A trauma‐related survival predictive model of acute respiratory distress syndrome. Issue 11 (20th September 2021)

• Record Type: Journal Article
• Authors: Tang, Rui; Wang, Hanghang; Peng, Junnan; Wang, Daoxin
• Is Part Of: Journal of clinical laboratory analysis. Volume 35:Issue 11(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-20
• Subjects: acute respiratory distress syndrome -- laboratory Inspection -- predictive model -- trauma; Diagnosis, Laboratory -- Periodicals; Medical laboratory technology -- Periodicals; 616
• DOI: 10.1002/jcla.24006
• Languages: English
• ISSNs: 0887-8013

Abstract

Abstract: Purpose: The purpose of this study was to construct and validate a simple model for the prediction of survival in patients with trauma‐related ARDS. Methods: This is a single‐center, retrospective cohort study using MIMIC‐III Clinical Database. Results: 842 patients were included in this study. 175 (20.8%) died in‐hospital, whereas 215 (25.5%) died within 90 days. The deceased group had higher Acute Physiology Score (APS III), Sequential Organ Failure Assessment (SOFA), and Simplified Acute Physiology Score II (SAPS II). In multivariate logistic regression model, independent risk factors for mortality in ARDS patients included age ([odds ratio] OR, 1.035; 95% confidence interval [CI], 1.020–1.049), body mass index (OR, 0.957; 95% CI, 0.926–0.989), red blood cell distribution width (OR, 1.283; 95% CI, 1.141–1.443), hematocrit (OR, 1.055; 95% CI, 1.017–1.095), lactate (OR, 1.226; 95% CI, 1.127–1.334), blood urea nitrogen (OR, 1.025; 95% CI, 1.007–1.044), acute kidney failure (OR, 1.875; 95% CI, 1.188–2.959), sepsis (OR, 1.917; 95% CI, 1.165–3.153), type of admission (emergency vs. elective [OR, 2.822; 95% CI, 1.647–4.837], and urgent vs. elective [OR, 5.156; 95% CI, 1.896–14.027]). The area under the curve (AUC) of the model was 0.826, which was superior than the SAPS II (0.776), APS III (0.718), and SOFA (0.692). In the cross‐validation model, the accuracy of the test set was 0.823, the precision was 0.643, and the AUC was 0.813. Conclusions: We established a prediction model using data commonly used in the clinic, which has high accuracy and precision and is worthy of use in clinical practice. Abstract : Acute respiratory distress syndrome is a heterogeneous disease. We established a prognostic model for in‐hospital death in patients with traumatic acute respiratory distress syndrome. So that we can timely identify high‐risk patients and formulate intervention strategies.