Research challenges and opportunities in the Caribbean area: first bibliometric study in the French West Indies, from 1989 to 2018.

OBJECTIVE: To analyze, describe, and quantify the collaborations and scientific output of the two university teaching hospitals of Martinique and Guadeloupe, at the regional, national, and international level. METHODS: A bibliometrics analysis was performed from the international databases Web of Science and PubMed, for the period from 1989 to 2018, inclusive (30 years). Three types of bibliometric indicators were used, namely quantitative indicators, performance indicators, and organization-specific indicators. Affiliations of the first and last authors were identified from PubMed. RESULTS: Between 1989 and 2018, a total of 1 522 indexed articles were published with at least one author affiliated to either the University Hospital of Martinique (n = 827) or the University Hospital of Guadeloupe (n = 685). The majority of articles were in category Q1 (35.8% for Martinique and 35.2% for Guadeloupe). In Martinique, over the last 30 years, the three main research areas have been clinical neurology, ophthalmology, and surgery, together representing 28.7% of all research areas, with the highest number of articles published in the field of clinical neurology (n = 81). In the University Hospital of Guadeloupe, the area of hematology was largely represented, with 79 articles published. For both hospitals, the first and last authors of the articles published were mainly from mainland France. CONCLUSIONS: This quantitative analysis shows the development of medical and scientific research in Martinique and Guadeloupe over the last three decades, as well as the extent of their collaborative partnerships at the national and international levels.

International cooperation in public health in Martinique: geostrategic utility for cancer surveillance in the Caribbean.

BACKGROUND: Cooperation in public health and in oncology in particular, is currently a major issue for the island of Martinique, given its geopolitical position in the Caribbean region. The region of Martinique shares certain public health problems with other countries of the Caribbean, notably in terms of diagnostic and therapeutic management of patients with cancer. We present here a roadmap of cooperation priorities and activities in cancer surveillance and oncology in Martinique. MAIN BODY: The fight against cancer is a key public health priority that features high on the regional health policy for Martinique. In the face of these specific epidemiological conditions, Martinique needs to engage in medical cooperation in the field of oncology within the Caribbean, to improve skills and knowledge in this field, and to promote the creation of bilateral relations that will help to improve cancer management in an international healthcare environment. CONCLUSIONS: These collaborative exchanges will continue throughout 2020 and will lead to the implementation of mutual research projects across a larger population basin, integrating e-health approaches and epidemiological e-cohorts.

Impact of Preoperative Iron Deficiency on Blood Transfusion in Elective Cardiac Surgery.

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 noncardiac thoracic surgeries, surgeries of the descending aorta, endovascular procedures, and patients affected by an iron-metabolism disease. INTERVENTIONS: Transferrin saturation and serum ferritin levels were systematically assessed before surgery, and the care of patients was 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% v 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 transfusions 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 associated with both anemia and transfusion.

Combining information from a clinical data warehouse and a pharmaceutical database to generate a framework to detect comorbidities in electronic health records.

BACKGROUND: Medical 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. METHODS: We 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. RESULTS: Among 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. CONCLUSIONS: This simple algorithm based on combining accessible and immediately reusable data from knowledge databases, drug prescriptions and laboratory test results can detect comorbidities.

Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review.

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.

Vaccine effectiveness against severe COVID-19 outcomes within the French overseas territories: A cohort study of 2-doses vaccinated individuals matched to unvaccinated ones followed up until September 2021 and based on the National Health Data System.

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.

The Role of Heterogenous Real-world Data for Dengue Surveillance in Martinique: Observational Retrospective Study.

BACKGROUND: Traditionally, dengue prevention and control rely on vector control programs and reporting of symptomatic cases to a central health agency. However, case reporting is often delayed, and the true burden of dengue disease is often underestimated. Moreover, some countries do not have routine control measures for vector control. Therefore, researchers are constantly assessing novel data sources to improve traditional surveillance systems. These studies are mostly carried out in big territories and rarely in smaller endemic regions, such as Martinique and the Lesser Antilles. OBJECTIVE: The aim of this study was to determine whether heterogeneous real-world data sources could help reduce reporting delays and improve dengue monitoring in Martinique island, a small endemic region. METHODS: Heterogenous data sources (hospitalization data, entomological data, and Google Trends) and dengue surveillance reports for the last 14 years (January 2007 to February 2021) were analyzed to identify associations with dengue outbreaks and their time lags. RESULTS: The dengue hospitalization rate was the variable most strongly correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.70) with a time lag of -3 weeks. Weekly entomological interventions were also correlated with the increase in dengue positivity rate by real-time reverse transcription polymerase chain reaction (Pearson correlation coefficient=0.59) with a time lag of -2 weeks. The most correlated query from Google Trends was the "Dengue" topic restricted to the Martinique region (Pearson correlation coefficient=0.637) with a time lag of -3 weeks. CONCLUSIONS: Real-word data are valuable data sources for dengue surveillance in smaller territories. Many of these sources precede the increase in dengue cases by several weeks, and therefore can help to improve the ability of traditional surveillance systems to provide an early response in dengue outbreaks. All these sources should be better integrated to improve the early response to dengue outbreaks and vector-borne diseases in smaller endemic territories.

How to Identify Potential Candidates for HIV Pre-Exposure Prophylaxis: An AI Algorithm Reusing Real-World Hospital Data.

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.

A Semi-Automated Approach for Multilingual Terminology Matching: Mapping the French Version of the ICD-10 to the ICD-10 CM.

The aim of this study was to develop a simple method to map the French International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) with the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10 CM). We sought to map these terminologies forward (ICD-10 to ICD-10 CM) and backward (ICD-10 CM to ICD-10) and to assess the accuracy of these two mappings. We used several terminology resources such as the Unified Medical Language System (UMLS) Metathesaurus, Bioportal, the latest version available of the French ICD-10 and several official mapping files between different versions of the ICD-10. We first retrieved existing partial mapping between the ICD-10 and the ICD-10 CM. Then, we automatically matched the ICD-10 with the ICD-10-CM, using our different reference mapping files. Finally, we used manual review and natural language processing (NLP) to match labels between the two terminologies. We assessed the accuracy of both methods with a manual review of a random dataset from the results files. The overall matching was between 94.2 and 100%. The backward mapping was better than the forward one, especially regarding exact matches. In both cases, the NLP step was highly accurate. When there are no available experts from the ontology or NLP fields for multi-lingual ontology matching, this simple approach enables secondary reuse of Electronic Health Records (EHR) and billing data for research purposes in an international context.

Comparison of Unplanned 30-Day Readmission Prediction Models, Based on Hospital Warehouse and Demographic Data.

Anticipating unplanned hospital readmission episodes is a safety and medico-economic issue. We compared statistics (Logistic Regression) and machine learning algorithms (Gradient Boosting, Random Forest, and Neural Network) for predicting the risk of all-cause, 30-day hospital readmission using data from the clinical data warehouse of Rennes and from other sources. The dataset included hospital stays based on the criteria of the French national methodology for the 30-day readmission rate (i.e., patients older than 18 years, geolocation, no iterative stays, and no hospitalization for palliative care), with a similar pre-processing for all algorithms. We calculated the area under the ROC curve (AUC) for 30-day readmission prediction by each model. In total, we included 259114 hospital stays, with a readmission rate of 8.8%. The AUC was 0.61 for the Logistic Regression, 0.69 for the Gradient Boosting, 0.69 for the Random Forest, and 0.62 for the Neural Network model. We obtained the best performance and reproducibility to predict readmissions with Random Forest, and found that the algorithms performed better when data came from different sources.

Retrieving the Vital Status of Patients with Cancer Using Online Obituaries.

The aim of this study was to develop a methodology to link mortality data from Internet sources with administrative data from electronic health records and to assess the performance of different record linkage methods. We extracted the electronic health records of all adult patients hospitalized at Rennes comprehensive cancer center between January 1, 2010 and December 31, 2015 and separated them in two groups (training and test set). We also extracted all available online obituaries from the most exhaustive French funeral home website using web scraping techniques. We used and evaluated three different algorithms (deterministic, approximate deterministic and probabilistic) to link the patients' records with online obituaries. We optimized the algorithms using the training set and then evaluated them in the test set. The overall precision was between 98 and 100%. The three classification algorithms performed better for men than women. The probabilistic classification decreased the number of manual reviews, but slightly increased the number of false negatives. To address the problem of long delays in the publication or sharing of mortality data, online obituary data could be considered for real-time surveillance of mortality in patients with cancer because they are easily available and time-efficient.

Patients with preexisting psychiatric disorders admitted to ICU: a descriptive and retrospective cohort study.

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. METHODS: ICU 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. RESULTS: Among 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. CONCLUSIONS: The 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.

The Role of Percutaneous Biopsy and Prognostic Factors of Malignancy in Solitary Breast Papilloma: A Retrospective Multicenter Study of 259 Cases.

AIM: Management of papillary breast lesions is a controversial issue, as complete excision implies surgery of numerous benign lesions. The purpose of this study was to assess concordance between percutaneous and surgical biopsy of papillomas along with factors predictive of malignancy. PATIENTS AND METHODS: The study consisted of a retrospective review of papilloma cases between 2009 and 2013 at three breast cancer centers. All cases of papilloma histologically diagnosed by percutaneous biopsy and confirmed by surgical specimen were included. The biopsy results were compared with final surgical pathology. Lesion size and clinical and radiological features were recorded. RESULTS: A total of 259 cases were included (188 simple and 71 complex papillomas). Concordance between histology after percutaneous and surgical biopsy was lower for complex papillomas, regardless of type, than for simple papillomas (p<0.001). The risk of having a complex papilloma was shown to be significantly higher in postmenopausal patients (p=0.023), and was 20 times higher if the percutaneous biopsy was malignant as opposed to benign (p<2.10). However, the false-negatives for percutaneous biopsy in complex papilloma cases were mainly related to atypical and in situ lesions. CONCLUSION: Percutaneous biopsy does not appear adequate for identifying papillomas requiring surgical excision due to the risk of underestimation of cancerous lesions. However, certain factors predictive of malignancy may assist with surgical management, such as age and menopausal status, lesions peripheral to the nipple, and atypia on percutaneous biopsy.

An Integrated Workflow For Secondary Use of Patient Data for Clinical Research.

This work proposes an integrated workflow for secondary use of medical data to serve feasibility studies, and the prescreening and monitoring of research studies. All research issues are initially addressed by the Clinical Research Office through a research portal and subsequently redirected to relevant experts in the determined field of concentration. For secondary use of data, the workflow is then based on the clinical data warehouse of the hospital. A datamart with potentially eligible research candidates is constructed. Datamarts can either produce aggregated data, de-identified data, or identified data, according to the kind of study being treated. In conclusion, integrating the secondary use of data process into a general research workflow allows visibility of information technologies and improves the accessability of clinical data.

Vaccination coverage of children with inflammatory bowel disease after an awareness campaign on the risk of infection.

BACKGROUND: Children with inflammatory bowel disease are at risk of vaccine-preventable diseases mostly due to immunosuppressive drugs. AIM: To evaluate coverage after an awareness campaign informing patients, their parents and general practitioner about the vaccination schedule. METHODS: Vaccination coverage was firstly evaluated and followed by an awareness campaign on the risk of infection via postal mail. The trial is a case-control study on the same patients before and after the awareness campaign. Overall, 92 children were included. A questionnaire was then completed during a routine appointment to collect data including age at diagnosis, age at data collection, treatment history, and vaccination status. RESULTS: Vaccination rates significantly increased for vaccines against diphtheria-tetanus-poliomyelitis (92% vs. 100%), Haemophilus influenzae (88% vs. 98%), hepatitis B (52% vs. 71%), pneumococcus (36% vs. 57%), and meningococcus C (17% vs. 41%) (p<0.05). Children who were older at diagnosis were 1.26 times more likely to be up-to-date with a minimum vaccination schedule (diphtheria-tetanus-poliomyelitis, pertussis, H. influenzae, measles-mumps-rubella, tuberculosis) (p=0.002). CONCLUSION: Informing inflammatory bowel disease patients, their parents and general practitioner about the vaccination schedule via postal mail is easy, inexpensive, reproducible, and increases vaccination coverage. This method reinforces information on the risk of infection during routine visits.