Functional Status Outperforms Comorbidities in Predicting Acute Care Readmissions in Medically Complex Patients

Journal of General Internal Medicine

Volumn 30, Issue 11, 2015, Pages 1688-1695

  Shih, Shirley L ^a,b   Gerrard, Paul ^c   Goldstein, Richard ^a   Mix, Jacqueline ^d   Ryan, Colleen M ^b,e   Niewczyk, Paulette ^d,f   Kazis, Lewis ^g   Hefner, Jaye ^a   Ackerly, D Clay ^b,h   Zafonte, Ross ^a,b   Schneider, Jeffrey C ^a,b  

^a American Journal of Physical Medicine and Rehabilitation   (United States)

^b MASSACHUSETTS GENERAL HOSPITAL   (United States)

^c New England Rehabilitation Hospital   (United States)

^d Uniform Data System for Medical Rehabilitation   (United States)

^e SHRINERS HOSPITALS FOR CHILDREN   (United States)

^f DAEMEN COLLEGE   (United States)

^g BOSTON UNIVERSITY SCHOOL OF PUBLIC HEALTH   (United States)

^h HARVARD MEDICAL SCHOOL   (United States)

Author keywords

care transitions; functional status; outcomes; risk assessment; statistical modeling

Indexed keywords

AGED; ARTICLE; BATH; CHARLSON COMORBIDITY INDEX; COMORBIDITY; EATING; ELIXHAUSER COMORBIDITY INDEX; EMERGENCY CARE; FEMALE; FUNCTIONAL INDEPENDENCE MEASURE; FUNCTIONAL STATUS; GROOMING; HOSPITAL READMISSION; HUMAN; MAJOR CLINICAL STUDY; MALE; MEDICARE; PATIENT; REHABILITATION CARE; RETROSPECTIVE STUDY; DAILY LIFE ACTIVITY; DISABILITY; HEALTH STATUS INDICATOR; MIDDLE AGED; MOTOR ACTIVITY; PROCEDURES; PROGNOSIS; REHABILITATION CENTER; RISK ASSESSMENT; STATISTICS AND NUMERICAL DATA; UNITED STATES; VERY ELDERLY;

ACTIVITIES OF DAILY LIVING; AGED; AGED, 80 AND OVER; COMORBIDITY; DISABILITY EVALUATION; FEMALE; HEALTH STATUS INDICATORS; HUMANS; MALE; MIDDLE AGED; MOTOR ACTIVITY; PATIENT READMISSION; PROGNOSIS; REHABILITATION CENTERS; RETROSPECTIVE STUDIES; RISK ASSESSMENT; UNITED STATES;

EID: 84945451773     PISSN: 08848734     EISSN: 15251497     Source Type: Journal    
DOI: 10.1007/s11606-015-3350-2     Document Type: Article

References (65)

1. Pfuntner A, Wier LM, Steiner C. Costs for Hospital Stays in the United States, 2010. HCUP Statistical Brief #146. Rockville: Agency for Healthcare Research and Quality; 2013.
2. Pfuntner A, Wier LM, Steiner C. Costs for Hospital Stays in the United States, 2011. HCUP Statistical Brief #168. Rockville: Agency for Healthcare Research and Quality; 2013.
3. Hines AL, Barrett ML, Jiang HJ, Steiner CA. Conditions with the Largest Number of Adult Hospital Readmissions by Payer, 2011. HCUP Statistical Brief #172. Rockville: Healthcare Research and Quality; 2014.
4. “Readmission Reductions Program.” Centers for Medicare and Medicaid Services. . Aug 04, 2014. Last accessed April 1, 2015.
5. James J. Health Policy Brief: Medicare Hospital Readmission Reduction Program. Health Aff. 2013;1–5.
6. Kansagara D, Englander H, Salanitro A, et al. Risk Prediction Models for Hospital Readmission: A Systematic Review. JAMA. 2011;306(15):1688–98.
7. Marks E. Complexity Science and the Readmission Dilemma: Comment on “Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients” and “Association of Self-Reported Hospital Discharge Handoffs With 30-Day Readmissions”. JAMA Intern Med. 2013;173(8):629–31.
8. Gerhardt G, Yemane A, Hickman P, et al. Data Shows Reduction in Medicare Hospital Readmission Rates During 2012. Medicare Medicaid Res Rev. 2013;3(2):E1–12.
9. Coleman EA, Min SJ, Chomiak A, Kramer AM. Posthospital Care Transitions: Patterns, Complications, and Risk Identification. Health Serv Res. 2004;39(5):1449–65.
10. Burke RE, Coleman EA. Interventions to Decrease Hospital Readmissions: Keys for Cost-Effectiveness. JAMA Intern Med. 2013;173(8):695–8.
11. Hamilton BB, Laughlin JA, Fiedler RC, Granger CV. Interrater Reliability of the 7-Level Functional Independence Measure (FIM). Scand J Rehabil Med. 1994;26(3):115–9.
12. “Bipartisan, Bicameral Effort Underway to Advance Medicare Post-Acute Reform.” U.S. House of Representatives. Committee on Ways and Means. 2014 Mar 18. Available at: http://waysandmeans.house.gov/news/documentsingle.aspx?DocumentID=373213. Last accessed April 1, 2015.
13. Roberts PS, DiVita MA, Riggs RV, et al. Risk factors for discharge to an acute care hospital from inpatient rehabilitation among stroke patients. PM&R. 2014;6(1):50–5.
14. Chung DM, Niewczyk P, DiVita M, et al. Predictors of discharge to acute care after inpatient rehabilitation in severely affected stroke patients. Am J Phys Med Rehabil. 2012;91(5):387–92.
15. Ottenbacher KJ, Graham JE, Ottenbacher AJ, et al. Hospital readmission in persons with stroke following postacute inpatient rehabilitation. J Gerontol A Biol Sci Med Sci. 2012;67(8):875–81.
16. Roth EJ, Lovell L, Harvey RL, et al. Incidence of and risk factors for medical complications during stroke rehabilitation. Stroke. 2001;32(2):523–9.
17. Stineman MG, Ross R, Maislin G, et al. Risks of acute hospital transfer and mortality during stroke rehabilitation. Arch Phys Med Rehabil. 2003;84(5):712–8.
18. Gerrard P, Black-Schaffer R, DiVita M, et al. Functional status as a predictor of acute care readmissions in the stroke population [Abstract]. Am J Phys Med Rehabil. 2014;93(3)(Supplement 2):a7.
19. Schneider JC, Gerrard P, Goldstein R, et al. Predictors of transfer from rehabilitation to acute care in burn injuries. J Trauma Acute Care Surg. 2012;73(6):1596–601.
20. Asher A, Roberts PS, Bresee C, et al. Transferring inpatient rehabilitation facility cancer patients back to acute care (TRIPBAC). PM&R. 2014;6(9):808–13.
21. Hoyer EH, Needham DM, Atanelov L, et al. Association of impaired functional status at hospital discharge and subsequent rehospitalization. J Hosp Med. 2014;9(5):277–82.
22. UB Foundation Activities. The Inpatient Rehabilitation Facility-Patient Assessment Instrument (IRF-PAI) Training Manual. 2004. Available at: http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/downloads//irfpaimanual040104.pdf. Last accessed April 1, 2015.
23. Schneider JC, Tan WH, Goldstein R, et al. Time to Rehabilitation in the Burn Population: Incidence of Zero Onset Days in the UDSMR National Dataset. J Burn Care Res. 2013;34(6):666–73.
24. Centers for Medicare and Medicaid Services. Medicare Hospital Quality Chartbook: Performance Report on Outcome Measures. Sept 2013. Yale New Haven Health Services Corporation, Centers for Outcomes Research and Evaluation.
25. Ouslander JG, Diaz S, Hain D, Tappen R. Frequency and Diagnoses Associated with 7- and 30-Day Readmission of Skilled Nursing Facility Patients to a Nonteaching Community Hospital. J Am Med Dir Assoc. 2011;12:195–203.
26. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):8–27.
27. Comorbidity Software [computer program]. Version 3.5. Available at: http://www.hcup-us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp. 2010. Last accessed April 1, 2015.
28. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–9.
29. Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–51.
30. Centers for Medicare and Medicaid Services. Medicare Program; Prospective Payment System for Inpatient Rehabilitation Facilities. Fed Regist. 2001;66:41316–430.
31. Carter GM, Buntin MB, Hayden O, et al. In: Carter GM, Buntin BM, Hayden O, et al., eds. Analyses for the Initial Implementation of the Inpatient Rehabilitation Facility Prospective Payment System. Santa Monica: RAND Corporation; 2002:99–154.
32. Stineman MG, Shea JA, Jette A, et al. The Functional Independence Measure: Tests of Scaling, Assumptions, Structure, and Reliability Across 20 Diverse Impairment Categories. Arch Phys Med Rehabil. 1996;77(11):1101–8.
33. Grunkemeier GL, Wu YX. Bootstrap resampling methods: Something for nothing? Ann Thorac Surg. 2004;77:1142–4.
34. Lin M, Lucas, Jr. HC, Shmueli G. Too Big to Fail: Large Samples and the p-Value Problem. Inform Syst Res. 2013;24(4):906–17.
35. Harrell FE Jr. Regression Modeling Strategies: With Application to Linear Models, Logistic Regression, and Survival Analysis. New York: Springer-Verlag New York; 2001.
36. Williams R. A Note on Robust Variance Estimation for Cluster-Correlated Data. Biometrics. 2000;56:645–6.
37. McNeish DM. Modeling sparsely clustered data: design-based, model-based, and single-level methods. Psychol Methods. 2014;19(4):552–63.
38. Donzé J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173(8):632–8.
39. Corrigan JD, Smith-Knapp K, Granger CV. Validity of the Functional Independence Measure for Persons with Traumatic Brain Injury. Arch Phys Med Rehabil. 1997;78(8):828–34.
40. Disler PB, Roy CW, Smith BP. Predicting hours of care needed. Arch Phys Med Rehabil. 1993;74(2):139–43.
41. Granger CV, Cotter AC, Hamilton BB, Fiedler RC. Functional Assessment Scales: A Study of Persons after Stroke. Arch Phys Med Rehabil. 1993;74(2):133–8.
42. Granger CV, Cotter AC, Hamilton BB, et al. Functional Assessment Scales: A Study of Persons with Multiple Sclerosis. Arch Phys Med Rehabil. 1990;71(11):870–5.
43. Hamilton BB, Deutsch A, Russell C, et al. Relation of Disability Costs to Function: Spinal Cord Injury. Arch Phys Med Rehabil. 1999;80(4):385–91.
44. Anderson DJ, Chen LF, Schmader KE, et al. Poor Functional Status as a Risk Factor for Surgical Site Infection Due to Methicillin-Resistant Staphylococcus aureus. Infect Control Hosp Epidemiol. 2008;29:832–9.
45. Sellier E, Labarere J, Sevestre A-M, et al. Risk Factors for Deep Vein Thrombosis in Older Patients: A Multicenter Study with Systematic Compression Ultrasonography in Postacute Care Facilities in France. J Am Geriatr Soc. 2008;56:224–30.
46. Robbins AS, Rubenstein LZ, Josephson KR, et al. Predictors of Falls Among Elderly People: Results of Two Population-Based Studies. Arch Intern Med. 1989;149(7):1628–33.
47. Adler J, Malone D. Early Mobilization in the Intensive Care Unit: A Systematic Review. Cardiopulm Phys Ther J. 2012;23(1):5–13.
48. Needham DM. Mobilizing Patients in the Intensive Care Unit: Improving Neuromuscular Weakness and Physical Function. JAMA. 2008;300(14):1685–90.
49. Needham DM, Korupolu R, Zanni JM, et al. Early Physical Medicine and Rehabilitation for Patients with Acute Respiratory Failure: A Quality Improvement Project. Arch Phys Med Rehabil. 2010;91:536–42.
50. Stillman G, Granger C, Niewczyk P. Projecting function of stroke patients in rehabilitation using the AlphaFIM instrument in acute care. PM&R. 2009;1(3):234–9.
51. ® instrument in stroke rehabilitation. Int J Stroke. 2012;7(2):118–24.
52. ® Instrument: Its Background, Structure, and Usefulness. Buffalo: UDSMR; 2012.
53. Brezinski D, Stone PH, Muller JE, et al. Prognostic significance of the Karnofsky Performance Status score in patients with acute myocardial infarction: comparison with the left ventricular ejection fraction and the exercise treadmill test performance. The MILIS Study Group. Am Heart J. 1991;121(5):1374–81.
54. Cesari M, Kritchevsky SB, Penninx BW, et al. Prognostic value of usual gait speed in well-functioning older people—results from the Health, Aging and Body Composition Study. J Am Geriatr Soc. 2005;53(10):1675–80.
55. Cesari M, Onder G, Zamboni V, et al. Physical function and self-rated health status as predictors of mortality: results from longitudinal analysis in the ilSIRENTE study. BMC Geriatr. 2008;8:34.
56. Cesari M, Kritchevsky SB, Newman AB, et al. Added value of physical performance measures in predicting adverse health-related events: results from the Health, Aging And Body Composition Study. J Am Geriatr Soc. 2009;57(2):251–9.
57. Penninx BW, Ferrucci L, Leveille SG, et al. Lower extremity performance in nondisabled older persons as a predictor of subsequent hospitalization. J Gerontol A Biol Sci Med Sci. 2000;55(11):M691–7.
58. Stommel M, Given BA, Given CW. Depression and functional status as predictors of death among cancer patients. Cancer. 2002;94(10):2719–27.
59. Inouye SK, Peduzzi PN, Robison JT, et al. Importance of functional measures in predicting mortality among older hospitalized patients. JAMA. 1996;279(15):1187–93.
60. Landi F, Liperoti R, Russo A, et al. Disability, more than multimorbidity, was predictive of mortality among older persons aged 80 years and older. J Clin Epidemiol. 2010;63:752–9.
61. Marengoni A, von Strauss E, Rizzuto D, et al. The impact of chronic multimorbidity and disability on functional decline and survival in elderly persons. A community-based, longitudinal study. J Intern Med. 2009;265(2):288–95.
62. Lee SJ, Go AS, Lindquist K, et al. Chronic conditions and mortality among the oldest old. Am J Public Health. 2008;98(7):1209–14.
63. Krumholz HM, Lin Z, Drye EE, et al. An administrative claims measure suitable for profiling hospital performance based on 30-day all-cause readmission rates among patients with acute myocardial infarction. Circ Cardiovasc Qual Outcomes. 2011;4(2):243–52.
64. Keenan PS, Normand SL, Lin Z, et al. An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure. Circ Cardiovasc Qual Outcomes. 2008;1:29–37.
65. Lindenauer PK, Normand SL, Drye EE. Development, validation, and results of a measure of 30-day readmission following hospitalization for pneumonia. J Hosp Med. 2011;6(3):142–50.