CC BY-NC-ND 4.0 · Arq Neuropsiquiatr 2019; 77(05): 321-329
DOI: 10.1590/0004-282X20190041
Article

Predictors of readmission and long length of stay in elders admitted with neurological disorders in a tertiary center: a real-world investigation

Preditores de readmissão hospitalar e de longo tempo de internação em idosos admitidos com doenças neurológicas em centro terciário: uma investigação no mundo real
1   São Rafael Hospital, D’Or Institute for Research and Education (IDOR), Salvador BA, Brasil
2   Universidade Federal Fluminense, Departamento de Neurologia, Rio de Janeiro RJ, Brasil
,
Bruno B. Pedreira
1   São Rafael Hospital, D’Or Institute for Research and Education (IDOR), Salvador BA, Brasil
,
Gersonita Costa
1   São Rafael Hospital, D’Or Institute for Research and Education (IDOR), Salvador BA, Brasil
,
Telma Assis
1   São Rafael Hospital, D’Or Institute for Research and Education (IDOR), Salvador BA, Brasil
,
Camila Lobo
1   São Rafael Hospital, D’Or Institute for Research and Education (IDOR), Salvador BA, Brasil
,
Osvaldo Nascimento
2   Universidade Federal Fluminense, Departamento de Neurologia, Rio de Janeiro RJ, Brasil
› Author Affiliations

ABSTRACT

Hospital readmission and long length of stay (LOS) increase morbidity and hospital mortality and are associated with excessive costs to health systems.

Objective: This study aimed to identify predictors of hospital readmission and long LOS among elders with neurological disorders (NDs).

Methods: Patients ≥ 60 years of age admitted to the hospital between January 1, 2009, and December 31, 2010, with acute NDs, chronic NDs as underpinnings of acute clinical disorders, and neurological complications of other diseases were studied. We analyzed demographic factors, NDs, and comorbidities as independent predictors of readmission and long LOS (≥ 9 days). Logistic regression was performed for multivariate analysis.

Results: Overall, 1,154 NDs and 2,679 comorbidities were identified among 798 inpatients aged ≥ 60 years (mean 75.8 ± 9.1). Of the patients, 54.5% were female. Patient readmissions were 251(31%) and 409 patients (51%) had an LOS ≥ 9 days (95% confidence interval 48%–55%). We found no predictors for readmission. Low socioeconomic class (p = 0.001), respiratory disorder (p < 0.001), infection (p < 0.001), genitourinary disorder (p < 0.033), and arterial hypertension (p = 0.002) were predictors of long LOS. Identified risks of long LOS explained 22% of predictors.

Conclusions: Identifying risk factors for patient readmission are challenges for neurology teams and health system stakeholders. As low socioeconomic class and four comorbidities, but no NDs, were identified as predictors for long LOS, we recommend studying patient multimorbidity as well as functional and cognitive scores to determine whether they improve the risk model of long LOS in this population.

RESUMO

Readmissão hospitalar e tempo longo de internação aumentam a morbidade, a mortalidade hospitalar e estão associados a custos excessivos para os sistemas de saúde.

Objetivo: Este estudo almejou identificar preditores de readmissões hospitalares e longo tempo de internação (TDI) entre idosos com doenças neurológicas (DN).

Métodos: Pacientes de idade ≥ 60 anos admitidos no hospital entre 1 de janeiro de 2009 e 31 de dezembro de 2010 com DN aguda, DN crçnica subjacente a transtorno clínico agudo e complicações neurológicas de outras doenças foram estudados. Nos analisamos fatores demográficos, DN e comorbidades como preditores independentes de readmissão hospitalar e TDI (≥ 9 dias). Utilizamos regressão logística para analise multivariada.

Resultados: Um total de 1154 DN e 2679 comorbidades foram identificadas entre 798 pacientes com idade ≥ 60 anos (media 75.8 ± 9.1). Desses pacientes 54.5% foram mulheres. Foram 251(31%) readmissões de pacientes e 409 (51%) dos pacientes tiveram um TDI≥9 dias (intervalo de confiança 95%, 48%–55%). Não encontramos preditores para readmissões. Baixa classe social (p = 0,001), distúrbio respiratório (p < 0,001), infecção (p < 0,001), distúrbio genito-urinário (p = 0,033) e hipertensão arterial (p = 0,002) foram os preditores de longo tempo de internação. Esses fatores de risco compõem 22% dos preditores para longo TDI.

Conclusões: A identificação de fatores de risco para readmissão hospitalar é um desafio para equipes neurológicas e gestores dos sistemas de saúde. Conquanto baixa classe social e 4 comorbidades, todavia nenhuma DN, foram identificadas como preditoras para longo TDI nós recomendamos investigar multimorbidade, escores funcionais e cognitivos para saber se eles melhoram o modelo de risco para longo TDI nesta população.

Support

The Hospital Sao Rafael–Monte Tabor Foundation partially reimburses manuscript-processing charges to motivate research.




Publication History

Received: 03 October 2018

Accepted: 01 February 2019

Article published online:
16 August 2023

© 2023. Academia Brasileira de Neurologia. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commecial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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  • References

  • 1 Wong EL, Cheung AW, Leung MC, Yam CH, Chan FW, Wong FY, et al. Unplanned readmission rates, length of hospital stay, mortality, and medical costs of ten common medical conditions: a retrospective analysis of Hong Kong hospital data. BMC Health Serv Res. 2011 Jun;11(1):149. https://doi.org/10.1186/1472-6963-11-149
  • 2 Zook CJ, Savickis SF, Moore FD. Repeated hospitalization for the same disease: a multiplier of national health costs. Milbank Mem Fund Q Health Soc. 1980;58(3):454-71. https://doi.org/10.2307/3349734
  • 3 Ishak KJ, Stolar M, Hu MY, Alvarez P, Wang Y, Getsios D, et al. Accounting for the relationship between per diem cost and LOS when estimating hospitalization costs. BMC Health Serv Res. 2012 Dec;12(1):439. https://doi.org/10.1186/1472-6963-12-439
  • 4 Group GB; GBD 2015 Neurological Disorders Collaborator Group. Global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol. 2017 Nov;16(11):877-97. https://doi.org/10.1016/S1474-4422(17)30299-5
  • 5 Fletcher P, Leake A, Marion MH. Patients with Parkinson's disease dementia stay in the hospital twice as long as those without dementia. Mov Disord. 2011 Apr;26(5):919. https://doi.org/10.1002/mds.23573
  • 6 Koch S, Spuler S, Deja M, Bierbrauer J, Dimroth A, Behse F, et al. Critical illness myopathy is frequent: accompanying neuropathy protracts ICU discharge. J Neurol Neurosurg Psychiatry. 2011 Mar;82(3):287-93. https://doi.org/10.1136/jnnp.2009.192997
  • 7 Naidech AM, Beaumont JL, Rosenberg NF, Maas MB, Kosteva AR, Ault ML, et al. Intracerebral hemorrhage and delirium symptoms. Length of stay, function, and quality of life in a 114-patient cohort. Am J Respir Crit Care Med. 2013 Dec;188(11):1331-7. https://doi.org/10.1164/rccm.201307-1256OC
  • 8 Daiello LA, Gardner R, Epstein-Lubow G, Butterfield K, Gravenstein S. Association of dementia with early rehospitalization among Medicare beneficiaries. Arch Gerontol Geriatr. 2014 Jul-Aug;59(1):162-8. https://doi.org/10.1016/j.archger.2014.02.010
  • 9 Rocha MS, Almeida AC, Abath Neto O, Porto MP, Brucki SM. Impact of stroke unit in a public hospital on length of hospitalization and rate of early mortality of ischemic stroke patients. Arq Neuropsiquiatr. 2013 Oct;71(10):774-9. https://doi.org/10.1590/0004-282X20130120
  • 10 Lewsey J, Ebueku O, Jhund PS, Gillies M, Chalmers JW, Redpath A, et al. Temporal trends and risk factors for readmission for infections, gastrointestinal and immobility complications after an incident hospitalisation for stroke in Scotland between 1997 and 2005. BMC Neurol. 2015 Jan;15(1):3. https://doi.org/10.1186/s12883-014-0257-1
  • 11 André C, Py MO, Mariño RG. [Causes of unjustified hospital stay following cerebral infarction]. Arq Neuropsiquiatr. 1997 Sep;55(3B):569-72. https://doi.org/10.1590/S0004-282X1997000400009
  • 12 Hammond CL, Phillips MF, Pinnington LL, Pearson BJ, Fakis A. Appropriateness of acute admissions and last in-patient day for patients with long term neurological conditions. BMC Health Serv Res. 2009 Feb;9(1):40. https://doi.org/10.1186/1472-6963-9-40
  • 13 García-Pérez L, Linertová R, Lorenzo-Riera A, Vázquez-Díaz JR, Duque-González B, Sarría-Santamera A. Risk factors for hospital readmissions in elderly patients: a systematic review. QJM. 2011 Aug;104(8):639-51. https://doi.org/10.1093/qjmed/hcr070
  • 14 Agência IBGE Notícias. Numero de idosos cresce 18% em cinco anos e ultrapassa 30 milhões em 2017. 2018 Apr 26. Available from:https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/20980-numero-de-idosos-cresce
  • 15 Bacellar A, Pedreira BB, Costa G, Assis T. Frequency, associated features, and burden of neurological disorders in older adult inpatients in Brazil: a retrospective cross-sectional study. BMC Health Serv Res. 2017 Jul;17(1):504. https://doi.org/10.1186/s12913-017-2260-x
  • 16 Bacellar A, Assis T, Pedreira BB, Costa G, Nascimento OJ. Hospital mortality among elderly patients admitted with neurological disorders was not predicted by any particular diagnosis in a tertiary medical center. Open Neurol J. 2018 Jan;12(1):1-11. https://doi.org/10.2174/1874205X01812010001
  • 17 Brazilian Market Research Association. Critério de Classificação Econçmica Brasil. Brazilian Criteria 2015 and social class distribution update for 2016. 2016. [Cited 2016 Apr 16]. Available from:http://www.abep.org/Servicos/Download.aspx?id=13
  • 18 Bós AM, Bós AJ. [Determinants of elders’ choice between private and public health care providers]. Rev Saude Publica. 2004 Feb;38(1):113-20. Portuguese. https://doi.org/10.1590/S0034-89102004000100016
  • 19 Boccolini CS, Souza Junior PR. Inequities in Healthcare utilization: results of the Brazilian National Health Survey, 2013. Int J Equity Health. 2016 Nov;15(1):150. https://doi.org/10.1186/s12939-016-0444-3
  • 20 van Drimmelen-Krabbe JJ, Bradley WG, Orgogozo JM, Sartorius N. The application of the International Statistical Classification of Diseases to neurology: ICD-10 NA. J Neurol Sci. 1998 Nov;161(1):2-9. https://doi.org/10.1016/S0022-510X(98)00217-2
  • 21 American Psychiatric Association. Diagnostic and statistical manual of mental disorders, 4th ed. Washington, DC: American Psychiatric Association; 1994.
  • 22 Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003 May;289(19):2560-72. https://doi.org/10.1001/jama.289.19.2560
  • 23 Grundy SM, Cleeman JI, Merz CN, Brewer HB Jr, Clark LT, Hunninghake DB, et al. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III Guidelines. J Am Coll Cardiol. 2004 Aug;44(3):720-32. https://doi.org/10.1016/j.jacc.2004.07.001
  • 24 Genuth S, Alberti KG, Bennett P, Buse J, Defronzo R, Kahn R, et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care. 2003 Nov;26(11):3160-7. https://doi.org/10.2337/diacare.26.11.3160
  • 25 Oni T, McGrath N, BeLue R, Roderick P, Colagiuri S, May CR, et al. Chronic diseases and multi-morbidity: conceptual modification to the WHO ICCC model for countries in health transition. BMC Public Health. 2014 Jun;14(1):575. https://doi.org/10.1186/1471-2458-14-575
  • 26 Hosmer DW Jr, Lemeshow S. Applied logistic regression. 2nd ed. New York: Wiley-Interscience; 2000.
  • 27 Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012 Jul;380(9836):37-43. https://doi.org/10.1016/S0140-6736(12)60240-2
  • 28 Yam CH, Wong EL, Chan FW, Leung MC, Wong FY, Cheung AW, et al. Avoidable readmission in Hong Kong: system, clinician, patient or social factor? BMC Health Serv Res. 2010 Nov;10(1):311. https://doi.org/10.1186/1472-6963-10-311
  • 29 Hesselink G, Zegers M, Vernooij-Dassen M, Barach P, Kalkman C, Flink M, et al. Improving patient discharge and reducing hospital readmissions by using Intervention Mapping. BMC Health Serv Res. 2014 Sep;14(1):389. https://doi.org/10.1186/1472-6963-14-389
  • 30 Knox J, Chuni C, Naqvi Z, Crawford P, Waring W. Presentations to an acute medical unit due to headache: a review of 306 consecutive cases. Acute Med. 2012;11(3):144-9.
  • 31 Shiyovich A, Munchak I, Zelingher J, Grosbard A, Katz A. Admission for syncope: evaluation, cost and prognosis according to etiology. Isr Med Assoc J. 2008 Feb;10(2):104-8.
  • 32 Delgado-Rodríguez M, Medina-Cuadros M, Gómez-Ortega A, Martínez-Gallego G, Mariscal-Ortiz M, Martinez-Gonzalez MA, et al. Cholesterol and serum albumin levels as predictors of cross infection, death, and length of hospital stay. Arch Surg. 2002 Jul;137(7):805-12. https://doi.org/10.1001/archsurg.137.7.805
  • 33 Garcia-Subirats I, Vargas I, Mogollón-Pérez AS, De Paepe P, Silva MR, Unger JP, et al. Inequities in access to health care in different health systems: a study in municipalities of central Colombia and north-eastern Brazil. Int J Equity Health. 2014 Jan;13(1):10. https://doi.org/10.1186/1475-9276-13-10
  • 34 Corrao G, Rea F, Merlino L, Mazzola P, Annoni F, Annoni G. Management, prognosis and predictors of unfavourable outcomes in patients newly hospitalized for transient ischemic attack: a real-world investigation from Italy. BMC Neurol. 2017 Jan;17(1):12. https://doi.org/10.1186/s12883-017-0796-3
  • 35 Instituto Brasileiro de Geografia e Estatística – IBGE. Brazilian health services availability and utilization: a population-based study. (1998, Dec 15). Available from:http://www.ibge.gov.br/home/estatistica/populacao/trabalhoerendimento/pnad98/saude/analise.shtm
  • 36 Gadre SK, Duggal A, Mireles-Cabodevila E, Krishnan S, Wang XF, Zell K, et al. Acute respiratory failure requiring mechanical ventilation in severe chronic obstructive pulmonary disease (COPD). Medicine (Baltimore). 2018 Apr; 97(17): e0487. https://doi.org/10.1097/MD.201900410000010487
  • 37 Djordjevic Z, Jankovic S, Gajovic O, Djonovic N, Folic N, Bukumiric Z. Hospital infections in a neurological intensive care unit: incidence, causative agents and risk factors. J Infect Dev Ctries. 2012 Nov;6(11):798-805. https://doi.org/10.3855/jidc.2659
  • 38 Yong TY, Fok JS, Ng PZ, Hakendorf P, Ben-Tovim DI, Roberts S, et al. The significance of reduced kidney function among hospitalized acute general medical patients. QJM. 2013 Jan;106(1):59-65. https://doi.org/10.1093/qjmed/hcs192
  • 39 Sanders JS, Skipworth JR, Cooper JA, Brull DJ, Humphries SE, Mythen M, et al. Duration of preceding hypertension is associated with prolonged length of ICU stay. Int J Cardiol. 2012 May;157(2):180-4. https://doi.org/10.1016/j.ijcard.2010.12.011
  • 40 Aleluia IR, Medina MG, Almeida PF, Vilasbças AL. Care coordination in primary health care: an evaluative study in a municipality in the Northeast of Brazil. Cien Saúde Colet. 2017 Jun;22(6):1845-56. https://doi.org/10.1590/1413-81232017226.02042017
  • 41 Campbell SE, Seymour DG, Primrose WR. A systematic literature review of factors affecting outcome in older medical patients admitted to hospital. Age Ageing. 2004 Mar;33(2):110-5. https://doi.org/10.1093/ageing/afh036