Multimodal Remote Home Monitoring of Lung Transplant Recipients during COVID-19 Vaccinations: Usability Pilot Study of the COVIDA Desk Incorporating Wearable Devices.

Background and Objectives: Remote patient monitoring (RPM) of vital signs and symptoms for lung transplant recipients (LTRs) has become increasingly relevant in many situations. Nevertheless, RPM research integrating multisensory home monitoring in LTRs is scarce. We developed a novel multisensory home monitoring device and tested it in the context of COVID-19 vaccinations. We hypothesize that multisensory RPM and smartphone-based questionnaire feedback on signs and symptoms will be well accepted among LTRs. To assess the usability and acceptability of a remote monitoring system consisting of wearable devices, including home spirometry and a smartphone-based questionnaire application for symptom and vital sign monitoring using wearable devices, during the first and second SARS-CoV-2 vaccination. Materials and Methods: Observational usability pilot study for six weeks of home monitoring with the COVIDA Desk for LTRs. During the first week after the vaccination, intensive monitoring was performed by recording data on physical activity, spirometry, temperature, pulse oximetry and self-reported symptoms, signs and additional measurements. During the subsequent days, the number of monitoring assessments was reduced. LTRs reported on their perceptions of the usability of the monitoring device through a purpose-designed questionnaire. Results: Ten LTRs planning to receive the first COVID-19 vaccinations were recruited. For the intensive monitoring study phase, LTRs recorded symptoms, signs and additional measurements. The most frequent adverse events reported were local pain, fatigue, sleep disturbance and headache. The duration of these symptoms was 5-8 days post-vaccination. Adherence to the main monitoring devices was high. LTRs rated usability as high. The majority were willing to continue monitoring. Conclusions: The COVIDA Desk showed favorable technical performance and was well accepted by the LTRs during the vaccination phase of the pandemic. The feasibility of the RPM system deployment was proven by the rapid recruitment uptake, technical performance (i.e., low number of errors), favorable user experience questionnaires and detailed individual user feedback.

Medication adherence and persistence among patients with non-small cell lung cancer receiving tyrosine kinase inhibitors and estimation of the economic burden associated with the unused medicines.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a leading cause of cancer deaths. Its treatment includes specific oral tyrosine kinases inhibitors (TKIs). OBJECTIVES: To estimate adherence and persistence among patients receiving TKIs and to assess the economic burden of the unused medicines in Alsace (France). METHOD: This retrospective study was carried out using the Insurance Healthcare database. MAIN OUTCOME MEASURES: Adherence was calculated using medication possession ratio (MPR), persistence using estimated level of persistence with therapy (ELPT) and economic impact using prescription refill data. RESULTS: 242 patients were receiving TKIs. The most common TKIs prescribed were erlotinib (75.6%, n = 183) and crizotinib (12.8%, n = 31). Total of 149 patients were included in the adherence analysis. Overall MPR was 0.98. 180 patients were included in the persistence analysis. Almost half of patients had stopped treatment at 60 days and only 38.3% (n = 69) were still persistent with the therapy at 120 days. The expenses related to unused TKIs amounted to €356,392 and were related majorly to treatment discontinuation followed by overlapping refills, patient deaths and dose- or drug-switching, respectively. CONCLUSIONS: Our data indicated overall adherence medicines above the acceptable limit of 0.80 but also pointed out a significant decline in persistence over time. The resulting economic losses justify the need for physicians and pharmacists to closely monitor their patients to ensure continuity of treatment. To limit the cost associated with unused medicines, interventions such as app-based monitoring, dispensing TKIs per unit over shorter periods and not only on monthly intervals could be implemented.

Statin (mis)use in older people: A cross-sectional study using French health insurance databases.

PURPOSE: To describe the occurrence of potential statin misuse, its characteristics and associated factors in older people. METHODS: A retrospective cross-sectional study was conducted in Alsace and Lorraine (France) using French health insurance databases. Study population comprised community-dwelling patients and nursing homes residents aged 80 and over, who received at least one statin prescription between January 1, 2017 to April 30, 2017 (N = 38 268). Potential statin misuse was identified considering off-label uses, high-intensity doses, drug contraindications and statin-drug interactions. RESULTS: At least one potential statin misuse was detected for 19 468 patients (50.9%). Off-label prescription was the most frequent misuse observed (53.6%), followed by high-intensity statin dose (30.5%). Polypharmacy (five to nine drugs) and excessive polypharmacy (10 or more drugs) were the main risk factors associated with potential statin misuse (adjusted OR = 2.6 and 4.7 respectively). Nursing home, multi-morbidity and the presence of multiple prescriptions from different doctors for the same patient were other risk factors significantly associated. Two sensitivity analyzes (reconsiderations of off-label and high statin dose definitions) revealed the same statistical trend. CONCLUSION: Potential statin misuse is frequent in people aged 80 and over. These findings should serve as a warning to health care professionals and hopefully will contribute to ensure an appropriate and safe use of statin in aged population.

Memantine misuse and social networks: A content analysis of Internet self-reports.

PURPOSE: Memantine is a N-methyl-D-aspartate (NMDA) receptor antagonist currently used for moderate-to-severe Alzheimer's disease. Although the risk of memantine abuse is very low, other NMDA receptor antagonists, such as phencyclidine and ketamine, are well known to drug users. The purpose of this study was to collect data on social networks in order to identify unexpected forms of memantine abuse. METHOD: A Google Trends search was used to highlight reddit.com as a major source of social discussions about memantine. Self-reported users experiences posted on reddit.com were recorded and sorted using representative keywords. RESULTS: From 2010 to November 2019, 307 topics citing memantine were identified on reddit.com and 136 users experiences extracted from the topics were recorded. The main use identified was "self-medication" based on off-label uses of memantine such as anxiety, depression, attention-deficit hyperactivity disorder, or obsessive-compulsive disorder (n = 87 reports), followed by nootropic (42) and recreational (39) uses. The average reported doses for acute and chronic uses were 156 ± 110 mg and 23 ± 24 mg respectively. For chronic use, the average duration was 15 ± 29 weeks. Most chronic users (77 out of 100) reported at least one side effect. CONCLUSION: Memantine misuse seems to be a growing phenomenon. Beyond expected use for recreational purpose; the main uses identified on reddit.com were not reported in the medical literature. Off-label uses and nootropic purposes seemed to be key features of memantine misuse.

IoT-Based Home Monitoring: Supporting Practitioners' Assessment by Behavioral Analysis.

This paper introduces technical solutions devised to support the Deployment Site - Regione Emilia Romagna (DS-RER) of the ACTIVAGE project. The ACTIVAGE project aims at promoting IoT (Internet of Things)-based solutions for Active and Healthy ageing. DS-RER focuses on improving continuity of care for older adults (65+) suffering from aftereffects of a stroke event. A Wireless Sensor Kit based on Wi-Fi connectivity was suitably engineered and realized to monitor behavioral aspects, possibly relevant to health and wellbeing assessment. This includes bed/rests patterns, toilet usage, room presence and many others. Besides hardware design and validation, cloud-based analytics services are introduced, suitable for automatic extraction of relevant information (trends and anomalies) from raw sensor data streams. The approach is general and applicable to a wider range of use cases; however, for readability's sake, two simple cases are analyzed, related to bed and toilet usage patterns. In particular, a regression framework is introduced, suitable for detecting trends (long and short-term) and labeling anomalies. A methodology for assessing multi-modal daily behavioral profiles is introduced, based on unsupervised clustering techniques. The proposed framework has been successfully deployed at several real-users' homes, allowing for its functional validation. Clinical effectiveness will be assessed instead through a Randomized Control Trial study, currently being carried out.

A first higher-level time-calibrated phylogeny of antlions (Neuroptera: Myrmeleontidae).

In this study, we reconstruct the first time-calibrated phylogeny of the iconic antlion family, the Myrmeleontidae (Neuroptera: Myrmeleontiformia). We use maximum likelihood and Bayesian inference to analyse a molecular dataset based on seven mitochondrial and nuclear gene markers. The dataset encompasses 106 species of Neuroptera, including 94 antlion species. The resulting phylogenetic framework provides support for a myrmeleontid classification distinguishing four subfamilies: Acanthaclisinae, Myrmeleontinae, Palparinae, and Stilbopteryginae. Within Myrmeleontinae, Myrmecaelurini and Nemoleontini are recovered as monophyletic clades; Gepini also appears as a valid tribe, distinct from Myrmecaelurini whereas Myrmecaelurini and Nesoleontini on one hand and Brachynemurini and Dendroleontini on the other hand, appear closely related. Some preliminary information related to generic and specific levels are also implied from our results, such as the paraphyly of several genera. Dating analyses based on thoroughly evaluated fossil calibrations indicate that the antlion family likely originated in the Cretaceous, between 135 and 138 million years ago (depending on the set of fossil calibrations), and that all higher-level lineages appeared during the Early Cretaceous. This first phylogenetic hypothesis will provide a valuable basis to further expand the taxonomic coverage and molecular sampling, and to lay the foundations of future systematic revisions.

Drug-dispensing problems community pharmacists face when patients are discharged from hospitals: a study about 537 prescriptions in Alsace.

OBJECTIVES: To identify both type and frequency of the challenges community pharmacists face when dispensing drugs from hospital discharge prescriptions, to describe the measures undertaken to resolve the issues at stake and to list their consequences. DESIGN: We carried out an observational study in the community pharmacies of the French region of Alsace and asked the community pharmacy staff to review 537 hospital discharge prescriptions in 2013 using anonymous data collection forms. SETTING AND PARTICIPANTS: Nineteen community pharmacies. MAIN OUTCOME MEASURES: Number of patients informed about their medication (at hospital and/or community pharmacy), type and frequency of issues encountered during drug dispensing, type and frequency of measures undertaken to resolve the issues, type and frequency of the consequences regarding drug dispensing. RESULTS: Community pharmacists faced 165 challenges from 145 hospital discharge prescriptions (i.e. 27.5% out of 528 analysed prescriptions), mostly correlated to the quality of the prescriptions (n = 100, 60.6%) or to logistical matters (n = 54, 32.7%). A mere 36.8% of the patients received information pertaining to their medication while being hospitalized. Of note, 40.5% of the prescriptions were delivered to pharmacies within 2 days following the patients' discharge. In order to resolve the different issues preventing drugs from being dispensed (n = 33/145 prescriptions), pharmacists sought information, mainly from patients, colleagues and hospital prescribers. The pharmacists were able to dispense all the drugs prescribed in 138 out of 145 cases (95.2%). CONCLUSIONS: This study highlighted the challenges encountered by community pharmacists and their significant contribution to the continuity of care upon patients being discharged from hospitals.

Anticoagulant-related hospital admissions: serious adverse reactions identified through hospital databases.

PURPOSE: A growing number of patients today receive anticoagulants. These drugs can cause serious adverse reactions leading to patients' hospitalization. The present study aimed to assess the number of hospital admissions as a result of anticoagulant adverse reactions in Alsace, a French region of 1.8 million inhabitants, and to estimate the economic burden associated with their management. METHODS: A retrospective analysis was performed using data extracted from the regional and anonymous hospital Programme de Médicalisation des Systèmes d'Information (PMSI) database to assess the number of hospital admissions and the associated costs. Stays from public and private hospitals were extracted from the database using two International Classification of Diseases, 10th revision, codes referring to anticoagulant drugs: 'T45.5-Poisoning by anticoagulants' and 'Y44.2-Anticoagulants' adverse effect in therapeutic use'. Costs were calculated from official French tariffs. RESULTS: Within a 2-year period from 1 Januray 2010 to 31 December 2011, 462 anticoagulant-related hospital admissions, predominantly in elderly patients, were identified in Alsace. These stays, as a result of anticoagulant adverse reactions, represented a cost of 2 050 127.86 euros (including hospitalization and expensive drugs). CONCLUSION: Regional PMSI database constitutes an effective tool to explore anticoagulant-related hospital admissions. Based on our study, one can state that the cost of anticoagulation therapies lies not only in the price of the drugs but also in the cost of adverse reaction management. Policy makers should be aware of this reality and should focus on better medication supervision in order to improve patient safety and reduce expenses.

Asprothrips bimaculatus sp.n. (Thripidae, Dendrothripinae) from Martinique.

Asprothrips bimaculatus sp. n. is described from Martinique. It is unique among the five species now recognised in this genus in having the body sharply bicoloured, with the head and thorax dark but the abdomen white.

Deep learning classification of drug-related problems from pharmaceutical interventions issued by hospital clinical pharmacists during medication prescription review: a large-scale descriptive retrospective study in a French university hospital.

OBJECTIVES: Pharmaceutical interventions are proposals made by hospital clinical pharmacists to address sub-optimal uses of medications during prescription review. Pharmaceutical interventions include the identification of drug-related problems, their prevention and resolution. The objective of this study was to exploit a newly developed deep neural network classifier to identify drug-related problems from pharmaceutical interventions and perform a large retrospective descriptive analysis of them in a French university hospital over a 3-year period. METHODS: Data were collected from prescription support software from 2018 to 2020. A classifier running in Python 3.8 and using Keras library was then used to automatically categorise drug-related problems from pharmaceutical interventions according to the coding of the French Society of Clinical Pharmacy. RESULTS: 2 930 656 prescription lines were analysed for a total of 119 689 patients. Among these prescription lines, 153 335 (5.2%) resulted in pharmaceutical interventions (n=48 202 patients; 40.2%). Pharmaceutical interventions were predominantly observed in patients aged 65 years or older (n=26 141 patients out of 53 186; 49.1%) and in patients taking five or more medications (44 702 patients out of 93 419; 47.8%). The most frequently identified types of drug-related problems associated with pharmaceutical interventions were 'Non-conformity to guidelines or contra-indication' (n=88 523; 57.7%), 'Overdosage' (16 975; 11.1%) and 'Improper administration' (13 898; 9.1%). The most frequently encountered drugs were: paracetamol (n=10 585; 6.9%), esomeprazole (6031; 3.9%), hydrochlorothiazide (2951; 1.9%), enoxaparin (2191; 1.4%), tramadol (1879; 1.2%), calcium (2073; 1.3%), perindopril (1950; 1.2%), amlodipine (1716; 1.1%), simvastatin (1560; 1.0%) and insulin (1019; 0.7%). CONCLUSIONS: The deep neural network classifier used met the challenge of automatically classifying drug-related problems from pharmaceutical interventions from a large database without mobilising significant human resources. The use of such a classifier can lead to alerting caregivers about certain risky practices in prescription and administration, and triggering actions to improve patients' therapeutic outcomes.

Deep learning application to automated classification of recommendations made by hospital pharmacists during medication prescription review.

PURPOSE: Recommendations to improve therapeutics are proposals made by pharmacists during the prescription review process to address suboptimal use of medicines. Recommendations are generated daily as text documents but are rarely reused beyond their primary use to alert prescribers and caregivers. If recommendation data were easier to summarize, they could be used retrospectively to improve safeguards for better prescribing. The objective of this work was to train a deep learning algorithm for automated recommendation classification to valorize the large amount of recommendation data. METHODS: The study was conducted in a French university hospital, at which recommendation data were collected throughout 2017. Data from the first 6 months of 2017 were labeled by 2 pharmacists who assigned recommendations to 1 of the 29 possible classes of the French Society of Clinical Pharmacy classification. A deep neural network classifier was trained to predict the class of recommendations. RESULTS: In total, 27,699 labeled recommendations from the first half of 2017 were used to train and evaluate a classifier. The prediction accuracy calculated on a validation dataset was 78.0%. We also predicted classes for unlabeled recommendations collected during the second half of 2017. Of the 4,460 predictions reviewed, 67 required correction. When these additional labeled data were concatenated with the original dataset and the neural network was retrained, accuracy reached 81.0%. CONCLUSION: To facilitate analysis of recommendations, we have implemented an automated classification system using deep learning that achieves respectable performance. This tool can help to retrospectively highlight the clinical significance of daily medication reviews performed by hospital clinical pharmacists.

Clinical risk assessment of modelled situations in a pharmaceutical decision support system: a modified e-Delphi exploratory study.

BACKGROUND: Pharmaceutical decision support systems (PDSSs) use reasoning software to match patient data to modelled situations likely to cause drug-related problems (DRPs) or adverse drug events. To aid decision-making, modelled situations must be linked to well-defined systemic clinical risks. AIM: To obtain expert consensus on the level of clinical risk for patients associated with each modelled situation that could be addressed using a PDSS. METHOD: A two-round e-Delphi survey was conducted from February to April 2022, involving 20 experts from four French-speaking countries. Participants had to rate modelled situations on two five-point Likert scales, assessing the likelihood of clinical consequences and their severity. The degree of consensus was determined as the proportion of participants providing risk scores in line with the median. The combined median scores for likelihood and severity provided the level of risk according to the Clinical Risk Situation for Patients (CRiSP) scale, formalized via validated tools. RESULTS: The expert panel achieved consensus (≥ 75% agreement) on 48 out of 52 modelled clinical situations. Among these, 45 were categorized as high or extreme risk. The most common DRP identified was overdosing, accounting for 22% of cases. Furthermore, DRPs involving cardiovascular, psychiatric, and endocrinological drug classes were prevalent, constituting 45, 13, and 9% of cases, respectively. CONCLUSION: Through consensus, our study identified 45 modelled clinical situations associated with high or extreme risks. This study highlights the interest of using PDSSs to prevent harm in patients and, on a large scale, document the impact of the pharmacist in preventing, intercepting and managing iatrogenic drug risk.

Safety and efficacy of colchicine in crystal-induced arthritis flare in 54 patients with severe chronic kidney disease.

INTRODUCTION: Colchicine, commonly used in gout flare, is contraindicated in severe chronic kidney disease (CKD) (estimated glomerular filtration rate <30 mL/min). However, in this context, there are few alternatives, and colchicine use persists. We evaluated the tolerance of colchicine and its efficacy in patients with severe CKD. PATIENTS AND METHODS: All prescriptions of colchicine for managing crystal-induced arthritis flare (gout or calcium pyrophosphate deposition (CPPD) disease) in a hospitalised patient with severe CKD were screened from September 2020 to September 2021. After patient consent and treatment information, clinical and biological safety and efficacy data were prospectively collected from day 1 (D1) to D11. RESULTS: We included 54 patients (median age 75 years (IQR 67-83)) with 62 colchicine prescriptions (cases). Twelve (22%) patients were on dialysis. The main reason for hospitalisation was heart failure (31.5%), acute renal failure (22.2%), infection (18.5%) or an acute joint episode (9.3%). In total, 59.3% of patients had diabetes. The prescriptions concerned 58 cases of gout flares, 1 case of CPPD and 3 cases of both. Initial colchicine dosages were ≤0.5 mg/day in 47/62 (75.8%) cases; no dosage exceeded 1 mg/day (median duration of 6 days (IQR 3-11)). Colchicine was well tolerated in 47/61 (77%) cases. No serious adverse event was reported. Colchicine was considered completely effective by the medical team in 48/58 (83%) of cases. CONCLUSION: The use of colchicine, at reduced doses, was mostly effective to treat crystal-induced arthritis flare in 54 patients with severe CKD and was well tolerated, without any serious adverse events.

A new species of Gymnocnemia Schneider, 1845 from Morocco with additional diagnostic data for the genus (Neuroptera, Myrmeleontidae).

Gymnnocnemia editaerevayae nov. sp. is described from the High Atlas Mountains in Morocco and compared to Gymnocneinia variegata Schneider, 1845. Descriptions of male genitalia and male and female terminalia of G. variegata are provided. Both species are illustrated.

Description of three new species of Myrmeleon Linnaeus, 1767 from West Africa (Neuroptera: Myrmeleontidae).

Three antlion species, Myrmeleon croceus n. sp., Myrmeleon foucarti n. sp. and Myrmeleon nemoleontoides n. sp. from West Africa are described and illustrated. Information on their distribution, ecology and predators is provided.

Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review.

OBJECTIVES: The emergence of artificial intelligence (AI) is catching the interest of hospital pharmacists. A massive collection of health data is now available to train AI models and hold the promise of disrupting codes and practices. The objective of this systematic review was to examine the state of the art of machine learning or deep learning models that detect inappropriate hospital medication orders. METHODS: A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. MEDLINE and Embase databases were searched from inception to May 2023. Studies were included if they reported and described an AI model intended for use by clinical pharmacists in hospitals. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS: 13 articles were selected after review: 12 studies were judged to have high risk of bias; 11 studies were published between 2020 and 2023; 8 were conducted in North America and Asia; 6 analysed orders and detected inappropriate prescriptions according to patient profiles and medication orders; and 7 detected specific inappropriate prescriptions, such as detecting antibiotic resistance, dosage abnormality in prescriptions, high alert drugs errors from prescriptions or predicting the risk of adverse drug events. Various AI models were used, mainly supervised learning techniques. The training datasets used were very heterogeneous; the length of study varied from 2 weeks to 7 years and the number of prescription orders analysed went from 31 to 5 804 192. CONCLUSIONS: This systematic review points out that, to date, few original research studies report AI tools based on machine or deep learning in the field of hospital clinical pharmacy. However, these original articles, while preliminary, highlighted the potential value of integrating AI into clinical hospital pharmacy practice.

Do proton pump inhibitors alter the response to immune checkpoint inhibitors in cancer patients? A meta-analysis.

Introduction: Gut microbiota can significantly affect the effectiveness of immune checkpoint inhibitors (ICIs) in cancer patients. Recently, antibiotics were shown to decrease survival rate of patients treated by ICIs. Proton pump inhibitors (PPIs) can indeed modulate microbiota's diversity, therefore altering ICIs response. A meta-analysis was performed based on published data to verify this hypothesis. Methods: In this study, over 41 publications, exploring the impact of concomitant PPI treatment on outcomes of ICI-treated patients, were analyzed. Evaluated endpoints were overall survival (OS) and progression-free survival (PFS). Pooled hazard ratios (HRs) with a 95% confidence interval (CI) were reported in ICIs in PPI users versus non-PPI users. Subgroup analyses were performed to minimize the impact of study heterogeneity and to investigate the influence of PPI on the different groups of interest. There was no evidence of publication bias for OS and PFS analysis in subgroup analysis. Results: Forty-one studies were included in the meta-analysis, including a total of 20,042 patients. OS of patients receiving ICIs was negatively correlated in patients concomitantly treated with PPI (HR=1.37; 95%CI, 1.23-1.52). PFS of cancer patients receiving ICIs was also negatively correlated with PPI treatment (HR=1.28; 95%CI, 1.15-1.42). PPI and ICI use was associated with worst OS and PFS not only for non-small-cell lung cancer (NSCLC) or urothelial cancer patients but also for patients treated with anti PD-1 (OS) and anti PD-L1 (OS and PFS) immunotherapies when administered in non-first line and when PPI was received as baseline treatment or in 60 days before ICI initiation. PPI and ICI use also showed the worst OS and PFS for patients from Europe and Asia. Conclusion: This meta-analysis suggests that PPI treatment leads to significantly worse outcomes in advanced cancer patients treated by ICIs in terms of PFS and OS.

[Evaluation of prescription practices for drugs charged in addition to DRG-based fees in Alsace]. / Évaluation des pratiques de prescription relatives à 6 médicaments facturés en sus des GHS en Alsace.

Two studies led in parallel from May till June, 2009, were proposed in Alsace in order to analyze the hospital practices of prescription relative to 6 drugs charged in addition to the GHS: bevacizumab, gemcitabine, trastuzumab, etanercept, adalimumab and infliximab. The first study, led within 9 hospitals, allowed the collection of 343 situations of prescription. The second approach, based on the extraction of the PMSI data from the same hospitals, allowed the exploitation of 771 situations of prescription. The data collected on sites and from the PMSI respectively put in evidence 86.3% and 73.0% of prescriptions corresponding to guidelines. No unacceptable situation was revealed. The differences between approaches can be explained by the important proportion of unclassifiable situations extracted from the PMSI. These approaches bring complementary lightings and allow the OMEDIT of Alsace to take position in its missions of expertise and follow-up of therapeutic innovations.