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Did Duty Hour Reform Lead to Better Outcomes Among the Highest Risk Patients?

  • Hospital Medicine
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Journal of General Internal Medicine Aims and scope Submit manuscript

Abstract

Background

Earlier work demonstrated that ACGME duty hour reform did not adversely affect mortality, with slight improvement noted among specific subgroups.

Objective

To determine whether resident duty hour reform differentially affected the mortality risk of high severity patients or patients who experienced post-operative complications (failure-to-rescue).

Design

Observational study using interrupted time series analysis with data from July 1, 2000 - June 30, 2005. Fixed effects logistic regression was used to examine the change in the odds of mortality or failure-to-rescue (FTR) in more versus less teaching-intensive hospitals before and after duty hour reform.

Participants

All unique Medicare patients (n = 8,529,595) admitted to short-term acute care non-federal hospitals and all unique VA patients (n = 318,636 patients) with principal diagnoses of acute myocardial infarction, congestive heart failure, gastrointestinal bleeding, stroke or a DRG classification of general, orthopedic or vascular surgery.

Measurements and Main Results

We measured mortality within 30 days of hospital admission and FTR, measured by death among patients who experienced a surgical complication. The odds of mortality and FTR generally changed at similar rates for higher and lower risk patients in more vs. less teaching intensive hospitals. For example, comparing the mortality risk for the 10% of Medicare patients with highest risk to the other 90% of patients in post-reform year 1 for combined medical an OR of 1.01 [95% CI 0.90, 1.13], for combined surgical an OR of 0.91 [95% CI 0.80, 1.04], and for FTR an OR of 0.94 [95% CI 0.80, 1.09]. Findings were similar in year 2 for both Medicare and VA. The two exceptions were a relative increase in mortality for the highest risk medical (OR 1.63 [95% CI 1.08, 2.46]) and a relative decrease in the high risk surgical patients within VA in post-reform year 1 (OR 0.52 [95% CI 0.29, 0.96]).

Conclusions

ACGME duty hour reform was not associated with any consistent improvements or worsening in mortality or failure-to-rescue rates for high risk medical or surgical patients.

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References

  1. Volpp KG, Rosen AK, Rosenbaum PR, et al. Mortality among hospitalized Medicare beneficiaries in the first two years following ACGME resident duty hour reform. JAMA. 2007;298:975–83.

    Article  CAS  PubMed  Google Scholar 

  2. Volpp KG, Rosen AK, Rosenbaum PR, et al. Mortality among patients in VA hospitals in the first two years following ACGME resident duty hour reform. JAMA. 2007;298:984–92.

    Article  CAS  PubMed  Google Scholar 

  3. Shetty KD, Bhattacharya J. Changes in hospital mortality associated with residency work-hour regulations. Ann Intern Med. 2007;147(2):73–80.

    PubMed  Google Scholar 

  4. Laine C, Goldman L, Soukup JR, Hayes JG. The impact of a regulation restricting medical house staff working hours on the quality of patient care. JAMA. 1993;269(3):374–8.

    Article  CAS  PubMed  Google Scholar 

  5. Drazen JM. Awake and informed. N Engl J Med. 2004;351(18):884.

    Article  Google Scholar 

  6. Mukherjee S. A precarious exchange. N Engl J Med. 2004;351(18):1822–4.

    Article  CAS  PubMed  Google Scholar 

  7. Ofri D. Residency regulations–resisting our reflexes. N Engl J Med. 2004;351(18):1824–6.

    Article  CAS  PubMed  Google Scholar 

  8. Silber JH, Williams SV, Krakauer H, Schwartz JS. Hospital and patient characteristics associated with death after surgery: A study of adverse occurrence and failure-to-rescue. Med Care. 1992;30(7):615–29.

    Article  CAS  PubMed  Google Scholar 

  9. Silber JH, Rosenbaum PR, Ross RN. Comparing the contributions of groups of predictors: Which outcomes vary with hospital rather than patient characteristics? J Am Stat Assoc. 1995;90(429):7–18.

    Article  Google Scholar 

  10. Silber JH, Rosenbaum PR, Schwartz JS, Ross RN, Williams SV. Evaluation of the complication rate as a measure of quality of care in coronary artery bypass graft surgery. JAMA. 1995;274(4):317–23.

    Article  CAS  PubMed  Google Scholar 

  11. Silber JH, Romano PS, Rosen AK, Wang Y, Even-Shoshan O, Volpp KG. Failure-to-rescue: Comparing definitions to measure quality of care. Med Care. 2007; 45(10):918–25.

    Article  PubMed  Google Scholar 

  12. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27.

    Article  CAS  PubMed  Google Scholar 

  13. Glance LG, Dick AW, Osler TM, Mukamel DB. Does date stamping ICD-9-CM codes increase the value of clinical information in administrative data? Health Serv Res. 2006;41(1):231–51.

    Article  PubMed  Google Scholar 

  14. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130–9.

    Article  PubMed  Google Scholar 

  15. Southern DA, Quan H, Ghali WA. Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data. Med Care. 2004;42(4):355–60.

    Article  PubMed  Google Scholar 

  16. Stukenborg GJ, Wagner DP, Connors AF Jr. Comparison of the performance of two comorbidity measures, with and without information from prior hospitalizations. Med Care. 2001;39(7):727–39.

    Article  CAS  PubMed  Google Scholar 

  17. Deyo R, Cherkin D, Ciol M. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613–619.

    Article  CAS  PubMed  Google Scholar 

  18. Silber J, Rosenbaum P, Schwartz J, Ross R. Comparing the contributions of groups of predictors: which outcomes vary with hospital rather than patient characteristics? J Am Stat Assoc. 1995;90(429):7–18.

    Article  Google Scholar 

  19. Haberman SJ. Generalized residuals for log-linear models. Proceedings of the 9th International Biometric Conference. 1st ed. Boston: The Biometric Society; 1976:104–23.

    Google Scholar 

  20. Haberman SJ. The analysis of frequency data. Chicago: The University of Chicago Press; 1974.

    Google Scholar 

  21. Ayanian JZ, Weissman JS. Teaching hospitals and quality of care: a review of the literature. Milbank Q. 2002;80(3):569–93.

    Article  PubMed  Google Scholar 

  22. Keeler EB, Rubenstein LV, Kahn KL, et al. Hospital characteristics and quality of care. JAMA. 1992;268(13):1709–14.

    Article  CAS  PubMed  Google Scholar 

  23. Allison JJ, Kiefe CI, Weissman NW, et al. Relationship of hospital teaching status with quality of care and mortality for Medicare patients with acute MI. JAMA. 2000;284(10):1256–62.

    Article  CAS  PubMed  Google Scholar 

  24. Taylor DH, Whellan DJ, Sloan FA. Effects of admission to a teaching hospital on the cost and quality of care for medicare beneficiaries. N Engl J Med. 1999;340(4):293–9.

    Article  PubMed  Google Scholar 

  25. Cox DR. Note on grouping. J Am Stat Assoc. 1957;52(280):543–7.

    Article  Google Scholar 

  26. Cochran WG. The effectiveness of adjustment by subclassification in removing bias in observational studies. Biometrics. 1968;24(2):295–313.

    Article  CAS  PubMed  Google Scholar 

  27. Campbell DT, Stanley JC. Experimental and quasi-experimental designs for research. Dallas: Houghton Mifflin Company; 1963.

    Google Scholar 

  28. Shadish WR, Cook TD, Campbell DT. Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton-Mifflin; 2002.

    Google Scholar 

  29. Rosenbaum PR. Stability in the absence of treatment. J Am Stat Assoc. 2001;96:210–9.

    Article  Google Scholar 

  30. Lawthers AG, McCarthy EP, Davis RB, Peterson LE, Palmer RH, Iezzoni LI. Identification of in-hospital complications from claims data. Is it valid? Med Care. 2000;38:785–95.

    Article  CAS  PubMed  Google Scholar 

  31. McCarthy EP, Iezzoni LI, Davis RB, et al. Does clinical evidence support ICD-9-CM diagnosis coding of complications? Med Care. 2000;38:868–76.

    Article  CAS  PubMed  Google Scholar 

  32. Weingart SN, Iezzoni LI, Davis RB, et al. Use of administrative data to find substandard care. Validation of the complications screening program. Med Care. 2000;38:796–806.

    Article  CAS  PubMed  Google Scholar 

  33. California Office of Statewide Health Planning and Development. (OSHPD). Second report of the California Hospitals Outcomes Project. Acute myocardial infarction. 1996 May.

  34. Institute of Medicine. Optimizing graduate medical trainee (resident) hours and work schedules to improve patient safety. 2007.

  35. Myers JS, Bellini LM, Morris JB, et al. Internal medicine and general surgery residents’ attitudes about the ACGME duty hours regulations: A multicenter study. Acad Med. 2006;81(12):1052–8.

    Article  PubMed  Google Scholar 

  36. Jagsi R, Shapiro J, Weissman JS, Dorer DJ, Weinstein DF. The educational impact of ACGME limits on resident and fellow duty hours: A pre–post survey study. Acad Med. 2006;81:1059–68.

    Article  PubMed  Google Scholar 

  37. Rosen AK, Loveland SA, Romano PS, et al. Effects of resident duty hour reform on patient safety among hospitalized VA and medicare patients. Med Care. 2009. in press.

  38. Silber J, Rosenbaum PR, Rosen AK, et al. Prolonged hospital stays and the resident duty hour rules of 2003. Med Care. 2009. in press.

  39. Landrigan CP, Rothschild JM, Cronin JW, et al. Effect of reducing interns’ work hours on serious medical errors in intensive care units. N Engl J Med. 2004;351(18):1838–48.

    Article  CAS  PubMed  Google Scholar 

  40. Lockley SW, Cronin JW, Evans EE, et al. Effect of reducing interns’ weekly work hours on sleep and attentional failures. N Engl J Med. 2004;351(18):1829–37.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported primarily by grant VA HSR&D IIR 04.202.1 and NHLBI R01 HL082637, with additional support from National Science Foundation grant SES-0646002. The sponsors/funders have had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. Dr. Volpp had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. We also thank Yun Teng for her assistance with the analyses. Everyone who contributed significantly to this work has been acknowledged.

Conflict of Interest

None disclosed.

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Correspondence to Kevin G. Volpp MD, PhD.

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Volpp, K.G., Rosen, A.K., Rosenbaum, P.R. et al. Did Duty Hour Reform Lead to Better Outcomes Among the Highest Risk Patients?. J GEN INTERN MED 24, 1149–1155 (2009). https://doi.org/10.1007/s11606-009-1011-z

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  • DOI: https://doi.org/10.1007/s11606-009-1011-z

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