Arron Lacey

Ashley Akbari

Daniel Harris

Fatemeh Torabi

Julian Halcox

Michael Gravenor

Ronan Lyons

English

Cronfa at Swansea University

Machine learning approaches for prediction of major bleeding events in anticoagulated atrial fibrillation patients.Major bleeding events are the most serious complications of anticoagulation therapy (AC) in Atrial Fibrillation (AF) patients, with standardised bleeding risk scores used to guide therapy selection in practice. We explored whether a machine learning (ML) approach could improve bleeding risk modelling.IntroductionWe developed two ML-based decision support systems andcompared their performance with the HAS-BLED (the bleedingrisk score used routinely in UK clinical practice) score forprediction of 1-year risk of major-bleeding events in the ACtreated Welsh AF population. Multiple data sources were linkedto capture potential contributing clinical variables. Figure 1 & 2AF: Atrial Fibrillation WLGP: Welsh Longitudinal General Practice dataset WDSD: Welsh Demographic Service DatasetADDE: Annual District Death Extract PEDW: Patient Episode Dataset for Wales LSOA: Lower layer Super Output Area codeIndividuals in WLGP in SAIL(n=4,048,319)Diagnosis of AF, satisfactory linkage status, alive at the index date (2017-01-01)(n=70,471)AF diagnosis linked PEDW record 180+ days of GP coverage(n=59,937)AF, Welsh LSOA, age 18+(n=59,293)AF, Welsh LSOA, age 18+, Receiving AC at index date(n=38,784)Assessed for eligibility WLGPAF and alive at the index ADDELinked hospital records PEDWWelsh residency LSOA WDSDProject linked datasetRF: RANDOM FORESTIs an ensemble learning method of classificationNB: NAÏVE BAYESis a probabilistic classifier based on conditionalprobability of event of interest and Bayestheorem2000-01-012017-01-01Baseline comorbiditiesBASELINEExisting diagnosis or prescriptionrecords for any comorbiditiesprior to study start date at2017-01-01NBIncident comorbiditiesDelta Features2018-01-01RFDelta feature (Chao et al.,2018) absolute changes in baseline comorbidities within study period  ResultsDuring 37,133 person-years 2,416 bleeding events occurred (6,506per 100,000 patients/year).The Random Forest model achieved the highest AUC=71.1%compared to HAS-BLED (AUC=69.2%), Naïve Bayes (AUC=64.3%)and logistic regression (AUC=60.5%). (Figure 4)Figure 4. ROC graph of true positive vs false positives for RF, Naïve Bayes, Logistic Regression and HAS-BLED scheme for prediction of bleeding events in atrial fibrillation population who are receiving anticoagulant drugs in Wales.Hypertension Congestive Heart FailureP-value < 0.05Age 65+Age 65-74Age 75+P-value < 0.05Stroke/Thrombo-embolism Diabetes mellitusVascular disease Abnormal renal/liver functionP-value(9.12E-08)Bleeding historyP-value(6.38E-06)Drug or Alcohol useP-value(9.12E-08)CHA2DS2-VASCHAS-BLEDLabile INRP-value < 0.05FemaleSexP-value(6.38E-06)HAS-BLED was validated using multivariable logistic regression.Bootstrapping and 5-fold cross validation were used fordevelopment of Random Forest and Naïve Bayes algorithms.Incident event rate in relation to contributing clinical variables inthe routinely used bleeding (HAS-BLED) and stroke(CHA2DS2VASc) risk scores were used for model optimization.Hazard ratios of the respective models were evaluated using Coxproportional hazard ratio modelling. McNemar test was used forevaluating marginal homogeneity of the final models.Clinical data were analysed throughout 2017 for 38,784 (43.5%female) AC treated AF patients (aged 76 ± 9.84years). BaselineHAS-BLED and CHA2DS2VASc scores were 2.18 (CI [2.17, 2.19])and 3.45 (CI [3.44, 3.46]) respectively. Figure 3 illustratesvariables in the models and statistical significance level in thelogistic regression model.Age>75 (P-value < 0.05), female-sex (P-value=6.38E-06), Incident and baseline congestiveheart failure(P-value<0.05), baseline and incident bleeding (P-value<2e-16) and incidentevent of chronic kidney disease stage 4-5 (P-value=9.12E-08) , baseline measure of excessivealcohol intake (P-value<0.05), regular nonsteroidal anti-inflammatory drug use at thebaseline (P-value=0.06), incident liver disease (P-value4.20E-14) and INR level above 5 (P-value<0.05) were significant predictors of bleeding.Fatemeh Torabi, Arron Lacey, Ashley Akbari, Daniel Harris, Ronan A Lyons, Mike Gravenor, Julian HalcoxUsing feature selection methods we were able to reach betterperformance with existing random forest models; we areevaluating predictive performance of other machine learningapproaches and conducting a comparison between statisticalmodelling and machine learning classifications.RF modelling achieved a small but significantly better predictionof bleeding than conventional HAS-BLED score, indicating apotential role for ML-based decision support algorithms tooptimise evaluation of bleeding risk in AF patients on orconsidering AC treatment.ConclusionFuture workFigure 1. Study timeline, baseline and delta variables definition.Figure 3. HAS-BLED and CHA2DS2-VASc variables used in the analysis and their level of significance in logistic regression fit. Figure 2. Linked datasets and consort diagram of SAIL-AF cohort receiving AC therapy in 2017-01-01.Methods

Machine learning approaches in for prediction of 1-year risk of major bleeding events in anticoagulated atrial fibrillation patients with atrial fibrillation in Wales.

https://cronfa.swan.ac.uk/Record/cronfa53813/Download/53813__16839__0cd85122692544dd8d8b5105dffd2bf6.pdf

Machine learning approaches in for prediction of 1-year risk of major bleeding events in anticoagulated atrial fibrillation patients with atrial fibrillation in Wales.

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