Model-Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation and Clinical Simulation Trial.

Levothyroxine is one of the most prescribed drugs in the western world. Dosing is challenging due to high-interindividual differences in effective dosage and the narrow therapeutic window. Model-informed precision dosing (MIPD) using machine learning could assist general practitioners (GPs), but no such models exist for primary care. Furthermore, introduction of decision-support algorithms in healthcare is limited due to the substantial gap between developers and clinicians' perspectives. We report the development, validation, and a clinical simulation trial of the first MIPD application for primary care. Stable maintenance dosage of levothyroxine was the model target. The multiclass model generates predictions for individual patients, for different dosing classes. Random forest was trained and tested on a national primary care database (n = 19,004) with a final weighted AUC across dosing options of 0.71, even in subclinical hypothyroidism. TSH, fT4, weight, and age were most predictive. To assess the safety, feasibility, and clinical impact of MIPD for levothyroxine, we performed clinical simulation studies in GPs and compared MIPD to traditional prescription. Fifty-one GPs selected starting dosages for 20 primary hypothyroidism cases without and then with MIPD 2 weeks later. Overdosage and underdosage were defined as higher and lower than 12.5 µg relative to stable maintenance dosage. MIPD decreased overdosage in number (30.5 to 23.9%, P < 0.01) and magnitude (median 50 to 37.5 µg, P < 0.01) and increased optimal starting dosages (18.3 to 30.2%, P < 0.01). GPs considered lab results more often with MIPD and most would use the model frequently. This study demonstrates the clinical relevance, safety, and effectiveness of MIPD for levothyroxine in primary care.

Location in the right hemi-colon is an independent risk factor for delayed post-polypectomy hemorrhage: a multi-center case-control study.

OBJECTIVES: Delayed hemorrhage is an infrequent, but serious complication of colonoscopic polypectomy. Large size is the only polyp-related factor that has been unequivocally proven to increase the risk of delayed bleeding. It has been suggested that location in the right hemi-colon is also a risk factor. The objective of this study was to determine whether polyp location is an independent risk factor for delayed post-polypectomy hemorrhage. METHODS: A retrospective case-control study was conducted in two university hospitals and two community hospitals. RESULTS: Thirty-nine cases and 117 controls were identified. In multivariate analysis, size and location were found to be independent polyp-related risk factors for delayed type hemorrhage. The risk increased by 13% for every 1 mm increase in polyp diameter (odds ratio (OR) 1.13, 95% confidence interval (CI) 1.05-1.20, P<0.001). Polyps located in the right hemi-colon had an OR of 4.67 (1.88-11.61, P=0.001) for delayed hemorrhage. Polyps in the cecum seemed to be especially at high risk in univariate analysis (OR 13.82, 95% CI 2.66-71.73), but this could not be assessed in multivariate analysis as the number of cases was too small. Polyp type (sessile or pedunculated) was not a risk factor. CONCLUSIONS: Polyp location in the right hemi-colon seems to be an independent and substantial risk factor for delayed post-polypectomy hemorrhage. A low threshold for preventive hemostatic measures is advised when removing polyps from this region.