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

Copyright © 2023 Xavier, Seringa, Pinto and Magalhães. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.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.This study was funded by Fundação Ciência e Tecnologia, IP national support through CHRC (UIDP/04923/2020).info:eu-repo/semantics/publishedVersio

Machine learning prediction of mortality in acute myocardial infarction

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients with AMI upon admission, using different variables to analyse their impact on predictive models. Methods: Three experiments were built for mortality in AMI in a Portuguese hospital between 2013 and 2015 using various machine learning techniques. The three experiments differed in the number and type of variables used. We used a discharged patients' episodes database, including administrative data, laboratory data, and cardiac and physiologic test results, whose primary diagnosis was AMI. Results: Results show that for Experiment 1, Stochastic Gradient Descent was more suitable than the other classification models, with a classification accuracy of 80%, a recall of 77%, and a discriminatory capacity with an AUC of 79%. Adding new variables to the models increased AUC in Experiment 2 to 81% for the Support Vector Machine method. In Experiment 3, we obtained an AUC, in Stochastic Gradient Descent, of 88% and a recall of 80%. These results were obtained when applying feature selection and the SMOTE technique to overcome imbalanced data. Conclusions: Our results show that the introduction of new variables, namely laboratory data, impacts the performance of the methods, reinforcing the premise that no single approach is adapted to all situations regarding AMI mortality prediction. Instead, they must be selected, considering the context and the information available. Integrating Artificial Intelligence (AI) and machine learning with clinical decision-making can transform care, making clinical practice more efficient, faster, personalised, and effective. AI emerges as an alternative to traditional models since it has the potential to explore large amounts of information automatically and systematically.The present publication was funded by Fundação Ciência e Tecnologia, IP national support through CHRC (UIDP/04923/2020).info:eu-repo/semantics/publishedVersio

Custos Diretos de Internamento por COVID-19 num Centro Hospitalar Universitário Português

Background: The COVID-19 pandemic has posed greater financial pressure on health systems and institutions that had to respond to the specific needs of COVID-19 patients while ensuring the safety of the diagnosis and treatment of all patients and healthcare professionals. To assess the financial impact of COVID-19 patients admitted to hospitals, we have characterized the cost of COVID-19 admissions, using inpatient data from a Portuguese Tertiary Care University Centre. Methods: We analysed inpatient data from adult patients di agnosed with COVID-19 who were admitted between March 1, 2020 and May 31, 2020. Admissions were eligible if the ICD-10-CM principal diagnosis was coded U07.1. We excluded admissions from patients under 18 years old, admissions with incomplete records, admissions from patients who had been transferred to or from other hospitals or those whose inpatient stay was under 24 h. Pregnancy, childbirth, and puerperium admissions were also excluded, as well as adsions from patients who had undergone surgery. Results: We identified 223 admissions of patients diagnosed with COVID-19. Most were men (64.1%) and aged 45–64 years (30.5%). Around 13.0% of patients were admitted to intensive care units and 9.9% died in hospital. The average length of hospital stay was 12.7 days (SD = 10.2) and the average estimated cost per admission was EUR 8,177 (SD = 11,534), which represents more than triple the inpatient base price (EUR 2,386). Human resources accounted for the highest proportion of the total costs per admission (50.8%). About 92.4% of the admissions were assigned to Diagnosis Related Group (DRG) 723, whose inpatient price is lower than COVID-19 inpatient costs for all degrees of severity. Conclusion: COVID-19 admissions represent a substantial financial bur den for the Portuguese NHS. For each COVID-19 hospitalized patient it would have been possible to treat three other hos pitalized patients. Also, the price set for DRG 723 is not adjusted to the cost of COVID-19 patients. These findings highlight the need for additional financial resources for the health system and, in particular, for hospitals that have treated high volumes of hospitalized patients diagnosed with COVID-19.info:eu-repo/semantics/publishedVersio