Construction and validation of a predictive model for septic shock based on propensity score matching

FANG Yang; LI Ying; CHEN Zhihong; ZHENG Shengnan; GONG Jian; WU Qihua; YANG Xiaoyu; WEN Xiuping; LIN Donghong

doi:10.7619/jcmp.20242165

Journal of Clinical Medicine in Practice > 2024 > 28(21): 53-59. > DOI: 10.7619/jcmp.20242165

FANG Yang, LI Ying, CHEN Zhihong, ZHENG Shengnan, GONG Jian, WU Qihua, YANG Xiaoyu, WEN Xiuping, LIN Donghong. Construction and validation of a predictive model for septic shock based on propensity score matching[J]. Journal of Clinical Medicine in Practice, 2024, 28(21): 53-59. DOI: 10.7619/jcmp.20242165

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Construction and validation of a predictive model for septic shock based on propensity score matching

1.
the Third People's Hospital of Longgang District in Shenzhen of Guangdong Province, Shenzhen, Guangdong, 518112
2.
Health Management and Medical Center, Xiangya Third Hospital of Central South University, Changsha, Hunan, 410013

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Received Date: May 22, 2024
Revised Date: September 02, 2024

Graphical Abstract

Abstract

Abstract

Objective
To construct a predictive model for septic shock based on the propensity score matching (PSM) method and validate its effectiveness.
Methods
A total of 114 patients with sepsis were enrolled as study objects, and were divided into septic shock group (40 patients) and sepsis group (74 patients) according to whether they developed septic shock. PSM was performed with a ratio of septic shock to sepsis of 1∶2, resulting in the inclusion of 30 patients in the septic shock group and 60 patients in the sepsis group after matching. The levels of C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), serum amyloid A (SAA), soluble endothelial protein C receptor (sEPCR), endothelial cell-specific molecule 1 (ESM-1), clusterin (CLU), and the Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score and Sequential Organ Failure Assessment (SOFA) score at admission were compared between the two groups. Cox proportional hazards regression analysis was used to identify the factors influencing septic shock, and a predictive model for septic shock was constructed and internally validated using the receiver operating characteristic (ROC) curve. Kaplan-Meier survival curves were plotted to analyze the differences in survival prognosis among patients with different expression levels of the indicators.
Results
After matching, there were no statistically significant differences in general information between the two groups (P>0.05). At admission, the septic shock group had higher levels of serum PCT, CRP, SAA, IL-6, sEPCR, ESM-1, and higher APACHE Ⅱ and SOFA scores, as well as a lower level of serum CLU compared with the sepsis group (P < 0.05). Cox regression analysis showed that PCT, CRP, SAA, IL-6, sEPCR, ESM-1, APACHE Ⅱ score, and SOFA score were independent risk factors for septic shock (P < 0.05), while CLU was an independent protective factor (P < 0.05). The predictive model for septic shock, constructed based on these factors, showed an internal validation accuracy of 94.44%, an area under the curve of 0.950, a sensitivity of 93.33%, and a specificity of 96.67%. Dead patients had higher levels of PCT, CRP, SAA, IL-6, sEPCR, ESM-1, and higher APACHE Ⅱ and SOFA scores, as well as a lower level of CLU at admission compared with survivors (P < 0.05). Compared with patients with low expression levels or low scores, patients with high expression levels of PCT, CRP, SAA, IL-6, sEPCR, ESM-1, and high APACHE Ⅱ and SOFA scores had higher fatality rates, while patients with high CLU expression levels had a lower fatality rate (P < 0.05).
Conclusion
The serum biomarkers including PCT, CRP, SAA, IL-6, sEPCR, ESM-1, CLU, and the APACHE Ⅱ and SOFA scores in sepsis patients are closely related to the occurrence of septic shock and survival prognosis. The predictive model constructed by combining these indicators can accurately predict the occurrence of septic shock.
- sepsis,
- septic shock,
- C-reactive protein,
- procalcitonin,
- interleukin-6,
- serum amyloid A

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References (29)

References

[1]	SINGER M, DEUTSCHMAN C S, SEYMOUR C W, et al. The third international consensus definitions for sepsis and septic shock (sepsis-3)[J]. JAMA, 2016, 315(8): 801-810. doi: 10.1001/jama.2016.0287
[2]	FONT M D, THYAGARAJAN B, KHANNA A K. Sepsis and Septic Shock-Basics of diagnosis, pathophysiology and clinical decision making[J]. Med Clin North Am, 2020, 104(4): 573-585. doi: 10.1016/j.mcna.2020.02.011
[3]	中国中西医结合学会急救医学专业委员会, 《中国中西医结合急救杂志》编辑委员会. 脓毒性休克中西医结合诊治专家共识[J]. 中华危重病急救医学, 2019, 31(11): 1317-1323. doi: 10.3760/cma.j.issn.2095-4352.2019.11.002
[4]	SONG Y L, YAO C, YAO Y M, et al. XueBiJing injection versus placebo for critically ill patients with severe community-acquired pneumonia: a randomized controlled trial[J]. Crit Care Med, 2019, 47(9): e735-e743. doi: 10.1097/CCM.0000000000003842
[5]	庄雪明, 王诗波, 虞大为, 等. 脓毒性休克患者抗菌肽LL-37与降钙素原联合检测价值[J]. 实用临床医药杂志, 2021, 25(5): 96-100. doi: 10.7619/jcmp.20201396
[6]	OSGOOD A M, HOLLENBECK D, YANKIN I. Evaluation of quick sequential organ failure scores in dogs with severe sepsis and septic shock[J]. J Small Anim Pract, 2022, 63(10): 739-746. doi: 10.1111/jsap.13522
[7]	GHOSH R, DEY R, SAWOO R, et al. Endogenous neutralization of TGF-β and IL-6 ameliorates septic arthritis by altering RANKL/OPG interaction in lymphocytes[J]. Mol Immunol, 2022, 152: 183-206. doi: 10.1016/j.molimm.2022.10.015
[8]	中国医师协会急诊医师分会, 中国研究型医院学会休克与脓毒症专业委员会. 中国脓毒症/脓毒性休克急诊治疗指南(2018)[J]. 感染、炎症、修复, 2019, 20(1): 3-22. doi: 10.3969/j.issn.1672-8521.2019.01.001
[9]	朱沙沙, 兰天, 李战炜. 脓毒性休克院内死亡的风险预测模型构建与验证[J]. 数理医药学杂志, 2023, 36(8): 592-600.
[10]	周思颖, 顾国嵘, 慕婉晴, 等. 细胞因子、经皮氧分压/吸入氧浓度联合危重评分对脓毒症患者进展为脓毒性休克及预后的预测价值[J]. 中国临床医学, 2023, 30(3): 426-431.
[11]	石颖, 於江泉, 张文娟. 内质网应激在脓毒症中的研究进展[J]. 实用临床医药杂志, 2023, 27(14): 121-125. doi: 10.7619/jcmp.20230103
[12]	唐梅, 马艳, 张西京, 等. 脓毒症脑病的诊疗进展[J]. 空军军医大学学报, 2023, 44(4): 375-379, 384.
[13]	王泽田, 齐越, 唐建国. 基于蛋白组学筛选的外周血蛋白在脓毒症诊断中的应用[J]. 实用临床医药杂志, 2023, 27(20): 80-85. doi: 10.7619/jcmp.20231071
[14]	廖海燕, 柴彦, 郭喆, 等. 脓毒症生物标志物的研究进展[J]. 医学综述, 2023, 29(2): 250-255.
[15]	杨美程, 李润发, 陈辉, 等. 人工智能辅助诊疗在脓毒症管理中的应用进展[J]. 药学进展, 2023, 47(10): 758-768.
[16]	EVANS L, RHODES A, ALHAZZANI W, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021[J]. Crit Care Med, 2021, 49(11): e1063-e1143. doi: 10.1097/CCM.0000000000005337
[17]	李赵进, 冯爽, 赵根明, 等. 常见倾向性评分匹配方法在真实世界研究中的应用[J]. 中国卫生统计, 2023, 40(2): 307-311.
[18]	MOLANO-FRANCO D, AREVALO-RODRIGUEZ I, MURIEL A, et al. Basal procalcitonin, C-reactive protein, interleukin-6, and presepsin for prediction of mortality in critically ill septic patients: a systematic review and meta-analysis[J]. Diagn Progn Res, 2023, 7(1): 15. doi: 10.1186/s41512-023-00152-2
[19]	李烽辉, 彭志允, 唐钟祥, 等. C反应蛋白及降钙素原对脓毒血症休克短期预后的预测价值[J]. 中国医药导报, 2021, 18(25): 93-97.
[20]	雷玲玲, 彭军, 曹晓琴. 降钙素原、D-二聚体、全程C反应蛋白联合检测在脓毒症患儿中的诊断价值[J]. 临床医学研究与实践, 2023, 8(1): 88-91.
[21]	喻淑慧, 李园园, 胡克. PCT、CRP、SAA及FIB水平检测对脓毒血症的诊断价值[J]. 医学研究杂志, 2016, 45(3): 123-126.
[22]	陈君, 王妮, 陈栩栩, 等. ICU脓毒症患者血清Presepsin、内毒素、IL-6、PCT水平与预后的相关性[J]. 中华医院感染学杂志, 2022, 32(3): 356-359.
[23]	孙彦博, 滕思勇, 罗振立, 等. 血清Clusterin对急性STEMI患者PCI术后主要不良心血管事件的预测价值[J]. 中国循证心血管医学杂志, 2022, 14(8): 1008-1011.
[24]	SEO H Y, LEE S H, LEE J H, et al. Clusterin attenuates hepatic fibrosis by inhibiting hepatic stellate cell activation and downregulating the Smad3 signaling pathway[J]. Cells, 2019, 8(11): 1442. doi: 10.3390/cells8111442
[25]	PARK J S, LEE W K, KIM H S, et al. Clusterin overexpression protects against western diet-induced obesity and NAFLD[J]. Sci Rep, 2020, 10(1): 17484. doi: 10.1038/s41598-020-73927-y
[26]	KRAUTZ C, MAIER S L, BRUNNER M, et al. Reduced circulating B cells and plasma IgM levels are associated with decreased survival in sepsis-A meta-analysis[J]. J Crit Care, 2018, 45: 71-75. doi: 10.1016/j.jcrc.2018.01.013
[27]	郭俊, 许强宏. 脓毒症患者血栓调节蛋白和纤溶酶原激活物抑制剂-1及内皮细胞损伤程度的研究初探[J]. 中华内科杂志, 2021, 60(2): 143-146.
[28]	钱骏, 黄中伟, 徐建如, 等. sEPCR在脓毒症患者中的表达及临床意义[J]. 中国医药导报, 2021, 18(10): 88-91.
[29]	杨晓梅, 杭敏, 许磊, 等. 脓毒症患者血清ESM-1、D-D、miR-155-5p、PCT的变化与患者预后的关系[J]. 医学临床研究, 2019, 36(4): 759-761.

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Construction and validation of a predictive model for septic shock based on propensity score matching

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