Skip to main content

Advertisement

Springer Nature Link
Log in

The Predictive Factors on Extended Hospital Length of Stay in Patients with AMI: Laboratory and Administrative Data

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The length of hospital stay (LOS) is an important measure of efficiency in the use of hospital resources. Acute Myocardial Infarction (AMI), as one of the diseases with higher mortality and LOS variability in the OECD countries, has been studied with predominant use of administrative data, particularly on mortality risk adjustment, failing investigation in the resource planning and specifically in LOS. This paper presents results of a predictive model for extended LOS (LOSE - above 75th percentile of LOS) using both administrative and clinical data, namely laboratory data, in order to develop a decision support system. Laboratory and administrative data of a Portuguese hospital were included, using logistic regression to develop this predictive model. A model with three laboratory data and seven administrative data variables (six comorbidities and age ≥ 69 years), with excellent discriminative ability and a good calibration, was obtained. The model validation shows also good results. Comorbidities were relevant predictors, mainly diabetes with complications, showing the highest odds of LOSE (OR = 37,83; p = 0,001). AMI patients with comorbidities (diabetes with complications, cerebrovascular disease, shock, respiratory infections, pulmonary oedema), with pO2 above level, aged 69 years or older, with cardiac dysrhythmia, neutrophils above level, pO2 below level, and prothrombin time above level, showed increased risk of extended LOS. Our findings are consistent with studies that refer these variables as predictors of increased risk.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kulinskaya, E., Kornbrot, D., and Gao, H., Length of stay as a performance indicator: robust statistical methodology. IMA J. Manag. Math. 16:369–381, 2005.

    Article  Google Scholar 

  2. Park, S., et al., Quality of care and in-hospital resource use in acute myocardial infarction: evidence from Japan. Health Policy 111:264–272, 2013.

    Article  PubMed  Google Scholar 

  3. Kaplan, R. M., and Babad, Y. M., Balancing influence between actors in healthcare decision making. BMC Health Serv. Res. 11:85, 2011.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Meyfroidt, G., et al., Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model. BMC Med. Inform. Decis. Mak. 11:64, 2011.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Barbini, P., Barbini, E., Furini, S., and Cevenini, G., A straightforward approach to designing a scoring system for predicting length-of-stay of cardiac surgery patients. BMC Med. Inform. Decis. Mak. 14:89, 2014.

    Article  PubMed  PubMed Central  Google Scholar 

  6. OECD (2013) Health at a Glance 2013: OECD Indicators, OECD Publishing. http://dx.doi.org/10.1787/health_glance-2013-en. Accessed 28 September 2014

  7. DGS (2014) Portugal: Doenças Cérebro-Cardiovasculares em números 2014. Direção-Geral da Saúde. http://www.dgs.pt/estatisticas-de-saude/estatisticas-de-saude/publicacoes/portugal-doencas-cerebro-cardiovasculares-em-numeros-2014.aspx. Accessed 20 November 2014

  8. Mathers, C. D., and Loncar, D., Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 3(11):e442, 2006.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Chevreul, K., et al., Does lay media ranking of hospitals reflect lower mortality in treating acute myocardial infarction? Arch. Cardiovasc. Dis. 105(10):489–498, 2012.

    Article  PubMed  Google Scholar 

  10. Hamm, C. W., et al., ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation. Eur. Heart J. 32:2999–3054, 2011.

    Article  PubMed  Google Scholar 

  11. Steg, G., et al., ESC guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation. Eur. Heart J. 33:2569–2619, 2012.

    Article  PubMed  CAS  Google Scholar 

  12. Grines, C.L., et al, Safety and cost-effectiveness of early discharge after primary angioplasty in low risk patients with acute myocardial infarction. PAMI-II Investigators. Primary Angioplasty in Myocardial Infarction. J Am Coll Cardiol. 31,5:967–972, 1998.

  13. Kotowycz, M., Syal, R. P., Afzal, R., and Natarajan, M. K., Can we improve length of hospitalization in ST elevation myocardial infarction patients treated with primary percutaneous coronary intervention? Can. J. Cardiol. 25(10):585–588, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Tu, J. V., Austin, P. C., Walld, R., Roos, L., Agras, J., and McDonald, K. M., Development and validation of the Ontario acute myocardial infarction mortality prediction rules. J. Am. Coll. Cardiol. 37:992–997, 2001.

    Article  PubMed  CAS  Google Scholar 

  15. Stargardt, T., Schreyogg, J., and Kondofersky, I., Measuring the relationship between costs and outcomes: the example of acute myocardial infarction in German hospitals. Hamburg center for health economics. Health Econ. 23:653–669, 2014.

    Article  PubMed  Google Scholar 

  16. Wright, S. P., et al., Factors influencing the length of hospital stay of patients with heart failure. Eur. J. Heart Fail. 5(2):201–209, 2003.

    Article  PubMed  CAS  Google Scholar 

  17. Paulus, J. K., Shah, N. D., and Kent, D. M., Cardiovascular perspective: all else being equal. Men and women are still not the same: using risk models to understand gender disparities in care. Circ. Cardiovasc. Qual. Outcomes 8:317–320, 2015.

    Article  PubMed  Google Scholar 

  18. Qi Fan, G., et al., A medical costs study of older patients with acute myocardial infarction and metabolic syndrome in hospital. Clin. Interv. Aging 10:329–337, 2015.

    Google Scholar 

  19. Steyerberg, E. W., Eijkemans, M. J. C., Boersma, E., and Habbema, J. D. F., Applicability of clinical prediction models in acute myocardial infarction: a comparison of traditional and empirical Bayes adjustment methods. Am. Heart J. 150(5):11–17, 2005.

    Article  Google Scholar 

  20. Saczynski, J. S., et al., Declining length of stay for patients hospitalized with AMI: impact on mortality and readmissions. Am. J. Med. 11:1007–1015, 2010.

    Article  Google Scholar 

  21. Iezzoni, L. I., Risk Adjustment for Measuring Healthcare Outcomes, 2nd edition. Health Administration Press, Chicago, 1997. ISBN 1-56793-054-9.

    Google Scholar 

  22. Bertomeu, V., et al., In-hospital Mortality due to acute myocardial infarction. relevance of type of hospital and care provided. RECALCAR study. Rev. Esp. Cardiol. (Engl. Ed.) 66(12):935–942, 2013.

    Article  Google Scholar 

  23. McCullough, E., et al., Challenges and benefits of adding laboratory data to a mortality risk adjustment method. Qual. Manag.Health Care 20(4):253–262, 2011.

    Article  PubMed  Google Scholar 

  24. Park, H. K., Comparison of risk-adjustment models using administrative or clinical data for outcome prediction in patients after myocardial infarction or coronary bypass surgery in Korea. Int. J. Clin. Pract. 61(7):1086–1090, 2007.

    Article  PubMed  Google Scholar 

  25. Tanuja, S., Acharya, U. D., and Shailesh, K. R., Comparison of different data mining techniques to predict hospital length of stay. J. Pharm. Biom. Sci. 7:1–4, 2011.

    Google Scholar 

  26. Asadollahi, K., Hastings, I. M., Gill, G. V., and Beeching, N. J., Prediction of hospital mortality from admission laboratory data and patient age: a simple model. Emerg. Med. Australas. 23:354–363, 2011.

    Article  PubMed  Google Scholar 

  27. Hosmer, D. W., Jr., Lemeshow, S., and Sturdivant, R. X., Applied Logistic Regression, 3rd edition. Wiley, Hoboken, 2013.

    Book  Google Scholar 

  28. Faraway, J. J., Does Data Splitting Improve Prediction? Cornell University Library, Ithaca, 2013.

    Google Scholar 

  29. Takahashi, T., et al., Relation between neutrophil counts on admission, microvascular injury, and left ventricular functional recovery in patients with an anterior wall first acute myocardial infarction treated with primary coronary angioplasty. Am. J. Cardiol. 100(1):35–40, 2007.

    Article  PubMed  Google Scholar 

  30. Khan, H. A., Alhomida, A. S., Rammah, T. Y. A., Sobki, S. H., and Ola, M. S., Alterations in prothrombin time and activated partial thromboplastin time in patients with acute myocardial infarction. Int. J. Clin. Exp. Med. 6(4):294–297, 2013.

    PubMed  PubMed Central  Google Scholar 

  31. Moradkhan, R., and Sinoway, L. I., Revisiting the role of oxygen therapy in cardiac patients. J. Am. Coll. Cardiol. 56(13):1013–1016, 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Stub, D., et al., Air versus oxygen in ST-segment elevation myocardial infarction. Circulation 131:2143–2150, 2015.

    Article  PubMed  CAS  Google Scholar 

  33. Fontainea, P., et al., Assessing the causes inducing lengthening of hospital stays by means of the appropriateness evaluation protocol. Health Policy 99:66–71, 2011.

    Article  Google Scholar 

  34. Geissler, A., Kreinsen, D. S., and Quentin, W., Do diagnoses-related groups appropriately explain variations in costs and length of stay of hip replacement? A comparative assessment of DRG systems across 10 European countries. Health Econ. 21:103–115, 2012.

    Article  PubMed  Google Scholar 

  35. Van Walraven, C., Escobar, G. J., Greene, J. D., and Forster, A. J., The Kaiser Permanente inpatient risk adjustment methodology was valid in an external patient population. J. Clin. Epidemiol. 63(7):798–803, 2010.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

We want to thank to Centro Hospitalar de Setúbal, EPE and Fundação para a Ciência e a Tecnologia (UID/MAT/04561/2013) for their support.

Author information

Authors and Affiliations

  1. Public Health Research Center, Department of Health Systems Policy and Management, National School of Public Health, Universidade Nova de Lisboa, Lisbon, Portugal

    Teresa Magalhães & Sílvia Lopes

  2. Centro de Matemática e Aplicações Fundamentais, Faculdade de Ciências da Universidade de Lisboa, Lisbon, Portugal

    João Gomes

  3. Centro Hospitalar de Setúbal, EPE, Setúbal, Portugal

    Filipe Seixo

Authors
  1. Teresa Magalhães
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sílvia Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. João Gomes
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Filipe Seixo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Teresa Magalhães.

Ethics declarations

Conflicts of interest

There are no conflicts of interest.

Consent

The participating hospital and the Portuguese National Commission for Data Protection approved the data collection.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Magalhães, T., Lopes, S., Gomes, J. et al. The Predictive Factors on Extended Hospital Length of Stay in Patients with AMI: Laboratory and Administrative Data. J Med Syst 40, 2 (2016). https://doi.org/10.1007/s10916-015-0363-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-015-0363-7

Keywords

Search

Navigation