Regression Models to Study the Total LOS Related to Valvuloplasty

Scala, Arianna; Trunfio, Teresa Angela; Coppi, Lucia De; Rossi, Giovanni; Borrelli, Anna; Triassi, Maria; Improta, Giovanni

doi:10.3390/ijerph19053117

Cited by 37 publications

(11 citation statements)

References 33 publications

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“…In contrast to our study, Hernandez et al [ 22 ] show how female sex is associated with prolonged LOS, but both highlight how there is no relationship with factors such as myocardial infarction or atrial fibrillation. Scala et al [ 37 ], on the other hand, demonstrate how preoperative hospital stay is a strong predictor, partly because it is included in total LOS by definition. The type of endarterectomy, as evidenced by our study, also significantly influences LOS.…”

Section: Discussionmentioning

confidence: 99%

Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

2022

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Background: In recent years, the length of hospital stay (LOS) following endarterectomy has decreased significantly from 4 days to 1 day. LOS is influenced by several common complications and factors that can adversely affect the patient’s health and may vary from one healthcare facility to another. The aim of this work is to develop a forecasting model of the LOS value to investigate the main factors affecting LOS in order to save healthcare cost and improve management. Methods: We used different regression and machine learning models to predict the LOS value based on the clinical and organizational data of patients undergoing endarterectomy. Data were obtained from the discharge forms of the “San Giovanni di Dio e Ruggi d’Aragona” University Hospital (Salerno, Italy). R2 goodness of fit and the results in terms of accuracy, precision, recall and F1-score were used to compare the performance of various algorithms. Results: Before implementing the models, the preliminary correlation study showed that LOS was more dependent on the type of endarterectomy performed. Among the regression algorithms, the best was the multiple linear regression model with an R2 value of 0.854, while among the classification algorithms for LOS divided into classes, the best was decision tree, with an accuracy of 80%. The best performance was obtained in the third class, which identifies patients with prolonged LOS, with a precision of 95%. Among the independent variables, the most influential on LOS was type of endarterectomy, followed by diabetes and kidney disorders. Conclusion: The resulting forecast model demonstrates its effectiveness in predicting the value of LOS that could be used to improve the endarterectomy surgery planning.

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Section: Discussionmentioning

confidence: 99%

Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

2022

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“…While ML and simulation, taken separately, have been widely investigated in the literature (e.g. to forecast complications and adverse events to achieve timely and more accurate clinical decisions [14][15][16][17][18] or with the aim of improving the management of healthcare emergency processes [19][20][21] and reduce the length of hospital stay (LOS) [20,[22][23][24][25][26][27][28]), there is only a limited subset of studies [14,29] that exploit a hybrid ML-powered simulation methodology in the healthcare field.…”

Section: Introductionmentioning

confidence: 99%

Combining simulation models and machine learning in healthcare management: strategies and applications

Ponsiglione,

Zaffino,

Ricciardi

et al. 2024

Prog. Biomed. Eng.

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Simulation models and artificial intelligence are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and artificial intelligence could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and artificial intelligence approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and artificial intelligence as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed artificial intelligence strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligent in-silico models of healthcare processes and to provide effective translation to the clinics.

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“…Verburg et al [ 24 ] compared the performance of eight regression models when predicting intensive care unit LOS, failing to obtain optimal results for any of them, while Lee et al [ 25 ] show the high performance of robust gamma mixed regression for the study of pediatric LOS. In addition to regression models, multiple linear regression was used to predict the LOS for patients undergoing valvuloplasty by considering their characteristics [ 26 ]. Austin et al [ 27 ] use statistical analysis or analyzing LOS in a cohort of patients undergoing CABG surgery, while Scala et al [ 28 ] show the benefits of implementing classifiers for predicting LOS [ 29 – 33 ].…”

Section: Introductionmentioning

confidence: 99%

Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy

Trunfio¹,

Scala

Giglio

et al. 2022

BMC Med Inform Decis Mak

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Background The rapid growth in the complexity of services and stringent quality requirements present a challenge to all healthcare facilities, especially from an economic perspective. The goal is to implement different strategies that allows to enhance and obtain health processes closer to standards. The Length Of Stay (LOS) is a very useful parameter for the management of services within the hospital and is an index evaluated for the management of costs. In fact, a patient's LOS can be affected by a number of factors, including their particular condition, medical history, or medical needs. To reduce and better manage the LOS it is necessary to be able to predict this value. Methods In this study, a predictive model was built for the total LOS of patients undergoing laparoscopic appendectomy, one of the most common emergency procedures. Demographic and clinical data of the 357 patients admitted at “San Giovanni di Dio e Ruggi d’Aragona” University Hospital of Salerno (Italy) had used as independent variable of the multiple linear regression model. Results The obtained model had an R2 value of 0.570 and, among the independent variables, the significant variables that most influence the total LOS were Age, Pre-operative LOS, Presence of Complication and Complicated diagnosis. Conclusion This work designed an effective and automated strategy for improving the prediction of LOS, that can be useful for enhancing the preoperative pathways. In this way it is possible to characterize the demand and to be able to estimate a priori the occupation of the beds and other related hospital resources.

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Regression Models to Study the Total LOS Related to Valvuloplasty

Cited by 37 publications

References 33 publications

Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

Combining simulation models and machine learning in healthcare management: strategies and applications

Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomy

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