Risk factors for early mortality of lung cancer patients in France: A nationwide analysis.

BACKGROUND: Despite therapeutic advances, lung cancer remains the first cause of death from cancer. The main objective of this study was to identify risk factors associated with death within 3-months of the first hospitalization for lung cancer in France. METHODS: This analysis included patients with a first hospitalization for lung cancer (between January 1, 2016 and December 31, 2018) according to diagnosis-related groups entered into the French national medical-administrative database. Clinical and socioeconomic parameters and characteristics of that first hospitalization were analyzed. A model predictive of early mortality was developed based on those variables. RESULTS: The 144,087 included patients were 67% men; median age of 68 [interquartile range 60-76] years; 47% had metastatic disease at diagnosis; and 34% and 23%, respectively, had received systemic treatment or undergone curative surgery. The 3-month mortality was 19%, and significantly higher for those ≥70 versus <70 years old (OR 1.33, 1.22-1.45), men versus. women (OR 1.50, 1.44-1.55), those with metastatic disease at diagnosis (OR, 3.30, 3.18-3.43), first hospitalization via the emergency room (OR 1.65 1.59-1.71) and first hospitalization lasting >30 days (OR, 1.58 1.49-1.68). In contrast, no socioeconomic characteristic was associated with early mortality. CONCLUSION: Almost 1 in 5 patients diagnosed with lung cancer in France died within 3 months post-diagnosis. Improving survival requires diagnosis at an earlier stage and better organization of diagnosis and specific care pathways.

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. RESULTS: The two populations were significantly different, with Tenon having more severe cases than Créteil, although internal validations show comparable results (C-index of 60% for both populations). As for the external validation, the XGBoost model stands out as being more stable than Cox regression, with the latter having a higher performance loss (C-index of 60% and 58%, respectively). The explicability method indicates that the XGBoost model relies strongly on features such as the ages of a couple, causes of infertility, and the woman's body mass index or infertility duration, which is consistent with the ART literature about risk factors. CONCLUSIONS: Overall performances are still relatively modest, which is coherent with all reported ART predictive models. Explicability-based methods would allow access to new knowledge, to gain a greater comprehension of which characteristics and interactions really influence a couple's journey. These models can be used by practitioners and patients to make better informed decisions about performing ART.

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. Machine learning and explainability methods were used to construct an aggravation risk score and analyzed feature effects. The model had a robust AUC ROC Score of 81%. Most important features were age, chest CT Severity and biological variables such as CRP, O2 Saturation and Eosinophils. Several features showed strong non-linear effects, especially for CT Severity. Interaction effects were also detected between age and gender as well as age and Eosinophils. Clustering techniques stratified inpatients in three main subgroups (low aggravation risk with no risk factor, medium risk due to their high age, and high risk mainly due to high CT Severity and abnormal biological values). This in-depth analysis determined significantly distinct typologies of inpatients, which facilitated definition of medical protocols to deliver the most appropriate cares for each profile. Graphical Abstract Graphical abstract represents main methods used and results found with a focus on feature impact on aggravation risk and identified groups of patients.

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. On 1076 hospitalized patients, severe forms concerned 29% (123/429), 31% (66/214) and 18% (79/433) of each wave. Risk factors of the first wave included old age (≥ 70 years), male gender, diabetes and obesity while cardiovascular issues appeared to be a protective factor. Influence of age, gender and comorbidities on the occurrence of severe COVID-19 was less marked in the 3rd wave compared to the first 2, and the interactions between age and comorbidities less important. Typology of hospitalized patients with severe forms evolved rapidly through the waves. This evolution may be due to the changes of hospital practices and the early vaccination campaign targeting the people at high risk such as elderly and patients with comorbidities.

Trends in metabolic bariatric surgery in adolescents in France: a nationwide analysis on an 11- year period.

BACKGROUND: As the prevalence of obesity in adolescents has reached an alarming level of 16%, the rate of metabolic bariatric surgery (MBS) in this population is also rising in several countries. OBJECTIVES: This study aimed to compare the trends in types of MBS, short-term safety, and revisional rates, in younger adolescents aged < 18 years, compared with older adolescents (aged 18-19 yr) and adults aged >20 years. SETTING: Clinical research center, general hospital in France. METHODS: Using a national administrative database (Programme de Médicalisation des Systèmes d'Information [PMSI]), data regarding all patients undergoing MBS between 2008 and 2018 in France were examined. Demographic parameters, body mass index (BMI), co-morbidities, types of surgery, early complications, and long-term revisional rates were analyzed, comparing younger adolescents (<18 yr), older adolescents (18-19 yr), and adults (≥20 yr). RESULTS: The number of bariatric procedures in adolescents initially increased from 59 in 2008 to 135 in 2014, and then progressively declined to 56 procedures in 2018. Adjustable gastric banding (AGB) decreased from 83.1% (n = 49) of procedures to 32.1% (n = 18) of procedures during the study period, while sleeve gastrectomy (SG) increased from 6.8% (n = 4) to 46.4% (n = 26). In the early postoperative period, younger adolescents undergoing MBS experienced fewer episodes of reoperation (1.0% versus 1.3% in older adolescents and 2.6% in adults, P < .001) and intensive care unit (ICU) stays (.2% versus .2% in older adolescents and .6% in adults, P < .001), and no deaths were observed in younger adolescents (.02% in older adolescents and .1% in adults, P = .18). At 10 years, the AGB removal rate was lower in younger adolescents (24.8%) compared with that in older adolescents (29.6%) and adults (50.3%, P < .001). Similarly, rates of revisional surgery after SG were different in the 3 groups: 2.9%, 4.6% and 12.2% in younger adolescents, older adolescents, and adults, respectively. CONCLUSION: Despite significantly lower early complication rates and long-term revisional rates in young adolescents (<18 yr), we observed a progressive decrease in the utilization of MBS in this population in France, compared with adults (≥20 yr) and older adolescents (18-19 yr).