The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Intérêt d'un réseau de transmission d'électrocardiogrammes par les médecins généralistes pour la prise en charge des syndromes coronariens aigus dans l'Indre

TOURS-BU Médecine (372612103) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Etude observationnelle et descriptive des complications hémorragiques graves de patients sous traitement AVK, admis et pris en charge au sein des structures d'urgence du Centre Hospitalier de Châteauroux de mai 2009 à mai 2010

TOURS-BU Médecine (372612103) / SudocSudocFranceF

Quelle est la perception des médecins généralistes de l'Indre concernant leur place dans la prise en charge de patients victimes d'arrêt cardiaque ?

TOURS-BU Médecine (372612103) / SudocSudocFranceF

Prise en charge de la douleur thoracique chez les personnes âgées (l'âge est-il un facteur discriminant ?)

TOURS-BU Médecine (372612103) / SudocSudocFranceF

Ground-level falls among nonagenarians: the impact of pre-injury antithrombotic therapy

International audienceAmong nonagenarians admitted to our emergency department (ED) for ground-level falls, we assessed the impact of pre-injury antithrombotic (AT) treatment on the post-traumatic consequences, and identified risk factors for 1-month mortality. All eligible patients were consecutively included over an 18-month period. Head trauma was attested by reliable medical history, witnesses or recent external signs. Patient characteristics, post-traumatic consequences and outcomes were compared across patients with and without AT. Risk factors for 1-month mortality were assessed using multivariate logistic regression analyses. 1014 consecutive nonagenarians were analysed, 675 (66.6%) with AT and 339 (33.4%) without. Head trauma (n = 429, 42.3%) was significantly more frequent among patients with AT (49.2 vs 28.6%, p < 0.001). Intracranial hemorrhage (ICH, n = 43, 4.2%), mostly subdural hematomas (58%), were more frequently found among patients with AT (p < 0.015). At least one fracture was diagnosed for 23.9% of the population, mostly hip fractures, without any significant association with AT. At 1 month, 103 patients (10.2%) had died. The independent risk factors for 1-month mortality were: ICH associated with head trauma (OR = 5.9, 95% CI 2.5-14), Glasgow coma score <= 12 at admission (OR = 10; 95% CI 2.2-46), atrial fibrillation (OR = 2.2, 95% CI 1.4-3.4) and age >= 95 years (OR = 1.6, 95% CI 1.0-2.5). Our results support accurate and regular assessment of the benefit/risk ratio for antithrombotic treatment among elderly people at high risk for falls

Machine learning is the key to diagnose COVID-19: a proof-of-concept study

International audienceThe reverse transcription-polymerase chain reaction (RT-PCR) assay is the accepted standard for coronavirus disease 2019 (COVID-19) diagnosis. As any test, RT-PCR provides false negative results that can be rectified by clinicians by confronting clinical, biological and imaging data. The combination of RT-PCR and chest-CT could improve diagnosis performance, but this would requires considerable resources for its rapid use in all patients with suspected COVID-19. The potential contribution of machine learning in this situation has not been fully evaluated. The objective of this study was to develop and evaluate machine learning models using routine clinical and laboratory data to improve the performance of RT-PCR and chest-CT for COVID-19 diagnosis among post-emergency hospitalized patients. All adults admitted to the ED for suspected COVID-19, and then hospitalized at Rennes academic hospital, France, between March 20, 2020 and May 5, 2020 were included in the study. Three model types were created: logistic regression, random forest, and neural network. Each model was trained to diagnose COVID-19 using different sets of variables. Area under the receiving operator characteristics curve (AUC) was the primary outcome to evaluate model’s performances. 536 patients were included in the study: 106 in the COVID group, 430 in the NOT-COVID group. The AUC values of chest-CT and RT-PCR increased from 0.778 to 0.892 and from 0.852 to 0.930, respectively, with the contribution of machine learning. After generalization, machine learning models will allow increasing chest-CT and RT-PCR performances for COVID-19 diagnosis

Out-of-Hospital Cardiac Arrest Detection by Machine Learning Based on the Phonetic Characteristics of the Caller’s Voice

International audienceINTRODUCTION: Out-of-hospital cardiac arrest (OHCA) is a major public health issue. The prognosis is closely related to the time from collapse to return of spontaneous circulation. Resuscitation efforts are frequently initiated at the request of emergency call center professionals who are specifically trained to identify critical conditions over the phone. However, 25% of OHCAs are not recognized during the first call. Therefore, it would be interesting to develop automated computer systems to recognize OHCA on the phone. The aim of this study was to build and evaluate machine learning models for OHCA recognition based on the phonetic characteristics of the caller’s voice. METHODS: All patients for whom a call was done to the emergency call center of Rennes, France, between 01/01/2017 and 01/01/2019 were eligible. The predicted variable was OHCA presence. Predicting variables were collected by computer-automatized phonetic analysis of the call. They were based on the following voice parameters: fundamental frequency, formants, intensity, jitter, shimmer, harmonic to noise ratio, number of voice breaks, and number of periods. Three models were generated using binary logistic regression, random forest, and neural network. The area under the curve (AUC) was the primary outcome used to evaluate each model performance. RESULTS: 820 patients were included in the study. The best model to predict OHCA was random forest (AUC=74.9, 95% CI=67.4-82.4). CONCLUSION: Machine learning models based on the acoustic characteristics of the caller’s voice can recognize OHCA. The integration of the acoustic parameters identified in this study will help to design decision-making support systems to improve OHCA detection over the phone

Use of Drugs, Tobacco, Alcohol and Illicit Substances in a French Student Population

Objective. To investigate perceived health status and prevalence of drug use, tobacco smoking, consumption of alcohol and illicit substances in a student population. Methods. Data were obtained from an anonymous questionnaire distributed to first-year students of the Toulouse University. Collected data concerned socio-demographic characteristics, perceived health status, and consumption of tobacco, alcohol, illicit substances and drugs. Results. Fifty seven percent of the 3 561 responders declared to have taken at least one drug during the week preceding the questionnaire. Most commonly Anatomical, Therapeutic and Chemical (ATC) classes used were genito-urinary system and sex hormones (29.6%), nervous system (16.4%) and alimentary tract and metabolism (14.1%). Twenty three percent of students were smokers. Differences according to health perception were found for tobacco and cannabis consumption. Anxiety was significantly more prevalent among students reporting that they did not consume alcohol (p<0.05). Conclusion. More than half of students use drugs. Other consumptions (tobacco, alcohol and illicit substances) are related with perceived health status

Effectiveness of a multimodal intervention to improve blood culture collection in an adult emergency department

International audienceWe investigated the impact of a multimodal intervention to improve the compliance of BC collections as a composite outcome, taking into account both blood volume collected and absence of solitary BC. We performed a quasi-experimental study using a before-after design (5 months for pre- and post-intervention evaluation) in an adult emergency department at a tertiary care hospital that showed that a multimodal intervention was associated with a dramatic increase in the proportion of blood cultures that were collected as recommended per national guidelines, from 17.3% (328/1896) to 68.9% (744/1080), P &lt; 0.0001. The implementation of such intervention in other settings could improve the diagnosis of bloodstream infections and reduce irrelevant costs