Systematic review of hypothermia prediction models in patients with severe trauma_West China Medical Journal

Authors：

LIU Yining ¹ ,  LI Gang ¹ , SONG Qinghua ¹ , DUAN Keli ¹ , HUANG Xu ²

1. Department of Plastic and Aesthetic Surgery, Trauma Treatment Center, Shenzhen University General Hospital, Shenzhen, Guangdong 518055, P. R. China;
2. Chinese Center for Disease Control and Prevention, Beijing 102206, P. R. China;

Corresponding?author：

LI Gang, Email: 656061380@qq.com

Keywords：

Hypothermia; prediction model; systematic review; severe trauma patients; risk prediction; trauma care

DOI：

10.7507/1002-0179.202510158

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Objective To systematically evaluate risk prediction models for hypothermia in patients with severe trauma, in order to provide an evidence base for the selection and refinement of predictive tools in clinical practice. Methods A comprehensive search was conducted in electronic databases including PubMed, Web of Science, Embase, Cochrane Library, CINAHL, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, VIP Network, and SinoMedfrom from inception to July 31, 2025, to identify studies on risk prediction models for hypothermia in severe trauma patients. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias and applicability using the Prediction Model Risk of Bias Assessment Tool. Results A total of 8 studies detailing the development of prediction models were included, yielding 8 models. The single sample sizes ranged from 91 to 489, with the number of outcome events ranging from 61 to 231. The area under the receiver operating characteristic curve for the model ranged from 0.704 to 0.990. Calibration assessment was conducted in all 8 studies, with 6 of them employing classic calibration testing methods such as the Hosmer-Lemeshow test. The most frequently identified predictors included the modified Glasgow Coma Scale score, Revised Trauma Score, Injury Severity Score, wet clothing on admission, and ambient temperature at the time of injury. PredictionModel Risk of Bias Assessment Tool indicated that while all models demonstrated good applicability, they exhibited a high risk of bias, primarily attributable to insufficient sample sizes and suboptimal handling of predictive variables, inadequate standardization of hypothermia measurement, and lack of effective validation. Conclusions Current prediction models show good discriminative potential, but due to the high risk of bias, there is insufficient evidence to recommend their routine use in clinical decision-making. Clinicians may refer to core predictors for empirical risk assessment. Future research should prioritize large-scale, multicenter external validation of existing models with high discriminative power and strictly standardize methodological aspects.

Citation： LIU Yining, LI Gang, SONG Qinghua, DUAN Keli, HUANG Xu. Systematic review of hypothermia prediction models in patients with severe trauma. West China Medical Journal, 2026, 41(5): 799-805. doi: 10.7507/1002-0179.202510158 Copy

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2.	李虹彥, 劉濤, 殷欣. 中國創傷急救 10 年研究現狀回顧及分析. 創傷外科雜志, 2012, 14(2): 104-107.
3.	Frischknecht A, Lustenberger T, Bukur M, et al. Damage control in severely injured trauma patients--a ten-year. experience. J Emerg Trauma Shock, 2011, 4(4): 450-454.
4.	Forristal C, Van Aarsen K, Columbus M, et al. Predictors of hypothermia upon trauma center arrival in severe trauma patients transported to hospital via EMS. Prehosp Emerg Care, 2020, 24(1): 15-22.
5.	Zhou LH, Hong JF, Qin RM, et al. Post-traumatic growth and its influencing factors among Chinese women diagnosed with gynecological cancer: a cross-sectional study. Eur J Oncol Nurs, 2021, 51: 101903.
6.	Moons KG, Hooft L, Williams K, et al. Implementing systematic reviews of prognosis studies in Cochrane. Cochrane Database syst Rev, 2018, 10(10): ED000129.
7.	Lapostolle F, Sebbah JL, Couvreur J, et al. Risk factors for onset of hypothermia in trauma victims: the HypoTraum study. Crit Care, 2012, 16(4): R142.
8.	Weuster M, Brück A, Lippross S, et al. Epidemiology of accidental hypothermia in polytrauma patients: an analysis of 15, 230 patients of the trauma register DGU?. J Trauma Acute Care Surg, 2016, 81(5): 905-912.
9.	Lapostolle F, Couvreur J, Koch FX, et al. Hypothermia in trauma victims at first arrival of ambulance personnel: an observational study with assessment of risk factors. Scand J Trauma Resusc Emerg Med, 2017, 25(1): 43.
10.	劉力行, 聶時南, 劉云, 等. 創傷后自發性低體溫急救處理現狀與臨床問題的研究進展. 中華護理雜志, 2016, 51(6): 725-729.
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14.	涂加園, 劉云, 孫琳, 等. 創傷患者低體溫風險評估量表的編制及信效度檢驗. 中華護理雜志, 2020, 55(12): 1813-1818.
15.	陳天喜, 沈紅五, 姜琴, 等. 急診創傷患者低體溫風險評分量表的研制. 護理學雜志, 2021, 36(23): 38-41.
16.	顏雷雷, 廖德珺, 金倩倩, 等. 急性創傷患者低體溫發生風險的列線圖模型構建. 中國護理管理, 2022, 22(7): 1015-1020.
17.	梅潤, 何乾峰, 徐璐瑤, 等. 基于決策樹構建急診創傷患者低體溫早期預警模型及驗證. 軍事護理, 2023, 40(5): 14-17, 85.
18.	張文英, 皇芳芳, 徐洪云. 基于 logistic 回歸分析構建急診創傷后患者自發性低體溫預測模型. 國際護理學雜志, 2023, 42(1):165-168.
19.	何聰聰, 張瑜, 周立敏. 急性嚴重創傷患者自發性低體溫風險預測模型的構建及驗證. 護理學, 2024, 13(11): 1615-1622.
20.	張悅, 潘慧斌, 溫曉紅, 等. 嚴重創傷后自發性低體溫風險預測模型的構建. 湖州師范學院學報, 2025, 47(2): 49-56.
21.	Feng X, Zhu F, Qiao A, et al. Factors influencing spontaneous hypothermia after emergency trauma and the construction of a predictive model. Open Life Sci, 2024, 19(1): 20220862.

1. 張陽春, 季學麗, 張麗, 等. 急診嚴重創傷患者體溫管理的循證護理實踐. 護理學雜志, 2019, 34(22): 20-25.
2. 李虹彥, 劉濤, 殷欣. 中國創傷急救 10 年研究現狀回顧及分析. 創傷外科雜志, 2012, 14(2): 104-107.
3. Frischknecht A, Lustenberger T, Bukur M, et al. Damage control in severely injured trauma patients--a ten-year. experience. J Emerg Trauma Shock, 2011, 4(4): 450-454.
4. Forristal C, Van Aarsen K, Columbus M, et al. Predictors of hypothermia upon trauma center arrival in severe trauma patients transported to hospital via EMS. Prehosp Emerg Care, 2020, 24(1): 15-22.
5. Zhou LH, Hong JF, Qin RM, et al. Post-traumatic growth and its influencing factors among Chinese women diagnosed with gynecological cancer: a cross-sectional study. Eur J Oncol Nurs, 2021, 51: 101903.
6. Moons KG, Hooft L, Williams K, et al. Implementing systematic reviews of prognosis studies in Cochrane. Cochrane Database syst Rev, 2018, 10(10): ED000129.
7. Lapostolle F, Sebbah JL, Couvreur J, et al. Risk factors for onset of hypothermia in trauma victims: the HypoTraum study. Crit Care, 2012, 16(4): R142.
8. Weuster M, Brück A, Lippross S, et al. Epidemiology of accidental hypothermia in polytrauma patients: an analysis of 15, 230 patients of the trauma register DGU?. J Trauma Acute Care Surg, 2016, 81(5): 905-912.
9. Lapostolle F, Couvreur J, Koch FX, et al. Hypothermia in trauma victims at first arrival of ambulance personnel: an observational study with assessment of risk factors. Scand J Trauma Resusc Emerg Med, 2017, 25(1): 43.
10. 劉力行, 聶時南, 劉云, 等. 創傷后自發性低體溫急救處理現狀與臨床問題的研究進展. 中華護理雜志, 2016, 51(6): 725-729.
11. 國家衛生健康委員會. 創傷中心建設與管理指南 (2023 版). 北京: 國家衛生健康委, 2023.
12. American College of Surgeons Committee on Trauma. Resources for optimal care of the injured patient. 9th ed. Chicago: American College of Surgeons, 2016.
13. 中華醫學會創傷分會. 《簡明損傷定級標準》2005 (AIS2005). 北京: 人民衛生出版社, 2005.
14. 涂加園, 劉云, 孫琳, 等. 創傷患者低體溫風險評估量表的編制及信效度檢驗. 中華護理雜志, 2020, 55(12): 1813-1818.
15. 陳天喜, 沈紅五, 姜琴, 等. 急診創傷患者低體溫風險評分量表的研制. 護理學雜志, 2021, 36(23): 38-41.
16. 顏雷雷, 廖德珺, 金倩倩, 等. 急性創傷患者低體溫發生風險的列線圖模型構建. 中國護理管理, 2022, 22(7): 1015-1020.
17. 梅潤, 何乾峰, 徐璐瑤, 等. 基于決策樹構建急診創傷患者低體溫早期預警模型及驗證. 軍事護理, 2023, 40(5): 14-17, 85.
18. 張文英, 皇芳芳, 徐洪云. 基于 logistic 回歸分析構建急診創傷后患者自發性低體溫預測模型. 國際護理學雜志, 2023, 42(1):165-168.
19. 何聰聰, 張瑜, 周立敏. 急性嚴重創傷患者自發性低體溫風險預測模型的構建及驗證. 護理學, 2024, 13(11): 1615-1622.
20. 張悅, 潘慧斌, 溫曉紅, 等. 嚴重創傷后自發性低體溫風險預測模型的構建. 湖州師范學院學報, 2025, 47(2): 49-56.
21. Feng X, Zhu F, Qiao A, et al. Factors influencing spontaneous hypothermia after emergency trauma and the construction of a predictive model. Open Life Sci, 2024, 19(1): 20220862.

West China Medical Journal

Systematic review of hypothermia prediction models in patients with severe trauma

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