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Stable warfarin dose prediction in sub-Saharan African patients: A machine-learning approach and external validation of a clinical dose-initiation algorithm.

ABSTRACT: Warfarin remains the most widely prescribed oral anticoagulant in sub-Saharan Africa. However, because of its narrow therapeutic index, dosing can be challenging. We have therefore (a) evaluated and compared the performance of 21 machine-learning techniques in predicting stable warfarin dose in sub-Saharan Black-African patients and (b) externally validated a previously developed Warfarin Anticoagulation in Patients in Sub-Saharan Africa (War-PATH) clinical dose-initiation algorithm. The development cohort included 364 patients recruited from eight outpatient clinics and hospital departments in Uganda and South Africa (June 2018-July 2019). Validation was conducted using an external validation cohort (270 patients recruited from August 2019 to March 2020 in 12 outpatient clinics and hospital departments). Based on the mean absolute error (MAE; mean of absolute differences between the actual and predicted doses), random forest regression (12.07 mg/week; 95% confidence interval [CI], 10.39-13.76) was the best performing machine-learning technique in the external validation cohort, whereas the worst performing technique was model trees (17.59 mg/week; 95% CI, 15.75-19.43). By comparison, the simple, commonly used regression technique (ordinary least squares) performed similarly to more complex supervised machine-learning techniques and achieved an MAE of 13.01 mg/week (95% CI, 11.45-14.58). In summary, we have demonstrated that simpler regression techniques perform similarly to more complex supervised machine-learning techniques. We have also externally validated our previously developed clinical dose-initiation algorithm, which is being prospectively tested for clinical utility.

SUBMITTER: Asiimwe IG

PROVIDER: S-EPMC8752108 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Stable warfarin dose prediction in sub-Saharan African patients: A machine-learning approach and external validation of a clinical dose-initiation algorithm.

Asiimwe Innocent G IG Blockman Marc M Cohen Karen K Cupido Clint C Hutchinson Claire C Jacobson Barry B Lamorde Mohammed M Morgan Jennie J Mouton Johannes P JP Nakagaayi Doreen D Okello Emmy E Schapkaitz Elise E Sekaggya-Wiltshire Christine C Semakula Jerome R JR Waitt Catriona C Zhang Eunice J EJ Jorgensen Andrea L AL Pirmohamed Munir M

CPT: pharmacometrics & systems pharmacology 20211209 1

Warfarin remains the most widely prescribed oral anticoagulant in sub-Saharan Africa. However, because of its narrow therapeutic index, dosing can be challenging. We have therefore (a) evaluated and compared the performance of 21 machine-learning techniques in predicting stable warfarin dose in sub-Saharan Black-African patients and (b) externally validated a previously developed Warfarin Anticoagulation in Patients in Sub-Saharan Africa (War-PATH) clinical dose-initiation algorithm. The develop ...[more]

PMID: 34889080

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Project description:BackgroundWarfarin dosing in cardiac surgery patients is complicated by a heightened sensitivity to the drug, predisposing patients to adverse events. Predictive algorithms are therefore needed to guide warfarin dosing in cardiac surgery patients.ObjectiveThis study aimed to develop and validate an algorithm for predicting the warfarin dose needed to attain a therapeutic international normalized ratio (INR) at the time of discharge in cardiac surgery patients.MethodsWe abstracted variables influencing warfarin dosage from the records of 1031 encounters initiating warfarin between April 1, 2011, and November 29, 2019, at St Michael's Hospital in Toronto, Ontario, Canada. We compared the performance of penalized linear regression, k-nearest neighbors, random forest regression, gradient boosting, multivariate adaptive regression splines, and an ensemble model combining the predictions of the 5 regression models. We developed and validated separate models for predicting the warfarin dose required for achieving a discharge INR of 2.0-3.0 in patients undergoing all forms of cardiac surgery except mechanical mitral valve replacement and a discharge INR of 2.5-3.5 in patients receiving a mechanical mitral valve replacement. For the former, we selected 80% of encounters (n=780) who had initiated warfarin during their hospital admission and had achieved a target INR of 2.0-3.0 at the time of discharge as the training cohort. Following 10-fold cross-validation, model accuracy was evaluated in a test cohort comprised solely of cardiac surgery patients. For patients requiring a target INR of 2.5-3.5 (n=165), we used leave-p-out cross-validation (p=3 observations) to estimate model performance. For each approach, we determined the mean absolute error (MAE) and the proportion of predictions within 20% of the true warfarin dose. We retrospectively evaluated the best-performing algorithm in clinical practice by comparing the proportion of cardiovascular surgery patients discharged with a therapeutic INR before (April 2011 and July 2019) and following (September 2021 and May 2, 2022) its implementation in routine care.ResultsRandom forest regression was the best-performing model for patients with a target INR of 2.0-3.0, an MAE of 1.13 mg, and 39.5% of predictions of falling within 20% of the actual therapeutic discharge dose. For patients with a target INR of 2.5-3.5, the ensemble model performed best, with an MAE of 1.11 mg and 43.6% of predictions being within 20% of the actual therapeutic discharge dose. The proportion of cardiovascular surgery patients discharged with a therapeutic INR before and following implementation of these algorithms in clinical practice was 47.5% (305/641) and 61.1% (11/18), respectively.ConclusionsMachine learning algorithms based on routinely available clinical data can help guide initial warfarin dosing in cardiac surgery patients and optimize the postsurgical anticoagulation of these patients.

Dataset Information

Stable warfarin dose prediction in sub-Saharan African patients: A machine-learning approach and external validation of a clinical dose-initiation algorithm.

Publications

Stable warfarin dose prediction in sub-Saharan African patients: A machine-learning approach and external validation of a clinical dose-initiation algorithm.

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