Relation of Acute Heart Failure Hospital Length of Stay to Subsequent Readmission and All-Cause Mortality Kristi Reynolds, PhDa,*, Melissa G. Butler, PharmD, MPH, PhDb, Teresa M. Kimes, MSc, A. Gabriela Rosales, MSc, Wing Chan, MSd, and Gregory A. Nichols, PhDc Heart failure (HF) hospitalization length of stay (LOS) has been associated with the risk of subsequent readmission and mortality. We identified 19,927 hospitalized patients with HF who were discharged alive from 2008 to 2011 from 3 Kaiser Permanente regions. In adjusted Cox models using LOS 3 to 4 days as the reference category, shorter LOS was not significantly associated with hospital readmissions. LOS of 5 to 10 days was associated with 17% greater risk of readmission within 30 days (hazard ratio [HR] 1.17, 95% confidence interval [CI] 1.07 to 1.28) and 9% greater risk within 1 year (HR 1.09, 95% CI 1.03 to 1.15). LOS of 11 to 29 days was associated with increased readmission risk of 52% at 30 days (HR 1.52, 95% CI 1.30 to 1.76) and 25% at 1 year (HR 1.25, 95% CI 1.16 to 1.35). Mortality risk within 30 days among those with LOS of 1 day was 47% lower (HR 0.53, 95% CI 0.43 to 0.65) and 32% lower at 1 year (HR 0.68, 95% CI 0.62 to 0.74). LOS of 2 days was associated with lower mortality risk of 17% (HR 0.83, 95% CI 0.76 to 0.90) at 1 year. At LOS 5 to 10 days, 30-day and 1-year risk of mortality was increased by 52% (HR 1.52, 95% CI 1.30 to 1.76) and 25% (HR 1.25, 95% CI 1.16 to 1.35), respectively. LOS of 11 to 29 days was associated with 171% higher mortality risk at 30 days (HR 2.71, 95% CI 2.19 to 3.35) and 73% at 1 year (HR 1.73, 95% CI 1.53 to 1.97). Longer LOS during the index HF hospitalization was associated with readmission and mortality within 30 days and 1 year independent of co-morbidities and cardiovascular risk factors. These results suggest that LOS may be a proxy for the severity of HF during the index hospitalization. Ó 2015 Elsevier Inc. All rights reserved. (Am J Cardiol 2015;116:400e405) Heart failure (HF) is a complex, progressive condition with significant and growing public health and economic burdens. Approximately 20% of US adults will develop HF during their lifetime.1 By 2030, the total cost of HF is projected to increase markedly by 127% to $70 billion.2 Much of the cost of HF care is because of hospitalizations and readmissions.3 Nearly 20% of patients discharged with a principle diagnosis of HF are readmitted within 30 days,4 whereas 44% are readmitted for any cause within 6 months.5 A longer length of stay (LOS) during an initial HF hospitalization has been associated with risk of poor outcomes, and it has also been suggested as a predictor of readmission.6e8 However, reducing LOS in patients with more severe HF may not reduce risk of readmission or mortality.9 Therefore, it is important to understand whether LOS is independently associated with hospital readmissions or mortality or is a marker for clinical characteristics that have not been included in previous analyses. We evaluated the importance of HF hospitalization LOS as a predictor of hospital readmission and all-cause mortality within 30 days and 1 year after
a Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California; bKaiser Permanente Center for Clinical and Outcomes Research, Atlanta, Georgia; cKaiser Permanente Center for Health Research, Portland, Oregon; and dNovartis Pharmaceuticals Corporation, East Hanover, New Jersey. Manuscript received February 5, 2015; revised manuscript received and accepted April 16, 2015. See page 404 for disclosure information. *Corresponding author: Tel: (626) 564-5103; fax: (626) 564-3403. E-mail address: [email protected] (K. Reynolds).
0002-9149/15/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.amjcard.2015.04.052
controlling for variables extracted from patient electronic medical records (EMRs) in a contemporary cohort of patients from an integrated health system operating in 3 distinct regions of the United States. Methods The retrospective cohort used in this study comprised members from Kaiser Permanente Southern California, serving approximately 3.6 million subjects of Southern California; Kaiser Permanente Northwest (KPNW), serving approximately 480,000 subjects in the Portland, Oregon, service area; and Kaiser Permanente Georgia, serving approximately 250,000 subjects in the Atlanta, Georgia, metropolitan area. These geographically diverse regions provide care to an ethnically and socioeconomically diverse population. Data on the medical care patients receive are captured through structured administrative and clinical databases and EMRs at each region. A Virtual Data Warehouse (VDW) at each site served as a distributed standardized data resource. The VDW comprised electronic data sets, populated with linked information on demographics, administrative, pharmacy, laboratory, and health care utilization data (including diagnoses and procedures from ambulatory visits and network and non-network hospitalizations).10 This study was approved by the KPNW Institutional Review Board, and the Kaiser Permanente Southern California and Kaiser Permanente Georgia sites ceded oversight to the KPNW Institutional Review Board. A waiver of informed consent was obtained because of the observational nature of the study. www.ajconline.org
Heart Failure/Heart Failure Hospital Length of Stay Table 1 Baseline characteristics of patients hospitalized with heart failure Variable Number of patients Mean LOS of Index Hospitalization LOS > 7 Days Age (years) Female Hispanic African-American Body Mass Index (kg/m2) Current Smoker Systolic Blood Pressure (mm Hg) Diastolic Blood Pressure (mm Hg) Heart Rate (beats per minute) ACEIs or ARBs b-Blockers Diuretics Digoxin Insulin Statins Metformin Sulfonylureas Calcium Channel Blockers Existing Heart Failure Coronary Artery Disease Arrhythymia Stroke Diabetes Hypertension Chronic Kidney Disease Depression eGFR (mL/min/1.73m2) LDL Cholesterol (mg/dL) Sodium (mmol/L) Hemoglobin Hemoglobin A1c (%) Fasting Glucose (mg/dL) B-Type Natriuretic Peptide (pg/mL) Troponin (ng/mL) C-Reactive Protein (mg/dL)
Mean (SD) or %
% missing
19,927 3.8 (4.8) 12.7% 73.9 (13.1) 47.1% 17.0% 15.3% 30 (8) 8.0% 130 (16) 69 (10) 75 (13) 46.2% 50.2% 47.1% 10.1% 15.7% 47.0% 9.7% 15.3% 26.6% 57.1% 45.4% 47.7% 8.7% 46.8% 85.7% 62.3% 23.3% 59 (25) 92 (33) 139 (3) 12.6 (1.7) 7.2 (1.5) 119 (45) 758 (816) 0.10 (.28) 2.1 (3.0)
– 0 0 0 0 0 0 6.4% 0 3.6% 3.6% 3.7% 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14.2% 22.4% 20.1% 24.9% 49.6% 64.0% 84.5% 95.8% 97.9%
ACEIs ¼ angiotensin converting enzyme inhibitors; ARBs ¼ angiotensin receptor blockers; eGFR ¼ estimated glomerular filtration rate; LOS ¼ length of stay.
We identified all members aged 18 years of these health systems who had a hospitalization with a primary diagnosis of HF (International Classification of Diseases, Ninth Edition, Clinical Modification [ICD-9-CM], 428.xx) and were discharged alive from January 1, 2008, to December 31, 2011, with no previous hospitalization for HF in the preceding 12 months. Patients with 30 days to avoid undue influence of these uncommon cases on the overall results. Baseline patient demographic characteristics, medical history, and clinical data were extracted from the VDW during the index hospitalization or within 12 months before the index hospitalization if not available during the hospitalization. Demographic variables included age, gender, and race/ethnicity. We ascertained risk factors for HF including ambulatory systolic and diastolic blood pressures, body mass
401
index, smoking status, LDL cholesterol, and heart rate. History of HF (ICD-9-CM 428.xx) and co-morbidities including coronary heart disease (ICD-9-CM 410.x to 414.x), arrhythmia (ICD-9-CM 427.x), stroke (ICD-9-CM code 430.x to 432.x and 434.x to 436.x), diabetes mellitus (ICD-9-CM 250.xx), hypertension (ICD-9 code 401.x to 405.x), chronic kidney disease (ICD-9-CM 585.x), and depression (ICD-9-CM code 296.2 to 296.8 and 311) were extracted from inpatient EMR, outpatient EMR, and claims diagnoses. We characterized baseline kidney function using outpatient serum creatinine concentration values and estimated glomerular filtration rate using the Modification Diet in Renal Disease equation.11 We also collected other laboratory measures particularly relevant to HF, including sodium, hemoglobin, and B-type natriuretic peptide and HbA1c, fasting glucose, troponin, and C-reactive protein (CRP). Baseline exposure to relevant cardiovascular prescription medications including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, diuretics, b blockers, calcium channel blockers, digoxin, and diabetic medications were extracted from ambulatory pharmacy records. We initially evaluated 3 study outcomes: (1) hospital readmission with a primary diagnosis of HF, (2) hospital readmission for any reason, and (3) all-cause mortality, each over 30 days, 6 months, and 1 year after the index hospitalization. However, all associations between LOS and hospital readmission with a primary diagnosis of HF were nonsignificant. Furthermore, results of 6-month and 1-year time periods for all outcomes were nearly identical. Therefore, we focused on the following 4 outcomes: (1) hospital readmission for any reason within 30 days; (2) hospital readmission for any reason within 1 year; (3) all-cause mortality within 30 days; and (4) all-cause mortality within 1 year. Deaths were identified from health plan databases, state death registries, and Social Security Administration Death Master files. Baseline patient characteristics are described in terms of means and SDs for continuous variables and percentages for categorical variables. We plotted cumulative incidence functions (CIF ¼ 1 Kaplan-Meier) to calculate unadjusted hospital readmission and all-cause mortality rates. For hospital readmission, the estimated CIF was created using the SAS macro %CIF that implements appropriate nonparametric methods for estimating CIF and accounts for the competing risk of mortality.12 The primary variable of interest was LOS (defined as the difference between the discharge and admission dates) during the index hospitalization for HF. Preliminary analyses indicated that the relation between LOS and all outcomes was nonlinear, and inclusion of a quadratic term was not statistically significant. Therefore, we created 5 categories of LOS: 1, 2, 3 to 4, 5 to 10, and 11 to 30 days. To isolate the impact of LOS on the outcomes, we constructed a series of Cox proportional hazards models of the association between LOS categories and risk of readmission and all-cause mortality within 30 days and 1 year, using 3 to 4 days as the reference category. We evaluated the univariate associations between patient characteristics and outcomes. In multivariable analyses, the initial model (model A) included adjustment for variables
402
The American Journal of Cardiology (www.ajconline.org)
Table 2 Prevalence (95% CI) of hospital readmission and all-cause mortality at 30 Days and 1 Year, overall and by categories of length of stay of the index hospitalization All-Cause Readmission 30 Days All Patients (n¼19,927) Index Length of Stay (days): 1 (n¼4,457) 2 (n¼4,872) 3-4 (n¼5,748) 5-10 (n¼4,064) 11-30 (n¼786)
All-Cause Mortality 1 Year
30 Days
1 Year
20.8% (20.2 - 21.4)
60.0% (59.3 - 60.6)
6.3% (6.0 - 6.7)
25.7% (25.1 - 26.3)
19.4% (18.2 - 20.5) 19.1% (18.0 - 20.2) 20.5% (19.5 - 21.6) 23.3% (22.0 - 24.6) 28.9% (25.7 - 32.1)
60.9% (59.5 - 62.4) 59.9% (58.5 - 61.3) 59.0% (57.7 - 60.3) 59.7% (58.2 - 61.3) 62.5% (59.0 - 65.8)
3.1% (2.7 - 3.7) 5.2% (4.6 - 5.9) 6.2% (5.6 - 6.9) 9.4% (8.5 - 10.3) 15.6% (13.1 - 18.2)
19.0% (17.9 - 20.2) 22.9% (21.7 - 24.1) 27.2% (26.0 - 28.4) 31.6% (30.1 - 33.0) 39.4% (35.9 - 42.9)
Figure 1. Cumulative incidence of hospital readmission for any reason, accounting for mortality as a competing risk. Shaded areas represent 95% confidence intervals.
that are easily obtainable at any given clinic encounter including age, gender, race, Hispanic ethnicity, body mass index, current cigarette smoking, blood pressure, and heart rate. The subsequent model (model B) added use of pharmaceutical agents for the treatment of cardiovascular diseases and diabetes including ACE inhibitors, angiotensin receptor blockers, b blockers, diuretics, digoxin, calcium channel blockers, statins, metformin, sulfonylureas, and insulin and the presence of co-morbidities including existing HF, coronary artery disease, arrhythmia, stroke diabetes, hypertension, chronic kidney disease, and depression. The final model (model C) added routinely measured laboratory data including estimated glomerular filtration rate, LDL cholesterol, sodium, and hemoglobin. Laboratory variables with >25% of the data missing (hemoglobin A1c, glucose, B-type natriuretic peptide, troponin, and CRP) were not considered routinely measured and were not included in the final model. All analyses were conducted using SAS software, version 9.2 (SAS Institute, Cary, North Carolina).
Figure 2. Cumulative incidence of all-cause mortality. Shaded areas represent 95% confidence intervals.
Results Baseline characteristics of the 19,927 patients with a hospital discharge for HF are presented in Table 1. Risk factors for HF (blood pressure, heart rate, LDL cholesterol, and cigarette smoking) were well controlled. Nearly 50% of the cohort was prescribed medications for cardiovascular diseases. Co-morbidity burden was high and 57% had a preexisting diagnosis of HF. Use of potentially predictive laboratory results (HbA1c, fasting glucose, B-type natriuretic peptide, troponin, and CRP) was infrequent in this population. Within 30 days, 21% of patients had a hospital readmission and 60% had a readmission within 1 year (Table 2, Figure 1). Within 30 days, 6% of patients had died and by 1 year, 25% of patients had died (Table 2, Figure 2). In models A and B, using LOS 3 to 4 days as the reference category and adjusting for mortality as a competing risk, LOS of 1 or 2 days was not significantly associated with hospital readmission within 30 days or
Heart Failure/Heart Failure Hospital Length of Stay
403
Figure 3. Hazard ratios with 95% error bars for index length of stay categories for readmissions at 30 days (A) and 1 year (B) and all-cause mortality at 30 days (C) and 1 year (D). Hazards are adjusted using model A and model B variables. Inclusion of model C variables did not change the size or significance of any displayed associations.
1 year (Figure 3). However, LOS >5 days was associated with a greater risk of readmission within 30 days and within 1 year compared with LOS of 3 to 4 days. The results were similar for model C, which included the addition of routinely measured laboratory results. All associations between LOS categories and risk of mortality at 30 days and 1 year were statistically significant in models A and B (Figure 3). Compared with LOS of 3 to 4 days, risk of mortality was lower at 30 days and at 1 year in those with LOS of 1 or 2 days but higher in those with LOS >5 days. The results were similar for model C, which included the addition of routinely measured laboratory results. Discussion In this diverse cohort of 19,927 patients with HF from an integrated health system, hospital readmission and mortality rates were high (30-day and 1-year readmission was 21% and 60%, respectively, and 30-day and 1-year mortality was 6% and 25%, respectively). Furthermore, longer LOS of the initial hospitalization for HF was modestly associated with risk of hospital readmission and more strongly associated with all-cause mortality within 30 days and 1 year. These
findings were robust across the modeling strategy; all models for all 4 outcomes performed similarly regardless of the variables included for adjustment. Given that the inclusion of covariates obtainable from the EMR had little effect on the associations, these results suggest that LOS is an independent predictor of 30-day and 1-year hospital readmission and mortality and may be a proxy for the severity of HF during the index hospitalization. Others have reported a longer LOS in patients with more severe HF and co-morbidities.13e15 Of 1,009 hospitalized patients with HF, worsening renal function occurred frequently and LOS >10 days increased threefold.13 Formiga et al14 found that women and patients with worse NYHA functional class at hospital admission were more likely to have LOS >4 days. With our large sample size of nearly 20,000 patients with HF and the comprehensive capture of co-morbidities in the EMRs, our findings strengthen the notion that LOS may be a useful proxy for severity of HF and complicated patients with multiple co-morbidities, especially when predicting future mortality. In the United States, 30-day readmission and mortality rates vary widely across hospitals, ranging from 10% to >50%.16 Overall, the readmission rates we report (21%
404
The American Journal of Cardiology (www.ajconline.org)
within 30 days and 60% within 1 year) are not only consistent with previous studies17e19 but also vary widely depending on the LOS of the initial hospitalization, with higher readmission rates when stays are 5 to 10 days and much higher rates for even longer stays. However, our results do not imply that readmission rates could be reduced by shortening HF hospitalizations unless this can be achieved through more aggressive and effective therapies that reduce the severity of HF in the hospital, thus allowing for earlier discharge. The all-cause mortality rates we report are also in line with previous studies,19e21 but again varied substantially by LOS of the initial hospitalization. Indeed, LOS shorter than 3 to 4 days was associated with lower mortality and the association between LOS of 5 to 10 days and 11 days was remarkably strong, especially for 30-day mortality. These results suggest that LOS is a clear surrogate for HF severity. In any case, because our focus was on outcomes after an HF hospitalization, we did not include patients who died during their initial hospital stay. These patients clearly had the most severe cases of HF, so arguing that LOS is a marker for severity that only applies to patients who survive the initial hospitalization. There are several potential limitations to this study. Kaiser Permanente is an integrated system of insured subjects with substantial health information technology support. Therefore, our results may not be fully representative of the general population. However, the geographic and demographic diversity represented across the 3 health plans and the population-based nature of health care suggests that our findings are likely to be generalizable to patients with HF in real-world clinical settings. Furthermore, our results were largely unaffected by covariates, suggesting that the relation between HF LOS and outcomes is independent. Nevertheless, we used observational data available in the EMR, and capture of potentially important measures was incomplete. It is, therefore, possible that inclusion of unmeasured covariates could have altered our findings. The findings from this large and racially/ethnically diverse cohort have important clinical implications. Preventing readmission and mortality after hospitalization for HF is complex, and rates of readmission have remained relatively unchanged in recent years.22,23 Given that hospitals in the United States struggle to contain readmission rates and hospitals with excess readmissions are now financially penalized, identifying potential targets for intervention are essential. Our results suggest that the initial hospital LOS may be a proxy for the severity of HF. Therefore, identifying key social factors early and developing a patient-focused plan, including self-management education and care coordination efforts, before the patient is discharged would likely be very beneficial. Future studies should examine the effect of aggressive management strategies aimed at reducing HF severity on LOS and readmission and mortality. Acknowledgment: Concept and design were performed by KR, MGB, WC, and GAN. Data were collected and analyzed by Kimes with interpretation from KR, MGB, GR, WC, and GAN. The manuscript was written by KR and GAN and revised by MGB, TMK, GR, and WC.
Disclosures This study was funded by a contractual agreement between the Kaiser Permanente Center for Health Research, Portland, Oregon, and Novartis Pharmaceuticals Corporation, East Hanover, New Jersey. GAN has received grant funding from Novartis Pharmaceuticals, Merck & Co., Inc., AstraZeneca, Boehringer-Ingelheim, and Incyte Corporation. KR has received grant funding from Amgen Inc., Novartis Pharmaceuticals, Merck & Co., Inc., and AstraZeneca. MGB, TMK, and AGR report no disclosures. WC is an employee of Novartis Pharmaceuticals Corporation. 1. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, Dai S, Ford ES, Fox CS, Franco S, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Huffman MD, Judd SE, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Mackey RH, Magid DJ, Marcus GM, Marelli A, Matchar DB, McGuire DK, Mohler ER III, Moy CS, Mussolino ME, Neumar RW, Nichol G, Pandey DK, Paynter NP, Reeves MJ, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Wong ND, Woo D, Turner MB; American Heart Association Statistics Committee, Stroke Statistics Subcommittee. Heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation 2014;129:e28ee292. 2. Heidenreich PA, Albert NM, Allen LA, Bluemke DA, Butler J, Fonarow GC, Ikonomidis JS, Khavjou O, Konstam MA, Maddox TM, Nichol G, Pham M, Pina IL, Trogdon JG; American Heart Association Advocacy Coordinating Committee; Council on Arteriosclerosis Thrombosis and Vascular Biology; Council on Cardiovascular Radiology and Intervention; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Stroke Council. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail 2013;6:606e619. 3. Hunt SA, Abraham WT, Chin MH, Feldman AM, Francis GS, Ganiats TG, Jessup M, Konstam MA, Mancini DM, Michl K, Oates JA, Rahko PS, Silver MA, Stevenson LW, Yancy CW; American College of Cardiology Foundation, American Heart Association. 2009 focused update incorporated into the ACC/AHA 2005 Guidelines for the diagnosis and management of heart failure in adults a report of the American College of Cardiology Foundation/American heart association Task Force on Practice Guidelines Developed in Collaboration with the International Society for Heart and Lung Transplantation. J Am Coll Cardiol 2009;53:e1ee90. 4. Au AG, McAlister FA, Bakal JA, Ezekowitz J, Kaul P, van Walraven C. Predicting the risk of unplanned readmission or death within 30 days of discharge after a heart failure hospitalization. Am Heart J 2012;164: 365e372. 5. Krumholz HM, Parent EM, Tu N, Vaccarino V, Wang Y, Radford MJ, Hennen J. Readmission after hospitalization for congestive heart failure among Medicare beneficiaries. Arch Intern Med 1997;157:99e104. 6. Zaya M, Phan A, Schwarz ER. Predictors of re-hospitalization in patients with chronic heart failure. World J Cardiol 2012;4:23e30. 7. Tsuchihashi M, Tsutsui H, Kodama K, Kasagi F, Setoguchi S, Mohr M, Kubota T, Takeshita A. Medical and socioenvironmental predictors of hospital readmission in patients with congestive heart failure. Am Heart J 2001;142:E7. 8. Solomon SD, Dobson J, Pocock S, Skali H, McMurray JJ, Granger CB, Yusuf S, Swedberg K, Young JB, Michelson EL, Pfeffer MA; Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity (CHARM) Investigators. Influence of nonfatal hospitalization for heart failure on subsequent mortality in patients with chronic heart failure. Circulation 2007;116:1482e1487. 9. Foraker RE, Rose KM, Chang PP, Suchindran CM, McNeill AM, Rosamond WD. Hospital length of stay for incident heart failure: Atherosclerosis Risk in Communities (ARIC) cohort: 1987-2005. J Healthc Qual 2014;36:45e51. 10. Ross TR, Ng D, Brown JS, Pardee R, Hornbrook MC, Hart G, Steiner JF. The HMO research network virtual data warehouse: a public data model to support collaboration. EGEMS 2014;2:1049; Article 2. 11. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999;130:461e470.
Heart Failure/Heart Failure Hospital Length of Stay 12. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999;94:496e509. 13. Forman DE, Butler J, Wang Y, Abraham WT, O’Connor CM, Gottlieb SS, Loh E, Massie BM, Rich MW, Stevenson LW, Young JB, Krumholz HM. Incidence, predictors at admission, and impact of worsening renal function among patients hospitalized with heart failure. J Am Coll Cardiol 2004;43:61e67. 14. Formiga F, Chivite D, Manito N, Mestre AR, Llopis F, Pujol R. Admission characteristics predicting longer length of stay among elderly patients hospitalized for decompensated heart failure. Eur J Intern Med 2008;19:198e202. 15. Krumholz HM, Chen YT, Bradford WD, Cerese J. Variations in and correlates of length of stay in academic hospitals among patients with heart failure resulting from systolic dysfunction. Am J Manag Care 1999;5:715e723. 16. Joynt KE, Jha AK. Who has higher readmission rates for heart failure, and why? Implications for efforts to improve care using financial incentives. Circ Cardiovasc Qual Outcomes 2011;4:53e59. 17. Bueno H, Ross JS, Wang Y, Chen J, Vidan MT, Normand SL, Curtis JP, Drye EE, Lichtman JH, Keenan PS, Kosiborod M, Krumholz HM. Trends in length of stay and short-term outcomes among Medicare patients hospitalized for heart failure, 1993-2006. JAMA 2010;303:2141e2147. 18. Dharmarajan K, Hsieh AF, Lin Z, Bueno H, Ross JS, Horwitz LI, Barreto-Filho JA, Kim N, Bernheim SM, Suter LG, Drye EE,
19.
20.
21.
22. 23.
405
Krumholz HM. Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA 2013;309:355e363. Hernandez AF, Greiner MA, Fonarow GC, Hammill BG, Heidenreich PA, Yancy CW, Peterson ED, Curtis LH. Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA 2010;303: 1716e1722. Foraker RE, Rose KM, Suchindran CM, Chang PP, McNeill AM, Rosamond WD. Socioeconomic status, Medicaid coverage, clinical comorbidity, and rehospitalization or death after an incident heart failure hospitalization: Atherosclerosis Risk in Communities Cohort (1987 to 2004). Circ Heart Fail 2011;4:308e316. Krumholz HM, Merrill AR, Schone EM, Schreiner GC, Chen J, Bradley EH, Wang Y, Wang Y, Lin Z, Straube BM, Rapp MT, Normand SL, Drye EE. Patterns of hospital performance in acute myocardial infarction and heart failure 30-day mortality and readmission. Circ Cardiovasc Qual Outcomes 2009;2:407e413. Chen J, Normand SL, Wang Y, Krumholz HM. National and regional trends in heart failure hospitalization and mortality rates for Medicare beneficiaries, 1998-2008. JAMA 2011;306:1669e1678. Gheorghiade M, Vaduganathan M, Fonarow GC, Bonow RO. Rehospitalization for heart failure: problems and perspectives. J Am Coll Cardiol 2013;61:391e403.
a Department of Research and Evaluation, Kaiser Permanente Southern California, Pasadena, California; bKaiser Permanente Center for Clinical and Outcomes Research, Atlanta, Georgia; cKaiser Permanente Center for Health Research, Portland, Oregon; and dNovartis Pharmaceuticals Corporation, East Hanover, New Jersey. Manuscript received February 5, 2015; revised manuscript received and accepted April 16, 2015. See page 404 for disclosure information. *Corresponding author: Tel: (626) 564-5103; fax: (626) 564-3403. E-mail address: [email protected] (K. Reynolds).
0002-9149/15/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.amjcard.2015.04.052
controlling for variables extracted from patient electronic medical records (EMRs) in a contemporary cohort of patients from an integrated health system operating in 3 distinct regions of the United States. Methods The retrospective cohort used in this study comprised members from Kaiser Permanente Southern California, serving approximately 3.6 million subjects of Southern California; Kaiser Permanente Northwest (KPNW), serving approximately 480,000 subjects in the Portland, Oregon, service area; and Kaiser Permanente Georgia, serving approximately 250,000 subjects in the Atlanta, Georgia, metropolitan area. These geographically diverse regions provide care to an ethnically and socioeconomically diverse population. Data on the medical care patients receive are captured through structured administrative and clinical databases and EMRs at each region. A Virtual Data Warehouse (VDW) at each site served as a distributed standardized data resource. The VDW comprised electronic data sets, populated with linked information on demographics, administrative, pharmacy, laboratory, and health care utilization data (including diagnoses and procedures from ambulatory visits and network and non-network hospitalizations).10 This study was approved by the KPNW Institutional Review Board, and the Kaiser Permanente Southern California and Kaiser Permanente Georgia sites ceded oversight to the KPNW Institutional Review Board. A waiver of informed consent was obtained because of the observational nature of the study. www.ajconline.org
Heart Failure/Heart Failure Hospital Length of Stay Table 1 Baseline characteristics of patients hospitalized with heart failure Variable Number of patients Mean LOS of Index Hospitalization LOS > 7 Days Age (years) Female Hispanic African-American Body Mass Index (kg/m2) Current Smoker Systolic Blood Pressure (mm Hg) Diastolic Blood Pressure (mm Hg) Heart Rate (beats per minute) ACEIs or ARBs b-Blockers Diuretics Digoxin Insulin Statins Metformin Sulfonylureas Calcium Channel Blockers Existing Heart Failure Coronary Artery Disease Arrhythymia Stroke Diabetes Hypertension Chronic Kidney Disease Depression eGFR (mL/min/1.73m2) LDL Cholesterol (mg/dL) Sodium (mmol/L) Hemoglobin Hemoglobin A1c (%) Fasting Glucose (mg/dL) B-Type Natriuretic Peptide (pg/mL) Troponin (ng/mL) C-Reactive Protein (mg/dL)
Mean (SD) or %
% missing
19,927 3.8 (4.8) 12.7% 73.9 (13.1) 47.1% 17.0% 15.3% 30 (8) 8.0% 130 (16) 69 (10) 75 (13) 46.2% 50.2% 47.1% 10.1% 15.7% 47.0% 9.7% 15.3% 26.6% 57.1% 45.4% 47.7% 8.7% 46.8% 85.7% 62.3% 23.3% 59 (25) 92 (33) 139 (3) 12.6 (1.7) 7.2 (1.5) 119 (45) 758 (816) 0.10 (.28) 2.1 (3.0)
– 0 0 0 0 0 0 6.4% 0 3.6% 3.6% 3.7% 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14.2% 22.4% 20.1% 24.9% 49.6% 64.0% 84.5% 95.8% 97.9%
ACEIs ¼ angiotensin converting enzyme inhibitors; ARBs ¼ angiotensin receptor blockers; eGFR ¼ estimated glomerular filtration rate; LOS ¼ length of stay.
We identified all members aged 18 years of these health systems who had a hospitalization with a primary diagnosis of HF (International Classification of Diseases, Ninth Edition, Clinical Modification [ICD-9-CM], 428.xx) and were discharged alive from January 1, 2008, to December 31, 2011, with no previous hospitalization for HF in the preceding 12 months. Patients with 30 days to avoid undue influence of these uncommon cases on the overall results. Baseline patient demographic characteristics, medical history, and clinical data were extracted from the VDW during the index hospitalization or within 12 months before the index hospitalization if not available during the hospitalization. Demographic variables included age, gender, and race/ethnicity. We ascertained risk factors for HF including ambulatory systolic and diastolic blood pressures, body mass
401
index, smoking status, LDL cholesterol, and heart rate. History of HF (ICD-9-CM 428.xx) and co-morbidities including coronary heart disease (ICD-9-CM 410.x to 414.x), arrhythmia (ICD-9-CM 427.x), stroke (ICD-9-CM code 430.x to 432.x and 434.x to 436.x), diabetes mellitus (ICD-9-CM 250.xx), hypertension (ICD-9 code 401.x to 405.x), chronic kidney disease (ICD-9-CM 585.x), and depression (ICD-9-CM code 296.2 to 296.8 and 311) were extracted from inpatient EMR, outpatient EMR, and claims diagnoses. We characterized baseline kidney function using outpatient serum creatinine concentration values and estimated glomerular filtration rate using the Modification Diet in Renal Disease equation.11 We also collected other laboratory measures particularly relevant to HF, including sodium, hemoglobin, and B-type natriuretic peptide and HbA1c, fasting glucose, troponin, and C-reactive protein (CRP). Baseline exposure to relevant cardiovascular prescription medications including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, diuretics, b blockers, calcium channel blockers, digoxin, and diabetic medications were extracted from ambulatory pharmacy records. We initially evaluated 3 study outcomes: (1) hospital readmission with a primary diagnosis of HF, (2) hospital readmission for any reason, and (3) all-cause mortality, each over 30 days, 6 months, and 1 year after the index hospitalization. However, all associations between LOS and hospital readmission with a primary diagnosis of HF were nonsignificant. Furthermore, results of 6-month and 1-year time periods for all outcomes were nearly identical. Therefore, we focused on the following 4 outcomes: (1) hospital readmission for any reason within 30 days; (2) hospital readmission for any reason within 1 year; (3) all-cause mortality within 30 days; and (4) all-cause mortality within 1 year. Deaths were identified from health plan databases, state death registries, and Social Security Administration Death Master files. Baseline patient characteristics are described in terms of means and SDs for continuous variables and percentages for categorical variables. We plotted cumulative incidence functions (CIF ¼ 1 Kaplan-Meier) to calculate unadjusted hospital readmission and all-cause mortality rates. For hospital readmission, the estimated CIF was created using the SAS macro %CIF that implements appropriate nonparametric methods for estimating CIF and accounts for the competing risk of mortality.12 The primary variable of interest was LOS (defined as the difference between the discharge and admission dates) during the index hospitalization for HF. Preliminary analyses indicated that the relation between LOS and all outcomes was nonlinear, and inclusion of a quadratic term was not statistically significant. Therefore, we created 5 categories of LOS: 1, 2, 3 to 4, 5 to 10, and 11 to 30 days. To isolate the impact of LOS on the outcomes, we constructed a series of Cox proportional hazards models of the association between LOS categories and risk of readmission and all-cause mortality within 30 days and 1 year, using 3 to 4 days as the reference category. We evaluated the univariate associations between patient characteristics and outcomes. In multivariable analyses, the initial model (model A) included adjustment for variables
402
The American Journal of Cardiology (www.ajconline.org)
Table 2 Prevalence (95% CI) of hospital readmission and all-cause mortality at 30 Days and 1 Year, overall and by categories of length of stay of the index hospitalization All-Cause Readmission 30 Days All Patients (n¼19,927) Index Length of Stay (days): 1 (n¼4,457) 2 (n¼4,872) 3-4 (n¼5,748) 5-10 (n¼4,064) 11-30 (n¼786)
All-Cause Mortality 1 Year
30 Days
1 Year
20.8% (20.2 - 21.4)
60.0% (59.3 - 60.6)
6.3% (6.0 - 6.7)
25.7% (25.1 - 26.3)
19.4% (18.2 - 20.5) 19.1% (18.0 - 20.2) 20.5% (19.5 - 21.6) 23.3% (22.0 - 24.6) 28.9% (25.7 - 32.1)
60.9% (59.5 - 62.4) 59.9% (58.5 - 61.3) 59.0% (57.7 - 60.3) 59.7% (58.2 - 61.3) 62.5% (59.0 - 65.8)
3.1% (2.7 - 3.7) 5.2% (4.6 - 5.9) 6.2% (5.6 - 6.9) 9.4% (8.5 - 10.3) 15.6% (13.1 - 18.2)
19.0% (17.9 - 20.2) 22.9% (21.7 - 24.1) 27.2% (26.0 - 28.4) 31.6% (30.1 - 33.0) 39.4% (35.9 - 42.9)
Figure 1. Cumulative incidence of hospital readmission for any reason, accounting for mortality as a competing risk. Shaded areas represent 95% confidence intervals.
that are easily obtainable at any given clinic encounter including age, gender, race, Hispanic ethnicity, body mass index, current cigarette smoking, blood pressure, and heart rate. The subsequent model (model B) added use of pharmaceutical agents for the treatment of cardiovascular diseases and diabetes including ACE inhibitors, angiotensin receptor blockers, b blockers, diuretics, digoxin, calcium channel blockers, statins, metformin, sulfonylureas, and insulin and the presence of co-morbidities including existing HF, coronary artery disease, arrhythmia, stroke diabetes, hypertension, chronic kidney disease, and depression. The final model (model C) added routinely measured laboratory data including estimated glomerular filtration rate, LDL cholesterol, sodium, and hemoglobin. Laboratory variables with >25% of the data missing (hemoglobin A1c, glucose, B-type natriuretic peptide, troponin, and CRP) were not considered routinely measured and were not included in the final model. All analyses were conducted using SAS software, version 9.2 (SAS Institute, Cary, North Carolina).
Figure 2. Cumulative incidence of all-cause mortality. Shaded areas represent 95% confidence intervals.
Results Baseline characteristics of the 19,927 patients with a hospital discharge for HF are presented in Table 1. Risk factors for HF (blood pressure, heart rate, LDL cholesterol, and cigarette smoking) were well controlled. Nearly 50% of the cohort was prescribed medications for cardiovascular diseases. Co-morbidity burden was high and 57% had a preexisting diagnosis of HF. Use of potentially predictive laboratory results (HbA1c, fasting glucose, B-type natriuretic peptide, troponin, and CRP) was infrequent in this population. Within 30 days, 21% of patients had a hospital readmission and 60% had a readmission within 1 year (Table 2, Figure 1). Within 30 days, 6% of patients had died and by 1 year, 25% of patients had died (Table 2, Figure 2). In models A and B, using LOS 3 to 4 days as the reference category and adjusting for mortality as a competing risk, LOS of 1 or 2 days was not significantly associated with hospital readmission within 30 days or
Heart Failure/Heart Failure Hospital Length of Stay
403
Figure 3. Hazard ratios with 95% error bars for index length of stay categories for readmissions at 30 days (A) and 1 year (B) and all-cause mortality at 30 days (C) and 1 year (D). Hazards are adjusted using model A and model B variables. Inclusion of model C variables did not change the size or significance of any displayed associations.
1 year (Figure 3). However, LOS >5 days was associated with a greater risk of readmission within 30 days and within 1 year compared with LOS of 3 to 4 days. The results were similar for model C, which included the addition of routinely measured laboratory results. All associations between LOS categories and risk of mortality at 30 days and 1 year were statistically significant in models A and B (Figure 3). Compared with LOS of 3 to 4 days, risk of mortality was lower at 30 days and at 1 year in those with LOS of 1 or 2 days but higher in those with LOS >5 days. The results were similar for model C, which included the addition of routinely measured laboratory results. Discussion In this diverse cohort of 19,927 patients with HF from an integrated health system, hospital readmission and mortality rates were high (30-day and 1-year readmission was 21% and 60%, respectively, and 30-day and 1-year mortality was 6% and 25%, respectively). Furthermore, longer LOS of the initial hospitalization for HF was modestly associated with risk of hospital readmission and more strongly associated with all-cause mortality within 30 days and 1 year. These
findings were robust across the modeling strategy; all models for all 4 outcomes performed similarly regardless of the variables included for adjustment. Given that the inclusion of covariates obtainable from the EMR had little effect on the associations, these results suggest that LOS is an independent predictor of 30-day and 1-year hospital readmission and mortality and may be a proxy for the severity of HF during the index hospitalization. Others have reported a longer LOS in patients with more severe HF and co-morbidities.13e15 Of 1,009 hospitalized patients with HF, worsening renal function occurred frequently and LOS >10 days increased threefold.13 Formiga et al14 found that women and patients with worse NYHA functional class at hospital admission were more likely to have LOS >4 days. With our large sample size of nearly 20,000 patients with HF and the comprehensive capture of co-morbidities in the EMRs, our findings strengthen the notion that LOS may be a useful proxy for severity of HF and complicated patients with multiple co-morbidities, especially when predicting future mortality. In the United States, 30-day readmission and mortality rates vary widely across hospitals, ranging from 10% to >50%.16 Overall, the readmission rates we report (21%
404
The American Journal of Cardiology (www.ajconline.org)
within 30 days and 60% within 1 year) are not only consistent with previous studies17e19 but also vary widely depending on the LOS of the initial hospitalization, with higher readmission rates when stays are 5 to 10 days and much higher rates for even longer stays. However, our results do not imply that readmission rates could be reduced by shortening HF hospitalizations unless this can be achieved through more aggressive and effective therapies that reduce the severity of HF in the hospital, thus allowing for earlier discharge. The all-cause mortality rates we report are also in line with previous studies,19e21 but again varied substantially by LOS of the initial hospitalization. Indeed, LOS shorter than 3 to 4 days was associated with lower mortality and the association between LOS of 5 to 10 days and 11 days was remarkably strong, especially for 30-day mortality. These results suggest that LOS is a clear surrogate for HF severity. In any case, because our focus was on outcomes after an HF hospitalization, we did not include patients who died during their initial hospital stay. These patients clearly had the most severe cases of HF, so arguing that LOS is a marker for severity that only applies to patients who survive the initial hospitalization. There are several potential limitations to this study. Kaiser Permanente is an integrated system of insured subjects with substantial health information technology support. Therefore, our results may not be fully representative of the general population. However, the geographic and demographic diversity represented across the 3 health plans and the population-based nature of health care suggests that our findings are likely to be generalizable to patients with HF in real-world clinical settings. Furthermore, our results were largely unaffected by covariates, suggesting that the relation between HF LOS and outcomes is independent. Nevertheless, we used observational data available in the EMR, and capture of potentially important measures was incomplete. It is, therefore, possible that inclusion of unmeasured covariates could have altered our findings. The findings from this large and racially/ethnically diverse cohort have important clinical implications. Preventing readmission and mortality after hospitalization for HF is complex, and rates of readmission have remained relatively unchanged in recent years.22,23 Given that hospitals in the United States struggle to contain readmission rates and hospitals with excess readmissions are now financially penalized, identifying potential targets for intervention are essential. Our results suggest that the initial hospital LOS may be a proxy for the severity of HF. Therefore, identifying key social factors early and developing a patient-focused plan, including self-management education and care coordination efforts, before the patient is discharged would likely be very beneficial. Future studies should examine the effect of aggressive management strategies aimed at reducing HF severity on LOS and readmission and mortality. Acknowledgment: Concept and design were performed by KR, MGB, WC, and GAN. Data were collected and analyzed by Kimes with interpretation from KR, MGB, GR, WC, and GAN. The manuscript was written by KR and GAN and revised by MGB, TMK, GR, and WC.
Disclosures This study was funded by a contractual agreement between the Kaiser Permanente Center for Health Research, Portland, Oregon, and Novartis Pharmaceuticals Corporation, East Hanover, New Jersey. GAN has received grant funding from Novartis Pharmaceuticals, Merck & Co., Inc., AstraZeneca, Boehringer-Ingelheim, and Incyte Corporation. KR has received grant funding from Amgen Inc., Novartis Pharmaceuticals, Merck & Co., Inc., and AstraZeneca. MGB, TMK, and AGR report no disclosures. WC is an employee of Novartis Pharmaceuticals Corporation. 1. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Blaha MJ, Dai S, Ford ES, Fox CS, Franco S, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Huffman MD, Judd SE, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Mackey RH, Magid DJ, Marcus GM, Marelli A, Matchar DB, McGuire DK, Mohler ER III, Moy CS, Mussolino ME, Neumar RW, Nichol G, Pandey DK, Paynter NP, Reeves MJ, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Wong ND, Woo D, Turner MB; American Heart Association Statistics Committee, Stroke Statistics Subcommittee. Heart disease and stroke statistics—2014 update: a report from the American Heart Association. Circulation 2014;129:e28ee292. 2. Heidenreich PA, Albert NM, Allen LA, Bluemke DA, Butler J, Fonarow GC, Ikonomidis JS, Khavjou O, Konstam MA, Maddox TM, Nichol G, Pham M, Pina IL, Trogdon JG; American Heart Association Advocacy Coordinating Committee; Council on Arteriosclerosis Thrombosis and Vascular Biology; Council on Cardiovascular Radiology and Intervention; Council on Clinical Cardiology; Council on Epidemiology and Prevention; Stroke Council. Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ Heart Fail 2013;6:606e619. 3. Hunt SA, Abraham WT, Chin MH, Feldman AM, Francis GS, Ganiats TG, Jessup M, Konstam MA, Mancini DM, Michl K, Oates JA, Rahko PS, Silver MA, Stevenson LW, Yancy CW; American College of Cardiology Foundation, American Heart Association. 2009 focused update incorporated into the ACC/AHA 2005 Guidelines for the diagnosis and management of heart failure in adults a report of the American College of Cardiology Foundation/American heart association Task Force on Practice Guidelines Developed in Collaboration with the International Society for Heart and Lung Transplantation. J Am Coll Cardiol 2009;53:e1ee90. 4. Au AG, McAlister FA, Bakal JA, Ezekowitz J, Kaul P, van Walraven C. Predicting the risk of unplanned readmission or death within 30 days of discharge after a heart failure hospitalization. Am Heart J 2012;164: 365e372. 5. Krumholz HM, Parent EM, Tu N, Vaccarino V, Wang Y, Radford MJ, Hennen J. Readmission after hospitalization for congestive heart failure among Medicare beneficiaries. Arch Intern Med 1997;157:99e104. 6. Zaya M, Phan A, Schwarz ER. Predictors of re-hospitalization in patients with chronic heart failure. World J Cardiol 2012;4:23e30. 7. Tsuchihashi M, Tsutsui H, Kodama K, Kasagi F, Setoguchi S, Mohr M, Kubota T, Takeshita A. Medical and socioenvironmental predictors of hospital readmission in patients with congestive heart failure. Am Heart J 2001;142:E7. 8. Solomon SD, Dobson J, Pocock S, Skali H, McMurray JJ, Granger CB, Yusuf S, Swedberg K, Young JB, Michelson EL, Pfeffer MA; Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity (CHARM) Investigators. Influence of nonfatal hospitalization for heart failure on subsequent mortality in patients with chronic heart failure. Circulation 2007;116:1482e1487. 9. Foraker RE, Rose KM, Chang PP, Suchindran CM, McNeill AM, Rosamond WD. Hospital length of stay for incident heart failure: Atherosclerosis Risk in Communities (ARIC) cohort: 1987-2005. J Healthc Qual 2014;36:45e51. 10. Ross TR, Ng D, Brown JS, Pardee R, Hornbrook MC, Hart G, Steiner JF. The HMO research network virtual data warehouse: a public data model to support collaboration. EGEMS 2014;2:1049; Article 2. 11. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999;130:461e470.
Heart Failure/Heart Failure Hospital Length of Stay 12. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999;94:496e509. 13. Forman DE, Butler J, Wang Y, Abraham WT, O’Connor CM, Gottlieb SS, Loh E, Massie BM, Rich MW, Stevenson LW, Young JB, Krumholz HM. Incidence, predictors at admission, and impact of worsening renal function among patients hospitalized with heart failure. J Am Coll Cardiol 2004;43:61e67. 14. Formiga F, Chivite D, Manito N, Mestre AR, Llopis F, Pujol R. Admission characteristics predicting longer length of stay among elderly patients hospitalized for decompensated heart failure. Eur J Intern Med 2008;19:198e202. 15. Krumholz HM, Chen YT, Bradford WD, Cerese J. Variations in and correlates of length of stay in academic hospitals among patients with heart failure resulting from systolic dysfunction. Am J Manag Care 1999;5:715e723. 16. Joynt KE, Jha AK. Who has higher readmission rates for heart failure, and why? Implications for efforts to improve care using financial incentives. Circ Cardiovasc Qual Outcomes 2011;4:53e59. 17. Bueno H, Ross JS, Wang Y, Chen J, Vidan MT, Normand SL, Curtis JP, Drye EE, Lichtman JH, Keenan PS, Kosiborod M, Krumholz HM. Trends in length of stay and short-term outcomes among Medicare patients hospitalized for heart failure, 1993-2006. JAMA 2010;303:2141e2147. 18. Dharmarajan K, Hsieh AF, Lin Z, Bueno H, Ross JS, Horwitz LI, Barreto-Filho JA, Kim N, Bernheim SM, Suter LG, Drye EE,
19.
20.
21.
22. 23.
405
Krumholz HM. Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA 2013;309:355e363. Hernandez AF, Greiner MA, Fonarow GC, Hammill BG, Heidenreich PA, Yancy CW, Peterson ED, Curtis LH. Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA 2010;303: 1716e1722. Foraker RE, Rose KM, Suchindran CM, Chang PP, McNeill AM, Rosamond WD. Socioeconomic status, Medicaid coverage, clinical comorbidity, and rehospitalization or death after an incident heart failure hospitalization: Atherosclerosis Risk in Communities Cohort (1987 to 2004). Circ Heart Fail 2011;4:308e316. Krumholz HM, Merrill AR, Schone EM, Schreiner GC, Chen J, Bradley EH, Wang Y, Wang Y, Lin Z, Straube BM, Rapp MT, Normand SL, Drye EE. Patterns of hospital performance in acute myocardial infarction and heart failure 30-day mortality and readmission. Circ Cardiovasc Qual Outcomes 2009;2:407e413. Chen J, Normand SL, Wang Y, Krumholz HM. National and regional trends in heart failure hospitalization and mortality rates for Medicare beneficiaries, 1998-2008. JAMA 2011;306:1669e1678. Gheorghiade M, Vaduganathan M, Fonarow GC, Bonow RO. Rehospitalization for heart failure: problems and perspectives. J Am Coll Cardiol 2013;61:391e403.
