Background While the psychiatric disorders are conditions frequently encountered in hospitalized patients, there are little or no data regarding the characteristics and short- and long-term outcomes in patients with preexisting psychiatric disorders in ICU. Such assessment may provide the opportunity to determine the respective impact on mortality in the ICU and after ICU discharge with reasons for admission, including modalities of self-harm, of underlying psychiatric disorders and prior psychoactive medications.MethodsICU and 1-year survival analysis performed on a retrospective cohort of patients with preexisting psychiatric disorders admitted from 2000 through 2013 in a 21-bed polyvalent ICU in a university hospital.ResultsAmong the 1751 patients of the cohort, 1280 (73%) were admitted after deliberate self-harm. Psychiatric diagnoses were: schizophrenia, n = 97 (6%); non-schizophrenia psychotic disorder, n = 237 (13%); depression disorder, n = 1058 (60%), bipolar disorder, n = 172 (10%), and anxiety disorder, n = 187 (11%). ICU mortality rate was significantly lower in patients admitted after self-harm than in patients admitted for other reasons than self-harm [38/1288 patients (3%) vs. 53/463 patients (11%), respectively, p < 0.0001]. Compared with patients admitted for deliberate self-poisoning with psychoactive medications, patients admitted for self-harm by hanging, drowning, jumping from buildings, or corrosive chemicals ingestion had a significantly higher ICU mortality rate. In the ICU, SAPS II score [adjusted odds ratio (OR) 1.061, 95% CI 1.041–1.079, p < 0.0001], use of vasopressors (adjusted OR 7.40, 95% CI 2.94–18.51, p < 0.001), out-of-hospital cardiac arrest (adjusted OR 14.70, 95% CI 3.86–38.51, p < 0.001), and self-harm by hanging, drowning, jumping from buildings, or corrosive chemicals ingestion (adjusted OR 11.49, 95% CI 3.76–35.71, p < 0.001) were independently associated with mortality. After ICU discharge SAPS II score [adjusted hazard ratio (HR) 1.023, 95% CI 1.010–1.036, p < 0.01], age (adjusted HR 1.030, 95% CI 1.016–1.044, p < 0.0001), admission for respiratory failure (adjusted HR 2.23, 95% CI 1.19–4.57, p = 0.01), and shock (adjusted HR 3.72, 95% CI 1.97–6.62, p < 0.001) were independently associated with long-term mortality. Neither psychiatric diagnoses nor psychoactive medications received before admission to the ICU were independently associated with mortality.ConclusionsThe study provides data on the short- and long-term outcomes of patients with prepsychiatric disorders admitted to the ICU that may guide decisions when considering ICU admission and discharge in these patients.

words)Objective: To evaluate the incidence and consequences of preoperative iron deficiency in elective cardiac surgery.Design: A prospective observational study. Setting:The cardiac surgery unit of a university hospital, from November 2016 to February 2017.Participants: All patients presenting for elective cardiac surgery during the study period, with the exclusion of non-cardiac thoracic surgeries, surgeries of the descending aorta, endovascular procedures, and patients affected by an iron-metabolism disease.Intervention: Transferrin saturation and serum ferritin levels were systematically assessed before surgery and the care of patients maintained as usual. Measurements and Main Results:Routine analyses, clinical data, and the number of blood transfusions were recorded during the hospital stay. Among the 272 patients included, 31% had preoperative iron deficiency, and 13% were anemic. Patients with iron deficiency had significantly lower hemoglobin levels throughout the hospital stay and received blood transfusions more frequently during surgical procedures (31% vs. 19%, p = 0.0361). Detailed analysis showed that patients with iron deficiency received more red blood cell units. There were no differences in postoperative bleeding, morbidity, or mortality. Conclusions:Iron deficiency appears to be related to lower hemoglobin levels and more frequent transfusion in elective cardiac surgery. Assessing iron status preoperatively and correcting any iron deficiencies should be one of the numerous actions involved in patient blood management for such surgeries, with the aim of reducing morbidity due to both anemia and transfusion. * Mean +/-standard deviation • Number of patients (percentage) Redux: first reoperation after one cardiac surgery. Tridux: second reoperation. CPB: cardiopulmonary bypass.

BackgroundMedical coding is used for a variety of activities, from observational studies to hospital billing. However, comorbidities tend to be under-reported by medical coders. The aim of this study was to develop an algorithm to detect comorbidities in electronic health records (EHR) by using a clinical data warehouse (CDW) and a knowledge database.MethodsWe enriched the Theriaque pharmaceutical database with the French national Comorbidities List to identify drugs associated with at least one major comorbid condition and diagnoses associated with a drug indication. Then, we compared the drug indications in the Theriaque database with the ICD-10 billing codes in EHR to detect potentially missing comorbidities based on drug prescriptions. Finally, we improved comorbidity detection by matching drug prescriptions and laboratory test results. We tested the obtained algorithm by using two retrospective datasets extracted from the Rennes University Hospital (RUH) CDW. The first dataset included all adult patients hospitalized in the ear, nose, throat (ENT) surgical ward between October and December 2014 (ENT dataset). The second included all adult patients hospitalized at RUH between January and February 2015 (general dataset). We reviewed medical records to find written evidence of the suggested comorbidities in current or past stays.ResultsAmong the 22,132 Common Units of Dispensation (CUD) codes present in the Theriaque database, 19,970 drugs (90.2%) were associated with one or several ICD-10 diagnoses, based on their indication, and 11,162 (50.4%) with at least one of the 4878 comorbidities from the comorbidity list. Among the 122 patients of the ENT dataset, 75.4% had at least one drug prescription without corresponding ICD-10 code. The comorbidity diagnoses suggested by the algorithm were confirmed in 44.6% of the cases. Among the 4312 patients of the general dataset, 68.4% had at least one drug prescription without corresponding ICD-10 code. The comorbidity diagnoses suggested by the algorithm were confirmed in 20.3% of reviewed cases.ConclusionsThis simple algorithm based on combining accessible and immediately reusable data from knowledge databases, drug prescriptions and laboratory test results can detect comorbidities.Electronic supplementary materialThe online version of this article (10.1186/s12911-018-0586-x) contains supplementary material, which is available to authorized users.

Background Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. Methodology/Principal findings We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts. Conclusions/Significance Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders.

No abstract

HIV Pre-Exposure Prophylaxis (PrEP) is effective in Men who have Sex with Men (MSM), and is reimbursed by the social security in France. Yet, PrEP is underused due to the difficulty to identify people at risk of HIV infection outside the “sexual health” care path. We developed and validated an automated algorithm that re-uses Electronic Health Record (EHR) data available in eHOP, the Clinical Data Warehouse of Rennes University Hospital (France). Using machine learning methods, we developed five models to predict incident HIV infections with 162 variables that might be exploited to predict HIV risk using EHR data. We divided patients aged 18 or more having at least one hospital admission between 2013 and 2019 in two groups: cases (patients with known HIV infection in the study period) and controls (patients without known HIV infection and no PrEP in the study period, but with at least one HIV risk factor). Among the 624,708 admissions, we selected 156 cases (incident HIV infection) and 761 controls. The best performing model for identifying incident HIV infections was the combined model (LASSO, Random Forest, and Generalized Linear Model): AUC = 0.88 (95% CI: 0.8143-0.9619), specificity = 0.887, and sensitivity = 0.733 using the test dataset. The algorithm seems to efficiently identify patients at risk of HIV infection.

No abstract

Importance Although several observational studies on the effectiveness of SARS-CoV-2 vaccination have been published, vaccination coverage by August, 3 2021, remained low in the French overseas territories, despite Martinique and Guadeloupe experiencing an unprecedented number of COVID-19-related hospitalizations. We aimed to determine the association between COVID-19 vaccination and severe COVID-19 in the French overseas territories. Methods The French National Health Data System was used to conduct a 1:1 matched-cohort study. For each individual receiving a first dose of BNT162b2, mRNA-1273, ChAdOx1 nCoV-19, or Ad26.COV2-S vaccine between December 27, 2020, and July 31, 2021, one unvaccinated individual was randomly selected and matched for year of birth, sex, and overseas territories on the date of vaccination. We estimated vaccine effectiveness against COVID-19-related hospitalization and in-hospital death after a full vaccination schedule, defined as ≥14 days after the second dose. Analyses were stratified according to the number of comorbidities. Results 276,778 vaccinated individuals had a double-dose vaccination during the follow-up period and were followed with their paired unvaccinated control. The average age was 50 years and 53% were women. During a median 77 days of follow-up from day 14 after the second injection, 96 COVID-19-related hospitalizations occurred among vaccinated individuals and 1,465 among their unvaccinated counterparts. Overall, vaccine effectiveness against hospitalization was 94% (95%CI [93–95]) and exceeded 90% in each overseas territory, except Mayotte. The results were similar looking specifically at hospitalizations between July 15 and September 30, 2021. Vaccine effectiveness against in-hospital death was similar (94% [95%CI 91–96]). The risk of COVID-19-related hospitalization increased with the number of comorbidities, especially among vaccinated individuals. Conclusions and relevance In conclusion, vaccination has a major effect in reducing the risk of severe Covid-19 in the French overseas territories. The risk of COVID-19-hospitalization was very low among vaccinated individuals, especially in the absence of comorbidities. These results aim to increase confidence in vaccine effectiveness in overseas territories in hope of achieving better vaccination coverage.