The relationship between hospital lung cancer resection volume and patient mortality risk

Annals of Surgery

Volumn 254, Issue 6, 2011, Pages 1032-1037

  Kozower, Benjamin D ^a,b   Stukenborg, George J ^b  

^a Departments of Medicine and Surgery   (United States)

^b UNIVERSITY OF VIRGINIA HEALTH SYSTEM   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACQUIRED IMMUNE DEFICIENCY SYNDROME; ADULT; ALCOHOL ABUSE; ARTHRITIS; ARTICLE; BLEEDING; CANCER MORTALITY; CANCER PATIENT; CANCER SURGERY; CHRONIC LUNG DISEASE; COMORBIDITY; CONGESTIVE HEART FAILURE; DEPRESSION; DIABETES MELLITUS; DRUG ABUSE; FEMALE; HEALTH CARE DELIVERY; HEALTH CARE QUALITY; HOSPITAL DISCHARGE; HUMAN; HYPERTENSION; HYPOTHYROIDISM; KIDNEY FAILURE; LIVER DISEASE; LUNG CANCER; LYMPHOMA; MALE; METASTASIS; NEUROLOGIC DISEASE; OBESITY; PARAPLEGIA; PEPTIC ULCER; PERIPHERAL VASCULAR DISEASE; PRIORITY JOURNAL; RISK ASSESSMENT; SOLID TUMOR; STATISTICAL MODEL; STATISTICAL SIGNIFICANCE; TUMOR VOLUME; VALVULAR HEART DISEASE; WEIGHT REDUCTION;

COMORBIDITY; HEALTH FACILITY SIZE; HOSPITAL MORTALITY; HUMANS; LIKELIHOOD FUNCTIONS; LINEAR MODELS; LUNG NEOPLASMS; PNEUMONECTOMY; POSTOPERATIVE COMPLICATIONS; QUALITY INDICATORS, HEALTH CARE; REGRESSION ANALYSIS; STATISTICS AS TOPIC; UNITED STATES;

EID: 81855194064     PISSN: 00034932     EISSN: 15281140     Source Type: Journal    
DOI: 10.1097/SLA.0b013e31821d4bdd     Document Type: Article

References (43)

1. Luft HS, Bunker JP, Enthoven AC. Should operations be regionalized?. The empirical relation between surgical volume and mortality. N Engl J Med. 1979;301(25):1364-1369.
2. Birkmeyer JD, Dimick JB, Birkmeyer NJ. Measuring the quality of surgical care: structure, process, or outcomes?. J Am Coll Surg. 2004;198(4):626-632.
3. Birkmeyer JD, Siewers AE, Finlayson EV, et al. Hospital volume and surgical mortality in the united states. N Engl J Med. 2002;346(15):1128-1137.
4. Bach PB, Cramer LD, Schrag D, et al. The influence of hospital volume on survival after resection for lung cancer. N Engl J Med. 2001;345(3):181-188.
5. Romano PS, Mark DH. Patient and hospital characteristics related to in-hospital mortality after lung cancer resection. Chest. 1992;101(5):1332-1337.
6. Finlayson EV, Goodney PP, Birkmeyer JD. Hospital volume and operative mortality in cancer surgery: a national study. Arch Surg. 2003;138(7): 721-725.
7. Halm EA, Lee C, Chassin MR. Is volume related to outcome in health care?. A systematic review and methodologic critique of the literature. Ann Intern Med. 2002;137(6):511-520.
8. Livingston EH, Cao J. Procedure volume as a predictor of surgical outcomes. JAMA. 2010;304(1):95-97.
9. Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006;25(1):127-141.
10. Livingston EH, Elliot A, Hynan L, et al. Effect size estimation: a necessary component of statistical analysis. Arch Surg. 2009;144(8):706-712.
11. Leapfrog Group. Evidence-based hospital referral. Available at: http://www.leapfroggroup. org/media/file/Leapfrog-Evidence Based-Hospital- Referral-Fact-Sheet. pdf. Accessed February 2, 2011.
12. Shahian DM, Normand SL. The volume-outcome relationship: from luft to leapfrog. Ann Thorac Surg. 2003;75(3):1048-1058.
13. Kozower BD, Stukenborg GJ, Lau CL, et al. Measuring the quality of surgical outcomes in general thoracic surgery: should surgical volume be used to direct patient referrals?. Ann Thorac Surg. 2008;86(5):1405-1408.
14. Nationwide inpatient sample. Available at: http://www.hcup-us. ahrq. gov/nisoverview. jsp. Accessed February 2, 2011.
15. Whalen D, Houchens R, Elixhauser A. 2004 HCUP nationwide inpatient sample (NIS) comparison report. HCUP methods series report # 2007-03 online. february 2, 2007. U. S. agency for healthcare research and quality. Available at: http://www.hcup-us. ahrq. gov/reports/methods. jsp. Accessed February 2, 2011.
16. Stukenborg GJ, Kilbridge KL, Wagner DP, et al. Present-at-admission diagnoses improve mortality risk adjustment and allow more accurate assessment of the relationship between volume of lung cancer operations and mortality risk. Surgery. 2005;138(3):498-507.
17. Stukenborg GJ, Wagner DP, Harrell FE. Temporal order and nonlinearity in the relationship between lung cancer resection volume and in-hospital mortality. Health Serv Outcomes Res Methodol. 2004;5:59.
18. Gilboa SM, Correa A, Alverson CJ. Use of spline regression in an analysis of maternal prepregnancy body mass index and adverse birth outcomes: does it tell us more than we already know?. Ann Epidemiol. 2008;18(3):196-205.
19. Greenland S. Dose-response and trend analysis in epidemiology: alternatives to categorical analysis. Epidemiology. 1995;6(4):356-365.
20. Livingston EH. Bariatric surgery outcomes at designated centers of excellence vs nondesignated programs. Arch Surg. 2009;144(4):319-325.
21. Greenland S. Avoiding power loss associated with categorization and ordinal scores in dose-response and trend analysis. Epidemiology. 1995;6(4):450-454.
22. Elixhauser A, Steiner C, Harris DR, et al. Comorbidity measures for use with administrative data. Med Care 1998;36(1):8-27.
23. 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):727-739.
24. 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):355-360.
25. Li B, Evans D, Faris P, et al. Risk adjustment performance of charlson and elixhauser comorbidities in ICD-9 and ICD-10 administrative databases. BMC Health Serv Res. 2008;8:12.
26. Weinhandl ED, Snyder JJ, Israni AK, et al. Effect of comorbidity adjustment on CMS criteria for kidney transplant center performance. Am J Transplant. 2009;9(3):506-516.
27. Kozower BD, Ailawadi G, Jones DR, et al. Predicted risk of mortality models: Surgeons need to understand limitations of the university HealthSystem consortium models. J Am Coll Surg. 2009;209(5):551-556.
28. Panageas KS, Schrag D, Riedel E, et al. . The effect of clustering of outcomes on the association of procedure volume and surgical outcomes. Ann Intern Med. 2003;139(8):658-665.
29. Willan AR, Ross W, Mackenzie TA. Comparing in-patient classification systems: a problem of non-nested regression models. Stat Med. 1992;11:1321.
30. Thabut G, Christie JD, Kremers WK, et al. Survival differences following lung transplantation among US transplant centers. JAMA. 2010;304(1):53-60.
31. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29-36.
32. Harrell FE, Jr, Califf RM, Pryor DB, et al. Evaluating the yield of medical tests. JAMA. 1982;247(18):2543-2546.
33. Ash AS, ShwartzM. A useful measure of model performance when predicting a dichotomous outcome. Stat Med. 1999;18:375-384.
34. Harrell FE, Jr, Lee KL, Pollock BG. Regression models in clinical studies: determining relationships between predictors and response. J Natl Cancer Inst. 1988;80(15):1198-1202.
35. Livingston EH, Elliott AC, Hynan LS, et al. When policy meets statistics: the very real effect that questionable statistical analysis has on limiting health care access for bariatric surgery. Arch Surg. 2007;142(10):979-987.
36. Kozower BD, Sheng S, O'Brien SM, et al. STS database riskmodels: predictors of mortality and major morbidity for lung cancer resection. Ann Thorac Surg. 2010;90(3):875-881.
37. Wright CD, Kucharczuk JC, O'Brien SM, et al. Society of Thoracic Surgeons General Thoracic Surgery, Database. Predictors of majormorbidity andmortality after esophagectomy for esophageal cancer: a society of thoracic surgeons general thoracic surgery database risk adjustment model. J Thor Cardiovasc Surg. 2009;137(3):587-595.
38. Iezzoni LI. The risks of risk adjustment. JAMA. 1997;278(19):1600-1607.
39. Romano PS, Roos LL, Luft HS, Jollis JG, et al. A comparison of administrative versus clinical data: coronary artery bypass surgery as an example. Ischemic heart disease patient outcomes research team. J Clin Epidemiol. 1994;47(3):249-260.
40. Iezzoni LI, Foley SM, Daley J, et al. Comorbidities, complications, and coding bias. Does the number of diagnosis codes matter in predicting in-hospital mortality?. JAMA. 1992;267(16):2197-2203.
41. Birkmeyer JD, Stukel TA, Siewers AE, et al. Surgeon volume and operative mortality in the united states. N Engl J Med. 2003;349(22):2117-2127.
42. The leapfrog group. Available at: http://www.leapfroggroup. org. Accessed February, 2011.
43. Hannan EL. The relation between volume and outcome in health care. N Engl J Med. 1999;340(21):1677-1679.