Key result
Model combining clinical congestion, age, and LUS predicts 30-day AHF mortality with AUC ~0.85.
Why the study?
Existing mortality prediction models for ICU patients with HF rely on admission variables and lack adequate discriminative ability, prompting development of a model using ICU discharge variables.
Does a prognostic model combining clinical congestion score, age, and lung ultrasound score improve the prediction of 30-day all-cause mortality in ICU patients with acute heart failure compared to existing scores?
Does a prognostic model combining clinical congestion score, age, and lung ultrasound score improve the prediction of 30-day all-cause mortality in ICU patients with acute heart failure compared to existing scores?
A novel prognostic model combining clinical congestion score, age, and lung ultrasound score provides superior discrimination for 30-day mortality in acute heart failure ICU patients compared to traditional scoring systems.
May enhance post-ICU risk stratification in acute HF; hypothesis-generating and requires external validation before adoption.
Background Existing mortality prediction models for intensive care unit (ICU) patients with heart failure (HF) often rely on variables collected at ICU admission and lacked adequate discriminative ability. This study aimed to develop a new prediction model based on ICU discharge variables to improve predictive performance in patients with acute HF. Methods This single‐center prospective observational study aimed to assess all‐cause mortality within 30 days of emergency ICU admission. Candidate variables with p < 0.05 in univariable analysis were dichotomized based on clinical characteristics or a restricted cubic spline model, and included in the multivariate logistic regression model. The clinical congestion score (CCS) score was revised using forward multivariable regression analysis. The discrimination, calibration, and clinical utility of CCS, lung ultrasound (LUS) score, age, LUS score combined with age, CCS combined with LUS score, CCS combined with age, the new prognostic model, and APACHE‐HF were assessed using the bootstrap method with 500 resamples. Results Seventy‐one participants were enrolled in this study, with a median age of 79 years (71.5–84), and 50.7% (36/71) were male. The multivariate logistic regression model revealed that a 1‐point increase in CCS (OR: 2.883, 95% CI: 1.358–6.121, p = 0.006), age ≥85 years (OR: 15.271, 95% CI: 2.434–95.803, p = 0.004), and LUS score ≥3 (OR: 4.646, 95% CI: 1.366–15.799, p = 0.014) were significantly associated with higher all‐cause mortality risk. Based on 500 bootstrap resamples, the new prognostic model (CCS combined with age and LUS score) had the highest AUC of 0.849 among prediction models. Furthermore, this new model demonstrated superior calibration and clinical utility compared to other scoring systems. Conclusion The CCS enhanced by age and LUS score enabled good prediction of all‐cause mortality within 30 days after admission to ICUs, potentially aiding clinical decision‐making for acute HF patients in ICUs including emergency intensive care units.
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Xu et al. (2026) studied this question. A prognostic model combining clinical congestion score, age ≥85, and lung ultrasound score ≥3 predicted 30-day mortality with AUC 0.849 in acute heart failure ICU patients.
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