Artificial intelligence-based prescription of personalized scalp cosmetics improved the scalp condition: efficacy results from 100 participants.

Background: Scalp-related symptoms such as dandruff and itching are common with diverse underlying etiologies. We previously proposed a novel classification and scoring system for scalp conditions, called the scalp photographic index (SPI); it grades five scalp features using trichoscopic images with good reliability. However, it requires trained evaluators.Aim: To develop artificial intelligence (AI) algorithms for assessment of scalp conditions and to assess the feasibility of AI-based recommendations on personalized scalp cosmetics.Methods: Using EfficientNet, convolutional neural network (CNN) models (SPI-AI) ofeach scalp feature were established. 101,027 magnified scalp images graded according to the SPI scoring were used for training, validation, and testing the model Adults with scalp discomfort were prescribed shampoos and scalp serums personalized according to their SPI-AI-defined scalp types. Using the SPI, the scalp conditions were evaluated at baseline and at weeks 4, 8, and 12 of treatment.Results: The accuracies of the SPI-AI for dryness, oiliness, erythema, folliculitis, and dandruff were 91.3%, 90.5%, 89.6%, 87.3%, and 95.2%, respectively. Overall, 100 individuals completed the 4-week study; 43 of these participated in an extension study until week 12. The total SPI score decreased from 32.70 ± 7.40 at baseline to 15.97 ± 4.68 at week 4 (p < 0.001). The efficacy was maintained throughout 12 weeks.Conclusions: SPI-AI accurately assessed the scalp condition. AI-based prescription of tailored scalp cosmetics could significantly improve scalp health.

[Combating antimicrobial resistance: audits of practices to target actions]. / Lutte contre l'antibiorésistance : audits des pratiques pour cibler les actions.

Antibiotic resistance is a public health emergency requiring a concerted approach and motivating the implementation of antibiotic stewardship programmes. As part of an institutional project at the Centre hospitalier universitaire vaudois, we collected data on the appropriateness of antimicrobial prescriptions in various medical and surgical departments in order to identify areas for improvement. The results show that there is room for improvement and that there are differences between departments, particularly regarding surgical prophylaxis administered beyond the operating time, adaptation of the spectrum and duration. Prescribing appropriateness data is an essential complement to consumption data for adapting effective intervention strategies.

L'antibiorésistance constitue une urgence de santé publique justifiant une approche concertée et motivant la mise en place de programmes de gestion des antibiotiques (Antibiotic Stewardship). Dans le cadre d'un projet institutionnel conduit au Centre hospitalier universitaire vaudois, nous avons collecté dans différents services médico-chirurgicaux des données d'adéquation des prescriptions afin d'identifier les domaines d'amélioration. Les résultats obtenus montrent qu'il existe une marge d'amélioration et des différences interservices, en particulier concernant les prophylaxies chirurgicales administrées au-delà du temps opératoire, l'adaptation du spectre et la durée. Les données d'adéquation des prescriptions sont un complément essentiel aux données de consommation pour adapter des stratégies d'intervention efficaces.

Decision support systems for antibiotic prescription in hospitals: a survey with hospital managers on factors for implementation.

BACKGROUND: Inappropriate antimicrobial use, such as antibiotic intake in viral infections, incorrect dosing and incorrect dosing cycles, has been shown to be an important determinant of the emergence of antimicrobial resistance. Artificial intelligence-based decision support systems represent a potential solution for improving antimicrobial prescribing and containing antimicrobial resistance by supporting clinical decision-making thus optimizing antibiotic use and improving patient outcomes. OBJECTIVE: The aim of this research was to examine implementation factors of artificial intelligence-based decision support systems for antibiotic prescription in hospitals from the perspective of the hospital managers, who have decision-making authority for the organization. METHODS: An online survey was conducted between December 2022 and May 2023 with managers of German hospitals on factors for decision support system implementation. Survey responses were analyzed from 118 respondents through descriptive statistics. RESULTS: Survey participants reported openness towards the use of artificial intelligence-based decision support systems for antibiotic prescription in hospitals but little self-perceived knowledge in this field. Artificial intelligence-based decision support systems appear to be a promising opportunity to improve quality of care and increase treatment safety. Along with the Human-Organization-Technology-fit model attitudes were presented. In particular, user-friendliness of the system and compatibility with existing technical structures are considered to be important for implementation. The uptake of decision support systems also depends on the ability of an organization to create a facilitating environment that helps to address the lack of user knowledge as well as trust in and skepticism towards these systems. This includes the training of user groups and support of the management level. Besides, it has been assessed to be important that potential users are open towards change and perceive an added value of the use of artificial intelligence-based decision support systems. CONCLUSION: The survey has revealed the perspective of hospital managers on different factors that may help to address implementation challenges for artificial intelligence-based decision support systems in antibiotic prescribing. By combining factors of user perceptions about the systems´ perceived benefits with external factors of system design requirements and contextual conditions, the findings highlight the need for a holistic implementation framework of artificial intelligence-based decision support systems.

Adverse Event Signal Detection Using Patients' Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models.

BACKGROUND: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients' subjective opinions (patients' voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients' narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients' daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients. OBJECTIVE: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients' concerns at pharmacies was also assessed. METHODS: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients' concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs. RESULTS: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients' daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. "Pain or numbness" (n=57, 36.3%), "fever" (n=46, 29.3%), and "nausea" (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients' daily lives. CONCLUSIONS: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients' subjective information recorded in pharmaceutical care records accumulated during pharmacists' daily work.

Prescription opioid dispensing patterns among patients with schizophrenia or bipolar disorder.

BACKGROUND: Patients with schizophrenia (SZ) or bipolar disorder (BD) may have increased risk of complications from prescribed opioids, including opioid-induced respiratory depression. We compared prescription opioid pain medication dispensing for patients with SZ or BD versus controls over 5 years to assess dispensing trends. METHODS: This retrospective, observational study analysed US claims data from the IBM® MarketScan® Commercial and Multi-State Medicaid databases for individuals aged 18-64 years with prevalent SZ or BD for years 2015-2019 compared with age- and sex-matched controls. Baseline characteristics, comorbidities, and medication use were assessed. Proportions of individuals dispensed prescription opioids chronically (ie, ≥70 days over a 90-day period or ≥ 6 prescriptions annually) or nonchronically (≥1 prescription, chronic definition not met) were assessed. RESULTS: In 2019, the Commercial and Medicaid databases contained records for 4773 and 30,179 patients with SZ and 52,780 and 63,455 patients with BD, respectively. Patients with SZ or BD had a higher prevalence of comorbidities, including pain, versus controls in each analysis year. From 2015 to 2019, among commercially insured patients with SZ, chronic opioid-dispensing proportions decreased from 6.1% (controls: 2.7%) to 2.3% (controls: 1.2%) and, for patients with BD, from 11.4% (controls: 2.7%) to 6.4% (controls: 1.6%). Chronic opioid dispensing declined in Medicaid-covered patients with SZ from 15.0% (controls: 14.7%) to 6.7% (controls: 6.0%) and, for patients with BD, from 27.4% (controls: 12.0%) to 12.4% (controls: 4.7%). Among commercially insured patients with SZ, nonchronic opioid dispensing decreased from 15.5% (controls: 16.4%) to 10.7% (controls: 11.0%) and, for patients with BD, from 26.1% (controls: 17.5%) to 20.0% (controls: 12.2%). In Medicaid-covered patients with SZ, nonchronic opioid dispensing declined from 22.5% (controls: 24.4%) to 15.1% (controls: 12.7%) and, for patients with BD, from 32.3% (controls: 25.9%) to 24.6% (controls: 13.6%). CONCLUSIONS: The proportions of individuals dispensed chronic or nonchronic opioid medications each year were similar between commercially and Medicaid-insured patients with SZ versus controls and were higher for patients with BD versus controls. From 2015 to 2019, the proportions of individuals who were dispensed prescription opioids chronically or nonchronically decreased for patients with SZ or BD and controls.

[Optimization of prediction model for personalized water pills based on semantic analysis of traditional Chinese medicine materials].

This study aims to optimize the prediction model of personalized water pills that has been established by our research group. Dioscoreae Rhizoma, Leonuri Herba, Codonopsis Radix, Armeniacae Semen Amarum, and calcined Oyster were selected as model medicines of powdery, fibrous, sugary, oily, and brittle materials, respectively. The model prescriptions were obtained by uniform mixing design. With hydroxypropyl methylcellulose E5(HPMC-E5) aqueous solution as the adhesive, personalized water pills were prepared by extrusion and spheronizaition. The evaluation indexes in the pill preparation process and the multi-model statistical analysis were employed to optimize and evaluate the prediction model of personalized water pills. The prediction equation of the adhesive concentration was obtained as follows: Y_1=-4.172+3.63X_A+15.057X_B+1.838X_C-0.997X_D(adhesive concentration of 10% when Y_1<0, and 20% when Y_1>0). The overall accuracy of the prediction model for adhesive concentration was 96.0%. The prediction equation of adhesive dosage was Y_2=6.051+94.944X_A~(1.5)+161.977X_B+70.078X_C~2+12.016X_D~(0.3)+27.493X_E~(0.3)-2.168X_F~(-1)(R~2=0.954, P<0.001). Furthermore, the semantic prediction model for material classification of traditional Chinese medicines was used to classify the materials contained in the prescription, and thus the prediction model of personalized water pills was evaluated. The results showed that the prescriptions for model evaluation can be prepared with one-time molding, and the forming quality was better than that established by the research group earlier. This study has achieved the optimization of the prediction model of personalized water pills.

[Molding law of traditional Chinese medicine gel plaster based on physicochemical properties of raw materials and intermediates].

This paper aims to study the correlation between the physicochemical properties of raw materials and intermediates and the molding quality and law of traditional Chinese medicine(TCM) gel plaster by using TCM slices and powder as raw materials. 48 TCM compounds are selected as model prescriptions to prepare gel plasters. The rotational rheometer is used to determine the rheological parameters of the plaster, including storage modulus(G'), loss modulus(G″), yield stress(τ), and creep compliance [J(t)]. The molding quality of the prepared TCM gel plaster is evaluated by subjective and objective measures. Clustering and principal component analysis are conducted to evaluate the physical properties of the plaster. By measuring the rheological properties of the plaster, the molding quality of the TCM gel plaster can be predicted, with an accuracy of 83.72% after seven days of modeling and 88.37% after 30 days of modeling. When the parameters such as G' and G″ of the plaster are large, and the [J(t)] is small, the molding quality of the plaster is better. When the plaster coating point is no less than 3, it is difficult to be coated. In addition, when the proportion of metal ions in the prescription is higher, the 30-day forming quality of the plaster is mainly affected, and the viscosity of the plaster is poor. If the prescription contains many acidic chemical components, the 7-day forming quality of the plaster is mainly affected, with many residuals. The results suggest that the rheological properties of the plaster can be used to predict the molding quality of TCM slice and powder gel plaster. It can provide a reference for the development of TCM gel plaster prescriptions.

[Preparation process selection of presonalized traditional Chinese medicine concentrated watered pills containing heat-unstable components].

In the process of preparing presonalized concentrated watered pills, the decoction needs to be concentrated by heat and mixed with medicinal slices or powder to prepare a wet mass. However, some of the traditional Chinese medicine(TCM) components are easily decomposed or transformed by heat. In order to optimize the preparation process of presonalized TCM concentrated watered pills and reduce the loss of heat-unstable components in prescriptions, this study uses five compound TCM prescriptions containing heat-unstable components as model prescriptions, namely the Linggui Zhugan Formula, Xiaochengqi Formula, Sanpian Formula, Xiaoer Qixing Formula, and Xiaoyao Formula. Based on the two kinds of preparation process of presonalized concentrated watered pills previously established by our research group, whole extract concentrated watered pills and concentrated watered pills without excipients are prepared, respectively. Characteristic maps are measured and compared with those of the corresponding decoction. The results show that the characteristic maps of the concentrated watered pills without excipients of the five model prescriptions are very close to those of the decoction, and the number of characteristic peaks and peak areas are higher than those of whole extract concentrated watered pills. In addition, the peak area of some peaks is higher than that of the corresponding decoction. Thus, it is recommended to select the preparation process of prescription-based concentrated watered pills without excipients based on the "unification of medicines and excipients" to preserve those heat-unstable components more effectively when the prescription contains a heat-unstable component of TCM. This study provides a basis for the subsequent reasonable development and application of presonalized TCM pills.

The use of prescription medications and non-prescription medications during lactation in a prospective Canadian cohort study.

BACKGROUND: A lack of safety data on postpartum medication use presents a potential barrier to breastfeeding and may result in infant exposure to medications in breastmilk. The type and extent of medication use by lactating women requires investigation. METHODS: Data were collected from the CHILD Cohort Study which enrolled pregnant women across Canada between 2008 and 2012. Participants completed questionnaires regarding medications and non-prescription medications used and breastfeeding status at 3, 6 and 12 months postpartum. Medications, along with self-reported reasons for medication use, were categorized by ontologies [hierarchical controlled vocabulary] as part of a large-scale curation effort to enable more robust investigations of reasons for medication use. RESULTS: A total of 3542 mother-infant dyads were recruited to the CHILD study. Breastfeeding rates were 87.4%, 75.3%, 45.5% at 3, 6 and 12 months respectively. About 40% of women who were breastfeeding at 3 months used at least one prescription medication during the first three months postpartum; this proportion decreased over time to 29.5% % at 6 months and 32.8% at 12 months. The most commonly used prescription medication by breastfeeding women was domperidone at 3 months (9.0%, n = 229/2540) and 6 months (5.6%, n = 109/1948), and norethisterone at 12 months (4.1%, n = 48/1180). The vast majority of domperidone use by breastfeeding women (97.3%) was for lactation purposes which is off-label (signifying unapproved use of an approved medication). Non-prescription medications were more often used among breastfeeding than non-breastfeeding women (67.6% versus 48.9% at 3 months, p < 0.0001), The most commonly used non-prescription medications were multivitamins and Vitamin D at 3, 6 and 12 months postpartum. CONCLUSIONS: In Canada, medication use is common postpartum; 40% of breastfeeding women use prescription medications in the first 3 months postpartum. A diverse range of medications were used, with many women taking more than one prescription and non-prescription medicines. The most commonly used prescription medication by breastfeeding women were domperidone for off-label lactation support, signalling a need for more data on the efficacy of domperidone for this indication. This data should inform research priorities and communication strategies developed to optimize care during lactation.

Influence of sex on the timing of coronary angiography and the prescription of antiplatelet therapy in patients with nonST-segment elevation myocardial infarction. / Impacto del sexo en los tiempos de cateterismo y en el uso de pretratamiento antiagregante plaquetario en pacientes con síndrome coronario agudo sin elevación del segmento ST (SCASEST).

OBJECTIVES: To assess differences in the clinical management of nonST-segment elevation myocardial infarction (NSTEMI), including in-hospital events, according to biological sex. MATERIAL AND METHODS: Prospective observational multicenter study of patients diagnosed with NSTEMI and atherosclerosis who underwent coronary angiography. RESULTS: We enrolled 1020 patients in April and May 2022; 240 (23.5%) were women. Women were older than men on average (72.6 vs 66.5 years, P .001), and more women were frail (17.1% vs 5.6%, P .001). No difference was observed in pretreatment with any P2Y12 inhibitor (prescribed in 68.8% of women vs 70.2% of men, P = .67); however, more women than men were prescribed clopidogrel (56% vs 44%, P = .009). Women prescribed clopidogrel were more often under the age of 75 years and not frail. Coronary angiography was performed within 24 hours less corooften in women (29.8% vs 36.9%, P = .03) even when high risk was recognized. Frailty was independently associated with deferring coronary angiography in the adjusted analysis; biological sex by itself was not related. The frequency and type of revascularization were the same in both sexes, and there were no differences in in-hospital cardiovascular events. CONCLUSION: Women were more often prescribed less potent antithrombotic therapy than men. Frailty, but not sex, correlated independently with deferral of coronary angiography. However, we detected no differences in the frequency of coronary revascularization or in-hospital events according to sex.

OBJETIVO: Evaluar las diferencias en el manejo clínico y eventos intrahospitalarios en una cohorte de pacientes con síndrome coronario agudo sin elevación del segmento ST (SCASEST) en función del sexo. METODO: Estudio observacional, prospectivo y multicéntrico que incluyó pacientes consecutivos con diagnóstico de SCASEST sometidos a coronariografía con enfermedad ateroesclerótica responsable. RESULTADOS: Entre abril y mayo de 2022 se incluyeron 1.020 pacientes; de ellos, 240 eran mujeres (23,5%). En comparación con los hombres, las mujeres fueron mayores (72,6 años vs 66,5 años; p 0,001) y más frágiles (17,1% vs 5,6%; p 0,001). No hubo diferencias en el pretratamiento con un inhibidor del receptor P2Y12 (68,8% vs 70,2%, p = 0,67), aunque las mujeres recibieron más pretratamiento con clopidogrel (56% vs 44%, p = 0,009), principalmente aquellas de edad 75 años y sin fragilidad. En las mujeres se realizaron menos coronariografías precoces (# 24 h) (29,8% vs 36,9%; p = 0,03) a pesar de presentar la misma indicación (criterios de alto riesgo). En el análisis ajustado, la fragilidad, pero no el sexo, se asoció de forma independiente con la realización de una coronariografía diferida. La tasa y el tipo de revascularización fue igual en ambos sexos, y no hubo diferencias en los eventos cardiovasculares intrahospitalarios. CONCLUSIONES: Las mujeres recibieron con mayor frecuencia un tratamiento antitrombótico menos potente. La fragilidad y no el sexo se asoció con la realización de coronariografía diferida. Sin embargo, no hubo diferencias en la tasa de revascularización coronaria ni en los eventos intrahospitalarios en función del sexo.

Receipt of prescription opioid medication is associated with increased mortality in an Israeli population.

BACKGROUND: Despite Israel's increased use of prescription opioids, reported deaths resulting or associated with opioids have decreased, in fact dramatically, since 2005. This contrast is unique and difficult to explain. We sought to examine whether higher prescribed opioid dosages among adults without oncologic diagnoses were associated with higher all-cause mortality rates. METHODS: A historical cohort study in Clalit Health Services, using a data repository including all adult patients prescribed opiates between 2010 and 2020, excluding patients with oncologic diagnoses. Patients were classified into three groups according to opioid use: below 50 Morphine milligram equivalents (MME) per day, 50 to 90 MME per day, and above 90 MME per day. Sex, Charlson comorbidity score, age and socioeconomic status were recorded. Mortality rates were compared between the dosage groups and compared to age-standardized mortality rates in the general population. RESULTS: On multivariate analysis, patients receiving 90 or more MME per day were 2.37 (95%CI 2.1 to 2.68) more likely to have died compared to patients receiving below 50 MME per day. The respective hazard ratio among patients receiving between 50 and 90 MME per day was 2.23 (2.01 to 2.46). Among patients aged 18 to 50, standardized mortality ratios (SMRs) compared to the general population ranged between 5.4 to 8.6 among women, receiving between 50 and 90 MME per day, and between 8.07 and 10.7 among women receiving 90 or more MME per day. The respective SMRs among men were 1.2 to 3.8 and 2.7 to 5.4. CONCLUSION: Increased opioid use is independently associated with increased all-cause mortality among non-oncological patients. This result is most notable among young adults with little or no known comorbidities. These findings are consistent with results in other countries and seem more credible than previous Israeli reports. Healthcare regulators and providers should, therefore, act to curtail the increasing opioid prescriptions and devise and enhance controls in the healthcare system, which, until 2020, had very limited mechanisms in place.

Online Tools to Decrease Out-of-Pocket Prescription Costs for Patients: A Practical Guide for Urologists.

INTRODUCTION: Patients who seek urologic care have recently reported a high degree of financial toxicity from prescription medications, including management for nephrolithiasis, urinary incontinence, and urological oncology. Estimating out-of-pocket costs can be challenging for urologists in the US because of variable insurance coverage, local pharmacy distributions, and complicated prescription pricing schemes. This article discusses resources that urologists can adopt into their practice and share with patients to help lower out-of-pocket spending for prescription medications. METHODS: We identify 4 online tools that are designed to direct patients toward more affordable prescription medication options: the Medicare Part D Plan Finder, GoodRx, Amazon, and the Mark Cuban Cost Plus Drug Company. A brief historical overview and summary for patients and clinicians are provided for each online resource. A patient-centered framework is provided to help navigate these 4 available tools in clinic. RESULTS: Among the 4 tools we identify, there are multiples tradeoffs to consider as financial savings and features can vary. First, patients insured by Medicare should explore the Part D Plan Finder each year to compare drug plans. Second, patients who need to urgently refill a prescription at a local pharmacy should visit GoodRx. Third, patients who are prescribed recurrent generic prescriptions for chronic conditions can utilize the Mark Cuban Cost Plus Drug Company. Finally, patients who are prescribed 3 or more chronic medications can benefit from subscribing to Amazon RxPass. CONCLUSIONS: Prescription medications for urologic conditions can be expensive. This article includes 4 online resources that can help patients access medications at their most affordable costs. Urologists can provide this framework to their patients to help support lowering out-of-pocket drug costs.

Trends in pediatric prescription-opioid overdoses in U.S. emergency departments from 2008-2020: An epidemiologic study of pediatric opioid overdose ED visits.

BACKGROUND: Opioid overdose was declared a public health emergency in the United States, but much of the focus has been on adults. Child and adolescent exposure and access to unused prescription-opioid medications is a big concern. More research is needed on the trend of pediatric (age 0-17) prescription-opioid overdose emergency department (ED) visits in the United States, particularly during the COVID-19 pandemic year. METHODS: This retrospective epidemiological study used the 2008-2020 Nationwide Emergency Department Sample to provide a national estimate of ED visits related to prescription-opioid overdose. Inclusion criteria were 0-17-year-old patients treated at the ED due to prescription-opioid overdose. Eligible visits were identified if their medical records included any administrative billing codes for prescription-opioid overdose. National estimates were broken down by age groups, sex, geographic region, primary payer, median household income by zip code, ED disposition, and hospital location/teaching status. Incidence rate per 100,000 U.S. children was calculated for age groups, sex, and geographic region. RESULTS: Overall, the prescription-opioid overdose ED visits for patients from 0-17 years old in the United States decreased by 22% from 2008 to 2019, then increased by 12% in 2020. Most patients were discharged to home following their ED visit; however, there was a 42% increase in patients admitted from 2019 to 2020. The prescription-opioid overdose rate per 100,000 U.S. children was highest in the 0 to 1 and 12 to 17 age groups, with the 12 to 17 group increasing by 27% in 2020. ED visits in the West and Midwest saw prescription-opioid visits increase by 58% and 20%, respectively, from 2019-2020. CONCLUSIONS: Prescription-opioid overdose ED visits among U.S. children and adolescents decreased over the past decade until 2019. However, there was a substantial increase in ED visits from 2019 to 2020, suggesting the potential impact due to the then-emerging COVID-19 pandemic. Findings suggest focusing on young children and adolescents to reduce further prescription-opioid overdoses in the United States.

Analysis of belimumab prescription and outcomes in a 10-year monocentric cohort: is there an advantage with early use?

OBJECTIVE: The objective is to evaluate perscriptions of belimumab (BEL), how these have changed over the years and their impact on clinical outcomes in patients with systemic lupus erythematosus (SLE). METHODS: This is a retrospective analysis of prospectively collected data. We retrieved demographic and clinical data and concomitant therapies at BEL starting (baseline). Disease activity was assessed at baseline and after 6 and 12 months and organ damage at baseline and at the last visit. RESULTS: From 422 patients followed in the Pisa SLE cohort, 102 patients received BEL and were included and 22 (21.6%) were immunosuppressant (IS)-naïve. Lupus Low Disease Activity State (LLDAS) with a glucocorticoid (GC) dosage ≤5 mg/day (LLDAS5) and remission were achieved by 47% and 38% of patients at 6 months, and by 75% and 66% at 12 months. Comparing IS-naïve patients with those who received BEL after at least one conventional IS, we did not find significant differences in baseline characteristics and in the achievement of LLDAS5 and remission. Despite at baseline we did not observe significant differences in mean GC daily dosage, IS-naïve patients were taking a significantly lower GC daily dose at 6 and 12 months. Interestingly, IS-naïve patients were more common in the most recent years. CONCLUSIONS: Our data confirm that BEL is effective in controlling disease activity, and in recent years BEL has been considered as an earlier treatment option before other IS. Early introduction of BEL can be at least as effective as a step-up approach and can help to reduce the GC dosage.

Prescription opioid use increases resource utilization following ventral hernia repair.

BACKGROUND: Previous studies showed that preoperative opioid use is associated with increased postoperative opioid use and surgical site infection (SSI) in patients undergoing ventral hernia repair (VHR). Orthopedic surgery literature cites increased resource utilization with opioid use. This study aimed to determine the effect of preoperative opioid use on resource utilization after open VHR. METHODS: A retrospective institutional review board-approved study of VHRs from a single tertiary care practice between 2013 and 2020 was performed. Medical records, the National Surgical Quality Improvement Program database, and Kentucky All Schedule Prescription Electronic Reporting data were reviewed for patient demographics, comorbidities, dispensed opiate prescriptions, hernia characteristics, and outcomes. Univariate logistic regression analyses assessed the effect of each patient's demographic and clinical characteristics. Multivariate logistic regression models analyzed significant factors from the univariate analyses. The primary outcome was resource utilization measured as readmission, emergency department visit, or >2 postoperative clinic visits within 45 days after VHR. RESULTS: Overall, 381 patients who underwent VHR were identified; of which 101 patients had preoperative dispensed opioids. Multivariate analysis demonstrated that patient gender at birth, any new-onset SSI, and any preoperative opioid use were associated with increased postoperative resource utilization (odds ratio, 1.76; P = .026). CONCLUSION: Preoperative opioid use was determined as a risk factor that increased resource utilization after open VHR. An understanding of the drivers of the increased use of resources is essential in developing strategies to improve healthcare value. Future research will focus on strategies to reduce the utilization of resources among patients who use opioids.

Safety vs price in the generic drug market: metformin.

OBJECTIVES: Generic medications represent 90% of prescriptions in the US market and provide a tremendous financial benefit for patients. Recently, multiple generic drugs have been recalled due to the presence of carcinogens, predominantly N-nitrosodimethylamine (NDMA), including an extensive recall of extended-release (ER) metformin products in 2020. STUDY DESIGN: Primary pharmaceutical quality testing and database analysis. METHODS: We tested marketed metformin immediate-release (IR) and ER tablets from a wide sample of generic manufacturers for the presence of carcinogenic impurities NDMA and N,N-dimethylformamide (DMF). We examined the association of level of impurity with drug price and the impact of the 2020 FDA recalls on unit price and prescription fill rate. RESULTS: Postrecall NDMA levels were significantly lower in metformin ER samples (standardized mean difference = -2.0; P = .01); however, we found continued presence of carcinogens above the FDA threshold in 2 of 30 IR samples (6.67%). Overall, the presence of contaminant levels was not significantly associated with price for either IR (NDMA: R2 = 0.142; P = .981; DMF: R2 = 0.382; P = .436) or ER (NDMA: R2 = 0.124; P = .142; DMF: R2 = 0.199; P = .073) samples. Despite recalls, metformin ER prescription fills increased by 8.9% while unit price decreased by 19.61% (P < .05). CONCLUSIONS: Recalls of metformin ER medications were effective in lowering NDMA levels below the FDA threshold; however, some samples of generic metformin still contained carcinogens even after FDA-announced recalls. The absence of any correlation with price indicates that potentially safer products are available on the market for the same price as poorer-quality products.

Strengthening Protocols for Rapid Response to Disruptions in Access to Prescription Opioids: Considerations for State and Territorial Health Agencies.

Prescription opioid disruptions pose a danger and lead to adverse health outcomes for patients taking prescription opioids for pain or medication for opioid use disorder. State and territorial health agencies are uniquely positioned to respond to disruptions and potentially prevent risks associated with service disruptions. Responding to disruptions in access to prescription opioids necessitates a multifaceted, collaborative approach that prioritizes care continuity and patient well-being. State and territorial health agencies may benefit from developing and exercising a formal response protocol that outlines roles and activities during these types of events, strengthening capacity to rapidly respond and serve patient needs.