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      Risk-adjustment models for heart failure patients’ 30-day mortality and readmission rates: the incremental value of clinical data abstracted from medical charts beyond hospital discharge record

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          Abstract

          Background

          Hospital discharge records (HDRs) are routinely used to assess outcomes of care and to compare hospital performance for heart failure. The advantages of using clinical data from medical charts to improve risk-adjustment models remain controversial. The aim of the present study was to evaluate the additional contribution of clinical variables to HDR-based 30-day mortality and readmission models in patients with heart failure.

          Methods

          This retrospective observational study included all patients residing in the Local Healthcare Authority of Bologna (about 1 million inhabitants) who were discharged in 2012 from one of three hospitals in the area with a diagnosis of heart failure. For each study outcome, we compared the discrimination of the two risk-adjustment models (i.e., HDR-only model and HDR-clinical model) through the area under the ROC curve (AUC).

          Results

          A total of 1145 and 1025 patients were included in the mortality and readmission analyses, respectively. Adding clinical data significantly improved the discrimination of the mortality model (AUC = 0.84 vs. 0.73, p < 0.001), but not the discrimination of the readmission model (AUC = 0.65 vs. 0.63, p = 0.08).

          Conclusions

          We identified clinical variables that significantly improved the discrimination of the HDR-only model for 30-day mortality following heart failure. By contrast, clinical variables made little contribution to the discrimination of the HDR-only model for 30-day readmission.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12913-016-1731-9) contains supplementary material, which is available to authorized users.

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          Most cited references30

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          Systolic blood pressure at admission, clinical characteristics, and outcomes in patients hospitalized with acute heart failure.

          The association between systolic blood pressure (SBP) at admission, clinical characteristics, and outcomes in patients hospitalized for heart failure who have reduced or relatively preserved systolic function has not been well studied. To evaluate the relationship between SBP at admission, clinical profile, and outcomes in patients hospitalized for acute heart failure. Cohort study using data from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF) registry and performance-improvement program for patients hospitalized with heart failure at 259 US hospitals between March 2003 and December 2004. Patients were divided into quartiles by SBP at hospital admission ( 161 mm Hg). In-hospital outcomes were based on 48,612 patients aged 18 years or older with heart failure. Of the 41,267 patients with left ventricular function assessed, 21,149 (51%) had preserved left ventricular function. Postdischarge outcomes were based on a prespecified subgroup (n = 5791, 10% of patients) with follow-up data assessed between 60 and 90 days. In-hospital and postdischarge mortality. Patients with higher SBP were more likely to be female and black and to have preserved systolic function. Fifty percent of the patients had SBP higher than 140 mm Hg at admission. Patients with lower SBP at admission had higher in-hospital and postdischarge mortality rates. Higher SBP at admission was associated with lower in-hospital mortality rates: 7.2% ( 161 mm Hg) (P<.001 for overall difference). Postdischarge mortality rates in the follow-up cohort by SBP at admission were 14.0%, 8.4%, 6.0%, and 5.4%, respectively (P<.001 for overall difference). Systolic hypertension is common in patients hospitalized for heart failure. Systolic blood pressure is an independent predictor of morbidity and mortality in patients with heart failure with either reduced or relatively preserved systolic function. Low SBP (<120 mm Hg) at hospital admission identifies patients who have a poor prognosis despite medical therapy. These findings may have important therapeutic implications because characteristics and outcomes differ greatly among patients with heart failure with varying SBP.
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            Renal insufficiency and heart failure: prognostic and therapeutic implications from a prospective cohort study.

            The prevalence, prognostic import, and impact of renal insufficiency on the benefits of ACE inhibitors and beta-blockers in community-dwelling patients with heart failure are uncertain. We analyzed data from a prospective cohort of 754 patients with heart failure who had ejection fraction, serum creatinine, and weight measured at baseline. Median age was 69 years, and 43% had an ejection fraction > or =35%. By the Cockcroft-Gault equation, 118 patients (16%) had creatinine clearances or =60 mL/min, although these drugs were used less frequently in patients with renal insufficiency. Renal insufficiency is more prevalent in patients with heart failure than previously reported and is an independent prognostic factor in diastolic and systolic dysfunction. ACE inhibitors and beta-blockers were associated with similar reductions in mortality in patients with and without renal insufficiency.
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              A validated risk score for in-hospital mortality in patients with heart failure from the American Heart Association get with the guidelines program.

              Effective risk stratification can inform clinical decision-making. Our objective was to derive and validate a risk score for in-hospital mortality in patients hospitalized with heart failure using American Heart Association Get With the Guidelines-Heart Failure (GWTG-HF) program data. A cohort of 39 783 patients admitted January 1, 2005, to June 26, 2007, to 198 hospitals participating in GWTG-HF was divided into derivation (70%, n=27 850) and validation (30%, n=11 933) samples. Multivariable logistic regression identified predictors of in-hospital mortality in the derivation sample from candidate demographic, medical history, and laboratory variables collected at admission. In-hospital mortality rate was 2.86% (n=1139). Age, systolic blood pressure, blood urea nitrogen, heart rate, sodium, chronic obstructive pulmonary disease, and nonblack race were predictive of in-hospital mortality. The model had good discrimination in the derivation and validation datasets (c-index, 0.75 in each). Effect estimates from the entire sample were used to generate a mortality risk score. The predicted probability of in-hospital mortality varied more than 24-fold across deciles (range, 0.4% to 9.7%) and corresponded with observed mortality rates. The model had the same operating characteristics among those with preserved and impaired left ventricular systolic function. The morality risk score can be calculated on the Web-based calculator available with the GWTG-HF data entry tool. The GWTG-HF risk score uses commonly available clinical variables to predict in-hospital mortality and provides clinicians with a validated tool for risk stratification that is applicable to a broad spectrum of patients with heart failure, including those with preserved left ventricular systolic function.
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                Author and article information

                Contributors
                jacopo.lenzi2@unibo.it
                vera.avaldi@gmail.com
                boussard@stanford.edu
                carlo.descovich@ausl.bo.it
                ilaria.castaldini@ausl.bologna.it
                stefano.urbinati@ausl.bologna.it
                giuseppe.dipasquale@ausl.bologna.it
                paola.rucci2@unibo.it
                0039 051 20 94 836 , mariapia.fantini@unibo.it
                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central (London )
                1472-6963
                6 September 2016
                6 September 2016
                2016
                : 16
                : 1
                : 473
                Affiliations
                [1 ]Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum – University of Bologna, via San Giacomo 12, 40126 Bologna, Italy
                [2 ]Department of Surgery, Stanford University, 300 Pasteur Drive, Stanford, CA 94305-2200 USA
                [3 ]Department of Clinical Governance, Bologna Local Healthcare Authority, via Castiglione 29, 40124 Bologna, Italy
                [4 ]Department of Programming and Control, Bologna Local Healthcare Authority, via Castiglione 29, 40124 Bologna, Italy
                [5 ]Department of Cardiology, Bellaria Hospital, via Altura 3, 40139 Bologna, Italy
                [6 ]Department of Cardiology, Maggiore Hospital, Largo Nigrisoli 2, 40133 Bologna, Italy
                Article
                1731
                10.1186/s12913-016-1731-9
                5012069
                27600617
                477991ca-1e9f-4e56-ab66-8e202231c4be
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 25 November 2015
                : 31 August 2016
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Health & Social care
                heart failure,risk-adjustment,mortality,readmissions
                Health & Social care
                heart failure, risk-adjustment, mortality, readmissions

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