ArticleFrontiers in medicine2026
Early risk prediction model for intra-abdominal hypertension in adult ICU patients: a prospective cohort study.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To identify independent risk factors for intra-abdominal hypertension (IAH) in adult intensive care unit (ICU) patients, develop a predictive nomogram, and perform internal validation. Methods: This prospective cohort study enrolled 261 adult ICU patients from two tertiary hospitals in Sichuan Province, China, between August and December 2022, using convenience sampling. Patients with IAH at admission were excluded. A standardized questionnaire captured demographic, clinical, and laboratory data. Cox proportional hazards regression identified independent predictors of IAH within 1-6 days post-ICU admission. A nomogram was constructed and internally validated using Bootstrap resampling (1,000 iterations). Model performance was assessed by the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). Results: Intra-abdominal hypertension occurred in 198 patients (75.9%), with a median onset of 12 h (range: 12-72 h). Multivariable Cox analysis revealed six independent risk factors: pre-ICU surgery (HR = 1.524, 95%CI: 1.075-2.161), abdominal distension (HR = 2.082, 95%CI: 1.505-2.881), increased intra-abdominal contents (HR = 1.857, 95%CI: 1.343-2.567), intraperitoneal fluid accumulation (HR = 1.500, 95%CI: 1.074-2.095), shock (HR = 1.806, 95%CI: 1.218-2.677), and APACHE II score (HR = 1.047 per point, 95%CI: 1.016-1.078). The nomogram demonstrated excellent discrimination (C-index: 0.826; bootstrap-corrected: 0.826) and calibration. Time-dependent AUC at 12 h, 24 h, and 36 h were 0.836 (95% CI: 0.787-0.884), 0.932 (95% CI: 0.900-0.964), and 0.913 (95% CI: 0.877-0.950), respectively. DCA confirmed clinical utility across a range of threshold probabilities. Conclusion: This nomogram, based on six readily available clinical variables, enables early identification of ICU patients at high risk for IAH, facilitating targeted monitoring and preventive interventions. Relevance to clinical practice: The model provides a simple bedside tool for risk stratification, guiding proactive intra-abdominal pressure monitoring and individualized care plans.
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