早期预测新型冠状病毒肺炎患者病情严重程度列线图的建立及应用

Establishment of a nomogram for early prediction of the severity of patients withcoronavirus disease 2019 and its application

目的建立早期预测新型冠状病毒肺炎患者病情严重程度的列线图,以指导临床治疗。方法选择2020年1月17日至2020年2月14日温州市中心医院收治的116例新型冠状病毒肺炎患者,根据临床表现,将116例患者分为轻型组(4例)、普通型组(90例)、重型组(18例)和危重型组(4例)。记录所有患者的住院时间和并发症,比较4组患者的一般资料及临床指标。通过多因素Logistic回归分析得到影响新型冠状病毒肺炎患者预后的危险因素,并用R语言软件建立可视化的回归列线图,最后采用受试者工作特征(ROC)曲线检测该列线图的效能。结果轻型组、普通型组、重型组和危重型组新型冠状病毒肺炎患者年龄[(39±11)、(43±12)、(53±13)、(60±8)岁,F=5.815,P=0.001]、C反应蛋白质[1.7(0.6,7.1)、7.9(2.9,21.6)、28.4(13.9,42.5)、61.7(44.7,79.8)mg/L,H=8.424,P<0.001]、红细胞比容[(36±5)%、(41±4)%、(39±4)%、(37±5)%,F=4.344,P=0.006]、血小板计数[318.0(251.0,409.0)×10^9/L、180.5(140.0,225.5)×10^9/L、162.0(130.0,222.8)×10^9/L、108.5(82.0,103.0)×10^9/L,H=7.225,P<0.001]、天冬氨酸氨基转移酶[16.0(15.5,19.5)、23.0(19.0,31.0)、34.5(26.3,55.0)、39.5(29.0,82.3)U/L,H=6.159,P=0.001]、白蛋白[(44±6)、(43±16)、(39±3)、(33±4)g/L,F=9.508,P<0.001]和乳酸脱氢酶[142.5(107.8,189.3)、198.0(159.5,238.0)、295.0(251.0,323.0)、369.5(295.2,436.3)U/L,H=14.225,P<0.001]水平比较,差异均有统计学意义。将年龄、C反应蛋白质、红细胞比容、血小板计数、天冬氨酸氨基转移酶、白蛋白和乳酸脱氢酶纳入多因素Logistic回归分析,结果显示,白蛋白[比值比(OR)=0.756,95%置信区间(CI)(0.581,0.982),P=0.036]和乳酸脱氢酶[OR=1.019,95%CI(1.007,1.032),P=0.002]为影响新型冠状病毒肺炎患者预后的危险因素。通过R语言软件得到可靠直观的列线图。ROC曲线分析结果显示,该列线图对重型及危重型患者[曲线下面积(AUC)=0.903,95%CI(0.831,0.975),P<0.001]、单独预测危重型患者[AUC=0.974,95%CI(0.932,1.000),P<0.001]、单独预测重型患者[AUC=0.848,95%CI(0.759,0.937),P<0.001]均具有优秀的预测能力。结论该列线图可以早期并有效预测新型冠状病毒肺炎患者严重程度,可能成为指导临床治疗的一项实用工具。

Objective To establish a nomogram for early prediction of the severity of patients with coronavirus disease 2019(COVID-19)to guide clinical treatment.Methods From January 17 to February 14,2020,116 patients with COVID-19 admitted to Wenzhou Central Hospital were selected.According to their clinical manifestations,116 patients were divided into a mild group(n=4),a common group(n=90),a severe group(n=18)and a critical group(n=4).The hospitalization time and complications of all patients were recorded,and the general data and clinical indicators were compared among the 4 groups.Risk factors for the prognosis of patients with COVID-19 were obtained by multivariate Logistic regression analysis.Then a visual regression nomogram was established using R language software.Finally,efficacy of the nomogram was detected by a receiver operating characteristic(ROC)curve.Results The age[(39±11),(43±12),(53±13),(60±8)years;F=5.815,P=0.001],C-reactive protein[1.7(0.6,7.1),7.9(2.9,21.6),28.4(13.9,42.5),61.7(44.7,79.8)mg/L;H=8.424,P<0.001],hematocrit[(36±5)%,(41±4)%,(39±4)%,(37±5)%;F=4.344,P=0.006],platelet count[318.0(251.0,409.0)×10^9/L,180.5(140.0,225.5)×10^9/L,162.0(130.0,222.8)×10^9/L,108.5(82.0,103.0)×10^9/L;H=7.225,P<0.001],aspartate aminotransferase[16.0(15.5,19.5),23.0(19.0,31.0),34.5(26.3,55.0),39.5(29.0,82.3)U/L;H=6.159,P=0.001],albumin[(44±6),(43±16),(39±3),(33±4)g/L;F=9.508,P<0.001],and lactate dehydrogenase[142.5(107.8,189.3),198.0(159.5,238.0),295.0(251.0,323.0),369.5(295.2,436.3)U/L;H=14.225,P<0.001]of patients with COVID-19 in the 4 groups were statistically significantly different.Then the age,C-reactive protein,hematocrit,platelet count,aspartate aminotransferase,albumin,and lactate dehydrogenase were incorporated into multivariate Logistic regression analysis.It showed that the albumin[odds ratio(OR)=0.756,95%confidence interval(CI)(0.581,0.982),P=0.036]and lactate dehydrogenase[OR=1.019,95%CI(1.007,1.032),P=0.002]were risk factors for the prognosis of patients with COVID-19.In the meantime,a reliable and intuitive nomogram was obtained by R language software.ROC curve analysis showed that this nomogram had excellent predictive ability for severe and critical patients[area under the curve(AUC)=0.903,95%CI(0.831,0.975),P<0.001],for critical patients alone[AUC=0.974,95%CI(0.932,1.000),P<0.001]and for severe patients alone[AUC=0.848,95%CI(0.759,0.937),P<0.001].Conclusion This nomogram can predict the severity of patients with COVID-19 early and effectively,and may be a practical tool to guide clinical treatment.