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Development and evaluation of a predictive nomogram for survival in heat stroke patients: a retrospective cohort study

摘要BACKGROUND: This study aimed to establish an eff ective 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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作者单位 Department of Emergency Medicine,Beijing Chaoyang Hospital,Capital Medical University,Beijing 100020,China;Department of Emergency Medicine,Hebei Yanda Hospital,Langfang 065201,China [1] Department of Emergency Medicine,Beijing Huairou Hospital,Beijing 101400,China [2] Department of Emergency Medicine,the Second Hospital of Hebei Medical University,Shijiazhuang 050000,China [3] Department of Emergency Medicine,Hebei Yanda Hospital,Langfang 065201,China [4] Department of Emergency Medicine,the Second Affiliated Hospital of Shandong First Medical University,Tai'an 271000,China [5] Department of Emergency Medicine,the First Affiliated Hospital of Dalian Medical University,Dalian 116011,China [6] Department of Emergency Medicine,China-Japan Friendship Hospital,Beijing 100029,China [7] Department of Emergency Medicine,Beijing Friendship Hospital,Beijing 100050,China [8] Department of Emergency Medicine,Henan Provincial People's Hospital,Zhengzhou 450003,China [9] Department of Emergency Medicine,Beijing Tsinghua Changung Hospital,Beijing 102218,China [10] Department of Emergency Medicine,General Hospital of Ningxia Medical University,Yinchuan 750004,China [11] Department of Emergency Medicine,the Affiliated Hospital of Inner Mongolia Medical University,Hohhot 010050,China [12] Department of Emergency Medicine,the Second Hospital of Jilin University,Changchun 130021,China [13] Department of Emergency Medicine,the First Hospital of China Medical University,Shenyang 110001,China [14] Department of Emergency Medicine,the Second Hospital of Shanxi Medical University,Taiyuan 030001,China [15] Department of Emergency Medicine,Beijing Chaoyang Hospital,Capital Medical University,Beijing 100020,China [16] Department of Big Data Research,Goodwill Hessian Health Technology Co.,Ltd.,Beijing 100085,China [17] School of Public Health,Peking University,Beijing 100083,China [18] Department of Emergency Medicine,Beijing Chaoyang Hospital,Capital Medical University,Beijing 100020,China;Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation,Beijing 100020,China [19] Department of Critical Care Medicine,Beijing Friendship Hospital,Capital Medical University,Beijing 100050,China [20]
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DOI 10.5847/wjem.j.1920-8642.2022.092
发布时间 2022-09-05(万方平台首次上网日期,不代表论文的发表时间)
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