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

Our objective is to develop and validate a predictive model based on the random forest algorithm to estimate the readmission risk to an outpatient rheumatology clinic after discharge. We included patients from the Hospital Clínico San Carlos rheumatology outpatient clinic, from 1 April 2007 to 30 November 2016, and followed-up until 30 November 2017. Only readmissions between 2 and 12 months after the discharge were analyzed. Discharge episodes were chronologically split into training, validation, and test datasets. Clinical and demographic variables (diagnoses, treatments, quality of life (QoL), and comorbidities) were used as predictors. Models were developed in the training dataset, using a grid search approach, and performance was compared using the area under the receiver operating characteristic curve (AUC-ROC). A total of 18,662 discharge episodes were analyzed, out of which 2528 (13.5%) were followed by outpatient readmissions. Overall, 38,059 models were developed. AUC-ROC, sensitivity, and specificity of the reduced final model were 0.653, 0.385, and 0.794, respectively. The most important variables were related to follow-up duration, being prescribed with disease-modifying anti-rheumatic drugs and corticosteroids, being diagnosed with chronic polyarthritis, occupation, and QoL. We have developed a predictive model for outpatient readmission in a rheumatology setting. Identification of patients with higher risk can optimize the allocation of healthcare resources.

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

Therapeutic Advances in Musculoskeletal Disease ◽

10.1177/1759720x211034867 ◽

2021 ◽

Vol 13 ◽

pp. 1759720X2110348

Author(s):

Alfredo Madrid-García ◽

Isabel Montuenga-Fernández ◽

Judit Font-Urgelles ◽

Leticia León-Mateos ◽

Esperanza Pato ◽

...

Keyword(s):

Quality Of Life ◽

Outpatient Clinic ◽

Outpatient Setting ◽

P Value ◽

Marginal Effect ◽

Rheumatology Clinic ◽

Related Quality ◽

Health Related ◽

San Carlos

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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Penerapan Sistem Menu Engineering Dalam Upaya Peningkatan Kualitas Menu di Restoran Bintan Inti Executive Village Clubhouse

Jurnal Pariwisata Terapan ◽

10.22146/jpt.66724 ◽

2021 ◽

Vol 5 (1) ◽

pp. 60

Author(s):

Adiguna Tumpuan

Keyword(s):

Production Costs ◽

Engineering System ◽

Contribution Margin ◽

Analysis And Evaluation ◽

Food And Beverage ◽

Menu Analysis ◽

And Performance ◽

Suggestive Selling

This research is a form of analysis and evaluation of the menu at the Bintan Inti Executive Village (BIEV) Clubhouse through a menu engineering system with the aim of improving the quality and effectiveness of the menu served. The formulation of the problem which is the basis of this research is how to improve the quality of the food and beverage menu through the menu engineering system and what recommendation actions should be taken based on the resulting data from the analysis that appears. This study uses variables, which are menu analysis in improving the quality of the menu at BIEV Clubhouse with sub variables, such as food cost, menu mix, and contribution margin. The results of this study that there were 8 menus or 30.77% menus with star categories, 5 menus or 19.23% menus with the plowhorse category, 4 menus or 15.38% menus with the puzzle category, and 9 menus or 34.62%. menu with dog category. The recommended follow-up actions to BIEV Clubhouse management is reducing the menu with the dog category, evaluating product prices and production costs for menus with the plowhorse category, taking suggestive selling steps for menus with the puzzle category, and maintaining the quality and performance of menus with the star category.

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Feasibility and Impact of an 8-Week Integrative Yoga Program in People with Moderate Multiple Sclerosis–Related Disability

International Journal of MS Care ◽

10.7224/1537-2073.2015-046 ◽

2017 ◽

Vol 19 (1) ◽

pp. 30-39 ◽

Cited By ~ 11

Author(s):

Evan T. Cohen ◽

David Kietrys ◽

Susan Gould Fogerite ◽

Mariella Silva ◽

Kristen Logan ◽

...

Keyword(s):

Quality Of Life ◽

Multiple Sclerosis ◽

Short Form ◽

Repeated Measurements ◽

Walk Test ◽

Sf 36 ◽

Serial Addition ◽

And Performance

Background: This pilot study determined the feasibility of a specifically designed 8-week yoga program for people with moderate multiple sclerosis (MS)–related disability. We explored the program's effect on quality of life (QOL) and physical and mental performance. Methods: We used a single-group design with repeated measurements at baseline, postintervention, and 8-week follow-up. Feasibility was examined through cost, recruitment, retention, attendance, and safety. Outcomes included the Multiple Sclerosis Quality of Life Inventory (MSQLI), 12-item Multiple Sclerosis Walking Scale (MSWS-12), Timed 25-Foot Walk test (T25FW), 6-Minute Walk Test (6MWT), Nine-Hole Peg Test (NHPT), Five-Times Sit-to-Stand Test (FTSTS), Multidirectional Reach Test (MDRT), maximum expiratory pressure, and Paced Auditory Serial Addition Test-3″ (PASAT-3″). Results: Fourteen participants completed the study. The program was feasible. There were significant main effects on the 36-item Short Form Health Status Survey Mental Component Summary (SF-36 MCS), Modified Fatigue Impact Scale (MFIS), Bladder Control Scale (BLCS), Perceived Deficits Questionnaire (PDQ), Mental Health Inventory (MHI), MSWS-12, T25FW, NHPT, PASAT-3″, 6MWT, FTSTS, and MDRT-Back. Improvements were found on the SF-36 MCS, MFIS, BLCS, PDQ, MHI, and MSWS-12 between baseline and postintervention. The effect on PDQ persisted at follow-up. Improvements were found on the T25FW, NHPT, 6MWT, FTSTS, and MDRT-Back between baseline and postintervention that persisted at follow-up. The PASAT-3″ did not change between baseline and postintervention but did between postintervention and follow-up. Conclusions: The yoga program was safe and feasible. Improvements in certain measures of QOL and performance were seen at postintervention and follow-up.

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Importance-performance analysis of private health facilities serving within the scope of medical tourism: Istanbul example

PASOS Revista de Turismo y Patrimonio Cultural ◽

10.25145/j.pasos.2021.19.038 ◽

2021 ◽

Vol 19 (3) ◽

pp. 589-603

Author(s):

Yeliz Pekerşen ◽

◽

Erkan Guneş ◽

Fatma Canöz ◽

◽

...

Keyword(s):

Quality Of Service ◽

Performance Analysis ◽

Information Quality ◽

Medical Tourism ◽

Health Facilities ◽

Wage Policy ◽

Survey Technique ◽

And Performance

The aim of this study is to evaluate the factors presented in the private health facilities operating in Istanbul within the scope of medical tourism with importance‑performance analysis, to determine the situation, to compare the levels of importance and performance and to reveal which factors should be protected through the obtained findings and which should be focused on and which are in the spectrum of low priority and possible excesses. For this purpose, data has been collected from medical tourists who has received medical services from 15 private health facilities operating in Istanbul between December 2019 and January 2020, using a survey technique. The data obtained has been evaluated by the Importance‑Performance Analysis. As a result of the study, the factors whose low priority should be maintained has been defined as waiting period, providing accurate hospital information, quality of service, legal inspections, follow‑up treatment and the factors that need to be focused on has been defined as the transparency of the wage policy.

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Predictive Model of Quality of Life in Patients with Parkinson’s Disease

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph19020672 ◽

2022 ◽

Vol 19 (2) ◽

pp. 672

Author(s):

Eduardo Candel-Parra ◽

María Pilar Córcoles-Jiménez ◽

Victoria Delicado-Useros ◽

Marta Carolina Ruiz-Grao ◽

Antonio Hernández-Martínez ◽

...

Keyword(s):

Quality Of Life ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Predictive Model ◽

Health Related Quality ◽

Predictive Capacity ◽

Related Quality ◽

Health Related

Parkinson’s disease is a chronic, progressive, and disabling neurodegenerative disease which evolves until the end of life and triggers different mood and organic alterations that influence health-related quality of life. The objective of our study was to identify the factors that negatively impact the quality of life of patients with Parkinson’s disease and construct a predictive model of health-related quality of life in these patients. Methods: An analytical, prospective observational study was carried out, including Parkinson’s patients at different stages in the Albacete Health Area. The sample consisted of 155 patients (T0) who were followed up at one (T1) and two years (T2). The instruments used were a purpose-designed data collection questionnaire and the “Parkinson’s Disease Questionnaire” (PDQ-39), with a global index where a higher score indicates a worse quality of life. A multivariate analysis was performed by multiple linear regression at T0. Next, the model’s predictive capacity was evaluated at T1 and T2 using the area under the ROC curve (AUROC). Results: Predictive factors were: sex, living in a residence, using a cane, using a wheelchair, having a Parkinson’s stage of HY > 2, having Alzheimer’s disease or a major neurocognitive disorder, having more than five non-motor symptoms, polypharmacy, and disability greater than 66%. This model showed good predictive capacity at one year and two years of follow-up, with an AUROC of 0.89 (95% CI: 0.83–0.94) and 0.83 (95% CI: 0.76–0.89), respectively. Conclusions: A predictive model constructed with nine variables showed a good discriminative capacity to predict the quality of life of patients with Parkinson’s disease at one and two years of follow-up.

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The Lifestyle Eating and Performance (LEAP) Program for Improving Quality of Life in Women With PCOS: A Pilot Study of Dietitian-Directed Therapy

Current Developments in Nutrition ◽

10.1093/cdn/nzab061_007 ◽

2021 ◽

Vol 5 (Supplement_2) ◽

pp. 1123-1123

Author(s):

Nadine Braunstein ◽

Michal Hogan ◽

Rafael Diaz Escamilla

Keyword(s):

Quality Of Life ◽

Pilot Study ◽

Immunoglobulin E ◽

Food Allergies ◽

Registered Dietitians ◽

Activation Assay ◽

The Mean ◽

And Performance

Abstract Objectives To investigate the effectiveness of the Lifestyle Eating and Performance (LEAP) program for reducing health-related Quality of Life (QoL) symptoms in women with Polycystic Ovary Syndrome (PCOS). Methods A retrospective chart review was conducted of PCOS clients seen by registered dietitians from a private group practice during 2010–2018. The in-vitro Leukocyte Activation Assay (LAA-MRT) was used to identify hidden non-immunoglobulin E (non-IgE) mediated food allergies and chemical sensitivities. The registered dietitians developed a patient-tailored oligoantigenic diet program for each subject. The LEAP program is an elimination diet built on the selection of less reactive food and chemicals based on the LAA-MRT results. A symptom survey was used to assess the QoL at the first visit and each follow-up visit. The severity of symptoms over the past month was recorded and quantified based on the frequency of the symptoms from a scale of 0 (low) to 4 (high) with a minimum score of 0 and a maximum of 248 points. Descriptive statistics were created and reported as means for continuous variables. Mixed model analysis of variance (ANOVA) was performed using R Studio Version 1.1.414 . The study received Institutional Review Board (IRB) approval by California State University Sacramento. Results Subjects’ (n = 42) mean age was 35.2 years, and BMI was 34.4 kg/m2. The mean symptoms score at baseline was 72.5. After a personal eating plan was implemented based on the LAA-MRT results (mean 18.1 days following the plan), scores reduced to 29.3 (P < 0.001). The mean score at the second follow-up (mean 44.1 days) was 19.9 (P < 0.001), and at the third (mean 60.0 days) was 14.7 (P < 0.001). Conclusions Findings from this pilot study highlight that a comprehensive, tailored dietary program can effectively achieve improvements in QoL for women living with PCOS. Funding Sources There was no funding for this research.

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DEVELOPMENT AND VALIDATION OF A MODEL FOR THE PREDICTION OF MORTALITY IN CHILDREN UNDER FIVE YEARS WITH CLINICAL PNEUMONIA IN RURAL GAMBIA

10.1101/2021.08.04.21260737 ◽

2021 ◽

Author(s):

Alexander Jarde ◽

David Jeffries ◽

Grant A Mackenzie

Keyword(s):

Machine Learning ◽

Predictive Model ◽

Prediction Models ◽

Characteristic Curve ◽

Machine Learning Algorithms ◽

Support Vector ◽

Learning Methods ◽

Final Model ◽

Machine Learning Methods ◽

Prediction Of Mortality

Background: Pneumonia is the leading cause of death in children aged 1-59 months. Prediction models for child pneumonia mortality have been developed using regression methods but their performance is insufficient for clinical use. Methods: We used a variety of machine learning methods to develop a predictive model for mortality in children with clinical pneumonia enrolled in population-based surveillance in the Basse Health and Demographic Surveillance System in rural Gambia (n=11,012). Four machine learning algorithms (support vector machine, random forest, artifical neural network, and regularized logistic regression) were implemented, fitting all possible combinations of two or more of 16 selected features. Models were shortlisted based on their training set performance , the number of included features, and the reliability of feature measurement. The final model was selected considering its clinical interpretability. Results: When we applied the final model to the test set (55 deaths), the area under the Receiver Operating Characteristic Curve was 0.88 (95% confidence interval: 0.84, 0.91), sensitivity was 0.78 and specificity was 0.77. Conclusions: Our evaluation of multiple machine learning methods combined with minimal and pragmatic feature selection led to a predictive model with very good performance. We plan further validation of our model in different populations.

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