{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T10:08:05Z","timestamp":1789553285709,"version":"build-2803163510"},"reference-count":33,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"12","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,6,16]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Extracorporeal cardiopulmonary resuscitation (ECPR) has demonstrated survival benefit in selected patients with out\u2010of\u2010hospital cardiac arrest, yet optimal selection criteria remain uncertain. Machine\u2010learning\u2013based individualized treatment effect (ITE) modeling may identify patients most likely to benefit by capturing heterogeneity of treatment response, potentially providing a more accurate strategy for ECPR candidate selection than current rule\u2010based criteria.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">We retrospectively analyzed adult, nontraumatic, emergency medical services\u2013attended patients with out\u2010of\u2010hospital cardiac arrest from 4 tertiary centers in Taiwan between 2016 and 2024. After propensity score matching for shockable rhythm and witnessed arrest, a gradient\u2010boosted trees\u2013based causal forest model was developed to estimate ITE and predict the survival benefit of ECPR to hospital discharge. Absolute observed treatment effect across different ITE thresholds was compared with trial\u2010based (ARREST [Advanced reperfusion strategies for patients with out\u2010of\u2010hospital cardiac arrest and refractory ventricular fibrillation] trial, PRAGUE OHCA [Effect of Intra\u2010arrest Transport, Extracorporeal Cardiopulmonary Resuscitation, and Immediate Invasive Assessment and Treatment on Functional Neurologic Outcome in Refractory Out\u2010of\u2010Hospital Cardiac Arrest] trial, and INCEPTION [Early Extracorporeal CPR for Refractory Out\u2010of\u2010Hospital Cardiac Arrest] trial) and hospital\u2010specific criteria.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">\n                      Among 1953 matched patients, 977 received ECPR. Overall survival was similar between ECPR and non\u2010ECPR groups (11.1% versus 12.8%; standardized mean difference=0.054). In the top 10% of patients with the highest predicted benefit, survival to discharge was 50.0% with ECPR versus 20.0% without, yielding an absolute observed treatment effect of 30.0% (95% CI, 1.56\u201357.58;\n                      <jats:italic toggle=\"no\">P<\/jats:italic>\n                      =0.042). The ITE model identified subgroups with higher observed survival benefit compared with those selected by rule\u2010based criteria. Factors associated with higher ECPR treatment effect included lower serum pH and partial pressure of carbon dioxide level, higher lactate level, younger age, and bystander cardiopulmonary resuscitation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">The ITE\u2010based model was associated with greater observed survival benefit compared with current rule\u2010based criteria and may demonstrate potential as a data\u2010driven framework for candidate selection for ECPR. Further validation in prospective settings is warranted.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.125.047815","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T12:12:18Z","timestamp":1781525538000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Optimizing Extracorporeal Cardiopulmonary Resuscitation Candidate Selection in out\u2010of\u2010Hospital Cardiac Arrest: A Machine\u2010Learning Individualized Treatment Effect Approach Versus Rule\u2010Based Criteria"],"prefix":"10.1161","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1826-6598","authenticated-orcid":false,"given":"Chi\u2010Hsin","family":"Chen","sequence":"first","affiliation":[{"name":"National Taiwan University Hospital Hsin\u2010Chu Branch","place":["Hsinchu, Taiwan"]},{"name":"Taipei Medical University","place":["Taipei, Taiwan"]},{"name":"National Taiwan 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