RESUMO
To evaluate the methodologic rigor and predictive performance of models predicting ICU readmission; to understand the characteristics of ideal prediction models; and to elucidate relationships between appropriate triage decisions and patient outcomes. DATA SOURCES: PubMed, Web of Science, Cochrane, and Embase. STUDY SELECTION: Primary literature that reported the development or validation of ICU readmission prediction models within from 2010 to 2021. DATA EXTRACTION: Relevant study information was extracted independently by two authors using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist. Bias was evaluated using the Prediction model Risk Of Bias ASsessment Tool. Data sources, modeling methodology, definition of outcomes, performance, and risk of bias were critically evaluated to elucidate relevant relationships. DATA SYNTHESIS: Thirty-three articles describing models were included. Six studies had a high overall risk of bias due to improper inclusion criteria or omission of critical analysis details. Four other studies had an unclear overall risk of bias due to lack of detail describing the analysis. Overall, the most common (50% of studies) source of bias was the filtering of candidate predictors via univariate analysis. The poorest performing models used existing clinical risk or acuity scores such as Acute Physiologic Assessment and Chronic Health Evaluation II, Sequential Organ Failure Assessment, or Stability and Workload Index for Transfer as the sole predictor. The higher-performing ICU readmission prediction models used homogenous patient populations, specifically defined outcomes, and routinely collected predictors that were analyzed over time. CONCLUSIONS: Models predicting ICU readmission can achieve performance advantages by using longitudinal time series modeling, homogenous patient populations, and predictor variables tailored to those populations.
RESUMO
Background Acute aortic syndromes may be prone to misdiagnosis by nonreferral aortic centers with less diagnostic experience. We evaluated regional variability in these misdiagnosis trends among patients transferred to different regional quaternary care centers with presumed acute aortic syndromes. Methods and Results Two institutional aortic center databases were retrospectively reviewed for emergency transfers in patients diagnosed with acute aortic dissection, intramural hematoma, penetrating aortic ulcer, thoracic aortic aneurysm, or aortic pseudoaneurysm between 2008 and 2020. Transferring diagnoses versus actual diagnoses were reviewed using physician notes and radiology reports. Misdiagnoses were confirmed by a board-certified cardiothoracic surgeon. A total of 3772 inpatient transfers were identified, of which 1762 patients were classified as emergency transfers. The mean age was 64 years (58% male). Patients were transferred from 203 medical centers by ground (51%) or air (49%). Differences in transfer diagnosis and actual diagnosis were identified in 188 (10.7%) patients. Of those, incorrect classification of Type A versus B dissections was identified among 23%, and 30% of patients with a referring diagnosis of an acute aortic dissection did not have one. In addition, 14% transferred for contained/impending rupture did not have signs of rupture. All misdiagnoses were secondary to misinterpretation of imaging, with motion artifacts (n=32, 17%) and postsurgical changes (n=44, 23%) being common sources of diagnostic error. Conclusions Misdiagnosis of acute aortic syndromes commonly occurred in patients transferred to 2 separate large aortic referral centers. Although diagnostic accuracy may be improving, there are opportunities for improved physician awareness through standardized web-based imaging education.
