of CS, including gradual onset and nonspecific symptoms. Our objective was to develop an algorithm that can continuously monitor heart failure patients, and partition them into cohorts of high- and low-risk for CS.Methods: We retrospectively studied 24,461 patients hospitalized with acute decompensated heart failure, 265 of whom developed CS, in the Johns Hopkins Healthcare system. Our cohort identification approach is based on logistic regression, and makes use of vital signs, lab values, and medication administrations recorded during the normal course of care. Results: Our algorithm identified patients at high-risk of CS. Patients in the high-risk cohort had 10.2 times (95% confidence interval 6.1-17.2) higher prevalence of CS than those in the low-risk cohort. Patients who experienced cardiogenic shock while in the high-risk cohort were first deemed high-risk a median of 1.7 days (interquartile range 0.8 to 4.6) before cardiogenic shock diagnosis was made by their clinical team. Conclusions: This risk model was able to predict patients at higher risk of CS in a time frame that allowed a change in clinical care. Future studies need to evaluate if CS analysis of high-risk cohort identification may affect outcomes.