Identification of clinical factors predicting warfarin sensitivity after cardiac surgery

Tyson, Karen W.; Hutchinson, Nevil; Williams, Siân; Scutt, Greg

doi:10.1177/2042098618776541

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…However, the risk factors of bleeding during hospitalization are different from outpatients, they were treated in a various physiological and pathological state. 15 Studies have also found that patients are more sensitive to warfarin after valve replacement, 17,18 it means that the therapeutic strategy of AF is different from valve replacement, which requires a lower warfarin dose at initial stage. Hence, existing prediction models may not be suitable for predicting bleeding risk in valve replacement patients during a hospital stay.…”

Section: Introductionmentioning

confidence: 99%

Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized

Hu,

Zhang,

Wei

et al. 2024

Pharmacoepidemiology and Drug

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BackgroundDistinguishing warfarin‐related bleeding risk at the bedside remains challenging. Studies indicate that warfarin therapy should be suspended when international normalized ratio (INR) ≥ 4.5, or it may sharply increase the risk of bleeding. We aim to develop and validate a model to predict the high bleeding risk in valve replacement patients during hospitalization.MethodCardiac valve replacement patients from January 2016 to December 2021 across Nanjing First Hospital were collected. Five different machine‐learning (ML) models were used to establish the prediction model. High bleeding risk was an INR ≥4.5. The area under the receiver operating characteristic curve (AUC) was used for evaluating the prediction performance of different models. The SHapley Additive exPlanations (SHAP) was used for interpreting the model. We also compared ML with ATRIA score and ORBIT score.ResultsA total of 2376 patients were finally enrolled in this model, 131 (5.5%) of whom experienced the high bleeding risk after anticoagulation therapy of warfarin during hospitalization. The extreme gradient boosting (XGBoost) exhibited the best overall prediction performance (AUC: 0.882, confidence interval [CI] 0.817–0.946, Brier score, 0.158) compared to other prediction models. It also shows superior performance compared with ATRIA score and ORBIT score. The top 5 most influential features in XGBoost model were platelet, thyroid stimulation hormone, body surface area, serum creatinine and white blood cell.ConclusionA model for predicting high bleeding risk in valve replacement patients who treated with warfarin during hospitalization was successfully developed by using machine learning, which may well assist clinicians to identify patients at high risk of bleeding and allow timely adjust therapeutic strategies in evaluating individual patient.

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Section: Introductionmentioning

confidence: 99%

Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized

Hu,

Zhang,

Wei

et al. 2024

Pharmacoepidemiology and Drug

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“…Warfarin is a commonly prescribed oral anticoagulant for patients who have undergone cardiac surgery and remains the only guideline-endorsed anticoagulant for patients with mechanical heart valves. However, considerable interindividual variability exists in the response to warfarin, with patient age, BMI, concomitant medications, and genetic status imparting considerable influence on warfarin maintenance dose requirements [1][2][3][4][5]. Complicating matters in cardiac surgery patients is an exaggerated sensitivity to the effects of warfarin in the immediate postoperative period, predisposing individuals to supratherapeutic international normalized ratio (INR) values and bleeding [3,6,7].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Approaches for Predicting Warfarin Discharge Dose in Cardiac Surgery Patients: Retrospective Algorithm Development and Validation Study

Dryden,

Song,

Valenzano

et al. 2023

JMIR Cardio

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Background Warfarin 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. Objective This 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. Methods We 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. Results Random 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. Conclusions Machine 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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“…Then liver congestion may affect the metabolism of warfarin and the synthesis of coagulation factors. Since warfarin is 99% bound to plasma proteins (mainly albumin), hypoalbuminemia is caused by in ammation response or blood dilution during CPB, which may also amplify the anticoagulant effect of warfarin [20] . Therefore, traditional warfarin dose prediction algorithms may not be suitable for patients after aortic surgery.…”

Section: Introductionmentioning

confidence: 99%

Warfarin dosing prediction algorithm assessments in Asian patients after aortic surgery

Peng

Shi

Zhang

et al. 2022

Preprint

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Background: To evaluate the accuracy and predictive performance of multiple linear regression algorithms, Gage algorithm and International Warfarin Pharmacogenetics Consortium (IWPC) algorithm, and Bayesian algorithm for the maintenance dose of warfarin in Asian patients after aortic surgery. Methods:The predictive performance of Gage, IWPC and Bayesian algorithm were compared by calculating mean prediction error (MPE), mean squared error (MSE), root-mean-squared error (RMSE), and the percentage of patients whose predicted warfarin dose fell within 20% of the maintenance dose. Then the predictive performance of each algorithm was calculated separately in conventional dose group (dose of warfarin between 3mg and 3.75mg) and unconventional dose group (dose less than 3mg or more than 3.75mg). Results: Among three algorithms, the Gage algorithm predicted the warfarin dose with the highest percentage within 20% (58.1%). Bayesian-priori (dose estimation before starting therapy) algorithm had the highest RMSE (1.45 mg/day) and the lowest percentage within 20% (40.9%). The algorithm of Gage method tended to perform better in the conventional dose group (percentage within 20%: 70.0%), and Bayesian-priori method performed better in the unconventional dose group (percentage within 20%: 38.5%). And as the number of international normalized ratio (INR) observations increases, the predictive performance of Bayesian-posteriori (dose estimation after initiating therapy) algorithm showed an increasing trend in both groups and a significantly higher advantage, especially in the unconventional dose group. Conclusion: For Asian patients after aortic surgery, the Gage algorithm was appropriate for patients requiring conventional dose (2.25-3.75mg). Bayesian-posteriori algorithm might be more appropriate for patients requiring high dose or low dose (＜2.25mg or＞3.75mg).

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Identification of clinical factors predicting warfarin sensitivity after cardiac surgery

Cited by 7 publications

References 25 publications

Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized

Using machine learning to predict the bleeding risk for patients with cardiac valve replacement treated with warfarin in hospitalized

Evaluation of Machine Learning Approaches for Predicting Warfarin Discharge Dose in Cardiac Surgery Patients: Retrospective Algorithm Development and Validation Study

Warfarin dosing prediction algorithm assessments in Asian patients after aortic surgery

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