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Multiparameter volcanic unrest has been recorded since April 2019 on the Montagne Pelée volcano located on Martinique. There have been only very few periods of seismic unrest since the last magmatic eruption of 1929−1932. This is therefore a rare opportunity to examine its origin. In April 2019 the number of shallow volcano-tectonic (VT) earthquakes increased drastically above the reference monthly rate of 19 VT/month and then exceeded it consistently for several months. Deep (> 10 km) VT events occurred at the onset of the unrest and harmonic tremor was first recorded in November 2020. Continuous Global Navigation Satellite System data reveal that a minor horizontal deformation began around mid-2021. The modeling of these data favors an inflation source located at about 1 km below and slightly SW of the summit, in the area of the hydrothermal system and where most of the shallow VT events are located. Zones of degraded and dead vegetation on the upper flanks of Montagne Pelée were detected with satellite imagery starting in November 2019 and shown to be associated with elevated passive CO 2 soil degassing. This protracted unrest most likely reflects the ascent of a limited volume of deep magmatic fluids that reinvigorated the shallow hydrothermal circulation.

Background and ImportanceDrug iatrogenia is responsible for 10,000 deaths per year in France. The reporting of Serious adverse drug events (SADE) lead to retrospective implementation of barrier measures aimed at reducing their occurrence. The analysis of SADEs and the transcription of risk situations into a pharmaceutical decision support system (PDSS) would contribute to securing patient medication care in an innovative way.Aim and ObjectivesTo present the importance of using databases of SADE as a source of pharmaceutical algorithms (PAs) utilised by PDSS.Material and MethodsTwo databases of SADEs were analysed by three clinical pharmacists: a key elements synthesis relating to prescription errors identified in the National Database of reporting serious adverse events related to care provided by the French National Authority for Health and a sample of SADEs notified to the Mutual insurance of the French health corps. The potential for formalising each SADE into PAs was evaluated based on several aspects: the existence of a drug related problem (DRP) with possible solutions, the explicit, encodable, and queryable nature of the assessment elements to represent the SADE in a PA and the availability of related health data in the PDSS.ResultsWith a sample of 125 SADEs, 57 events (45.6%) were identified as potentially detectable and preventable (53) or mitigable through the creation of 34 PAs. Most of them involved cardiology drugs (8 PAs, 23.5%), followed by endocrinology (5) and analgesics (6). The primary DRPs identified were contraindications based on patient history (9 PAs, 26.5%), such as allergies and organ deficiencies; untreated indications (5) (e.g., absence of anticoagulants, antiplatelet agents); overdoses (5) (including anticoagulants and acetaminophen); and inadequate monitoring (liver enzymes, electrolyte panels).Conclusion and RelevanceSADE reporting system seems a valuable source of PAs for spreading detection of these situations causing patient harm. The underreporting of these events related to prescription, monitoring, or side effects remains a significant issue that could be addressed through the educational and innovative applications of artificial intelligence. Nevertheless, additional solutions are still necessary to reduce SADEs resulting from drug administration processes.References and/or AcknowledgementsAuger C, et al. Serious adverse events in France: a reporting and learning system. International Forum on Quality & Safety in Healthcare. Copenhagen 2020Conflict of InterestNo conflict of interest

Background and ImportanceFor inpatients, the risk of thromboembolic disease is significantly reduced through the administration of pharmacological thromboprophylaxis using either unfractionated heparin or low molecular weight heparin. The Padua score is validated in the literature for determining the indication for thromboprophylaxis. In our 450-bed healthcare facility, the therapeutic strategy for thromboprophylaxis is well-established and validated by the medical committee.Aim and ObjectivesThe objective of the study is to determine the proportion of thromboprophylaxis misuse among patients who should receive this medication and to identify actions to address this issue.Material and MethodsA retrospective analysis was carried out on the records of patients hospitalised for more than 3 days within our healthcare facility between January and June 2024. Patients with thrombocytopenia <20G/L, active severe haemorrhage, or those already receiving therapeutic anticoagulation were excluded. All prescriptions, medical and surgical history, morphological data, medical and paramedical observations, and biological results were reviewed.ResultsAmong 1,684 eligible patients, 204 were randomly selected. After analysis, 72 patients were excluded: 52 were receiving therapeutic-dose anticoagulants, and 20 had insufficiently documented records. Among the 132 included patients, 17 (12.9%) were not treated despite having an indication according to the Padua score. Six patients (4.5%) were treated with an insufficient dose – enoxaparin 4,000 IU/day in morbidly obese patients – resulting in a total underuse rate of 17.4%. Two cases of overdosing were identified: enoxaparin 4,000 IU/day instead of 2,000 IU/day in patients with severe renal impairment or low body weight. Thromboprophylaxis misuse affected 18.9% of patients.Conclusion and RelevanceA limitation of this study is its retrospective nature, which includes challenges in determining whether a patient is bedridden for the calculation of the Padua score. Despite routine pharmaceutical analysis, misuse was identified in nearly one out of five patients. The pharmacy team was informed, and a rapid training session on the systematic use of the Padua score was organised. A systematic approach to detect thromboprophylaxis misuse through the modelling of clinical situations within a pharmaceutical decision support system is currently being evaluated.References and/or AcknowledgementsBarbar. A risk assessment model for the identification of hospitalised medical patients at risk for venous thromboembolism: the Padua Prediction Score. J Thromb Haemost. 2010;8(11):2450–7Conflict of InterestNo conflict of interest

Background and ImportanceEach Effervescent Acetaminophen Formulation (EAF) (500 mg or 1,000 mg) contains high quantity of salt: 1 g.An EAF induced salt-regimen could reach 8 g of salt per day.But adults should consume less than 5 g of salt per dayRecent studies suggest that EAFs lead to cardiovascular events.Aim and ObjectivesThis study highlights the ability of a pharmaceutical decision support system (PDSS) to detect the prescription of EAF in cardiovascular patients.Material and MethodsA prospective study was implemented from April 2020 to August 2022 at two facilities – 1,600 beds.Our PDSS operates on Pharmaclass (Keenturtle) using real-time patient data and modelled situations to generate alerts.The first version of the situation for identifying EAF prescriptions evolved into a second (May 2022), more sensitive in detecting various cardiovascular comorbidities through biological markers, patient‘s cardiac history and incorporating all EAF specialties.Data collected include analysed alerts, EAF consumption and patient‘s cardiovascular diseases.Data analysis is performed in Excel.ResultsWith the first version, on 159 alerts, 101 Drug related problems (DRP) required a pharmacist’s intervention in 28 months of whom 49.5% were accepted by prescribers. Technical false positives were 13 (8.2%) and 39 situations do not correspond to a DRP (24.5%). EAFs on demand concerned 120 patients. With the second version, on 124 alerts, 35 DRP required a pharmacist’s intervention in 4 months of whom 57.1% were accepted. Technical false positives were 29 (23.4%) and 33 situations do not correspond to a DRP (26.6%). EAFs on demand concerned 46 patients.Only 10 alerts (8.1%) on 124 were detected by both of the two versions.At least one cardiovascular disease is present in 99 on 102 patients (general hospital) versus 146 on 156 (university hospital): 83 high blood pressure, 11 cardiac insufficiency and 61 ischaemic pathologies) versus respectively 142, 23 and 106.Before EAF interruption, the overall salt intake was 2,910 grams over 987 days of treatment at the general hospital and 624 grams over 361 days of treatment at the university hospital.Conclusion and RelevanceUsing a PDSS reveals the salt intake through EAF among cardiovascular patients.However, the increased sensitivity is accompanied by a rise in false positives, highlighting the importance of the modelling process.References and/or AcknowledgementsConflict of InterestNo conflict of interest

Background Clinical decision support systems (CDSSs) enable the automated, real-time detection of situations associated with a risk of adverse drug events (ADEs). However, the effectiveness of CDSS in reducing ADEs has yet to be demonstrated. We have chosen to focus on the detection of ADE such as hyperkalemia and/or acute kidney injury (AKI), which are common among hospitalized older adults. The present study’s primary objective is to use a CDSS to reduce the number of ADEs (such as AKI and/or hyperkalemia) that occur in hospitalized older adults. Methods This is a multicenter, stepped-wedge, cluster-randomized study involving five hospitals. Each hospital will start with a control period (i.e., routine care, during which each center’s CDSS is deactivated) and then switch to an intervention period (during which the CDSS is activated). The intervention will be the use of a CDSS and a strategy for managing and transmitting alerts to clinical pharmacists. The rules concerning AKI and hyperkalemia have been drafted and reviewed by a multidisciplinary group. Each rule created in the CDSS is associated with a standardized procedure, based on a review of the literature. Older patients (aged 65 or over) admitted to a participating general medicine ward, a surgical ward, or obstetrics ward will be eligible for inclusion after the provision of verbal informed consent. Discussion This study will assess the effectiveness of the CDSS in reducing the incidence of AKI and hyperkalemia. The implementation of the CDSS can assist clinical pharmacists in their daily work and is expected to prevent ADEs. Trial registration ClinicalTrials.gov Identifier: NCT05923983. Registered February 02, 2023.

Background and ImportanceThe EAHP statement integrates pharmaceutical analysis into our practices mentioning that all prescriptions should be reviewed and validated as soon as possible by a pharmacist.However this practice is highly variable. Reviewing all prescriptions as soon as possible by a pharmacist and detecting drug-related problems remains a challenge.Pharmaceutical decision support systems (PDSS) are associated with the decrease of adverse drug events and the improvement of prescribing practices.Our PDSS works on the patient’s data, modelled situations and Pharmaclass® (Keenturtle – F) in real time.Aim and objectivesThis study aims to present pharmacists’ ability to detect drug-related problems (DRP) in usual care by using a PDSS.Material and MethodsAn observational prospective study has been ongoing from November 2019 to June 2023 in two facilities (1600 beds). PDSS is applied in addition to standard care.Up to a maximum of 201 modelled situations were integrated in the PDSS.A DRP resolution strategy structure the pharmaceutical analysis of DRPs. It is the support of the human supervision of the PDSS.Data collected are the number alerts analysed, DRPs, PIs and accepted PIs.Data analysis is performed by using Pandas library in Python.ResultsThe data are collected during 663 non-consecutive days.On 14331 alerts 3157 were technical false positives (22.0%) and 3821 situations do not correspond to a DRP (26.7%).DRP detection is performed for 7,353 situations by the pharmacists using the PDSS (51.3% of analysed alerts).5,062 DRP (68.9% of all DRP detected) required a pharmacist’s intervention that analyses the alert.For 2648 of them a pharmacist had missed the identification of the DRP during his analysis.In addition, 838 PIs were transmitted for DRPs identified following the overall analysis of the situation. These last two comments constitute the specific added value of using a PDSS.Another 927 DRPs (12.6% of all DRP detected) had already benefited from a PI by another pharmacist.For 1364 DRPs (18.5% of all DRP detected) the physician changed the drug management just before analysis of the alert.Conclusion and RelevanceA PDSS is both efficient and offers added value in routine care to secure the patient‘s medication management.References and/or AcknowledgementsConflict of InterestNo conflict of interest.

Background and ImportancePharmaceutical decision support system (PDSS) is a positive triangulation between patients’ data, modelled situations standing for drug-related problems and a reasoning software sending alerts. So the pharmaceutical interventions better prevent adverse drug events and better reduce healthcare costs. But to be optimal the PDSS has also to link the modelled situations to a clinical well-defined risk. As consequences each pharmaceutical intervention’s impact will be documented and the PDSS’s interest in patients’ safety sustained.Aim and ObjectivesTo present the results of an e-Delphi study during which health professional experts evaluate the clinical risk’s level of 52 modelled situations standing for drug-related problems or adverse drug events.Material and MethodsTwenty experts across 4 francophone countries were involved because of their clinical skills. Based on their experience, physicians (5) or pharmacists (15) scored the likelihood of occurrence of clinical consequences and its severity for each of the 52 modelled patients’ situations using a five-point Likert scale. These situations were chosen among a panel of 199 one, according to their high frequency in the health facilities. The degree of consensus between participants was defined as the proportion that gave a risk score in the same category as the median. Consensus was obtained if the score was 75% or more. Then the 2 median scores -occurrence and severity- were combined to produce the risk level for each situation. Only 2 Delphi rounds were necessary.ResultsAfter the first round a consensus was reached for 8 situations. Experts agreed on the level of risk associated with 48 out of 52 modelled situations. A high or extreme consensus risk level is determined for 45 modelled situations. These situations represent a variety of drug-related problems. Overdosing was the most frequent situation [12 (22%)]. Cardiovascular, Psychiatric and Endocrinological drug classes are the most common involved in respectively [25 (45%)], [7 (13%)] and [5 (9%)] situations.Conclusion and RelevanceThe symbolic artificial intelligence to detect drug-related problems in patients’ medications will be much more shared if pharmaceutical algorithms including the clinical risk are defined through consensus.References and/or AcknowledgementsHealth Regional Agency, Innovation Department, Région Grand Est, FranceConflict of InterestNo conflict of interest

Background and importanceAnticoagulants are sources of iatrogenia when they are used, misused or not used, especially when medication errors are involved. The EAHP statement integrates pharmaceutical analysis into our practices mentioning that all prescriptions should be reviewed and validated as soon as possible by a pharmacist. Pharmaceutical analysis practice is highly variable. Clinical decision support systems have proven to be effective globally in reducing morbidity, improving the detection of drug related problems (DRP) and reducing adverse drug events and costs. The threefold alliance, AVICENNE, as a real time clinical decision support system, works on the patient’s data, pharmaceutical algorithms and Pharmaclass (Keenturtle-F).Aim and objectivesThe aim of the study was present the ability of AVICENNE to detect DRP when working on anticoagulation therapy compared with other drugs.Material and methodsAn observational prospective study has been ongoing from January 2019 to September 2020 in two facilities (1600 beds). 20 to about 135 pharmaceutical algorithms encoded in Pharmaclass detected patients with an anticoagulant related problem. Guidelines structured the pharmaceutical analysis of selected DRP analysed from anamnesis to transmission of the pharmaceutical interventions (PI). In the two algorithms, the number of accepted PIs were collected via computerised patient order entries.ResultsThe data were collected over 260 non-consecutive days. Of 4121 alerts 1301 were about anticoagulant medications (31%) and 2820 about other medications (69%). DRP detection was better with the algorithm on anticoagulants than with the other algorithm (1029 (79%) vs 1271 (45%)) because of fewer technical false positives. Pharmacist issued 437 PI targeting anticoagulant medicines, of which 266 PI (61%) were accepted by physicians. On the other hand, 1075 transmitted PI resulted in 505 accepted PI (47%). The difference was statistically significant (χ2=23.99; p<10–6). For both of the algorithms’ sets, transmission had the same importance: for the oral route, 29% vs 27%, respectively (NS). The acceptance rate was similar (81% and 75%, respectively (NS)).Conclusion and relevanceAlgorithms about anticoagulant therapy medications were more efficient in the detection of DRP because of explicit clinical practice guidelines. The acceptance rate of PI by physicians was better. AVICENNE improved patient safety.References and/or acknowledgementsConflict of interestNo conflict of interest

Background and importancePharmacological thromboprophylaxis reduces the risk of pulmonary embolism and deep vein thrombosis. Enoxaparin once a day is more relevant than unfractionated heparin (UFH) twice a day when glomerular filtration rate is >30 mL/min. The threefold alliance AVICENNE, as a real time clinical decision support system, works on the patient’s data, pharmaceutical algorithms (PA) and Pharmaclass (Keenturtle-F).Aim and objectivesTo show the value of one AVICENNE algorithm in detecting UFH which was not indicated, and the acceptance by the physician of the switch to enoxaparin proposed by the pharmacist.Material and methodsA prospective study was carried out from March 2019 to September 2020 in two health facilities (1600 beds). One algorithm was encoded in Pharmaclass to detect patients with a UFH prescription and two glomerular filtration rate measurements >30 mL/min, the second higher than the first. A guideline detailed the pharmaceutical analysis, from history taking of detected DRPs to reporting of pharmaceutical interventions (PI). The first outcome was the number of detected DRPs and accepted PIs. The second outcome was the number of injections and hospital cost avoided.ResultsThe data were collected over 250 non-consecutive days. First, the pharmacist confirmed 98 DRPs after anamnesis and 96 PIs proposing the switch from UFH and enoxaparin. A total of 41 PIs (43%) were accepted by physicians. The secondary outcome included savings of 353 injections, providing a minimal cost saving of 1700€.Conclusion and relevanceAVICENNE optimises patients’ thromboprophylaxis management by triggering a pharmaceutical analysis on DRPs which are complex to detect. What is original is that this study showed that pharmaceutical analysis stayed relevant although the clinical and biological situation of the patient was improving.References and/or acknowledgementsConflict of interestNo conflict of interest

Background and importanceThe CLEO Scale is a three-dimensional tool to assess the clinical, economic and organisational impact of pharmacists’ interventions (PI) which would resolve drug-related problems in prescriptions.AVICENNE is an advanced real-time pharmaceutical decision support system based on the patient’s data, pharmaceutical algorithms and Pharmaclass (Keenturtle) which enhances the PI relevance.Aim and objectivesThis study aimed to analyse the reliability of the CLEO scale.Material and methodsFrom the 171 modelled clinical situations in Pharmaclass, the 50 most frequent were chosen. For each situation a PI was retrospectively and randomly selected between November 2019 and November 2020 in the AVICENNE database. It contained 1263 PI transmitted after of Pharmaclass alerts’ analysis in two 1700 beds health facilities.A multiprofessional panel of 11 clinicians have rated independently the PIs using the CLEO scale. CLEO evaluates the clinical, economical and organisational impact of PI. The panel re-rated the PIs after a 1-month washout period.Intra-class correlation coefficients in absolute agreement on single unit (ICCA,1) are calculated using the ‘Psych’ package on Rstudio to measure inter- and intra-rater reliabilities of the panel.ResultsThe PIs were rated as having a minor, medium, major or vital clinical impact in, respectively, 10%, 70%, 16% and 4% of situations.Direct drug management costs were reduced by the PI in 24%, unchanged in 62% and increased in 14% of the situations. The care process did not change in 78% of the situations, 20% of PIs improved it and 2% of PIs altered it. On average less than 3 min are needed per evaluation.Inter-rater reliability (ICCA,1) was poor for clinical (ICCA,1 = 0.297) and organiaational (ICCA,1 = 0.338) dimensions and moderate for economic dimensions (ICCA,1 = 0.665). Intra-rater reliability was moderate for clinical (ICCA,1 = 0.611) and organisational (ICCA,1 = 0.726) dimensions and excellent for economic dimensions (ICCA,1 = 0.914).Conclusion and relevanceAlmost all of AVICENNE PIs prevent a temporary or permanent damage or the need of care to reduce their gravity. The CLEO tool offers a limited validity when used by untrained clinicians. Symbolic artificial intelligence reinforces the therapeutic safety of patients and the relevance of care.References and/or acknowledgementsConflict of interestNo conflict of interest