Readmission after hospitalization for heart failure among patients with chronic kidney disease

a prediction model.

Robert M. Perkins, Amir Rahman, Ion D. Bucaloiu, Evan Norfolk, William DiFilippo, James E. Hartle, H. Lester Kirchner

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

30-day readmission rates after hospitalization for heart failure (HF) approach 25%, and patients with chronic kidney disease (CKD) are disproportionately represented. A retrospective cohort study was conducted to develop a prediction tool for 30-day readmission after hospitalization for HF among those with non-dialysis dependent CKD. Geisinger primary care patients with Stage 3 - 5 CKD hospitalized with a primary discharge diagnosis of HF during the period July 1, 2004 through February 28, 2010 were eligible. Multivariate logistic regression was employed to build models from predictors of 30-day readmission, drawn from demographic, clinical, laboratory, and pharmaceutical variables in the electronic health record. Variables were manually removed to achieve a model with satisfactory goodness-of-fit and parsimony while maximizing area under the receiver operating characteristic curve (AUC). Internal validation was performed using the bootstrap resampling method (1,000 samples) to provide a bias-corrected AUC. 607 patients with CKD were admitted for HF during the study period; 116 (19.1%) were readmitted within 30 days. A model incorporating 23 variables across domains of medical history, active outpatient pharmaceuticals, vital signs, laboratory tests, and recent inpatient and outpatient resource utilization yielded an AUC (95% CI) of 0.792 (0.746 - 0.838). The bias-corrected AUC was 0.743. At an estimated readmission probability of 20%, the model correctly classified readmission status for 73% of the population, with a sensitivity of 69% and a specificity of 73%. A robust electronic health record may facilitate the identification of CKD patients at risk for readmission after hospitalization for HF.

Original languageEnglish (US)
Pages (from-to)433-440
Number of pages8
JournalClinical Nephrology
Volume80
Issue number6
StatePublished - Dec 2013
Externally publishedYes

Fingerprint

Chronic Renal Insufficiency
Hospitalization
Heart Failure
Area Under Curve
Electronic Health Records
Outpatients
Vital Signs
ROC Curve
Pharmaceutical Preparations
Inpatients
Primary Health Care
Cohort Studies
Retrospective Studies
Logistic Models
Demography
Population

ASJC Scopus subject areas

  • Nephrology
  • Medicine(all)

Cite this

Perkins, R. M., Rahman, A., Bucaloiu, I. D., Norfolk, E., DiFilippo, W., Hartle, J. E., & Kirchner, H. L. (2013). Readmission after hospitalization for heart failure among patients with chronic kidney disease: a prediction model. Clinical Nephrology, 80(6), 433-440.

Readmission after hospitalization for heart failure among patients with chronic kidney disease : a prediction model. / Perkins, Robert M.; Rahman, Amir; Bucaloiu, Ion D.; Norfolk, Evan; DiFilippo, William; Hartle, James E.; Kirchner, H. Lester.

In: Clinical Nephrology, Vol. 80, No. 6, 12.2013, p. 433-440.

Research output: Contribution to journalArticle

Perkins, RM, Rahman, A, Bucaloiu, ID, Norfolk, E, DiFilippo, W, Hartle, JE & Kirchner, HL 2013, 'Readmission after hospitalization for heart failure among patients with chronic kidney disease: a prediction model.', Clinical Nephrology, vol. 80, no. 6, pp. 433-440.
Perkins RM, Rahman A, Bucaloiu ID, Norfolk E, DiFilippo W, Hartle JE et al. Readmission after hospitalization for heart failure among patients with chronic kidney disease: a prediction model. Clinical Nephrology. 2013 Dec;80(6):433-440.
Perkins, Robert M. ; Rahman, Amir ; Bucaloiu, Ion D. ; Norfolk, Evan ; DiFilippo, William ; Hartle, James E. ; Kirchner, H. Lester. / Readmission after hospitalization for heart failure among patients with chronic kidney disease : a prediction model. In: Clinical Nephrology. 2013 ; Vol. 80, No. 6. pp. 433-440.
@article{65a907c0dcf1420a9beaf7a416f3a75e,
title = "Readmission after hospitalization for heart failure among patients with chronic kidney disease: a prediction model.",
abstract = "30-day readmission rates after hospitalization for heart failure (HF) approach 25{\%}, and patients with chronic kidney disease (CKD) are disproportionately represented. A retrospective cohort study was conducted to develop a prediction tool for 30-day readmission after hospitalization for HF among those with non-dialysis dependent CKD. Geisinger primary care patients with Stage 3 - 5 CKD hospitalized with a primary discharge diagnosis of HF during the period July 1, 2004 through February 28, 2010 were eligible. Multivariate logistic regression was employed to build models from predictors of 30-day readmission, drawn from demographic, clinical, laboratory, and pharmaceutical variables in the electronic health record. Variables were manually removed to achieve a model with satisfactory goodness-of-fit and parsimony while maximizing area under the receiver operating characteristic curve (AUC). Internal validation was performed using the bootstrap resampling method (1,000 samples) to provide a bias-corrected AUC. 607 patients with CKD were admitted for HF during the study period; 116 (19.1{\%}) were readmitted within 30 days. A model incorporating 23 variables across domains of medical history, active outpatient pharmaceuticals, vital signs, laboratory tests, and recent inpatient and outpatient resource utilization yielded an AUC (95{\%} CI) of 0.792 (0.746 - 0.838). The bias-corrected AUC was 0.743. At an estimated readmission probability of 20{\%}, the model correctly classified readmission status for 73{\%} of the population, with a sensitivity of 69{\%} and a specificity of 73{\%}. A robust electronic health record may facilitate the identification of CKD patients at risk for readmission after hospitalization for HF.",
author = "Perkins, {Robert M.} and Amir Rahman and Bucaloiu, {Ion D.} and Evan Norfolk and William DiFilippo and Hartle, {James E.} and Kirchner, {H. Lester}",
year = "2013",
month = "12",
language = "English (US)",
volume = "80",
pages = "433--440",
journal = "Clinical Nephrology",
issn = "0301-0430",
publisher = "Dustri-Verlag Dr. Karl Feistle",
number = "6",

}

TY - JOUR

T1 - Readmission after hospitalization for heart failure among patients with chronic kidney disease

T2 - a prediction model.

AU - Perkins, Robert M.

AU - Rahman, Amir

AU - Bucaloiu, Ion D.

AU - Norfolk, Evan

AU - DiFilippo, William

AU - Hartle, James E.

AU - Kirchner, H. Lester

PY - 2013/12

Y1 - 2013/12

N2 - 30-day readmission rates after hospitalization for heart failure (HF) approach 25%, and patients with chronic kidney disease (CKD) are disproportionately represented. A retrospective cohort study was conducted to develop a prediction tool for 30-day readmission after hospitalization for HF among those with non-dialysis dependent CKD. Geisinger primary care patients with Stage 3 - 5 CKD hospitalized with a primary discharge diagnosis of HF during the period July 1, 2004 through February 28, 2010 were eligible. Multivariate logistic regression was employed to build models from predictors of 30-day readmission, drawn from demographic, clinical, laboratory, and pharmaceutical variables in the electronic health record. Variables were manually removed to achieve a model with satisfactory goodness-of-fit and parsimony while maximizing area under the receiver operating characteristic curve (AUC). Internal validation was performed using the bootstrap resampling method (1,000 samples) to provide a bias-corrected AUC. 607 patients with CKD were admitted for HF during the study period; 116 (19.1%) were readmitted within 30 days. A model incorporating 23 variables across domains of medical history, active outpatient pharmaceuticals, vital signs, laboratory tests, and recent inpatient and outpatient resource utilization yielded an AUC (95% CI) of 0.792 (0.746 - 0.838). The bias-corrected AUC was 0.743. At an estimated readmission probability of 20%, the model correctly classified readmission status for 73% of the population, with a sensitivity of 69% and a specificity of 73%. A robust electronic health record may facilitate the identification of CKD patients at risk for readmission after hospitalization for HF.

AB - 30-day readmission rates after hospitalization for heart failure (HF) approach 25%, and patients with chronic kidney disease (CKD) are disproportionately represented. A retrospective cohort study was conducted to develop a prediction tool for 30-day readmission after hospitalization for HF among those with non-dialysis dependent CKD. Geisinger primary care patients with Stage 3 - 5 CKD hospitalized with a primary discharge diagnosis of HF during the period July 1, 2004 through February 28, 2010 were eligible. Multivariate logistic regression was employed to build models from predictors of 30-day readmission, drawn from demographic, clinical, laboratory, and pharmaceutical variables in the electronic health record. Variables were manually removed to achieve a model with satisfactory goodness-of-fit and parsimony while maximizing area under the receiver operating characteristic curve (AUC). Internal validation was performed using the bootstrap resampling method (1,000 samples) to provide a bias-corrected AUC. 607 patients with CKD were admitted for HF during the study period; 116 (19.1%) were readmitted within 30 days. A model incorporating 23 variables across domains of medical history, active outpatient pharmaceuticals, vital signs, laboratory tests, and recent inpatient and outpatient resource utilization yielded an AUC (95% CI) of 0.792 (0.746 - 0.838). The bias-corrected AUC was 0.743. At an estimated readmission probability of 20%, the model correctly classified readmission status for 73% of the population, with a sensitivity of 69% and a specificity of 73%. A robust electronic health record may facilitate the identification of CKD patients at risk for readmission after hospitalization for HF.

UR - http://www.scopus.com/inward/record.url?scp=84896589048&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84896589048&partnerID=8YFLogxK

M3 - Article

VL - 80

SP - 433

EP - 440

JO - Clinical Nephrology

JF - Clinical Nephrology

SN - 0301-0430

IS - 6

ER -