Rapid learning in practice: a lung cancer survival decision support system in routine patient care data

Andre Dekker, Shalini Vinod, Lois Holloway, Cary Oberije, Armia George, Gary Goozee, Geoff P Delaney, Philippe Lambin, David Thwaites

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)

Abstract

BACKGROUND AND PURPOSE: A rapid learning approach has been proposed to extract and apply knowledge from routine care data rather than solely relying on clinical trial evidence. To validate this in practice we deployed a previously developed decision support system (DSS) in a typical, busy clinic for non-small cell lung cancer (NSCLC) patients.

MATERIAL AND METHODS: Gender, age, performance status, lung function, lymph node status, tumor volume and survival were extracted without review from clinical data sources for lung cancer patients. With these data the DSS was tested to predict overall survival.

RESULTS: 3919 lung cancer patients were identified with 159 eligible for inclusion, due to ineligible histology or stage, non-radical dose, missing tumor volume or survival. The DSS successfully identified a good prognosis group and a medium/poor prognosis group (2 year OS 69% vs. 27/30%, p<0.001). Stage was less discriminatory (2 year OS 47% for stage I-II vs. 36% for stage IIIA-IIIB, p=0.12) with most good prognosis patients having higher stage disease. The DSS predicted a large absolute overall survival benefit (∼40%) for a radical dose compared to a non-radical dose in patients with a good prognosis, while no survival benefit of radical radiotherapy was predicted for patients with a poor prognosis.

CONCLUSIONS: A rapid learning environment is possible with the quality of clinical data sufficient to validate a DSS. It uses patient and tumor features to identify prognostic groups in whom therapy can be individualized based on predicted outcomes. Especially the survival benefit of a radical versus non-radical dose predicted by the DSS for various prognostic groups has clinical relevance, but needs to be prospectively validated.

Original languageEnglish
Pages (from-to)47-53
Number of pages7
JournalRadiotherapy and Oncology
Volume113
Issue number1
DOIs
Publication statusPublished - Oct 2014

Keywords

  • Aged
  • Carcinoma, Non-Small-Cell Lung
  • Decision Support Techniques
  • Female
  • Humans
  • Kaplan-Meier Estimate
  • Lung Neoplasms
  • Male
  • Neoplasm Staging
  • Netherlands
  • New South Wales
  • Patient Care
  • Professional Practice
  • Prognosis

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