Emulating 3 Clinical Trials That Compare Stroke Rehabilitation at Inpatient Rehabilitation Facilities With Skilled Nursing Facilities

被引:4
作者
Simmonds, Kent P. [1 ]
Burke, James [2 ]
Kozlowski, Allan J. [1 ,3 ]
Andary, Michael [4 ]
Luo, Zhehui [1 ]
Reeves, Mathew J. [1 ,5 ]
机构
[1] Michigan State Univ, Coll Human Med, Dept Epidemiol & Biostat, E Lansing, MI USA
[2] Ohio State Univ, Dept Neurol, Columbus, OH USA
[3] Mary Free Bed Rehabil Hosp, John F Butzer Ctr Res & Innovat, Grand Rapids, MI USA
[4] Michigan State Univ, Coll Osteopath Med, Dept Phys Med & Rehabil, E Lansing, MI USA
[5] 909 Wilson Rd, E Lansing, MI 48824 USA
来源
ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION | 2022年 / 103卷 / 07期
关键词
Comparative effectiveness research; Epidemiology; Health policy; Rehabilitation; Stroke rehabilitation; POSTACUTE CARE; SUBACUTE REHABILITATION; THERAPY INTENSITY; OUTCOMES; HOSPITALS; GUIDELINES; RECOVERY;
D O I
10.1016/j.apmr.2021.12.029
中图分类号
R49 [康复医学];
学科分类号
100215 ;
摘要
Objective: To inform the design of a potential future randomized controlled trial (RCT), we emulated 3 trials where patient-level outcomes were compared after stroke rehabilitation at inpatient rehabilitation facilities (IRFs) with skilled nursing facilities (SNFs). Design: Trials were emulated using a 1:1 matched propensity score analysis. The 3 trials differed because facilities from rehabilitation networks with different case volumes were compared. Rehabilitation network case volumes were based on the number of patients with stroke that each hospital discharged to each specific IRF or SNF. Trial 1 included 60,529 patients from all networks, trial 2 included 34,444 patients from networks with medium and large case volumes (ie, >= 5 patients), and trial 3 included 19,161 patients from networks with large case volumes (ie, >= 10 patients). The E values were calculated to estimate the minimum strength that an unmeasured confounder would need to be to nullify the results.Setting: A national sample of IRFs and SNFs from across the United States.Participants: Fee-for-service Medicare patients with acute stroke who received IRF or SNF based rehabilitation.Interventions: Not applicable.Main Outcome Measures: One-year successful community discharge (home for > 30 consecutive days) and all-cause mortality.Results: Overall, 29,500, 15,156, and 7450 patients were matched for trials 1, 2, and 3. For 1-year successful community discharge, absolute risk differences for IRF patients were 0.21 (95% CI, 0.20-0.22), 0.17 (95% CI, 0.16-0.19), and 0.12 (95% CI, 0.10-0.14) in trials 1, 2, and 3, respectively. For 1-year all-cause mortality, corresponding risk differences were-0.11 (95% CI,-0.12 to-0.11),-0.11 (95% CI,-0.12 to-0.09), and-0.08 (95% CI,-0.10 to-0.06). The E values indicated that a moderately sized unmeasured confounder, with a relative risk of 1.6-2.0 would nullify differences in successful community discharge. Conclusions: IRF patients had superior outcomes, but differences were attenuated when IRFs and SNFs from larger rehabilitation networks were compared. The vulnerability of the findings to unmeasured confounding supports the need for an RCT. (c) 2022 Published by Elsevier Inc. on behalf of the American Congress of Rehabilitation Medicine
引用
收藏
页码:1311 / 1319
页数:9
相关论文
共 41 条
  • [1] Emulating a Novel Clinical Trial Using Existing Observational Data Predicting Results of the PreVent Study
    Admon, Andrew J.
    Donnelly, John P.
    Casey, Jonathan D.
    Janz, David R.
    Russell, Derek W.
    Joffe, Aaron M.
    Vonderhaar, Derek J.
    Dischert, Kevin M.
    Stempek, Susan B.
    Dargin, James M.
    Rice, Todd W.
    Iwashyna, Theodore J.
    Semler, Matthew W.
    [J]. ANNALS OF THE AMERICAN THORACIC SOCIETY, 2019, 16 (08) : 998 - 1007
  • [2] The use of fixed- and random-effects models for classifying hospitals as mortality outliers: A Monte Carlo assessment
    Austin, PC
    Alter, DA
    Tu, JV
    [J]. MEDICAL DECISION MAKING, 2003, 23 (06) : 526 - 539
  • [3] The use of bootstrapping when using propensity-score matching without replacement: a simulation study
    Austin, Peter C.
    Small, Dylan S.
    [J]. STATISTICS IN MEDICINE, 2014, 33 (24) : 4306 - 4319
  • [4] An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
    Austin, Peter C.
    [J]. MULTIVARIATE BEHAVIORAL RESEARCH, 2011, 46 (03) : 399 - 424
  • [5] Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies
    Austin, Peter C.
    [J]. PHARMACEUTICAL STATISTICS, 2011, 10 (02) : 150 - 161
  • [6] Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples
    Austin, Peter C.
    [J]. STATISTICS IN MEDICINE, 2009, 28 (25) : 3083 - 3107
  • [7] Contemporary Trends and Predictors of Postacute Service Use and Routine Discharge Home After Stroke
    Bettger, Janet Prvu
    McCoy, Lisa
    Smith, Eric E.
    Fonarow, Gregg C.
    Schwamm, Lee H.
    Peterson, Eric D.
    [J]. JOURNAL OF THE AMERICAN HEART ASSOCIATION, 2015, 4 (02):
  • [8] Using observational data to emulate a randomized trial of dynamic treatment-switching strategies: an application to antiretroviral therapy
    Cain, Lauren E.
    Saag, Michael S.
    Petersen, Maya
    May, Margaret T.
    Ingle, Suzanne M.
    Logan, Roger
    Robins, James M.
    Abgrall, Sophie
    Shepherd, Bryan E.
    Deeks, Steven G.
    Gill, M. John
    Touloumi, Giota
    Vourli, Georgia
    Dabis, Francois
    Vandenhende, Marie-Anne
    Reiss, Peter
    van Sighem, Ard
    Samji, Hasina
    Hogg, Robert S.
    Rybniker, Jan
    Sabin, Caroline A.
    Jose, Sophie
    del Amo, Julia
    Moreno, Santiago
    Rodriguez, Benigno
    Cozzi-Lepri, Alessandro
    Boswell, Stephen L.
    Stephan, Christoph
    Perez-Hoyos, Santiago
    Jarrin, Inma
    Guest, Jodie L.
    Monforte, Antonella D'Arminio
    Antinori, Andrea
    Moore, Richard
    Campbell, Colin N. J.
    Casabona, Jordi
    Meyer, Laurence
    Seng, Remonie
    Phillips, Andrew N.
    Bucher, Heiner C.
    Egger, Matthias
    Mugavero, Michael J.
    Haubrich, Richard
    Geng, Elvin H.
    Olson, Ashley
    Eron, Joseph J.
    Napravnik, Sonia
    Kitahata, Mari M.
    Van Rompaey, Stephen E.
    Teira, Ramon
    [J]. INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 2016, 45 (06) : 2038 - 2049
  • [9] Successful Community Discharge Following Postacute Rehabilitation for Medicare Beneficiaries: Analysis of a Patient-Centered Quality Measure
    Cary, Michael P., Jr.
    Bettger, Janet Prvu
    Jarvis, Jessica M.
    Ottenbacher, Kenneth J.
    Graham, James E.
    [J]. HEALTH SERVICES RESEARCH, 2018, 53 (04) : 2470 - 2482
  • [10] The state-of-the-science: Challenges in designing postacute care payment policy
    Chan, Leighton
    [J]. ARCHIVES OF PHYSICAL MEDICINE AND REHABILITATION, 2007, 88 (11): : 1522 - 1525