急性呼吸窘迫综合征患者短期预后的风险模型

程秉山; 符之月; 何声萍; 王妙; 何云蕾

doi:10.16369/j.oher.issn.1007-1326.2021.03.013

急性呼吸窘迫综合征患者短期预后的风险模型

Risk model of short-term prognosis for patients with acute respiratory distress syndrome

摘要

摘要:
目的建立急性呼吸窘迫综合征（acute respiratory distress syndrome，ARDS）28 d死亡的风险预测模型，旨在为患者危险分层、改善短期预后提供早期干预策略。
方法回顾性分析2010年3月—2020年3月琼海市中医院综合内科和琼海市人民医院呼吸内科诊治的628例ARDS患者的临床资料，依据28 d生存状况分为死亡组267例、存活组361例。采用多因素logistic回归分析ARDS患者28 d死亡的影响因素，并构建风险预测模型，采用ROC曲线对风险模型进行评定。
结果多因素logistic回归分析结果显示：相比体质量指数（body mass index，BMI）≤ 25.00 kg/m²、氧合指数（PaO₂/FiO₂）≤ 122.50，BMI > 25.00 kg/m²（OR=0.37，95% CI：0.15~0.72，P=0.002）、PaO₂/FiO₂ > 122.50（OR=0.59，95% CI：0.21~0.83，P=0.007）分别是ARDS患者28 d死亡的保护因素；相比中性粒细胞淋巴细胞比值NLR ≤ 13.85、Clara细胞蛋白-16（CC16）≤ 53.50 ng/L、急性生理学和慢性健康状况评价（acute physiology and chronic health evaluation，APACHE）Ⅱ评分≤ 23.80分，NLR > 13.85（OR=1.58，95% CI：1.26~3.39，P < 0.001）、CC16 > 53.50 ng/L（OR=1.66，95% CI：1.13~3.90，P=0.013）、APACHEⅡ评分> 23.80分（OR=2.39，95% CI：1.55~4.12，P=0.030）分别是ARDS患者28 d死亡的危险因素。根据回归方程，拟合新变量Predicting，并构建风险预测模型，ROC曲线显示：Predicting预测ARDS患者28 d死亡的曲线下面积（area under the curve，AUC）为0.893（95% CI：0.847~0.939，P < 0.001），准确率83.28%，敏感度83.90%，特异度为82.83%，临界值为0.768。
结论依据NLR、CC16、APACHEⅡ评分、BMI、PaO₂/FiO₂构建的风险模型有助于早期识别ARDS危重症患者，指导临床工作。

Abstract:
Objective A 28-day death risk prediction model for acute respiratory distress syndrome (ARDS) was explored to provide early intervention strategies for stratification of patient risk and improvement of short-term prognosis.
Methods A retrospective analysis of the clinical data of 628 ARDS patients diagnosed and treated in the respiratory medicine of department of comprehensive internal medicine, Qionghai City Hospital of Traditional Chinese Medicine and department of respiratory medicine, Qionghai City People's Hospital from March 2010 to March 2020 was done. The patients were divided into the death group(267 cases)and survival group(361 cases)based on the 28-day survival status. Multivariate logistic regression analysis was used to show the factors influencing fate of ARDS patients at day 28. A risk prediction model was constructed, and the ROC curve was used to evaluate the risk model.
Results Multivariate logistic regression analysis showed that compared with body mass index (BMI) ≤ 25.00 kg/m², or oxygenation index (PaO₂/FiO₂) ≤ 122.50, the factors BMI > 25.00 kg/m² (OR = 0.37, 95% CI: 0.15-0.72, P = 0.002) and PaO₂/FiO₂ > 122.50 (OR = 0.59, 95%CI: 0.21-0.83, P = 0.007)were protective factors. Compared with the neutrophils lymphocyte ratio(NLR)≤ 13.85, Clara cell protein-16(CC16)≤ 53.50 μg/L, or acute physiology and chronic health evaluation (APACHE) II score ≤ 23.80 points, the factors NLR > 13.85 (OR = 1.58, 95%CI: 1.26-3.39, P < 0.001), CC16 > 53.50 ng/L (OR = 1.66, 95%CI: 1.13-3.90, P = 0.013), and APACHEⅡ score > 23.80 (OR = 2.39, 95%CI: 1.55-4.12, P = 0.030) were the risk factors. Then the risk prediction model was constructed by using new derived variable "Predicting" based on the regression equation. ROC curve analysis showed that the AUC of Predicting to predict the fate of ARDS patients at day 28 was 0.893(95%CI: 0.847-0.939, P < 0.001), and the accuracy rate was 83.28%, the sensitivity was 83.90%, the specificity was 82.83%, and the critical value was 0.768.
Conclusions The risk model constructed based on factors NLR, CC16, APACHEⅡ score, BMI, and PaO₂/FiO₂ can help early identification of critically ill patients with ARDS and guide clinical work.

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