摘要
目的构建慢性阻塞性肺疾病急性加重(AECOPD)风险预测列线图模型并开发其网络计算器。方法回顾性选取2021年1月—2023年6月安徽医科大学附属六安医院收治的慢性阻塞性肺疾病稳定期(SCOPD)患者155例为研究对象。根据患者AECOPD发生情况,将其分为AECOPD组(108例)和非AECOPD组(47例)。采用ROC曲线确定血小板计数(PLT)、平均血小板体积(MPV)、血小板分布宽度(PDW)预测SCOPD患者发生AECOPD的最佳截断值并评估患者血小板指数评分(PIS);采用多因素Logistic回归分析探讨SCOPD患者发生AECOPD的影响因素;基于多因素Logistic回归分析结果构建AECOPD风险预测列线图模型;通过ROC曲线和决策曲线评估该列线图模型的区分度和临床适用性;使用R 4.0.3软件的DynNom包开发AECOPD风险预测网络计算器。结果AECOPD组年龄、MPV、PDW大于非AECOPD组,有糖尿病者占比、改良英国医学研究委员会(mMRC)分级评分、C反应蛋白(CRP)、纤维蛋白原(FIB)、D-二聚体(D-D)、PLT高于非AECOPD组,血红蛋白(Hb)低于非AECOPD组,凝血酶原时间(PT)短于非AECOPD组,活化部分凝血活酶时间(APTT)长于非AECOPD组(P<0.05)。ROC曲线分析结果显示,PLT、MPV、PDW预测SCOPD患者发生AECOPD的最佳截断值分别为203.4×10^(9)/L、10.4 fl、12.5%。根据PLT、MPV、PDW预测SCOPD患者发生AECOPD的最佳截断值评估患者PIS。AECOPD组PIS高于非AECOPD组(P<0.05)。多因素Logistic回归分析结果显示,年龄、CRP、Hb、FIB、PIS是SCOPD患者发生AECOPD的独立影响因素(P<0.05)。基于多因素Logistic回归分析结果构建AECOPD风险预测列线图模型。ROC曲线分析结果显示,该列线图模型预测SCOPD患者发生AECOPD的AUC为0.975〔95%CI(0.936~0.993)〕。决策曲线分析结果显示,当阈值概率>0.062时,该列线图模型的临床净获益>0。基于AECOPD风险预测列线图模型开发其网络计算器(https://npmcls.shinyapps.io/DynNomapp1/)。结论年龄、CRP、Hb、FIB、PIS是SCOPD患者发生AECOPD的独立影响因素,本研究基于上述影响因素构建的AECOPD风险预测列线图模型的区分度较高,且具有较好的临床适用性,同时开发了AECOPD风险预测网络计算器。
Objective To construct the nomogram model for predicting the risk of acute exacerbation of chronic obstructive pulmonary disease(AECOPD)and develop its web calculator.Methods One hundred and fifty-five patients with stable chronic obstructive pulmonary disease(SCOPD)admitted to Lu'an Hospital of Anhui Medical University from January 2021 to June 2023 were retrospectively selected as the study objects.According to the occurrence of AECOPD,the patients were divided into AECOPD group(108 cases)and non-AECOPD group(47 cases).The ROC curve was used to determine the optimal cut-off value of platelet count(PLT),mean platelet volume(MPV),and platelet distribution width(PDW)in predicting AECOPD in SCOPD patients and the patients'platelet index score(PIS)was evaluated;multivariate Logistic regression analysis was used to explore the influencing factors of AECOPD in SCOPD patients;based on the results of multivariate Logistic regression analysis,the nomogram model for predicting the risk of AECOPD was constructed;the differentiability and clinical applicability of the nomogram model were evaluated by ROC curve and decision curve;and the AECOPD risk prediction web calculator was developed using the DynNom package of R 4.0.3 software.Results Age,MPV and PDW of AECOPD group were older than those of non-AECOPD group,and the proportion of patients with diabetes mellitus,modified Medical Research Council(mMRC)grading score,C-reactive protein(CRP),fibrinogen(FIB),D-dimer(D-D)and PLT were higher than those of non-AECOPD group,hemoglobin(Hb)was lower than that of non-AECOPD group,prothrombin time(PT)was shorter than that of non-AECOPD group,and activated partial thromboplastin time(APTT)was longer than that of non-AECOPD group(P<0.05).The results of ROC curve analysis showed that the optimal cut-off values of PLT,MPV,and PDW in predicting AECOPD in patients with SCOPD were 203.4×10^(9)/L,10.4 fl,and 12.5%respectively.PIS of patients was assessed based on the optimal cut-off values of PLT,MPV,and PDW in predicting AECOPD in SCOPD patients.PIS in the AECOPD group was higher than that in the non-AECOPD group(P<0.05).The results of multivariate Logistic regression analysis showed that age,CRP,Hb,FIB,and PIS were independent influencing factors of AECOPD in SCOPD patients(P<0.05).Based on the results of multivariate Logistic regression analysis,the nomogram model for predicting the risk of AECOPD was constructed.The results of ROC curve analysis showed that the AUC of the nomogram model in predicting AECOPD in patients with SCOPD was 0.975[95%CI(0.936-0.993)].The results of the decision curve analysis showed that the net benefit of the nomogram model was>0 when the threshold probability was greater than 0.062.The AECOPD risk prediction web calculator(https://npmcls.shinyapps.io/DynNomapp1/)was developed based on the nomogram model for predicting the risk of AECOPD.Conclusion Age,CRP,Hb,FIB,and PIS are independent influencing factors of AECOPD in SCOPD patients,and the nomogram model for predicting the risk of AECOPD constructed based on the above influencing factors in this study has a high degree of differentiation and good clinical applicability,and an AECOPD risk prediction web calculator was also developed.
作者
虞玉兰
林承奎
朱成圣
王宇
YU Yulan;LIN Chengkui;ZHU Chengsheng;WANG Yu(Department of Respiratory and Critical Care Medicine,Lu'an Hospital of Anhui Medical University/Lu'an People's Hospital of Anhui Province,Liuan 237000,China)
出处
《实用心脑肺血管病杂志》
2024年第6期16-21,共6页
Practical Journal of Cardiac Cerebral Pneumal and Vascular Disease
基金
安徽高校科学研究立项重大项目(2022AH040355)。
关键词
肺疾病
慢性阻塞性
慢性阻塞性肺疾病急性加重
列线图
网络计算器
Pulmonary disease,chronic obstructive
Acute exacerbations of chronic obstructive pulmonary disease
Nomograms
Web calculator
