A prediction model for acute respiratory distress syndrome among patients with severe acute pancreatitis: a retrospective analysis. (September 2022)

• Record Type: Journal Article
• Authors: Lin, Fengyu; Lu, Rongli; Han, Duoduo; Fan, Yifei; Zhang, Yan; Pan, Pinhua
• Is Part Of: Therapeutic advances in respiratory disease. Volume 16(2022)
• Publication Date: 2022-09
• Subjects: acute respiratory distress syndrome -- ARDS -- SAP -- severe acute pancreatitis; Respiratory organs -- Diseases -- Periodicals; Respiratory agents -- Periodicals; Pulmonary pharmacology -- Periodicals; Respiratory Tract Diseases -- Periodicals; Respiratory System Agents -- therapeutic use -- Periodicals; Respiratory Tract Diseases -- drug therapy -- Periodicals; Lung Diseases -- drug therapy -- Periodicals; Appareil respiratoire -- Maladies -- Traitement -- Périodiques; Agents respiratoires -- Périodiques; Pharmacologie pulmonaire -- Périodiques; 616.2005
• DOI: 10.1177/17534666221122592
• Languages: English
• ISSNs: 1753-4658

Abstract

Background: Acute respiratory distress syndrome (ARDS) is a severe complication among patients with severe acute pancreatitis (SAP), which may be associated with increased mortality in hospitalized patients. Thus, an effective model to predict ARDS in patients with SAP is urgently required. Methods: We retrospectively analyzed the data from the patients with SAP who recruited in Xiangya Hospital between April 2017 and May 2021. Patients meeting the Berlin definition of ARDS were categorized into the ARDS group. Logistic regression models and a nomogram were utilized in the study. Descriptive statistics, logistic regression models, and a nomogram were used in the current study. Results: Comorbidity of ARDS occurred in 109 (46.58%) of 234 patients with SAP. The SAP patients with ARDS group had a higher 60-day mortality rate, an increased demand for invasive mechanical ventilation, and a longer intensive care unit (ICU) stay than those without ARDS ( p < .001 for all). Partial pressure of oxygen (PaO2): fraction of inspired oxygen (FiO2) < 200, platelets <125 × 109/L, lactate dehydrogenase >250 U/L, creatinine >111 mg/dL, and procalcitonin >0.5 ng/mL were independent risk variables for development of ARDS in SAP patients. The area under the curve for the model was 0.814, and the model fit was acceptable [ p = .355 (Hosmer–Lemeshow)]. Incorporating these 5 factors, a nomogram was established with sufficient discriminatory power (C-index 0.814). Calibration curve indicated the proper discrimination and good calibration in the predicting nomogram model. Conclusion: The prediction nomogram for ARDS in patients with SAP can be applied using clinical common variables after the diagnosis of SAP. Future studies would be warranted to verify the potential clinical benefits of this model.