目的 探讨劳力性热射病患者早期死亡的危险因素,构建并验证其列线图预测模型。方法 回顾性分析2022-06至2024-06联勤保障部队第910医院急诊科就诊的78例劳力性热射病患者的临床资料,根据预后结果将患者分为死亡组和生存组,比较两组患者基线临床资料,通过多元logistic回归分析早期死亡的危险因素,应用R软件建立列线图预测模型并验证其准确性。结果 78例中, 18例（23.08%）为早期死亡（死亡组）,60例为生存组。多元logistic回归分析显示,体温是劳力性热射病患者早期死亡的独立危险因素（OR>1,P<0.05）,淋巴细胞百分比、格拉斯哥昏迷评分法（GCS）评分是独立保护因素（OR<1,P<0.05）。建立列线图预测模型,受试者工作特征曲线（ROC）显示,列线图预测模型的曲线下面积（AUC）为0.902（95%CI：0.850~0.937）,显示出较好的预测精准度;校准曲线显示,预测与观测吻合良好,Hosmer-Lemeshow检验结果,P=0.478。结论 基于体温、淋巴细胞百分比、GCS评分建立的列线图预测模型,对劳力性热射病患者早期死亡预测的准确性良好,早期识别和改善预后有一定作用。
Abstract
Objective To investigate the risk factors of early death in patients with exertional heatstroke and validate a nomogram prediction model. Methods A total of 78 patients diagnosed with exertional heatstroke in No. 910 Hospital of PLA Joint Logistics Support Force from June 2022 to June 2024 were retrospectively included, and they were divided into death group and survival group according to the prognosis. The baseline clinical data of the two groups were compared, and the risk factors of early death were analyzed by multivariate logistic regression. A nomogram prediction model was constructed by R software and its accuracy was verified. Results The incidence of early death among 78 patients with exertional heatstroke was 23.08%（18/78）. Multivariate logistic regression analysis showed that body temperature was an independent risk factor for early death in patients with exertional heatstroke（OR>1, P<0.05）, while lymphocyte percentage and GCS score were independent protective factors（OR<1, P<0.05）. The receiver operating characteristic curve (ROC) showed that the area under the curve (AUC) of the nomogram prediction model was 0.902（95%CI 0.850~0.937）, indicating good prediction accuracy. The calibration curve showed good agreement between prediction and observation, and the P-value of the Hosmer-Lemeshow test was 0.478. Conclusion The nomogram prediction model based on body temperature, lymphocyte percentage and GCS score can accurately predict the risk for early death in patients with exertional heatstroke, and play a role in early identification and improving prognosis.
关键词
劳力性热射病 /
早期死亡 /
危险因素 /
预测模型 /
列线图
Key words
exertional heatstroke /
early death /
risk factors /
prediction model /
nomogram
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参考文献
[1] Lipman G S, Gaudio F G, Eifling K P, et al. Wilderness medical society clinical practice guidelines for the prevention and treatment of heat illness: 2019 update[J]. Wilderness Environ Med, 2019, 30(4S): S33-S46.
[2] Epstein Y, Yanovich R.Heatstroke[J]. N Engl J Med,2019, 380(25): 2449-2459.
[3] Yang J, Gong F, Shi X, et al. A nomogram based on lymphocyte percentage for predicting hospital mortality in exertional heatstroke patients: a 13-year retrospective study[J]. World J Emerg Med,2023, 14(6): 434-441.
[4] Bouchama A, Abuyassin B, Lehe C, et al. Classic and exertional heatstroke[J],Nat Rev Dis Primers, 2022, 8(1): 8.
[5] 刘树元,宋景春,毛汉丁,等.中国热射病诊断与治疗专家共识[J].解放军医学杂志,2019,44(3):181-196.
[6] Emleek E M, Khalil A A.The International society of thrombosis and hemostasis (isth) criteria in intensive care units[J]. J Vasc Nurs,2023, 41(4): 158-163.
[7] Cabral BM I, Edding S N, Portocarrero J P, et al. Rhabdomyolysis[J]. Dis Mon,2020, 66(8): 101015.
[8] Zhao J, Wang H, Li Y, et al. Heatstroke recovery at home as predicted by human thermoregulation modeling[J]. Build Environ, 2020, 173: 106752.
[9] Laitano O, Leon LR, Roberts W O, et al. Controversies in exertional heat stroke diagnosis, prevention, and treatment[J]. J Appl Physiol (1985), 2019, 127(5): 1338-1348.
[10] Chen L, Xu S, Yang X, et al. Association between cooling temperature and outcomes of patients with heat stroke[J]. Intern Emerg Med,2023, 18(6): 1831-1842.
[11] Cao C, Yu M, Chai Y.Pathological alteration and therapeutic implications of sepsis-induced immune cell apoptosis[J]. Cell Death Dis, 2019, 10(10): 782.
[12] Ji J, Su P, Lin W, et al. Immune Cells Characteristics and their prognostic effects in exertional heatstroke patients: a retrospective cohort study[J]. Front Med (Lausanne),2022, 9: 867774.
[13] Bruse N, Leijte G P, Pickkers P, et al. New frontiers in precision medicine for sepsis-induced immunoparalysis[J]. Expert Rev Clin Immunol,2019, 15(3): 251-263.
[14] Murray K O, Brant JO, Iwaniec J D, et al. Exertional heat stroke leads to concurrent long-term epigenetic memory, immunosuppression and altered heat shock response in female mice[J]. J Physiol,2021, 599(1): 119-141.
[15] Garcia CK, Renteria LI, Leite S G, et al. Exertional heat stroke: pathophysiology and risk factors[J]. BMJ Med,2022, 1(1): e000239.
[16] Schlader Z J, Davis M S, Bouchama A.Biomarkers of heatstroke-induced organ injury and repair[J]. Exp Physiol, 2022, 107(10): 1159-1171.
[17] Mahajan C, Sengupta D, Kapoor I, et al. Evaluation of the GCS-pupils score for prognosis in traumatic brain injury- the COMA study[J]. Brain Inj,2023, 37(9): 1041-1047.
[18] Marchand M, Gin K.The Cardiovascular System in Heat Stroke[J]. CJC Open, 2021, 4(2): 158-163.
[19] Wang F, Gong F, Shi X, et al. Monocyte HLA-DR level on admission predicting in-hospital mortality rate in exertional heatstroke: a 12-year retrospective study[J]. Immun Inflamm Dis,2024, 12(4): e1240.
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