Outpatient Readmission in Rheumatology: A Machine Learning Predictive Model of Patient’s Return to the Clinic

Madrid, Alfredo; Font, J.; Vega-Barbas, Mario; León-Mateos, Leticia; Freites, D.; Lajas, Cristina; Pato, Esperanza; Jover, Juan A.; Abásolo-Alcazar, Lydia; Rodríguez‐Rodríguez, Luis

doi:10.3390/jcm8081156

Cited by 13 publications

(11 citation statements)

References 39 publications

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“…Recent works have repeatedly demonstrated the superior performance of the ANN model compared to other models [9,13]. The advantages offered by the unique characteristics of the ANN model have been con rmed by statistical analyses.…”

Section: Discussionmentioning

confidence: 96%

“…The advantages offered by the unique characteristics of the ANN model have been con rmed by statistical analyses. For example, using an ANN model can enable more appropriate and more accurate processing of inputs that are incomplete or inputs that introduce noise [9,27]. Another advantage is that linear and non-linear ANN models with good potential for use in large-scale medical databases can be constructed using data that are highly correlated but not normally distributed.…”

Section: Discussionmentioning

confidence: 99%

“…Although many models for predicting outcomes of stroke treatments have been proposed in recent years, models for predicting 30-day readmission have had major shortcomings: (1) recently proposed forecasting models have shown lower prediction accuracy compared to conventional models [4][5][6][7][8][9], (2) proposed forecasting models require use of health insurance claims data, which would not be available in a real-time clinical setting [8,10], (3) predictions of 30-day readmission do not consider post-acute care (PAC) program, patient attributes, clinical attributes and functional status score before rehabilitation [11][12][13][14][15]. In the current study, the best predictors of hospital readmission within 30 days after stroke were identi ed using arti cial neural network (ANN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classi er (NBC) and Cox regression (COX) models.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning algorithms to predict 30-day readmission in patients with stroke: a prospective cohort study

Chen¹,

Chung²,

Yeh³

et al. 2020

Preprint

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Background No studies have discussed machine learning algorithms to predict the risk of 30-day readmission in patients with stroke. The objective of the present study was to compare the accuracy of the artificial neural network (ANN), K nearest neighbor (KNN), support vector machine (SVM), naive Bayes classifier (NBC), and Cox regression (COX) models and to explore the significant factors in predicting 30-day readmission after stroke. Methods This study prospectively compared the accuracy of the models using clinical data for 1,476 patients with stroke treated in six hospitals between March, 2014 and September, 2019. A training dataset (n=1,033) was used for model development, a testing dataset (n=443) was used for internal validation, and a validating dataset (n=167) was used for external validation. A global sensitivity analysis was performed to compare the significance of the selected input variables. Results Of all forecasting models, the ANN model had the highest accuracy in predicting 30-day readmission after stroke and had the highest overall performance indices. According to the ANN model, 30-day readmission was significantly associated with post-acute care (PAC) program, patient attributes, clinical attributes, and functional status scores before re-habilitation (all P <0.05). Additionally, PAC program was the most significant variable affecting 30-day readmission, followed by nasogastric tube insertion, and stroke type ( P <0.05). Conclusions Comparisons of the five forecasting models indicated that the ANN model had the highest accuracy in predicting 30-day readmission in stroke patients. Before stroke patients are discharged from hospitalization, they should be counseled regarding their potential for recovery and other possible outcomes. These important predictors can also be used to educate candidates for stroke patients who underwent PAC rehabilitation with respect to the course of recovery and health outcomes.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning algorithms to predict 30-day readmission in patients with stroke: a prospective cohort study

Chen¹,

Chung²,

Yeh³

et al. 2020

Preprint

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“…The study population was selected from the Hospital Clínico San Carlos Musculoskeletal Cohort (HCSC-MSKC), 14 a routine clinical practice cohort that includes subjects seen at the rheumatology outpatient clinic of our center whose clinical information and management was carried out using a departmental electronic health record (EHR). The HCSC-MSKC contains information from more than 35,000 patients attending our clinic from 1 April 2007 until 30 November 2017 and comprising more than 117,000 clinical visits.…”

Section: Methodsmentioning

confidence: 99%

There and back again: analyzing the effect of outpatient readmission on the quality of life of patients attending a rheumatology clinic

Madrid

Montuenga-Fernández

Font

et al. 2021

Therapeutic Advances in Musculoskeletal

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Aims: The aim of this study was to assess the effect of “outpatient readmissions” on the health-related quality of life (HR-QoL) of outpatients from a rheumatology clinic, meaning the effect of the patient’s return to the outpatient clinic after having received care and been discharged. Methods: We conducted an observational longitudinal retrospective study, with patients selected from the Hospital Clínico San Carlos Musculoskeletal cohort, based on having received at least one discharge from the outpatient clinic and having returned (readmission) at least once after the discharge. The main outcomes were the patients’ baseline HR-QoL (measured on the first visit of each episode) and the ΔHR-QoL (difference between the HR-QoL in the last and the first visit of each episode). Successive episodes of admission and readmission were chronologically ordered, paired and analyzed using nested linear mixed models, nested by patients and by admission–readmission tandem. We carried out bivariable and multivariable analyses to assess the effect of demographic, clinical, treatment and comorbidity-related variables in both main outcomes. Results: For the first main outcome, 5887 patients (13,772 episodes) were analyzed. Based on the multivariable level, readmission showed no significant marginal effect on the baseline HR-QoL ( p-value = 0.17). Conversely, when analyzing the ΔHR-QoL, we did observe a negative and significant marginal effect ( p-value = 0.028), meaning that readmission was associated with a lower gain in the HR-QoL during the follow-up, compared with the previous episode. Conclusion: In the outpatient setting, readmission exerts a deleterious effect in patients undergoing this process. Identification of outpatients more likely to be readmitted could increase the value of the care provided.

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“…Hospital readmission is a common occurrence that can impose a multitude of burdens such as increased costs and an increased risk of mortality. 1 Studies indicate that the percentage of patients readmitted to hospitals in 30 days following discharge may be as high as 20%. 2 Due to its cost and reoccurrence, in 2012 the Center for Medicare & Medicaid Services (CMS) instituted the Hospital Readmissions Reduction Program to reduce the frequency of rehospitalization by establishing financial penalties for hospitals with readmission rates deemed excessive.…”

Section: Introductionmentioning

confidence: 99%

<p>Prediction of 30-Day Hospital Readmissions for All-Cause Dental Conditions using Machine Learning</p>

Hung¹,

Li²,

Hon³

et al. 2020

RMHP

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Introduction: It is unknown whether patients admitted for all-cause dental conditions (ACDC) are at high risk for hospital readmission, or what are the risk factors for dental hospital readmission. Objective: We examined the prevalence of, and risk factors associated with, 30-day hospital readmission for patients with an all-cause dental admission. We applied artificial intelligence to develop machine learning (ML) algorithms to predict patients at risk of 30-day hospital readmission. Methods: This study used data extracted from the 2013 Nationwide Readmissions Database (NRD). There were a total of 11,341 cases for all-cause index admission for dental patients admitted to the hospitals. Descriptive statistics were used to analyze patient characteristics. This study applied five techniques to build risk prediction models and to identify risk factors. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), and accuracy, sensitivity, specificity and precision. Results: There were 11% of patients admitted for ACDC readmitted within 30 days of hospital discharge. On average, the total charge per patient was $131,004 for those with 30day readmission (n=1254) and $69,750 for those without readmission (n=10,087). Factors significantly associated with 30-day hospital readmission included total charges, number of diagnoses, age, number of chronic conditions, length of hospital stays, number of procedures, Medicare insurance and Medicaid insurance, and severity of illness. Model performance from all methods was similar with the artificial neural network showing the highest AUC of 0.739. Conclusion: Our results demonstrate that readmission after hospitalization with ACDC is fairly common. If one-third of the 30-day readmission cases can be avoided, there is a potential annual saving of over $25 million among the twenty-one states represented in the NRD. The ML algorithms can predict hospital readmission in dental patients and should be further tested to aid the reduction of hospital readmission and enhancement of patientcentered care.

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Outpatient Readmission in Rheumatology: A Machine Learning Predictive Model of Patient’s Return to the Clinic

Cited by 13 publications

References 39 publications

Machine learning algorithms to predict 30-day readmission in patients with stroke: a prospective cohort study

Machine learning algorithms to predict 30-day readmission in patients with stroke: a prospective cohort study

There and back again: analyzing the effect of outpatient readmission on the quality of life of patients attending a rheumatology clinic

<p>Prediction of 30-Day Hospital Readmissions for All-Cause Dental Conditions using Machine Learning</p>

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