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Medline ® Abstract for Reference 48

of 'Palliative care for patients with advanced heart failure: Indications and strategies'

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Validation and Comparison of Seven Mortality Prediction Models for Hospitalized Patients With Acute Decompensated Heart Failure.
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Lagu T, Pekow PS, Shieh MS, Stefan M, Pack QR, Kashef MA, Atreya AR, Valania G, Slawsky MT, Lindenauer PK
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Circ Heart Fail. 2016;9(8)
 
BACKGROUND: Heart failure (HF) inpatient mortality prediction models can help clinicians make treatment decisions and researchers conduct observational studies; however, published models have not been validated in external populations.
METHODS AND RESULTS: We compared the performance of 7 models that predict inpatient mortality in patients hospitalized with acute decompensated heart failure: 4 HF-specific mortality prediction models developed from 3 clinical databases (ADHERE [Acute Decompensated Heart Failure National Registry], EFFECT study [Enhanced Feedback for Effective Cardiac Treatment], and GWTG-HF registry [Get With the Guidelines-Heart Failure]); 2 administrative HF mortality prediction models (Premier, Premier+); and a model that uses clinical data but is not specific for HF (Laboratory-Based Acute Physiology Score [LAPS2]). Using a multihospital, electronic health record-derived data set (HealthFacts [Cerner Corp], 2010-2012), we identified patients≥18 years admitted with HF. Of 13 163 eligible patients, median age was 74 years; half were women; and 27% were black. In-hospital mortality was 4.3%. Model-predicted mortality ranges varied: Premier+ (0.8%-23.1%), LAPS2 (0.7%-19.0%), ADHERE (1.2%-17.4%), EFFECT (1.0%-12.8%), GWTG-Eapen (1.2%-13.8%), and GWTG-Peterson (1.1%-12.8%). The LAPS2 and Premier models outperformed the clinical models (C statistics: LAPS2 0.80 [95% confidence interval 0.78-0.82], Premier models 0.81 [95% confidence interval 0.79-0.83]and 0.76 [95% confidence interval 0.74-0.78], and clinical models 0.68 to 0.70).
CONCLUSIONS: Four clinically derived, inpatient, HF mortality models exhibited similar performance, with C statistics near 0.70. Three other models, 1 developed in electronic health record data and 2 developed in administrative data, also were predictive, with C statistics from 0.76 to 0.80. Because every model performed acceptably, the decision to use a given model should depend on practical concerns and intended use.
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From the Center for Quality of Care Research (T.L., P.S.P., M.-S.S., M.S., Q.R.P., G.V., M.T.S., P.K.L.), Division of Hospital Medicine, Department of Medicine (T.L., M.S., P.K.L.), and Division of Cardiology (Q.R.P., M.A.K., A.R.A., G.V., M.T.S.), Baystate Medical Center, Springfield, MA; Department of Medicine, Tufts University School of Medicine, Boston, MA (T.L., M.S., Q.R.P., M.A.K., A.R.A., G.V., M.T.S., P.K.L.); and School of Public Health and Health Sciences, University of Massachusetts-Amherst (P.S.P.). lagutc@gmail.com.
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