Admission diagnosis and mortality risk prediction in a contemporary cardiac intensive care unit population - 19/06/20
Abstract |
Background |
Critical care risk scores can stratify mortality risk among cardiac intensive care unit (CICU) patients, yet risk score performance across common CICU admission diagnoses remains uncertain.
Methods |
We evaluated performance of the Acute Physiology and Chronic Health Evaluation (APACHE)-III, APACHE-IV, Sequential Organ Failure Assessment (SOFA) and Oxford Acute Severity of Illness Score (OASIS) scores at the time of CICU admission in common CICU admission diagnoses. Using a database of 9,898 unique CICU patients admitted between 2007 and 2015, we compared the discrimination (c-statistic) and calibration (Hosmer-Lemeshow statistic) of each risk score in patients with selected admission diagnoses.
Results |
Overall hospital mortality was 9.2%. The 3182 (32%) patients with a critical care diagnosis such as cardiac arrest, shock, respiratory failure, or sepsis accounted for >85% of all hospital deaths. Mortality discrimination by each risk score was comparable in each admission diagnosis (c-statistic 95% CI values were generally overlapping for all scores), although calibration was variable and best with APACHE-III. The c-statistic values for each score were 0.85-0.86 among patients with acute coronary syndromes, and 0.76-0.79 among patients with heart failure. Discrimination for each risk score was lower in patients with critical care diagnoses (c-statistic range 0.68-0.78) compared to non-critical cardiac diagnoses (c-statistic range 0.76-0.86).
Conclusions |
The tested risk scores demonstrated inconsistent performance for mortality risk stratification across admission diagnoses in this CICU population, emphasizing the need to develop improved tools for mortality risk prediction among critically-ill CICU patients.
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Disclosures: The authors declare that they have no competing financial interests or conflicts of interest to disclose relevant to this work. |
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Source of funding: No extramural funding source was involved in the collection, analysis or interpretation of study data. |
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Marc Jolicoeur, MD MSc MHS served as guest editor for this article. |
Vol 224
P. 57-64 - Giugno 2020 Ritorno al numeroBenvenuto su EM|consulte, il riferimento dei professionisti della salute.
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