Estimated equipotent conversion ratios of morphine, sufentanil and fentanyl as continuous infusion in neonatal intensive care units: a pharmacoepidemiologic cohort study.

Purpose: Opioids are frequently used to treat pain in neonatal intensive care units (NICU) with fentanyl, morphine and sufentanil being mainly used agents. Equianalgesic potency between opioids is not clearly described in the neonatal population. The aim of this study was to compare theoretical and actual equipotent conversion ratios between morphine, sufentanil and fentanyl based on prescriptions.

Frequencies, Modalities, Doses and Duration of Computerized Prescriptions for Sedative, Analgesic, Anesthetic and Paralytic Drugs in Neonates Requiring Intensive Care: A Prospective Pharmacoepidemiologic Cohort Study in 30 French NICUs From 2014 to 2020.

No consensus exists about the doses of analgesics, sedatives, anesthetics, and paralytics used in critically ill neonates. Large-scale, detailed pharmacoepidemiologic studies of prescription practices are a prerequisite to future research. This study aimed to describe the detailed prescriptions of these drug classes in neonates hospitalized in neonatal intensive care units (NICU) from computerized prescription records and to compare prescriptions by gestational age.

Local Explanation-Based Method for Healthcare Risk Stratification.

Decision support tools in healthcare require a strong confidence in the developed Machine Learning (ML) models both in terms of performances and in their ability to provide users a deeper understanding of the underlying situation. This study presents a novel method to construct a risk stratification based on ML and local explanations. An open-source dataset was used to demonstrate the efficiency of this method that well identified the main subgroups of patients.

Comparison of predictive models for cumulative live birth rate after treatment with ART.

Research Question: Can a machine learning model better predict the cumulative live birth rate for a couple after intrauterine insemination or embryo transfer than Cox regression based on their personal characteristics?

Study Design: Retrospective cohort study conducted in two French infertility centres (Créteil and Tenon Hospitals) between 2012 and 2019, including 1819 and 1226 couples at Créteil and Tenon, respectively. Two models were applied: a Cox regression, which is almost exclusively used in assisted reproductive technology (ART) predictive modelling, and a tree ensemble-based model using XGBoost implementation. Internal validations were performed on each hospital dataset separately; an external validation was then carried out on the Tenon Hospital's population.

Analysis of COVID-19 inpatients in France during first lockdown of 2020 using explainability methods.

The COVID-19 pandemic rapidly puts a heavy pressure on hospital centers, especially on intensive care units. There was an urgent need for tools to understand typology of COVID-19 patients and identify those most at risk of aggravation during their hospital stay. Data included more than 400 patients hospitalized due to COVID-19 during the first wave in France (spring of 2020) with clinical and biological features.

Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis.

Characteristics of patients at risk of developing severe forms of COVID-19 disease have been widely described, but very few studies describe their evolution through the following waves. Data was collected retrospectively from a prospectively maintained database from a University Hospital in Paris area, over a year corresponding to the first three waves of COVID-19 in France. Evolution of patient characteristics between non-severe and severe cases through the waves was analyzed with a classical multivariate logistic regression along with a complementary Machine-Learning-based analysis using explainability methods.

Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs.

Objective: To assess the ability of a deep learning model to distinguish between diabetic retinopathy (DR), sickle cell retinopathy (SCR), retinal vein occlusions (RVOs) and healthy eyes using ultra-widefield colour fundus photography (UWF-CFP).

Methods And Analysis: In this retrospective study, UWF-CFP images of patients with retinal vascular disease (DR, RVO, and SCR) and healthy controls were included. The images were used to train a multilayer deep convolutional neural network to differentiate on UWF-CFP between different vascular diseases and healthy controls.

Effect of bariatric surgery on cancer risk: results from an emulated target trial using population-based data.

Background: The impact of weight loss induced by bariatric surgery on cancer occurrence is controversial. To study the causal effect of bariatric surgery on cancer risk from an observational database, a target-trial emulation technique was used to mimic an RCT.

Methods: Data on patients admitted between 2010 and 2019 with a diagnosis of obesity were extracted from a national hospital discharge database.