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Nurs Res. 2018 Jul/Aug;67(4):314-323. doi: 10.1097/NNR.0000000000000287.

Risk Adjustment for Hospital Characteristics Reduces Unexplained Hospital Variation in Pressure Injury Risk.

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Daniel T. Linnen, PhD, MS, RN-BC, is former PhD candidate and Jonas Scholar, University of California San Francisco School of Nursing; Predoctoral Fellow, Kaiser Permanente Northern California Nurse Scholars Academy, Kaiser Foundation Hospitals; and Health Systems Research Resident, Kaiser Permanente Division of Research, Oakland, California. Patricia Kipnis, PhD, is Associate Director of Healthcare Quality and Affordability Analytics, Kaiser Permanente Northern California, Kaiser Foundation Health Plan, and Biostatistical Consultant at the Kaiser Permanente Northern California Division of Research, Oakland, California. June Rondinelli, PhD, RN, CNS, is Director of the Regional Nursing Research Program, Kaiser Permanente Southern California, Patient Care Services, Pasadena. John D. Greene, MA, is Senior Data Consultant, Kaiser Permanente Northern California Division of Research, Oakland. Vincent Liu, MD, MS, is Research Scientist, Kaiser Permanente Northern California Division of Research and Regional Director of Hospital Advanced Analytics, The Permanente Medical Group, Oakland, California. Gabriel J. Escobar, MD, is Research Scientist and Director of the Systems Research Initiative, Kaiser Permanente Northern California Division of Research, Oakland.



Research investigating risk factors for hospital-acquired pressure injury (HAPI) has primarily focused on the characteristics of patients and nursing staff. Limited data are available on the association of hospital characteristics with HAPI.


We aimed to quantify the association of hospital characteristics with HAPI and their effect on residual hospital variation in HAPI risk.


We employed a retrospective cohort study design with split validation using hierarchical survival analysis. This study extends the analysis "Hospital-Acquired Pressure Injury (HAPI): Risk Adjusted Comparisons in an Integrated Healthcare Delivery System" by Rondinelli et al. (2018) to include hospital-level factors. We analyzed 1,661 HAPI episodes among 728,266 adult hospitalization episodes across 35 California Kaiser Permanente hospitals, an integrated healthcare delivery system between January 1, 2013, and June 30, 2015.


After adjusting for patient-level and hospital-level variables, 2 out of 12 candidate hospital variables were statistically significant predictors of HAPI. The hazard for HAPI decreased by 4.8% for every 0.1% increase in a hospital's mean mortality ([6.3%, 2.6%], p < .001), whereas every 1% increase in a hospital's proportion of patients with a history of diabetes increased HAPI hazard by 5% ([-0.04%, 10.0%], p = .072). Addition of these hierarchical variables decreased unexplained hospital variation of HAPI risk by 35%.


We found hospitals with higher patient mortality had lower HAPI risk. Higher patient mortality may decrease the pool of patients who live to HAPI occurrence. Such hospitals may also provide more resources (specialty staff) to care for frail patient populations. Future research should aim to combine hospital data sets to overcome power limitations at the hospital level and should investigate additional measures of structure and process related to HAPI care.

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