The Maccabi Glaucoma Study

Despite effective medications, rates of uncontrolled hypertension remain high. Treatment protocols are largely based on randomized trials and meta‐analyses of these studies. The objective of this study was to test the utility of machine learning of big data in gaining insight into the treatment of hypertension. We applied machine learning techniques such as decision trees and neural networks, to identify determinants that contribute to the success of hypertension drug treatment on a large set of patients. We also identified concomitant drugs not considered to have antihypertensive activity, which may contribute to lowering blood pressure (BP) control. Higher initial BP predicts lower success rates. Among the medication options and their combinations, treatment with beta blockers appears to be more commonly effective, which is not reflected in contemporary guidelines. Among numerous concomitant drugs taken by hypertensive patients, proton pump inhibitors (PPIs), and HMG CO‐A reductase inhibitors (statins) significantly improved the success rate of hypertension. In conclusions, machine learning of big data is a novel method to identify effective antihypertensive therapy and for repurposing medications already on the market for new indications. Our results related to beta blockers, stemming from machine learning of a large and diverse set of big data, in contrast to the much narrower criteria for randomized clinic trials (RCTs), should be corroborated and affirmed by other methods, as they hold potential promise for an old class of drugs which may be presently underutilized. These previously unrecognized effects of PPIs and statins have been very recently identified as effective in lowering BP in preliminary clinical observations, lending credibility to our big data results.

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Machine learning of big data in gaining insight into successful treatment of hypertension

Koren

Nordon

Radinsky

et al. 2018

Pharmacology Res & Perspec

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Identification of repurposable drugs with beneficial effects on glucose control in type 2 diabetes using machine learning

Koren

Nordon

Radinsky

et al. 2019

Pharmacology Res & Perspec

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Despite effective medications, rates of uncontrolled glucose levels in type 2 diabetes remain high. We aimed to test the utility of machine learning applied to big data in identifying the potential role of concomitant drugs not taken for diabetes which may contribute to lowering blood glucose. Success in controlling blood glucose was defined as achieving HgA1c levels < 6.5% after 90‐365 days following diagnosis and initiating treatment. Among numerous concomitant drugs taken by type 2 diabetic patients, alpha 1 (α1)‐adrenoceptor antagonist drugs were the only group of medications that significantly improved the success rate of glucose control. Searching the published literature, this effect of α1‐adrenoceptor antagonists has been shown in animal models, where this class of medications appears to induce insulin secretion. In conclusion, machine learning of big data is a novel method to identify effective antidiabetic effects for potential repurposable medications already on the market for other indications. Because these α1‐adrenoceptor antagonists are widely used in men for treating benign prostate hyperplasia (BPH) at age groups exhibiting increased rates of type 2 diabetes, this finding is of potential clinical significance.

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The effect of trabeculectomy on serum brain-derived neurotrophic factor levels in primary open-angle glaucoma

Uzel

Elgin

Boral

et al. 2018

Graefes Arch Clin Exp Ophthalmol

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The Maccabi Glaucoma Study

Abstract: This large-scale analysis of real-world use of glaucoma medications reveals that adherence to glaucoma therapy is associated with medication type, patient's sex, age, socioeconomic status, type of glaucoma, follow-up visits, and baseline intraocular pressure.

Cited by 14 publications

References 28 publications

Machine learning of big data in gaining insight into successful treatment of hypertension

Machine learning of big data in gaining insight into successful treatment of hypertension

Identification of repurposable drugs with beneficial effects on glucose control in type 2 diabetes using machine learning

The effect of trabeculectomy on serum brain-derived neurotrophic factor levels in primary open-angle glaucoma

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