Unravelling the tangled web of hypertension and cancer.

Cardiovascular disease remains the primary cause of mortality globally, being responsible for an estimated 17 million deaths every year. Cancer is the second leading cause of death on a global level with roughly 9 million deaths per year being attributed to neoplasms. The two share multiple common risk factors such as obesity, poor physical exercise, older age, smoking and there exists rare monogenic hypertension syndromes. Hypertension is the most important risk factor for cardiovascular disease and affects more than a billion people worldwide and may also be a risk factor for the development of certain types of cancer (e.g. renal cell carcinoma (RCC)). The interaction space of the two conditions becomes more complicated when the well-described hypertensive effect of certain antineoplastic drugs is considered along with the extensive amount of literature on the association of different classes of antihypertensive drugs with cancer risk/prevention. The cardiovascular risks associated with antineoplastic treatment calls for efficient management of relative adverse events and the development of practical strategies for efficient decision-making in the clinic. Pharmacogenetic interactions between cancer treatment and hypertension-related genes is not to be ruled out, but the evidence is not still ample to be incorporated in clinical practice. Precision Medicine has the potential to bridge the gap of knowledge regarding the full spectrum of interactions between cancer and hypertension (and cardiovascular disease) and provide novel solutions through the emerging field of cardio-oncology. In this review, we aimed to examine the bidirectional associations between cancer and hypertension including pharmacotherapy.

Survey and Evaluation of Hypertension Machine Learning Research.

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.