Bruno Batinica
Postdoctoral AssociateAbout
Research
Publications
2026
Artificial Intelligence-enhanced Electrocardiography for Heart Failure Screening and Risk Stratification
Dhingra L, Croon P, Batinica B, Aminorroaya A, Pedroso A, Khera R. Artificial Intelligence-enhanced Electrocardiography for Heart Failure Screening and Risk Stratification. Current Heart Failure Reports 2026, 23: 10. PMID: 41838300, DOI: 10.1007/s11897-026-00748-x.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsRisk factor surveillancePublic health challengeHeart failure screeningHF risk assessmentHF screeningRoutine careCommunity programsHF riskHealth challengesECG testRisk scoreECG interpretationRisk stratificationConfirmatory imagingSymptom onsetRiskCost-effectiveClinical implementationProspective validationScreeningTherapy decisionsFunctional abnormalitiesCareRisk assessmentCohortPopulation-level cardiovascular risk prediction models including biochemical predictors in 800 000 individuals
Batinica B, Mehta S, Liang J, Jackson R, Poppe K. Population-level cardiovascular risk prediction models including biochemical predictors in 800 000 individuals. European Heart Journal Open 2026, 6: oeag051. PMID: 42125027, PMCID: PMC13160000, DOI: 10.1093/ehjopen/oeag051.Peer-Reviewed Original ResearchCVD risk prediction modelsAdministrative data-baseHealth administrative databasesRisk prediction modelCardiovascular disease eventsHealthcare organizationsAdministrative databasesCardiovascular diseaseTotal cholesterol/high-density lipoprotein cholesterol ratioSex-specific Cox modelsCardiovascular risk prediction modelsAdministrative health datasetsHealth administrative dataPrior cardiovascular diseaseCholesterol/high-density lipoprotein cholesterol ratioNew Zealand adultsTraditional health servicesHealth planning effortsCardiovascular disease riskPre-screened populationsLipoprotein cholesterol ratioHealth servicesStatistically significant independent predictorsCVD eventsAdministrative datasetsTARGET-AI: A Foundational Approach for the Targeted Deployment of Artificial Intelligence Electrocardiography in the Electronic Health Record
Oikonomou E, Batinica B, Dhingra L, Aminorroaya A, Coppi A, Khera R. TARGET-AI: A Foundational Approach for the Targeted Deployment of Artificial Intelligence Electrocardiography in the Electronic Health Record. NEJM AI 2026, 3 DOI: 10.1056/aioa2500588.Peer-Reviewed Original Research
2025
Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges
Biswas D, Aminorroaya A, Croon P, Batinica B, Pedroso A, Khera R. Transforming Population Health Screening for Atherosclerotic Cardiovascular Disease with AI-Enhanced ECG Analytics: Opportunities and Challenges. Current Atherosclerosis Reports 2025, 27: 86. PMID: 40888973, DOI: 10.1007/s11883-025-01337-4.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsAtherosclerotic cardiovascular diseasePopulation health screeningPopulation-level screeningCardiovascular diseaseLow riskHealth screeningStandard risk factorsHospital-basedCardiovascular healthSubclinical coronary artery diseaseWorkflow integrationSingle-lead ECGPersonalized interventionsPatient outcomesDiverse populationsTraditional risk modelsECG interpretationRisk factorsAscertainment biasImplementation challengesAdverse cardiovascular eventsProspective studyLogistical challengesRe-classifying patientsCoronary artery disease
2023
Development and validation of cardiovascular risk prediction equations in 76 000 people with known cardiovascular disease
Holt A, Batinica B, Liang J, Kerr A, Crengle S, Hudson B, Wells S, Harwood M, Selak V, Mehta S, Grey C, Lamberts M, Jackson R, Poppe K. Development and validation of cardiovascular risk prediction equations in 76 000 people with known cardiovascular disease. European Journal Of Preventive Cardiology 2023, 31: 218-227. PMID: 37767960, DOI: 10.1093/eurjpc/zwad314.Peer-Reviewed Original ResearchConceptsCardiovascular risk prediction equationsRisk prediction equationsHealth databasesCardiovascular diseaseStudy outcomesAdministrative health databasesHealth administrative databasesHigh-risk categorizationHeart failureAotearoa New ZealandIschaemic heart diseaseTotal cholesterol/HDL cholesterolStatistically significant independent predictorsAdministrative databasesBlood pressure loweringSignificant independent predictorsChronic renal diseasePrediction equationsEncrypted identifiersMultivariable FinePeripheral vascular diseaseNew ZealandSmoking historyHemoglobin A1cMedian ageTIBIAL METAPHYSEAL CONES COMBINED WITH SHORT STEMS PERFORM AS WELL AS LONG STEMS IN REVISION TOTAL KNEE ARTHROPLASTY
Batinica B, Bolam S, Zhu M, D'Arcy M, Peterson R, Young S, Monk A, Munro J. TIBIAL METAPHYSEAL CONES COMBINED WITH SHORT STEMS PERFORM AS WELL AS LONG STEMS IN REVISION TOTAL KNEE ARTHROPLASTY. Orthopaedic Proceedings 2023, 105-B: 38-38. DOI: 10.1302/1358-992x.2023.2.038.Peer-Reviewed Original ResearchOxford Knee ScoreFollow-upLS groupSS groupRevision total knee arthroplastyTwo-year clinical follow-upMean time of follow-upFailure free survivalMean Oxford Knee ScoreMean timeClinical follow-upTime of follow-upProsthetic joint infectionTwo-year follow-upMulti-centre studyPost-operative radiographsFree survivalImplant retentionClinical chartsRadiographic outcomesJoint infectionFunctional outcomesKnee scoreEuroQol-5DPatients
2022
Tibial metaphyseal cones combined with short stems perform as well as long stems in revision total knee arthroplasty
Batinica B, Bolam S, D'Arcy M, Zhu M, Monk A, Munro J. Tibial metaphyseal cones combined with short stems perform as well as long stems in revision total knee arthroplasty. Australian And New Zealand Journal Of Surgery 2022, 92: 2254-2260. PMID: 35754371, PMCID: PMC9539956, DOI: 10.1111/ans.17864.Peer-Reviewed Original ResearchConceptsOxford Knee ScoreYear Follow-UpFollow-upLS groupRevision total knee arthroplastySS groupMean time of follow-upFailure free survivalMean Oxford Knee ScoreTotal knee arthroplastyMean timeTime of follow-upProsthetic joint infectionMulti-centre studyPost-operative radiographsKnee arthroplastyFree survivalImplant retentionClinical chartsRadiographic outcomesJoint infectionFunctional outcomesKnee scoreEuroQol-5DTM cones
2021
Remote Patient Monitoring with Wearable Sensors Following Knee Arthroplasty
Bolam S, Batinica B, Yeung T, Weaver S, Cantamessa A, Vanderboor T, Yeung S, Munro J, Fernandez J, Besier T, Monk A. Remote Patient Monitoring with Wearable Sensors Following Knee Arthroplasty. Sensors 2021, 21: 5143. PMID: 34372377, PMCID: PMC8347411, DOI: 10.3390/s21155143.Peer-Reviewed Original ResearchConceptsRemote patient monitoringRange of motionOxford Knee ScoreFlexion angleLoad asymmetryKnee rangeEQ-5DThree-dimensional gait analysisMaximum knee flexion angleKnee range of motionPatient-reported outcome measuresKnee flexion angleEuroQol visual analogue scaleSpatiotemporal gait characteristicsMaximum flexion angleMinute walk testNon-operative limbEuroQol five dimensionsGait characteristicsKnee arthroplastyGait analysisPost-operative week 2Visual analogue scaleKnee arthroplasty patientsWalk test