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血清学指标联合急性生理学与慢性健康状况评分系统Ⅱ评分预测急性加重期慢性阻塞性肺疾病的预后

Value of serological indicators combined with Acute Physiology and Chronic Health Evaluation Ⅱ score in predicting prognosis of acute exacerbation of chronic obstructive pulmonary disease

    摘要
  • 摘要:
    目的 探讨血清学指标联合急性生理学与慢性健康状况评分系统Ⅱ(APACHEⅡ)评分预测急性加重期慢性阻塞性肺疾病(AECOPD)患者预后的价值。
    方法 前瞻性选取359例AECOPD患者作为研究对象, 根据出院后6个月随访结果分为预后良好组190例和预后不良组169例, 记录2组患者血清学指标血小板与淋巴细胞比值(PLR)、中性粒细胞与淋巴细胞比值(NLR)、红细胞体积分布宽度(RDW)、中性粒细胞与单核细胞乘积(NMP)和APACHEⅡ评分等临床资料, 筛选AECOPD患者预后的影响因素, 构建预后预警模型, 并将预警模型转化为简易评分工具(AECOPD患者预后不良评分表), 绘制受试者工作特征(ROC)曲线进行前瞻性验证。
    结果 多因素Logistic回归分析显示, APACHEⅡ评分、PLR、NLR、RDW、NMP是AECOPD患者预后不良的影响因素(OR=22.651、16.042、12.599、17.669、11.289, P < 0.05); 基于APACHEⅡ评分、PLR、NLR、RDW、NMP构建列线图预警模型并预测个体患者危险评分, 高风险患者的预后不良发生率为60.00%(96/160), 高于低风险患者的36.68%(73/199), 差异有统计学意义(χ2=18.292, P < 0.001); 前瞻性验证结果显示, AECOPD患者预后不良评分表预测预后不良的曲线下面积(AUC)为0.902(95%CI: 0.899~1.000), 敏感度、特异度分别为85.18%、93.93%。
    结论 基于APACHEⅡ评分和血清学指标PLR、NLR、RDW、NMP构建AECOPD患者预后预警模型及评分标准, 可帮助临床医师有效鉴别预后不良高危人群并制订合理诊治措施, 改善患者预后。

     

    Abstract:
    Objective To investigate the value of serological indicators combined with Acute Physiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ) score in predicting the prognosis of patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD).
    Methods A total of 359 patients with AECOPD were prospectively selected as study subjects, and were divided into good prognosis group(n=190) and poor prognosis group (n=169) according to the 6-month follow-up after discharge. Serological indicatorsplatelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), red blood cell volume distribution width (RDW), neutrophil to monocyte product (NMP), and APACHE Ⅱ score were recorded to screen influencing factors related to the prognosis of AECOPD patients, and a prognostic early warning model was constructed. The model was converted into a simple scoring tool (Poor Prognosis Rating Scale for AECOPD patients). Receiver Operating Characteristic (ROC) curves were plotted for prospective validation.
    Results Multivariate Logistic regression analysis showed that APACHE Ⅱ score, PLR, NLR, RDW and NMP were high risk factors for poor prognosis in patients with AECOPD (OR=22.651, 16.042, 12.599, 17.669, 11.289; P < 0.05); based on APACHEⅡ score, PLR, NLR, RDW and NMP, a nomogram warning model was constructed to predict individual patient risk score. The incidence of adverse prognosis in high-risk patients was 60.00% (96/160), which was higher than 36.68%(73/199) in low-risk patients, and the difference was statistically significant (χ2=18.292, P < 0.001). Prospective validation showed that the area under the curve (AUC) for predicting poor prognosis in AECOPD patients by Poor Prognosis Rating Scale was 0.902 (95%CI, 0.899 to 1.000), with sensitivities and specificities of 85.18% and 93.93%, respectively.
    Conclusion The prognostic warning model and scoring criteria for AECOPD patients based on APACHEⅡscore, PLR, NLR, RDW and NMP can help clinicians effectively identify high-risk groups with poor prognosis and formulate reasonable diagnosis and treatment measures to improve the prognosis of patients.

     

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