Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?

Trunfio, Teresa Angela; Borrelli, Anna; Improta, Giovanni

doi:10.3390/ijerph19106219

Cited by 32 publications

(12 citation statements)

References 42 publications

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“…Our model was also well calibrated during internal validation (optimism of −0.002, range −0.300-0.296). Compared to other predictive models, our model has a higher accuracy than the multiple linear regression model from Trunfio et al which achieved an overall accuracy of between 0.651 and 0.718 (20) and is comparable to the model by Knoll et al which had an accuracy of within 3 days of the true LOS for 0.758 of their series (19). There are numerous variations among predictive models in terms of inclusion criteria, candidate predictors and type of regression model that could potentially affect the accuracy of each model (19,20).…”

Section: Discussionmentioning

confidence: 72%

“…Various predictors for LOS in osteoporotic hip fractures have been reported including delay in time to surgery, previous hip fractures, cerebrovascular disease, smoking status, ASA classification, age and degree of decline in cognitive function (12)(13)(14)(15)(16)(17). Some models to predict LOS in osteoporotic hip fracture patients have been proposed (18)(19)(20). One study created a model to predict LOS in hip fractures (both intertrochanteric and femoral neck types) (19).…”

Section: Introductionmentioning

confidence: 99%

“…One study created a model to predict LOS in hip fractures (both intertrochanteric and femoral neck types) ( 19 ). Another created a predictive model using multiple linear regression model but only in patients who underwent hip-replacement surgery ( 20 ). Another study developed a predictive model using machine learning algorithms in osteoporotic femoral neck fractures ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

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Predictive model for prolonged length of hospital stay in patients with osteoporotic femoral neck fracture: A 5-year retrospective study

Manosroi

Koetsuk²,

Phinyo³

et al. 2023

Front. Med.

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Prolonged length of stay (LOS) in osteoporotic femoral neck fracture patients increased the hospital care cost and demonstrated in-hospital complications. This study aimed to develop an ease-of use predictive model of prolonged LOS in osteoporotic femoral neck fracture patients. In this 5-year retrospective study, the medical charts of 255 patients admitted to hospital with an osteoporotic femoral neck fracture resulting from a simple fall from January 2014 to December 2018 were reviewed. Multivariable fractional polynomials (MFP) algorithms was applied to develop the predictive model from candidate predictors of prolonged LOS. The discrimination performance of predictive model was evaluated using the receiver operating characteristic curve (ROC). Internal validity was assessed using bootstrapping. From 289 patients who were hospitalized with an osteoporotic fracture of femoral neck throughout this study, 255 (88%) fulfilled the inclusion criteria. There was 54.90% (140 of 255 patients) of patients who had prolonged LOS. The predictors of the predictive model were age, BMI, ASA score class 3 or 4, arthroplasty and time from injury to surgery. The area under ROC curve of the model was 0.83 (95% confidence interval 0.77–0.88). Internal validation with bootstrap re-sampling revealed an optimism of −0.002 (range −0.300–0.296) with an estimated shrinkage factor of 0.907 for the predictive model. The current predictive model developed from preoperative predictors which had a good discriminative ability to differentiate between length of hospitalization less than 14 days and prolonged LOS in osteoporotic femoral neck patients. This model can be applied as ease-of use calculator application to help patients, their families and clinicians make appropriate decisions in terms of treatment planning, postoperative care program, and cost-effectiveness before patients receiving the definitive treatments.

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Section: Discussionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive model for prolonged length of hospital stay in patients with osteoporotic femoral neck fracture: A 5-year retrospective study

Manosroi

Koetsuk²,

Phinyo³

et al. 2023

Front. Med.

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“…The work showed that infection control programs with dedicated hospital epidemiologists and surveillance programs reduced nosocomial infections by 32% compared to facilities without infection control programs [ 8 ]. To design an effective prevention program, it is necessary to consider the impact that SSIs have on the length of hospital stay [ 9 , 10 , 11 , 12 ], which is a performance indicator of the quality of health processes [ 13 , 14 , 15 , 16 , 17 , 18 , 19 ]. In addition, identifying risk factors associated with SSIs can help reduce the incidence of SSIs [ 11 , 20 ] and add value to HTA studies, which are widely used to support health decision-making [ 21 , 22 , 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

Scala

Loperto

Triassi

et al. 2022

IJERPH

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Background: Surgical site infections (SSIs) have a major role in the evolution of medical care. Despite centuries of medical progress, the management of surgical infection remains a pressing concern. Nowadays, the SSIs continue to be an important factor able to increase the hospitalization duration, cost, and risk of death, in fact, the SSIs are a leading cause of morbidity and mortality in modern health care. Methods: A study based on statistical test and logistic regression for unveiling the association between SSIs and different risk factors was carried out. Successively, a predictive analysis of SSIs on the basis of risk factors was performed. Results: The obtained data demonstrated that the level of surgery contamination impacts significantly on the infection rate. In addition, data also reveals that the length of postoperative hospital stay increases the rate of surgical infections. Finally, the postoperative length of stay, surgery department and the antibiotic prophylaxis with 2 or more antibiotics are a significant predictor for the development of infection. Conclusions: The data report that the type of surgery department and antibiotic prophylaxis there are a statistically significant predictor of SSIs. Moreover, KNN model better handle the imbalanced dataset (48 infected and 3983 healthy), observing highest accuracy value.

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“…The literature reports that the evaluation of LOS through advanced analytical techniques and artificial intelligence algorithms is the subject of numerous studies [ 8 , 9 , 10 , 11 , 12 ]. History has verified that some populations with high-grade carotid stenosis are at high risk of subsequent stroke [ 13 ].…”

Section: Introductionmentioning

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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Is It Possible to Predict the Length of Stay of Patients Undergoing Hip-Replacement Surgery?

Cited by 32 publications

References 42 publications

Predictive model for prolonged length of hospital stay in patients with osteoporotic femoral neck fracture: A 5-year retrospective study

Predictive model for prolonged length of hospital stay in patients with osteoporotic femoral neck fracture: A 5-year retrospective study

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study

Implementation of Predictive Algorithms for the Study of the Endarterectomy LOS

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