Use of machine learning to predict mortality in patients with type 2 diabetes mellitus, according to socioeconomic status

Abstract Introduction Patients with Type 2 diabetes mellitus (T2DM) are at increased risk of developing cardiovascular disease (CVD) that adversely affects prognosis. Socioeconomic deprivation may be associated with lifestyle choices that contribute to adverse outcomes. The influence of social deprivation on the prognosis of T2DM is rarely investigated in large cohorts. Therefore, we assessed the associations between socioeconomic status and survival in patients with T2DM by applying conventional statistical methods and state-of-the-art, machine learning (ML) models. Methods We obtained routinely collected, linked administrative data for individuals with T2DM aged >50 years from the National Health Service (NHS) Scotland, including demographic data, laboratory measurements, prescriptions, death records and primary and secondary care diagnostic codes. We developed a random survival forest model for predicting all-cause mortality, overall and for each quintile of socioeconomic status using the Scottish Index of Multiple Deprivation (SIMD), which is based on postcodes that reflect a combination of income, employment, local crime rates, education, housing and health. We used Cox proportional hazards models to investigate the risk between the most and least deprived quintiles. Subsequently, we applied Shapely Additive Explanations (SHAP), a state-of-the-art ML interpretability method, to identify key prognostic factors that influence the survival probability prediction score for each sub-group. Results 30,495 people had a newly recorded diagnosis of T2DM between 2009-2019, of whom 4,300 died within 10 years. Table 1 shows the c-index (global assessment of model discrimination) and time-brier score (measure of calibration for time-dependent probability prediction) for the whole cohort and for each SIMD quintile: use of loop diuretics, older age, lower serum concentrations of albumin and alanine transaminase (ALT) and estimated glomerular function rate (eGFR) were strong predictors of death for all quintiles. Prevalence of chronic obstructive pulmonary disease (COPD) was strongly associated with mortality for the most deprived quintile (Q1, whilst strokes were strongly associated with mortality for the most affluent (Q5). A history of heart failure, in addition to use of loop diuretics, predicted a higher risk, in 3 of the 5 quintiles. Overall Q1 had a 36% higher mortality than Q5 (HR: adjusted for age and sex 1.36 [95% CI 1.24 – 1.50 (