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Predicting prolonged dose titration in patients starting warfarin.

ABSTRACT: PURPOSE:Patients initiating warfarin therapy generally experience a dose-titration period of weeks to months, during which time they are at higher risk of both thromboembolic and bleeding events. Accurate prediction of prolonged dose titration could help clinicians determine which patients might be better treated by alternative anticoagulants that, while more costly, do not require dose titration. METHODS:A prediction model was derived in a prospective cohort of patients starting warfarin (n?=?390), using Cox regression, and validated in an external cohort (n?=?663) from a later time period. Prolonged dose titration was defined as a dose-titration period >12?weeks. Predictor variables were selected using a modified best subsets algorithm, using leave-one-out cross-validation to reduce overfitting. RESULTS:The final model had five variables: warfarin indication, insurance status, number of doctor's visits in the previous year, smoking status, and heart failure. The area under the ROC curve (AUC) in the derivation cohort was 0.66 (95%CI 0.60, 0.74) using leave-one-out cross-validation, but only 0.59 (95%CI 0.54, 0.64) in the external validation cohort, and varied across clinics. Including genetic factors in the model did not improve the area under the ROC curve (0.59; 95%CI 0.54, 0.65). Relative utility curves indicated that the model was unlikely to provide a clinically meaningful benefit compared with no prediction. CONCLUSIONS:Our results suggest that prolonged dose titration cannot be accurately predicted in warfarin patients using traditional clinical, social, and genetic predictors, and that accurate prediction will need to accommodate heterogeneities across clinical sites and over time. Copyright © 2016 John Wiley & Sons, Ltd.

SUBMITTER: Finkelman BS

PROVIDER: S-EPMC5093054 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Predicting prolonged dose titration in patients starting warfarin.

Finkelman Brian S BS French Benjamin B Bershaw Luanne L Brensinger Colleen M CM Streiff Michael B MB Epstein Andrew E AE Kimmel Stephen E SE

Pharmacoepidemiology and drug safety 20160726 11

<h4>Purpose</h4>Patients initiating warfarin therapy generally experience a dose-titration period of weeks to months, during which time they are at higher risk of both thromboembolic and bleeding events. Accurate prediction of prolonged dose titration could help clinicians determine which patients might be better treated by alternative anticoagulants that, while more costly, do not require dose titration.<h4>Methods</h4>A prediction model was derived in a prospective cohort of patients starting ...[more]

PMID: 27456080

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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.

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Predicting prolonged dose titration in patients starting warfarin.

Publications

Predicting prolonged dose titration in patients starting warfarin.

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