Dataset Information

Validation of the Erlangen Score Algorithm for the Prediction of the Development of Dementia due to Alzheimer's Disease in Pre-Dementia Subjects.

ABSTRACT: In previous studies, a dichotomous stratification of subjects into "cerebrospinal fluid (CSF) normal" and "CSF pathologic" was used to investigate the role of biomarkers in the prediction of progression to dementia in pre-dementia/mild cognitive impairment subjects. With the previously published Erlangen Score Algorithm, we suggested a division of CSF patterns into five groups, covering all possible CSF result combinations based on the presence of pathologic tau and/or amyloid-? CSF values.This study aimed to validate the Erlangen Score diagnostic algorithm based on the results of biomarkers analyses obtained in different patients cohorts, with different pre-analytical protocols, and with different laboratory analytical platforms.We evaluated the algorithm in two cohorts of pre-dementia subjects: the US-Alzheimer's Disease Neuroimaging Initiative and the German Dementia Competence Network.In both cohorts, the Erlangen scores were strongly associated with progression to Alzheimer's disease. Neither the scores of the progressors nor the scores of the non-progressors differed significantly between the two projects, in spite of significant differences in the cohorts, laboratory methods, and the samples treatment.Our findings confirm the utility of the Erlangen Score algorithm as a useful tool in the early neurochemical diagnosis of Alzheimer's disease.

SUBMITTER: Lewczuk P

PROVIDER: S-EPMC5127395 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Validation of the Erlangen Score Algorithm for the Prediction of the Development of Dementia due to Alzheimer's Disease in Pre-Dementia Subjects.

Lewczuk Piotr P Kornhuber Johannes J Toledo Jon B JB Trojanowski John Q JQ Knapik-Czajka Malgorzata M Peters Oliver O Wiltfang Jens J Shaw Leslie M LM

Journal of Alzheimer's disease : JAD 20150101 2

<h4>Background</h4>In previous studies, a dichotomous stratification of subjects into "cerebrospinal fluid (CSF) normal" and "CSF pathologic" was used to investigate the role of biomarkers in the prediction of progression to dementia in pre-dementia/mild cognitive impairment subjects. With the previously published Erlangen Score Algorithm, we suggested a division of CSF patterns into five groups, covering all possible CSF result combinations based on the presence of pathologic tau and/or amyloid ...[more]

PMID: 26402007

Similar Datasets

Project description:BACKGROUND:The previously described and validated Erlangen Score (ES) algorithm enables interpretation of the cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD), ordering them on an ordinal scale: from neurochemically normal (ES?=?0) through improbable AD (ES?=?1), possible AD (ES?=?2 or 3), to probable AD (ES?=?4). Here we assess the accuracy of the ES in predicting hazards of progression from the mild cognitive impairment (MCI) stage of AD to the dementia stage of the disease (Alzheimer's disease dementia (ADD)) in a novel, single-center cohort. METHODS:Baseline CSF biomarkers (amyloid beta (A?) 1-42, A?42/40, Tau, and pTau181), interpreted according to the ES, were used to estimate time to progression from the MCI stage of AD to ADD, conditional on age, gender, APOE ?4 genotype, and Mini Mental State Examination score in 144 MCI subjects, using the Extended Cox Model; the subjects were followed-up until they developed dementia or until they had been cognitively stable for at least 2?years. In addition, ES distributions were studied in 168 ADD cases and 66 neurologic controls. Further, we stratified MCI patients into those who progressed to ADD faster (within 3 years, n?=?47) and those who progressed slower (n?=?74). RESULTS:The distributions of the ES categories across the four diagnostic groups (Controls, MCI-Stable, MCI-AD, and ADD) were highly significantly different (Kruskal-Wallis ?2(df?=?3)?=?151.4, p?<?0.001), with significant contrasts between each pair (p?<?0.005), except between the ADD and the MCI-AD groups (p?=?1.0). MCI patients with ES?=?2 or 3 had 6-8 times higher hazards to progress to ADD compared to patients with ES?=?0 or 1 in the first 3 follow-up years, and then their hazards decreased to those of the group with ES?=?0 or 1. Patients with ES?=?4 had hazards 8-12 times higher compared to the ES?=?0 or 1 group. Faster progressors with ES?=?2 or 3 had, in comparison to slower progressors, significantly lower A?1-42, A?1-40, and A?42/40, but comparable Tau and pTau181. A highly significant difference of the ES distributions between these two groups was observed (p?<?0.001). CONCLUSIONS:Our current results reconfirm and extend the conclusions of the previously published report that the Erlangen Score is a useful tool facilitating interpretation of a complex pattern of the CSF AD biomarkers.

Project description:BackgroundHIV pre-exposure prophylaxis (PrEP) is effective but underused, in part because clinicians do not have the tools to identify PrEP candidates. We developed and validated an automated prediction algorithm that uses electronic health record (EHR) data to identify individuals at increased risk for HIV acquisition.MethodsWe used machine learning algorithms to predict incident HIV infections with 180 potential predictors of HIV risk drawn from EHR data from 2007-15 at Atrius Health, an ambulatory group practice in Massachusetts, USA. We included EHRs of all patients aged 15 years or older with at least one clinical encounter during 2007-15. We used ten-fold cross-validated area under the receiver operating characteristic curve (cv-AUC) with 95% CIs to assess the model's performance at identifying individuals with incident HIV and patients independently prescribed PrEP by clinicians. The best-performing model was validated prospectively with 2016 data from Atrius Health and externally with 2011-16 data from Fenway Health, a community health centre specialising in sexual health care in Boston (MA, USA). We calculated HIV risk scores (ie, probability of an incident HIV diagnosis) for every HIV-uninfected patient not on PrEP during 2007-15 at Atrius Health and assessed the distribution of scores for thresholds to determine possible candidates for PrEP in the three study cohorts.FindingsWe included 1 155 966 Atrius Health patients from 2007-15 (150 [<0·1%] patients with incident HIV) in our development cohort, 537 257 Atrius Health patients in 2016 (16 [<0·1%] with incident HIV) in our prospective validation cohort, and 33 404 Fenway Health patients from 2011-16 (423 [1·3%] with incident HIV) in our external validation cohort. The best-performing algorithm was obtained with least absolute shrinkage and selection operator (LASSO) and had a cv-AUC of 0·86 (95% CI 0·82-0·90) for identification of incident HIV infections in the development cohort, 0·91 (0·81-1·00) on prospective validation, and 0·77 (0·74-0·79) on external validation. The LASSO model successfully identified patients independently prescribed PrEP by clinicians at Atrius Health in 2016 (cv-AUC 0·93, 95% CI 0·90-0·96) or Fenway Health (0·79, 0·78-0·80). HIV risk scores increased steeply at the 98th percentile. Using this score as a threshold, we prospectively identified 9515 (1·8%) of 536 384 patients at Atrius Health in 2016 and 4385 (15·3%) of 28 702 Fenway Health patients as potential PrEP candidates.InterpretationAutomated algorithms can efficiently identify patients at increased risk for HIV acquisition. Integrating these models into EHRs to alert providers about patients who might benefit from PrEP could improve prescribing and prevent new HIV infections.FundingHarvard University Center for AIDS Research, Providence/Boston Center for AIDS Research, Rhode Island IDeA-CTR, the National Institute of Mental Health, and the US Centers for Disease Control and Prevention.

Project description:ImportanceEarly palliative care interventions drive high-value care but currently are underused. Health care professionals face challenges in identifying patients who may benefit from palliative care.ObjectiveTo develop a deep learning algorithm using longitudinal electronic health records to predict mortality risk as a proxy indicator for identifying patients with dementia who may benefit from palliative care.Design, setting, and participantsIn this retrospective cohort study, 6-month, 1-year, and 2-year mortality prediction models with recurrent neural networks used patient demographic information and topics generated from clinical notes within Partners HealthCare System, an integrated health care delivery system in Boston, Massachusetts. This study included 26 921 adult patients with dementia who visited the health care system from January 1, 2011, through December 31, 2017. The models were trained using a data set of 24 229 patients and validated using another data set of 2692 patients. Data were analyzed from September 18, 2018, to May 15, 2019.Main outcomes and measuresThe area under the receiver operating characteristic curve (AUC) for 6-month and 1- and 2-year mortality prediction models and the factors contributing to the predictions.ResultsThe study cohort included 26 921 patients (16 263 women [60.4%]; mean [SD] age, 74.6 [13.5] years). For the 24 229 patients in the training data set, mean (SD) age was 74.8 (13.2) years and 14 632 (60.4%) were women. For the 2692 patients in the validation data set, mean (SD) age was 75.0 (12.6) years and 1631 (60.6%) were women. The 6-month model reached an AUC of 0.978 (95% CI, 0.977-0.978); the 1-year model, 0.956 (95% CI, 0.955-0.956); and the 2-year model, 0.943 (95% CI, 0.942-0.944). The top-ranked latent topics associated with 6-month and 1- and 2-year mortality in patients with dementia include palliative and end-of-life care, cognitive function, delirium, testing of cholesterol levels, cancer, pain, use of health care services, arthritis, nutritional status, skin care, family meeting, shock, respiratory failure, and swallowing function.Conclusions and relevanceA deep learning algorithm based on patient demographic information and longitudinal clinical notes appeared to show promising results in predicting mortality among patients with dementia in different time frames. Further research is necessary to determine the feasibility of applying this algorithm in clinical settings for identifying unmet palliative care needs earlier.

Project description:The number of patients with Alzheimer's disease (AD) is increasing and so is the number of patients driving a car. To enable patients to retain their mobility while at the same time not endangering public safety, each patient should be assessed for fitness to drive. The aim of this study is to develop a method to assess fitness to drive in a clinical setting, using three types of assessments, i.e. clinical interviews, neuropsychological assessment and driving simulator rides. The goals are (1) to determine for each type of assessment which combination of measures is most predictive for on-road driving performance, (2) to compare the predictive value of clinical interviews, neuropsychological assessment and driving simulator evaluation and (3) to determine which combination of these assessments provides the best prediction of fitness to drive. Eighty-one patients with AD and 45 healthy individuals participated. All participated in a clinical interview, and were administered a neuropsychological test battery and a driving simulator ride (predictors). The criterion fitness to drive was determined in an on-road driving assessment by experts of the CBR Dutch driving test organisation according to their official protocol. The validity of the predictors to determine fitness to drive was explored by means of logistic regression analyses, discriminant function analyses, as well as receiver operating curve analyses. We found that all three types of assessments are predictive of on-road driving performance. Neuropsychological assessment had the highest classification accuracy followed by driving simulator rides and clinical interviews. However, combining all three types of assessments yielded the best prediction for fitness to drive in patients with AD with an overall accuracy of 92.7%, which makes this method highly valid for assessing fitness to drive in AD. This method may be used to advise patients with AD and their family members about fitness to drive.

Dataset Information

Validation of the Erlangen Score Algorithm for the Prediction of the Development of Dementia due to Alzheimer's Disease in Pre-Dementia Subjects.

Publications

Validation of the Erlangen Score Algorithm for the Prediction of the Development of Dementia due to Alzheimer's Disease in Pre-Dementia Subjects.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets