Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision‐making. However, applications in different specialities are variable and generally limited. Like other conditions, the number of studies employing ML in hypertension research is growing rapidly. In this study, we aimed to survey hypertension research using ML, evaluate the reporting quality, and identify barriers to ML's potential to transform hypertension care. Methods and Results The Harmonious Understanding of Machine Learning Analytics Network survey questionnaire was applied to 63 hypertension‐related ML research articles published between January 2019 and September 2021. The most common research topics were blood pressure prediction (38%), hypertension (22%), cardiovascular outcomes (6%), blood pressure variability (5%), treatment response (5%), and real‐time blood pressure estimation (5%). The reporting quality of the articles was variable. Only 46% of articles described the study population or derivation cohort. Most articles (81%) reported at least 1 performance measure, but only 40% presented any measures of calibration. Compliance with ethics, patient privacy, and data security regulations were mentioned in 30 (48%) of the articles. Only 14% used geographically or temporally distinct validation data sets. Algorithmic bias was not addressed in any of the articles, with only 6 of them acknowledging risk of bias. Conclusions Recent ML research on hypertension is limited to exploratory research and has significant shortcomings in reporting quality, model validation, and algorithmic bias. Our analysis identifies areas for improvement that will help pave the way for the realization of the potential of ML in hypertension and facilitate its adoption.

Cancer is a complex and multifactorial disease which can be associated with both genetic and environmental abnormalities. Gene therapy medicines are classified as advanced therapy medicinal products (ATMPs). The use of ATMPs in cancer is a new era in medicine, which requires the support from specialist and experienced staff and the regulatory authorities to introduce gene therapy medicines safely and effectively into clinical practice. In Europe, the European Medicines Agency (EMA) has approved a Gene therapy Medicinal Product (GTMP), Talimogene Laherperepvec (Imlygic) for melanoma, a Cell Therapy Medicines Product (CTMP), Zalmoxis for patients with haematopoietic Stem Cell Transplantation (SCT) and two Chimeric antigen receptor T-cell therapies (CAR-T cells), Tisagenlecleucel (Kymriah) and Axicabtagene ciloleucel (Yescarta), for the treatment of acute lymphoblastic leukaemia (ALL) and diffuse large B-cell lymphoma (DLBCL). Regulatory authorities, expert members in this field, and experience from clinical trials have come together to build guidance and provide advice on how to initiate these novel therapies in clinical practice. Risk assessments and standard operating procedures should be in place before gene therapy is introduced in healthcare centres. Gene therapy in cancer is an exciting new era, with challenges for healthcare professionals to implement, manage and monitor this new group of therapy. Clinical exposure and extensive research have provided a deeper understanding and improvement of medicines optimisation and safety. Scientific and safety challenges are being explored and answered in clinical trial settings, with safety precautions always being paramount to ensure the efficiency and the safe delivery of these therapies. In this review, we will summarize the gene therapy medicinal products that are licensed in the UK and the challenges that need to be taken into consideration before hospital pharmacy initiates those therapies in clinical practice.

Objective:Artificial intelligence and machine learning (AI/ML) are increasingly being applied to big clinical data to tackle research questions that cannot be answered with traditional statistical methods. The field is still in its nascent stages and there is a paucity of guidelines for conducting and reporting AI/ML research in hypertension. The objective was to apply the HUMANE checklist to survey the present landscape of AI/ML in hypertension to inform the development of hypertension-specific guidelines and recommendations.Design and method:The HUMANE checklist was developed by global clinical and AI/ML experts through the Delphi method. It assesses the quality of medical AI/ML articles based on whether they cover subjects expected in any peer-reviewed, clinical or AI/ML research publication. A cooping review was carried out to identify articles presenting original research in AI/ML and hypertension published in 2019–2021. Two independent reviewers applied the checklist to each article and in the case of discordance, the response was adjudicated by an AI/ML expert. Results were analysed to assess compliance with the survey (% of papers satisfying checklist requirements).Results:A total of 63 manuscripts was reviewed. A summary of results is shown in Figure 1. Highest compliance was seen for items relating to general article presentation, with compliance ranging from 68% to 98% (description of statistical analysis methods and background context, respectively). Lowest compliance was seen with checklist items relating to clinical research and AI/ML methods. 44% of reviewed articles described the demographics of their dataset and 48% stated their inclusion/exclusion criteria. Nonetheless, datasets were deemed appropriate for investigative aims in 93% of articles. 30% of manuscripts reported their calibration measures, while 73% stated their performance metrics. Internal validation was carried out in 75% of studies, but external validity was assessed in only 14% of cases. Algorithmic bias was addressed in 11% of papers.Conclusions:Application of AI/ML methods in hypertension research is growing, but the majority of current work has major shortfalls in reporting quality, model validation and algorithmic bias. Our study identifies areas of improvement to enable the full realisation of the potential of AI/ML in hypertension.

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