Clinical congestion score enhanced by age and lung ultrasound improves utility over clinical congestion score alone in patients with acute heart failure presenting to emergency intensive care unit

Abstract 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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Journal
Hong Kong Journal of Emergency Medicine
Published
2026-02-01
DOI
https://doi.org/10.1002/hkj2.70070
Primary Topic
Ultrasound in Clinical Applications
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article
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article

Clinical congestion score enhanced by age and lung ultrasound improves utility over clinical congestion score alone in patients with acute heart failure presenting to emergency intensive care unit

Cheng Zhang, Ziyin Xu, Kefeng Li, Dehao Zhou et al.
Hong Kong Journal of Emergency Medicine
Ultrasound in Clinical Applications
article

Clinical congestion score enhanced by age and lung ultrasound improves utility over clinical congestion score alone in patients with acute heart failure presenting to emergency intensive care unit

Cheng Zhang, Ziyin Xu, Kefeng Li, Dehao Zhou, Liang Li, Wenbin Huang, Ping Xu, Mingyan Fan
article en

Abstract

Abstract 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.

Hong Kong Journal of Emergency MedicineVol. 33(1)
Beijing Institute of Big Data Research (CN), Zigong First People's Hospital (CN), Macao Polytechnic University (MO)
Reduced inequalities
Openalex Percentile: Top 60%
Ultrasound in Clinical Applications
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