Predictors of Hospital Length of Stay and Readmissions in Ischemic Stroke Patients and the Impact of Inpatient Medication Management

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Predictors of Hospital Length of Stay and Readmissions in Ischemic Stroke Patients and the Impact of Inpatient Medication Management Arinze Nkemdirim Okere, PharmD, MS, MBA, BCPS,* Colleen M. Renier, Angela Frye, BSN, RN, CNRN‡

BS,†

and

Objective: This study was designed to evaluate predictors of hospital length of stay (LOS) and readmissions among nonsurgical ischemic stroke patient, and the impact of inpatient medication management. Methods: This retrospective cohort study includes adult patients (≥18 years) hospitalized with a diagnosis of nonsurgical ischemic stroke from November 2007 to March 2013. In November 2011, an inpatient medication management model was implemented in the stroke unit. At the end of the study period, patients were matched before and after implementation of the inpatient medication management model (non-PHC [pharmacist– hospitalist collaborative] and PHC, respectively) to evaluate change in outcomes. The primary outcome of the study is an evaluation of predictive factors affecting LOS and readmissions. Additionally, changes in LOS and all-cause readmission at 30, 60, and 90 days when compared between PHC and non-PHC were evaluated. Findings: A total of 151 PHC patients were matched to 248 non-PHC patients. There was no difference in LOS between the PHC and non-PHC patients (mean adjusted difference −.14; P = .66). Similar finding was observed for readmissions (P > .05). Insurance type was a significant predictor of LOS, with Medicare patients having an extended LOS compared to patients with private insurance (mean difference −1.00; P = .005). Patients taking statins and patients aged less than 80 years had a lower 30-day readmission rate compared to nonstatin users and patients aged 80 years or older, respectively (P < .05). Conclusions: Insurance type and severity of illness are important predictors of LOS, whereas readmissions are mostly influenced by age and statin use. Key Words: Inpatient medication therapy management—medication reconciliation—stroke—model of care—ischemic stroke. © 2016 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Background and Objective From the *College of Pharmacy Florida A&M University, Tallahassee Florida; †Essentia Health, Essentia Institute of Rural Health, Duluth, Minnesota; and ‡Neuroscience Unit, Spectrum Health, Butterworth Hospital, Grand Rapids, Michigan. Received January 29, 2016; revision received April 4, 2016; accepted April 13, 2016. Grant: Research funded by the Blue Cross Blue Shield of Michigan Foundation. Address correspondence to Arinze Nkemdirim Okere, PharmD, MS, MBA, BCPS, College of Pharmacy, Florida A&M University, 1415 S. Martin Luther King Jr. Blvd, Tallahassee, FL 32307. E-mail: [email protected]. 1052-3057/$ - see front matter © 2016 National Stroke Association. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2016.04.011

Stroke is the fourth leading cause of death and the second leading cause of disability. In the United States, 795,000 people suffer either new or recurrent stroke annually, and it is the cause of 1 in every 19 deaths.1 Although the management of stroke has improved over time, it comes with a high economic burden.2 In 2008, the total direct and indirect costs of both inpatient and outpatient care associated with stroke were approximately $65 billion.3 Over half of the cost associated with ischemic stroke is linked to inpatient care.2-4 Notably, the best predictive markers for cost of hospitalization among stroke patients are hospital length of stay (LOS) and readmission rates.2-7 Therefore, it is reasonable to focus attention on

Journal of Stroke and Cerebrovascular Diseases, Vol. ■■, No. ■■ (■■), 2016: pp ■■–■■

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sustainable quality improvement efforts that could reduce LOS and readmissions while providing optimal care. To accomplish the aforementioned goals, it is imperative to understand modifiable and unmodifiable predictive factors that may influence LOS and readmission. As a result, several studies had explored these predictive factors. First, the average LOS among ischemic stroke patients is at least 6 days.7 The observed extended LOS has been associated with 2 key factors as follows: In 2 studies, Koton et al and Chang et al demonstrated that the level of stroke severity is a strong predictor of hospital LOS.7,8 In another example, Zhu et al conducted a retrospective study comparing ischemic stroke patients admitted in medical wards and stroke unit. The authors observed that patients admitted in the stroke unit had a shorter length of days (15 days) compared to patients in the medical wards (19 days).9 They further observed that patients in the stroke units were less likely to have extended LOS greater than 7 days compared to patients admitted in the medical ward.9 Deductively, in a stroke unit, the level of stroke severity is an important predictor of LOS. With respect to readmissions, the frequency of hospital readmission among the subpopulation of ischemic stroke patient is 6.5%-24.5%.7,10,11 However, the data for predictors of readmissions are inconsistent across several institutions.12 Kilkenny et al showed that the 2 predictive factors associated with early readmissions among stroke patients in Australia were being dependent prior to admission and having several complications during admission.11 Most of the aforementioned studies were either performed in a different setting or a country with a different health care management system compared to the United States. For example, in contrast to the United States, Australia has a universal national care program.13 Therefore, the generalization of these studies to community hospitals located in the Untied States is limited. This is significant because the U.S. community hospitals serve patients from both rural and urban areas, and the absence of this information will negatively impact the development of cost-effective novel practices. Therefore, our aim is to identify the predictive factors of LOS and readmissions in a single community hospital. Furthermore, as secondary analysis, we will evaluate the impact of inpatient medication management on LOS and readmissions. On the latter aim, we hypothesize that inpatient medication management could influence hospital LOS. This hypothesis is based on our prior study in medically ill patients that showed that inpatient medication therapy management provided by pharmacist in collaboration with hospitalist was associated with reduction in hospital LOS compared to routine care (mean difference = −.73 days, P < .01).14 Similar findings were observed in other studies. For example, Maldonado et al.15 demonstrated that provision of inpatient medication therapy management by pharmacist in an inpatient kidney transplant team was associated with increased cost savings and reduction in hospital LOS.15 In another example, Kucukarslan

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et al. demonstrated that pharmacist participation in rounds and in the prevention of adverse drug events was associated with reduction of LOS and readmissions. However, the observed difference was not statistically significant.16 Given the results of our prior study and others, we decided to test if similar effect will be observed in nonsurgical ischemic stroke patients in the medical unit. In this study, inpatient medication management was defined following the steps as demonstrated in our earlier study (as will be shown in the Methods section).14 Therefore, the primary objective of this study is to evaluate the predictors of LOS and readmissions. The secondary objective is to evaluate the impact of inpatient medication management on LOS and readmissions among nonsurgical ischemic stroke patients.

Methods Design and Setting We conducted a retrospective cohort study of patients admitted with nonsurgical ischemic stroke between 2007 and March 2013. This institution received its certification as a primary stroke center by the Joint Commission in 2004. A primary stroke center certification is a designation that recognizes hospitals that meet the required standards to support better outcomes for stroke care.17 The study was approved by the Spectrum Health Institution Review Board, Grand Rapids, Michigan.

Description of Usual Model of Care In the usual model of care provided in the unit (routine care), stable ischemic stroke patients are admitted in the neurology medical floor to a hospitalist or neurologist care. Medication history is then collected by the pharmacy technician prior to provider’s encounter, and discharge counseling is provided by a health provider or by a registered nurse. Clinical pharmacists are only consulted for specified drug therapy problems or to assist with pharmacokinetics monitoring and evaluation. Therefore, there is no formal or direct clinical pharmacist involvement with ischemic stroke patients in the medical unit.

Intervention An inpatient medication therapy management and reconciliation stroke service was implemented from November 2011 to March 2013 among patients diagnosed with ischemic stroke. This was done following similar steps as described by Okere et al14 (see steps below). Furthermore, each step of the inpatient medication management was designed to be a collaboration between the pharmacist and the hospitalist (pharmacist–hospitalist collaborative model of care [PHC]). A clinical pharmacist was designated to provide this service during the day (8:00 a.m. to 4:00 p.m.) from Monday to Friday. The

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inpatient medication management service as provided by the designated pharmacist followed 4 essential steps: 1. Patient’s profile review: Pharmacist reviewed patient’s medication/s, social and medical history profiles, and laboratory results/imaging/vital signs. (Inpatient medications and laboratory results/imaging/ vital signs were reviewed daily.) 2. Verification through patient (or caregiver) interview: Pharmacist verified patients’ medication-adherence pattern. This was accomplished by face-to-face interviews and verifications from patients’ primary or secondary pharmacies where prescriptions were filled (student pharmacist assisted in verification of patient’s medication history). 3. Medication therapy management: Discrepancies were documented and reported. These discrepancies included noncompliance and core medications (based on stroke clinical guideline18) that were not included in patients’ therapeutic regimen. Medication adjustment or error based on patients’ comorbidities was discussed with the attending hospitalist. Home and prescribed medications were optimized based on current guidelines. (Note: Documentation was not part of the patients’ permanent Electronic Health Record (EHR) but discrepancies were communicated directly to the hospitalist via electronic means or phone). Of note, it is expected that the first 3 steps (steps 1-3) of the aforementioned process be provided within 24 or 48 hours to the patient being admitted in the medical unit. During step 3, in addition to optimizing all therapies, particular attention was given to the following recommendations (following the discussion with the attending physician): A. Ensure that blood pressure medications were optimized prior to discharge. For example, ensure that angiotensin-converting-enzyme inhibitor or angiotensin receptor blockers are initiated for appropriate patients (e.g., patients are excluded if they have history of adverse events or developed acute kidney injury). B. Initiate HMG CoA reductase inhibitors (statins) in appropriate patients irrespective of level of low-density lipoprotein cholesterol (exclude if they have previous history of rhabdomyolysis or are intolerant to statins) and modification of statins to appropriate dose (e.g., switching from low-intensity to high-intensity statins). C. Initiate B-blockers (BB) for all ischemic stroke patients with past history of cardiovascular events such as myocardial infarction (exclude BB if low heart rate <60 bpm; recommend carvedilol, bisoprolol, or metoprolol succinate if history of heart failure exists as recommended by heart failure guideline). 4. Patient education: Appropriate medication education was provided to these patients prior to discharge.

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Patient Selections Following a 12-month implementation of service, cohorts of ischemic stroke patients were identified through the institution electronic medical record from 2007 to 2013. Patients admitted prior to the implementation of the inpatient medication management (November 2007 to October 2011) (non-PHC) were compared to patients admitted during the intervention period from November 2011 to March 2013 (PHC). The principal inclusion criteria were adult patients (at least 18 years old) with primary diagnosis of ischemic stroke as identified using the following ICD-9-CM codes: 433.01, 433.11, 433.21, 433.31, 433.81, 434.01, 434.11, or 434.91 (from the International Classification of Diseases, 9th Revision). Patients were excluded if they died during hospitalization or if they had a surgical APR-DRG (All Patient Refined-Diagnosis Related Groups) classification.

Patient Matching Patients were separated into 4 annual non-PHC study periods (preintervention periods): November 2007 to October 2008, November 2008 to October 2009, November 2009 to October 2010, November 2010 to October 2011, with the PHC intervention period from November 2011 to March 2013. Patients were further stratified based on the APR-DRG severity of illness (SOI) levels I-IV (I—Minor, II—Moderate, III—Major, IV—Extreme).

Criteria Greedy matching methods were used to create sets matched exactly on the following: APR-DRG SOI (Minorto-Moderate, Major-to-Extreme) and modified Deyo– Charlson Comorbidity Index (CCI). During the CCI scoring, diagnosis codes for cerebrovascular disease and hemiplegia points were excluded because they reflect conditions associated with ischemic stroke, a methodology used by Zhu et al.9 The CCI distribution for both groups (intervention or nonintervention) and the quartiles (Q) were as follows: 25th percentile (Q1) = 0; 50th percentile (Q2 or median) = 1; and 75th percentile (Q3) = 2. To determine the final CCI strata for patient matching, a preliminary logistic regression analysis was conducted to determine if statistically significant differences in study primary outcomes (see “outcomes” below) existed between the CCI categories: Q1 = 0, Q2 = 1, Q3 = 2, greater than Q3 = 3+ , and greater than or equal to Q3 = 2+ with P values adjusted for multiple comparisons (Bonferroni’s). Significant differences existed for CCI scores: 0 versus 1, 0 versus 2+, 1 versus 2+, and 1 versus 3+, but there was no significant difference for 2 versus 3+ for any study endpoint. Therefore, patients were grouped and matched based on the following CCI categories: 0, 1, 2+.

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Process PHC patients were matched to non-PHC patients, with 2 non-PHC patients identified for each PHC patient and the non-PHC patients selected from different study periods (as available), based upon a greedy matching protocol. This resulted in 54 sets matched 1:1 and 97 sets matched 2:1 non-PHC to PHC patients per matched set.

Outcomes The primary outcome of the study is to identify the predictors of LOS and 30-, 60-, 90-day all-cause readmissions. Furthermore, as secondary outcomes, we evaluated the differences in LOS, as measured from time of admission to discharge alive, and 30-, 60-, 90-day allcause readmissions between PHC and non-PHC groups.

Statistical Analysis Descriptive analyses present population characteristics for the total cohort of PHC and non-PHC patients, with unadjusted statistical comparisons. A statistically significant difference was defined as P < .05. The cohort of nonsurgical stroke patients was analyzed using the chisquare test of significance for categorical variables and Student’s t-test for continuous variables. Descriptive analysis for matched sets utilized generalized estimating equations (GEEs) unadjusted statistical comparisons, with binary logistic models used to evaluate binary outcomes and linear models used in assessment of continuous variables. Because the LOS data were not normally distributed (Shapiro–Wilk = .979, P < .001), LOS was analyzed as natural log of LOS (LnLOS), then converted as exp (LnLOS). Multivariate and bivariate matched analyses (study primary endpoint, LOS [bivariate analysis was used], and all-cause readmission at 30, 60, and 90 days) were analyzed through GEE evaluation comparing non-PHC and PHC sets of patients. Models were evaluated stepwise with the following variables included as potential covariates: sex; race (white or nonwhite); age; insurance dummy variables for Medicaid, Medicare, and other insurances (private); discharge status and treatment variables (warfarin [Coumadin], statins, tissue plasminogen activator [alteplase], aspirin, and clopidogrel). The aforementioned treatment variables were chosen because they are the core medications for ischemic stroke management and most likely to impact LOS and readmissions. Criteria for covariate inclusion or exclusion in the model were P < .05 to enter and P > .1 to remove. All data analyses were performed with IBM SPSS Statistics 23 (IBM Corp., Armonk, N.Y., USA).

Out of 530 eligible patients, 169 were in the PHC group. Furthermore, 296 of the 530 patients had Medicare, and of the 296 Medicare patients, 105 were in the PHC group. Table 1 presents the characteristics of and unadjusted analysis for nonsurgical ischemic stroke patients, both for the full cohort and the matched sets. In the matched sets, there were no statistically significant differences between the non-PHC and PHC groups (see Table 1 [151 matched sets]).

Hospital LOS and Associated Predictors Inpatient Medication Management In the unadjusted analysis, non-PHC patients had a longer LOS (5.5 days) than PHC patients (5.2 days); however, this was not statistically significant (Table 2). Similarly, in the adjusted analysis, there was no significant difference in LOS between PHC and non-PHC patients. PHC patients had LOS of 5.89 days whereas nonPHC patients had LOS of 6.02 days (P = .660) (Table 3). Predictors In the adjusted analysis, insurance type and SOI were identified as significant factors affecting LOS. Patients with Medicare insurance had a longer LOS compared to patients with private insurance (mean difference = 1.00 days, P = .005). In addition, patients with higher level of SOI were more likely to experience an extended LOS (mean difference = 2.44 days, P < .001). Patients who received Coumadin were more likely to experience an extended LOS (mean difference = 1.81 days, P < .001) (Table 3). Finally, patients with diagnosis of hyperlipidemia had a shorter LOS compared to patients with no documented diagnosis (mean difference = −.60 days, P = .045) (Table 3).

All-Cause Readmissions (Rates/1000) and Associated Predictors Inpatient Medication Management In the unadjusted matched analysis, there was no significant difference between PHC and non-PHC patients for all-cause readmission at 30, 60, or 90 days. However, CCI, SOI, statin use, discharge status, and age were identified as significant factors (Table 4). Similarly, in the adjusted matched analysis, there was no significant difference observed between PHC patients and non-PHC patients. Predictors

Results Baseline Characteristics In this retrospective study, we began with 530 patients with a diagnosis of nonsurgical ischemic stroke.

Patients aged less than 80 years were less likely to be readmitted within 30 and 60 days post discharge compared to patients who are at least 80 years old (Table 5). Patients taking statins were less likely to be readmitted within 30 days (5.3%) compared to nonstatin users (15.4%)

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Table 1. Nonsurgical ischemic stroke patients Full cohort1,2 Non-PHC (n = 361) Study period: N (%)† November 2007 to October 2008 November 2008 to October 2009 November 2009 to October 2010 November 2010 to October 2011 November 2011 to March 2013 (PHC) Medical/Surgical × CCI Score# × Severity Strata‡ Medical—Severity = Minor-to-moderate CCI = 0 Medical—Severity = Minor-to-moderate CCI = 1 Medical—Severity = Minor-to-moderate CCI ≥ 2 Medical—Severity = Major-to-extreme CCI = 0 Medical—Severity = Major-to-extreme CCI = 1 Medical—Severity = Major-to-extreme CCI = ≥ 2 Insurance: N (%) None (including charity care) Medicaid (including hospice) Medicare‡ Other insured Medications: N (%) Statins Tissue plasminogen activator Warfarin (Coumadin) Aspirin Clopidogrel Sex—male: N (%)§ Age group: N (%)§,* <55 years 55-64 years 65-79 years ≥80 years Race—white: N (%)§ Comorbidities: N (%)§ Hyperlipidemia (LIPID) Essential hypertension Diabetes mellitus Chronic obstructive pulmonary disease Other cerebrovascular disease* Heart failure (congestive heart failure) Myocardial infarction Atrial fibrillation Other coronary artery disease Peripheral vascular disease Deceased at discharge: N (%)‖ Length of stay¶: mean (standard deviation) Discharged to home¶: N (%) All-cause readmission¶: N (%) 30 days 60 days 90 days

151 Matched sets3,4

PHC (n = 169)

60 (16.6) 90 (24.9) 123 (34.1) 88 (24.4) 169 (100.0)

Non-PHC (n = 248)

PHC (n = 151)

(matching criteria) 45 (18.1) 53 (21.4) 84 (33.9) 66 (26.6) 151 (100.0) (matching criteria) 60 (24.2) 31 (20.5) 34 (13.7) 24 (15.9) 20 (8.1) 16 (10.6) 36 (14.5) 19 (12.6) 34 (13.7) 24 (15.9) 64 (25.8) 37 (24.5)

94 (26.0) 40 (11.1) 39 (10.8) 54 (15.0) 43 (11.9) 91 (25.2)

33 (19.5) 24 (14.2) 16 (9.5) 22 (13.0) 27 (16.0) 47 (27.8)

27 (7.5) 48 (13.3) 191 (52.9) 95 (26.3)

10 (5.9) 13 (7.7) 105 (62.1) 41 (24.3)

18 (7.3) 27 (10.9) 144 (58.1) 59 (23.8)

8 (5.3) 13 (8.6) 93 (61.6) 37 (24.5)

289 (80.1) 71 (19.7) 94 (26.0) 269 (74.7) 134 (37.2) 193 (53.5)

144 (85.2) 32 (18.9) 31 (18.3) 125 (74.0) 61 (36.1) 83 (49.1)

203 (81.9) 50 (20.2) 65 (26.2) 184 (74.5) 91 (36.8) 129 (48.0)

130 (86.1) 28 (18.5) 27 (17.9) 113 (74.8) 55 (36.4) 73 (48.3)

98 (27.1) 74 (20.5) 102 (28.3) 87 (24.1) 313 (90.5)

27 (16.0) 39 (23.1) 62 (36.7) 41 (24.3) 150 (91.5)

52 (21.0) 54 (21.8) 81 (32.7) 61 (24.6) 215 (90.3)

26 (17.2) 35 (23.2) 52 (34.4) 38 (25.2) 134 (91.8)

237 (65.7) 242 (67.0) 107 (29.6) 45 (12.5) 48 (13.3) 53 (14.7) 41 (11.4) 85 (23.5) 97 (26.9) 21 (5.8) 8 (2.2) 5.4 (1.7) 167 (47.3)

108 (63.9) 115 (68.0) 51 (30.2) 28 (16.6) 37 (21.9) 24 (14.2) 20 (11.8) 52 (30.8) 49 (29.0) 12 (7.1) 5 (3.0) 5.2 (1.8) 84 (51.2)

163 (65.7) 96 (63.6) 168 (67.7) 103 (68.2) 74 (29.8) 43 (28.5) 32 (12.9) 22 (14.6) 29 (11.7) 21 (13.9) 37 (14.9) 23 (15.2) 29 (11.7) 19 (12.6) 61 (24.6) 45 (29.8) 68 (27.4) 43 (28.5) 14 (5.6) 10 (6.6) (excluded from matching) 5.5 (1.8) 5.2 (1.8) 113 (45.6) 75 (49.7)

36 (10.2) 54 (15.3) 68 (19.3)

20 (12.2) 25 (15.2) 34 (20.7)

22 (8.9) 37 (14.9) 50 (20.2)

18 (11.9) 22 (14.6) 31 (20.5)

Abbreviations: CCI, Charlson Comorbidity Index; PHC, pharmacist–hospitalist collaborative model. Full cohort—group comparisons: 1Categorical variables—chi-square test, 2Continuous variables—independent samples t-test (two tailed), * P < .05. Matched sets—generalized estimating equations: 3Categorical variables—binary logistic models, 4Continuous variables—linear models, **P < .05. †Matching criteria—Up to 2 Non-PHC per PHC, from different periods ‡Matching criteria—Exact match; c Included in Logistic regression. §Included in Logistic regression creation of propensity score (probability of PHC), matched +/- .05 (5%); ‖Excluded from matching. ¶Denominator is original n minus number deceased at discharge. #Cerebrovascular Disease and Hemiplegia/paraplegia points removed, if initially coded, to obtain a score comorbid to primary diagnosis of stroke.

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Table 2. Length of stay, in days—unadjusted associations with study covariates‡ All 151 matched sets Covariates Intervention group Non-PHC PHC Matching criteria APR-DRG severity of illness† Minor-to-moderate Major-to-extreme Deyo–Charlson Comorbidity Index† 0 1 2+ Insurance None (including charity care) Medicaid (including hospice) Medicare† Other insured Sex Male Female Age <55 years 55-64 years 65-79 years 80+ years Race—white No Yes Comorbidities Hyperlipidemia (LIPID) No Yes Essential hypertension No Yes Diabetes mellitus No Yes Chronic obstructive pulmonary disease No Yes Other cerebrovascular disease No Yes Heart failure (congestive heart failure) No Yes Myocardial infarction No Yes Atrial fibrillation No Yes Other coronary artery disease No Yes

N

Mean (SD)

Difference

248 151

5.5 (1.8) 5.2 (1.8)

— −.3

185 214

4.2 (1.6) 6.7 (1.8)

— 2.5***

146 116 137

4.7 (2.0) 5.6 (2.0) 6.0 (1.6)

—

26 40 237 96

5.5 (1.8) 6.4 (1.7) 5.7 (1.7) 4.4 (2.1)

−.2 .7 — −1.3**

207 192

5.3 (2.0) 5.5 (1.8)

−.2 —

78 89 133 99

5.0 (2.0) 5.0 (2.0) 5.7 (1.8) 5.8 (1.6)

−.8 −.8**** −.1 —

35 349

5.9 (1.7) 5.3 (1.9)

— −.5

140 259

5.8 (1.9) 5.2 (1.9)

— −.6****

128 271

5.7 (1.7) 5.3 (2.0)

— −.4

282 117

5.3 (1.9) 5.8 (1.7)

—

345 54

5.3 (1.9) 6.2 (1.7)

— 1.0*

349 50

5.3 (1.9) 6.5 (1.7)

— 1.2**

339 60

5.2 (1.9) 6.5 (1.7)

— 1.3**

351 48

5.3 (1.9) 5.9 (1.7)

— .6

293 106

5.1 (1.9) 6.4 (1.7)

— 1.3***

288 111

5.3 (1.9) 5.6 (1.8)

—

.9* 1.3**

.5

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Table 2.

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All 151 matched sets Covariates Peripheral vascular disease No Yes Medications Statins No Yes Tissue plasminogen activator No Yes Warfarin (Coumadin) No Yes Aspirin No Yes Clopidogrel No Yes

N

Mean (SD)

Difference

375 24

5.4 (1.9) 5.1 (1.6)

— −.3

66 333

5.5 (1.9) 5.4 (1.8)

— −.2

320 78

5.4 (1.9) 5.4 (1.6)

— .0

307 92

5.0 (1.8) 7.0 (1.6)

101 297

5.4 (1.7) 5.4 (1.9)

— −.1

252 146

5.4 (1.8) 5.3 (1.8)

— −.1

2.0***

Abbreviations: APR-DRG, All Patient Refined-Diagnosis Related Groups; PHC, pharmacist–hospitalist collaborative model. *P < .05; **P < .01; ***P < .001; ****P < .1 (not significant, but included in multivariate models). †Exact match. ‡Generalized estimating equations—linear models.

(P = .018). Furthermore, men were 7.5% more likely to be readmitted than women at 30 days (P = .014) and 8.9% more likely at 60 days (P = .022). Additionally, CCI was the only predictor for 90-day readmission post discharge. Patients with higher CCI (2+) were more likely to be readmitted compared to patients with CCI of 0 (P = .002) (Table 5). Finally, the reasons for readmissions were summarized in Appendix A. For adjusted 30-, 60-, and 90-day readmissions, besides ischemic stroke, diseases

of respiratory system and digestive system were the 2 most common reasons for readmission among ischemic stroke patients (see Appendix A).

Discussion Among the published studies on the predictors of LOS and readmission in ischemic stroke patients, this is the first that has been conducted in a community hospital

Table 3. 151 Matched sets: adjusted matched analysis*—length of stay, in days LOS

Covariates Intervention group APR-DRG SOI Type of insurance

Hyperlipidemia (LIPID) Warfarin (Coumadin)

Group 1 (Ref.)

Group 2

Group 1 Mean (SD)

Group 2 Mean (SD)

Mean difference

P value

Non-PHC Minor-to-moderate Medicare Medicare Medicare No No

PHC Major-to-extreme None Medicaid Other insurance Yes Yes

6.02 (1.84) 4.86 (1.75) 6.03 (1.61) 6.03 (1.61) 6.03 (1.61) 6.26 (1.77) 5.12 (1.97)

5.89 (1.88) 7.30 (2.01) 5.92 (1.61) 7.00 (1.56) 5.03 (1.84) 5.66 (1.97) 6.93 (1.66)

−.14 2.44 −.10 .97 −1.00 −.60 1.81

.660 <.001 .857 .048 .005 .045 <.001

Abbreviations: APR-DRG, All Patient Refined-Diagnosis Related Groups; PHC, pharmacist–hospitalist collaborative model; SD, standard deviation; SOI, severity of illness. *Generalized estimating equations—linear models—stepwise model entered intervention group (PHC/non-PHC) and tested the following covariates: Charlson Comorbidity Index, SOI, type of insurance, age group, LIPID, chronic obstructive pulmonary disease, other coronary artery disease, heart failure, atrial fibrillation, and warfarin (see Table 2).

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Table 4. 151 Matched sets: unadjusted matched analysis†—all-cause readmission, rate, and SE (%) ≤30 days Independent variables Intervention group Non-PHC PHC Matching criteria Deyo–Charlson Comorbidity Index‡ 0 1 2+ APR-DRG severity of illness‡ Minor-to-moderate Major-to-extreme Insurance None (including charity care) Medicaid (including hospice) Medicare‡ Other Sex Male Female Age <55 years 55-64 years 65-79 years 80+ years Race—white No Yes Discharged to home No Yes Comorbidities Hyperlipidemia (LIPID) No Yes Essential hypertension No Yes Diabetes mellitus No Yes Chronic obstructive pulmonary disease No Yes Other cerebrovascular disease No Yes Heart failure (congestive heart failure) No Yes Myocardial infarction No Yes Atrial fibrillation No Yes

≤60 days

≤90 days

N

Rate (SE)

Difference

Rate (SE)

Difference

Rate (SE)

Difference

248 151

8.9 (1.9) 11.9 (2.6)

—

14.9 (2.4) 14.6 (2.9)

— −.3

20.2 (2.6) 20.5 (3.3)

—

3.0

146 116 137

8.2 (2.5) 5.2 (2.0) 16.1 (3.0)

— −3.0 7.8*

11.0 (2.6) 11.2 (2.7) 21.9 (3.3)

—

15.1 (3.1) 17.2 (3.5) 28.5 (3.2)

—

.2 10.9**

185 214

5.9 (1.8) 13.6 (2.3)

—

12.4 (2.4) 16.8 (2.4)

— 4.4

15.1 (2.7) 24.8 (2.6)

—

7.6*

26 40 237 96

3.8 (3.6) 5.0 (3.5) 13.5 (2.2) 5.2 (2.2)

−9.7* −8.5* — −8.3**

3.8 (3.6) 5.0 (3.5) 18.6 (2.5) 12.5 (3.1)

−14.7** −13.6** — −6.1

7.7 (5.1) 20.0 (5.9) 23.6 (2.6) 15.6 (3.3)

−15.9 −3.6 — −8.0****

207 192

12.6 (2.4) 7.3 (1.9)

—

17.9 (2.8) 11.5 (2.3)

−6.4**** —

21.7 (3.0) 18.8 (2.8)

— −3.0

78 89 133 99

2.6 (1.8) 5.6 (2.4) 7.5 (2.5) 23.2 (4.1)

−20.7*** −17.6*** −15.7** —

7.7 (2.8) 10.1 (3.0) 12.0 (2.9) 28.3 (4.2)

−20.6*** −18.2*** −16.3** —

16.7 (3.9) 16.9 (3.7) 18.8 (3.4) 28.3 (4.2)

−11.6* −11.4* −9.5**** —

35 349

17.1 (6.1) 9.5 (1.6)

— −7.7

17.1 (6.1) 14.9 (1.9)

— −2.2

31.4 (7.4) 19.8 (2.1)

— −11.7

211 188

14.2 (2.4) 5.3 (1.6)

— −8.9**

18.0 (2.6) 11.2 (2.2)

— −6.8*

24.2 (2.9) 16.0 (2.6)

— −8.2*

140 259

8.6 (2.3) 10.8 (2.1)

—

15.7 (2.8) 14.3 (2.2)

— −1.4

20.0 (3.1) 20.5 (2.6)

—

2.2

128 271

11.7 (2.8) 9.2 (1.9)

— −2.5

18.8 (3.4) 12.9 (2.1)

— −5.8

25.0 (3.7) 18.1 (2.4)

— −6.9

282 117

10.6 (1.9) 8.5 (2.7)

— −2.1

14.9 (2.0) 14.5 (3.5)

—

19.5 (2.3) 22.2 (3.8)

—

−.4

345 54

10.7 (1.7) 5.6 (3.2) 9.7 (1.7)

— −5.2

14.5 (1.9) 16.7 (5.1)

—

19.7 (2.1) 24.1 (5.5)

—

2.2

—

19.5 (2.0) 26.0 (5.4)

—

1.4

5.3****

.4

2.2 13.4**

9.6*

.5

2.7

4.4

349 50

2.3

14.6 (1.9) 16.0 (4.9)

—

12.0 (4.4)

339 60

10.3 (1.7) 8.3 (3.5)

— −2.0

14.2 (1.9) 18.3 (4.8)

—

19.2 (2.1) 26.7 (5.1)

—

4.2

351 48

10.5 (1.7) 6.3 (3.5)

— −4.3

15.4 (1.9) 10.4 (4.3)

— −5.0

21.1 (2.2) 14.6 (4.9)

— −6.5

293 106

8.5 (1.6) 14.2 (3.5)

—

13.3 (1.9) 18.9 (3.7)

—

18.1 (2.3) 26.4 (4.1)

—

5.6

5.6

6.5

7.5

8.3****

(continued on next page)

ARTICLE IN PRESS PREDICTORS OF LENGTH OF STAY AND READMISSIONS

Table 4.

9 (continued)

≤30 days Independent variables Other coronary artery disease No Yes Peripheral vascular disease No Yes Medications Statins No Yes Tissue plasminogen activator No Yes Warfarin (Coumadin) No Yes Aspirin No Yes Clopidogrel No Yes

≤60 days

≤90 days

N

Rate (SE)

Difference

Rate (SE)

Difference

Rate (SE)

Difference

288 111

9.4 (1.8) 11.7 (3.0)

—

14.9 (2.1) 14.4 (3.2)

— −.5

20.8 (2.3) 18.9 (3.5)

—

2.3

375 24

10.4 (1.6) 4.2 (3.7)

—

15.2 (1.8) 8.3 (7.5)

— −6.9

20.8 (2.0) 12.5 (8.1)

—

−6.2

66 333

21.2 (4.9) 7.8 (1.5)

— −13.4*

24.2 (5.1) 12.9 (1.9)

— −11.3*

25.8 (5.1) 19.2 (2.2)

—

320 78

9.7 (1.7) 10.3 (3.4)

—

15.0 (2.0) 12.8 (3.7)

— −2.2

20.3 (2.2) 19.2 (4.3)

—

.6

307 92

10.1 (1.7) 9.8 (3.0)

—

15.0 (2.0) 14.1 (3.5)

— −.9

19.2 (2.2) 23.9 (4.2)

—

−.3

101 297

12.9 (3.6) 8.8 (1.7)

—

20.8 (4.3) 12.5 (1.9)

— −8.3****

22.8 (4.5) 19.2 (2.3)

—

−4.1

252 146

10.7 (1.9) 8.2 (2.4)

—

13.9 (2.1) 15.8 (2.9)

—

19.8 (2.3) 20.5 (3.3)

—

−2.5

1.9

−1.9 −8.3

−6.5 −1.1

4.7 −3.6

.7

Abbreviations: APR-DRG, All Patient Refined-Diagnosis Related Groups; PHC, pharmacist–hospitalist collaborative model; SE, standard error. *P < .05; **P < .01; ***P < .001; ****P < .1 (not significant, but included in multivariate models). †Generalized estimating equations—binary logistic models. ‡Exact match.

located in the United States. Based on our adjusted matched analysis, insurance type and level of SOI were significant independent factors affecting LOS. Medicare patients had an extended LOS (5.54 days) compared to patients with private insurance (4.63 days). This is consistent with other studies which have shown that Medicare patients tend to have higher LOS compared to other (private) insurances.19,20 Although our study did not investigate the reason for these differences, we speculate that the observed difference in LOS between Medicare patients and patients with private insurance may be related to the differences in the type of reimbursement system inherent in the health insurance plan. Unlike private insurance that covers outpatient facilities without a stipulated minimum hospital stay, Medicare only pays for outpatient facilities when patients have experienced at least a 3-day hospital stay.21 In a subanalysis of our study, we observed that 96.9% of our patients aged less than 65 years were covered by private insurance whereas 91.6% of stroke patients aged 65 years or older were covered by Medicare (see Appendix B). Therefore, the data suggest that the aforementioned differences in reimbursement plan may explain the reason for the extended LOS among Medicare patients when compared to patients with private

insurance. Furthermore, similar to studies conducted by Koton et al7 and Chang et al,8 level of severity was identified as a strong predictor of hospital LOS. Additionally, patients with a documented diagnosis of hyperlipidemia were more likely to have a shorter LOS compared to patients without documented diagnosis of hyperlipidemia. Although the rationale for this intriguing observation is unknown, we suspect that the fact that it is documented (i.e., diagnosis of hyperlipidemia) is a reflection of these patients receiving care from outpatient health care providers which translated to better outcome. Finally, the use of Coumadin is associated with extended LOS. Although the reason for this is multifactorial, we speculate that the observed extended LOS is related to the time of Coumadin administration and the wait time to reach target International normalized ratio (INR) for appropriate patients. However, with wide spread use of newer oral anticoagulants, this may not be the case. With respect to all-cause readmissions, the use of statin was a significant factor affecting all-cause readmissions at 30 days post discharge. Although patients taking statins were less likely to be readmitted at 60 days post discharge compared to patients not taking statins, the observed difference was not statistically significant. Our findings

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10

Table 5. 151 Matched sets: adjusted matched analysis—all-cause readmission, rate, and SE (%) ≤30 days* Covariates

Group 1 (Ref.)

Group 2

Group 1 Rate (SE)

Group 2 Rate (SE)

Mean difference

P value

Intervention group Sex Age (years)

Non-PHC Female 80+ 80+ 80+ No

PHC Male <55 55-64 65-79 Yes

7.5 (2.0) 6.1 (2.1) 30.2 (5.2) 30.2 (5.2) 30.2 (5.2) 15.4 (4.4)

11.2 (3.4) 13.6 (2.9) 3.1 (2.3) 6.8 (3.2) 9.3 (2.9) 5.3 (1.5)

3.7 7.5 −27.0 −23.3 −20.9 −10.1

.282 .014 <.001 <.001 .001 .018

Taking any statin(s)

≤60 days† Covariates

Group 1 (Ref.)

Group 2

Exposed Rate (SE)

Unexposed Rate (SE)

Mean difference

P value

Intervention group Sex Age (years)

Non-PHC Female 80+ 80+ 80+ No

PHC Male <55 55-64 65-79 Yes

14.8 (2.6) 10.9 (2.6) 32.8 (4.9) 32.8 (4.9) 32.8 (4.9) 19.7 (4.5)

14.8 (3.5) 19.8 (3.2) 8.6 (3.3) 11.0 (3.8) 13.7 (3.2) 10.9 (1.9)

.0 8.9 −24.1 −21.7 −19.1 −8.7

.994 .022 <.001 <.001 .001 .069

Taking any statin(s)

≤90 days‡ Covariates

Group 1 (Ref.)

Group 2

Group 1 Rate (SE)

Group 2 Rate (SE)

Mean difference

P value

Intervention group Deyo–CCI

Non-PHC 0 0

PHC 1 2+

19.6 (2.6) 15.1 (3.1) 15.1 (3.1)

19.7 (3.3) 17.2 (3.5) 28.5 (3.2)

.1 2.2 13.4

.980 .641 .002

Abbreviations: CCI, Charlson Comorbidity Index; PHC, pharmacist–hospitalist collaborative model; SE, standard error. *Generalized estimating equations—binary logistic models: Variables originally in model: intervention group, CCI, severity of illness, insurance type, sex, age group, discharged to home, and statins (see Table 4). †Generalized estimating equations—binary logistic models: Variables originally in model: intervention group, CCI, insurance type, sex, age group, any statins, aspirin, and discharged to home (see Table 4). ‡Generalized estimating equations—binary logistic models: Variables originally in model: intervention group, CCI, severity of illness, insurance type, age group, atrial fibrillation, and discharged to home (see Table 4).

revealed that patients taking statins were less likely to be readmitted compared to patients who were not taking statins. This is consistent with the study conducted by Flint et al., suggesting that the use of statins is associated with better discharge outcome compared to nonstatin users.22 These data underscore the role of statins among ischemic stroke patients. Therefore, consistent with other studies, statin should always be initiated in appropriate ischemic stroke patients. Additionally, age was a significant factor affecting frequency of readmission at 30 and 60 days post discharge. Based on our observation, patients who are at least 80 years old were more likely to be readmitted within 30 and 60 days compared to patients aged less than 80 years. Notably, in the adjusted analysis, discharge status was not identified as a predictor for all-cause readmissions. Therefore, the rate of readmission was not shown to be

dependent on whether a patient was discharged home or to another facility. Furthermore, gender was a significant predictor of readmissions at 30 and 60 days. A similar observation was reported by Guru et al.23 In their study, they showed that the rate of readmissions associated with stroke among women was lower than that among men (HR .9, 95% CI .84-.97).23 However, the reason for this difference is unknown. We speculate that this may be associated with increased use of primary care by women, which may translate to better outcomes. Our hypothesis is based on the rationale that women are more likely to use primary care than men.24,25 Future studies will investigate the reasons for gender differences in all-cause readmissions. Our findings highlight several important predictors that are responsive to quality improvement efforts in order to provide a cost-effective, high-quality inpatient care while

ARTICLE IN PRESS PREDICTORS OF LENGTH OF STAY AND READMISSIONS

avoiding unnecessary extended LOS or preventable readmissions. For example, in our analysis, the most frequent reasons for readmissions among these patients were diseases of respiratory system. There were no readmissions for hyperlipidemia, chronic obstructive pulmonary disease, atrial fibrillation, other coronary artery disease, or peripheral vascular disease (see Appendix A). Therefore, this evidence highlights the need to improve steps necessary to prevent respiratory disease such as pneumonia or influenza among ischemic stroke patients as suggested by Fonarow et al26 and Ovbiagele et al.27 Based on our secondary analysis, PHC did not impact either LOS or all-cause readmissions among nonsurgical ischemic stroke patients. Although we have attempted to explore this outcome from inpatient perspective, in contrast to our prior LOS study in medically ill patients,14 inpatient medication management alone did not reduce LOS. Unlike other clinical states that have been previously investigated, it is evident that short-term medication therapy management intervention may not alter the long-term clinical outcomes associated with ischemic stroke. It requires a long-term consistent prevention of adverse effect and maintenance in an optimal clinical state to observe a beneficial clinical state. This is evident from the observed modifiable and unmodifiable predictors of LOS or readmission. For example, for the positive impact of statin use to be significant as identified, it has to be taken consistently beyond discharge. In another example, emphasis in managing risk factors for ischemic stroke among the elderly, such as blood pressure and hyperlipidemia management, may require special attention in an outpatient setting. Although the knowledge of the extent of the patients’ exposure to outpatient medication therapy management is unknown to the authors, we speculate that collaboration with outpatient clinical pharmacist may reduce readmissions. This speculation is based on the published studies (on unrelated disease states) that effective transition of care with timely outpatient follow-up will improve readmissions.28,29 However, this remains an area of future research—to evaluate prospectively the impact of outpatient followup after discharge on readmission rates among ischemic stroke patients.

Limitation This study has several limitations. First, this study was conducted in a single institution. Therefore, secondary analysis of our readmission rate did not account for readmissions in surrounding hospitals and our outcome may have been influenced by our limited effect size. Therefore, the generalization of our secondary analysis of our readmission data is limited. Given that this study was done in a primary stroke center, the only available comparable group is the other cohort of nonsurgical

11

ischemic stroke patients admitted prior years, after receiving the Joint Commission certification as a stroke center. Furthermore, at the time of the study, there may have been policy changes or other ongoing quality improvement initiatives (unknown to the authors) in the overall health system that were designed to accomplish the goals of an accountable care organization. This may have also influenced the results of our study. The results of this study highlight the difficulty of simply designing a single intervention for reducing LOS and readmissions among ischemic stroke patients. Additionally, we only captured data at the time of discharge. Therefore, it is unknown to the investigators if patients were taking these medications (such as statins, antiplatelet) prior to admission. Because of the retrospective nature of the study, there is a potential risk for exposure misclassification which may have influenced our secondary objective of the study. Second, although one clinical pharmacist was designated in the stroke unit to provide consistent service, the quality of service provided or total percentage of medication recommendations accepted by the hospitalist was not evaluated. Additionally, while the designated pharmacist verified medication-adherence patterns, these patterns were not captured and evaluated in association with reasons for readmission. Despite these limitations, our study provides a direction for a randomized clinical trial as previously proposed. Future study will evaluate the quality of service provided and the effect of collaborating or partnering with outpatient clinical pharmacist.

Conclusion Based on our primary outcome, the retrospective analysis revealed that insurance type and SOI were factors affecting LOS. Whereas, all-cause readmissions at 30, 60, and 90 days were mostly predicted by age and statin use. Furthermore, following our secondary outcome analysis, implementation of inpatient medication management alone among hospitalized nonsurgical ischemic stroke patients did not result in statistically significant changes in LOS and all-cause readmissions. These observations have an important implication as we develop novel patient care services or predictive models that will translate to improved LOS and readmissions among ischemic stroke patients in a system-wide institution in the United States. Acknowledgments: The authors gratefully acknowledge Ryan Foster, PharmD, MBA, Director of Pharmacy, Spectrum Health, Michigan; Theresa Price, BSN, RN (Stroke Coordinator); and Kevin Schaefer (System Analyst) for their technical support.

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12

Appendix A

Reasons for Readmissions Frequency of readmission (rates %) 30 days Categories Stroke Transient ischemic attack Sepsis Organ dysfunction Essential hypertension Diabetes mellitus Other cerebrovascular disease Heart failure (congestive heart failure) Myocardial infarction Other infectious and parasitic diseases (001-139) Neoplasms (140-239) Other diseases of the nervous system and sense organs (320-389) Other diseases of the circulatory system (390-459) Other diseases of the respiratory system (460-519) Other diseases of the digestive system (520-579) Other diseases of the genitourinary system (580-629) Musculoskeletal system and connective tissue diseases (710-739) Congenital anomalies (740-759) Symptoms, signs, and ill-defined conditions (780-799) Injury and poisoning (800-999)

60 days

90 days

PHC (%)

Non-PHC (%)

Total (%)

PHC (%)

Non-PHC (%)

Total (%)

PHC (%)

Non-PHC (%)

Total (%)

33.3 5.6 5.6 .0 .0 .0 .0 .0 .0 .0

22.7 .0 4.5 4.5 .0 .0 9.1 4.5 4.5 .0

27.5 2.5 5.0 2.5 .0 .0 5.0 2.5 2.5 .0

27.3 4.5 4.5 .0 .0 .0 .0 4.5 .0 .0

18.9 2.7 2.7 2.7 2.7 2.7 8.1 2.7 2.7 .0

22.0 3.4 3.4 1.7 1.7 1.7 5.1 3.4 1.7 .0

22.6 6.5 6.5 3.2 .0 .0 .0 3.2 .0 3.2

18.0 6.0 4.0 4.0 2.0 2.0 8.0 4.0 2.0 .0

19.8 6.2 4.9 3.7 1.2 1.2 4.9 3.7 1.2 1.2

.0 5.6

9.1 .0

5.0 2.5

.0 9.1

5.4 .0

3.4 3.4

3.2 9.7

4.0 .0

3.7 3.7

11.1

4.5

7.5

9.1

2.7

5.1

9.7

2.0

4.9

16.7

13.6

15.0

18.2

13.5

15.3

12.9

10.0

11.1

11.1

9.1

10.0

9.1

8.1

8.5

6.5

12.0

9.9

5.6

4.5

5.0

4.5

5.4

5.1

3.2

4.0

3.7

.0

.0

.0

.0

2.7

1.7

.0

2.0

1.2

.0 .0

.0 4.5

.0 2.5

.0 .0

10.8 2.7

6.8 1.7

.0 3.2

12.0 2.0

7.4 2.5

5.6

4.5

5.0

9.1

2.7

5.1

6.5

2.0

3.7

Abbreviation: PHC, pharmacist–hospitalist collaborative model. There were no readmissions for hyperlipidemia (LIPID), chronic obstructive pulmonary disease, atrial fibrillation, other coronary artery disease, or peripheral vascular disease.

Appendix B

Statistical Comparisons of Age Groups (<65 versus 65+) by Type of Insurance Coverage All patients—151 matched sets Type of insurance coverage

Age group: N (row %) <55 years 55-64 years 65-79 years 80+ years

None—not applicable (including 6 (23.1) charity care) Medicaid (including hospice) 27 (67.5) Medicare 1 (.4) Other insured 44 (45.8) Total 78 (19.5)

10 (38.5)

6 (23.1)

11 (27.5) 19 (8.0) 49 (51.0) 89 (22.3)

2 (5.0) 122 (51.5) 3 (3.1) 133 (33.3)

4 (15.4)

<65 years

65+ years

Total: N (column %)

16 (61.5)

10 (38.5)

26 (6.5)

38 (95.0) 2 (5.0) 20 (8.4) 217 (91.6) 93 (96.9) 9 (3.1) 99 (24.8) 167 (41.9) 232 (58.1) 95 (40.1)

(Generalized estimating equation) Wald chi-square = 105.301, df = 3, P < .001 (matched analysis). Bolded numbers represents the majority of ischemic stroke patients in each age category per insurance type.

40 (10.0) 237 (59.4) 96 (24.1) 399 (100.0)

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