National ICU Registries as Enablers of Clinical Research and Quality Improvement.

OBJECTIVES: Clinical quality registries (CQRs) have been implemented worldwide by several medical specialties aiming to generate a better characterization of epidemiology, treatments, and outcomes of patients. National ICU registries were created almost 3 decades ago to improve the understanding of case-mix, resource use, and outcomes of critically ill patients. This narrative review describes the challenges, proposed solutions, and evidence generated by National ICU registries as facilitators for research and quality improvement. DATA SOURCES: English language articles were identified in PubMed using phrases related to ICU registries, CQRs, outcomes, and case-mix. STUDY SELECTION: Original research, review articles, letters, and commentaries, were considered. DATA EXTRACTION: Data from relevant literature were identified, reviewed, and integrated into a concise narrative review. DATA SYNTHESIS: CQRs have been implemented worldwide by several medical specialties aiming to generate a better characterization of epidemiology, treatments, and outcomes of patients. National ICU registries were created almost 3 decades ago to improve the understanding of case-mix, resource use, and outcomes of critically ill patients. The initial experience in European countries and in Oceania ensured that through locally generated data, ICUs could assess their performances by using risk-adjusted measures and compare their results through fair and validated benchmarking metrics with other ICUs contributing to the CQR. The accomplishment of these initiatives, coupled with the increasing adoption of information technology, resulted in a broad geographic expansion of CQRs as well as their use in quality improvement studies, clinical trials as well as international comparisons, and benchmarking for ICUs. CONCLUSIONS: ICU registries have provided increased knowledge of case-mix and outcomes of ICU patients based on real-world data and contributed to improve care delivery through quality improvement initiatives and trials. Recent increases in adoption of new technologies (i.e., cloud-based structures, artificial intelligence, machine learning) will ensure a broader and better use of data for epidemiology, healthcare policies, quality improvement, and clinical trials.

Comparison of physician prediction with 2 prognostic scoring systems in predicting 2-year mortality after intensive care admission: a linked-data cohort study.

PURPOSE: Patients who survive an episode of critical illness continue to experience significant mortality after hospital discharge. This study assessed the accuracy of physician prediction of 2-year mortality and compared it with 2 objective prognostic models. METHODS: Sensitivity (probability of a prediction of death in patients who died within 2 years) and specificity (probability of a prediction of survival in patients who survived at least 2 years) of physicians' 2-year prediction were compared with those from 2 objective prognostic models, Acute Physiology and Chronic Health Evaluation (APACHE) II and Predicted Risk Existing Disease Intensive Care Therapy (PREDICT). RESULTS: Physician prediction of 2-year mortality was available for 2497 (94.8%) intensive care unit admissions. Specificity was high (85.2%; 95% confidence interval [CI], 83.7-86.4), but sensitivity (65.0%; 95% CI, 61.1-68.8) and positive predictive value (57.4%; 95% CI, 53.6-61.2) were relatively low, suggesting overpessimistic prediction of 2-year mortality. Age, Charlson comorbidity index, and APACHE score were independent risk factors for an inaccurate physician prediction. The diagnostic odds ratio for the physician predictions was at least comparable with the APACHE and PREDICT models, which both had very good discrimination of mortality at 2-year follow-up. CONCLUSIONS: Physicians tended to overpredict the risk of 2-year mortality of critically ill patients, but accuracy was comparable with 2 objective prognostic models.