脓毒症患者发生脓毒症休克临床评分预测模型的建立及其意义

doi:10.19871/j.cnki.xfcrbzz.2024.03.005

新发传染病电子杂志 ›› 2024, Vol. 9 ›› Issue (3): 21-25.doi: 10.19871/j.cnki.xfcrbzz.2024.03.005

脓毒症患者发生脓毒症休克临床评分预测模型的建立及其意义

吴森泉¹, 莫伟良¹, 李少媚²

1.东莞市人民医院呼吸与危重症医学科,广东东莞 523059;
2.东莞市人民医院血液淋巴瘤科,广东东莞 523059

收稿日期:2024-02-02 出版日期:2024-06-30 发布日期:2024-07-23
通讯作者: 李少媚,Email: 214983353@qq.com
基金资助:
1.2020年东莞市社会科技发展一般项目(202050715001774）; 2.2023年东莞市人民医院博士启动基金(K202302）

The establishment and significance of clinical score prediction model for septic shock in patients with sepsis

Wu Senquan¹, Mo Weiliang¹, Li Shaomei²

1. Department of Respiratory and Critical Care Medicine, Dongguan People's Hospital, Guangdong Dongguan 523059, China;
2. Department of Hematologic lymphoma, Dongguan People's Hospital, Guangdong Dongguan 523059, China

Received:2024-02-02 Online:2024-06-30 Published:2024-07-23

摘要/Abstract

摘要： 目的建立脓毒症患者发生脓毒症休克的临床评分预测模型,从而早期识别和积极治疗脓毒症休克的高危患者。方法回顾性分析东莞市人民医院2018年1月1日至2021年12月31日以脓毒症入院的部分脓毒症患者82例,根据患者是否进展为脓毒症休克将其分为脓毒症组及脓毒症休克组。对患者的一般临床资料进行单因素分析,组间有统计学意义的连续变量指标应用受试者操作特征曲线寻找最佳截断值并分析其诊断价值;根据截断值对连续变量进行二分类资料转换,运用多因素二分类Logistic回归分析进一步筛选对脓毒症休克有预测价值的指标,根据各变量的β回归系数设立相应分值建立脓毒症休克预测模型。最后将2022年1月1日至2023年12月31日期间以脓毒症入院的64例患者对模型进行验证。结果单因素分析显示年龄、性别在两组间无统计学意义,其余观察指标均有统计学意义。将降钙素原（procalcitonin,PCT）≥12μg/L、C反应蛋白（C-reactive protein,CRP）≥181mg/L、中性粒细胞与淋巴细胞比值（neutrophil to lymphocyte ratio,NLR）≥17三项指标纳入多因素Logistic回归模型。预测模型方程：Y=2.471×PCT+1.76×CRP+1.009×NLR,截断值为2.62,即Y≥2.62时预示脓毒症进展为脓毒症休克可能性大,模型敏感度、特异度、准确度分别为89.5%、63.6%、85.9%。验证队列的验证结果为：敏感度88.2%、特异度83.3%、准确度85.9%。结论本研究建立的脓毒症休克临床评分预测模型简单易行,对于早期识别脓毒症患者是否进展为脓毒症休克有一定的价值,为临床及时救治脓毒症休克的高危患者提供理论依据。

关键词: 脓毒症, 脓毒症休克, 临床评分预测模型

Abstract: Objective To develop a predictive model for differentiating septic shock from sepsis. Thus, treating potential septic shock patients as soon as possible. Method A retrospective analysis was performed on 82 patients with sepsis hospitalized in Dongguan People's Hospital from 1 January 2018 to 31 December 2020, and they were divided into sepsis group and septic shock group according to whether they developed into septic shock. Single factor analysis was performed on the general clinical data of the patients. The receiver operating characteristic (ROC) curve was used to determine the cut-off value of continuous variables with statistical significance between the two groups. Binary data conversion was performed on the continuous variables according to the cut-off value. Multivariate binary logistic regression analysis was used to further screen the indexes with predictive value for septic shock, and the corresponding score was set up according to the β regression coefficient of each variable to establish the septic shock prediction model. Finally, 64 patients admitted with sepsis from 1 January 2022 to 31 December 2023 will be used to validate the model. Result Univariate analysis showed that age and gender had no statistical significance between the two groups, while the other observation indexes had statistical significance. However, only procalcitonin (PCT) ≥12μg/L,C-reactive protein (CRP) ≥181mg/L and neutrophil to lymphocyte ratio (NLR) ≥17 were included in the multivariate logistic regression model finally. The prediction model equation was as follows: Y=2.471×PCT+1.76×CRP+1.009×NLR, and the cut-off value was 2.62, that is, when Y value ≥2.62, sepsis was highly likely to develop into septic shock. The sensitivity, specificity and accuracy of the model were 89.5%, 63.6% and 85.9%, respectively. The validation results showed a sensitivity of 88.2%, a specificity of 83.3% and an accuracy of 85.9%．Conclusion The scoring model provides a simple and feasible way of facilitating a differential diagnosis of septic shock and sepsis, thus, providing evidence for the timely treatment of high-risk patients with septic shock.

Key words: Sepsis, Septic shock, Clinical score prediction model

中图分类号:

R446.11+2

吴森泉, 莫伟良, 李少媚. 脓毒症患者发生脓毒症休克临床评分预测模型的建立及其意义[J]. 新发传染病电子杂志, 2024, 9(3): 21-25.

Wu Senquan, Mo Weiliang, Li Shaomei. The establishment and significance of clinical score prediction model for septic shock in patients with sepsis[J]. Electronic Journal of Emerging Infectious Diseases, 2024, 9(3): 21-25.

参考文献

[1] SINGER M, DEUTSCHMAN CS, SEYMOUR CW, et al.The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)[J]. JAMA, 2016, 315(8): 801-810.
[2] NAMGUNG M, AHN C, PARK Y, et al.Mortality among adult patients with sepsis and septic shock in Korea: a systematic review and meta-analysis[J]. Clin Exp Emerg Med, 2023, 10(2):157-171.
[3] NEDEVA C, MENASSA J, DUAN M, et al.TREML4 receptor regulates inflammation and innate immune cell death during polymicrobial sepsis[J]. Nat Immunol, 2020, 21(12): 1585-1596.
[4] 张爱华,赖伟华,王敏,等. 双歧杆菌三联活菌胶囊联合肠内营养在脓毒症机械通气患者呼吸机相关性肺炎预防中的效果研究[J/CD]. 新发传染病电子杂志,2023,8(3):29-33.
[5] 吴森泉,方年新,莫伟良,等.山柰酚抑制NLRP3炎症小体活化和细胞焦亡提高脓毒症小鼠生存率[J/CD].新发传染病电子杂志,2022, 7(1):12-16.
[6] LIU Y, WANG K, FENG Q, et al.Alpinetin Attenuates Persistent Inflammation, Immune Suppression, and Catabolism Syndrome in a Septic Mouse Model[J]. J Immunol Res, 2021, 2021: 9998517.
[7] UMAKOSHI K, CHOUDHURY ME, NISHIOKA R, et al.B lymphocytopenia and Bregs in a not-to-die murine sepsis model[J]. Biochem Biophys Res Commun, 2020, 523(1): 202-207.
[8] XIE J, WANG H, KANG Y, et al.The Epidemiology of Sepsis in Chinese ICUs: A National Cross-Sectional Survey[J]. Crit Care Med, 2020, 48(3): e209-e218.
[9] DEMARET J, VENET F, FRIGGERI A, et al.Marked alterations of neutrophil functions during sepsis-induced immunosuppression[J]. J Leukoc Biol, 2015, 98(6): 1081-1090.
[10] KAUFMANN I, HOELZL A, SCHLIEPHAKE F, et al.Polymorphonuclear leukocyte dysfunction syndrome in patients with increasing sepsis severity[J]. Shock, 2006, 26(3): 254-261.
[11] YANG J, ZHU X, FENG J.The Changes in the Quantity of Lymphocyte Subpopulations during the Process of Sepsis[J]. Int J Mol Sci, 2024, 25(3):1902-1914.
[12] DAVIES K, MCLAREN JE.Destabilisation of T cell-dependent humoral immunity in sepsis[J]. Clin Sci (Lond), 2024, 138(1):65-85.
[13] FINFER S, VENKATESH B, HOTCHKISS RS, et al.Lymphopenia in sepsis-an acquired immunodeficiency?[J]. Immunol Cell Biol, 2023, 101(6):535-544.
[14] HUANG Z, FU Z, HUANG W, et al.Prognostic value of neutrophil-to-lymphocyte ratio in sepsis: A meta-analysis[J]. Am J Emerg Med, 2020, 38(3): 641-647.
[15] LORENTE L, MARTIN MM, ORTIZ-LOPEZ R, et al.Association between neutrophil-to-lymphocyte ratio in the first seven days of sepsis and mortality[J]. Enferm Infecc Microbiol Clin (Engl Ed), 2022, 40(5): 235-240.
[16] SARI R, KARAKURT Z, AY M, et al.Neutrophil to lymphocyte ratio as a predictor of treatment response and mortality in septic shock patients in the intensive care unit[J]. Turk J Med Sci, 2019, 49(5): 1336-1349.
[17] LIU Y, ZHENG J, ZHANG D, et al.Neutrophil-lymphocyte ratio and plasma lactate predict 28-day mortality in patients with sepsis[J]. J Clin Lab Anal, 2019, 33(7): e22942.
[18] RYOO SM, HAN KS, AHN S, et al.The usefulness of C-reactive protein and procalcitonin to predict prognosis in septic shock patients: A multicenter prospective registry-based observational study[J]. Sci Rep, 2019, 9(1): 6579.
[19] ZHANG H, WANG X, ZHANG Q, et al.Comparison of procalcitonin and high-sensitivity C-reactive protein for the diagnosis of sepsis and septic shock in the oldest old patients[J]. BMC Geriatr, 2017, 17(1): 173.
[20] CUI N, ZHANG H, CHEN Z, et al.Prognostic significance of PCT and CRP evaluation for adult ICU patients with sepsis and septic shock: retrospective analysis of 59 cases[J]. J Int Med Res, 2019, 47(4): 1573-1579.
[21] ASSICOT M, GENDREL D, CARSIN H, et al.High serum procalcitonin concentrations in patients with sepsis and infection[J]. Lancet, 1993, 341(8844): 515-518.
[22] JEKARL DW, LEE S, KIM M, et al.Procalcitonin as a prognostic marker for sepsis based on SEPSIS-3[J]. J Clin Lab Anal, 2019, 33(9): e22996.

脓毒症患者发生脓毒症休克临床评分预测模型的建立及其意义

The establishment and significance of clinical score prediction model for septic shock in patients with sepsis

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[1]	张爱华, 赖伟华, 王敏, 覃明. 双歧杆菌三联活菌胶囊联合肠内营养在脓毒症机械通气患者呼吸机相关性肺炎预防中的效果研究[J]. 新发传染病电子杂志, 2023, 8(3): 29-33.
[2]	吴森泉, 方年新, 莫伟良, 张平. 山柰酚抑制NLRP3炎症小体活化和细胞焦亡提高脓毒症小鼠生存率[J]. 新发传染病电子杂志, 2022, 7(1): 12-16.