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- Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windowsPublication . Pereira, Telma; Lemos, Luis; Cardoso, Sandra; Silva, Dina; Rodrigues, Ana; Santana, Isabel; de Mendonca, Alexandre; Guerreiro, Manuela; Madeira, Sara C.Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.
- Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling studyPublication . van Maurik, Ingrid S.; Vos, Stephanie J.; Bos, Isabelle; Bouwman, Femke H.; Teunissen, Charlotte E.; Scheitens, Philip; Barkhof, Frederik; Frolich, Lutz; Kornhuber, Johannes; Wiftfang, Jens; Maier, Wolfgang; Peters, Oliver; ROther, Eckart; Nobili, Flavio; Frisoni, Giovanni B.; Spiru, Luiza; Freund-Levi, Yvonne; Wallin, Asa K.; Hampel, Harald; Soininen, Hilkka; Tsolaki, Magda; Verhey, Frans; Kloszewska, Iwona; Mecocci, Patrizia; Vellas, Bruno; Lovestone, Simon; Gailuzzi, Samantha; Herukka, Sanna-Kaisa; Santana, Isabel; Baldeiras, Ines; de Mendonca, Alexandre; Silva, Dina; Chetelat, Gael; Egret, Stephanie; Palmqvist, Sebastian; Hansson, Oskar; Visser, Pieter Jelle; Berkhof, Johannes; van der Flier, Wiesje M.Background Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia. Methods In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer’s Disease (EMIF-AD; n=883), Alzheimer’s Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models—a demographics model, a hippocampal volume model, and a CSF biomarkers model—by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell’s C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer’s Association research framework. Findings We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell’s C 0·62, 95% CI 0·59–0·65), validated hippocampal volume model (0·67, 0·62–0·72), and updated CSF biomarkers model (0·72, 0·68–0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71–0·76). Interpretation We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer’s disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants’ understanding, emotional wellbeing, and behaviour. Funding ZonMW-Memorabel.
- Can Subjective Memory Complaints Identify A beta Positive and A beta Negative Amnestic Mild Cognitive Impairment Patients?Publication . Mendes, Tiago; Cardoso, Sandra; Guerreiro, Manuela; Maroco, Joao; Silva, Dina; Alves, Luisa; Schmand, Ben; Gerardo, Bianca; Lima, Marisa; Santana, Isabel; de Mendonca, AlexandreBackground: The use of biomarkers, in particular amyloid-beta (A(beta) changes, has allowed the possibility to identify patients with subjective memory complaints (SMCs) and amnestic mild cognitive impairment (aMCI) who suffer from Alzheimer's disease (AD). Since it is unfeasible that all patients with aMCI could presently undergo biomarkers assessment, it would be important that SMCs might contribute to identify the aMCI patients who have AD amyloid pathology. Objectives: To know whether aMCI patients with amyloid biomarkers (A beta(+)) present greater SMCs as compared to those without amyloid biomarkers (A beta(-)). Methods: Participants were selected from a cohort of nondemented patients with cognitive complaints and a comprehensive neuropsychological evaluation, on the basis of 1) diagnosis of aMCI
- Mental time travel in mild cognitive impairmentPublication . Coelho, Sara; Guerreiro, Manuela; Chester, Catarina; Silva, Dina; Maroco, Joao; Paglieri, Fabio; de Mendonca, AlexandreIntroduction: Mental Time Travel (MTT) is the people's ability to remember themselves in the past and to imagine themselves in the future, and influence important life domains such as making decisions and planning future actions. It is widely recognized that patients with aMCI have deficits in episodic memory, but they also show impairments in semantic memory. It has been controversial whether MTT tasks are disturbed in aMCI mainly in relation to internal details related to episodic information, or external details, representing semantic and other extraneous information. The present study assessed whether patients with aMCI are affected in MTT regarding generation of internal details and external details, in past and future dimensions. Furthermore, it analyzed production in individual detail categories (internal: event details, thought/emotion, place, time, perceptual
- Neuropsychological contribution to predict conversion to dementia in patients with mild cognitive impairment due to Alzheimer's diseasePublication . Silva, Dina; Cardoso, Sandra; Guerreiro, Manuela; Maroco, Joao; Mendes, Tiago; Alves, Luisa; Nogueira, Joana; Baldeiras, Ines; Santana, Isabel; de Mendonca, AlexandreBackground: Diagnosis of Alzheimer's disease (AD) confirmed by biomarkers allows the patient to make important life decisions. However, doubt about the fleetness of symptoms progression and future cognitive decline remains. Neuropsychological measures were extensively studied in prediction of time to conversion to dementia for mild cognitive impairment (MCI) patients in the absence of biomarker information. Similar neuropsychological measures might also be useful to predict the progression to dementia in patients with MCI due to AD. Objective: To study the contribution of neuropsychological measures to predict time to conversion to dementia in patients with MCI due to AD. Methods: Patients with MCI due toADwere enrolled from a clinical cohort and the effect of neuropsychological performance on time to conversion to dementia was analyzed. Results: At baseline, converters scored lower than non-converters at measures of verbal initiative, non-verbal reasoning, and episodic memory. The test of non-verbal reasoning was the only statistically significant predictor in a multivariate Cox regression model. A decrease of one standard deviation was associated with 29% of increase in the risk of conversion to dementia. Approximately 50% of patients with more than one standard deviation below the mean in the z score of that test had converted to dementia after 3 years of follow-up. Conclusion: In MCI due to AD, lower performance in a test of non-verbal reasoning was associated with time to conversion to dementia. This test, that reveals little decline in the earlier phases of AD, appears to convey important information concerning conversion to dementia.