{"id":"https://openalex.org/W7225802197","doi":"https://doi.org/10.9734/ajrcd/2026/v8i1173","title":"Artificial Intelligence\u2013Based Prediction of Major Adverse Cardiovascular Events Following Acute Myocardial Infarction: A Retrospective Cohort Study","display_name":"Artificial Intelligence\u2013Based Prediction of Major Adverse Cardiovascular Events Following Acute Myocardial Infarction: A Retrospective Cohort Study","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225802197","doi":"https://doi.org/10.9734/ajrcd/2026/v8i1173"},"language":"en","primary_location":{"id":"doi:10.9734/ajrcd/2026/v8i1173","doi":"https://doi.org/10.9734/ajrcd/2026/v8i1173","is_oa":true,"landing_page_url":"https://doi.org/10.9734/ajrcd/2026/v8i1173","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Asian Journal of Research in Cardiovascular Diseases","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.9734/ajrcd/2026/v8i1173","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5161468310","display_name":"Jeffrey Chidumebi Nebonta","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I131561025","display_name":"Enugu State University of Science and Technology","ror":"https://ror.org/04ntynb59","country_code":"NG","type":"education","lineage":["https://openalex.org/I131561025"]}],"countries":["NG"],"is_corresponding":true,"raw_author_name":"Jeffrey Chidumebi Nebonta","raw_affiliation_strings":["Enugu State University of Science and Technology, Enugu, Nigeria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Enugu State University of Science and Technology, Enugu, Nigeria","institution_ids":["https://openalex.org/I131561025"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161424632","display_name":"Chipuruime I. Ideh","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I166934088","display_name":"Imo State University","ror":"https://ror.org/0257ekp23","country_code":"NG","type":"education","lineage":["https://openalex.org/I166934088"]}],"countries":["NG"],"is_corresponding":false,"raw_author_name":"Chipuruime I. Ideh","raw_affiliation_strings":["Imo State University College of Medicine, Owerri, Nigeria"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Imo State University College of Medicine, Owerri, Nigeria","institution_ids":["https://openalex.org/I166934088"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161434593","display_name":"Benjamin O. C. Echendu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210131292","display_name":"National Pirogov Memorial Medical University, Vinnytsya","ror":"https://ror.org/03bcjfh39","country_code":"UA","type":"education","lineage":["https://openalex.org/I4210131292"]}],"countries":["UA"],"is_corresponding":false,"raw_author_name":"Benjamin O. C. Echendu","raw_affiliation_strings":["Vinnytsia National Pirogov Medical University, Ukraine"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vinnytsia National Pirogov Medical University, Ukraine","institution_ids":["https://openalex.org/I4210131292"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5134430015","display_name":"Chimaobi Nwevo","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chimaobi Nwevo","raw_affiliation_strings":["NYC Health + Hospital / South Brooklyn Health, 11235, Brooklyn NY, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NYC Health + Hospital / South Brooklyn Health, 11235, Brooklyn NY, United States","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5161468310"],"corresponding_institution_ids":["https://openalex.org/I131561025"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":"1","first_page":"382","last_page":"399"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.8622999787330627,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.8622999787330627,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10292","display_name":"Acute Myocardial Infarction Research","score":0.10209999978542328,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.016499999910593033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.7703999876976013},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7450000047683716},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.6575000286102295},{"id":"https://openalex.org/C2780739214","wikidata":"https://www.wikidata.org/wiki/Q6723023","display_name":"Mace","level":4,"score":0.6488000154495239},{"id":"https://openalex.org/C167135981","wikidata":"https://www.wikidata.org/wiki/Q2146302","display_name":"Retrospective cohort study","level":2,"score":0.6473000049591064},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6013000011444092},{"id":"https://openalex.org/C2778198053","wikidata":"https://www.wikidata.org/wiki/Q181754","display_name":"Heart failure","level":2,"score":0.508400022983551},{"id":"https://openalex.org/C500558357","wikidata":"https://www.wikidata.org/wiki/Q12152","display_name":"Myocardial infarction","level":2,"score":0.5056999921798706},{"id":"https://openalex.org/C35405484","wikidata":"https://www.wikidata.org/wiki/Q4967066","display_name":"Brier score","level":2,"score":0.5040000081062317},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4650000035762787},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.40869998931884766},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.385699987411499},{"id":"https://openalex.org/C72563966","wikidata":"https://www.wikidata.org/wiki/Q1303415","display_name":"Cohort","level":2,"score":0.36880001425743103},{"id":"https://openalex.org/C194828623","wikidata":"https://www.wikidata.org/wiki/Q2861470","display_name":"Emergency medicine","level":1,"score":0.3686000108718872},{"id":"https://openalex.org/C201903717","wikidata":"https://www.wikidata.org/wiki/Q1778788","display_name":"Cohort study","level":2,"score":0.3662000000476837},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36059999465942383},{"id":"https://openalex.org/C198433322","wikidata":"https://www.wikidata.org/wiki/Q3910099","display_name":"Predictive value of tests","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C535046627","wikidata":"https://www.wikidata.org/wiki/Q30612","display_name":"Clinical trial","level":2,"score":0.349700003862381},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C197934379","wikidata":"https://www.wikidata.org/wiki/Q2047938","display_name":"Adverse effect","level":2,"score":0.33079999685287476},{"id":"https://openalex.org/C188816634","wikidata":"https://www.wikidata.org/wiki/Q2113324","display_name":"Prospective cohort study","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.3131999969482422},{"id":"https://openalex.org/C2780040984","wikidata":"https://www.wikidata.org/wiki/Q79785","display_name":"Electrocardiography","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C11783203","wikidata":"https://www.wikidata.org/wiki/Q5478027","display_name":"Framingham Risk Score","level":3,"score":0.26429998874664307},{"id":"https://openalex.org/C76318530","wikidata":"https://www.wikidata.org/wiki/Q16833590","display_name":"Area under the curve","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.25609999895095825},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.9734/ajrcd/2026/v8i1173","doi":"https://doi.org/10.9734/ajrcd/2026/v8i1173","is_oa":true,"landing_page_url":"https://doi.org/10.9734/ajrcd/2026/v8i1173","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Asian Journal of Research in Cardiovascular Diseases","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.9734/ajrcd/2026/v8i1173","doi":"https://doi.org/10.9734/ajrcd/2026/v8i1173","is_oa":true,"landing_page_url":"https://doi.org/10.9734/ajrcd/2026/v8i1173","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Asian Journal of Research in Cardiovascular Diseases","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Background:":[0],"Major":[1],"adverse":[2],"cardiovascular":[3,72],"events":[4],"(MACE)":[5],"remain":[6],"a":[7],"substantial":[8],"contributor":[9],"to":[10,109],"morbidity":[11],"and":[12,62,94,112,125,171,195,212,226,235,261],"mortality":[13],"following":[14,46,246],"acute":[15],"myocardial":[16,75],"infarction":[17],"(AMI).":[18],"Conventional":[19],"risk":[20,256],"prediction":[21],"models":[22,98],"may":[23],"inadequately":[24],"capture":[25],"complex":[26],"relationships":[27],"among":[28],"clinical":[29,104,224,263],"predictors.":[30,218],"This":[31,49],"study":[32,53],"evaluated":[33],"the":[34,145,149],"predictive":[35,233],"performance":[36],"of":[37],"artificial":[38,95,163],"intelligence\u2013based":[39],"machine":[40,169],"learning":[41],"algorithms":[42],"for":[43,243,253],"12-month":[44,69,244],"MACE":[45,245],"AMI.":[47,247],"Methods:":[48],"multicentre":[50],"retrospective":[51],"cohort":[52],"included":[54],"adults":[55],"hospitalised":[56],"with":[57,239],"AMI":[58],"between":[59],"January":[60],"2016":[61],"December":[63],"2024.":[64],"The":[65,175],"primary":[66],"outcome":[67],"was":[68,118,181],"MACE,":[70],"comprising":[71],"death,":[73],"recurrent":[74],"infarction,":[76],"ischaemic":[77],"stroke,":[78],"or":[79],"heart":[80,214],"failure":[81,215],"hospitalisation.":[82],"Logistic":[83],"regression,":[84],"random":[85,160],"forest,":[86],"extreme":[87],"gradient":[88],"boosting":[89],"(XGBoost),":[90],"support":[91,167],"vector":[92,168],"machine,":[93],"neural":[96,164],"network":[97,165],"were":[99,107,216],"developed":[100],"using":[101,120],"routinely":[102],"collected":[103],"variables.":[105],"Patients":[106],"allocated":[108],"training":[110],"(80%)":[111],"held-out":[113],"testing":[114],"(20%)":[115],"datasets.":[116],"Performance":[117],"assessed":[119],"discrimination,":[121],"calibration,":[122],"classification":[123],"metrics,":[124],"decision":[126],"curve":[127,153],"analysis.":[128],"Results:":[129],"Among":[130],"20,846":[131],"patients":[132],"(mean":[133],"age,":[134,207],"64.8":[135],"years;":[136],"64.6%":[137],"male),":[138],"3,684":[139],"(17.7%)":[140],"experienced":[141],"MACE.":[142],"XGBoost":[143,189,230],"achieved":[144],"highest":[146],"area":[147],"under":[148],"receiver":[150],"operating":[151],"characteristic":[152],"(AUROC;":[154],"0.862;":[155],"95%":[156],"CI,":[157,184],"0.846\u20130.878),":[158],"outperforming":[159],"forest":[161],"(0.841),":[162],"(0.833),":[166],"(0.819),":[170],"logistic":[172,179,241],"regression":[173,180,242],"(0.784).":[174],"AUROC":[176],"improvement":[177],"over":[178],"0.078":[182],"(95%":[183],"0.057\u20130.099;":[185],"P":[186],"<":[187],".001).":[188],"demonstrated":[190,231],"81.6%":[191],"sensitivity,":[192],"77.9%":[193],"specificity,":[194],"favourable":[196,236],"calibration":[197,237],"(slope,":[198],"0.96;":[199],"Brier":[200],"score,":[201],"0.112).":[202],"Left":[203],"ventricular":[204],"ejection":[205],"fraction,":[206],"renal":[208],"function,":[209],"Killip":[210],"classification,":[211],"previous":[213],"influential":[217],"Discrimination":[219],"remained":[220],"broadly":[221],"consistent":[222],"across":[223],"subgroups":[225],"sensitivity":[227],"analyses.":[228],"Conclusions:":[229],"superior":[232],"discrimination":[234],"compared":[238],"conventional":[240],"These":[248],"findings":[249],"suggest":[250],"potential":[251],"value":[252],"individualized":[254],"post-infarction":[255],"stratification,":[257],"although":[258],"external":[259],"validation":[260],"prospective":[262],"evaluation":[264],"are":[265],"required":[266],"before":[267],"implementation.":[268]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}
