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. 2019 Sep 3;8(17):e013675.
doi: 10.1161/JAHA.119.013675. Epub 2019 Aug 29.

Derivation and Validation of a Novel Cardiac Intensive Care Unit Admission Risk Score for Mortality

Jacob C Jentzer  1   2 , Nandan S Anavekar  1 , Courtney Bennett  1   2 , Dennis H Murphree  3 , Mark T Keegan  4 , Brandon Wiley  1   2 , David A Morrow  5 , Joseph G Murphy  1 , Malcolm R Bell  1 , Gregory W Barsness  1
Affiliations

Derivation and Validation of a Novel Cardiac Intensive Care Unit Admission Risk Score for Mortality

Jacob C Jentzer et al. J Am Heart Assoc. .

Abstract

Background There are no risk scores designed specifically for mortality risk prediction in unselected cardiac intensive care unit (CICU) patients. We sought to develop a novel CICU-specific risk score for prediction of hospital mortality using variables available at the time of CICU admission. Methods and Results A database of CICU patients admitted from January 1, 2007 to April 30, 2018 was divided into derivation and validation cohorts. The top 7 predictors of hospital mortality were identified using stepwise backward regression, then used to develop the Mayo CICU Admission Risk Score (M-CARS), with integer scores ranging from 0 to 10. Discrimination was assessed using area under the receiver-operator curve analysis. Calibration was assessed using the Hosmer-Lemeshow statistic. The derivation cohort included 10 004 patients and the validation cohort included 2634 patients (mean age 67.6 years, 37.7% females). Hospital mortality was 9.2%. Predictor variables included in the M-CARS were cardiac arrest, shock, respiratory failure, Braden skin score, blood urea nitrogen, anion gap and red blood cell distribution width at the time of CICU admission. The M-CARS showed a graded relationship with hospital mortality (odds ratio 1.84 for each 1-point increase in M-CARS, 95% CI 1.78-1.89). In the validation cohort, the M-CARS had an area under the receiver-operator curve of 0.86 for hospital mortality, with good calibration (P=0.21). The 47.1% of patients with M-CARS <2 had hospital mortality of 0.8%, and the 5.2% of patients with M-CARS >6 had hospital mortality of 51.6%. Conclusions Using 7 variables available at the time of CICU admission, the M-CARS can predict hospital mortality in unselected CICU patients with excellent discrimination.

Keywords: cardiac intensive care unit; coronary care unit; mortality; risk scores.

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Figures

Figure 1
Figure 1
Flow diagram demonstrating inclusion and exclusion criteria for the derivation (A) and validation (B) cohorts.
Figure 2
Figure 2
Distribution of the M‐CARS in the derivation and validation cohorts. M‐CARS indicates Mayo CICU Admission Risk Score.
Figure 3
Figure 3
Observed hospital mortality as a function of the M‐CARS (range 0–10) in the derivation and validation cohorts. M‐CARS indicates Mayo cardiac intensive care unit (CICU) Admission Risk Score.
Figure 4
Figure 4
Observed hospital mortality as a function of the M‐CARS and Day 1 SOFA score, in the overall population, derivation, and validation cohorts. CICU indicates cardiac intensive care unit; M‐CARS, Mayo CICU Admission Risk Score; SOFA, Sequential Organ Failure Assessment.
See this image and copyright information in PMC

References

    1. Killip T III, Kimball JT. Treatment of myocardial infarction in a coronary care unit. A two year experience with 250 patients. Am J Cardiol. 1967;20:457–464. - PubMed
    1. Granger CB, Goldberg RJ, Dabbous O, Pieper KS, Eagle KA, Cannon CP, Van De Werf F, Avezum A, Goodman SG, Flather MD, Fox KA; Global Registry of Acute Coronary Events Investigators . Predictors of hospital mortality in the global registry of acute coronary events. Arch Intern Med. 2003;163:2345–2353. - PubMed
    1. Keegan MT, Gajic O, Afessa B. Severity of illness scoring systems in the intensive care unit. Crit Care Med. 2011;39:163–169. - PubMed
    1. Abraham WT, Fonarow GC, Albert NM, Stough WG, Gheorghiade M, Greenberg BH, O'Connor CM, Sun JL, Yancy CW, Young JB; OPTIMIZE‐HF Investigators and Coordinators. Predictors of in‐hospital mortality in patients hospitalized for heart failure: insights from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE‐HF). J Am Coll Cardiol. 2008;52:347–356. - PubMed
    1. Peterson PN, Rumsfeld JS, Liang L, Albert NM, Hernandez AF, Peterson ED, Fonarow GC, Masoudi FA; American Heart Association Get With the Guidelines‐Heart Failure Program . A validated risk score for in‐hospital mortality in patients with heart failure from the American Heart Association get with the guidelines program. Circ Cardiovasc Qual Outcomes. 2010;3:25–32. - PubMed

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