Decision-making support systems on extended hospital length of stay: Validation and recalibration of a model for patients with AMI

Background Cardiovascular diseases are still a significant cause of death and hospitalization. In 2019, circulatory diseases were responsible for 29.9% of deaths in Portugal. These diseases have a significant impact on the hospital length of stay. Length of stay predictive models is an efficient way to aid decision-making in health. This study aimed to validate a predictive model on the extended length of stay in patients with acute myocardial infarction at the time of admission. Methods An analysis was conducted to test and recalibrate a previously developed model in the prediction of prolonged length of stay, for a new set of population. The study was conducted based on administrative and laboratory data of patients admitted for acute myocardial infarction events from a public hospital in Portugal from 2013 to 2015. Results Comparable performance measures were observed upon the validation and recalibration of the predictive model of extended length of stay. Comorbidities such as shock, diabetes with complications, dysrhythmia, pulmonary edema, and respiratory infections were the common variables found between the previous model and the validated and recalibrated model for acute myocardial infarction. Conclusion Predictive models for the extended length of stay can be applied in clinical practice since they are recalibrated and modeled to the relevant population characteristics.

[1]  Ondrej Krejcar,et al.  Consequences of chronic diseases and other limitations associated with old age – a scoping review , 2019, BMC Public Health.

[2]  R. Swaminathan,et al.  Hospital length of stay and clinical outcomes in older STEMI patients after primary PCI: a report from the National Cardiovascular Data Registry. , 2015, Journal of the American College of Cardiology.

[3]  F. Hu,et al.  National trends in hospital length of stay for acute myocardial infarction in China , 2015, BMC Cardiovascular Disorders.

[4]  Sidney C. Smith,et al.  Association of inpatient vs outpatient onset of ST-elevation myocardial infarction with treatment and clinical outcomes. , 2014, JAMA.

[5]  Paolo Barbini,et al.  A straightforward approach to designing a scoring system for predicting length-of-stay of cardiac surgery patients , 2014, BMC Medical Informatics and Decision Making.

[6]  Raul Laureano,et al.  Previsão de tempos de internamento num hospital português: aplicação da metodologia CRISP-DM , 2014 .

[7]  W. Qiu,et al.  Risk scores in acute coronary syndrome and percutaneous coronary intervention: a review. , 2013, American heart journal.

[8]  A. Mehrotra,et al.  The Association Between Health Care Quality and Cost , 2013, Annals of Internal Medicine.

[9]  Pavel Brazdil,et al.  Factors influencing hospital high length of stay outliers , 2012, BMC Health Services Research.

[10]  M. G. Castillo.  Modelling patient length of stay in public hospitals in Mexico , 2012 .

[11]  Jean-Marie Aerts,et al.  Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model , 2011, BMC Medical Informatics Decis. Mak..

[12]  Dinesh U Acharya,et al.  Comparison of Different Data Mining Techniques to Predict Hospital Length of Stay , 2011 .

[13]  Jorge Yarzebski,et al.  Declining length of stay for patients hospitalized with AMI: impact on mortality and readmissions. , 2010, The American journal of medicine.

[14]  B. Tura,et al.  C-reactive protein diagnostic and prognostic value in patients presenting at the emergency room with chest pain. , 2006, Arquivos brasileiros de cardiologia.

[15]  G. Meyer,et al.  Use of administrative data for clinical quality measurement. , 2005, The Journal of thoracic and cardiovascular surgery.

[16]  Paul M Ridker,et al.  Should C-reactive protein be added to metabolic syndrome and to assessment of global cardiovascular risk? , 2004, Circulation.

[17]  E. Antman,et al.  The thrombolysis in myocardial infarction risk score in unstable angina/non-ST-segment elevation myocardial infarction. , 2003, Journal of the American College of Cardiology.

[18]  L. Iezzoni.  Assessing Quality Using Administrative Data , 1997, Annals of Internal Medicine.

[19]  Teresa Magalhães,et al.  The Predictive Factors on Extended Hospital Length of Stay in Patients with AMI: Laboratory and Administrative Data , 2015, Journal of Medical Systems.

[20]  C. Steiner,et al.  Comorbidity measures for use with administrative data. , 1998, Medical care.

[21]  Robert C. Wolpert,et al.  A Review of the , 1985 .

[22]  K. Shadan,et al.  Available online: , 2012 .