Department of Gastroenterology, Peking Union Medical College Hospital, Beijing 100730, China.
Medical Research Center, Peking Union Medical College Hospital, Beijing 100730, China.
World J Gastroenterol. 2022 May 21;28(19):2123-2136. doi: 10.3748/wjg.v28.i19.2123.
Acute respiratory distress syndrome (ARDS) is a major cause of death in patients with severe acute pancreatitis (SAP). Although a series of prediction models have been developed for early identification of such patients, the majority are complicated or lack validation. A simpler and more credible model is required for clinical practice.
To develop and validate a predictive model for SAP related ARDS.
Patients diagnosed with AP from four hospitals located at different regions of China were retrospectively grouped into derivation and validation cohorts. Statistically significant variables were identified using the least absolute shrinkage and selection operator regression method. Predictive models with nomograms were further built using multiple logistic regression analysis with these picked predictors. The discriminatory power of new models was compared with some common models. The performance of calibration ability and clinical utility of the predictive models were evaluated.
Out of 597 patients with AP, 139 were diagnosed with SAP (80 in derivation cohort and 59 in validation cohort) and 99 with ARDS (62 in derivation cohort and 37 in validation cohort). Four identical variables were identified as independent risk factors for both SAP and ARDS: heart rate [odds ratio (OR) = 1.05; 95%CI: 1.04-1.07; < 0.001; OR = 1.05, 95%CI: 1.03-1.07, < 0.001], respiratory rate (OR = 1.08, 95%CI: 1.0-1.17, = 0.047; OR = 1.10, 95%CI: 1.02-1.19, = 0.014), serum calcium concentration (OR = 0.26, 95%CI: 0.09-0.73, = 0.011; OR = 0.17, 95%CI: 0.06-0.48, = 0.001) and blood urea nitrogen (OR = 1.15, 95%CI: 1.09-1.23, < 0.001; OR = 1.12, 95%CI: 1.05-1.19, < 0.001). The area under receiver operating characteristic curve was 0.879 (95%CI: 0.830-0.928) and 0.898 (95%CI: 0.848-0.949) for SAP prediction in derivation and validation cohorts, respectively. This value was 0.892 (95%CI: 0.843-0.941) and 0.833 (95%CI: 0.754-0.912) for ARDS prediction, respectively. The discriminatory power of our models was improved compared with that of other widely used models and the calibration ability and clinical utility of the prediction models performed adequately.
The present study constructed and validated a simple and accurate predictive model for SAP-related ARDS in patients with AP.
急性呼吸窘迫综合征（ARDS）是重症急性胰腺炎（SAP）患者死亡的主要原因。尽管已经开发了一系列预测模型来早期识别此类患者，但大多数模型较为复杂或缺乏验证。临床实践需要更简单、更可信的模型。
建立和验证 SAP 相关 ARDS 的预测模型。
回顾性地将来自中国四个不同地区医院的诊断为 AP 的患者分为推导队列和验证队列。使用最小绝对收缩和选择算子回归方法识别有统计学意义的变量。使用这些有意义的预测因子进行多变量逻辑回归分析，进一步构建带有列线图的预测模型。比较新模型与一些常用模型的区分能力。评估预测模型的校准能力和临床实用性。
在 597 例 AP 患者中，有 139 例被诊断为 SAP（推导队列 80 例，验证队列 59 例），99 例患有 ARDS（推导队列 62 例，验证队列 37 例）。有四个相同的变量被确定为 SAP 和 ARDS 的独立危险因素：心率[比值比（OR）=1.05；95%置信区间（CI）：1.04-1.07；<0.001；OR=1.05，95%CI：1.03-1.07，<0.001]、呼吸频率（OR=1.08，95%CI：1.0-1.17，=0.047；OR=1.10，95%CI：1.02-1.19，=0.014）、血清钙浓度（OR=0.26，95%CI：0.09-0.73，=0.011；OR=0.17，95%CI：0.06-0.48，=0.001）和血尿素氮（OR=1.15，95%CI：1.09-1.23，<0.001；OR=1.12，95%CI：1.05-1.19，<0.001）。推导和验证队列中 SAP 预测的受试者工作特征曲线下面积分别为 0.879（95%CI：0.830-0.928）和 0.898（95%CI：0.848-0.949）。ARDS 预测的相应值分别为 0.892（95%CI：0.843-0.941）和 0.833（95%CI：0.754-0.912）。与其他广泛使用的模型相比，我们的模型的区分能力有所提高，预测模型的校准能力和临床实用性均表现良好。
本研究建立并验证了一种针对 AP 患者 SAP 相关 ARDS 的简单、准确的预测模型。
