脓毒症相关性血小板减少症患者死亡风险影响因素的列线图构建与验证

Construction and verification of a nomogram of factors influencing the risk of death in patient with sepsis-associated thrombocytopenia

目的构建重症监护病房(ICU)脓毒症相关性血小板减少症(SAT)患者死亡风险列线图预测模型,以早期识别、积极干预。方法回顾性收集2019年12月至2021年8月南京医科大学第一附属医院ICU收治的SAT患者临床资料,包括人口学资料、实验室指标等。根据28 d预后将患者分为死亡组和存活组,比较两组患者临床资料的差异。应用多因素Logistic回归分析死亡危险因素,构建列线图模型并采用内部数据验证该模型的性能。使用受试者工作特征曲线(ROC曲线)评估该列线图模型的诊断效能,采用临床决策曲线分析(DCA)其临床适应性。结果共纳入275例SAT患者,28 d死亡95例,28 d病死率为34.5%。与存活组相比,死亡组患者急性生理学与慢性健康状况评分Ⅱ(APACHEⅡ)、序贯器官衰竭评分(SOFA)、血乳酸(Lac)、入ICU第5天血小板体积分布宽度(PDW)、血尿素氮(BUN)、总胆红素(TBIL)、天冬氨酸转氨酶(AST)、C-反应蛋白(CRP)更高,活化部分凝血活酶时间(APTT)、凝血酶原时间(PT)更长,入ICU第3天和第5天血小板计数(PLT)、直接胆红素(DBIL)、纤维蛋白原(FIB)更低,慢性肺部疾病史、混合部位感染、肺部感染、血流感染、革兰阴性菌感染、真菌感染占比更高,糖尿病病史、泌尿系统感染、未培养出病原微生物占比更低,且血管活性药物、机械通气(MV)、连续性肾脏替代治疗(CRRT)、出血事件、输注血小板比例更高。多因素Logistic回归分析显示,APACHEⅡ评分〔优势比(OR)=1.417,95%可信区间(95%CI)为1.153~1.743,P=0.001〕、慢性肺部疾病史(OR=72.271,95%CI为4.475~1?167.126,P=0.003)、入ICU第5天PLT(OR=0.954,95%CI为0.922~0.987,P=0.007)、血管活性药物(OR=622.943,95%CI为10.060~38?575.340,P=0.002)、MV(OR=91.818,95%CI为3.973~2?121.966,P=0.005)是SAT患者死亡的独立危险因素。用以上独立危险因素构建列线图预测模型,ROC曲线分析其预测SAT患者死亡的曲线下面积(AUC)为0.979,敏感度为94.7%,特异度为91.7%,提示该模型具有较好的区分度。Hosmer-Lemeshow拟合优度检验显示该模型具有较好的拟合度(P>0.05),同时DCA提示该模型具有较好的临床适用性。结论 SAT患者死亡风险较高。基于APACHEⅡ评分、慢性肺部疾病史、入ICU第5天PLT、应用血管活性药物和MV等指标构建的列线图模型预测SAT患者28 d死亡具有很好的临床价值,区分度和适用性好,但仍需进一步验证。

Objective To construct a nomogram prediction model for predicting the risk of death in patients with sepsis-associated thrombocytopenia(SAT)in intensive care unit(ICU)for early indentification and active intervention.Methods Clinical data of SAT patients admitted to ICU of the First Affiliated Hospital of Nanjing Medical University from December 2019 to August 2021 were retrospectively collected,including demographic data,laboratory indicators,etc.According to the prognosis at 28 days,the patients were divided into the death group and the survival group,and the differences of clinical variables between the two groups were compared.Multivariate Logistic regression analysis was performed to analyze the independent risk factors influencing mortality of patients within 28 days,then a nomogram predictive model was constructed and its performance was verified with internal data.Receiver operator characteristic curve(ROC curve)was used to evaluate the diagnostic effectiveness of the nomogram model,and the clinical applicability of this model was evaluated by clinical decision curve analysis(DCA).Results A total of 275 patients were included,with 95 deaths at 28 days and a 28-day mortality of 34.5%.Compared with the survival group,acute physiology and chronic health evaluationⅡ(APACHEⅡ),sequential organ failure assessment(SOFA),lactic acid(Lac),platelet distribution width(PDW)on day 5 of ICU admission,blood urea nitrogen(BUN),total bilirubin(TBIL),aspartate aminotransferase(AST),C-reactive protein(CRP)of patients in the death group were higher,activated partial thromboplastin time(APTT)and prothrombin time(PT)were longer,platelet count(PLT)on day 3 and day 5 of ICU admission,direct bilirubin(DBIL),fibrinogen(FIB)were lower,the history of chronic lung disease,mixed site infection,lung infection,bloodstream infection,Gram-negative bacterial infection and fungal infection accounted for a higher proportion,the history of diabetes mellitus,urinary tract infection and no pathogenic microorganisms cultured accounted for a lower proportion,and the proportion of the use of vasoactive drugs,mechanical ventilation(MV),continuous renal replacement therapy(CRRT),bleeding events and platelet transfusion were higher.Multivariate Logistic regression analysis showed that APACHEⅡscore at the day of ICU admission[odds ratio(OR)=1.417,95%confidence interval(95%CI)was 1.153-1.743,P=0.001],chronic lung disease(OR=72.271,95%CI was 4.475-1167.126,P=0.003),PLT on day 5 of ICU admission(OR=0.954,95%CI was 0.922-0.987,P=0.007),vasoactive drug(OR=622.943,95%CI was 10.060-38575.340,P=0.002),MV(OR=91.818,95%CI was 3.973-2121.966,P=0.005)were independent risk factors of mortality in SAT patients.The above independent risk factors were used to build a nomogram prediction model,and the area under the curve(AUC),sensitivity and specificity were 0.979,94.7%and 91.7%,respectively,suggesting that the model had good discrimination.The Hosmer-Lemeshow goodness of fit test showed a good calibration with P>0.05.At the same time,DCA showed that the nomogram model had good clinical applicability.Conclusions Patients with SAT has a higher risk of death.The nomogram model based on APACHEⅡscore at the day of ICU admission,chronic lung disease,PLT on day 5 of ICU admission,the use of vasoactive drug and MV has good clinical significance for the prediction of 28-day mortality,and the discrimination and calibration are good,however,further verification is needed.