Predicting VTE in Cancer Patients: Candidate Biomarkers and Risk Assessment Models

Risk prediction of chemotherapy-associated venous thromboembolism (VTE) is a compelling challenge in contemporary oncology, as VTE may result in treatment delays, impaired quality of life, and increased mortality. Current guidelines do not recommend thromboprophylaxis for primary prevention, but assessment of the patient's individual risk of VTE prior to chemotherapy is generally advocated. In recent years, efforts have been devoted to building accurate predictive tools for VTE risk assessment in cancer patients. This review focuses on candidate biomarkers and prediction models currently under investigation, considering their advantages and disadvantages, and discussing their diagnostic performance and potential pitfalls.

Renal function monitoring in heart failure - what is the optimal frequency? A narrative review

The second most common cause of hospitalization due to adverse drug reactions in the UK is renal dysfunction due to diuretics, particularly in patients with heart failure, where diuretic therapy is a mainstay of treatment regimens. Therefore, the optimal frequency for monitoring renal function in these patients is an important consideration for preventing renal failure and hospitalization. This review looks at the current evidence for optimal monitoring practices of renal function in patients with heart failure according to national and international guidelines on the management of heart failure (AHA/NICE/ESC/SIGN). Current guidance of renal function monitoring is in large part based on expert opinion, with a lack of clinical studies that have specifically evaluated the optimal frequency of renal function monitoring in patients with heart failure. Furthermore, there is variability between guidelines, and recommendations are typically nonspecific. Safer prescribing of diuretics in combination with other antiheart failure treatments requires better evidence for frequency of renal function monitoring. We suggest developing more personalized monitoring rather than from the current medication-based guidance. Such flexible clinical guidelines could be implemented using intelligent clinical decision support systems. Personalized renal function monitoring would be more effective in preventing renal decline, rather than reacting to it.

EHDViz: clinical dashboard development using open-source technologies

OBJECTIVE

To design, develop and prototype clinical dashboards to integrate high-frequency health and wellness data streams using interactive and real-time data visualisation and analytics modalities.

MATERIALS AND METHODS

We developed a clinical dashboard development framework called electronic healthcare data visualization (EHDViz) toolkit for generating web-based, real-time clinical dashboards for visualising heterogeneous biomedical, healthcare and wellness data. The EHDViz is an extensible toolkit that uses R packages for data management, normalisation and producing high-quality visualisations over the web using R/Shiny web server architecture. We have developed use cases to illustrate utility of EHDViz in different scenarios of clinical and wellness setting as a visualisation aid for improving healthcare delivery.

RESULTS

Using EHDViz, we prototyped clinical dashboards to demonstrate the contextual versatility of EHDViz toolkit. An outpatient cohort was used to visualise population health management tasks (n=14,221), and an inpatient cohort was used to visualise real-time acuity risk in a clinical unit (n=445), and a quantified-self example using wellness data from a fitness activity monitor worn by a single individual was also discussed (n-of-1). The back-end system retrieves relevant data from data source, populates the main panel of the application and integrates user-defined data features in real-time and renders output using modern web browsers. The visualisation elements can be customised using health features, disease names, procedure names or medical codes to populate the visualisations. The source code of EHDViz and various prototypes developed using EHDViz are available in the public domain at http://ehdviz.dudleylab.org.

CONCLUSIONS

Collaborative data visualisations, wellness trend predictions, risk estimation, proactive acuity status monitoring and knowledge of complex disease indicators are essential components of implementing data-driven precision medicine. As an open-source visualisation framework capable of integrating health assessment, EHDViz aims to be a valuable toolkit for rapid design, development and implementation of scalable clinical data visualisation dashboards.