Hyperspectral image-based spare autoencoder network for TVB-N measurement in pork

Guo, Tengfei; Huang, Min; Zhu, Qibing; Guo, Yanbing

doi:10.13031/aim.201700450

Cited by 2 publications

(1 citation statement)

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“…Model performance was assessed through discrimination, calibration, and the Brier score. For discrimination, we calculated the integrated area under the receiver operating characteristic curve (iAUC), which averages all available AUC statistics over time . We also reported time-specific values at clinically relevant time points of 1, 2, 5, and 10 years.…”

Section: Methodsmentioning

confidence: 99%

Development and External Validation of a Mortality Prediction Model for Community-Dwelling Older Adults With Dementia

et al. 2022

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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.

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Section: Methodsmentioning

confidence: 99%