Literature DB >> 20057328

Is survival better at hospitals with higher "end-of-life" treatment intensity?

Amber E Barnato1, Chung-Chou H Chang, Max H Farrell, Judith R Lave, Mark S Roberts, Derek C Angus.   

Abstract

BACKGROUND: Concern regarding wide variations in spending and intensive care unit use for patients at the end of life hinges on the assumption that such treatment offers little or no survival benefit.
OBJECTIVE: To explore the relationship between hospital "end-of-life" (EOL) treatment intensity and postadmission survival. RESEARCH
DESIGN: Retrospective cohort analysis of Pennsylvania Health Care Cost Containment Council discharge data April 2001 to March 2005 linked to vital statistics data through September 2005 using hospital-level correlation, admission-level marginal structural logistic regression, and pooled logistic regression to approximate a Cox survival model.
SUBJECTS: A total of 1,021,909 patients > or =65 years old, incurring 2,216,815 admissions in 169 Pennsylvania acute care hospitals. MEASURES: EOL treatment intensity (a summed index of standardized intensive care unit and life-sustaining treatment use among patients with a high predicted probability of dying [PPD] at admission) and 30- and 180-day postadmission mortality.
RESULTS: There was a nonlinear negative relationship between hospital EOL treatment intensity and 30-day mortality among all admissions, although patients with higher PPD derived the greatest benefit. Compared with admission at an average intensity hospital, admission to a hospital 1 standard deviation below versus 1 standard deviation above average intensity resulted in an adjusted odds ratio of mortality for admissions at low PPD of 1.06 (1.04-1.08) versus 0.97 (0.96-0.99); average PPD: 1.06 (1.04-1.09) versus 0.97 (0.96-0.99); and high PPD: 1.09 (1.07-1.11) versus 0.97 (0.95-0.99), respectively. By 180 days, the benefits to intensity attenuated (low PPD: 1.03 [1.01-1.04] vs. 1.00 [0.98-1.01]; average PPD: 1.03 [1.02-1.05] vs. 1.00 [0.98-1.01]; and high PPD: 1.06 [1.04-1.09] vs. 1.00 [0.98-1.02]), respectively.
CONCLUSIONS: Admission to higher EOL treatment intensity hospitals is associated with small gains in postadmission survival. The marginal returns to intensity diminish for admission to hospitals above average EOL treatment intensity and wane with time.

Entities:  

Mesh:

Year:  2010        PMID: 20057328      PMCID: PMC3769939          DOI: 10.1097/MLR.0b013e3181c161e4

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  21 in total

1.  Resurrecting treatment histories of dead patients: a study design that should be laid to rest.

Authors:  Peter B Bach; Deborah Schrag; Colin B Begg
Journal:  JAMA       Date:  2004-12-08       Impact factor: 56.272

2.  Evaluating the efficiency of california providers in caring for patients with chronic illnesses.

Authors:  John E Wennberg; Elliott S Fisher; Laurence Baker; Sandra M Sharp; Kristen K Bronner
Journal:  Health Aff (Millwood)       Date:  2005 Jul-Dec       Impact factor: 6.301

3.  The value of medical spending in the United States, 1960-2000.

Authors:  David M Cutler; Allison B Rosen; Sandeep Vijan
Journal:  N Engl J Med       Date:  2006-08-31       Impact factor: 91.245

4.  Relation of pooled logistic regression to time dependent Cox regression analysis: the Framingham Heart Study.

Authors:  R B D'Agostino; M L Lee; A J Belanger; L A Cupples; K Anderson; W B Kannel
Journal:  Stat Med       Date:  1990-12       Impact factor: 2.373

5.  A national survey of end-of-life care for critically ill patients.

Authors:  T J Prendergast; M T Claessens; J M Luce
Journal:  Am J Respir Crit Care Med       Date:  1998-10       Impact factor: 21.405

6.  Method of Medicare reimbursement and the rate of potentially ineffective care of critically ill patients.

Authors:  D J Cher; L A Lenert
Journal:  JAMA       Date:  1997-09-24       Impact factor: 56.272

7.  The implications of regional variations in Medicare spending. Part 1: the content, quality, and accessibility of care.

Authors:  Elliott S Fisher; David E Wennberg; Thérèse A Stukel; Daniel J Gottlieb; F L Lucas; Etoile L Pinder
Journal:  Ann Intern Med       Date:  2003-02-18       Impact factor: 25.391

8.  Trends in Medicare payments in the last year of life.

Authors:  J D Lubitz; G F Riley
Journal:  N Engl J Med       Date:  1993-04-15       Impact factor: 91.245

9.  Development and validation of hospital "end-of-life" treatment intensity measures.

Authors:  Amber E Barnato; Max H Farrell; Chung-Chou H Chang; Judith R Lave; Mark S Roberts; Derek C Angus
Journal:  Med Care       Date:  2009-10       Impact factor: 2.983

10.  Use of hospitals, physician visits, and hospice care during last six months of life among cohorts loyal to highly respected hospitals in the United States.

Authors:  John E Wennberg; Elliott S Fisher; Thérèse A Stukel; Jonathan S Skinner; Sandra M Sharp; Kristen K Bronner
Journal:  BMJ       Date:  2004-03-13
View more
  46 in total

1.  Toward an integrated research agenda for critical illness in aging.

Authors:  Eric B Milbrandt; Basil Eldadah; Susan Nayfield; Evan Hadley; Derek C Angus
Journal:  Am J Respir Crit Care Med       Date:  2010-06-17       Impact factor: 21.405

2.  Comment on Silber et al.: Investing in postadmission survival—a “failure-to-rescue” U.S. population health.

Authors:  Amber E Barnato
Journal:  Health Serv Res       Date:  2010-12       Impact factor: 3.402

3.  Aggressive treatment style and surgical outcomes.

Authors:  Jeffrey H Silber; Robert Kaestner; Orit Even-Shoshan; Yanli Wang; Laura J Bressler
Journal:  Health Serv Res       Date:  2010-09-28       Impact factor: 3.402

Review 4.  The Impact of Sparse Follow-up on Marginal Structural Models for Time-to-Event Data.

Authors:  Nassim Mojaverian; Erica E M Moodie; Alex Bliu; Marina B Klein
Journal:  Am J Epidemiol       Date:  2015-11-20       Impact factor: 4.897

5.  Racial differences in mortality among patients with acute ischemic stroke: an observational study.

Authors:  Ying Xian; Robert G Holloway; Katia Noyes; Manish N Shah; Bruce Friedman
Journal:  Ann Intern Med       Date:  2011-02-01       Impact factor: 25.391

6.  Hospital spending and inpatient mortality: evidence from California: an observational study.

Authors:  John A Romley; Anupam B Jena; Dana P Goldman
Journal:  Ann Intern Med       Date:  2011-02-01       Impact factor: 25.391

7.  30-Day Episode Payments and Heart Failure Outcomes Among Medicare Beneficiaries.

Authors:  Rishi K Wadhera; Karen E Joynt Maddox; Yun Wang; Changyu Shen; Robert W Yeh
Journal:  JACC Heart Fail       Date:  2018-04-11       Impact factor: 12.035

8.  High resource utilization does not affect mortality in acute respiratory failure patients managed with tracheostomy.

Authors:  Bradley D Freeman; Dustin Stwalley; Dennis Lambert; Joshua Edler; Peter E Morris; Sofia Medvedev; Samuel F Hohmann; Steven M Kymes
Journal:  Respir Care       Date:  2013-04-30       Impact factor: 2.258

9.  The Impact of Race on Intensity of Care Provided to Older Adults in the Medical Intensive Care Unit.

Authors:  Chidinma Chima-Melton; Terrence E Murphy; Katy L B Araujo; Margaret A Pisani
Journal:  J Racial Ethn Health Disparities       Date:  2015-09-28

Review 10.  Healthcare disparities in critical illness.

Authors:  Graciela J Soto; Greg S Martin; Michelle Ng Gong
Journal:  Crit Care Med       Date:  2013-12       Impact factor: 7.598

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.