Dataset Information

Cost of diagnosing dementia in a German memory clinic.

ABSTRACT:

Background

Little is known about diagnostic work-ups or the costs of diagnosing dementia in specialized care. Here, we analyzed the costs of diagnosing dementia according to specific dementia disorders.

Methods

A prospective descriptive design was used to analyze the cost of diagnosing dementia for 120 patients with suspected dementia at a German memory clinic. The duration of clinical consultations and use of technical procedures were recorded by the memory clinic staff. To detect cost drivers, a multiple linear regression model was used.

Results

Of patients with suspected dementia, 44% were diagnosed with dementia. The total cost per patient and diagnostic process amounted to 501 € across all patients and 659 € for patients who were diagnosed with dementia. The costs varied between 649 € for patients with Alzheimer's disease, 662 € for patients with vascular or mixed dementia, and 705 € for patients with unspecific dementia. A final diagnosis of dementia was the only factor that was significantly associated with the diagnostic cost (b?=?356, CI^- 182, CI⁺ 531, p?=?0.001).

Conclusion

The high range of costs reflects differences in diagnostic demands depending on the etiology of dementia. This variation needs to be transferred into reimbursement. Further studies are needed to assess the influence of the type of cognitive impairment and of the setting on diagnostic costs.

SUBMITTER: Michalowsky B

PROVIDER: S-EPMC5568303 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Publications

Cost of diagnosing dementia in a German memory clinic.

Michalowsky Bernhard B Flessa Steffen S Hertel Johannes J Goetz Olav O Hoffmann Wolfgang W Teipel Stefan S Kilimann Ingo I

Alzheimer's research & therapy 20170822 1

<h4>Background</h4>Little is known about diagnostic work-ups or the costs of diagnosing dementia in specialized care. Here, we analyzed the costs of diagnosing dementia according to specific dementia disorders.<h4>Methods</h4>A prospective descriptive design was used to analyze the cost of diagnosing dementia for 120 patients with suspected dementia at a German memory clinic. The duration of clinical consultations and use of technical procedures were recorded by the memory clinic staff. To detec ...[more]

PMID: 28830516

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Project description:ImportanceMachine learning algorithms could be used as the basis for clinical decision-making aids to enhance clinical practice.ObjectiveTo assess the ability of machine learning algorithms to predict dementia incidence within 2 years compared with existing models and determine the optimal analytic approach and number of variables required.Design, setting, and participantsThis prognostic study used data from a prospective cohort of 15 307 participants without dementia at baseline to perform a secondary analysis of factors that could be used to predict dementia incidence. Participants attended National Alzheimer Coordinating Center memory clinics across the United States between 2005 and 2015. Analyses were conducted from March to May 2021.Exposures258 variables spanning domains of dementia-related clinical measures and risk factors.Main outcomes and measuresThe main outcome was incident all-cause dementia diagnosed within 2 years of baseline assessment.ResultsIn a sample of 15 307 participants (mean [SD] age, 72.3 [9.8] years; 9129 [60%] women and 6178 [40%] men) without dementia at baseline, 1568 (10%) received a diagnosis of dementia within 2 years of their initial assessment. Compared with 2 existing models for dementia risk prediction (ie, Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score, and the Brief Dementia Screening Indicator), machine learning algorithms were superior in predicting incident all-cause dementia within 2 years. The gradient-boosted trees algorithm had a mean (SD) overall accuracy of 92% (1%), sensitivity of 0.45 (0.05), specificity of 0.97 (0.01), and area under the curve of 0.92 (0.01) using all 258 variables. Analysis of variable importance showed that only 6 variables were required for machine learning algorithms to achieve an accuracy of 91% and area under the curve of at least 0.89. Machine learning algorithms also identified up to 84% of participants who received an initial dementia diagnosis that was subsequently reversed to mild cognitive impairment or cognitively unimpaired, suggesting possible misdiagnosis.Conclusions and relevanceThese findings suggest that machine learning algorithms could accurately predict incident dementia within 2 years in patients receiving care at memory clinics using only 6 variables. These findings could be used to inform the development and validation of decision-making aids in memory clinics.

Project description:BACKGROUND:Determination of β-amyloid (Aβ) positivity and likelihood of underlying Alzheimer's disease (AD) relies on dichotomous biomarker cut-off values. Individuals with mild cognitive impairment (MCI) and Aβ within the normal range may still have a substantial risk of developing dementia, primarily of Alzheimer type. Their prognosis, as well as predictors of clinical progression, are not fully understood. The aim of this study was to explore the associations of cerebrospinal fluid (CSF) biomarkers (Aβ42, total tau, phosphorylated tau) and other characteristics, including modifiable vascular factors, with the risk of progression to dementia among patients with MCI and normal CSF Aβ42. METHODS:Three hundred eighteen memory clinic patients with CSF and clinical data, and at least 1-year follow-up, were included. Patients had normal CSF Aβ42 levels based on clinical cut-offs. Cox proportional hazard models with age as time scale and adjusted for sex, education, and cognition (Mini-Mental State Examination) were used to investigate predictors of progression to dementia and Alzheimer-type dementia. Potential predictors included CSF biomarkers, cognitive performance (verbal learning and memory), apolipoprotein E (APOE) ε4 genotype, medial temporal lobe atrophy, family history of dementia, depressive symptoms, and vascular factors, including the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) risk score. Predictive performance of patient characteristics was further explored with Harrell C statistic. RESULTS:Lower normal Aβ42 and higher total tau and phosphorylated tau were associated with higher dementia risk, and the association was not driven by Aβ42 values close to cut-off. Additional predictors included poorer cognition, APOE ε4 genotype, higher systolic blood pressure, and lower body mass index, but not the CAIDE dementia risk score. Aβ42 individually and in combination with other CSF biomarkers improved the risk prediction compared to age and cognition alone. Medial temporal lobe atrophy or vascular factors did not increase the predictive performance. CONCLUSIONS:Possibility of underlying AD pathology and increased dementia risk should not be ruled out among MCI patients with CSF Aβ42 within the normal range. While cut-offs may be useful in clinical practice to identify high-risk individuals, personalized risk prediction tools incorporating continuous biomarkers may be preferable among individuals with intermediate risk. The role of modifiable vascular factors could be explored in this context.

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Dataset Information

Cost of diagnosing dementia in a German memory clinic.

Background

Methods

Results

Conclusion

Publications

Cost of diagnosing dementia in a German memory clinic.

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