Hepatocellular carcinoma risk in patients with chronic hepatitis B receiving tenofovir- vs. entecavir-based regimens: Individual patient data meta-analysis.

BACKGROUND & AIMS: The comparative risk of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) receiving tenofovir disoproxil fumarate (TDF) vs. entecavir (ETV) remains controversial. In this individual patient data (IPD) meta-analysis, we aimed to compare HCC risk between the two drugs and identify subgroups who may benefit more from one treatment than the other. METHODS: Published meta-analyses, electronic databases and congress proceedings were searched to identify eligible studies through January 2021. We compared HCC risk between the two drugs using a multivariable Cox proportional hazards model with anonymised IPD from treatment-naïve patients with CHB receiving TDF or ETV for ≥1 year. Treatment effect consistency was explored in propensity score matching (PSM), weighting (PSW) and subgroup analyses for age, sex, hepatitis B e-antigen (HBeAg) positivity, cirrhosis and diabetes status. RESULTS: We included 11 studies from Korea, Taiwan and Hong Kong involving 42,939 patients receiving TDF (n = 6,979) or ETV (n = 35,960) monotherapy. Patients receiving TDF had significantly lower HCC risk (adjusted hazard ratio [HR] 0.77; 95% CI 0.61-0.98; p = 0.03). Lower HCC risk with TDF was consistently observed in PSM (HR 0.73; 95% CI 0.59-0.88; p <0.01) and PSW (HR 0.83; 95% CI 0.67-1.03; p = 0.10) analyses and in all subgroups, with statistical significance in the ≥50 years of age (HR 0.76; 95% CI 0.58-1.00; p <0.05), male (HR 0.74; 95% CI 0.58-0.96; p = 0.02), HBeAg-positive (HR 0.69; 95% CI 0.49-0.97; p = 0.03) and non-diabetic (HR 0.79; 95% CI 0.63-1.00; p <0.05) subgroups. CONCLUSION: TDF was associated with significantly lower HCC risk than ETV in patients with CHB, particularly those with HBeAg positivity. Longer follow-up may be needed to better define incidence differences between the treatments in various subgroups. IMPACT AND IMPLICATIONS: Previous aggregate data meta-analyses have reported inconsistent conclusions on the relative effectiveness of tenofovir disoproxil fumarate and entecavir in reducing hepatocellular carcinoma risk in patients with chronic hepatitis B (CHB). This individual patient data meta-analysis on 11 studies involving 42,939 patients from Korea, Taiwan and Hong Kong suggested that tenofovir disoproxil fumarate-treated patients have a significantly lower hepatocellular carcinoma risk than entecavir-treated patients, which was observed in all subgroups of clinical interest and by different analytical methodologies. These findings should be taken into account by healthcare providers when determining the optimal course of treatment for patients with CHB and may be considered in ensuring that treatment guidelines for CHB remain pertinent.

Systematic literature review and network meta-analysis of sodium-glucose co-transporter inhibitors vs metformin as add-on to insulin in type 1 diabetes.

AIMS: To identify and synthesize phase 3 and phase 4 randomized controlled trials (RCTs) of sodium-glucose co-transporter (SGLT) inhibitors and metformin as adjuncts to insulin in type 1 diabetes (T1DM) using network meta-analysis (NMA). MATERIALS AND METHODS: A systematic literature review (SLR) identified relevant RCTs of ≥12 Weeks duration. MEDLINE, Embase, the Cochrane Library and grey literature were searched through October 2018. NMAs indirectly compared SGLT inhibitors and metformin for change from baseline in HbA1c, weight, total daily insulin dose and systolic blood pressure at Week 24 to 26 and Week 52. Safety outcomes were also explored. RESULTS: Nine trials (N = 6780) were included in the SLR. NMAs indicated that all therapies performed better than placebo for the efficacy outcomes at both time points. Compared with metformin at Week 24 to 26, the SGLT inhibitors dapagliflozin (5 mg), sotagliflozin (200 mg) and empagliflozin (10 mg) had larger reductions in HbA1c (mean difference [MD] = -0.24, 95% credible interval [CrI], -0.41 to -0.07, MD = -0.23, 95% CrI, -0.39 to -0.08 and MD = -0.35, 95% CrI, -0.51 to -0.19, respectively) and in weight, which were sustained in sensitivity analyses. There were few differences observed in the results of safety outcomes, such as risk of diabetic ketoacidosis (DKA), which should be interpreted cautiously because of wide CrIs. CONCLUSIONS: Adjunctive use of SGLT inhibitors in T1DM can improve glycaemic control compared with metformin while enabling weight loss, with consistent efficacy across the class. However, these results are based on indirect evidence so confirmation in a head-to-head study would be valuable.

A qualitative exploration of barriers to efficient and effective structured medication reviews in primary care: Findings from the DynAIRx study.

INTRODUCTION: Structured medication reviews (SMRs), introduced in the United Kingdom (UK) in 2020, aim to enhance shared decision-making in medication optimisation, particularly for patients with multimorbidity and polypharmacy. Despite its potential, there is limited empirical evidence on the implementation of SMRs, and the challenges faced in the process. This study is part of a larger DynAIRx (Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity) project which aims to introduce Artificial Intelligence (AI) to SMRs and develop machine learning models and visualisation tools for patients with multimorbidity. Here, we explore how SMRs are currently undertaken and what barriers are experienced by those involved in them. METHODS: Qualitative focus groups and semi-structured interviews took place between 2022-2023. Six focus groups were conducted with doctors, pharmacists and clinical pharmacologists (n = 21), and three patient focus groups with patients with multimorbidity (n = 13). Five semi-structured interviews were held with 2 pharmacists, 1 trainee doctor, 1 policy-maker and 1 psychiatrist. Transcripts were analysed using thematic analysis. RESULTS: Two key themes limiting the effectiveness of SMRs in clinical practice were identified: 'Medication Reviews in Practice' and 'Medication-related Challenges'. Participants noted limitations to the efficient and effectiveness of SMRs in practice including the scarcity of digital tools for identifying and prioritising patients for SMRs; organisational and patient-related challenges in inviting patients for SMRs and ensuring they attend; the time-intensive nature of SMRs, the need for multiple appointments and shared decision-making; the impact of the healthcare context on SMR delivery; poor communication and data sharing issues between primary and secondary care; difficulties in managing mental health medications and specific challenges associated with anticholinergic medication. CONCLUSION: SMRs are complex, time consuming and medication optimisation may require multiple follow-up appointments to enable a comprehensive review. There is a need for a prescribing support system to identify, prioritise and reduce the time needed to understand the patient journey when dealing with large volumes of disparate clinical information in electronic health records. However, monitoring the effects of medication optimisation changes with a feedback loop can be challenging to establish and maintain using current electronic health record systems.

Combinations of medicines in patients with polypharmacy aged 65-100 in primary care: Large variability in risks of adverse drug related and emergency hospital admissions.

BACKGROUND: Polypharmacy can be a consequence of overprescribing that is prevalent in older adults with multimorbidity. Polypharmacy can cause adverse reactions and result in hospital admission. This study predicted risks of adverse drug reaction (ADR)-related and emergency hospital admissions by medicine classes. METHODS: We used electronic health record data from general practices of Clinical Practice Research Datalink (CPRD GOLD) and Aurum. Older patients who received at least five medicines were included. Medicines were classified using the British National Formulary sections. Hospital admission cases were propensity-matched to controls by age, sex, and propensity for specific diseases. The matched data were used to develop and validate random forest (RF) models to predict the risk of ADR-related and emergency hospital admissions. Shapley Additive eXplanation (SHAP) values were calculated to explain the predictions. RESULTS: In total, 89,235 cases with polypharmacy and hospitalised with an ADR-related admission were matched to 443,497 controls. There were over 112,000 different combinations of the 50 medicine classes most implicated in ADR-related hospital admission in the RF models, with the most important medicine classes being loop diuretics, domperidone and/or metoclopramide, medicines for iron-deficiency anaemias and for hypoplastic/haemolytic/renal anaemias, and sulfonamides and/or trimethoprim. The RF models strongly predicted risks of ADR-related and emergency hospital admission. The observed Odds Ratio in the highest RF decile was 7.16 (95% CI 6.65-7.72) in the validation dataset. The C-statistics for ADR-related hospital admissions were 0.58 for age and sex and 0.66 for RF probabilities. CONCLUSIONS: Polypharmacy involves a very large number of different combinations of medicines, with substantial differences in risks of ADR-related and emergency hospital admissions. Although the medicines may not be causally related to increased risks, RF model predictions may be useful in prioritising medication reviews. Simple tools based on few medicine classes may not be effective in identifying high risk patients.

Knowledge support for optimising antibiotic prescribing for common infections in general practices: evaluation of the effectiveness of periodic feedback, decision support during consultations and peer comparisons in a cluster randomised trial (BRIT2) - study protocol.

INTRODUCTION: This project applies a Learning Healthcare System (LHS) approach to antibiotic prescribing for common infections in primary care. The approach involves iterations of data analysis, feedback to clinicians and implementation of quality improvement activities by the clinicians. The main research question is, can a knowledge support system (KSS) intervention within an LHS implementation improve antibiotic prescribing without increasing the risk of complications? METHODS AND ANALYSIS: A pragmatic cluster randomised controlled trial will be conducted, with randomisation of at least 112 general practices in North-West England. General practices participating in the trial will be randomised to the following interventions: periodic practice-level and individual prescriber feedback using dashboards; or the same dashboards plus a KSS. Data from large databases of healthcare records are used to characterise heterogeneity in antibiotic uses, and to calculate risk scores for clinical outcomes and for the effectiveness of different treatment strategies. The results provide the baseline content for the dashboards and KSS. The KSS comprises a display within the electronic health record used during the consultation; the prescriber (general practitioner or allied health professional) will answer standard questions about the patient's presentation and will then be presented with information (eg, patient's risk of complications from the infection) to guide decision making. The KSS can generate information sheets for patients, conveyed by the clinicians during consultations. The primary outcome is the practice-level rate of antibiotic prescribing (per 1000 patients) with secondary safety outcomes. The data from practices participating in the trial and the dashboard infrastructure will be held within regional shared care record systems of the National Health Service in the UK. ETHICS AND DISSEMINATION: Approved by National Health Service Ethics Committee IRAS 290050. The research results will be published in peer-reviewed journals and also disseminated to participating clinical staff and policy and guideline developers. TRIAL REGISTRATION NUMBER: ISRCTN16230629.

Improving the clinical accuracy and flexibility of the Alkaptonuria severity score index.

Alkaptonuria (AKU) is a rare genetic disorder where oxidised homogentisic acid accumulates in connective tissues, leading to multisystem disease. The clinical evaluation Alkaptonuria Severity Score Index (cAKUSSI) is a composite score that assesses the extent of AKU disease. However, some components assess similar disease features, are difficult to measure reliably or cannot be measured in resource-limited environments. cAKUSSI data from the 4-year SONIA 2 randomised controlled trial, which investigated nitisinone treatment in adults with AKU, were analysed (N = 125). Potentially biased or low-information cAKUSSI measurements were identified using clinical and statistical input to create a revised AKUSSI for use in AKU research (cAKUSSI 2.0). Additionally, resource-intensive measurements were removed to explore a flexible AKUSSI (flex-AKUSSI) for use in low-resource environments. Revised scores were compared to cAKUSSI in terms of correlation and how they capture disease progression and treatment response. Eight measurements were removed from the cAKUSSI to create the cAKUSSI 2.0, which performed comparably to the cAKUSSI in measuring disease extent, progression and treatment response. When removing resource-intensive measurements except for osteoarticular disease, the flex-AKUSSI was highly correlated with the cAKUSSI, indicating that they quantified disease extent similarly. However, when osteoarticular disease (measured using scans) was removed, the corresponding flex-AKUSSI underestimated disease progression and overestimated treatment response compared to the cAKUSSI. Clinicians may use the cAKUSSI 2.0 to reduce time, effort and patient burden. Clinicians in resource-limited environments may find value in computing a flex-AKUSSI score, offering potential for future global registries to expand knowledge about AKU.