Patricia A Zrelak1,2, Garth H Utter1,3, Kathryn M McDonald4,5, Robert L Houchens6, Sheryl M Davies4, Halcyon G Skinner6,7, Pamela L Owens8, Patrick S Romano1,9,10. 1. Center for Healthcare Policy and Research, University of California Davis, Sacramento, California, USA. 2. Kaiser Permanente, Sacramento, CA, USA. 3. Department of Surgery, Outcomes Research Group, University of California, Davis, Medical Center, Sacramento, California, USA. 4. Stanford University Center for Health Policy, Stanford University, Stanford, California, USA. 5. Johns Hopkins University Schools of Nursing and Medicine, Baltimore, MD, USA. 6. Truven Analytics, Ann Arbor, Michigan, USA. 7. Lehigh University College of Health, Bethlehem, PA, USA. 8. Agency for Healthcare Research and Quality, Rockville, Maryland, USA. 9. Department of Internal Medicine, University of California, Davis, Medical Center, Sacramento, California, USA. 10. Department of Pediatrics, University of California, Davis, Medical Center, Sacramento, California, USA.
Abstract
OBJECTIVE: To reweight the Agency for Healthcare Research and Quality Patient Safety for Selected Indicators Composite (Patient Safety Indicator [PSI] 90) from weights based solely on the frequency of component PSIs to those that incorporate excess harm reflecting patients' preferences for outcome-related health states. DATA SOURCES: National administrative and claims data involving hospitalizations in nonfederal, nonrehabilitation, acute care hospitals. STUDY DESIGN: We estimated the average excess aggregate harm associated with the occurrence of each component PSI using a cohort sample for each indicator based on denominator-eligible records. We used propensity scores to account for potential confounding in the risk models for each PSI and weighted observations to estimate the "average treatment effect in the treated" for those with the PSI event. We fit separate regression models for each harm outcome. Final PSI weights reflected both the disutilities and the frequencies of the harms. DATA COLLECTION/EXTRACTION METHODS: We estimated PSI frequencies from the 2012 Healthcare Cost and Utilization Project State Inpatient Databases with present on admission data and excess harms using 2012-2013 Centers for Medicare & Medicaid Services Medicare Fee-for-Service data. PRINCIPAL FINDINGS: Including harms in the weighting scheme changed individual component weights from the original frequency-based weighting. In the reweighted composite, PSIs 11 ("Postoperative Respiratory Failure"), 13 ("Postoperative Sepsis"), and 12 ("Perioperative Pulmonary Embolism or Deep Vein Thrombosis") contributed the greatest harm, with weights of 29.7%, 21.1%, and 20.4%, respectively. Regarding reliability, the overall average hospital signal-to-noise ratio for the reweighted PSI 90 was 0.7015. Regarding discrimination, among hospitals with greater than median volume, 34% had significantly better PSI 90 performance, and 41% had significantly worse performance than benchmark rates (based on percentiles). CONCLUSIONS: Reformulation of PSI 90 with harm-based weights is feasible and results in satisfactory reliability and discrimination, with a more clinically meaningful distribution of component weights.
OBJECTIVE: To reweight the Agency for Healthcare Research and Quality Patient Safety for Selected Indicators Composite (Patient Safety Indicator [PSI] 90) from weights based solely on the frequency of component PSIs to those that incorporate excess harm reflecting patients' preferences for outcome-related health states. DATA SOURCES: National administrative and claims data involving hospitalizations in nonfederal, nonrehabilitation, acute care hospitals. STUDY DESIGN: We estimated the average excess aggregate harm associated with the occurrence of each component PSI using a cohort sample for each indicator based on denominator-eligible records. We used propensity scores to account for potential confounding in the risk models for each PSI and weighted observations to estimate the "average treatment effect in the treated" for those with the PSI event. We fit separate regression models for each harm outcome. Final PSI weights reflected both the disutilities and the frequencies of the harms. DATA COLLECTION/EXTRACTION METHODS: We estimated PSI frequencies from the 2012 Healthcare Cost and Utilization Project State Inpatient Databases with present on admission data and excess harms using 2012-2013 Centers for Medicare & Medicaid Services Medicare Fee-for-Service data. PRINCIPAL FINDINGS: Including harms in the weighting scheme changed individual component weights from the original frequency-based weighting. In the reweighted composite, PSIs 11 ("Postoperative Respiratory Failure"), 13 ("Postoperative Sepsis"), and 12 ("Perioperative Pulmonary Embolism or Deep Vein Thrombosis") contributed the greatest harm, with weights of 29.7%, 21.1%, and 20.4%, respectively. Regarding reliability, the overall average hospital signal-to-noise ratio for the reweighted PSI 90 was 0.7015. Regarding discrimination, among hospitals with greater than median volume, 34% had significantly better PSI 90 performance, and 41% had significantly worse performance than benchmark rates (based on percentiles). CONCLUSIONS: Reformulation of PSI 90 with harm-based weights is feasible and results in satisfactory reliability and discrimination, with a more clinically meaningful distribution of component weights.
Authors: Patricia A Zrelak; Garth H Utter; Kathryn M McDonald; Robert L Houchens; Sheryl M Davies; Halcyon G Skinner; Pamela L Owens; Patrick S Romano Journal: Health Serv Res Date: 2022-01-09 Impact factor: 3.734
Authors: Patricia A Zrelak; Garth H Utter; Kathryn M McDonald; Robert L Houchens; Sheryl M Davies; Halcyon G Skinner; Pamela L Owens; Patrick S Romano Journal: Health Serv Res Date: 2022-01-09 Impact factor: 3.734