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Shao F, Shi X, Huo SH, Liu QY, Shi JX, Kang J, Gong P, Yan ST, Wang GX, Qin LJ, Wang F, Feng K, Chen FY, Yin YJ, Ma T, Li Y, Wu Y, Cui H, Yu CX, Yang S, Gan W, Wang S, Du LYZ, Zhao MC, Tang ZR, Zhao S. Development and evaluation of a predictive nomogram for survival in heat stroke patients: a retrospective cohort study. World J Emerg Med 2022; 13:355-360. [PMID: 36119776 PMCID: PMC9420659 DOI: 10.5847/wjem.j.1920-8642.2022.092] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 07/26/2022] [Indexed: 09/12/2023] Open
Abstract
BACKGROUND This study aimed to establish an effective nomogram to predict the survival of heat stroke (HS) based on risk factors. METHODS This was a retrospective, observational multicenter cohort study. We analyzed patients diagnosed with HS, who were treated between May 1 and September 30, 2018 at 15 tertiary hospitals from 11 cities in Northern China. RESULTS Among the 175 patients, 32 patients (18.29%) died before hospital discharge. After the univariate analysis, mechanical ventilation, initial mean arterial pressure <70 mmHg, maximum heart rate, lab results on day 1 (white blood cell count, alanine aminotransferase, creatinine), and Glasgow admission prediction score were included in multivariate analysis. Multivariate Cox regression showed that invasive ventilation, initial mean arterial pressure <70 mmHg (1 mmHg=0.133 kPa), and Glasgow admission prediction score were independent risk factors for HS. The nomogram was established for predicting 7-d and 14-d survival in the training cohort. The nomogram exhibited a concordance index (C-index) of 0.880 (95% confidence interval [95% CI] 0.831-0.930) by bootstrapping validation (B=1,000). Furthermore, the nomogram performed better when predicting 14-d survival, compared to 7-d survival. The prognostic index cut-off value was set at 2.085, according to the operating characteristic curve for overall survival prediction. The model showed good calibration ability in the internal and external validation datasets. CONCLUSION A novel nomogram, integrated with prognostic factors, was proposed; it was highly predictive of the survival in HS patients.
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Affiliation(s)
- Fei Shao
- Department of Emergency Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
- Department of Emergency Medicine, Hebei Yanda Hospital, Langfang 065201, China
| | - Xian Shi
- Department of Emergency Medicine, Beijing Huairou Hospital, Beijing 101400, China
| | - Shu-hua Huo
- Department of Emergency Medicine, the Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
| | - Qing-yu Liu
- Department of Emergency Medicine, Hebei Yanda Hospital, Langfang 065201, China
| | - Ji-xue Shi
- Department of Emergency Medicine, the Second Affiliated Hospital of Shandong First Medical University, Tai’an 271000, China
| | - Jian Kang
- Department of Emergency Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China
| | - Ping Gong
- Department of Emergency Medicine, the First Affiliated Hospital of Dalian Medical University, Dalian 116011, China
| | - Sheng-tao Yan
- Department of Emergency Medicine, China-Japan Friendship Hospital, Beijing 100029, China
| | - Guo-xing Wang
- Department of Emergency Medicine, Beijing Friendship Hospital, Beijing 100050, China
| | - Li-jie Qin
- Department of Emergency Medicine, Henan Provincial People’s Hospital, Zhengzhou 450003, China
| | - Fei Wang
- Department of Emergency Medicine, Beijing Tsinghua Changung Hospital, Beijing 102218, China
| | - Ke Feng
- Department of Emergency Medicine, General Hospital of Ningxia Medical University, Yinchuan 750004, China
| | - Feng-ying Chen
- Department of Emergency Medicine, the Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010050, China
| | - Yong-jie Yin
- Department of Emergency Medicine, the Second Hospital of Jilin University, Changchun 130021, China
| | - Tao Ma
- Department of Emergency Medicine, the First Hospital of China Medical University, Shenyang 110001, China
| | - Yan Li
- Department of Emergency Medicine, the Second Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Yang Wu
- Department of Emergency Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - Hao Cui
- Department of Emergency Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - Chang-xiao Yu
- Department of Emergency Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
| | - Song Yang
- Department of Emergency Medicine, Beijing Huairou Hospital, Beijing 101400, China
| | - Wei Gan
- Department of Big Data Research, Goodwill Hessian Health Technology Co., Ltd., Beijing 100085, China
| | - Sai Wang
- Department of Big Data Research, Goodwill Hessian Health Technology Co., Ltd., Beijing 100085, China
| | - Liu-ye-zi Du
- Department of Big Data Research, Goodwill Hessian Health Technology Co., Ltd., Beijing 100085, China
| | - Ming-chen Zhao
- School of Public Health, Peking University, Beijing 100083, China
| | - Zi-ren Tang
- Department of Emergency Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China
- Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing 100020, China
| | - Shen Zhao
- Department of Critical Care Medicine, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
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