Real-world Data Research Articles

Genetic predisposition and antipsychotic treatment effect on metabolic syndrome in schizophrenia: a ten-year follow-up study using the Estonian Biobank

Schizophrenia (SCZ) patients exhibit 30% higher prevalence of metabolic syndrome (MetS) compared to the general population with its suboptimal management contributing to increased mortality. Large-scale studies providing real-world evidence of the underlying causes remain limited. To address this gap, we used real-world health data from the Estonian Biobank, spanning a median follow-up of ten years, to investigate the impact of genetic predisposition and antipsychotic treatment on the development of MetS in SCZ patients. Specifically, we set out to characterize antipsychotic treatment patterns, genetic predisposition of MetS traits, MetS prognosis, and body mass index (BMI) trajectories, comparing SCZ cases (n=677) to age- and sex-matched controls (n=2708). SCZ cases exhibited higher genetic predisposition to SCZ (OR=1.75, 95% CI 1.58-1.94), but lower polygenic burden for increased BMI (OR=0.88, 95% CI 0.88-0.96) and C-reactive protein (OR=0.88, 95% CI 0.81-0.97) compared to controls. While SCZ cases showed worse prognosis of MetS (HR 1.95, 95% CI 1.54-2.46), higher antipsychotic adherence within the first treatment year was associated with reduced long-term MetS incidence. Linear mixed modelling, incorporating multiple BMI timepoints, underscored the significant contribution of both, antipsychotic medication, and genetic predisposition to higher BMI, driving the substantially upward trajectory of BMI in SCZ cases. These findings contribute to refining clinical risk prediction and prevention strategies for MetS among SCZ patients and emphasize the significance of incorporating genetic information, long-term patient tracking, and employing diverse perspectives when analyzing real-world health data. EU Horizon 2020, Swedish Research Council, Estonian Research Council, Estonian Ministry of Education and Research, University of Tartu.

A Mamdani fuzzy inference system with trapezoidal membership functions for investigating fishery production

Seas, marine ecosystems, and coastal regions are crucial components of our environment. Numerous scientific strategies have been adopted to boost fisheries and aquaculture productivity. This study proposes a fuzzy-logic-based model to produce fisheries in India, which ranks fourth worldwide for fisheries production. Five input variables, such as fish seed, export, post-harvesting, released fund, and temperature, are considered inputs, and the production of fisheries is taken as the output variable. A Mamdani-type fuzzy inference system with trapezoidal membership functions is prepared with 243 rules in the IF-THEN format. This mathematical model investigates the impacts of input parameters on the production of Indian fisheries. We fit the model with the real-world data and show that fish seed, export, released fund, and post-harvesting facilities positively impact fisheries production. However, a very high temperature is unsuitable for high production, even if all other parameters lie at their desired level.

Virtual testbed for development and evaluation of power system digital twins and their applications

Digital twins (DTs) show considerable promise for development of applications and solutions across a diverse range of power system services. Ensuring these applications function as intended without causing disruptions or unexpected issues requires comprehensive testing prior to practical deployment. However, current approaches to power system digital twin (PSDT) application development and testing experience significant obstacles, including limitations and risks associated with accessing real-world power systems, models and data. This article introduces a virtual testbed based on the concept of a virtual physical twin (VPT), that replaces the need for access to a physical twin (PT), as a platform designed to remove the heavy reliance on online data flows and provide a close to real-world environment. A testbed enables simultaneous development and testing of DTs and their related applications in a realistic, interactive, and safe setting without impact on the operation of the actual power system. A complete workflow for PSDT testbed implementations is proposed, with all necessary steps from concept to the eventual connection to the physical system. To validate the practical implementation steps, utility, and value of the testbed, a real-time transient stability analysis with two artificial intelligence algorithms is provided. The proposed testbed is expandable and adaptable, promoting the development of advanced and intelligent PSDTs. The key benefit of the testbed is that it enables the development of DT and its application closely in a realistic environment without affecting the operation of PT.

Real-world Impact of 3 and 4.5 mg Doses of Dulaglutide on Weight and Hemoglobin A1c in Patients With Type 2 Diabetes Mellitus.

Limited real-world data on the benefits and risks associated with 3 and 4.5 mg doses of dulaglutide currently exists, making it difficult to determine the impact of dose titration for patients currently managed with dulaglutide 1.5 mg weekly. To determine the impact of dulaglutide 3 and 4.5 mg doses on weight and hemoglobin A1c (HbA1c) in patients with type 2 diabetes mellitus (T2DM), in clinical practice. Retrospective, observational study of adult T2DM patients receiving dulaglutide 3 or 4.5 mg weekly within a large, university-affiliated, primary care network. The primary outcome was change in weight and HbA1c from baseline to 24 weeks. Secondary outcomes included incremental changes in weight and HbA1c, and describing trends related to dose reductions. Ninety-five patients were included, 62 in the dulaglutide 3 mg group and 33 in the dulaglutide 4.5 mg group. After 24 weeks, the mean changes in weight and HbA1c from baseline were -1.8 kg (P < 0.01) and -0.4% (P < 0.01) in the 3 mg group, and -4.2 kg (P < 0.01) and -0.4% (P = 0.119) in the 4.5 mg group. Incremental change in weight and HbA1c among patients who were titrated from dulaglutide 3 to 4.5 mg weekly were -2.6 kg (P < 0.01) and -0.2% (P = 0.04), respectively. Titration from dulaglutide 1.5 to 3 mg resulted in significant reductions in weight and HbA1c after 24 weeks. Additional, statistically significant, reductions in weight and HbA1c were seen when patients were further titrated to dulaglutide 4.5 mg weekly.

Enhancing Criminal Analysis through Multi-Model Integration: Addressing Challenges and Ensuring Ethical Implementation

Abstract: Law enforcement agencies face numerous challenges in criminal analysis, from data quality issues to ethical concerns. To address these challenges, this research proposes a novel approach: integrating multiple modelling techniques into a unified framework. By combining regression analysis, decision trees, support vector machines, neural networks, and ensemble methods, this approach aims to provide more robust and accurate solutions. The research seeks to develop a framework for integration, evaluate effectiveness using real-world data, and explore ethical implications. Ultimately, the goal is to advance criminal analysis, promote multi-model integration in law enforcement, and ensure ethical implementation for justice.

Assessing the Impact of Seasonal Decomposition on the Time Series Analysis Accuracy: A Comprehensive Study

Abstract: Time series forecasting plays a pivotal role in decision-making across various domains, ranging from finance to healthcare and weather prediction. The accurate prediction of future values in a time series is vital for informed planning and resource allocation. The aim of this study is to explore whether the utilization of seasonal decomposition techniques, such as classical decomposition, X-12-ARIMA, and seasonal decomposition of time series (STL), can improve the effectiveness of time series forecasting models by separating the data into its distinct components, including trend and seasonality. We conduct a comprehensive analysis using real-world time series data, employing popular forecasting models like ARIMA, exponential smoothing, and machine learning-based approaches. By comparing the forecasting accuracy of these models with and without the application of seasonal decomposition techniques, we provide empirical evidence to support the hypothesis. Our research results provide valuable insights into the tangible ad- vantages of integrating seasonal decomposition techniques in time series forecasting, potentially contributing to enhanced decision support systems across diverse application domains.

Artificial Intelligence and Machine Learning in Hepatocellular Carcinoma Screening, Diagnosis and Treatment - A Comprehensive Systematic Review

Background: Medical image analysis plays a crucial role in the screening, monitoring, diagnosis, and prognosis of diseases. Hepatocellular Carcinoma (HCC) often remain asymptomatic in their early stages. Timely identification and intervention are crucial to prevent the progression to decompensated liver diseases and advanced-stage HCC, minimizing morbidity and mortality. Methodology: This study examined twenty relevant research articles that met the inclusion criteria. Utilizing the PRISMA criteria, a systematic search was conducted on PubMed/MEDLINE and Google Scholar for studies on HCC screening employing Artificial Intelligence (AI) and Machine Learning (ML). The search focused on the keywords "artificial intelligence" and "hepatocellular carcinoma," with inclusion criteria specifying studies in the English language published in and after 2020. Exclusions were made for histology, animal research, and investigations conducted before 2020. Titles and abstracts were thoroughly reviewed, and any discrepancies were discussed. Results: The comprehensive review reveals the transformative impact of AI and ML on HCC screening, diagnosis, and therapy. ML models demonstrated effectiveness in early HCC diagnosis, distinguishing hepatic lesions, predicting treatment responses, and assessing recurrence risks across various techniques. Integration of mass spectrometry-based technologies, advanced imaging, and real-world data significantly improved diagnostic accuracy and clinical decision support. AI models exhibited diagnostic expertise with potential applications in therapy suggestions, personalized surveillance, and prognostic evaluations. However, further validation and seamless integration into clinical practice are essential for realizing their full potential. Conclusion: This systematic study underscores the progress made in the application of AI and ML in HCC screening, diagnosis, and treatment. Numerous studies highlight the capability of AI and ML systems to enhance hepatocellular carcinoma diagnosis, predict outcomes, and optimize therapy. The enduring and versatile nature of these technologies points towards a revolutionary future in personalized and efficient HCC management.

Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room.

Artificial intelligence (AI) and large language models (LLMs) can play a critical role in emergency room operations by augmenting decision-making about patient admission. However, there are no studies for LLMs using real-world data and scenarios, in comparison to and being informed by traditional supervised machine learning (ML) models. We evaluated the performance of GPT-4 for predicting patient admissions from emergency department (ED) visits. We compared performance to traditional ML models both naively and when informed by few-shot examples and/or numerical probabilities. We conducted a retrospective study using electronic health records across 7 NYC hospitals. We trained Bio-Clinical-BERT and XGBoost (XGB) models on unstructured and structured data, respectively, and created an ensemble model reflecting ML performance. We then assessed GPT-4 capabilities in many scenarios: through Zero-shot, Few-shot with and without retrieval-augmented generation (RAG), and with and without ML numerical probabilities. The Ensemble ML model achieved an area under the receiver operating characteristic curve (AUC) of 0.88, an area under the precision-recall curve (AUPRC) of 0.72 and an accuracy of 82.9%. The naïve GPT-4's performance (0.79 AUC, 0.48 AUPRC, and 77.5% accuracy) showed substantial improvement when given limited, relevant data to learn from (ie, RAG) and underlying ML probabilities (0.87 AUC, 0.71 AUPRC, and 83.1% accuracy). Interestingly, RAG alone boosted performance to near peak levels (0.82 AUC, 0.56 AUPRC, and 81.3% accuracy). The naïve LLM had limited performance but showed significant improvement in predicting ED admissions when supplemented with real-world examples to learn from, particularly through RAG, and/or numerical probabilities from traditional ML models. Its peak performance, although slightly lower than the pure ML model, is noteworthy given its potential for providing reasoning behind predictions. Further refinement of LLMs with real-world data is necessary for successful integration as decision-support tools in care settings.

Cost-effectiveness of sotagliflozin for the treatment of patients with diabetes and recent worsening heart failure.

Aim: To assesses the cost-effectiveness of sotagliflozin for the treatment of patients hospitalized with heart failure and comorbid diabetes. Materials & methods: Ade novo cost-effectiveness model with a Markov structure was created for patients hospitalized for heart failure with comorbid diabetes. Outcomes of interest included hospital readmissions, emergency department visits and all-cause mortality measured over a 30-year time horizon. Baseline event frequencies were derived from published real-world data studies; sotagliflozin's efficacy was estimated from SOLOIST-WHF. Health benefits were calculated quality-adjusted life years (QALYs). Costs included pharmaceutical costs, rehospitalization, emergency room visits and adverse events. Economic value was measured using the incremental cost-effectiveness ratio (ICER). Results: Sotagliflozin use decreased annualized rehospitalization rates by 34.5% (0.228 vs 0.348, difference: -0.120), annualized emergency department visits by 40.0% (0.091 vs 0.153, difference: -0.061) and annualized mortality by 18.0% (0.298 vs 0.363, difference: -0.065) relative to standard of care, resulting in a net gain in QAYs of 0.425 for sotagliflozin versus standard of care. Incremental costs using sotagliflozin increased by $19,374 over a 30-year time horizon of the patient, driven largely by increased pharmaceutical cost. Estimated ICER for sotagliflozin relative to standard of care was $45,596 per QALY. Conclusion: Sotagliflozin is a cost-effective addition to standard of care for patients hospitalized with heart failure and comorbid diabetes.

Real-life use of ropeg-interferon α2b in polycythemia vera: patient selection and clinical outcomes.

Ropeginterferon-alfa2b (ropegIFNα2b) is a long-acting IFN formulation with broad FDA/EMA approval as a therapy of polycythemia vera (PV) with no symptomatic splenomegaly. There is currently lack of information on the real-world patient selection, including the impact of local reimbursement policies, and drug management, particularly: type/timing of screening and follow-up tests; absolute/relative contraindications to therapy; ropegIFNα2b dose and combinations with hydroxyurea. As a sub-analysis of the PV-ARC retrospective study (NCT06134102), we here report our monocenter experience with ropegIFNα2b in the period from January 2021, corresponding to drug availability outside clinical trial, and December 2023. Among the 149 patients with EMA/FDA indication, only 55 (36.9%) met the local reimbursement criteria and 18 (12.1%) received ropegIFNα2b. Thanks to appropriate screening, relative/absolute contraindications to ropegIFNα2b were detected and managed in a multidisciplinary manner. Efficacy and safety of ropegIFNα2b was confirmed, with 3 cases of early molecular response. General use of low ropegIFNα2b dose, with frequent need for hydroxyurea combinations, was noted. This real-world experience suggests a significant impact of local regulations on drug prescription and the need for greater real-world data collection on ropegIFNα2b in PV patients. Also, it describes appropriate multidisciplinary screening and monitoring procedures during ropegIFNα2b therapy.

Success rates of intensive aphasia therapy: real-world data from 448 patients between 2003 and 2020.

Aphasia is a devastating consequence after stroke, affecting millions of patients each year. Studies have shown that intensive speech and language therapy (SLT) is effective in the chronic phase of aphasia. Leveraging a large single-center cohort of persons with aphasia (PWA) including patients also in the subacute phase, we assessed treatment effects of intensive aphasia therapy in a real-world setting. Data were collected at the Aachen aphasia ward in Germany between 2003 and 2020. Immediate treatment responses across different language domains were assessed with the Aachen Aphasia Test (AAT) using single-case psychometrics, conducted before and after 6-7 weeks of intensive SLT (10 h per week, median (IQR) dosage = 68 (61-76)). We adjusted for spontaneous recovery in subacute patients. Differential treatment effects between subgroups of chronicity and predictors of therapy response were investigated. A total of 448 PWA were included (29% female, median (IQR) age = 54 (46-62) years, median (IQR) time post-onset = 11 (6-20) months) with 12% in the early subacute, 15% in the late subacute and 74% in the chronic phase of aphasia. The immediate responder rate was 59%. Significant improvements in all AAT subtests und subscales were observed hinting at broad effectiveness across language domains. The degree of therapy-induced improvement did not differ between the chronicity groups. Time post-onset, dosage of therapy and aphasia severity at the beginning of treatment were predictors of immediate treatment response. Intensive therapy protocols for aphasia after stroke are yielding substantial responder rates in a routine clinical setting including a wide range of patients.

Tackling school segregation with transportation network interventions: an agent-based modelling approach

We address the emerging challenge of school segregation within the context of free school choice systems. Households take into account both proximity and demographic composition when deciding on which schools to send their children to, potentially exacerbating residential segregation. This raises an important question: can we strategically intervene in transportation networks to enhance school access and mitigate segregation? In this paper, we propose a novel, network agent-based model to explore this question. Through simulations in both synthetic and real-world networks, we demonstrate that enhancing school accessibility via transportation network interventions can lead to a reduction in school segregation, under specific conditions. We introduce group-based network centrality measures and show that increasing the centrality of certain neighborhood nodes with respect to a transportation network can be an effective strategy for strategic interventions. We conduct experiments in two synthetic network environments, as well as in an environment based on real-world data from Amsterdam, the Netherlands. In both cases, we simulate a population of representative agents emulating real citizens’ schooling preferences, and we assume that agents belong to two different groups (e.g., based on migration background). We show that, under specific homophily regimes in the population, school segregation can be reduced by up to 35%. Our proposed framework provides the foundation to explore how citizens’ preferences, school capacity, and public transportation can shape patterns of urban segregation.

Target trial emulation of carfilzomib safety among patients with relapsed/refractory multiple myeloma using a nationwide observational data in Korea

PurposeCarfilzomib, commonly used for relapsed/refractory multiple myeloma (RRMM), has been associated with various adverse events in randomized controlled trials (RCTs). However, real-world safety data for a more diverse population are needed, as carfilzomib received expedited approval. This study aimed to evaluate carfilzomib’s safety in Korea by comparing new users of KRd (carfilzomib, lenalidomide, and dexamethasone) to Rd (lenalidomide and dexamethasone) using a nationwide administrative claims database.MethodsThe retrospective cohort study utilized target trial emulation, focusing on adverse events in various organ systems similar to the ASPIRE trial.ResultsThis study included 4,580 RRMM patients between 2007 and 2020, and the KRd group showed significantly higher risks of hematologic adverse events (anemia, neutropenia, thrombocytopenia) and some non-hematologic adverse events (cough, hypokalemia, constipation, hypertension, heart failure) compared to the Rd group. Among non-hematologic adverse events, cardiovascular events (heart failure [HR 2.04; 95% CI 1.24–3.35], hypertension [HR 1.58; 95% CI 1.15–2.17]) had the highest risk in the KRd group.ConclusionThe safety profile of carfilzomib in Korean patients was similar to previous RCTs. Therefore, caution should be exercised when using carfilzomib in Asian individuals with RRMM due to the increased risk of cardiovascular adverse events.

Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data

Abstract Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains. These powerful models have demonstrated superior capability to learn complex relationships and often generalize better to new data and problems. This paper presents a novel transformer-powered approach for enhancing prediction accuracy in multi-modal output scenarios, where sparse experimental data is supplemented with simulation data. The proposed approach integrates transformer-based architecture with a novel graph-based hyper-parameter optimization technique. The resulting system not only effectively reduces simulation bias, but also achieves superior prediction accuracy compared to the prior method. We demonstrate the efficacy of our approach on inertial confinement fusion experiments, where only 10 shots of real-world data are available, as well as synthetic versions of these experiments.

Balancing Data Acquisition Benefits and Ordering Costs for Predictive Supplier Selection and Order Allocation

The strategic selection of suppliers and the allocation of orders across multiple periods have long been recognized as critical aspects influencing company expenditure and resilience. Leveraging the enhanced predictive capabilities afforded by machine learning models, direct lookahead models—linear programming models that optimize future decisions based on forecasts generated by external predictive modules—have emerged as viable alternatives to traditional deterministic and stochastic programming methodologies to solve related problems. However, despite these advancements, approaches implementing direct lookahead models typically lack mechanisms for updating forecasts over time. Yet, in practice, suppliers often exhibit dynamic behaviours, and failing to update forecasts can lead to suboptimal decision-making. This study introduces a novel approach based on parametrized direct lookahead models to address this gap. The approach explicitly addresses the hidden trade-offs associated with incorporating forecast updates. Recognizing that forecasts can only be updated by acquiring new data and that the primary means of acquiring supplier-related data is through order allocation, this study investigates the trade-offs between data acquisition benefits and order allocation costs. An experimental design utilizing real-world automotive sector data is employed to assess the potential of the proposed approach against various benchmarks. These benchmarks include decision scenarios representing perfect foresight, no data acquisition benefits, and consistently positive benefits. Empirical findings demonstrate that the proposed approach achieves performance levels comparable to those of decision-makers with perfect foresight while consistently outperforming benchmarks not balancing order allocation costs and data acquisition benefits.

Developing an Efficient Model for Online Grocery Order Fulfillment

Due to the convenience of online grocery apps and home delivery, online grocery shopping has become popular in recent years. Globally, consumer behavior has significantly changed the consumption and purchase patterns of online grocery shopping. This study aimed to develop an efficient model for online grocery order fulfillment that both reduces costs and increases supply chain efficiency and sustainability. This study first aimed to develop the current picking model by adopting real-world data from a store in Riyadh, Saudi Arabia. Subsequently, four proposed models were developed to improve the efficiency and sustainability of the online grocery order fulfillment process. The results show a significant improvement in all models over the current picking model. The percentage improvements in fulfillment time per product are as follows: single order picking—8.33%; batch order picking—6.78%; zone order picking—3.08%; and hybrid order picking—13.20%, which combines zone and batch order picking. Retailers and online grocery apps could adopt these models to increase efficiency and sustainability. Also, these models have great potential for future research and improvement by optimizing product placement, in addition to picking methods and picking routes, which are the focus of this study.

An Assessment of Semaglutide Safety Based on Real World Data: From Popularity to Spontaneous Reporting in EudraVigilance Database

Some glucagon-like peptide-1 receptor agonists (GLP-1 RAs), first used in the treatment of type 2 diabetes mellitus (T2DM), have been approved for the treatment of obesity in patients with or without T2DM (liraglutide—LIR, semaglutide—SEM, and tirzepatide—TIR). Social media had an important influence on the off-label use of GLP-1 RAs for obesity, especially for SEM. We analyzed the Google queries related to SEM to assess people’s interest in this drug. We also investigated the occurrence of adverse drug reactions (ADRs) by searching the EudraVigilance database (EV) for Individual Case Safety Reports (ICSRs) that reported SEM as the suspected drug and performed a descriptive and a disproportionality analysis. The data obtained for SEM were compared to other GLP-1 RAs. SEM had the highest proportions of searches on Google associated with the term “weight loss” and presented the lowest number of severe ADRs, but it also had the highest number of ICSRs reported in EV. Even though no unexpected safety issues have been reported for it until now, SEM has a hi3gh tendency for overdose reports. The most frequent off-label use was reported for SEM and TIR. In order to lower the risks of ADRs, the off-label use should be reduced and carefully monitored.

Validation of algorithms in studies based on routinely collected health data: general principles.

Clinicians, researchers, regulators, and other decision-makers increasingly rely on evidence from real-world data (RWD), including data routinely accumulating in health and administrative databases. RWD studies often rely on algorithms to operationalize variable definitions. An algorithm is a combination of codes or concepts used to identify persons with a specific health condition or characteristic. Establishing the validity of algorithms is a prerequisite for generating valid study findings that can ultimately inform evidence-based health care. This paper aims to systematize terminology, methods, and practical considerations relevant to the conduct of validation studies of RWD-based algorithms. We discuss measures of algorithm accuracy; gold/reference standard; study size; prioritizing accuracy measures; algorithm portability; and implication for interpretation. Information bias is common in epidemiologic studies, underscoring the importance of transparency in decisions regarding choice and prioritizing measures of algorithm validity. The validity of an algorithm should be judged in the context of a data source, and one size does not fit all. Prioritizing validity measures within a given data source depends on the role of a given variable in the analysis (eligibility criterion, exposure, outcome or covariate). Validation work should be part of routine maintenance of RWD sources.

Predictive Modelling on Competitor Analysis Performance by using Generalised Linear Models and Machine Learning Approach

Competitive analysis in digital and technology is trending in the business field. However, the field of digital and technology in the business world is vast and challenging to analyse. The purpose of this research is first to identify the success factor which represent the company performance. Second, is to identify the significant services provided by the company to their business user. Then, based on the first and second objectives, a predictive modelling is developed to produce the best solution to their business user. The research is implementing a case study from Telecommunication Company and using data science life cycle methodology. The statistical modelling that is used to develop the competitor’s analysis model is generalised linear model (GLM) which integrated with machine learning approach. Furthermore, the synthetic data set is created by using Gamma Distribution, Gaussian Distribution and Poisson Distribution due to some data from the case study is confidential. The synthetic data set is based on existing real data which are from Telecommunication Company sentiment analysis data, were used to investigate the performance of the proposed model. The machine learning technique is used to get the accuracy of the significant GLM which has been developed. The accuracy is tested by using the error rates of the machine learning technique which are Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared. This research discovered that the business solution to the significant service for the business user and discovered the best statistical model to be used for the business solution. The results show that the Gumbel distribution is the best fit model for the synthetic dataset where the values of RMSE is 1.0574, MAE is 0.9168 and R-squared is 0.3994, and the significant success factor that has been identified by using the GLM is advertising success factor. The model developed can be improved with another type of data set and different sizes of data. Hence, further studies and real-world data are required for better validation.

Effectiveness, safety and discontinuation rates of bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) in people with HIV using real-world data: a systematic review and meta-analysis.

The use of bictegravir/emtricitabine/tenofovir alafenamide (BIC/FTC/TAF) is based on the results of robust clinical trials. To assess the effectiveness and safety of BIC/FTC/TAF in treatment-naïve (TN) and treatment-experienced (TE) people with HIV using available real-world cohort studies. Systematic review and meta-analysis of publications and communications identified via Boolean search in Medline, PubMed and Embase, and conference abstracts reporting retrospective real-world use of BIC/FTC/TAF, published until 31 January 2024. The primary endpoint was the proportion of TN and TE people with HIV with viral load (VL) < 50 copies/mL at 48 weeks while on treatment. Of the 38 identified publications and conference abstracts, for the present analysis we included 12 publications (comprising 792 TN and 6732 TE individuals). For the three publications including 507 TN participants reporting the primary outcome, VL suppression was 97% [95% confidence intervals (CI): 89-100]. For the nine publications including 4946 TE participants reporting the primary outcome, VL suppression was 95% (95% CI: 94-96), with suppression >93% in all studies. Total discontinuations at 48 weeks in TE individuals were 3% (95% CI: 2-5), 1% (95% CI: 0-2) due to side effects. A total of four publications with 151 TE individuals with previous presence of M184V substitution were identified, reporting a suppression rate at 48 weeks of 95% (95% CI: 88-100). Real-world studies demonstrate low discontinuation rates and high rates of virologic suppression in individuals treated with BIC/FTC/TAF, both TN and TE with and without previous detection of M184V substitution.