Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China.
The Science & Education Office, The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong 510632, China.
Int J Clin Pract. 2022 Feb 22;2022:5435656. doi: 10.1155/2022/5435656. eCollection 2022.
The prognosis is poor when acute pancreatitis (AP) progresses to sepsis; therefore, it is necessary to accurately predict the probability of sepsis and develop a personalized treatment plan to reduce the disease burden of AP patients.
A total of 1295 patients with AP and 43 variables were extracted from the Medical Information Mart for Intensive Care (MIMIC) IV database. The included patients were randomly assigned to the training set and to the validation set at a ratio of 7 : 3. The chi-square test or Fisher's exact test was used to test the distribution of categorical variables, and Student's -test was used for continuous variables. Multivariate logistic regression was used to establish a prognostic model for predicting the occurrence of sepsis in AP patients. The indicators to verify the overall performance of the model included the area under the receiver operating characteristic curve (AUC), calibration curves, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and a decision curve analysis (DCA).
The multifactor analysis results showed that temperature, phosphate, calcium, lactate, the mean blood pressure (MBP), urinary output, Glasgow Coma Scale (GCS), Charlson Comorbidity Index (CCI), sodium, platelet count, and albumin were independent risk factors. All of the indicators proved that the prediction performance and clinical profitability of the newly established nomogram were better than those of other common indicators (including SIRS, BISAP, SOFA, and qSOFA).
The new risk-prediction system that was established in this research can accurately predict the probability of sepsis in patients with acute pancreatitis, and this helps clinicians formulate personalized treatment plans for patients. The new model can reduce the disease burden of patients and can contribute to the reasonable allocation of medical resources, which is significant for tertiary prevention.
急性胰腺炎（AP）发展为脓毒症时预后较差；因此，有必要准确预测脓毒症的概率，并制定个性化的治疗计划，以减轻 AP 患者的疾病负担。
从医疗信息重症监护（MIMIC）IV 数据库中提取了 1295 名 AP 患者和 43 个变量。纳入的患者按 7:3 的比例随机分配到训练集和验证集中。使用卡方检验或 Fisher 确切检验检验分类变量的分布，使用 Student's -检验检验连续变量。使用多变量逻辑回归建立预测 AP 患者发生脓毒症的预后模型。验证模型整体性能的指标包括受试者工作特征曲线下面积（AUC）、校准曲线、净重新分类改善（NRI）、综合判别改善（IDI）和决策曲线分析（DCA）。
多因素分析结果表明，体温、磷酸盐、钙、乳酸、平均血压（MBP）、尿量、格拉斯哥昏迷评分（GCS）、Charlson 合并症指数（CCI）、钠、血小板计数和白蛋白是独立的危险因素。所有指标均表明，新建立的列线图的预测性能和临床获益均优于其他常用指标（包括 SIRS、BISAP、SOFA 和 qSOFA）。
本研究建立的新风险预测系统可以准确预测急性胰腺炎患者发生脓毒症的概率，有助于临床医生为患者制定个性化治疗方案。新模型可以减轻患者的疾病负担，有助于合理分配医疗资源，对三级预防具有重要意义。
