Impact of Adopting Population Pharmacokinetics for Tailoring Prophylaxis in Haemophilia A Patients: A Historically Controlled Observational Study

Background Performing individual pharmacokinetics (PK) studies in clinical practice can be simplified by adopting population PK-based profiling on limited post-infusion samples. The objective of this study was to assess the impact of population PK in tailoring prophylaxis in patients with haemophilia A. Patients and Methods Individual weekly treatment plans were developed considering predicted plasma factor activity levels and patients' lifestyle. Patients were trained using a visual traffic-light scheme to help modulate their level of physical activity with respect to factor infusions timing. Annualized joint bleeding rate (ABJR), haemophilia-specific quality of life questionnaire for adults (Haemo-QoL-A) and factor utilization were measured for 12 months before and after tailoring, compared within patients and analysed separately for those previously on prophylaxis (P), situational prophylaxis (SP) or on-demand (OD). Results Sixteen patients previously on P, 10 on SP and 10 on OD were enrolled in the study. The median (lower, upper quartile) ABJR changed from 2.0 (0, 4.0) to 0 (0, 1.6) for P (p = 0.003), from 2.0 (2.0, 13.6) to 3.0 (1.4, 7.2) for SP (p = 0.183) and from 16.0 (13.0, 25.0) to 2.3 (0, 5.0) for OD (p = 0.003). The Haemo-QoL-A total score improved for 58% of P, 50% of SP and 29% of OD patients. Factor utilization (IU/kg/patient/year) increased by 2,400 (121; 2,586) for P, 1,052 (308; 1,578) for SP and 2,086 (1,498; 2,576) for OD. One of 138 measurements demonstrated a factor activity level below the critical threshold of 0.03 IU/mL while the predicted level was above the threshold. Conclusion Implementing tailored prophylaxis using a Bayesian forecasting approach in a routine clinical practice setting may improve haemophilia clinical outcomes

Effectiveness of interventions to prevent pre-frailty and frailty progression in older adults:a systematic review

OBJECTIVE: To summarize the best available evidence regarding the effectiveness of interventions for preventing frailty progression in older adults. INTRODUCTION: Frailty is an age-related state of decreased physiological reserves characterized by an increased risk of poor clinical outcomes. Evidence supporting the malleability of frailty, its prevention and treatment, has been presented. INCLUSION CRITERIA: The review considered studies on older adults aged 65 and over, explicitly identified as pre-frail or frail, who had been undergoing interventions focusing on the prevention of frailty progression. Participants selected on the basis of specific illness or with a terminal diagnosis were excluded. The comparator was usual care, alternative therapeutic interventions or no intervention. The primary outcome was frailty. Secondary outcomes included: (i) cognition, quality of life, activities of daily living, caregiver burden, functional capacity, depression and other mental health-related outcomes, self-perceived health and social engagement; (ii) drugs and prescriptions, analytical parameters, adverse outcomes and comorbidities; (iii) costs, and/or costs relative to benefits and/or savings associated with implementing the interventions for frailty. Experimental study designs, cost effectiveness, cost benefit, cost minimization and cost utility studies were considered for inclusion. METHODS: Databases for published and unpublished studies, available in English, Portuguese, Spanish, Italian and Dutch, from January 2001 to November 2015, were searched. Critical appraisal was conducted using standardized instruments from the Joanna Briggs Institute. Data was extracted using the standardized tools designed for quantitative and economic studies. Data was presented in a narrative form due to the heterogeneity of included studies. RESULTS: Twenty-one studies, all randomized controlled trials, with a total of 5275 older adults and describing 33 interventions, met the criteria for inclusion. Economic analyses were conducted in two studies. Physical exercise programs were shown to be generally effective for reducing or postponing frailty but only when conducted in groups. Favorable effects on frailty indicators were also observed after the interventions, based on physical exercise with supplementation, supplementation alone, cognitive training and combined treatment. Group meetings and home visits were not found to be universally effective. Lack of efficacy was evidenced for physical exercise performed individually or delivered one-to-one, hormone supplementation and problem solving therapy. Individually tailored management programs for clinical conditions had inconsistent effects on frailty prevalence. Economic studies demonstrated that this type of intervention, as compared to usual care, provided better value for money, particularly for very frail community-dwelling participants, and had favorable effects in some of the frailty-related outcomes in inpatient and outpatient management, without increasing costs. CONCLUSIONS: This review found mixed results regarding the effectiveness of frailty interventions. However, there is clear evidence on the usefulness of such interventions in carefully chosen evidence-based circumstances, both for frailty itself and for secondary outcomes, supporting clinical investment of resources in frailty intervention. Further research is required to reinforce current evidence and examine the impact of the initial level of frailty on the benefits of different interventions. There is also a need for economic evaluation of frailty interventions

Benefits and harms of direct oral anticoagulation and low molecular weight heparin for thromboprophylaxis in patients undergoing non-cardiac surgery : systematic review and network meta-analysis of randomised trials

OBJECTIVE To systematically compare the effect of direct oral anticoagulants and low molecular weight heparin for thromboprophylaxis on the benefits and harms to patients undergoing non-cardiac surgery. DESIGN Systematic review and network meta-analysis of randomised controlled trials. DATA SOURCES Medline, Embase, and the Cochrane Central Register of Controlled Trials (CENTRAL), up to August 2021. REVIEW METHODS Randomised controlled trials in adults undergoing non-cardiac surgery were selected, comparing low molecular weight heparin (prophylactic (low) or higher dose) with direct oral anticoagulants or with no active treatment. Main outcomes were symptomatic venous thromboembolism, symptomatic pulmonary embolism, and major bleeding. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used for network meta-analyses. Abstracts and full texts were screened independently in duplicate. Data were abstracted on study participants, interventions, and outcomes, and risk of bias was assessed independently in duplicate. Frequentist network meta-analysis with multivariate random effects models provided odds ratios with 95% confidence intervals, and GRADE (grading of recommendations, assessment, development, and evaluation) assessments indicated the certainty of the evidence. RESULTS 68 randomised controlled trials were included (51 orthopaedic, 10 general, four gynaecological, two thoracic, and one urological surgery), involving 45 445 patients. Low dose (odds ratio 0.33, 95% confidence interval 0.16 to 0.67) and high dose (0.19, 0.07 to 0.54) low molecular weight heparin, and direct oral anticoagulants (0.17, 0.07 to 0.41) reduced symptomatic venous thromboembolism compared with no active treatment, with absolute risk differences of 1-100 per 1000 patients, depending on baseline risks (certainty of evidence, moderate to high). None of the active agents reduced symptomatic pulmonary embolism (certainty of evidence, low to moderate). Direct oral anticoagulants and low molecular weight heparin were associated with a 2-3-fold increase in the odds of major bleeding compared with no active treatment (certainty of evidence, moderate to high), with absolute risk differences as high as 50 per 1000 in patients at high risk. Compared with low dose low molecular weight heparin, high dose low molecular weight heparin did not reduce symptomatic venous thromboembolism (0.57, 0.26 to 1.27) but increased major bleeding (1.87, 1.06 to 3.31); direct oral anticoagulants reduced symptomatic venous thromboembolism (0.53, 0.32 to 0.89) and did not increase major bleeding (1.23, 0.89 to 1.69). CONCLUSIONS Direct oral anticoagulants and low molecular weight heparin reduced venous thromboembolism compared with no active treatment but probably increased major bleeding to a similar extent. Direct oral anticoagulants probably prevent symptomatic venous thromboembolism to a greater extent than prophylactic low molecular weight heparin.Peer reviewe

A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation

BACKGROUND: A barrier to practicing evidence-based medicine is the rapidly increasing body of biomedical literature. Use of method terms to limit the search can help reduce the burden of screening articles for clinical relevance; however, such terms are limited by their partial dependence on indexing terms and usually produce low precision, especially when high sensitivity is required. Machine learning has been applied to the identification of high-quality literature with the potential to achieve high precision without sacrificing sensitivity. The use of artificial intelligence has shown promise to improve the efficiency of identifying sound evidence. OBJECTIVE: The primary objective of this research is to derive and validate deep learning machine models using iterations of Bidirectional Encoder Representations from Transformers (BERT) to retrieve high-quality, high-relevance evidence for clinical consideration from the biomedical literature. METHODS: Using the HuggingFace Transformers library, we will experiment with variations of BERT models, including BERT, BioBERT, BlueBERT, and PubMedBERT, to determine which have the best performance in article identification based on quality criteria. Our experiments will utilize a large data set of over 150,000 PubMed citations from 2012 to 2020 that have been manually labeled based on their methodological rigor for clinical use. We will evaluate and report on the performance of the classifiers in categorizing articles based on their likelihood of meeting quality criteria. We will report fine-tuning hyperparameters for each model, as well as their performance metrics, including recall (sensitivity), specificity, precision, accuracy, F-score, the number of articles that need to be read before finding one that is positive (meets criteria), and classification probability scores. RESULTS: Initial model development is underway, with further development planned for early 2022. Performance testing is expected to star in February 2022. Results will be published in 2022. CONCLUSIONS: The experiments will aim to improve the precision of retrieving high-quality articles by applying a machine learning classifier to PubMed searching. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/2939

Deep Learning to Refine the Identification of High-Quality Clinical Research Articles from the Biomedical Literature: Performance Evaluation

Background Identifying practice-ready evidence-based journal articles in medicine is a challenge due to the sheer volume of biomedical research publications. Newer approaches to support evidence discovery apply deep learning techniques to improve the efficiency and accuracy of classifying sound evidence. Objective To determine how well deep learning models using variants of Bidirectional Encoder Representations from Transformers (BERT) identify high-quality evidence with high clinical relevance from the biomedical literature for consideration in clinical practice. Methods We fine-tuned variations of BERT models (BERTBASE, BioBERT, BlueBERT, and PubMedBERT) and compared their performance in classifying articles based on methodological quality criteria. The dataset used for fine-tuning models included titles and abstracts of >160,000 PubMed records from 2012-2020 that were of interest to human health which had been manually labeled based on meeting established critical appraisal criteria for methodological rigor. The data was randomly divided into 80:10:10 sets for training, validating, and testing. In addition to using the full unbalanced set, the training data was randomly undersampled into four balanced datasets to assess performance and select the best performing model. For each of the four sets, one model that maintained sensitivity (recall) at ≥99% was selected and were ensembled. The best performing model was evaluated in a prospective, blinded test and applied to an established reference standard, the Clinical Hedges dataset. Results In training, three of the four selected best performing models were trained using BioBERTBASE. The ensembled model did not boost performance compared with the best individual model. Hence a solo BioBERT-based model (named DL-PLUS) was selected for further testing as it was computationally more efficient. The model had high recall (>99%) and 60% to 77% specificity in a prospective evaluation conducted with blinded research associates and saved >60% of the work required to identify high quality articles. Conclusions Deep learning using pretrained language models and a large dataset of classified articles produced models with improved specificity while maintaining >99% recall. The resulting DL-PLUS model identifies high-quality, clinically relevant articles from PubMed at the time of publication. The model improves the efficiency of a literature surveillance program, which allows for faster dissemination of appraised research

Additional file 2: of Efficacy of ultra-micronized palmitoylethanolamide (um-PEA) in geriatric patients with chronic pain: study protocol for a series of N-of-1 randomized trials

CENT 2015 checklist*; CONSORT 2010 checklist items with modifications or additions for individual or series of N-of-1 trials; empty items in the CENT 2015 column indicate no modification from the CONSORT 2010 item. (DOCX 97.2 kb