IMPORTANCE The comparative cardiovascular safety of analogue and human insulins in adults with type 2 diabetes who initiate insulin therapy in usual care settings has not been carefully evaluated using machine learning and other rigorous analytic methods. OBJECTIVE To examine the association of analogue vs human insulin use with mortality and major cardiovascular events. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study included 127 600 adults aged 21 to 89 years with type 2 diabetes at 4 health care delivery systems who initiated insulin therapy from January 1, 2000, through December 31, 2013. Machine learning and rigorous inference methods with time-varying exposures were used to evaluate associations of continuous exposure to analogue vs human insulins with mortality and major cardiovascular events. Data were analyzed

Key Points Question Are glyburide and subcutaneous insulin use associated with different rates of perinatal complications among individuals with gestational diabetes? Findings In this cohort study of 11 321 patients with gestational diabetes, the use of glyburide vs insulin was associated with similar rates of infant hypoglycemia, hyperbilirubinemia, appropriate size-for–gestational age, and cesarean delivery as well as significantly lower rates of infant neonatal intensive care unit admission after adjusting for baseline and time-varying covariates. Meaning These findings do not provide evidence of a difference in the outcomes examined among patients with gestational diabetes initiating glyburide compared with those initiating insulin.

IMPORTANCE Cardiovascular events and mortality are the principal causes of excess mortality and health care costs for people with type 2 diabetes. No large studies have specifically compared longacting insulin alone with long-acting plus short-acting insulin with regard to cardiovascular outcomes. OBJECTIVE To compare cardiovascular events and mortality in adults with type 2 diabetes receiving long-acting insulin who do or do not add short-acting insulin. DESIGN, SETTING, AND PARTICIPANTSThis retrospective cohort study emulated a randomized experiment in which adults with type 2 diabetes who experienced a qualifying glycated hemoglobin A 1c (HbA 1c ) level of 6.8% to 8.5% with long-acting insulin were randomized to continuing treatment with long-acting insulin (LA group) or adding short-acting insulin within 1 year of the qualifying HbA 1c level (LA plus SA group). Retrospective data in 4 integrated health care delivery systems from the

<p>  </p> <p>OBJECTIVE: Although diabetic retinopathy is a leading cause of blindness worldwide, diabetes-related blindness can be prevented through effective screening, detection and treatment of disease. The study goal was to develop risk stratification algorithms for the onset of retinal complications of diabetes, including proliferative diabetic retinopathy, referable retinopathy and macular edema.</p> <p>RESEARCH DESIGN and METHODS: Retrospective cohort analysis of patients from the Kaiser Permanente Northern California Diabetes Registry who had no evidence of diabetic retinopathy at a baseline diabetic retinopathy screening during 2008-2020. Machine learning and logistic regression prediction models for onset of proliferative diabetic retinopathy, diabetic macular edema and referable retinopathy detected through routine screening were trained and internally validated. Model performance was assessed using area under the curve (AUC) metrics.</p> <p>RESULTS: The study cohort (N=276,794) was 51.9 % male and 42.1% white. Mean (±SD) age at baseline was 60.0 (±13.1) years. A machine learning XGBoost algorithm was effective in identifying patients who developed proliferative diabetic retinopathy (AUC 0.86; 95% CI, 0.86-0.87), diabetic macular edema (AUC 0.76; 95%CI, 0.75-0.77) and referable retinopathy (AUC 0.78; 95% CI, 0.78-0.79). Similar results were found using a simpler 9-covariate logistic regression model: proliferative diabetic retinopathy (AUC 0.82; 95% CI, 0.80-0.83), diabetic macular edema (AUC 0.73; 95% CI, 0.72-0.74) and referable retinopathy (AUC 0.75; 95% CI, 0.75-0.76).</p> <p>CONCLUSIONS:  Relatively simple logistic regression models using 9 readily available clinical variables can be used to rank order patients for onset of diabetic eye disease and thereby more efficiently prioritize and target screening for at risk patients. </p>

OBJECTIVE Although diabetic retinopathy is a leading cause of blindness worldwide, diabetes-related blindness can be prevented through effective screening, detection, and treatment of disease. The study goal was to develop risk stratification algorithms for the onset of retinal complications of diabetes, including proliferative diabetic retinopathy, referable retinopathy, and macular edema. RESEARCH DESIGN AND METHODS Retrospective cohort analysis of patients from the Kaiser Permanente Northern California Diabetes Registry who had no evidence of diabetic retinopathy at a baseline diabetic retinopathy screening during 2008–2020 was performed. Machine learning and logistic regression prediction models for onset of proliferative diabetic retinopathy, diabetic macular edema, and referable retinopathy detected through routine screening were trained and internally validated. Model performance was assessed using area under the curve (AUC) metrics. RESULTS The study cohort (N = 276,794) was 51.9% male and 42.1% White. Mean (±SD) age at baseline was 60.0 (±13.1) years. A machine learning XGBoost algorithm was effective in identifying patients who developed proliferative diabetic retinopathy (AUC 0.86; 95% CI, 0.86–0.87), diabetic macular edema (AUC 0.76; 95% CI, 0.75–0.77), and referable retinopathy (AUC 0.78; 95% CI, 0.78–0.79). Similar results were found using a simpler nine-covariate logistic regression model: proliferative diabetic retinopathy (AUC 0.82; 95% CI, 0.80–0.83), diabetic macular edema (AUC 0.73; 95% CI, 0.72–0.74), and referable retinopathy (AUC 0.75; 95% CI, 0.75–0.76). CONCLUSIONS Relatively simple logistic regression models using nine readily available clinical variables can be used to rank order patients for onset of diabetic eye disease and thereby more efficiently prioritize and target screening for at risk patients.