Physicians' perceptions of the value of prognostic models: the benefits and risks of prognostic confidence

Hallen, Sarah; Hootsmans, Norbert; Blaisdell, Laura; Gutheil, Caitlin; Han, Paul K. J.

doi:10.1111/hex.12196

Cited by 40 publications

(49 citation statements)

References 28 publications

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“…These tools are developed to decide whether certain treatments bring enough gain in overall or progressionfree survival to be worthwhile for patients. Research has shown that oncologists tend to overestimate life expectancy of patients [7,8], and that they could benefit from using statistical tools providing survival estimates [7,[9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Needs with Regard to Decision Support Systems for Treating Patients with Incurable Non-small Cell Lung Cancer

et al. 2019

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Treatment decision-making for patients with incurable non-small cell lung cancer (NSCLC) is complex due to the rapidly increasing number of treatments and discovery of new biomarkers. Decision support systems (DSS) could assist thoracic oncologists (TO) weighing of the pros and cons of treatments in order to arrive at an evidence-based and personalized treatment advice. Our aim is to inventory (1) TO's needs with regard to DSS in the treatment of incurable (stage IIIB/IV) NSCLC patients, and (2) preferences regarding the development of future tools in this field. We disseminated an online inventory questionnaire among all members of the Section of Oncology within the Society of Physicians in Chest Medicine and Tuberculosis. Telephone interviews were conducted to better contextualize the findings from the questionnaire. In total, 58 TO completed the questionnaire and expressed a need for new DSS. They reported that it is important for tools to include genetic and immune markers, to be sufficiently validated, regularly updated, and time-efficient. Also, future DSS should incorporate multiple treatment options, integrate estimates of toxicity, quality of life and costeffectiveness of treatments, enhance communication between caregivers and patients, and use IT solutions for a clear interface and continuous updating of tools. With this inventory among Dutch TO, we summarized the need for new DSS to aid treatment decision-making for patients with incurable NSCLC. To meet the expressed needs, substantial additional efforts will be required by DSS developers, above already existing tools.

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Section: Introductionmentioning

confidence: 99%

Needs with Regard to Decision Support Systems for Treating Patients with Incurable Non-small Cell Lung Cancer

et al. 2019

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“…They are based on patient and tumor characteristics, and preferably compare various treatment options. Research has shown that such clinical prediction models in end-of-life care are valued by physicians, because they enhance prognostic confidence and improve communication with patients, although they can also cause emotional distress in patients and raise prognostic overconfidence despite uncertainty in palliative care [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Decision support systems for incurable non-small cell lung cancer: a systematic review

Révész

Engelhardt

Tamminga

et al. 2017

BMC Med Inform Decis Mak

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BackgroundIndividually tailored cancer treatment is essential to ensure optimal treatment and resource use. Treatments for incurable metastatic non-small cell lung cancer (NSCLC) are evolving rapidly, and decision support systems (DSS) for this patient population have been developed to balance benefits and harms for decision-making. The aim of this systematic review was to inventory DSS for stage IIIB/IV NSCLC patients.MethodsA systematic literature search was performed in Pubmed, Embase and the Cochrane Library. DSS were described extensively, including their predictors, model performances (i.e., discriminative ability and calibration), levels of validation and user friendliness.ResultsThe systematic search yielded 3531 articles. In total, 67 articles were included after additional reference tracking. The 39 identified DSS aim to predict overall survival and/or progression-free survival, but give no information about toxicity or cost-effectiveness. Various predictors were incorporated, such as performance status, serum and inflammatory markers, and patient and tumor characteristics. Some DSS were developed for the entire incurable NSCLC population, whereas others were specifically for patients with brain or spinal metastases. Few DSS had been validated externally using recent clinical data, and the discrimination and calibration were often poor.ConclusionsMany DSS have been developed for incurable NSCLC patients, but DSS are still lacking that are up-to-date with a good model performance, while covering the entire treatment spectrum. Future DSS should incorporate genetic and biological markers based on state-of-the-art evidence, and compare multiple treatment options to estimate survival, toxicity and cost-effectiveness.Electronic supplementary materialThe online version of this article (10.1186/s12911-017-0542-1) contains supplementary material, which is available to authorized users.

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“…The identification of end-of-life patients is quintessential for an adequate integration between qualitative and intensive care treatments during the ICU stay [13,14]. Notably, palliative care should not be considered as an alternative for the intensive care in these patients; this care should instead be concomitantly made available for the patients and their family early on from the ICU admission [15].…”

Section: Discussionmentioning

confidence: 99%

Validation of END-of-life ScorING-system to identify the dying patient: a prospective analysis

et al. 2020

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Background:The "END-of-Life ScorING-System" (ENDING-S) was previously developed to identify patients at highrisk of dying in the ICU and to facilitate a practical integration between palliative and intensive care. The aim of this study is to prospectively validate ENDING-S in a cohort of long-term critical care patients. Materials and methods: Adult long-term ICU patients (with a length-of-stay> 4 days) were considered for this prospective multicenter observational study. ENDING-S and SOFA score were calculated daily and evaluated against the patient's ICU outcome. The predictive properties were evaluated through a receiver operating characteristic (ROC) analysis.Results: Two hundred twenty patients were enrolled for this study. Among these, 21.46% died during the ICU stay. ENDING-S correctly predicted the ICU outcome in 71.4% of patients. Sensitivity, specificity, positive and negative predictive values associated with the previously identified ENDING-S cut-off of 11.5 were 68.1, 72.3, 60 and 89.3%, respectively. ROC-AUC for outcome prediction was 0.79 for ENDING-S and 0.88 for SOFA in this cohort. Conclusions: ENDING-S, while not as accurately as in the pilot study, demonstrated acceptable discrimination properties in identifying long-term ICU patients at very high-risk of dying. ENDING-S may be a useful tool aimed at facilitating a practical integration between palliative, end-of-life and intensive care.

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Physicians' perceptions of the value of prognostic models: the benefits and risks of prognostic confidence

Cited by 40 publications

References 28 publications

Needs with Regard to Decision Support Systems for Treating Patients with Incurable Non-small Cell Lung Cancer

Needs with Regard to Decision Support Systems for Treating Patients with Incurable Non-small Cell Lung Cancer

Decision support systems for incurable non-small cell lung cancer: a systematic review

Validation of END-of-life ScorING-system to identify the dying patient: a prospective analysis

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