A novel web-based calculator to predict 30-day all-cause in-hospital mortality for 7,202 elderly patients with heart failure in ICUs: a multicenter retrospective cohort study in the United States

Zhongjian Wang; Jian Huang; Yang Zhang; Xiaozhu Liu; Tingting Shu; Minjie Duan; Haolin Wang; Chengliang Yin; Junyi Cao

doi:10.3389/fmed.2023.1237229

A novel web-based calculator to predict 30-day all-cause in-hospital mortality for 7,202 elderly patients with heart failure in ICUs: a multicenter retrospective cohort study in the United States

Front Med (Lausanne). 2023 Sep 15:10:1237229. doi: 10.3389/fmed.2023.1237229. eCollection 2023.

Authors

Zhongjian Wang^#¹, Jian Huang^#², Yang Zhang^#^{3

4}, Xiaozhu Liu³, Tingting Shu⁵, Minjie Duan^{3

4}, Haolin Wang³, Chengliang Yin⁶, Junyi Cao⁷

Affiliations

¹ Artificial Intelligence Laboratory, Pharnexcloud Digital Technology (Chengdu) Co. Ltd., Chengdu, China.
² Graduate School, Guangxi University of Chinese Medicine, Nanning, China.
³ College of Medical Informatics, Chongqing Medical University, Chongqing, China.
⁴ Medical Data Science Academy, Chongqing Medical University, Chongqing, China.
⁵ Department of Cardiology, Daping Hospital, The Third Military Medical University (Army Medical University), Chongqing, China.
⁶ Faculty of Medicine, Macau University of Science and Technology, Macau, Macau SAR, China.
⁷ Department of Medical Quality Control, The First People's Hospital of Zigong City, Zigong, China.

^# Contributed equally.

Abstract

Background and aims: Heart failure (HF) is a significant cause of in-hospital mortality, especially for the elderly admitted to intensive care units (ICUs). This study aimed to develop a web-based calculator to predict 30-day in-hospital mortality for elderly patients with HF in the ICU and found a relationship between risk factors and the predicted probability of death.

Methods and results: Data (N = 4450) from the MIMIC-III/IV database were used for model training and internal testing. Data (N = 2,752) from the eICU-CRD database were used for external validation. The Brier score and area under the curve (AUC) were employed for the assessment of the proposed nomogram. Restrictive cubic splines (RCSs) found the cutoff values of variables. The smooth curve showed the relationship between the variables and the predicted probability of death. A total of 7,202 elderly patients with HF were included in the study, of which 1,212 died. Multivariate logistic regression analysis showed that 30-day mortality of HF patients in ICU was significantly associated with heart rate (HR), 24-h urine output (24h UOP), serum calcium, blood urea nitrogen (BUN), NT-proBNP, SpO₂, systolic blood pressure (SBP), and temperature (P < 0.01). The AUC and Brier score of the nomogram were 0.71 (0.67, 0.75) and 0.12 (0.11, 0.15) in the testing set and 0.73 (0.70, 0.75), 0.13 (0.12, 0.15), 0.65 (0.62, 0.68), and 0.13 (0.12, 0.13) in the external validation set, respectively. The RCS plot showed that the cutoff values of variables were HR of 96 bmp, 24h UOP of 1.2 L, serum calcium of 8.7 mg/dL, BUN of 30 mg/dL, NT-pro-BNP of 5121 pg/mL, SpO₂ of 93%, SBP of 137 mmHg, and a temperature of 36.4°C.

Conclusion: Decreased temperature, decreased SpO₂, decreased 24h UOP, increased NT-proBNP, increased serum BUN, increased or decreased SBP, fast HR, and increased or decreased serum calcium increase the predicted probability of death. The web-based nomogram developed in this study showed good performance in predicting 30-day in-hospital mortality for elderly HF patients in the ICU.

Keywords: big data; calculator; death; elderly; heart failure; web-based.

Grants and funding

This research was funded by the Dongguan Science and Technology of Social Development Program (202050715032200).