A prediction model for one- and three-year mortality in dementia: results from a nationwide hospital-based cohort of 50,993 patients in the Netherlands

Vorst, Irene E. van de; Golüke, Nienke M.S.; Vaartjes, Ilonca; Bots, Michiel L.; Koek, Huiberdina L.

doi:10.1093/ageing/afaa007

Cited by 16 publications

(18 citation statements)

References 24 publications

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“…Our models had good predictive accuracy, as shown by the calibration plots, and the discriminative ability of the models appeared to be modest yet comparable to that of other clinical prediction models such as the Framingham Coronary Heart Disease Score. 31,32 The high shrinkage factors and low optimism in the bootstrapped Harrell c index also indicated that our developed models perform well in Swedish people diagnosed with dementia. We expect our findings to be fairly generalizable to people with dementia in other Western European countries, especially countries with similar population structures and health care systems.…”

Section: Discussionmentioning

confidence: 65%

Survival time tool to guide care planning in people with dementia

et al. 2020

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ObjectiveTo develop survival prediction tables to inform physicians and patients about survival probabilities after the diagnosis of dementia and to determine whether survival after dementia diagnosis can be predicted with good accuracy.MethodsWe conducted a nationwide registry-linkage study including 829 health centers, i.e., all memory clinics and ≈75% of primary care facilities, across Sweden. Data including cognitive function from 50,076 people with incident dementia diagnoses ≥65 years of age and registered with the Swedish Dementia Register in 2007 to 2015 were used, with a maximum follow-up of 9.7 years for survival until 2016. Sociodemographic factors, comorbidity burden, medication use, and dates of death were obtained from nationwide registries. Cox proportional hazards regression models were used to create tables depicting 3-year survival probabilities for different risk factor profiles.ResultsBy August 2016, 20,828 (41.6%) patients in our cohort had died. Median survival time from diagnosis of dementia was 5.1 (interquartile range 2.9–8.0) years for women and 4.3 (interquartile range 2.3–7.0) years for men. Predictors of mortality were higher age, male sex, increased comorbidity burden and lower cognitive function at diagnosis, a diagnosis of non-Alzheimer dementia, living alone, and using more medications. The developed prediction tables yielded c indexes of 0.70 (95% confidence interval [CI] 0.69–0.71) to 0.72 (95% CI 0.71–0.73) and showed good calibration.ConclusionsThree-year survival after dementia diagnosis can be predicted with good accuracy. The survival prediction tables developed in this study may aid clinicians and patients in shared decision-making and advance care planning.

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Section: Discussionmentioning

confidence: 65%

Survival time tool to guide care planning in people with dementia

et al. 2020

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“…Study quality according to PROBAST criteria is shown in Table 2. Eight of the studies had concerns about applicability, either because they did not provide a usable probability table or provided only a simple cut-off with sensitivity and specificity (3 studies) [9][10][11], included hospitalized inpatients (2) [12,13], or used predictors that were difficult to obtain, poorly defined, subjective, or non-standardized (3) [14][15][16].…”

Section: Resultsmentioning

confidence: 99%

“…Common reasons included lack of information on discrimination and calibration, lack of internal validation, insufficient numbers of events, lack of accounting for missing data or using complete case analysis, and selecting final candidates based on the results of univariate p value testing (more details available in Supplementary Table 2). Of the 16 studies reporting model development, 2 had low risk of bias for ≥18 PROBAST signaling questions [17,18], 7 had low risk of bias for 15-17 signaling questions [9,10,12,[19][20][21], and 7 had low risk of bias for <15 signaling questions [11, 13-16, 22, 23]. The two highest quality studies were the Advanced Dementia Prognostic Tool (ADEPT) [17], derived to predict risk of death at 6 months among persons with advanced dementia in nursing homes, and a prognostic model for risk of death at 6 months among persons with dementia seen in outpatient primary care or dementia specialty clinics contributing data to a nationwide registry [18].…”

Section: Resultsmentioning

confidence: 99%

Mortality Risk Models for Persons with Dementia: A Systematic Review

Smith

Ismail

2021

JAD

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Background: Persons with dementia have higher mortality than the general population. Objective, standardized predictions of mortality risk in persons with dementia could help with planning resources for care close to the end of life. Objective: To systematically review prediction models for risk of death in persons with dementia. Methods: The Medline and PsycInfo databases were searched on November 29, 2020, for prediction models estimating the risk of death in persons with dementia. Study quality was assessed using the Prediction model Risk Of Bias ASsessment Tool. Results: The literature search identified 2,828 studies, of which 18 were included. These studies described 16 different prediction models with c statistics mostly ranging from 0.67 to 0.79. Five models were externally validated, of which four were applicable. There were two models that were both applicable and had reasonably low risk of bias. One model predicted risk of death at six months in persons with advanced dementia residing in a nursing home. The other predicted risk of death at three years in persons seen in primary care practice or a dementia specialty clinic, derived from a nationwide registry in Sweden but not externally validated. Conclusion: Valid, applicable models with low risk of bias were found in two settings: advanced dementia in a nursing home and outpatient practices. The outpatient model requires external validation. Better models are needed for persons with mild to moderate dementia in nursing homes, a common demographic. These models may be useful for educating persons living with dementia and care partners and directing resources for end of life care. Registration: The study protocol is registered on PROSPERO as RD4202018076.

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“…In 2,624 elderly nursing home patients, CCI predicted 6-month mortality [147]. In 50,993 patients with dementia, the CCI predicted 1- and 3-year survival [148]. In 1,001 elderly patients dependent on home care, the CCI predicted 1-year survival [149].…”

Section: Predictive Validitymentioning

confidence: 99%

Charlson Comorbidity Index: A Critical Review of Clinimetric Properties

Charlson

Carrozzino

Guidi

et al. 2022

Psychother Psychosom

677

317

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The present critical review was conducted to evaluate the clinimetric properties of the Charlson Comorbidity Index (CCI), an assessment tool designed specifically to predict long-term mortality, with regard to its reliability, concurrent validity, sensitivity, incremental and predictive validity. The original version of the CCI has been adapted for use with different sources of data, ICD-9 and ICD-10 codes. The inter-rater reliability of the CCI was found to be excellent, with extremely high agreement between self-report and medical charts. The CCI has also been shown either to have concurrent validity with a number of other prognostic scales or to result in concordant predictions. Importantly, the clinimetric sensitivity of the CCI has been demonstrated in a variety of medical conditions, with stepwise increases in the CCI associated with stepwise increases in mortality. The CCI is also characterized by the clinimetric property of incremental validity, whereby adding the CCI to other measures increases the overall predictive accuracy. It has been shown to predict long-term mortality in different clinical populations, including medical, surgical, intensive care unit (ICU), trauma, and cancer patients. It may also predict in-hospital mortality, although in some instances, such as ICU or trauma patients, the CCI did not perform as well as other instruments designed specifically for that purpose. The CCI thus appears to be clinically useful not only to provide a valid assessment of the patient’s unique clinical situation, but also to demarcate major diagnostic and prognostic differences among subgroups of patients sharing the same medical diagnosis.

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A prediction model for one- and three-year mortality in dementia: results from a nationwide hospital-based cohort of 50,993 patients in the Netherlands

Cited by 16 publications

References 24 publications

Survival time tool to guide care planning in people with dementia

Survival time tool to guide care planning in people with dementia

Mortality Risk Models for Persons with Dementia: A Systematic Review

Charlson Comorbidity Index: A Critical Review of Clinimetric Properties

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