Characteristics of Long-Term Care Residents That Predict Adverse Events after Hospitalization.
Activities of Daily Living
Aged
Aged, 80 and over
Case-Control Studies
Female
Homes for the Aged
/ statistics & numerical data
Hospitalization
/ statistics & numerical data
Humans
Long-Term Care
/ statistics & numerical data
Male
Nursing Homes
/ statistics & numerical data
Polypharmacy
Prospective Studies
Risk Factors
adverse event
hospitalization
nursing home residents
risk factors
transition of care
Journal
Journal of the American Geriatrics Society
ISSN: 1532-5415
Titre abrégé: J Am Geriatr Soc
Pays: United States
ID NLM: 7503062
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
11
05
2020
revised:
06
07
2020
accepted:
07
07
2020
pubmed:
21
8
2020
medline:
1
4
2021
entrez:
21
8
2020
Statut:
ppublish
Résumé
Adverse events (AEs) occur frequently in long-term care (LTC) residents transitioning from the hospital back to an LTC facility. Measuring the association between resident characteristics and AEs can inform AE risk reduction strategies. Prospective cohort analysis. A total of 32 nursing homes from six New England states. A total of 555 LTC residents contributing 762 transitions from the hospital back to LTC. We measured the association between all AEs and preventable AEs developing in the 45 days following discharge back to LTC and demographic variables, hospital length of stay (LOS), Charlson Comorbidity Index (CCI) (0-1, 2-3, 4-5 and ≥6), dependency in activities of daily living (ADLs) using the Minimum Data Set Long Form Scale (in quintiles 0-12, 13-15, 16, 17-18, and ≥19), and number of regularly scheduled medications (0-9, 10-13, 14-17, and ≥18). To understand the independent association of each resident characteristic with AEs and preventable AEs, we constructed multiple Cox proportional hazards models. There were 283 discharges with one or more AEs and 212 with preventable AEs. Characteristics independently associated with higher risk of an AE included hospital LOS 9 or more days (hazard ratio [HR] = 1.49; 95% confidence interval [CI] = 1.02-2.17); CCI of 4 to 5 (HR = 1.74; 95% CI = 1.13-2.67) or 6 or higher (HR = 1.58; 95% CI = 1.01-2.46); 18 or more regularly scheduled medications (HR = 1.53; 95% CI = 1.07-2.18); and 19 and above on ADL dependency (HR = 1.78; 95% CI = 1.21-2.62). Results from models with preventable AEs were similar to those with all AEs. Increased LOS, higher comorbidity burden, greater dependency in ADLs, and polypharmacy were the resident characteristics most strongly associated with risk of AEs and preventable AEs. We recommend heightened vigilance in the care of LTC residents with these characteristics transitioning back to LTC. We also recommend research to assess strategies to reduce the risk of AEs.
Sections du résumé
BACKGROUND/OBJECTIVES
Adverse events (AEs) occur frequently in long-term care (LTC) residents transitioning from the hospital back to an LTC facility. Measuring the association between resident characteristics and AEs can inform AE risk reduction strategies.
DESIGN
Prospective cohort analysis.
SETTING
A total of 32 nursing homes from six New England states.
PARTICIPANTS
A total of 555 LTC residents contributing 762 transitions from the hospital back to LTC.
MEASUREMENTS
We measured the association between all AEs and preventable AEs developing in the 45 days following discharge back to LTC and demographic variables, hospital length of stay (LOS), Charlson Comorbidity Index (CCI) (0-1, 2-3, 4-5 and ≥6), dependency in activities of daily living (ADLs) using the Minimum Data Set Long Form Scale (in quintiles 0-12, 13-15, 16, 17-18, and ≥19), and number of regularly scheduled medications (0-9, 10-13, 14-17, and ≥18). To understand the independent association of each resident characteristic with AEs and preventable AEs, we constructed multiple Cox proportional hazards models.
RESULTS
There were 283 discharges with one or more AEs and 212 with preventable AEs. Characteristics independently associated with higher risk of an AE included hospital LOS 9 or more days (hazard ratio [HR] = 1.49; 95% confidence interval [CI] = 1.02-2.17); CCI of 4 to 5 (HR = 1.74; 95% CI = 1.13-2.67) or 6 or higher (HR = 1.58; 95% CI = 1.01-2.46); 18 or more regularly scheduled medications (HR = 1.53; 95% CI = 1.07-2.18); and 19 and above on ADL dependency (HR = 1.78; 95% CI = 1.21-2.62). Results from models with preventable AEs were similar to those with all AEs.
CONCLUSION
Increased LOS, higher comorbidity burden, greater dependency in ADLs, and polypharmacy were the resident characteristics most strongly associated with risk of AEs and preventable AEs. We recommend heightened vigilance in the care of LTC residents with these characteristics transitioning back to LTC. We also recommend research to assess strategies to reduce the risk of AEs.
Types de publication
Journal Article
Multicenter Study
Research Support, U.S. Gov't, P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
2551-2557Subventions
Organisme : AHRQ HHS
ID : 5R01HS024422-03
Pays : United States
Informations de copyright
© 2020 The American Geriatrics Society.
Références
Agency for Healthcare Research and Quality. Adverse events, near misses, and errors. https://psnet.ahrq.gov/primers/primer/34/adverseevents-near-misses-and-errors. Accessed December 18, 2018.
Kapoor A, Field T, Handler S, et al. Adverse events in long-term care residents transitioning from hospital back to nursing home. JAMA Intern Med. 2019;179(9):1254-1261.
Levinson DR. Adverse Events in Skilled Nursing Facilities: National Incidence among Medicare Beneficiaries. Washington, DC: Office of Inspector General, Department of Health & Human Services; 2014.
Harris-Kojetin L, Sengupta M, Park-Lee E, Valverde R. Long-term care services in the United States: 2013 overview. National Center for Health Statistics. Vital Health Stat. 2013;3(37):26-40. http://www.cdc.gov/nchs/data/nsltcp/long_term_care_services_2013.pdf Accessed April 13, 2015.
Han JH, Morandi A, Ely EW, et al. Delirium in the nursing home patients seen in the emergency department. J Am Geriatr Soc. 2009;57(5):889-894.
Wang HE, Shah MN, Allman RM, Kilgore M. Emergency department visits by nursing home residents in the United States. J Am Geriatr Soc. 2011;59(10):1864-1872.
New England Nursing Home Quality Care Collaborative. https://healthcarefornewengland.org/initiatives/nhquality/. Accessed December 22, 2018.
Donovan JL, Kanaan AO, Gurwitz JH, et al. A pilot health information technology-based effort to increase the quality of transitions from skilled nursing facility to home: compelling evidence of high rate of adverse outcomes. J Am Med Dir Assoc. 2016;17(4):312-317.
Kanaan AO, Donovan JL, Duchin NP, et al. Adverse drug events after hospital discharge in older adults: types, severity, and involvement of beers criteria medications. J Am Geriatr Soc. 2013;61(11):1894-1899.
Institute for Healthcare Improvement. Trigger tool for measuring adverse drug events in the nursing home. http://www.ihi.org/resources/Pages/Tools/TriggerToolforMeasuringAdverseDrugEvents.aspx. Accessed April 1, 2015
Adler LMJ, Federico F. IHI Skilled Nursing Facility Trigger Tool for Measuring Adverse Events. Cambridge, MA: Institute for Healthcare Improvement; 2015.
Bates DW, Cullen DJ, Laird N, et al. Incidence of adverse drug events and potential adverse drug events: implications for prevention. JAMA. 1995;274(1):29-34.
Gurwitz JH, Field TS, Avorn J, et al. Incidence and preventability of adverse drug events in nursing homes. Am J Med. 2000;109(2):87-94.
Gurwitz JH, Field TS, Harrold LR, et al. Incidence and preventability of adverse drug events among older persons in the ambulatory setting. JAMA. 2003;289(9):1107-1116.
Leape LL, Bates DW, Cullen DJ, et al. Systems analysis of adverse drug events. ADE Prevention Study Group. JAMA. 1995;274(1):35-43.
Leape LL, Cullen DJ, Clapp MD, et al. Pharmacist participation on physician rounds and adverse drug events in the intensive care unit. JAMA. 1999;282(3):267-270.
Brennan TA, Leape LL, Laird NM, et al. Incidence of adverse events and negligence in hospitalized patients. Results of the Harvard medical practice study I. N Engl J Med. 1991;324(6):370-376.
Sackett DL, Haynes RB, Guyatt GH, Tugwell P. Clinical Epidemiology: A Basic Science for Clinical Medicine. 2nd ed. Boston, MA: Little, Brown; 1991.
Lee EW, Wei LJ, Amato DA. In: Klein JP, ed. Survival Analysis: State of the Art. Dordrecht, The Netherlands: Kluwer Academic; 1992.
Colin A, Trivedi PK. Regression Analysis of Count Data. Cambridge, UK: University Press; 1998.
Molenberghs G, Verbeke G. Models for Discrete Longitudinal Data. New York, NY: Springer Science+Business Media; 2005.
Little RJ, Rubin DB. Statistical Analysis with Missing Data. 2nd ed. New York, NY: John Wiley & Sons; 2002.
Raghunathan T, Berglund PA, Solenberger PW. Multiple Imputation in Practice with Examples Using IVEware. New York, NY: Chapman and Hall/CRC; 2018.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chron Dis. 1987;40(5):373-383.
Centers for Medicare & Medicaid Services. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/MDS30RAIManual. Accessed April 6, 2020.
Fortinsky RH, Covinsky KE, Palmer RM, Landefeld CS. Effects of functional status changes before and during hospitalization on nursing home admission of older adults. J Gerontol A Biol. 1999;54(10):M521-M526.
Burke RE, Hess E, Baron AE, Levy C, Donze JD. Predicting potential adverse events during a skilled nursing facility stay: a skilled nursing facility prognosis score. J Am Geriatr Soc. 2018;66(5):930-936.
Mor V, Zinn J, Angelelli J, Teno JM, Miller SC. Driven to tiers: socioeconomic and racial disparities in the quality of nursing home care. Milbank Q. 2004;82(2):227-256.
Yuan Y, Louis C, Cabral H, Schneider JC, Ryan CM, Kazis LE. Socioeconomic and geographic disparities in accessing nursing homes with high star ratings. J Am Med Dir Assoc. 2018;19(10):852-859.e2.
Pedersen MM, Holt NE, Grande L, et al. Mild cognitive impairment status and mobility performance: an analysis from the Boston RISE study. J Gerontol A Biol. 2014;69(12):1511-1518.
Tinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community. N Engl J Med. 1988;319(26):1701-1707.
Tyrovolas S, Koyanagi A, Lara E, Santini ZI, Haro JM. Mild cognitive impairment is associated with falls among older adults: findings from the Irish Longitudinal Study on Ageing (TILDA). Exp Gerontol. 2016;75:42-47.
van der Wardt V, Logan P, Hood V, Booth V, Masud T, Harwood R. The association of specific executive functions and falls risk in people with mild cognitive impairment and early-stage dementia. Dement Geriatr Cogn Disord. 2015;40(3-4):178-185.
Welmer AK, Rizzuto D, Laukka EJ, Johnell K, Fratiglioni L. Cognitive and physical function in relation to the risk of injurious falls in older adults: a population-based study. J Gerontol A Biol. 2017;72(5):669-675.