Influence of trends in hospital volume over time on patient outcomes for high-risk surgery

Cécile Payet, Stéphanie Polazzi, Jean-Christophe Lifante, Eddy Cotte, Daniel Grinberg, Matthew J Carty, Stéphane Sanchez, Muriel Rabilloud, Antoine Duclos, Cécile Payet, Stéphanie Polazzi, Jean-Christophe Lifante, Eddy Cotte, Daniel Grinberg, Matthew J Carty, Stéphane Sanchez, Muriel Rabilloud, Antoine Duclos

Abstract

Background: The "practice makes perfect" concept considers the more frequent a hospital performs a procedure, the better the outcome of the procedure. We aimed to study this concept by investigating whether patient outcomes improve in hospitals with a significantly increased volume of high-risk surgery over time and whether a learning effect existed at the individual hospital level.

Methods: We included all patients who underwent one of 10 digestive, cardiovascular and orthopaedic procedures between 2010 and 2014 from the French nationwide hospitals database. For each procedure, we identified three groups of hospitals according to volume trend (increased, decreased, or no change). In-hospital mortality, reoperation, and unplanned hospital readmission within 30 days were compared between groups using Cox regressions, taking into account clustering of patients within hospitals and potential confounders. Individual hospital learning effect was investigated by considering the interaction between hospital groups and procedure year.

Results: Over 5 years, 759,928 patients from 694 hospitals were analysed. Patients' mortality in hospitals with procedure volume increase or decrease over time did not clearly differ from those in hospitals with unchanged volume across the studied procedures (e.g., Hazard Ratios [95%] of 1.04 [0.93-1.17] and 1.08 [0.97-1.21] respectively for colectomy). Furthermore, patient outcomes did not improve or deteriorate in hospitals with increased or decreased volume of procedures over time (e.g., 1.01 [0.95-1.08] and 0.99 [0.92-1.05] respectively for colectomy).

Conclusions: Trend in hospital volume over time did not appear to influence patient outcomes based on real-world data.

Trial registration: NCT02788331, June 2, 2016.

Keywords: Surgery; Trend; Volume-outcome.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Number of hospitals by trend in volume of procedures between 2010 and 2014. PCI percutaneous coronary intervention, CABG coronary artery bypass grafting, AAA abdominal aortic aneurysm
Fig. 2
Fig. 2
Mortality difference and individual hospital learning effect between hospital groups according to trends in procedures volume from 2010 to 2014. CABG coronary artery bypass grafting, AAA abdominal aortic aneurysm, PCI percutaneous coronary intervention. a Comparison of patient mortality across hospitals by comparing hospitals with increasing or decreasing volume with hospitals with unchanged volume. Hazard-ratios estimated from Cox model with adjustment regarding patient (age, gender, Elixhauser list of comorbidities, type and year of procedure, transfer, emergency admission, and median income) and hospital characteristics (hospital status, volume of procedures, specialization degree, and attraction rate). b Analyse to determine if mortality improved or deteriorated over time within hospital that increased or decreased its volume. The ratio of hazard ratio (RHR) compared the change in the mortality rate between two groups. A RHR greater than 1 suggests that the increase of mortality over time was greater in hospitals experiencing volume increase/decrease than in hospitals with unchanged volume

References

    1. Payet C, Lifante J-C, Carty MJ, Rabilloud M, Duclos A. Methodological quality of surgical mortality studies using large hospital databases: a systematic review. Ann Surg. 2017;265:1113–1118. doi: 10.1097/SLA.0000000000002119.
    1. Dudley RA, Johansen KL, Brand R, Rennie DJ, Milstein A. Selective referral to high-volume hospitals: estimating potentially avoidable deaths. JAMA. 2000;283:1159. doi: 10.1001/jama.283.9.1159.
    1. Gandjour A, Bannenberg A, Lauterbach KW. Threshold volumes associated with higher survival in health care: a systematic review. Med Care. 2003;41:1129–1141. doi: 10.1097/.
    1. Halm EA, Lee C, Chassin MR. Is volume related to outcome in health care? Syst Rev Methodol Critique Lit Ann Intern Med. 2002;137:511.
    1. Killeen SD, O’Sullivan MJ, Coffey JC, Kirwan WO, Redmond HP. Provider volume and outcomes for oncological procedures. Br J Surg. 2005;92:389–402. doi: 10.1002/bjs.4954.
    1. Luft HS, Hunt SS, Maerki SC. The volume-outcome relationship: practice-makes-perfect or selective-referral patterns? Health Serv Res. 1987;22:157–182.
    1. Gordon TA, Bowman HM, Tielsch JM, Bass EB, Burleyson GP, Cameron JL. Statewide regionalization of pancreaticoduodenectomy and its effect on in-hospital mortality. Ann Surg. 1998;228:71–78. doi: 10.1097/00000658-199807000-00011.
    1. Learn PA, Bach PB. A decade of mortality reductions in major oncologic surgery: the impact of centralization and quality improvement. Med Care. 2010;48:1041–1049. doi: 10.1097/MLR.0b013e3181f37d5f.
    1. Birkmeyer JD, Dimick JB, Staiger DO. Operative mortality and procedure volume as predictors of subsequent hospital performance. Ann Surg. 2006;243:411–417. doi: 10.1097/01.sla.0000201800.45264.51.
    1. Reames BN, Ghaferi AA, Birkmeyer JD, Dimick JB. Hospital volume and operative mortality in the modern era. Ann Surg. 2014;260:244–251. doi: 10.1097/SLA.0000000000000375.
    1. Finks JF, Osborne NH, Birkmeyer JD. Trends in hospital volume and operative mortality for high-risk surgery. N Engl J Med. 2011;364:2128–2137. doi: 10.1056/NEJMsa1010705.
    1. de Cruppé W, Ohmann C, Blum K, Geraedts M. Evaluating compulsory minimum volume standards in Germany: how many hospitals were compliant in 2004? BMC Health Serv Res. 2007;7:165. doi: 10.1186/1472-6963-7-165.
    1. Kim W, Wolff S, Ho V. Measuring the volume-outcome relation for complex hospital surgery. Appl Health Econ Health Policy. 2016;14:453–464. doi: 10.1007/s40258-016-0241-6.
    1. LaPar DJ, Kron IL, Jones DR, Stukenborg GJ, Kozower BD. Hospital procedure volume should not be used as a measure of surgical quality. Ann Surg. 2012;256:606–615. doi: 10.1097/SLA.0b013e31826b4be6.
    1. Livingston EH, Cao J. Procedure volume as a predictor of surgical outcomes. JAMA. 2010;304:95–97. doi: 10.1001/jama.2010.905.
    1. Duclos A, Lifante J-C. Hospital administrative data should not be used to study thyroid surgery outcomes. Ann Surg. 2018;267:e78. doi: 10.1097/SLA.0000000000002157.
    1. Sund R. Modeling the volume-effectiveness relationship in the case of hip fracture treatment in Finland. BMC Health Serv Res. 2010;10:238. doi: 10.1186/1472-6963-10-238.
    1. Horwitz LI, Lin Z, Herrin J, Bernheim S, Drye EE, Krumholz HM, et al. Association of hospital volume with readmission rates: a retrospective cross-sectional study. BMJ. 2015;350:h447. doi: 10.1136/bmj.h447.
    1. Kanhere HA, Trochsler MI, Kanhere MH, Lord AN, Maddern GJ. Pancreaticoduodenectomy: outcomes in a low-volume, specialised Hepato Pancreato biliary unit. World J Surg. 2014;38:1484–1490. doi: 10.1007/s00268-013-2431-9.
    1. Goldschlager T, Selvanathan S, Walker DG. Can a “novice” do aneurysm surgery? Surgical outcomes in a low-volume, non-subspecialised neurosurgical unit. J Clin Neurosci. 2007;14:1055–1061. doi: 10.1016/j.jocn.2006.12.002.
    1. Jha AK. Back to the future: volume as a quality metric. JAMA. 2015;314:214–215. doi: 10.1001/jama.2015.7580.
    1. Gonzalez AA, Dimick JB, Birkmeyer JD, Ghaferi AA. Understanding the volume-outcome effect in cardiovascular surgery: the role of failure to rescue. JAMA Surg. 2014;149:119–123. doi: 10.1001/jamasurg.2013.3649.
    1. Xu R, Carty MJ, Orgill DP, Lipsitz SR, Duclos A. The teaming curve: a longitudinal study of the influence of surgical team familiarity on operative time. Ann Surg. 2013;258:953–957. doi: 10.1097/SLA.0b013e3182864ffe.
    1. Maruthappu M, El-Harasis MA, Nagendran M, Orgill DP, McCulloch P, Duclos A, et al. Systematic review of methodological quality of individual performance measurement in surgery. Br J Surg. 2014;101:1491–1498. doi: 10.1002/bjs.9642.
    1. Flood AB, Scott WR, Ewy W. Does practice make perfect? Part I: the relation between hospital volume and outcomes for selected diagnostic categories. Med Care. 1984;22:98–114. doi: 10.1097/00005650-198402000-00002.
    1. Flood AB, Scott WR, Ewy W. Does practice make perfect? Part II: the relation between volume and outcomes and other hospital characteristics. Med Care. 1984;22:115–125. doi: 10.1097/00005650-198402000-00003.
    1. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi J-C, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43:1130–1139. doi: 10.1097/01.mlr.0000182534.19832.83.
    1. Halfon P, Eggli Y, van Melle G, Chevalier J, Wasserfallen JB, Burnand B. Measuring potentially avoidable hospital readmissions. J Clin Epidemiol. 2002;55:573–587. doi: 10.1016/S0895-4356(01)00521-2.
    1. Halfon P, Eggli Y, Prêtre-Rohrbach I, Meylan D, Marazzi A, Burnand B. Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med Care. 2006;44:972–981. doi: 10.1097/01.mlr.0000228002.43688.c2.
    1. Hartigan JA, Wong MA. Algorithm AS 136: a K-means clustering algorithm. Appl Stat. 1979;28:100. doi: 10.2307/2346830.
    1. Varadhan R, Weiss CO, Segal JB, Wu AW, Scharfstein D, Boyd C. Evaluating health outcomes in the presence of competing risks: a review of statistical methods and clinical applications. Med Care. 2010;48(6 Suppl):S96–105. doi: 10.1097/MLR.0b013e3181d99107.
    1. Glidden DV, Vittinghoff E. Modelling clustered survival data from multicentre clinical trials. Stat Med. 2004;23:369–388. doi: 10.1002/sim.1599.
    1. Marrie RA, Dawson NV, Garland A. Quantile regression and restricted cubic splines are useful for exploring relationships between continuous variables. J Clin Epidemiol. 2009;62:511–517. doi: 10.1016/j.jclinepi.2008.05.015.
    1. Hamilton BH, Ho V. Does practice make perfect? Examining the relationship between hospital surgical volume and outcomes for hip fracture patients in Quebec. Med Care. 1998;36:892–903. doi: 10.1097/00005650-199806000-00012.
    1. Brown JB, Rosengart MR, Kahn JM, Mohan D, Zuckerbraun BS, Billiar TR, et al. Impact of volume change over time on trauma mortality in the United States. Ann Surg. 2017;266:173–178. doi: 10.1097/SLA.0000000000001838.
    1. Marcin JP, Romano PS. Impact of between-hospital volume and within-hospital volume on mortality and readmission rates for trauma patients in California. Crit Care Med. 2004;32:1477–1483. doi: 10.1097/01.CCM.0000127781.08985.03.
    1. Ghaferi AA, Birkmeyer JD, Dimick JB. Hospital volume and failure to rescue with high-risk surgery. Med Care. 2011;49:1076–1081. doi: 10.1097/MLR.0b013e3182329b97.
    1. Iezzoni LI. Assessing quality using administrative data. Ann Intern Med. 1997;127(8 Pt 2):666–674. doi: 10.7326/0003-4819-127-8_Part_2-199710151-00048.
    1. Lilford R, Mohammed MA, Spiegelhalter D, Thomson R. Use and misuse of process and outcome data in managing performance of acute medical care: avoiding institutional stigma. Lancet. 2004;363:1147–1154. doi: 10.1016/S0140-6736(04)15901-1.
    1. Maa J, Gosnell JE, Gibbs VC, Harris HW. Exporting excellence for Whipple resection to refine the leapfrog initiative. J Surg Res. 2007;138:189–197. doi: 10.1016/j.jss.2006.09.023.
    1. Stitzenberg KB, Sigurdson ER, Egleston BL, Starkey RB, Meropol NJ. Centralization of cancer surgery: implications for patient access to optimal care. J Clin Oncol. 2009;27:4671–4678. doi: 10.1200/JCO.2008.20.1715.
    1. Finlayson SR, Birkmeyer JD, Tosteson AN, Nease RF. Patient preferences for location of care: implications for regionalization. Med Care. 1999;37:204–209. doi: 10.1097/00005650-199902000-00010.
    1. Birkmeyer JD, Siewers AE, Marth NJ, Goodman DC. Regionalization of high-risk surgery and implications for patient travel times. JAMA. 2003;290:2703–2708. doi: 10.1001/jama.290.20.2703.

Source: PubMed

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