Inpatient rehabilitation volume and functional outcomes in stroke, lower extremity fracture, and lower extremity joint replacement

Abstract

Background: It is unclear if volume-outcome relationships exist in inpatient rehabilitation.

Objectives: Assess associations between facility volumes and 2 patient-centered outcomes in the 3 most common diagnostic groups in inpatient rehabilitation.

Research design: We used hierarchical linear and generalized linear models to analyze administrative assessment data from patients receiving inpatient rehabilitation services for stroke (n=202,423), lower extremity fracture (n=132,194), or lower extremity joint replacement (n=148,068) between 2006 and 2008 in 717 rehabilitation facilities across the United States. Facilities were assigned to quintiles based on average annual diagnosis-specific patient volumes.

Measures: Discharge functional status (FIM instrument) and probability of home discharge.

Results: Facility-level factors accounted for 6%-15% of the variance in discharge FIM total scores and 3%-5% of the variance in home discharge probability across the 3 diagnostic groups. We used the middle volume quintile (Q3) as the reference group for all analyses and detected small, but statistically significant (P<0.01) associations with discharge functional status in all 3 diagnosis groups. Only the highest volume quintile (Q5) reached statistical significance, displaying higher functional status ratings than Q3 each time. The largest effect was observed in FIM total scores among fracture patients, with only a 3.6-point difference in Q5 and Q3 group means. Volume was not independently related to home discharge.

Conclusions: Outcome-specific volume effects ranged from small (functional status) to none (home discharge) in all 3 diagnostic groups. Patients with these conditions can be treated locally rather than at higher volume regional centers. Further regionalization of inpatient rehabilitation services is not needed for these conditions.

Figure 1

Mean predicted discharge FIM ratings and home discharge probabilities for all facilities (N = 717 stroke; N = 713 fracture; N = 712 joint replacement) by diagnosis-specific volumes. Multiple linear regression was used to compute each patient's predicted discharge FIM rating, from which the mean FIM rating for each facility was calculated. Multiple logistic regression was used to compute each patient's probability of home discharge, from which the mean home discharge probability for each facility was calculated. The scores reflect the expected mean values for each facility when applying its specific case-mix to the average effects derived from the entire diagnosis-specific population.

Figure 2

Casemix-adjusted mean discharge FIM ratings and home discharge probabilities for all facilities (N = 717 stroke; N = 713 fracture; N = 712 joint replacement) by diagnosis-specific volumes. Values were calculated by taking a facility's actual (observed) mean values and dividing by the predicted (expected) values for that facility (displayed in Figure 1), and multiplying those ratios by the grand mean FIM rating and home discharge probability, respectively, within each diagnosis category. This is a form of indirect standardization, wherein the scores reflect the mean outcomes experienced in each facility relative to the outcomes experienced by the entire diagnosis-specific population with a similar case-mix.

Figure 3

Predicted discharge cognition, motor, and total functional status ratings by diagnosis-specific volume quintiles. The values were obtained from hierarchical linear models and represent the predicted FIM ratings for white, female patients with average clinical characteristics (age, comorbidity burden, duration to admission, admission functional status, and length of stay) within each of the three diagnosis categories. Sample sizes increase across quintiles and vary by diagnosis: stroke 12,867 (Q1) – 83,567 (Q5), fracture 5,609 (Q1) – 59,511 (Q5), joint replacement 4,326 (Q1) – 79,831 (Q5), see Table for more details. Reference category = Q3 for all tests of significance. Underlined value labels indicate significant (p < .01) difference compared to Q3.

Figure 4

Predicted home discharge probability by diagnosis-specific volume quintiles. The values were obtained from hierarchical generalized linear models and represent the predicted probabilities for white, female patients with average clinical characteristics (age, comorbidity burden, duration to admission, admission functional status, and length of stay) within each of the three diagnosis categories. Sample sizes increase across quintiles and vary by diagnosis: stroke 12,867 (Q1) – 83,567 (Q5), fracture 5,609 (Q1) – 59,511 (Q5), joint replacement 4,326 (Q1) – 79,831 (Q5), see Table for more details. Reference category = Q3 for all tests of significance, none of which reached p < .01.