老年重症肺炎患者机械通气发生谵妄对预后的影响及预测模型构建

Effect of delirium on prognosis of elderly patients with severe pneumonia undergoing mechanical ventilation and construction of a prediction model

目的分析谵妄对老年重症肺炎(SP)患者机械通气预后的影响,并构建发生谵妄的预测模型。方法选取397例老年SP患者,均行机械通气,根据其住院期间是否发生谵妄分为谵妄组80例、非谵妄组317例。从患者进入ICU开始对其进行28 d随访,比较两组累积生存率。收集谵妄组、非谵妄组临床资料,多因素Logistic回归分析老年SP患者机械通气发生谵妄的影响因素,并构建风险预测模型,受试者工作特征(ROC)曲线分析风险预测模型对老年SP患者机械通气发生谵妄的预测价值。结果谵妄组28 d累积生存率45.00%(36/80)低于非谵妄组14.20%(45/317),差异有统计学意义(P<0.05)。谵妄组病情严重程度、脑血管疾病比例、糖尿病比例、血乳酸、CRP、PCT水平高于非谵妄组,ICU时间、机械通气时间、停药后唤醒时间长于非谵妄组,血红蛋白水平低于非谵妄组(P均<0.05)。病情高危、脑血管疾病、糖尿病、机械通气时间延长、停药后唤醒时间延长、血乳酸升高、CRP升高、PCT升高为老年SP患者机械通气发生谵妄的独立危险因素(P均<0.05)。以Logistic回归拟合预测老年SP患者机械通气发生谵妄的风险预测模型,Ln(P/1-P)=-15.475+1.165×病情严重程度+1.476×脑血管疾病+1.019×糖尿病+0.008×机械通气时间+0.103×停药后唤醒时间+0.751×血乳酸+0.011×CRP+0.362×PCT。拟合优度检验χ^(2)=7.996,P=0.434。该风险预测模型预测老年SP患者机械通气发生谵妄的曲线下面积为0.929(95%CI:0.899~0.952),最佳截断值为0.217,敏感度为83.75%,特异度为86.44%,最大约登指数为0.702。结论谵妄会增加老年SP患者死亡风险。基于老年SP患者机械通气发生谵妄的危险因素成功构建预测模型,该模型预测价值较高。

Objective To analyze the influence of delirium on the prognosis of elderly patients with severe pneumo⁃nia(SP)undergoing mechanical ventilation,and to construct a prediction model for the occurrence of delirium.Meth⁃ods Three hundred and ninety-seven cases of SP patients undergoing mechanical ventilation were selected and were clas⁃sified into the delirium group(80 cases)and non-delirium group(317 cases)according to the occurrence of delirium dur⁃ing the hospitalization period.The patients were followed up for 28 days starting from their admission to the intensive care unit(ICU).The 28-d cumulative survival rates of the two groups were compared,and the clinical data of the two groups were collected.Multivariate Logistic regression was used to analyze the risk factors for the occurrence of delirium during the hospitalization period in elderly SP patients undergoing mechanical ventilation and we constructed a risk prediction model.The value of the risk prediction model in predicting the occurrence of delirium in elderly SP patients was analyzed by the receiver operating characteristic(ROC)curve.Results The 28-day cumulative survival rate of the delirium group was 45.00%(36/80),which was lower than that[14.20%(45/317)]of the non-delirium group,with statistically signifi⁃cant difference(P<0.05).Univariate analysis showed that the severity of disease,the proportion of diabetes mellitus,the proportion of cerebrovascular disease,blood lactate,C-reactive protein(CRP),and procalcitonin(PCT)levels were higher in the delirium group than in the non-delirium group,and the ICU time,mechanical ventilation time,and time to arousal after discontinuation of medication were longer than those in the non-delirium group,and the hemoglobin level was lower than that in the non-delirium group(all P<0.05).Multifactorial Logistic regression analysis showed that high-risk condi⁃tion,cerebrovascular disease,diabetes mellitus,prolonged mechanical ventilation,prolonged arousal time after discontin⁃uation of medication,elevated blood lactate,elevated CRP,and elevated PCT were the independent risk factors for deliri⁃um in elderly patients with SP undergoing mechanical ventilation(all P<0.05).The risk prediction model for the occur⁃rence of delirium in elderly patients with SP undergoing mechanical ventilation:Ln(P/1-P)=-15.475+1.165×severity of illness+1.019×diabetes mellitus+1.476×cerebrovascular disease+0.008×duration of mechanical ventilation+0.103×time of arousal after discontinuation of medication+0.751×blood lactate+0.011×CRP+0.362×PCT(χ^(2)=7.996,P=0.434).The ROC curve showed that the area under the curve of the risk prediction model in predicting the occurrence of delirium in elderly SP patients undergoing mechanical ventilation was 0.929(95%CI:0.899-0.952),with a optimal cut-off value of 0.217,a sensitivity of 83.75%,a specificity of 86.44%,and the maximum Youden index of 0.702,respec⁃tively.Conclusions Delirium will increase the risk of death in elderly SP patients.A prediction model is successfully constructed based on the risk factors for delirium in elderly SP patients undergoing mechanical ventilation,and this model has a high predictive value.