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Abstract Background The prescription of antitumor drugs has often been associated with drug-related problems. Pretherapeutic multidisciplinary risk assessment programs including pharmaceutical care have been established to secure the initiation of injectable and oral antitumor therapies. This prospective cross-sectional double-center study evaluated the clinical and economic impact of the pharmacist in detecting drug-related problems in patients initiating antitumor therapies. Materials and Methods Following pharmaceutical consultations, pharmaceutical interventions were validated by a multidisciplinary team. A committee of independent clinical experts assessed the potential clinical impact of drug-drug interactions. The association of clinical variables with pharmaceutical interventions was tested using a multivariate logistic regression model. Pharmacist cost of the program was assessed by valuing pharmacists’ time at their salaries and compared with potentially avoided costs. Results Four hundred thirty-eight patients with solid tumors were included: 62% males, mean age of 65 ± 13 years, and average of 6 medications. Half of the patients required at least one pharmaceutical intervention and independent factors associated with pharmaceutical interventions were the number of medications (5-9 vs <5: OR = 2.91 [95% CI 1.82-4.65], P < .001) and the type of antitumor treatment (immunotherapy vs intravenous chemotherapy: OR = 0.35 [95% CI 0.18-0.68], P = .002). One hundred seventy-four out of 266 pharmaceutical interventions (130 patients) involved clinically significant drug-drug interactions. Pharmacist costs were estimated to range between €4899 and €6125. Average costs were estimated at €11.4-14.3 per patient. Avoided hospitalization costs were estimated to be €180 633. Conclusion Clinical pharmacists contribute to the cost-effective reduction of drug-related problems in pre-therapeutic assessment programs for patients with cancer. Pre-therapeutic multidisciplinary risk assessment programs including pharmaceutical care have been established to secure the initiation of injectable and oral antitumor therapies. This study evaluated the clinical and economic impact of the pharmacist in detecting drug-related problems in patients initiating antitumor therapies. Graphical Abstract Graphical Abstract

Medication adherence plays a critical role in controlling the evolution of chronic disease, as low medication adherence may lead to worse health outcomes, higher mortality, and morbidity. Assessment of their patients' medication adherence by clinicians is essential for avoiding inappropriate therapeutic intensification, associated health care expenditures, and the inappropriate inclusion of patients in time- and resource-consuming educational interventions. In both research and clinical practices the most extensively used measures of medication adherence are patient-reported outcome measures (PROMs), because of their ability to capture subjective dimensions of nonadherence. Machine learning (ML), a subfield of artificial intelligence, uses computer algorithms that automatically improve through experience. In this context, ML tools could efficiently model the complexity of and interactions between multiple patient behaviors that lead to medication adherence. This study aimed to create and validate a PROM on medication adherence interpreted using an ML approach. This cross-sectional, single-center, observational study was carried out a French teaching hospital between 2021 and 2022. Eligible patients must have had at least 1 long-term treatment, medication adherence evaluation other than a questionnaire, the ability to read or understand French, an age older than 18 years, and provided their nonopposition. Included adults responded to an initial version of the PROM composed of 11 items, each item being presented using a 4-point Likert scale. The initial set of items was obtained using a Delphi consensus process. Patients were classified as poorly, moderately, or highly adherent based on the results of a medication adherence assessment standard used in the daily practice of each outpatient unit. An ML-derived decision tree was built by combining the medication adherence status and PROM responses. Sensitivity, specificity, positive and negative predictive values (NPVs), and global accuracy of the final 5-item PROM were evaluated. We created an initial 11-item PROM with a 4-point Likert scale using the Delphi process. After item reduction, a decision tree derived from 218 patients including data obtained from the final 5-item PROM allowed patient classification into poorly, moderately, or highly adherent based on item responses. The psychometric properties were 78% (95% CI 40%-96%) sensitivity, 71% (95% CI 53%-85%) specificity, 41% (95% CI 19%-67%) positive predictive values, 93% (95% CI 74%-99%) NPV, and 70% (95% CI 55%-83%) accuracy. We developed a medication adherence tool based on ML with an excellent NPV. This could allow prioritization processes to avoid referring highly adherent patients to time- and resource-consuming interventions. The decision tree can be easily implemented in computerized prescriber order-entry systems and digital tools in smartphones. External validation of this tool in a study including a larger number of patients with diseases associated with low medication adherence is required to confirm its use in analyzing and assessing the complexity of medication adherence.

Excess dietary sodium is associated with increased blood pressure (BP). Some drugs are associated with high sodium intake (in particular effervescent tablets), but the cardiovascular risk associated with such high sodium-containing drugs (HSCD) is largely underevaluated. To summarize the evidence for a potential cardiovascular risk associated with exposure to HSCD, and to highlight possible risk factors associated with this iatrogenic issue; in general and/or specific populations. We conducted a systematic review, by searching electronic databases including MEDLINE, EMBASE, Web of Science, CENTRAL and grey literature between 1960 and 2015. We included studies that reported modification of cardiovascular parameters or incidence/prevalence of cardiovascular outcomes, between a group of subjects exposed to HSCD relative to a non-exposed group. The threshold used to identify HSCD was 391 mg/day. We did not consider studies evaluating exposure to sodium as an active ingredient or those focusing on dialysis solutions or enteral/parenteral nutrition. Study quality was assessed using the EPHPP tool. A total of eight studies met our inclusion criteria. Four reported results for short-term exposure to HSCD (≤ 7 days) on BP fluctuations. One study reported an elevation of BP (associated sodium intake: 1,656 mg/day). Four studies evaluated a long-term exposure (≥ 2 years or discontinuation of a chronic treatment). Two studies reported iatrogenic risk. For these studies, drug associated sodium intake was high (> 1,500 mg/day) in patients with comorbidities (in particular, diabetes mellitus and hypertension). Despite numerous study limitations, this systematic review suggests three potential synergistic risk factors for cardiovascular complications after exposure to HSCD: a high sodium intake (≥ 1,500 mg/day), a long duration of exposure, and the presence of comorbidities. Further studies are required to characterize this iatrogenic risk. PROSPERO CRD42016047086.

The burden of Sedative-Hypnotics (SHs) has been known since the 1980s. Yet, their consumption remains high. A systematic review of the literature should help to assess efficient interventions to improve the appropriate use of SHs in sleep disorders. To identify and assess regulatory and educational interventions designed to improve the appropriate use of SHs for insomnia treatment. We conducted a systematic review of the literature according to PRISMA guidelines. A systematic search covering the period 1980-2015 was carried out in Medline, Web of Science, Embase and PsycInfo. We included studies reporting the implementation of regulatory or educational strategies directed towards patients and/or healthcare professionals to improve the appropriate use of SHs to treat insomnia in the community, hospitals and nursing homes. Thirty-one studies were included: 23 assessed educational interventions (recommendations by mail/email, computer alerts, meetings, mass media campaigns, prescription profile), 8 assessed regulatory interventions (prescription rule restriction, end of reimbursement). The most recent was implemented in 2009. Restrictive prescription rules were effective to reduce the consumption of targeted SHs but led to a switch to other non-recommended SHs. Among educational interventions, only 3 studies out of 7 reported positive results of mono-faceted interventions; whereas, 13 out of the 16 multi-faceted interventions were reported as efficient: particularly, the active involvement of healthcare professionals and patients and the spread of information through mass media were successful. The risk of bias was high for 24 studies (mainly due to the design), moderate for 3 studies and weak for 4 studies. Educational multifaceted studies are presented as the most efficient. But further better designed studies are needed to make evidence-based results more generalizable.

ObjectivesThe aim of this systematic review was to describe and analyse the performance statistics of validated risk scores identifying elderly inpatients at risk of early unplanned readmission.Data sourcesWe identified potentially eligible studies by searching MEDLINE, EMBASE, COCHRANE and Web of Science. Our search was restricted to original studies, between 1966 and 2018.Eligibility criteriaOriginal studies, which internally or externally validated the clinical scores of hospital readmissions in elderly inpatients.Data extraction and synthesisA data extraction grid based on Strengthening the Reporting of Observational Studies in Epidemiology and Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis statements was developed and completed by two reviewers to collect general data. The same process was used to extract metrological data of the selected scores.Quality assessment of the included studiesAssessment of the quality and risk of bias in individual studies was performed by two reviewers, using the validated Effective Public Health Practice Project quality assessment tool.ParticipantsElderly inpatients discharged to home from hospital or returning home after an accident and emergency department visit.ResultsA total of 12 studies and five different scores were included in the review. The five scores present area under the receiving operating characteristic curve between 0.445 and 0.69. Identification of Senior At Risk (ISAR) and Triage  Risk Screening Tool (TRST) scores were the more frequently validated scores with ISAR being more sensitive and TRST more specific.ConclusionsThe TRST and ISAR scores have been extensively studied and validated. The choice of the most suitable score relies on available patient data, patient characteristics and the foreseen clinical care intervention. In order to pair the intervention with the appropriate clinical score, further studies of external validation of clinical scores, identifying elderly patients at risk of early unplanned readmission, are needed.PROSPERO registration numberCRD42017054516.

Abstract Objectives To evaluate the return on investment (ROI) and quality improvement after implementation of a centralized automated-dispensing system after 8 years of use. Design Prospective evaluation of ROI; before and after study to evaluate dispensing errors; user satisfaction questionnaire after 8 years of use. Setting The study was conducted at a French teaching hospital in the pharmacy department, which is equipped with decentralized automated medication cabinets in the wards. Participants Pharmacy staff (technicians and residents). Intervention(s) Implementation of a centralized automated-dispensing robot. Main Outcome Measure(s) The true ROI was prospectively and annually compared to estimated returns calculated after implementation and upgrade of the robot; dispensing errors determined by observation of global deliveries and the satisfaction of users based on a validated questionnaire were evaluated. Results Following the upgrade, we found little difference for the ROI (+1.86%). The payback period increased by almost 3 years. There was a significant reduction of dispensing errors, from 2.9% to 1.7% (P < 0.001). User satisfaction of the robot by the pharmacy staff was reported (score of 5.52 ± 1.20 out of 7). Conclusions These systems are worthwhile investments and largely contribute to improving the quality and safety of the medication process.