Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features.

BACKGROUND: Cardiac Resynchronization Therapy (CRT) is a widely used, device-based therapy for patients with left ventricle (LV) failure. Unfortunately, many patients do not benefit from CRT, so there is potential value in identifying this group of non-responders before CRT implementation. Past studies suggest that predicting CRT response will require diverse variables, including demographic, biomarker, and LV function data. Accordingly, the objective of this study was to integrate diverse variable types into a machine learning algorithm for predicting individual patient responses to CRT. METHODS: We built an ensemble classification algorithm using previously acquired data from the SMART-AV CRT clinical trial (n = 794 patients). We used five-fold stratified cross-validation on 80% of the patients (n = 635) to train the model with variables collected at 0 months (before initiating CRT), and the remaining 20% of the patients (n = 159) were used as a hold-out test set for model validation. To improve model interpretability, we quantified feature importance values using SHapley Additive exPlanations (SHAP) analysis and used Local Interpretable Model-agnostic Explanations (LIME) to explain patient-specific predictions. RESULTS: Our classification algorithm incorporated 26 patient demographic and medical history variables, 12 biomarker variables, and 18 LV functional variables, which yielded correct prediction of CRT response in 71% of patients. Additional patient stratification to identify the subgroups with the highest or lowest likelihood of response showed 96% accuracy with 22 correct predictions out of 23 patients in the highest and lowest responder groups. CONCLUSION: Computationally integrating general patient characteristics, comorbidities, therapy history, circulating biomarkers, and LV function data available before CRT intervention can improve the prediction of individual patient responses.

Comparative study of antidiabetic activity of Cajanus cajan and Tamarindus indica in alloxan-induced diabetic mice with a reference to in vitro antioxidant activity.

BACKGROUND: Oxidative stress not only develops complications in diabetic (type 1 and type 2) but also contributes to beta cell destruction in type 2 diabetes in insulin resistance hyperglycemia. Glucose control plays an important role in the pro-oxidant/antioxidant balance. Some antidiabetic agents may by themselves have antioxidant properties independently of their role on glucose control. OBJECTIVE: The present investigation draws a comparison of the protective antioxidant activity, total phenol content and the antihyperglycemic activity of the methanolic extract of Cajanus cajan root (MCC) and Tamarindus indica seeds (MTI). MATERIALS AND METHODS: Antidiabetic potentials of the plant extracts were evaluated in alloxan-induced diabetic Swiss albino mice. The plant extracts at the doses of 200 and 400 mg/kg body weight was orally administered for glucose tolerance test during 1-hour study and hypoglycemic effect during 5-day study period in comparison with reference drug Metformin HCl (50 mg/kg). In vitro antioxidant potential of MCC and MTI was investigated by using 1, 1- diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging activity at 517 nm. Total phenolic content, total antioxidant capacity and reducing power activity was also assayed. RESULTS: There was a significant decrease in fasting serum glucose level (P < 0.001), reduction in blood glucose level (P < 0.001) in 5-days study, observed in the alloxan-induced diabetic mice. The reduction efficacy of blood glucose level of both the extracts is proportional to their dose but MCC is more potent than MTI. Antioxidant study and quantification of phenolic compound of both the extracts revealed that they have high antioxidant capacity. CONCLUSION: These studies showed that MCC and MTI have both hypoglycemic and antioxidant potential but MCC is more potent than MTI. The present study suggests that both MCC and MTI could be used in managing oxidative stress.