202 Management of stage I and II non-small cell lung cancer at Plymouth Hospitals NHS Trust

Iyer, Sunil; Roy, Anke Pisters-van; Marchbank, Adrian

doi:10.1016/s0169-5002(13)70202-2

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ‘over-prescription’ of surgery by the probabilistic decision support, on the other hand, is caused by the limitations of the one-dimensional analysis based on survival maximization. In either case, evidence from NLCA and the Society of Cardiothoracic Surgeons show that although the curative resection rates in the UK are rising, they are still not at optimum levels [ 49 , 50 ]. In this respect, the adoption of LCA, which lays out qualitative and quantitative indicators in support of surgical treatment plans at the MDT meetings, may help in optimizing resection decisions.…”

Section: Discussionmentioning

confidence: 99%

Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care

Sesen

Peake

Bañares‐Alcántara

et al. 2014

J. R. Soc. Interface.

View full text Add to dashboard Cite

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.

show abstract

Section: Discussionmentioning

confidence: 99%