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Comparative Effectiveness of Risk-adjusted Cumulative Sum and Periodic Evaluation for Monitoring Hospital Perioperative Mortality.
Massarweh, Nader N; Chen, Vivi W; Rosen, Tracey; Dong, Yongquan; Richardson, Peter A; Axelrod, David A; Harris, Alex H S; Wilson, Mark A; Petersen, Laura A.
Afiliación
  • Massarweh NN; Center for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey VA Medical Center.
  • Chen VW; Michael E DeBakey Department of Surgery, Baylor College of Medicine.
  • Rosen T; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX.
  • Dong Y; Michael E DeBakey Department of Surgery, Baylor College of Medicine.
  • Richardson PA; Center for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey VA Medical Center.
  • Axelrod DA; Center for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey VA Medical Center.
  • Harris AHS; Center for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey VA Medical Center.
  • Wilson MA; Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX.
  • Petersen LA; Department of Surgery, University of Iowa, Iowa City, IO.
Med Care ; 59(7): 639-645, 2021 07 01.
Article en En | MEDLINE | ID: mdl-33900272
BACKGROUND: National surgical quality improvement (QI) programs use periodic, risk-adjusted evaluation to identify hospitals with higher than expected perioperative mortality. Rapid, accurate identification of poorly performing hospitals is critical for avoiding potentially preventable mortality and represents an opportunity to enhance QI efforts. METHODS: Hospital-level analysis using Veterans Affairs (VA) Surgical Quality Improvement Program data (2011-2016) to compare identification of hospitals with excess, risk-adjusted 30-day mortality using observed-to-expected (O-E) ratios (ie, current gold standard) and cumulative sum (CUSUM) with V-mask. Various V-mask slopes and radii were evaluated-slope of 2.5 and radius of 1.0 was used as the base case. RESULTS: Hospitals identified by CUSUM and quarterly O-E were identified midway into a quarter [median 47 days; interquartile range (IQR): 24-61 days before quarter end] translating to a median of 129 (IQR: 60-187) surgical cases and 368 (IQR: 145-681) postoperative inpatient days occurring after a CUSUM signal, but before the quarter end. At hospitals identified by CUSUM but not O-E, a median of 2 deaths within a median of 5 days triggered a signal. In some cases, these clusters extended beyond CUSUM identification date with as many as 8 deaths undetected using O-E. Sensitivity and negative predictive values for CUSUM relative to O-E were 71.9% (95% confidence interval: 66.2%-77.1%) and 95.5% (94.4%-96.4%), respectively. CONCLUSIONS: CUSUM evaluation identifies hospitals with clusters of mortality in excess of expected more rapidly than periodic analysis. CUSUM represents an analytic tool national QI programs could utilize to provide participating hospitals with data that could facilitate more proactive implementation of local interventions to help reduce potentially avoidable perioperative mortality.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Evaluación de Resultado en la Atención de Salud / Mortalidad Hospitalaria / Periodo Perioperatorio / Hospitales de Veteranos Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: Med Care Año: 2021 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Evaluación de Resultado en la Atención de Salud / Mortalidad Hospitalaria / Periodo Perioperatorio / Hospitales de Veteranos Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans País/Región como asunto: America do norte Idioma: En Revista: Med Care Año: 2021 Tipo del documento: Article