ORIGINAL RESEARCH
Patient and process factors associated with all-cause 30-day readmission among patients with heart failure Beth D. Whittaker, DNP, ARNP (Nurse Practitioner)1 , Laurie A. Soine, PhD, ARNP (Teaching Associate)2 , & Kathleen M. Errico, PhD, ARNP (Chief Nurse Practitioner)3 1
Western Washington Cardiology, Everett, Washington Department of Radiology and Medicine, University of Washington, Seattle, Washington 3 Department of Patient Care Services, University of Washington, Seattle, Washington 2
Keywords Heart failure; cardiac; cardiology; hospital readmission; adults. Correspondence Beth D. Whittaker, DNP, ARNP, 12728 19th Ave SE #200, Everett, WA 98208. Fax: 425-225-2790; E-mail: [email protected] Received: 19 September 2012; accepted: 19 May 2013 doi: 10.1002/2327-6924.12123 Disclosure The authors report no conflicts of interest.
Abstract Purpose: To explore the patient and process factors associated with all-cause 30-day readmission after heart failure (HF) hospitalization and develop recommendations to reduce readmissions of patients with HF. Data sources: A retrospective, cohort study of 239 patients ages 18 years and older was performed using electronic medical chart review. All patients were discharged from the medical center between July 1, 2009 and June 30, 2010 with a principal diagnosis of HF. Patient and process factors were compared in readmitted and nonreadmitted groups. Conclusions: Renal failure/insufficiency was the only factor significantly associated with all-cause 30-day readmission among patients with HF. Implications for practice: Assessment of renal function during hospitalization will help identify patients with HF at high risk for all-cause 30-day readmission. Give careful consideration to the timing of discharge of patients who appear otherwise ready for discharge but still have creatinine levels above their baseline. Careful follow-up is needed for patients with impaired renal function.
Introduction In the United States, heart failure (HF) is the most common reason for hospital readmission in patients older than 65 years of age (Berkowitz, Blank, & Powell, 2005). Hospital discharges for HF have increased significantly in the past 10 years, and 30% of patients will be readmitted within 30 days (Jencks, Williams, & Coleman, 2009; Lloyd-Jones et al., 2010). HF readmission poses significant problems including decreased quality of life, increased costs, and increased resource utilization (Hobbs et al., 2002; Lloyd-Jones et al., 2010). To encourage hospitals to improve quality of care, the Medicare Payment Advisory Commission (MedPAC) recommended public reporting of hospital-specific readmission rates for a subset of conditions, including HF (MedPAC, 2007). In response to MedPAC’s recommendation, the Centers for Medicare and Medicaid Services (CMS) developed 30-day risk-standardized readmission measures for HF (QualityNet, n.d.), which are currently reported on the Hospital Compare website (http://www.hospitalcompare.hhs.gov). In an effort to Journal of the American Association of Nurse Practitioners 27 (2015) 105–113 C 2014 American Association of Nurse Practitioners
reduce readmission costs, MedPAC suggested adjusting the payment method to financially encourage lower admission rates (MedPAC, 2007). Beginning October 1, 2012, Medicare began reducing reimbursement to hospitals for “excess” patients readmitted for any reason (Stone & Hoffman, 2010). An excess readmission is a ratio of risk-adjusted actual readmission to risk-adjusted expected readmissions (Stone & Hoffman, 2010). A 30-day time period between discharge and readmission determines reduced payment. In response to the initiatives set forth by CMS, a large west coast academic medical center created its own initiative to reduce readmissions of patients with HF. To assist the medical center in developing a program targeting specific predictors of hospital readmission, center-specific predictors must be first identified. Numerous studies have identified patient and process factors associated with HF readmission; however, relatively few have examined 30day readmission and fewer yet have found consistent predictors of readmission (Ross et al., 2008). Patient factors are descriptive patient characteristics that include demographic variables and comorbidities. A 105
Factors associated with heart failure readmission
systematic review of 112 studies with varying lengths of follow-up found inconsistencies between patient characteristics and readmission after HF hospitalization (Ross et al., 2008). Age, gender, race, and marital status were analyzed, but none were consistently associated with readmission. Several studies have examined comorbid conditions such as diabetes, chronic obstructive pulmonary disease (COPD), hypertension, and coronary heart disease (CHD); again, none were consistently associated with readmission (Hamner & Ellison, 2005; Ross et al., 2008). In contrast, chronic renal failure and chronic renal insufficiency have been shown to predict 30-day readmission (Hallerbach et al., 2008; Muzzarelli et al., 2010; Patel et al., 2010). Process factors, also known as process measures, denote “what is actually done in giving and receiving care” (Donabedian, 1988, p. 1745). They include providers’ activities surrounding the diagnosis and treatment of patients as well as patients’ activities in seeking care and carrying out the treatment plan. The Joint Commission and the American College of Cardiology and American Heart Association (ACC/AHA) found evidence supporting certain processes of care strongly linked to readmission and patient outcomes (Fonarow et al., 2007b). As a result, the Joint Commission worked closely with CMS to develop HF core performance measures (i.e., process factors). The 2011 CMS HF core measures for adults hospitalized with HF include left ventricular (LV) function assessment, use of an angiotensin-converting enzyme (ACE) inhibitor or an angiotensin receptor blocker (ARB) in patients with LV systolic dysfunction, smoking cessation counseling, and complete discharge teaching in all patients with HF (Fonarow et al., 2007b). These measures are all included in the 2011 CMS HF core measure set to evaluate hospital performance; however, data validating these measures are limited (Fonarow et al., 2007a). HF core measures are currently used and publicly reported by CMS, the Joint Commission, and major insurers, and are used as criteria for reimbursement. Therefore, it is critical that the measures are valid and linked to improved patient outcomes. Additional process factors explored in the literature relate to hospitalization time and follow-up. Length of stay is often examined as a predictor of readmission, but few studies have found statistically significant associations (Luthi, Flanders, Burnier, Burnand, & McClellan, 2006). There is also data to suggest that comprehensive discharge planning and post-discharge support, such as home visits and telephone follow-up, are important in preventing readmission (Phillips et al., 2004). However, based on the literature search, only a small number of studies have looked specifically at patient follow-up within 30 days of discharge. 106
B. D. Whittaker et al.
In summary, there are few studies that have specifically examined patient or process factors and all-cause 30-day readmission among patients with HF. Because 30 days has become the accepted benchmark for quality measures and is linked to Medicare reimbursement, this time interval is particularly important and merits further examination. Findings from studies examining 30-day readmission will help hospitals target their efforts to improve quality of care.
Purpose The purpose of this study was to identify and explore the patient and process factors associated with all-cause 30-day readmission after HF hospitalization and develop recommendations for the care of patients with HF in order to reduce readmissions.
Methods Study design The study was a retrospective, cohort study using electronic medical chart review. Patients were separated into two groups: (a) patients with HF readmitted within 30 days and (b) patients with HF not readmitted within 30 days. Investigators examined 12 months of data collected within an electronic medical record and data acquired from the hospital billing system.
Human subjects A Medical Records Review Application was submitted to the Human Subjects Division and Institutional Review Board approval was received.
Setting and study population The study was completed at a large west coast academic medical center serving a five state region. All patients over 18 years of age discharged from the medical center between July 1, 2009 and June 30, 2010 with a principal diagnosis of HF were included in the study. Readmissions were based on index hospitalizations. Patients were excluded if they expired during the initial admission, were transferred to another hospital, or left against medical advice. The operational definition of HF was a diagnosis of HF based on 24 different ICD-9 codes (Hart & Hopkins, 2009).
Data collection Data were extracted from electronic medical records within the Horizon Performance Manager and Core
Factors associated with heart failure readmission
B. D. Whittaker et al.
Measures databases. It was then de-identified and converted into an Excel spreadsheet. The following patient factors were examined in both readmitted and nonreadmitted groups: age, gender, race, marital status, and current diagnoses of diabetes, COPD, hypertension, CHD, renal failure/insufficiency, and depression. Diagnoses were based on ICD-9 codes that were extracted from billing data. The following process factors were examined in both readmitted and nonreadmitted groups: 2011 CMS HF core measures (LV function assessment, ACE inhibitor or ARB use for LV systolic dysfunction, smoking cessation counseling, discharge teaching), discharging service, length of stay, discharge disposition, days between discharge and first outpatient clinic appointment, and provider type seen during first discharge appointment. Point of re-entry into the hospital was also examined in the readmitted group. In order to obtain data on HF core measures, patients had to meet additional specific criteria from the existing data set. Core measures data included patients admitted to the hospital for inpatient acute care with the 24 ICD9 HF Codes (Hart & Hopkins, 2009). Patients were excluded from analysis if they had a diagnosis of HF and received a LV assist device or a heart transplant and/or had a length of stay greater than 120 days. For each core measure, there were four possible results: complete, incomplete, not applicable, and unable to determine. “Complete” indicated that the patient was in the core measures HF population and providers met the specifications of the process factor. “Incomplete” signified that the patient was in the core measures HF population, but the providers did not fully meet the specifications of the process factor. “Not applicable” indicated that the patient was in the core measures HF population, but the process factor did not apply and/or the patient was exempt from the process factor. “Unable to determine” denoted that the patient was not in the core measures HF population and process factor data were not available. Patient and process factors were chosen based on a review of the literature, the clinical experience of the researchers, and to inform improvement measures specific to the medical center.
Analysis Data were downloaded from the Excel file into SPSS version 15.0 for statistical analysis. Pearson chi-square tabular model tests were performed on all nominal variables and two-tailed t-tests were performed on interval variables. In addition, the interval variables were recoded into nominal form and subjected to Pearson chi-square tabular model tests. A p-value of ࣘ.05 was considered statistically sig-
Table 1 Baseline characteristics of patients
Characteristic Age (years), mean ± SD, range Male gender, n (%) Race, n (%) Caucasian Black or African American Asian Hispanic Other/unknown Marital status (married), n (%) Diabetes mellitus, n (%) COPD, n (%) Hypertension, n (%) CHD, n (%) Renal failure/insufficiency, n (%) Depression, n (%) LOS (days), mean ± SD, range ACE inhibitor or ARB use for LVSD at discharge, n (%) Discharging service, n (%) General cardiology Heart failure/transplant Cardiac surgery Internal medicine Other
All patients n = 239 59 ± 17.0,19–95 148 (61.9) 170 (71.1) 31 (13.0) 18 (7.5) 6 (2.5) 14 (5.9) 113 (47.3) 88 (36.8) 43 (18.0) 117 (49.0) 90 (37.7) 119 (49.8) 5 (2.1) 9.7 ± 14.9, 1–139 119 (49.8) 84 (35.1) 84 (35.1) 22 (9.2) 27 (11.3) 22 (9.2)
Note. ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease; LOS, length of stay; LVSD, left ventricular systolic dysfunction.
nificant. Binary logistic regression was performed for statistically significant variables controlling for age, gender, and comorbidities, and adjusted odds ratios (ORs) were obtained.
Results Analysis included 239 patients. Baseline characteristics of the study cohort are listed in Table 1. Of the 239 patients, 50 (20.9%) were readmitted for any reason within 30 days of hospital discharge. Frequencies of patient factors and the associations with all-cause 30-day readmission are displayed in Table 2. The only two variables that were significantly associated with 30-day readmission were gender (p = .048) and renal failure/insufficiency (p = .004). After controlling for age, gender, and comorbidities, only renal failure/insufficiency remained statistically significant. Patients with renal failure/insufficiency were found to be three times more likely (OR 3.06) to be readmitted within 30 days than those without renal failure/insufficiency (95% CI [1.47–6.35], p = .003). Although gender was not significant, it showed
107
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Table 2 Patient factors: clinical characteristics 30-Day all-cause readmission; n = 239
Variable Age (years), n (%) 18–49 50–54 55–59 60–64 65–69 70–74 75–79 >80 Gender, n (%) Male Female Race, n (%) White Black or African American Asian Hispanic American Indian or Alaska Native Multiracial Unknown Marital status, n (%) Married Single Divorced Widowed Other Unknown Diabetes mellitus, n (%) COPD, n (%) Hypertension, n (%) CHD, n (%) Renal failure/ insufficiency, n (%) Depression, n (%)
All patients n = 239
Readmission
No readmission
p value
61 (25.5) 23 (9.6) 41 (17.2) 32 (13.4) 22 (9.2) 15 (6.3) 14 (5.9) 31 (13.0)
14 (23.0) 7 (30.4) 7 (17.1) 6 (18.8) 7 (31.8) 2 (13.3) 3 (21.4) 4 (12.9)
47 (77.0) 16 (69.6) 34 (82.9) 26 (81.3) 15 (68.2) 13 (86.7) 11 (78.6) 27 (87.1)
148 (61.9) 91 (38.1)
37 (25.0) 13 (14.3)
111 (75.0) 78 (85.7)
170 (71.1) 31 (13.0)
42 (24.7) 3 (9.7)
128 (75.3) 28 (90.3)
18 (7.5) 6 (2.5) 4 (1.7)
3 (16.7) 0 (0) 0 (0)
15 (83.3) 6 (100.0) 4 (100.0)
2 (0.8) 8 (3.3)
1 (50.0) 1 (12.5)
1 (50.0) 7 (87.5)
113 (47.3) 80 (33.5) 14 (5.9) 23 (9.6) 2 (0.8) 7 (2.9) 88 (36.8)
23 (20.4) 22 (27.5) 1 (7.1) 3 (13.0) 0 (0) 1 (14.3) 20 (22.7)
90 (79.6) 58 (72.5) 13 (92.9) 20 (87.0) 2 (100.0) 6 (85.7) 68 (77.3)
.60
43 (18.0) 117 (49.0) 90 (37.7) 119 (49.8)
9 (20.9) 19 (16.2) 18 (20.0) 34 (28.6)
34 (79.1) 98 (83.8) 72 (80.0) 85 (71.4)
1.00 .08 .79 .004
5 (2.1)
2 (40.0)
3 (60.0)
.29
.64a
.048
.24
Discussion
.38
Note. CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease. a t-test also performed, p = .20.
a trend toward males being more likely to be readmitted within 30 days (OR 0.48, 95% CI [0.23–1.02], p = .06). Tables 3 and 4 display the frequencies of process factors and the associations to 30-day readmission. No process factors were significantly associated with all-cause 30-day readmission. The mean length of stay for all patients was 9.7 ± 14.9 days (Table 1), while the median was 5.0 days. The mean 108
length of stay for those readmitted was 12.8 ± 22.2 days compared to 8.9 ± 12.2 for those not readmitted, but as shown in Table 3, length of stay was not significantly associated with readmission. Approximately 60% of patients did not receive outpatient follow-up within 30 days of discharge at a hospitalbased clinic. Of those patients seen in a hospital-based clinic within 30 days, the mean number of days between discharge and the first outpatient visit was 9.5 ± 7.3 (median of 6.0 days). Of patients followed-up with a known provider type, physicians saw 38.9% of the patients and nurse practitioners (NPs) saw 61.1%. Tables 5 and 6 display specifics of outpatient follow-up within 30 days of discharge and outpatient follow-up provider type by discharging service, respectively. To further explore process factors related to readmission, the readmission source (site of readmission) was examined. Table 7 provides the frequencies with which patients were readmitted from different locations.
The association between patient and process factors and all-cause 30-day readmission after HF hospitalization was examined by comparing patient and process-specific variables in readmitted and nonreadmitted groups. Identifying factors associated with readmission is the first step in assisting medical centers in developing a plan to reduce readmission. Of the 239 patients in the present study, 50 (20.9%) were readmitted for any reason within 30 days of hospital discharge. The readmission rate is better than the U.S. national median of 24.7%, but room for improvement remains (CMS, 2011).
Patient factors Gender was found to be significant upon initial analysis (p = .048), but not after controlling for covariates (p = .06). There was a trend toward males being more likely to be readmitted within 30 days. Other studies examining gender and 30-day readmission also found nonsignificant results (Muus et al., 2010; Muzzarelli et al., 2010), and a large systematic review that included studies with varying lengths of follow-up had similar inconclusive findings (Ross et al., 2008). Thus, gender as a predictor of 30-day readmission is unlikely. Renal failure/insufficiency was the only significant patient factor associated with readmission. Approximately 50% of the patients in this study had a diagnosis of renal failure/insufficiency, which speaks to the high acuity of the HF patient population in this cohort. The association between renal failure/insufficiency and 30day readmission after HF hospitalization is supported by
Factors associated with heart failure readmission
B. D. Whittaker et al.
Table 3 Process factors: hospitalization characteristics
Variable LV function assessment, n (%) Complete Incomplete N/A Unable to determine ACE inhibitor or ARB use for LVSD, n (%) Complete Incomplete N/A Unable to determine Smoking cessation counseling, n (%) Complete Incomplete N/A Unable to determine Discharge teaching, n (%) Complete Incomplete N/A Unable to determine Discharging service, n (%) General cardiology Heart failure/transplant Cardiac surgery Internal medicine Other Length of stay (days), n (%) 1–2 3–5 6–11 ࣙ12 Discharge disposition, n (%) Home, self-care Home health service Skilled nursing facility Transferred to rehab Facility Hospice home
30-Day all-cause readmission; n = 239
All patients n = 239
Readmission
No readmission
216 (90.4) 2 (0.8) 3 (1.3) 18 (7.5)
42 (19.4) 1 (50.0) 0 (0) 7 (38.9)
174 (80.6) 1 (50.0) 3 (100.0) 11 (61.1)
119 (49.8) 2 (0.8) 100 (41.8) 18 (7.5)
25 (21.0) 0 (0) 18 (18.0) 7 (38.9)
94 (79.0) 2 (100.0) 82 (82.0) 11 (61.1)
33 (13.8) 0 (0) 188 (78.7) 18 (7.5)
6 (18.2) 0 (0) 37 (19.7) 7 (38.9)
27 (81.8) 0 (0) 151 (80.3) 11 (61.1)
175 (73.2) 29 (12.1) 17 (7.1) 18 (7.5)
34 (19.4) 7 (24.1) 2 (11.8) 7 (38.9)
141 (80.6) 22 (75.9) 15 (88.2) 11 (61.1)
84 (35.1) 84 (35.1) 22 (9.2) 27 (11.3) 22 (9.2)
13 (15.5) 21 (25.0) 8 (36.4) 6 (22.2) 2 (9.1)
71 (84.5) 63 (75.0) 14 (63.6) 21 (77.8) 20 (90.9)
p value .13
.21
.15
.19
.12
.77a 59 (24.7) 65 (27.2) 57 (23.8) 58 (24.3)
12 (20.3) 11 (16.9) 13 (22.8) 14 (24.1)
47 (79.7) 54 (83.1) 44 (77.2) 44 (75.9)
198 (82.8) 23 (9.6) 12 (5.0) 4 (1.7)
40 (20.2) 8 (34.8) 2 (16.7) 0 (0)
158 (79.8) 15 (65.2) 10 (83.3) 4 (100.0)
.35
2 (0.8)
0 (0)
2 (100.0)
Note. ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; LV, left ventricular; LVSD, left ventricular systolic dysfunction. a t-test also performed, p = .24.
multiple studies (Hallerbach et al., 2008; Luthi et al., 2006; Muzzarelli et al., 2010; Patel et al., 2010; Thakar, Parikh, & Liu, 2012). As renal function deteriorates, patients with HF have an impaired ability to excrete sodium and fluid, resulting in volume overload, HF exacerbation, and subsequent readmission (Cole et al., 2012). The prevalence of worsening renal failure (WRF), defined as a change in serum creatinine ࣙ0.3 mg/dL, is higher in patients with multiple comorbidities and baseline renal failure/insufficiency (Patel et al., 2010). A retrospective study
of over 20,000 patients found that WRF during hospitalization was an independent predictor of all-cause 30-day readmission in patients with reduced and preserved systolic ventricular function. Studies with longer periods of follow-up also found associations between baseline renal failure/insufficiency and HF readmission. A retrospective study of 109 patients found that decreased renal function (estimated glomerular filtration rate or = 65 years of age with chronic congestive heart failure. American Journal of Cardiology, 86(10), 1151–1153. Thakar, C. V., Parikh, P. J., & Liu, Y. (2012). Acute kidney injury (AKI) and risk of readmissions in patients with heart failure. American Journal of Cardiology, 109(10), 1482–1486. doi:10.1016/j.amjcard.2012.01. 362 The SOLVD Investigators. (1991). Effect of enalapril on survival in patients with reduced left ventricular ejection fractions and congestive heart failure. New England Journal of Medicine, 325(5), 293–302. doi:10.1056/NEJM199108013250501 Vansuch, M., Naessens, J. M., Stroebel, R. J., Huddleston, J. M., & Williams, A. R. (2006). Effect of discharge instructions on readmission of hospitalized patients with heart failure: Do all of the Joint Commission on Accreditation of Healthcare Organizations heart failure core measures reflect better care? Quality and Safety in Health Care, 15(6), 414–417. doi:10.1136/qshc.2005.017640
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Patient and process factors associated with all-cause 30-day readmission among patients with heart failure Beth D. Whittaker, DNP, ARNP (Nurse Practitioner)1 , Laurie A. Soine, PhD, ARNP (Teaching Associate)2 , & Kathleen M. Errico, PhD, ARNP (Chief Nurse Practitioner)3 1
Western Washington Cardiology, Everett, Washington Department of Radiology and Medicine, University of Washington, Seattle, Washington 3 Department of Patient Care Services, University of Washington, Seattle, Washington 2
Keywords Heart failure; cardiac; cardiology; hospital readmission; adults. Correspondence Beth D. Whittaker, DNP, ARNP, 12728 19th Ave SE #200, Everett, WA 98208. Fax: 425-225-2790; E-mail: [email protected] Received: 19 September 2012; accepted: 19 May 2013 doi: 10.1002/2327-6924.12123 Disclosure The authors report no conflicts of interest.
Abstract Purpose: To explore the patient and process factors associated with all-cause 30-day readmission after heart failure (HF) hospitalization and develop recommendations to reduce readmissions of patients with HF. Data sources: A retrospective, cohort study of 239 patients ages 18 years and older was performed using electronic medical chart review. All patients were discharged from the medical center between July 1, 2009 and June 30, 2010 with a principal diagnosis of HF. Patient and process factors were compared in readmitted and nonreadmitted groups. Conclusions: Renal failure/insufficiency was the only factor significantly associated with all-cause 30-day readmission among patients with HF. Implications for practice: Assessment of renal function during hospitalization will help identify patients with HF at high risk for all-cause 30-day readmission. Give careful consideration to the timing of discharge of patients who appear otherwise ready for discharge but still have creatinine levels above their baseline. Careful follow-up is needed for patients with impaired renal function.
Introduction In the United States, heart failure (HF) is the most common reason for hospital readmission in patients older than 65 years of age (Berkowitz, Blank, & Powell, 2005). Hospital discharges for HF have increased significantly in the past 10 years, and 30% of patients will be readmitted within 30 days (Jencks, Williams, & Coleman, 2009; Lloyd-Jones et al., 2010). HF readmission poses significant problems including decreased quality of life, increased costs, and increased resource utilization (Hobbs et al., 2002; Lloyd-Jones et al., 2010). To encourage hospitals to improve quality of care, the Medicare Payment Advisory Commission (MedPAC) recommended public reporting of hospital-specific readmission rates for a subset of conditions, including HF (MedPAC, 2007). In response to MedPAC’s recommendation, the Centers for Medicare and Medicaid Services (CMS) developed 30-day risk-standardized readmission measures for HF (QualityNet, n.d.), which are currently reported on the Hospital Compare website (http://www.hospitalcompare.hhs.gov). In an effort to Journal of the American Association of Nurse Practitioners 27 (2015) 105–113 C 2014 American Association of Nurse Practitioners
reduce readmission costs, MedPAC suggested adjusting the payment method to financially encourage lower admission rates (MedPAC, 2007). Beginning October 1, 2012, Medicare began reducing reimbursement to hospitals for “excess” patients readmitted for any reason (Stone & Hoffman, 2010). An excess readmission is a ratio of risk-adjusted actual readmission to risk-adjusted expected readmissions (Stone & Hoffman, 2010). A 30-day time period between discharge and readmission determines reduced payment. In response to the initiatives set forth by CMS, a large west coast academic medical center created its own initiative to reduce readmissions of patients with HF. To assist the medical center in developing a program targeting specific predictors of hospital readmission, center-specific predictors must be first identified. Numerous studies have identified patient and process factors associated with HF readmission; however, relatively few have examined 30day readmission and fewer yet have found consistent predictors of readmission (Ross et al., 2008). Patient factors are descriptive patient characteristics that include demographic variables and comorbidities. A 105
Factors associated with heart failure readmission
systematic review of 112 studies with varying lengths of follow-up found inconsistencies between patient characteristics and readmission after HF hospitalization (Ross et al., 2008). Age, gender, race, and marital status were analyzed, but none were consistently associated with readmission. Several studies have examined comorbid conditions such as diabetes, chronic obstructive pulmonary disease (COPD), hypertension, and coronary heart disease (CHD); again, none were consistently associated with readmission (Hamner & Ellison, 2005; Ross et al., 2008). In contrast, chronic renal failure and chronic renal insufficiency have been shown to predict 30-day readmission (Hallerbach et al., 2008; Muzzarelli et al., 2010; Patel et al., 2010). Process factors, also known as process measures, denote “what is actually done in giving and receiving care” (Donabedian, 1988, p. 1745). They include providers’ activities surrounding the diagnosis and treatment of patients as well as patients’ activities in seeking care and carrying out the treatment plan. The Joint Commission and the American College of Cardiology and American Heart Association (ACC/AHA) found evidence supporting certain processes of care strongly linked to readmission and patient outcomes (Fonarow et al., 2007b). As a result, the Joint Commission worked closely with CMS to develop HF core performance measures (i.e., process factors). The 2011 CMS HF core measures for adults hospitalized with HF include left ventricular (LV) function assessment, use of an angiotensin-converting enzyme (ACE) inhibitor or an angiotensin receptor blocker (ARB) in patients with LV systolic dysfunction, smoking cessation counseling, and complete discharge teaching in all patients with HF (Fonarow et al., 2007b). These measures are all included in the 2011 CMS HF core measure set to evaluate hospital performance; however, data validating these measures are limited (Fonarow et al., 2007a). HF core measures are currently used and publicly reported by CMS, the Joint Commission, and major insurers, and are used as criteria for reimbursement. Therefore, it is critical that the measures are valid and linked to improved patient outcomes. Additional process factors explored in the literature relate to hospitalization time and follow-up. Length of stay is often examined as a predictor of readmission, but few studies have found statistically significant associations (Luthi, Flanders, Burnier, Burnand, & McClellan, 2006). There is also data to suggest that comprehensive discharge planning and post-discharge support, such as home visits and telephone follow-up, are important in preventing readmission (Phillips et al., 2004). However, based on the literature search, only a small number of studies have looked specifically at patient follow-up within 30 days of discharge. 106
B. D. Whittaker et al.
In summary, there are few studies that have specifically examined patient or process factors and all-cause 30-day readmission among patients with HF. Because 30 days has become the accepted benchmark for quality measures and is linked to Medicare reimbursement, this time interval is particularly important and merits further examination. Findings from studies examining 30-day readmission will help hospitals target their efforts to improve quality of care.
Purpose The purpose of this study was to identify and explore the patient and process factors associated with all-cause 30-day readmission after HF hospitalization and develop recommendations for the care of patients with HF in order to reduce readmissions.
Methods Study design The study was a retrospective, cohort study using electronic medical chart review. Patients were separated into two groups: (a) patients with HF readmitted within 30 days and (b) patients with HF not readmitted within 30 days. Investigators examined 12 months of data collected within an electronic medical record and data acquired from the hospital billing system.
Human subjects A Medical Records Review Application was submitted to the Human Subjects Division and Institutional Review Board approval was received.
Setting and study population The study was completed at a large west coast academic medical center serving a five state region. All patients over 18 years of age discharged from the medical center between July 1, 2009 and June 30, 2010 with a principal diagnosis of HF were included in the study. Readmissions were based on index hospitalizations. Patients were excluded if they expired during the initial admission, were transferred to another hospital, or left against medical advice. The operational definition of HF was a diagnosis of HF based on 24 different ICD-9 codes (Hart & Hopkins, 2009).
Data collection Data were extracted from electronic medical records within the Horizon Performance Manager and Core
Factors associated with heart failure readmission
B. D. Whittaker et al.
Measures databases. It was then de-identified and converted into an Excel spreadsheet. The following patient factors were examined in both readmitted and nonreadmitted groups: age, gender, race, marital status, and current diagnoses of diabetes, COPD, hypertension, CHD, renal failure/insufficiency, and depression. Diagnoses were based on ICD-9 codes that were extracted from billing data. The following process factors were examined in both readmitted and nonreadmitted groups: 2011 CMS HF core measures (LV function assessment, ACE inhibitor or ARB use for LV systolic dysfunction, smoking cessation counseling, discharge teaching), discharging service, length of stay, discharge disposition, days between discharge and first outpatient clinic appointment, and provider type seen during first discharge appointment. Point of re-entry into the hospital was also examined in the readmitted group. In order to obtain data on HF core measures, patients had to meet additional specific criteria from the existing data set. Core measures data included patients admitted to the hospital for inpatient acute care with the 24 ICD9 HF Codes (Hart & Hopkins, 2009). Patients were excluded from analysis if they had a diagnosis of HF and received a LV assist device or a heart transplant and/or had a length of stay greater than 120 days. For each core measure, there were four possible results: complete, incomplete, not applicable, and unable to determine. “Complete” indicated that the patient was in the core measures HF population and providers met the specifications of the process factor. “Incomplete” signified that the patient was in the core measures HF population, but the providers did not fully meet the specifications of the process factor. “Not applicable” indicated that the patient was in the core measures HF population, but the process factor did not apply and/or the patient was exempt from the process factor. “Unable to determine” denoted that the patient was not in the core measures HF population and process factor data were not available. Patient and process factors were chosen based on a review of the literature, the clinical experience of the researchers, and to inform improvement measures specific to the medical center.
Analysis Data were downloaded from the Excel file into SPSS version 15.0 for statistical analysis. Pearson chi-square tabular model tests were performed on all nominal variables and two-tailed t-tests were performed on interval variables. In addition, the interval variables were recoded into nominal form and subjected to Pearson chi-square tabular model tests. A p-value of ࣘ.05 was considered statistically sig-
Table 1 Baseline characteristics of patients
Characteristic Age (years), mean ± SD, range Male gender, n (%) Race, n (%) Caucasian Black or African American Asian Hispanic Other/unknown Marital status (married), n (%) Diabetes mellitus, n (%) COPD, n (%) Hypertension, n (%) CHD, n (%) Renal failure/insufficiency, n (%) Depression, n (%) LOS (days), mean ± SD, range ACE inhibitor or ARB use for LVSD at discharge, n (%) Discharging service, n (%) General cardiology Heart failure/transplant Cardiac surgery Internal medicine Other
All patients n = 239 59 ± 17.0,19–95 148 (61.9) 170 (71.1) 31 (13.0) 18 (7.5) 6 (2.5) 14 (5.9) 113 (47.3) 88 (36.8) 43 (18.0) 117 (49.0) 90 (37.7) 119 (49.8) 5 (2.1) 9.7 ± 14.9, 1–139 119 (49.8) 84 (35.1) 84 (35.1) 22 (9.2) 27 (11.3) 22 (9.2)
Note. ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease; LOS, length of stay; LVSD, left ventricular systolic dysfunction.
nificant. Binary logistic regression was performed for statistically significant variables controlling for age, gender, and comorbidities, and adjusted odds ratios (ORs) were obtained.
Results Analysis included 239 patients. Baseline characteristics of the study cohort are listed in Table 1. Of the 239 patients, 50 (20.9%) were readmitted for any reason within 30 days of hospital discharge. Frequencies of patient factors and the associations with all-cause 30-day readmission are displayed in Table 2. The only two variables that were significantly associated with 30-day readmission were gender (p = .048) and renal failure/insufficiency (p = .004). After controlling for age, gender, and comorbidities, only renal failure/insufficiency remained statistically significant. Patients with renal failure/insufficiency were found to be three times more likely (OR 3.06) to be readmitted within 30 days than those without renal failure/insufficiency (95% CI [1.47–6.35], p = .003). Although gender was not significant, it showed
107
Factors associated with heart failure readmission
B. D. Whittaker et al.
Table 2 Patient factors: clinical characteristics 30-Day all-cause readmission; n = 239
Variable Age (years), n (%) 18–49 50–54 55–59 60–64 65–69 70–74 75–79 >80 Gender, n (%) Male Female Race, n (%) White Black or African American Asian Hispanic American Indian or Alaska Native Multiracial Unknown Marital status, n (%) Married Single Divorced Widowed Other Unknown Diabetes mellitus, n (%) COPD, n (%) Hypertension, n (%) CHD, n (%) Renal failure/ insufficiency, n (%) Depression, n (%)
All patients n = 239
Readmission
No readmission
p value
61 (25.5) 23 (9.6) 41 (17.2) 32 (13.4) 22 (9.2) 15 (6.3) 14 (5.9) 31 (13.0)
14 (23.0) 7 (30.4) 7 (17.1) 6 (18.8) 7 (31.8) 2 (13.3) 3 (21.4) 4 (12.9)
47 (77.0) 16 (69.6) 34 (82.9) 26 (81.3) 15 (68.2) 13 (86.7) 11 (78.6) 27 (87.1)
148 (61.9) 91 (38.1)
37 (25.0) 13 (14.3)
111 (75.0) 78 (85.7)
170 (71.1) 31 (13.0)
42 (24.7) 3 (9.7)
128 (75.3) 28 (90.3)
18 (7.5) 6 (2.5) 4 (1.7)
3 (16.7) 0 (0) 0 (0)
15 (83.3) 6 (100.0) 4 (100.0)
2 (0.8) 8 (3.3)
1 (50.0) 1 (12.5)
1 (50.0) 7 (87.5)
113 (47.3) 80 (33.5) 14 (5.9) 23 (9.6) 2 (0.8) 7 (2.9) 88 (36.8)
23 (20.4) 22 (27.5) 1 (7.1) 3 (13.0) 0 (0) 1 (14.3) 20 (22.7)
90 (79.6) 58 (72.5) 13 (92.9) 20 (87.0) 2 (100.0) 6 (85.7) 68 (77.3)
.60
43 (18.0) 117 (49.0) 90 (37.7) 119 (49.8)
9 (20.9) 19 (16.2) 18 (20.0) 34 (28.6)
34 (79.1) 98 (83.8) 72 (80.0) 85 (71.4)
1.00 .08 .79 .004
5 (2.1)
2 (40.0)
3 (60.0)
.29
.64a
.048
.24
Discussion
.38
Note. CHD, coronary heart disease; COPD, chronic obstructive pulmonary disease. a t-test also performed, p = .20.
a trend toward males being more likely to be readmitted within 30 days (OR 0.48, 95% CI [0.23–1.02], p = .06). Tables 3 and 4 display the frequencies of process factors and the associations to 30-day readmission. No process factors were significantly associated with all-cause 30-day readmission. The mean length of stay for all patients was 9.7 ± 14.9 days (Table 1), while the median was 5.0 days. The mean 108
length of stay for those readmitted was 12.8 ± 22.2 days compared to 8.9 ± 12.2 for those not readmitted, but as shown in Table 3, length of stay was not significantly associated with readmission. Approximately 60% of patients did not receive outpatient follow-up within 30 days of discharge at a hospitalbased clinic. Of those patients seen in a hospital-based clinic within 30 days, the mean number of days between discharge and the first outpatient visit was 9.5 ± 7.3 (median of 6.0 days). Of patients followed-up with a known provider type, physicians saw 38.9% of the patients and nurse practitioners (NPs) saw 61.1%. Tables 5 and 6 display specifics of outpatient follow-up within 30 days of discharge and outpatient follow-up provider type by discharging service, respectively. To further explore process factors related to readmission, the readmission source (site of readmission) was examined. Table 7 provides the frequencies with which patients were readmitted from different locations.
The association between patient and process factors and all-cause 30-day readmission after HF hospitalization was examined by comparing patient and process-specific variables in readmitted and nonreadmitted groups. Identifying factors associated with readmission is the first step in assisting medical centers in developing a plan to reduce readmission. Of the 239 patients in the present study, 50 (20.9%) were readmitted for any reason within 30 days of hospital discharge. The readmission rate is better than the U.S. national median of 24.7%, but room for improvement remains (CMS, 2011).
Patient factors Gender was found to be significant upon initial analysis (p = .048), but not after controlling for covariates (p = .06). There was a trend toward males being more likely to be readmitted within 30 days. Other studies examining gender and 30-day readmission also found nonsignificant results (Muus et al., 2010; Muzzarelli et al., 2010), and a large systematic review that included studies with varying lengths of follow-up had similar inconclusive findings (Ross et al., 2008). Thus, gender as a predictor of 30-day readmission is unlikely. Renal failure/insufficiency was the only significant patient factor associated with readmission. Approximately 50% of the patients in this study had a diagnosis of renal failure/insufficiency, which speaks to the high acuity of the HF patient population in this cohort. The association between renal failure/insufficiency and 30day readmission after HF hospitalization is supported by
Factors associated with heart failure readmission
B. D. Whittaker et al.
Table 3 Process factors: hospitalization characteristics
Variable LV function assessment, n (%) Complete Incomplete N/A Unable to determine ACE inhibitor or ARB use for LVSD, n (%) Complete Incomplete N/A Unable to determine Smoking cessation counseling, n (%) Complete Incomplete N/A Unable to determine Discharge teaching, n (%) Complete Incomplete N/A Unable to determine Discharging service, n (%) General cardiology Heart failure/transplant Cardiac surgery Internal medicine Other Length of stay (days), n (%) 1–2 3–5 6–11 ࣙ12 Discharge disposition, n (%) Home, self-care Home health service Skilled nursing facility Transferred to rehab Facility Hospice home
30-Day all-cause readmission; n = 239
All patients n = 239
Readmission
No readmission
216 (90.4) 2 (0.8) 3 (1.3) 18 (7.5)
42 (19.4) 1 (50.0) 0 (0) 7 (38.9)
174 (80.6) 1 (50.0) 3 (100.0) 11 (61.1)
119 (49.8) 2 (0.8) 100 (41.8) 18 (7.5)
25 (21.0) 0 (0) 18 (18.0) 7 (38.9)
94 (79.0) 2 (100.0) 82 (82.0) 11 (61.1)
33 (13.8) 0 (0) 188 (78.7) 18 (7.5)
6 (18.2) 0 (0) 37 (19.7) 7 (38.9)
27 (81.8) 0 (0) 151 (80.3) 11 (61.1)
175 (73.2) 29 (12.1) 17 (7.1) 18 (7.5)
34 (19.4) 7 (24.1) 2 (11.8) 7 (38.9)
141 (80.6) 22 (75.9) 15 (88.2) 11 (61.1)
84 (35.1) 84 (35.1) 22 (9.2) 27 (11.3) 22 (9.2)
13 (15.5) 21 (25.0) 8 (36.4) 6 (22.2) 2 (9.1)
71 (84.5) 63 (75.0) 14 (63.6) 21 (77.8) 20 (90.9)
p value .13
.21
.15
.19
.12
.77a 59 (24.7) 65 (27.2) 57 (23.8) 58 (24.3)
12 (20.3) 11 (16.9) 13 (22.8) 14 (24.1)
47 (79.7) 54 (83.1) 44 (77.2) 44 (75.9)
198 (82.8) 23 (9.6) 12 (5.0) 4 (1.7)
40 (20.2) 8 (34.8) 2 (16.7) 0 (0)
158 (79.8) 15 (65.2) 10 (83.3) 4 (100.0)
.35
2 (0.8)
0 (0)
2 (100.0)
Note. ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; LV, left ventricular; LVSD, left ventricular systolic dysfunction. a t-test also performed, p = .24.
multiple studies (Hallerbach et al., 2008; Luthi et al., 2006; Muzzarelli et al., 2010; Patel et al., 2010; Thakar, Parikh, & Liu, 2012). As renal function deteriorates, patients with HF have an impaired ability to excrete sodium and fluid, resulting in volume overload, HF exacerbation, and subsequent readmission (Cole et al., 2012). The prevalence of worsening renal failure (WRF), defined as a change in serum creatinine ࣙ0.3 mg/dL, is higher in patients with multiple comorbidities and baseline renal failure/insufficiency (Patel et al., 2010). A retrospective study
of over 20,000 patients found that WRF during hospitalization was an independent predictor of all-cause 30-day readmission in patients with reduced and preserved systolic ventricular function. Studies with longer periods of follow-up also found associations between baseline renal failure/insufficiency and HF readmission. A retrospective study of 109 patients found that decreased renal function (estimated glomerular filtration rate or = 65 years of age with chronic congestive heart failure. American Journal of Cardiology, 86(10), 1151–1153. Thakar, C. V., Parikh, P. J., & Liu, Y. (2012). Acute kidney injury (AKI) and risk of readmissions in patients with heart failure. American Journal of Cardiology, 109(10), 1482–1486. doi:10.1016/j.amjcard.2012.01. 362 The SOLVD Investigators. (1991). Effect of enalapril on survival in patients with reduced left ventricular ejection fractions and congestive heart failure. New England Journal of Medicine, 325(5), 293–302. doi:10.1056/NEJM199108013250501 Vansuch, M., Naessens, J. M., Stroebel, R. J., Huddleston, J. M., & Williams, A. R. (2006). Effect of discharge instructions on readmission of hospitalized patients with heart failure: Do all of the Joint Commission on Accreditation of Healthcare Organizations heart failure core measures reflect better care? Quality and Safety in Health Care, 15(6), 414–417. doi:10.1136/qshc.2005.017640
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