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中暑危重症患者继发感染预测列线图模型:一项来自中国的初步研究

Nomogram model for predicting secondary infection in critically ill patients with heatstroke: A pilot study from China.

作者信息

Lin Guodong, Peng Hailun, Yin Bingling, Xu Chongxiao, Zhao Yueli, Liu Anwei, Guo Haiyang, Pan Zhiguo

机构信息

The First Clinical Medical College, Southern Medical University, Guangzhou, Guangdong, China.

Department of Critical Care Medicine, General Hospital of Southern Theatre Command of PLA, Guangzhou, Guangdong, China.

出版信息

PLoS One. 2024 Dec 26;19(12):e0316254. doi: 10.1371/journal.pone.0316254. eCollection 2024.

DOI:10.1371/journal.pone.0316254
PMID:39724279
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11670984/
Abstract

OBJECTIVE

In this retrospective analysis, we explored the clinical characteristics and risk factors of secondary infections in patients with severe heatstroke with the aim to gain epidemiological insights and identify risk factors for secondary infections.

METHOD

The study included 129 patients with severe heatstroke admitted to the General Hospital of the Southern Theater Command of the PLA between January 1, 2011, and December 31, 2021. Patients were divided into an infection group (n = 24) and a non-infection group (n = 105) based on infection occurrence within 48 h of intensive care unit (ICU) admission. Clinical indicators, infection indicators, and clinical outcomes within 24 h of ICU admission were collected and compared between the groups. Independent risk factors for infection in patients with severe heatstroke were analyzed using univariate and multivariate analyses. A nomogram model was constructed, evaluated, and validated.

RESULT

Among the 129 patients with heatstroke, 24 developed secondary infections. Infections occurred between days 3 and 10 post-ICU admission, primarily affecting the lungs. Multivariate analysis identified vasopressor use, serum creatinine level, and gastrointestinal dysfunction at admission as independent risk factors, while elevated lymphocyte count (odds ratio [OR] = 0.167; 95% confidence interval [CI] 0.049-0.572; P = 0.004) was protective against severe heatstroke. Infected patients required longer durations of mechanical ventilation (OR = 2.764; 95% CI, 1.735-4.405; P = 0.044) and total hospital stay than those in the non-infection group. The nomogram model demonstrated clinical feasibility.

CONCLUSION

Increased lymphocyte count is an independent protective factor against infections in patients with severe heatstroke. Vasopressor use, gastrointestinal dysfunction, and elevated serum creatinine levels are independent risk factors. These indicators can aid clinicians in assessing infection risk in patients with severe heatstroke.

摘要

目的

在这项回顾性分析中,我们探究了重症中暑患者继发感染的临床特征及危险因素,旨在获得流行病学见解并确定继发感染的危险因素。

方法

本研究纳入了2011年1月1日至2021年12月31日期间在中国人民解放军南部战区总医院收治的129例重症中暑患者。根据重症监护病房(ICU)入院后48小时内是否发生感染,将患者分为感染组(n = 24)和非感染组(n = 105)。收集并比较两组患者ICU入院后24小时内的临床指标、感染指标及临床结局。采用单因素和多因素分析方法分析重症中暑患者感染的独立危险因素。构建、评估并验证列线图模型。

结果

129例中暑患者中,24例发生继发感染。感染发生在ICU入院后第3天至第10天,主要影响肺部。多因素分析确定血管活性药物的使用、血清肌酐水平及入院时的胃肠功能障碍为独立危险因素,而淋巴细胞计数升高(比值比[OR]=0.167;95%置信区间[CI]0.049 - 0.572;P = 0.004)对重症中暑具有保护作用。与非感染组相比,感染患者需要更长时间的机械通气(OR = 2.764;95%CI,1.735 - 4.405;P = 0.044)及住院总时长。列线图模型显示出临床可行性。

结论

淋巴细胞计数升高是重症中暑患者感染的独立保护因素。血管活性药物的使用、胃肠功能障碍及血清肌酐水平升高是独立危险因素。这些指标有助于临床医生评估重症中暑患者的感染风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/8acb5f3c37f9/pone.0316254.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/38fc74ed0017/pone.0316254.g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/416e60953140/pone.0316254.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/232c41c9605c/pone.0316254.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/9dce1e09b5f8/pone.0316254.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/63938d222d08/pone.0316254.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/3976cea43066/pone.0316254.g007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/b6be048294c0/pone.0316254.g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/8acb5f3c37f9/pone.0316254.g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/38fc74ed0017/pone.0316254.g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/416e60953140/pone.0316254.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/232c41c9605c/pone.0316254.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/9dce1e09b5f8/pone.0316254.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/63938d222d08/pone.0316254.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/3976cea43066/pone.0316254.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/5e712c2dd12e/pone.0316254.g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/71c8/11670984/8acb5f3c37f9/pone.0316254.g010.jpg

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