A machine learning approach for investigating delirium as a multifactorial syndrome

Delirium is a psycho-organic syndrome common in hospitalized patients, especially the elderly, and is associated with poor clinical outcomes. This study aims to identify the predictors that are mostly associated with the risk of delirium episodes using a machine learning technique (MLT). A random forest (RF) algorithm was used to evaluate the association between the subject’s characteristics and the 4AT (the 4 A’s test) score screening tool for delirium. RF algorithm was implemented using information based on demographic characteristics, comorbidities, drugs and procedures. Of the 78 patients enrolled in the study, 49 (63%) were at risk for delirium, 32 (41%) had at least one episode of delirium during the hospitalization (38% in orthopedics and 31% both in internal medicine and in the geriatric ward). The model explained 75.8% of the variability of the 4AT score with a root mean squared error of 3.29. Higher age, the presence of dementia, physical restraint, diabetes and a lower degree are the variables associated with an increase of the 4AT score. Random forest is a valid method for investigating the patients’ characteristics associated with delirium onset also in small case-series. The use of this model may allow for early detection of delirium onset to plan the proper adjustment in healthcare assistance

Propensity score analysis with partially observed baseline covariates: A practical comparison of methods for handling missing data

(1) Background: Propensity score methods gained popularity in non-interventional clinical studies. As it may often occur in observational datasets, some values in baseline covariates are missing for some patients. The present study aims to compare the performances of popular statistical methods to deal with missing data in propensity score analysis. (2) Methods: Methods that account for missing data during the estimation process and methods based on the imputation of missing values, such as multiple imputations, were considered. The methods were applied on the dataset of an ongoing prospective registry for the treatment of unprotected left main coronary artery disease. The performances were assessed in terms of the overall balance of baseline covariates. (3) Results: Methods that explicitly deal with missing data were superior to classical complete case analysis. The best balance was observed when propensity scores were estimated with a method that accounts for missing data using a stochastic approximation of the expectation-maximization algorithm. (4) Con-clusions: If missing at random mechanism is plausible, methods that use missing data to estimate propensity score or impute them should be preferred. Sensitivity analyses are encouraged to evaluate the implications methods used to handle missing data and estimate propensity score

Using social networks to estimate the number of covid-19 cases: The incident (hidden covid-19 cases network estimation) study protocol

Recent literature has reported a high percentage of asymptomatic or paucisymptomatic cases in subjects with COVID-19 infection. This proportion can be difficult to quantify; therefore, it constitutes a hidden population. This study aims to develop a proof-of-concept method for estimating the number of undocumented infections of COVID-19. This is the protocol for the INCIDENT (Hidden COVID-19 Cases Network Estimation) study, an online, cross-sectional survey with snowball sampling based on the network scale-up method (NSUM). The original personal network size estimation method was based on a fixed-effects maximum likelihood estimator. We propose an extension of previous Bayesian estimation methods to estimate the unknown network size using the Markov chain Monte Carlo algorithm. On 6 May 2020, 1963 questionnaires were collected, 1703 were completed except for the random questions, and 1652 were completed in all three sections. The algorithm was initialized at the first iteration and applied to the whole dataset. Knowing the number of asymptomatic COVID-19 cases is extremely important for reducing the spread of the virus. Our approach reduces the number of questions posed. This allows us to speed up the completion of the questionnaire with a subsequent reduction in the nonresponse rate

Physical activity assessment with wearable devices in rheumatic diseases: A systematic review and meta-analysis

Objectives: In the management of rheumatic musculoskeletal disorders (RMDs), regular physical activity (PA) is an important recognized non-pharmacological intervention. This systematic review and meta-analysis aims to evaluate how the use of wearable devices (WDs) impacts physical activity in patients with noninflammatory and inflammatory rheumatic diseases. Methods: A comprehensive search of articles was performed in PubMed, Embase, CINAHL and Scopus. A random-effect meta-analysis was carried out on the number of steps and moderate-vigorous physical activity (MVPA). Univariable meta-regression models were computed to assess the possibility that the study characteristics may act as modifiers on the final meta-analysis estimate. Results: In the analysis, 51 articles were included, with a total of 7488 participants. Twenty-two studies considered MVPA outcome alone, 16 studies considered the number of steps alone, and 13 studies reported information on both outcomes. The recommended PA threshold was reached for MVPA (36.35, 95% CI 29.39, 43.31) but not for daily steps (-1092.60, -1640.42 to -544.77). Studies on patients with fibromyalgia report a higher number (6290, 5198.65-7381.62) of daily steps compared with other RMDs. Patients affected by chronic inflammatory arthropathies seemed to fare better in terms of daily steps than the other categories. Patients of younger age reported a higher overall level of PA than elderly individuals for both the number of steps and MVPA. Conclusion: Physical activity can be lower than the recommended threshold in patients with RMDs when objectively measured using WD. WDs could be a useful and affordable instrument for daily monitoring physical activity in RMDs and may support an increase in activity levels. PROSPERO trial registration: CRD42021227681, https://www.crd.york.ac.uk/prospero/displayrecord.php?RecordID=227681

The surplus transplant lung allocation system in italy: An evaluation of the allocation process via stochastic modeling

Background: Lung transplantation is a specialized procedure used to treat chronic end-stage respiratory diseases. Due to the scarcity of lung donors, constructing fair and equitable lung transplant allocation methods is an issue that has been addressed with different strategies worldwide. This work aims to describe how Italy\u2019s \u201cnational protocol for the management of surplus organs in all transplant programs\u201d functions through an online app to allocate lung transplants. We have developed two probability models to describe the allocation process among the various transplant centers. An online app was then created. The first model considers conditional probabilities based on a protocol flowchart to compute the probability for each area and transplant center to receive each n-th organ in the period considered. The second probability model is based on the generalization of the binomial distribution to correlated binary variables, which is based on Bahadur\u2019s representation, to compute the cumulative probability for each transplant center to receive at least nth organs. Our results show that the impact of the allocation of a surplus organ depends mostly on the region where the organ was donated. The discrepancies shown by our model may be explained by a discrepancy between the northern and southern regions in relation to the number of organs donated