Dataset Information

Development, validation and clinical impact of a prediction model for 6-month mortality in older cancer patients: the GRADE.

ABSTRACT:

Background

To develop, validate, and assess the clinical impact of a clinical score to predict a 6-month mortality risk among older cancer patients.

Results

The mean age was 81.2 ± 6.1 years (women: 54%, various cancers, metastatic cancer: 45%). The score, namely the GRADE, included two geriatric variables (unintentional weight loss, impaired mobility), two oncological variables (cancer site, cancer extension), and exclusively supportive care. Up to a 14% risk of early death, the decision curves suggest that cancer treatment should be instated.

Conclusion

We have developed and validated a simple score, easy to implement in daily oncological practice, to predict early death among older cancer patients which could guide oncologists in their treatment decisions.

Methods

603 outpatients prospectively included in the Physical Frailty in Elderly Cancer patients cohort study. We created a multivariate prediction model by evaluating the strength of the individual components of the Geriatric Assessment regarding risk of death at 6 months. Each component was evaluated by univariate analysis and the significant variables (P ≤ 0.20) were carried on as covariates in the multivariate cox proportion hazard analysis. The beta coefficients from the model were used to build a point-based scoring system. Clinical impact was assessed using decision curves.

SUBMITTER: Angeli E

PROVIDER: S-EPMC7093177 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development, validation and clinical impact of a prediction model for 6-month mortality in older cancer patients: the GRADE.

Angeli Eurydice E Chouahnia Kader K Canoui-Poitrine Florence F Duchemann Boris B Aparicio Thomas T Paillaud Elena E Zelek Laurent L Bousquet Guilhem G Pamoukdjian Frédéric F

Aging 20200310 5

<h4>Background</h4>To develop, validate, and assess the clinical impact of a clinical score to predict a 6-month mortality risk among older cancer patients.<h4>Results</h4>The mean age was 81.2 ± 6.1 years (women: 54%, various cancers, metastatic cancer: 45%). The score, namely the GRADE, included two geriatric variables (unintentional weight loss, impaired mobility), two oncological variables (cancer site, cancer extension), and exclusively supportive care. Up to a 14% risk of early death, the ...[more]

PMID: 32156833

Similar Datasets

Project description:BackgroundThe COVID-19 pandemic has affected millions of individuals and caused hundreds of thousands of deaths worldwide. Predicting mortality among patients with COVID-19 who present with a spectrum of complications is very difficult, hindering the prognostication and management of the disease. We aimed to develop an accurate prediction model of COVID-19 mortality using unbiased computational methods, and identify the clinical features most predictive of this outcome.MethodsIn this prediction model development and validation study, we applied machine learning techniques to clinical data from a large cohort of patients with COVID-19 treated at the Mount Sinai Health System in New York City, NY, USA, to predict mortality. We analysed patient-level data captured in the Mount Sinai Data Warehouse database for individuals with a confirmed diagnosis of COVID-19 who had a health system encounter between March 9 and April 6, 2020. For initial analyses, we used patient data from March 9 to April 5, and randomly assigned (80:20) the patients to the development dataset or test dataset 1 (retrospective). Patient data for those with encounters on April 6, 2020, were used in test dataset 2 (prospective). We designed prediction models based on clinical features and patient characteristics during health system encounters to predict mortality using the development dataset. We assessed the resultant models in terms of the area under the receiver operating characteristic curve (AUC) score in the test datasets.FindingsUsing the development dataset (n=3841) and a systematic machine learning framework, we developed a COVID-19 mortality prediction model that showed high accuracy (AUC=0·91) when applied to test datasets of retrospective (n=961) and prospective (n=249) patients. This model was based on three clinical features: patient's age, minimum oxygen saturation over the course of their medical encounter, and type of patient encounter (inpatient vs outpatient and telehealth visits).InterpretationAn accurate and parsimonious COVID-19 mortality prediction model based on three features might have utility in clinical settings to guide the management and prognostication of patients affected by this disease. External validation of this prediction model in other populations is needed.FundingNational Institutes of Health.

Project description:ObjectiveThis research aims to construct and authenticate a comprehensive predictive model for all-cause mortality, based on a multifaceted array of risk factors.MethodsThe derivation cohort for this study was the Chinese Longitudinal Healthy Longevity Survey (CLHLS), while the Healthy Ageing and Biomarkers Cohort Study (HABCS) and the China Health and Retirement Longitudinal Study (CHARLS) were used as validation cohorts. Risk factors were filtered using lasso regression, and predictive factors were determined using net reclassification improvement. Cox proportional hazards models were employed to establish the mortality risk prediction equations, and the model's fit was evaluated using a discrimination concordance index (C-index). To evaluate the internal consistency of discrimination and calibration, a 10x10 cross-validation technique was employed. Calibration plots were generated to compare predicted probabilities with observed probabilities. The prediction ability of the equations was demonstrated using nomogram.ResultsThe CLHLS (mean age 88.08, n = 37074) recorded 28158 deaths (179683 person-years) throughout the course of an 8-20 year follow-up period. Additionally, there were 1384 deaths in the HABCS (mean age 86.74, n = 2552), and 1221 deaths in the CHARLS (mean age 72.48, n = 4794). The final all-cause mortality model incorporated demographic characteristics like age, sex, and current marital status, as well as functional status indicators including cognitive function and activities of daily living. Additionally, lifestyle factors like past smoking condition and leisure activities including housework, television viewing or radio listening, and gardening work were included. The C-index for the derivation cohort was 0.728 (95% CI: 0.724-0.732), while the external validation results for the CHARS and HABCS cohorts were 0.761 (95% CI: 0.749-0.773) and 0.713 (95% CI: 0.697-0.729), respectively.ConclusionThis study introduces a reliable, validated, and acceptable mortality risk predictor for older adults in China. These predictive factors have potential applications in public health policy and clinical practice.

Project description:ImportanceEstimating mortality risk in older adults with dementia is important for guiding decisions such as cancer screening, treatment of new and chronic medical conditions, and advance care planning.ObjectiveTo develop and externally validate a mortality prediction model in community-dwelling older adults with dementia.Design, setting, and participantsThis cohort study included community-dwelling participants (aged ≥65 years) in the Health and Retirement Study (HRS) from 1998 to 2016 (derivation cohort) and National Health and Aging Trends Study (NHATS) from 2011 to 2019 (validation cohort).ExposuresCandidate predictors included demographics, behavioral/health factors, functional measures (eg, activities of daily living [ADL] and instrumental activities of daily living [IADL]), and chronic conditions.Main outcomes and measuresThe primary outcome was time to all-cause death. We used Cox proportional hazards regression with backward selection and multiple imputation for model development. Model performance was assessed by discrimination (integrated area under the receiver operating characteristic curve [iAUC]) and calibration (plots of predicted and observed mortality).ResultsOf 4267 participants with probable dementia in HRS, the mean (SD) age was 82.2 (7.6) years, 2930 (survey-weighted 69.4%) were female, and 785 (survey-weighted 12.1%) identified as Black. Median (IQR) follow-up time was 3.9 (2.0-6.8) years, and 3466 (81.2%) participants died by end of follow-up. The final model included age, sex, body mass index, smoking status, ADL dependency count, IADL difficulty count, difficulty walking several blocks, participation in vigorous physical activity, and chronic conditions (cancer, heart disease, diabetes, lung disease). The optimism-corrected iAUC after bootstrap internal validation was 0.76 (95% CI, 0.75-0.76) with time-specific AUC of 0.73 (95% CI, 0.70-0.75) at 1 year, 0.75 (95% CI, 0.73-0.77) at 5 years, and 0.84 (95% CI, 0.82-0.85) at 10 years. On external validation in NHATS (n = 2404), AUC was 0.73 (95% CI, 0.70-0.76) at 1 year and 0.74 (95% CI, 0.71-0.76) at 5 years. Calibration plots suggested good calibration across the range of predicted risk from 1 to 10 years.Conclusions and relevanceWe developed and externally validated a mortality prediction model in community-dwelling older adults with dementia that showed good discrimination and calibration. The mortality risk estimates may help guide discussions regarding treatment decisions and advance care planning.

Dataset Information

Development, validation and clinical impact of a prediction model for 6-month mortality in older cancer patients: the GRADE.

Background

Results

Conclusion

Methods

Publications

Development, validation and clinical impact of a prediction model for 6-month mortality in older cancer patients: the GRADE.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets