Prevention of cancer-associated thromboembolism (TE) remains a significant clinical challenge and priority world-wide safety initiative. In this prospective non-small cell lung cancer (NSCLC) cohort, longitudinal TE risk profiling (clinical and biomarker) was undertaken to develop risk stratification models for targeted TE prevention. These were compared with published models from Khorana, CATS, PROTECHT, CONKO, and CATS/MICA. The NSCLC cohort of 129 patients, median follow-up 22.0 months (range 5.6—31.3), demonstrated a hypercoagulable profile in >75% patients and TE incidence of 19%. High TE risk patients were those receiving chemotherapy with baseline fibrinogen ≥ 4 g/L and d-dimer ≥ 0.5 mg/L; or baseline d-dimer ≥ 1.5 mg/L; or month 1 d-dimer ≥ 1.5 mg/L. The model predicted TE with 100% sensitivity and 34% specificity (c-index 0.67), with TE incidence 27% vs. 0% for high vs. low-risk. A comparison using the Khorana, PROTECHT, and CONKO methods were not discriminatory; TE incidence 17–25% vs. 14–19% for high vs. low-risk (c-index 0.51–0.59). Continuous d-dimer (CATS/MICA model) was also not predictive of TE. Independent of tumour stage, high TE risk was associated with cancer progression (HR 1.9, p = 0.01) and mortality (HR 2.2, p = 0.02). The model was tested for scalability in a prospective gastrointestinal cancer cohort with equipotency demonstrated; 80% sensitivity and 39% specificity. This proposed TE risk prediction model is simple, practical, potent and can be used in the clinic for real-time, decision-making for targeted thromboprophylaxis. Validation in a multicentre randomised interventional study is underway (ACTRN12618000811202).

Myelodysplastic syndromes (MDS) are a heterogeneous group of myeloid neoplasms. With the emergence of therapeutic options, attempts to standardize diagnostic, prognostic and response criteria to guide treatment decisions are increasingly important. This has been achieved in part by the revised 2008 World Health Organization classification and consensus guidelines outlining refined definitions and standards. Conventional criteria have limitations in terms of sensitivity and specificity. Multiparameter flow cytometry (FC) can be used real-time, and is a highly reproducible and objective way of assessing the pattern of expression of multiple antigens on a single hematopoietic cell and defined subpopulations. By comparing antigen expression within maturing myelomonocytic populations with that identified on the equivalent normal cells, abnormalities identified may provide a diagnostic indication of stem cell dysmaturation. There are now increasingly robust data demonstrating the capacity of FC to discriminate MDS from non-clonal cytopenias and dysplasia, as well as further refine disease classification and prognostication, which will be reviewed here.

A clinical decision support system (CDSS) is designed to assist health professionals in perioperative patient management. Robust CDSSs are vital to deliver enhanced healthcare services. Incorporating the latest advancements in digital technologies, mobile device based CDSSs are being introduced to healthcare settings at a considerable pace. However, given the nascency of this tech-health synergy, well-defined systematic approaches to be followed to design and develop mobile CDSSs to ensure developed technological solutions are of best fit-for-purpose, are lacking. To address this void, this study proposes an approach combining Task Technology Fit theory and Design Science Research Methodology, to guide the design and development of mobile CDSSs. The proposed approach is applied to a case study to design a mobile CDSS to assist perioperative optimization of surgery patients. The learnings from the case study are reported.