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.
To explore the relationship between hospital “end-of-life” (EOL) treatment intensity and postadmission survival.
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.
A total of 1,021,909 patients ≥65 years old, incurring 2,216,815 admissions in 169 Pennsylvania acute care hospitals.
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.
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.
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.
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From the *Department of Medicine, University of Pittsburgh, Pittsburgh, PA; †Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA; ‡The CRISMA (Clinical Research, Investigation, and Systems Modeling of Acute Illness) Laboratory, Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA; §Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA; and ¶Department of Economics, University of Michigan, Ann Arbor, MI.
Supported by NIH grant K08 AG021921 (Barnato PI), with additional support from P01 AG019783 (Skinner PI) and 1UL1 RR024153 (Reis PI).
The authors conducted the analysis under a data use agreement with the Pennsylvania Health Care Cost Containment Council (PHC4). The following statement is provided and required by the Pennsylvania Health Care Cost Containment Council (PHC4): PHC4 has provided this data in an effort to further PHC4's mission of educating the public and containing health care costs in Pennsylvania. PHC4, its agents and staff, have made no representation, guarantee, or warranty, expressed or implied, that the data—financial, patient, payor and physician specific information—are error-free, or that the use of the data will avoid differences of opinion or interpretation, or disputes with those who use published reports or purchased data. PHC4, its agents and staff, will bear no responsibility or liability for the results of the analysis, or consequences of its use.
Reprints: Amber E. Barnato, MD, MPH, MS, Center for Research on Health Care, 200 Meyran Avenue, Suite 200, Pittsburgh, PA 15213. E-mail: email@example.com.
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