Abstract
BackgroundThe study aimed to assess the performance of a multidisciplinary-team diabetes care program called DIABETIMSS on glycemic control of type 2 diabetes (T2D) patients, by using available observational patient data and machine-learning-based targeted learning methods.MethodsWe analyzed electronic health records and laboratory databases from the year 2012 to 2016 of T2D patients from six family medicine clinics (FMCs) delivering the DIABETIMSS program, and five FMCs providing routine care. All FMCs belong to the Mexican Institute of Social Security and are in Mexico City and the State of Mexico. The primary outcome was glycemic control. The study covariates included: patient sex, age, anthropometric data, history of glycemic control, diabetic complications and comorbidity. We measured the effects of DIABETIMSS program through 1) simple unadjusted mean differences; 2) adjusted via standard logistic regression and 3) adjusted via targeted machine learning. We treated the data as a serial cross-sectional study, conducted a standard principal components analysis to explore the distribution of covariates among clinics, and performed regression tree on data transformed to use the prediction model to identify patient sub-groups in whom the program was most successful. To explore the robustness of the machine learning approaches, we conducted a set of simulations and the sensitivity analysis with process-of-care indicators as possible confounders.ResultsThe study included 78,894 T2D patients, from which 37,767patients received care through DIABETIMSS. The impact of DIABETIMSS ranged, among clinics, from 2 to 8% improvement in glycemic control, with an overall (pooled) estimate of 5% improvement. T2D patients with fewer complications have more significant benefit from DIABETIMSS than those with more complications. At the FMC’s delivering the conventional model the predicted impacts were like what was observed empirically in the DIABETIMSS clinics. The sensitivity analysis did not change the overall estimate average across clinics.ConclusionsDIABETIMSS program had a small, but significant increase in glycemic control. The use of machine learning methods yields both population-level effects and pinpoints the sub-groups of patients the program benefits the most. These methods exploit the potential of routine observational patient data within complex healthcare systems to inform decision-makers.
Highlights
The study aimed to assess the performance of a multidisciplinary-team diabetes care program called DIABETIMSS on glycemic control of type 2 diabetes (T2D) patients, by using available observational patient data and machine-learning-based targeted learning methods
We found that the predicted impact is quite like what was observed empirically in the DIABETIMSS clinics, that is, there is some variation, but one would expect about a 5% improvement in glycemic control if the program were implemented in these clinics (Fig. 4)
The study provides evidence on the positive effect of DIABETIMSS program and shows the potential and challenges in using routine observational patient data and machine learning methods to evaluate the performance of health interventions within complex healthcare institutions to inform decision-makers
Summary
The study aimed to assess the performance of a multidisciplinary-team diabetes care program called DIABETIMSS on glycemic control of type 2 diabetes (T2D) patients, by using available observational patient data and machine-learning-based targeted learning methods. In Mexico, type 2 diabetes (T2D) is a major public health concern. The prevalence of this condition is above 9.4% in the adult population and increasing [1]. The chronic hyperglycemia causes damage of multiple organ systems and development of micro- and macrovascular complications. Macrovascular complications are coronary artery disease, peripheral arterial disease, and stroke. These complications are accountable for most of the morbidity, hospitalizations, and deaths that occur in patients with diabetes mellitus [3, 4]. A recent meta-analysis of 28 randomized trials that included 34,912 T2D patients found that targeting intensive glycemic control (HbA1C < 7%) reduces the risk of microvascular complications, compared with conventional glycemic control; yet, it increases the risk of hypoglycemia and did not show significant differences for all-cause and cardiovascular mortality [5]
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