Crossover effect: causal machine learning reveals opposing mortality responses to mean arterial pressure targets among phenotypically distinct hypertensive patients with septic shock

AIMS: To quantify the heterogeneity of treatment effects of higher versus standard MAP targets on 28-day mortality, to identify distinct clinical phenotypes that respond differentially to MAP targets, and to derive simple bedside decision rules to guide individualised haemodynamic management. BACKGROUND: Current guidelines recommend a uniform mean arterial pressure (MAP) target of ≥65 mmHg 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. METHODS: 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-weighted average MAP (TWA-MAP) during the first 24 h, dichotomised into high target (>75 mmHg) versus standard target (65-75 mmHg). The primary outcome was 28-day mortality. Individual treatment effects (ITEs) were estimated using a T-Learner 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. RESULTS: We observed marked HTE (ITE range: -0.54 to +0.86). In the Strong Benefit group (Decompensated Phenotype) - characterised by tachycardia, hyperlactatemia and renal dysfunction - the high MAP target dramatically reduced 28-day mortality from 62.6% to 22.9% (absolute risk reduction = 39.7%, P < 0.001). Conversely, in the Harm group (Frail Phenotype) - characterised by older age and lower heart rate - the high target increased mortality from 14.7% to 33.9% (ARR = -19.2%, P < 0.001). The CART model identified heart rate >105 bpm and creatinine >1.65 mg/dL as simple bedside criteria to identify patients likely to benefit from higher targets. CONCLUSIONS: The impact of higher MAP targets in hypertensive septic shock exhibits a distinct crossover pattern: life-saving 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 'one-size-fits-all' targets towards precision haemodynamic management.

Authors

Institutions

Publication Details

Journal
Internal Medicine Journal
Published
2026-06-29
DOI
https://doi.org/10.1111/imj.70515
Primary Topic
Sepsis Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Crossover effect: causal machine learning reveals opposing mortality responses to mean arterial pressure targets among phenotypically distinct hypertensive patients with septic shock

Zekai Yu
Internal Medicine Journal
Sepsis Diagnosis and Treatment
article

Crossover effect: causal machine learning reveals opposing mortality responses to mean arterial pressure targets among phenotypically distinct hypertensive patients with septic shock

Zekai Yu
article en

Abstract

AIMS: To quantify the heterogeneity of treatment effects of higher versus standard MAP targets on 28-day mortality, to identify distinct clinical phenotypes that respond differentially to MAP targets, and to derive simple bedside decision rules to guide individualised haemodynamic management. BACKGROUND: Current guidelines recommend a uniform mean arterial pressure (MAP) target of ≥65 mmHg 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. METHODS: 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-weighted average MAP (TWA-MAP) during the first 24 h, dichotomised into high target (>75 mmHg) versus standard target (65-75 mmHg). The primary outcome was 28-day mortality. Individual treatment effects (ITEs) were estimated using a T-Learner 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. RESULTS: We observed marked HTE (ITE range: -0.54 to +0.86). In the Strong Benefit group (Decompensated Phenotype) - characterised by tachycardia, hyperlactatemia and renal dysfunction - the high MAP target dramatically reduced 28-day mortality from 62.6% to 22.9% (absolute risk reduction = 39.7%, P < 0.001). Conversely, in the Harm group (Frail Phenotype) - characterised by older age and lower heart rate - the high target increased mortality from 14.7% to 33.9% (ARR = -19.2%, P < 0.001). The CART model identified heart rate >105 bpm and creatinine >1.65 mg/dL as simple bedside criteria to identify patients likely to benefit from higher targets. CONCLUSIONS: The impact of higher MAP targets in hypertensive septic shock exhibits a distinct crossover pattern: life-saving 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 'one-size-fits-all' targets towards precision haemodynamic management.

Internal Medicine Journal
Hangzhou Dianzi University (CN)
Good health and well-being
Openalex Percentile: Top 8%
Sepsis Diagnosis and Treatment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.