Unmasking the chameleons: A benchmark for out-of-distribution detection in medical tabular data.

Background: Machine Learning (ML) models often struggle to generalize effectively to data that deviates from the training distribution. This raises significant concerns about the reliability of real-world healthcare systems encountering such inputs known as out-of-distribution (OOD) data. These concerns can be addressed by real-time detection of OOD inputs.

Comparing causal random forest and linear regression to estimate the independent association of organisational factors with ICU efficiency.

Purpose: Parametric regression models have been the main statistical method for identifying average treatment effects. Causal machine learning models showed promising results in estimating heterogeneous treatment effects in causal inference. Here we aimed to compare the application of causal random forest (CRF) and linear regression modelling (LRM) to estimate the effects of organisational factors on ICU efficiency.

Performance of federated learning-based models in the Dutch TAVI population was comparable to central strategies and outperformed local strategies.

Background: Federated learning (FL) is a technique for learning prediction models without sharing records between hospitals. Compared to centralized training approaches, the adoption of FL could negatively impact model performance.

Aim: This study aimed to evaluate four types of multicenter model development strategies for predicting 30-day mortality for patients undergoing transcatheter aortic valve implantation (TAVI): (1) , learning one model from a centralized dataset of all hospitals; (2) , learning one model per hospital; (3) (), averaging of local model coefficients; and (4) , aggregating local model predictions.

Evaluation of no-show rate in outpatient clinics with open access scheduling system: A systematic review.

Background: Patients' missed appointments can cause interference in the functions of the clinics and the visit of other patients. One of the most effective strategies to solve the problem of no-show rate is the use of an open access scheduling system (OA). This systematic review was conducted with the aim of investigating the impact of OA on the rate of no-show of patients in outpatient clinics.

A systematic review of fall prediction models for community-dwelling older adults: comparison between models based on research cohorts and models based on routinely collected data.

Background: Prediction models can identify fall-prone individuals. Prediction models can be based on either data from research cohorts (cohort-based) or routinely collected data (RCD-based). We review and compare cohort-based and RCD-based studies describing the development and/or validation of fall prediction models for community-dwelling older adults.

Models to predict length of stay in the emergency department: a systematic literature review and appraisal.

Introduction: Prolonged Length of Stay (LOS) in ED (Emergency Department) has been associated with poor clinical outcomes. Prediction of ED LOS may help optimize resource utilization, clinical management, and benchmarking. This study aims to systematically review models for predicting ED LOS and to assess the reporting and methodological quality about these models.

Topic evolution before fall incidents in new fallers through natural language processing of general practitioners' clinical notes.

Background: Falls involve dynamic risk factors that change over time, but most studies on fall-risk factors are cross-sectional and do not capture this temporal aspect. The longitudinal clinical notes within electronic health records (EHR) provide an opportunity to analyse fall risk factor trajectories through Natural Language Processing techniques, specifically dynamic topic modelling (DTM). This study aims to uncover fall-related topics for new fallers and track their evolving trends leading up to falls.

Author Response.

Rahmatinejad Z, Hoseini B, Pourmand A, Reihani H, Rahmatinejad F, Eslami S, Author Response. Indian J Crit Care Med 2024;28(2):183-184.

The effect of computerised decision support alerts tailored to intensive care on the administration of high-risk drug combinations, and their monitoring: a cluster randomised stepped-wedge trial.

Background: Drug-drug interactions (DDIs) can harm patients admitted to the intensive care unit (ICU). Yet, clinical decision support systems (CDSSs) aimed at helping physicians prevent DDIs are plagued by low-yield alerts, causing alert fatigue and compromising patient safety. The aim of this multicentre study was to evaluate the effect of tailoring potential DDI alerts to the ICU setting on the frequency of administered high-risk drug combinations.

Prolonged use of closed-loop inspired oxygen support in preterm infants: a randomised controlled trial.

Objective: This randomised study in preterm infants on non-invasive respiratory support investigated the effectiveness of automated oxygen control (A-FiO) in keeping the oxygen saturation (SpO) within a target range (TR) during a 28-day period compared with manual titration (M-FiO).

Design: A single-centre randomised control trial.

Setting: A level III neonatal intensive care unit.

Effects of a clinical decision support system and patient portal for preventing medication-related falls in older fallers: Protocol of a cluster randomized controlled trial with embedded process and economic evaluations (ADFICE_IT).

Background: Falls are the leading cause of injury-related mortality and hospitalization among adults aged ≥ 65 years. An important modifiable fall-risk factor is use of fall-risk increasing drugs (FRIDs). However, deprescribing is not always attempted or performed successfully.

Adverse drug events caused by three high-risk drug-drug interactions in patients admitted to intensive care units: A multicentre retrospective observational study.

Aims: Knowledge about adverse drug events caused by drug-drug interactions (DDI-ADEs) is limited. We aimed to provide detailed insights about DDI-ADEs related to three frequent, high-risk potential DDIs (pDDIs) in the critical care setting: pDDIs with international normalized ratio increase (INR ) potential, pDDIs with acute kidney injury (AKI) potential, and pDDIs with QTc prolongation potential.

Methods: We extracted routinely collected retrospective data from electronic health records of intensive care units (ICUs) patients (≥18 years), admitted to ten hospitals in the Netherlands between January 2010 and September 2019.

Personal Continuity and Appropriate Prescribing in Primary Care.

Purpose: Personal continuity between patient and physician is a core value of primary care. Although previous studies suggest that personal continuity is associated with fewer potentially inappropriate prescriptions, evidence on continuity and prescribing in primary care is scarce. We aimed to determine the association between personal continuity and potentially inappropriate prescriptions, which encompasses potentially inappropriate medications (PIMs) and potential prescribing omissions (PPOs), by family physicians among older patients.

Temporal validation of 30-day mortality prediction models for transcatheter aortic valve implantation using statistical process control - An observational study in a national population.

Background: Various mortality prediction models for Transcatheter Aortic Valve Implantation (TAVI) have been developed in the past years. The effect of time on the performance of such models, however, is unclear given the improvements in the procedure and changes in patient selection, potentially jeopardizing the usefulness of the prediction models in clinical practice. We aim to explore how time affects the performance and stability of different types of prediction models of 30-day mortality after TAVI.

Leveraging Multi-Word Concepts to Predict Acute Kidney Injury in Intensive Care.

Acute kidney injury (AKI) is an abrupt decrease in kidney function widespread in intensive care. Many AKI prediction models have been proposed, but only few exploit clinical notes and medical terminologies. Previously, we developed and internally validated a model to predict AKI using clinical notes enriched with single-word concepts from medical knowledge graphs.

Validation of the ADFICE_IT Models for Predicting Falls and Recurrent Falls in Geriatric Outpatients.

Objectives: Before being used in clinical practice, a prediction model should be tested in patients whose data were not used in model development. Previously, we developed the ADFICE_IT models for predicting any fall and recurrent falls, referred as Any_fall and Recur_fall. In this study, we externally validated the models and compared their clinical value to a practical screening strategy where patients are screened for falls history alone.

Gestational age and socio-demographic factors associated with school performance at the age of 12 years, a population-based study.

Background: Gestational age is positively associated with cognitive development, but socio-demographic factors also influence school performance. Previous studies suggested possible interaction, putting children with low socio-economic status (SES) at increased risk of the negative effects of prematurity.

Objectives: To investigate the association between gestational age in weeks, socio-demographic characteristics, and school performance at the age of 12 years among children in regular primary education.

Comparing In-Hospital Mortality Prediction by Senior Emergency Resident's Judgment and Prognostic Models in the Emergency Department.

Background: A comparison of emergency residents' judgments and two derivatives of the Sequential Organ Failure Assessment (SOFA), namely, the mSOFA and the qSOFA, was conducted to determine the accuracy of predicting in-hospital mortality among critically ill patients in the emergency department (ED).

Methods: A prospective cohort research was performed on patients over 18 years of age presented to the ED. We used logistic regression to develop a model for predicting in-hospital mortality by using qSOFA, mSOFA, and residents' judgment scores.