Hospital length of stay prediction tools for all hospital admissions and general medicine populations: systematic review and meta-analysis.

Background: Unwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively. Objective: This systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions. Method: LOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist. Results: Overall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting. Conclusion: To the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021272198.

Hospital length of stay prediction for general surgery and total knee arthroplasty admissions: Systematic review and meta-analysis of published prediction models.

Objective: Systematic review of length of stay (LOS) prediction models to assess the study methods (including prediction variables), study quality, and performance of predictive models (using area under receiver operating curve (AUROC)) for general surgery populations and total knee arthroplasty (TKA). Method: LOS prediction models published since 2010 were identified in five major research databases. The main outcomes were model performance metrics including AUROC, prediction variables, and level of validation. Risk of bias was assessed using the PROBAST checklist. Results: Five general surgery studies (15 models) and 10 TKA studies (24 models) were identified. All general surgery and 20 TKA models used statistical approaches; 4 TKA models used machine learning approaches. Risk scores, diagnosis, and procedure types were predominant predictors used. Risk of bias was ranked as moderate in 3/15 and high in 12/15 studies. Discrimination measures were reported in 14/15 and calibration measures in 3/15 studies, with only 4/39 externally validated models (3 general surgery and 1 TKA). Meta-analysis of externally validated models (3 general surgery) suggested the AUROC 95% prediction interval is excellent and ranges between 0.803 and 0.970. Conclusion: This is the first systematic review assessing quality of risk prediction models for prolonged LOS in general surgery and TKA groups. We showed that these risk prediction models were infrequently externally validated with poor study quality, typically related to poor reporting. Both machine learning and statistical modelling methods, plus the meta-analysis, showed acceptable to good predictive performance, which are encouraging. Moving forward, a focus on quality methods and external validation is needed before clinical application.

Using participatory action research to develop a new self-management program: Results from the design stage of Managing My MS My Way.

BACKGROUND: Self-management programs have been used with success in several clinical populations, and there is a growing body of evidence to support their use among persons with multiple sclerosis (MS). This group aimed to develop a novel self-management program, Managing My MS My Way (M4W), which is based in social cognitive theory and contains evidence-based strategies that have been shown to be effective for persons with MS. Furthermore, persons with MS would serve as stakeholders throughout the development process to ensure that the program would be useful and encourage adoption. This paper outlines the initial development stages of M4W, including determining 1) stakeholders' interest in a self-management program, 2) the general focus of the program, 3) the delivery method of the program, 4) the content of the program, and 5) potential barriers and adaptations. METHODS: A three-stage study consisting of an anonymous survey (n = 187) to determine interest, topic, and delivery format; semi-structured interviews (n = 6) to follow-up on the survey results; and semi-structured interviews (n = 10) to refine the content and identify barriers. RESULTS: Over 80% of survey participants were somewhat or very interested in a self-management program. Fatigue was the topic with the greatest amount of interest (64.7%). An internet-based program (e.g., mobile health or mHealth) was the most preferred delivery method (37.4%), with the first group of stakeholders proposing a module-based system with an initial in-person orientation session. The second group of stakeholders were overall enthusiastic about the program, giving moderate to high confidence scores for each of the proposed interventional strategies. Suggestions included skipping sections that were not applicable to them, setting reminders, and seeing their progress (e.g., visualizing their fatigue scores as they move through the program). In addition, stakeholders recommended larger font sizes and speech-to-text entry. CONCLUSIONS: Input from the stakeholders has been incorporated into the prototype of M4W. The next steps will be to test this prototype with another group of stakeholders to assess its initial usability and identify issues before developing the functional prototype.

An efficient approach to identifying anti-government sentiment on Twitter during Michigan protests.

Trust in the government is an important dimension of happiness according to the World Happiness Report (Skelton, 2022). Recently, social media platforms have been exploited to erode this trust by spreading hate-filled, violent, anti-government sentiment. This trend was amplified during the COVID-19 pandemic to protest the government-imposed, unpopular public health and safety measures to curb the spread of the coronavirus. Detection and demotion of anti-government rhetoric, especially during turbulent times such as the COVID-19 pandemic, can prevent the escalation of such sentiment into social unrest, physical violence, and turmoil. This article presents a classification framework to identify anti-government sentiment on Twitter during politically motivated, anti-lockdown protests that occurred in the capital of Michigan. From the tweets collected and labeled during the pair of protests, a rich set of features was computed from both structured and unstructured data. Employing feature engineering grounded in statistical, importance, and principal components analysis, subsets of these features are selected to train popular machine learning classifiers. The classifiers can efficiently detect tweets that promote an anti-government view with around 85% accuracy. With an F1-score of 0.82, the classifiers balance precision against recall, optimizing between false positives and false negatives. The classifiers thus demonstrate the feasibility of separating anti-government content from social media dialogue in a chaotic, emotionally charged real-life situation, and open opportunities for future research.