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Warfarin Management Pathway: A clear and safe algorithm, from admission to discharge.

ABSTRACT: Warfarin is the most commonly prescribed anticoagulant in the UK and the one most frequently associated with both fatal medication errors and litigation claims [1]. Its life-threatening interactions and side effects are a concern for all doctors. Identifying and implementing solutions to achieve safer prescribing and monitoring is imperative to improve patient safety. The National Patient Safety Agency (NPSA) has outlined the major risks associated with anticoagulant therapy and sought to establish safer practice [1]. The monitoring of safety indicators has been highlighted as a solution. This quality improvement project (QIP) introduces a management algorithm for oral anticoagulant therapy in hospital patients, validated through a completed audit cycle. It was completed at one district general hospital (DGH) in England and involved all inpatient wards. Doctors and pharmacists were interviewed to assess their knowledge of the correct pathways for management of patients on warfarin. The number of errors on hospital warfarin charts was audited over three weeks. These results, coupled with senior haematological advice led to the production of an algorithm illustrating the gold-standard pathway for warfarin management from admission to discharge. It was emailed to all doctors in the Trust and a laminated copy attached to hospital Pneumatic Tube System (PTS) machines. The warfarin charts were re-audited over the following three weeks. The results showed a marked decrease in errors and incomplete anticoagulation referrals as well as a reduction in doctors' anxiety around prescribing warfarin.

SUBMITTER: Hart-George A

PROVIDER: S-EPMC4663821 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Warfarin Management Pathway: A clear and safe algorithm, from admission to discharge.

Hart-George Alice A

BMJ quality improvement reports 20131025 2

Warfarin is the most commonly prescribed anticoagulant in the UK and the one most frequently associated with both fatal medication errors and litigation claims [1]. Its life-threatening interactions and side effects are a concern for all doctors. Identifying and implementing solutions to achieve safer prescribing and monitoring is imperative to improve patient safety. The National Patient Safety Agency (NPSA) has outlined the major risks associated with anticoagulant therapy and sought to establ ...[more]

PMID: 26734214

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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.

Project description:BackgroundPoor anticoagulation management of warfarin may lead to patient admission, prolonged hospital stays, and even death due to anticoagulation-related adverse events. Traditional non-web-based outpatient clinics struggle to provide ideal anticoagulation management services for patients, and there is a need to explore a safer, more effective, and more convenient mode of warfarin management.ObjectiveThis study aimed to compare differences in the quality of anticoagulation management and clinical adverse events between a web-based management model (via a smartphone app) and the conventional non-web-based outpatient management model.MethodsThis study is a prospective cohort research that includes multiple national centers. Patients meeting the nadir criteria were split into a web-based management group using the Alfalfa app or a non-web-based management group with traditional outpatient management, and they were then monitored for a 6-month follow-up period to collect coagulation test results and clinical events. The effectiveness and safety of the 2 management models were assessed by the following indicators: time in therapeutic range (TTR), bleeding events, thromboembolic events, all-cause mortality events, cumulative event rates, and the distribution of the international normalized ratio (INR).ResultsThis national multicenter cohort study enrolled 522 patients between June 2019 and May 2021, with 519 (99%) patients reaching the follow-up end point, including 260 (50%) in the non-web-based management group and 259 (50%) in the web-based management group. There were no observable differences in baseline characteristics between the 2 patient groups. The web-based management group had a significantly higher TTR than the non-web-based management group (82.4% vs 71.6%, P<.001), and a higher proportion of patients received effective anticoagulation management (81.2% vs 63.5%, P<.001). The incidence of minor bleeding events in the non-web-based management group was significantly higher than that in the web-based management group (12.1% vs 6.6%, P=.048). Between the 2 groups, there was no statistically significant difference in the incidence of severe bleeding and thromboembolic and all-cause death events. In addition, compared with the non-web-based management group, the web-based management group had a lower proportion of INR in the extreme subtreatment range (17.6% vs 21.3%) and severe supertreatment range (0% vs 0.8%) and a higher proportion in the treatment range (50.4% vs 43.1%), with statistical significance.ConclusionsCompared with traditional non-web-based outpatient management, web-based management via the Alfalfa app may be more beneficial because it can enhance patient anticoagulation management quality, lower the frequency of small bleeding events, and improve INR distribution.

Project description:Warfarin is the most common oral anticoagulant. Because of a narrow therapeutic range, interindividual differences in drug responses, and the risk of bleeding, there are many challenges in using warfarin. We need to predict the warfarin maintenance dose. However, ethnic-specific algorithms may be required, and some Chinese algorithms do not perform adequately. Therefore, we aimed to establish a Han Chinese appropriate algorithm.We recruited a study group consisting of 361 Han Chinese patients receiving warfarin treatment who had heart valve replacements. Genotyping of 38 single nucleotide polymorphisms (SNPs) in 13 candidate genes was carried out using the MassARRAY. In the derivation cohort, a multiple linear regression model was constructed to predict the warfarin dosage. We evaluated the accuracy of our algorithm in the validation cohort and compared it with the other 5 algorithms based on Han Chinese and other races.We established a Han Chinese-specific pharmacogenetic-guided warfarin dosing algorithm. Warfarin maintenance dosage (mg/day) = 1.787 - 0.023 × (Age) + 1.151 × (BSA [m]) + 0.917 × (VKORC1 AG) + 4.619 × (VKORC1 GG) + 0.595 × (CYP4F2 TT) + 0.707 × (CYP2C19 CC). It explained 58.3% of the variance in warfarin doses in Han Chinese patients and was superior to the other 5 algorithms. The ability of the 6 algorithms which estimate the required dose correctly was tested. Our model had a mean absolute error of 0.74 mg/day, the other 5 models have mean absolute error of 0.81 mg/day,1.05 mg/day, 1.24 mg/day, 1.18 mg/day, and 0.85 mg/day, respectively. Our model had a mean percentage error of 26.9%, the other 5 models have the mean percentage error of 27.7%, 27.2%, 52.3%, 45.7%, and 29.3%, respectively.Physicians can not adopt algorithm from other race directly to predict warfarin dose in patients with heart valve replacements, they should establish a new algorithm or adjust another algorithm to fit their patients. The algorithm established in this study has the potential to assist physicians in determining warfarin doses that are close to the appropriate doses.

Dataset Information

Warfarin Management Pathway: A clear and safe algorithm, from admission to discharge.

Publications

Warfarin Management Pathway: A clear and safe algorithm, from admission to discharge.

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