{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T11:11:03Z","timestamp":1788779463979,"version":"build-2803163510"},"reference-count":35,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Internal Medicine Journal"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>To quantify the heterogeneity of treatment effects of higher versus standard MAP targets on 28\u2010day mortality, to identify distinct clinical phenotypes that respond differentially to MAP targets, and to derive simple bedside decision rules to guide individualised haemodynamic management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Current guidelines recommend a uniform mean arterial pressure (MAP) target of \u226565\u2009mmHg for septic shock. However, trials like SEPSISPAM found no mortality benefit from higher targets in hypertensive patients. We hypothesised that this null finding results from the masking of heterogeneous treatment effects (HTEs) among phenotypically distinct individuals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective cohort study of 7665 hypertensive patients with septic shock from the Medical Information Mart for Intensive Care IV database (version 3.1). The primary exposure was time\u2010weighted average MAP (TWA\u2010MAP) during the first 24 h, dichotomised into high target (&gt;75\u2009mmHg) versus standard target (65\u201375\u2009mmHg). The primary outcome was 28\u2010day mortality. Individual treatment effects (ITEs) were estimated using a T\u2010Learner causal machine learning framework with gradient boosting. Patients were stratified by ITE quartiles, and a Classification and Regression Tree (CART) was used to derive bedside clinical rules.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We observed marked HTE (ITE range: \u22120.54 to +0.86). In the Strong Benefit group (Decompensated Phenotype) \u2013 characterised by tachycardia, hyperlactatemia and renal dysfunction \u2013 the high MAP target dramatically reduced 28\u2010day mortality from 62.6% to 22.9% (absolute risk reduction\u2009=\u200939.7%,\n                      <jats:italic>P<\/jats:italic>\n                      &lt;\u20090.001). Conversely, in the Harm group (Frail Phenotype) \u2013 characterised by older age and lower heart rate \u2013 the high target increased mortality from 14.7% to 33.9% (ARR = \u221219.2%,\n                      <jats:italic>P<\/jats:italic>\n                      &lt;\u20090.001). The CART model identified heart rate &gt;105 bpm and creatinine &gt;1.65 mg\/dL as simple bedside criteria to identify patients likely to benefit from higher targets.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The impact of higher MAP targets in hypertensive septic shock exhibits a distinct crossover pattern: life\u2010saving in decompensated patients but harmful in frail patients. This HTE explains the null results of previous landmark trials. Utilising simple parameters like heart rate and creatinine can guide a shift from \u2018one\u2010size\u2010fits\u2010all\u2019 targets towards precision haemodynamic management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/imj.70515","type":"journal-article","created":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T09:34:43Z","timestamp":1782725683000},"page":"1617-1629","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Crossover effect: causal machine learning reveals opposing mortality responses to mean arterial pressure targets among phenotypically distinct hypertensive patients with septic shock"],"prefix":"10.1111","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4754-6534","authenticated-orcid":false,"given":"Zekai","family":"Yu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology Hangzhou Dianzi University  Hangzhou 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