Literature DB >> 20558582

How accurate are physicians in the prediction of patient survival in advanced lung cancer?

Christelle Clément-Duchêne1, Charlotte Carnin, Francis Guillemin, Yves Martinet.   

Abstract

BACKGROUND: Because most cases of non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage with a poor prognosis, patient inclusion in clinical trials is critical. Most trials require an estimated life expectancy >3 months, based on clinician estimates of patient survival probability, without providing formal guidelines. The aim of this study was to assess the accuracy of clinicians' predictions of survival in NSCLC patients (stages IIIB, and IV) and the possible impact of patient quality of life on survival estimation.
METHODS: At diagnosis, clinical, biological, and quality of life data (QLQ-C30 questionnaire) were recorded, and doctors "forecast" each patient's estimated survival. Concordance between predicted and actual survival was assessed with the intraclass correlation coefficient.
RESULTS: Eighty-five patients with a mean age of 62.2 years, 81.1% male, were included (squamous cell carcinoma, 33; adenocarcinoma, 42; large cell carcinoma, 8; neuroendocrine carcinoma, 2). The mean follow-up was 40 months and median survival time was 11.7 (range, 0.4-143.7) weeks. All clinicians (residents, registrars, and consultants) overestimated patient survival time, with a moderate concordance between predicted and actual survival time. A worse global health status was associated with a lower discrepancy between estimated and actual patient survival, and a worse role functioning was associated with a larger difference between estimated and actual patient survival.
CONCLUSION: The absence of specific recommendations to estimate patient survival may introduce major selection in clinical studies. Further research should investigate whether the accuracy of patient survival estimates by clinicians would be improved by taking into account patient quality of life.

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Year:  2010        PMID: 20558582      PMCID: PMC3228014          DOI: 10.1634/theoncologist.2009-0149

Source DB:  PubMed          Journal:  Oncologist        ISSN: 1083-7159


  29 in total

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2.  Terminal cancer. duration and prediction of survival time.

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3.  Predictive survival markers in patients with surgically resected non-small cell lung carcinoma.

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Journal:  Clin Cancer Res       Date:  2000-03       Impact factor: 12.531

4.  Prognostic value of stage grouping and TNM descriptors in lung cancer.

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6.  Extent and determinants of error in doctors' prognoses in terminally ill patients: prospective cohort study.

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7.  Quality-of-life scores predict outcome in metastatic but not early breast cancer. International Breast Cancer Study Group.

Authors:  A S Coates; C Hürny; H F Peterson; J Bernhard; M Castiglione-Gertsch; R D Gelber; A Goldhirsch
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8.  Quality of life and survival prediction in terminal cancer patients: a multicenter study.

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Authors:  A Depierre; J L Lagrange; S Theobald; P Astoul; P Baldeyrou; E Bardet; B Bazelly; J M Bréchot; J L Breton; J Y Douillard; M Grivaux; P Jacoulet; A Khalil; E Lemarié; Y Martinet; G Massard; B Milleron; T Molina; D Moro-Sibilot; M Paesmans; J L Pujol; E Quoix; E Ranfaing; A Rivière; H Sancho-Garnier; P J Souquet; D Spaeth; A Stoebner-Delbarre; L Thiberville; E Touboul; F Vaylet; J M Vergnon; V Westeel
Journal:  Br J Cancer       Date:  2003-08       Impact factor: 7.640

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  14 in total

1.  Prediction of lung cancer patient survival via supervised machine learning classification techniques.

Authors:  Chip M Lynch; Behnaz Abdollahi; Joshua D Fuqua; Alexandra R de Carlo; James A Bartholomai; Rayeanne N Balgemann; Victor H van Berkel; Hermann B Frieboes
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2.  A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making.

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3.  How palliative care professionals deal with predicting life expectancy at the end of life: predictors and accuracy.

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5.  The accuracy of probabilistic versus temporal clinician prediction of survival for patients with advanced cancer: a preliminary report.

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Journal:  Oncologist       Date:  2011-10-05

6.  Accuracy of Oncologists' Life-Expectancy Estimates Recalled by Their Advanced Cancer Patients: Correlates and Outcomes.

Authors:  Jason Lambden; Baohui Zhang; Robert Friedlander; Holly G Prigerson
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7.  Performance status is the most powerful risk factor for early death among patients with advanced soft tissue sarcoma: the European Organisation for Research and Treatment of Cancer-Soft Tissue and Bone Sarcoma Group (STBSG) and French Sarcoma Group (FSG) study.

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Journal:  Br J Cancer       Date:  2011-04-19       Impact factor: 7.640

8.  Machine Learning methods for Quantitative Radiomic Biomarkers.

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Review 9.  Symptomatic spinal metastasis: A systematic literature review of the preoperative prognostic factors for survival, neurological, functional and quality of life in surgically treated patients and methodological recommendations for prognostic studies.

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10.  Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.

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