Jeremy M Kahn1, Billie S Davis2, Tri Q Le3, Jonathan G Yabes4, Chung-Chou H Chang4, Derek C Angus5. 1. CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; Department of Health Policy & Management, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, United States. Electronic address: kahnjm@upmc.edu. 2. CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States. 3. Department of Health Policy & Management, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, United States. 4. Center for Research on Health Care, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; Department of Biostatistics, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, United States. 5. CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States; Department of Health Policy & Management, University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, United States.
Abstract
PURPOSE: We sought to examine variation in long-term acute care hospital (LTACH) quality based on 90-day in-hospital mortality for patients admitted for weaning from mechanical ventilation. METHODS: We developed an administrative risk-adjustment model using data from Medicare claims. We validated the administrative model against a clinical model using data from LTACHs participating in a 2002 to 2003 clinical registry. We then used our validated administrative model to assess national variation in 90-day in-hospital mortality rates in LTACHs from 2013. RESULTS: The administrative risk-adjustment model was derived using data from 9447 patients admitted to 221 LTACHs in 2003. The model had good discrimination (C statistic=0.72) and calibration. Compared to a clinically derived model using data from 1163 patients admitted to 14 LTACHs, the administrative model generated similar performance estimates. National variation in risk-adjusted mortality was assessed using data from 20,453 patients admitted to 380 LTACHs in 2013. LTACH-specific risk-adjusted mortality rates varied from 8.4% to 48.1% (median: 24.2%, interquartile range: 19.7%-30.7%). CONCLUSIONS: LTACHs vary widely in mortality rates, underscoring the need to better understand the sources of this variation and improve the quality of care for patients requiring long-term ventilator weaning.
PURPOSE: We sought to examine variation in long-term acute care hospital (LTACH) quality based on 90-day in-hospital mortality for patients admitted for weaning from mechanical ventilation. METHODS: We developed an administrative risk-adjustment model using data from Medicare claims. We validated the administrative model against a clinical model using data from LTACHs participating in a 2002 to 2003 clinical registry. We then used our validated administrative model to assess national variation in 90-day in-hospital mortality rates in LTACHs from 2013. RESULTS: The administrative risk-adjustment model was derived using data from 9447 patients admitted to 221 LTACHs in 2003. The model had good discrimination (C statistic=0.72) and calibration. Compared to a clinically derived model using data from 1163 patients admitted to 14 LTACHs, the administrative model generated similar performance estimates. National variation in risk-adjusted mortality was assessed using data from 20,453 patients admitted to 380 LTACHs in 2013. LTACH-specific risk-adjusted mortality rates varied from 8.4% to 48.1% (median: 24.2%, interquartile range: 19.7%-30.7%). CONCLUSIONS: LTACHs vary widely in mortality rates, underscoring the need to better understand the sources of this variation and improve the quality of care for patients requiring long-term ventilator weaning.
Authors: Curtis H Weiss; Farzad Moazed; Colleen A McEvoy; Benjamin D Singer; Igal Szleifer; Luís A N Amaral; Mary Kwasny; Charles M Watts; Stephen D Persell; David W Baker; Jacob I Sznajder; Richard G Wunderink Journal: Am J Respir Crit Care Med Date: 2011-05-26 Impact factor: 21.405
Authors: David J Scheinhorn; Meg Stearn Hassenpflug; John J Votto; David C Chao; Scott K Epstein; Gordon S Doig; E Bert Knight; Richard A Petrak Journal: Chest Date: 2007-01 Impact factor: 9.410
Authors: Hude Quan; Vijaya Sundararajan; Patricia Halfon; Andrew Fong; Bernard Burnand; Jean-Christophe Luthi; L Duncan Saunders; Cynthia A Beck; Thomas E Feasby; William A Ghali Journal: Med Care Date: 2005-11 Impact factor: 2.983
Authors: Timothy D Girard; John P Kress; Barry D Fuchs; Jason W W Thomason; William D Schweickert; Brenda T Pun; Darren B Taichman; Jan G Dunn; Anne S Pohlman; Paul A Kinniry; James C Jackson; Angelo E Canonico; Richard W Light; Ayumi K Shintani; Jennifer L Thompson; Sharon M Gordon; Jesse B Hall; Robert S Dittus; Gordon R Bernard; E Wesley Ely Journal: Lancet Date: 2008-01-12 Impact factor: 79.321
Authors: Christopher E Cox; Shannon S Carson; Jennifer H Lindquist; Maren K Olsen; Joseph A Govert; Lakshmipathi Chelluri Journal: Crit Care Date: 2007 Impact factor: 9.097
Authors: Kimberly J Rak; Laura Ellen Ashcraft; Courtney C Kuza; Jessica C Fleck; Lisa C DePaoli; Derek C Angus; Amber E Barnato; Nicholas G Castle; Tina B Hershey; Jeremy M Kahn Journal: Am J Respir Crit Care Med Date: 2020-04-01 Impact factor: 21.405
Authors: Louise Rose; Laura Istanboulian; Laura Allum; Lisa Burry; Craig Dale; Nicholas Hart; Kalliopi Kydonaki; Pam Ramsay; Natalie Pattison; Bronwen Connolly Journal: Crit Care Explor Date: 2019-04-17