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Introduction: Alzheimer's disease (AD) is highly heterogeneous, with substantial individual variabilities in clinical progression and neurobiology. Amyloid deposition has been thought to drive cognitive decline and thus a major contributor to the variations in cognitive deterioration in AD. However, the clinical heterogeneity of patients with early symptomatic AD (mild cognitive impairment or mild dementia due to AD) already with evidence of amyloid abnormality in the brain is still unknown. Methods: Participants with a baseline diagnosis of mild cognitive impairment or mild dementia, a positive amyloid-PET scan, and more than one follow-up Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) administration within a period of 5-year follow-up were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 421; age = 73±7; years of education = 16 ± 3; percentage of female gender = 43%; distribution of APOE4 carriers = 68%). A non-parametric k-means longitudinal clustering analysis in the context of the ADAS-Cog-13 data was performed to identify cognitive subtypes. Results: We found a highly variable profile of cognitive decline among patients with early AD and identified 4 clusters characterized by distinct rates of cognitive progression. Among the groups there were significant differences in the magnitude of rates of changes in other cognitive and functional outcomes, clinical progression from mild cognitive impairment to dementia, and changes in markers presumed to reflect neurodegeneration and neuronal injury. A nomogram based on a simplified logistic regression model predicted steep cognitive trajectory with an AUC of 0.912 (95% CI: 0.88 - 0.94). Simulation of clinical trials suggested that the incorporation of the nomogram into enrichment strategies would reduce the required sample sizes from 926.8 (95% CI: 822.6 - 1057.5) to 400.9 (95% CI: 306.9 - 516.8). Discussion: Our findings show usefulness in the stratification of patients in early AD and may thus increase the chances of finding a treatment for future AD clinical trials.

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OBJECTIVES: Systemic sclerosis (SSc) is heterogeneous in its clinical presentation. Common manifestations cluster together, defining unique subgroups. This investigation aims to characterize gastrointestinal (GI) phenotypes and determine whether they can be distinguished by temporal progression. METHODS: We examine a well-established SSc patient cohort with a modified Medsger GI severity score measured over time to determine heterogeneity in disease progression. Growth mixture models estimate each patient's phenotype and disease severity trajectory over time. We compare the characteristics of estimated phenotypes using non-parametric statistics and linear and logistic regression to compare patient characteristics between phenotypes while adjusting for disease duration. RESULTS: We examined 2696 SSc patients with at least two Medsger GI scores, identifying four unique phenotypes. The most common phenotype (n = 2325) ("Stable") had an average score of 1 that was consistent over time. Two phenotypes were progressive ["Early Progressive" (n = 142) and "Late Progressive" (n = 115)] with an initial average score of 1. The Early Progressive group increased initially and stabilized, and the Late Progressive group worsened slowly over time. A fourth phenotype ["Early Severe GI"; (n = 114)] had an initial average Medsger GI score just below 3 with high mortality and improving GI severity over time. CONCLUSIONS: Clinically distinct GI phenotypes exist among patients with SSc. These phenotypes are not only distinguished by GI and extra-intestinal SSc clinical complications, but they are also temporally distinct. Distinct autoantibody profiles are associated strongly with more severe GI disease.

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CONTEXT: Identifying clusters (i.e., subgroups) of patients from the analysis of medico-administrative databases is particularly important to better understand disease heterogeneity. However, these databases contain different types of longitudinal variables which are measured over different follow-up periods, generating truncated data. It is therefore fundamental to develop clustering approaches that can handle this type of data. OBJECTIVE: We propose here cluster-tracking approaches to identify clusters of patients from truncated longitudinal data contained in medico-administrative databases. MATERIAL AND METHODS: We first cluster patients at each age. We then track the identified clusters over ages to construct cluster-trajectories. We compared our novel approaches with three classical longitudinal clustering approaches by calculating the silhouette score. As a use-case, we analyzed antithrombotic drugs used from 2008 to 2018 contained in the Échantillon Généraliste des Bénéficiaires (EGB), a French national cohort. RESULTS: Our cluster-tracking approaches allow us to identify several cluster-trajectories with clinical significance without any imputation of data. The comparison of the silhouette scores obtained with the different approaches highlights the better performances of the cluster-tracking approaches. CONCLUSION: The cluster-tracking approaches are a novel and efficient alternative to identify patient clusters from medico-administrative databases by taking into account their specificities.

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BACKGROUND: Allergic diseases often develop jointly during early childhood but differ in timing of onset, remission, and progression. Their disease course over time is often difficult to predict and determinants are not well understood. OBJECTIVES: We aimed to identify trajectories of allergic diseases up to adolescence and to investigate their association with early-life and genetic determinants and clinical characteristics. METHODS: Longitudinal k-means clustering was used to derive trajectories of allergic diseases (asthma, atopic dermatitis, and rhinitis) in two German birth cohorts (GINIplus/LISA). Associations with early-life determinants, polygenic risk scores, food and aeroallergen sensitization, and lung function were estimated by multinomial models. The results were replicated in the independent Swedish BAMSE cohort. RESULTS: Seven allergic disease trajectories were identified: "Intermittently allergic," "rhinitis," "early-resolving dermatitis," "mid-persisting dermatitis," "multimorbid," "persisting dermatitis plus rhinitis," and "early-transient asthma." Family history of allergies was more prevalent in all allergic disease trajectories compared the non-allergic controls with stronger effect sizes for clusters comprising more than one allergic disease (e.g., RRR = 5.0, 95% CI = [3.1-8.0] in the multimorbid versus 1.8 [1.4-2.4] in the mild intermittently allergic cluster). Specific polygenic risk scores for single allergic diseases were significantly associated with their relevant trajectories. The derived trajectories and their association with genetic effects and clinical characteristics showed similar results in BAMSE. CONCLUSION: Seven robust allergic clusters were identified and showed associations with early life and genetic factors as well as clinical characteristics.

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Purpose: Preeclampsia is a leading cause of maternal morbidity and mortality. Calcium-based antacids and proton pump inhibitors (PPIs) are commonly used during pregnancy to treat symptoms of gastroesophageal reflux disease. Both have been hypothesized to reduce the risk of preeclampsia. We determined associations of calcium-based antacid and PPI use during pregnancy with late-onset preeclampsia (≥34 weeks of gestation), taking into account dosage and timing of use. Patients and Methods: We included 9058 pregnant women participating in the PRIDE Study (2012-2019) or The Dutch Pregnancy Drug Register (2014-2019), two prospective cohorts in The Netherlands. Data were collected through web-based questionnaires and obstetric records. We estimated risk ratios (RRs) for late-onset preeclampsia for any use and trajectories of calcium-based antacid and PPI use before gestational day 238, and hazard ratios (HRs) for time-varying exposures after gestational day 237. Results: Late-onset preeclampsia was diagnosed in 2.6% of pregnancies. Any use of calcium-based antacids (RR 1.2 [95% CI 0.9-1.6]) or PPIs (RR 1.4 [95% CI 0.8-2.4]) before gestational day 238 was not associated with late-onset preeclampsia. Use of low-dose calcium-based antacids in gestational weeks 0-16 (<1 g/day; RR 1.8 [95% CI 1.1-2.9]) and any use of PPIs in gestational weeks 17-33 (RR 1.6 [95% CI 1.0-2.8]) seemed to increase risks of late-onset preeclampsia. We did not observe associations between late-onset preeclampsia and use of calcium-based antacids (HR 1.0 [95% CI 0.6-1.5]) and PPIs (HR 1.4 [95% CI 0.7-2.9]) after gestational day 237. Conclusion: In this prospective cohort study, use of calcium-based antacids and PPIs during pregnancy was not found to reduce the risk of late-onset preeclampsia.

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Creating homogeneous groups (clusters) of patients from medico-administrative databases provides a better understanding of health determinants. But in these databases, patients have truncated care pathways. We developed an approach based on patient networks to construct care trajectories from such truncated data. We tested this approach on antithrombotic treatments prescribed from 2008 to 2018 contained in the échantillon généraliste des bénéficiaires (EGB). We constructed a patient network for each patients' age (years from birth). We then applied the Markov clustering algorithm in each network. The care trajectories were finally constructed by matching clusters identified in two consecutive networks. We calculated the silhouette score to assess the performance of this network approach compared to three existing approaches. We identified 12 care trajectories that we were able to associate with pathologies. The best silhouette score was obtained for the network approach. Our approach allowed to highlight care trajectories taking into account the longitudinal, multidimensional and truncated nature of data from medico-administrative databases.

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BACKGROUND: We employed machine learning approaches to (I) determine distinct progression trajectories in Parkinson's disease (PD) (unsupervised clustering task), and (II) predict progression trajectories (supervised prediction task), from early (years 0 and 1) data, making use of clinical and imaging features. METHODS: We studied PD-subjects derived from longitudinal datasets (years 0, 1, 2 & 4; Parkinson's Progressive Marker Initiative). We extracted and analyzed 981 features, including motor, non-motor, and radiomics features extracted for each region-of-interest (ROIs: left/right caudate and putamen) using our standardized standardized environment for radiomics analysis (SERA) radiomics software. Segmentation of ROIs on dopamine transposer - single photon emission computed tomography (DAT SPECT) images were performed via magnetic resonance images (MRI). After performing cross-sectional clustering on 885 subjects (original dataset) to identify disease subtypes, we identified optimal longitudinal trajectories using hybrid machine learning systems (HMLS), including principal component analysis (PCA) + K-Means algorithms (KMA) followed by Bayesian information criterion (BIC), Calinski-Harabatz criterion (CHC), and elbow criterion (EC). Subsequently, prediction of the identified trajectories from early year data was performed using multiple HMLSs including 16 Dimension Reduction Algorithms (DRA) and 10 classification algorithms. RESULTS: We identified 3 distinct progression trajectories. Hotelling's t squared test (HTST) showed that the identified trajectories were distinct. The trajectories included those with (I, II) disease escalation (2 trajectories, 27% and 38% of patients) and (III) stable disease (1 trajectory, 35% of patients). For trajectory prediction from early year data, HMLSs including the stochastic neighbor embedding algorithm (SNEA, as a DRA) as well as locally linear embedding algorithm (LLEA, as a DRA), linked with the new probabilistic neural network classifier (NPNNC, as a classifier), resulted in accuracies of 78.4% and 79.2% respectively, while other HMLSs such as SNEA + Lib_SVM (library for support vector machines) and t_SNE (t-distributed stochastic neighbor embedding) + NPNNC resulted in 76.5% and 76.1% respectively. CONCLUSIONS: This study moves beyond cross-sectional PD subtyping to clustering of longitudinal disease trajectories. We conclude that combining medical information with SPECT-based radiomics features, and optimal utilization of HMLSs, can identify distinct disease trajectories in PD patients, and enable effective prediction of disease trajectories from early year data.

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BACKGROUND AND OBJECTIVE: In the difficult problem of comparing countries regarding their lockdown measures or deaths caused by the COVID-19, there is still no agreement on what is the best strategy to follow. Thus, we propose a new way of comparison countries that avoids the main difficulties in the comparison by using three-dimensional trajectories for this type of data. METHODS: We introduce a new index to analyze the level of confinement that each country was subject to overtime, based on the Community Mobility Reports published by Google resorting to Principal Component Analysis. Subsequently, by using longitudinal clustering, we divide the European countries into similar groups according to the COVID-19 obits and also to the confinement index. However, to make the most out of the clustering methods we resort to artificial longitudinal data to evaluate both the methods and the indices. RESULTS: By using artificial data, we discover that Calinski-Harabasz outperformed other internal indices in indicating the real number of clusters. The tests also suggested that K-means with Euclidean distance was the best method among the ones studied. With the application to both the mobility and fatalities datasets, we found two groups in each one. CONCLUSIONS: Our analysis enables us to discover that European northern countries had more mobility during the first confinement and that the deaths caused by COVID-19 started to drop around the 40th day since the first death.

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PURPOSE: Quantitative computed tomography (qCT) imaging-based cluster analysis identified clinically meaningful COPD former-smoker subgroups (clusters) based on cross-sectional data. We aimed to identify progression clusters for former smokers using longitudinal data. PATIENTS AND METHODS: We selected 472 former smokers from SPIROMICS with a baseline visit and a one-year follow-up visit. A total of 150 qCT imaging-based variables, comprising 75 variables at baseline and their corresponding progression rates, were derived from the respective inspiration and expiration scans of the two visits. The COPD progression clusters identified were then associated with subject demography, clinical variables and biomarkers. RESULTS: COPD severities at baseline increased with increasing cluster number. Cluster 1 patients were an obese subgroup with rapid progression of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%). Cluster 2 exhibited a decrease of fSAD% and Emph%, an increase of tissue fraction at total lung capacity and airway narrowing over one year. Cluster 3 showed rapid expansion of Emph% and an attenuation of fSAD%. Cluster 4 demonstrated severe emphysema and fSAD and significant structural alterations at baseline with rapid progression of fSAD% over one year. Subjects with different progression patterns in the same cross-sectional cluster were identified by longitudinal clustering. CONCLUSION: qCT imaging-based metrics at two visits for former smokers allow for the derivation of four statistically stable clusters associated with unique progression patterns and clinical characteristics. Use of baseline variables and their progression rates enables identification of longitudinal clusters, resulting in a refinement of cross-sectional clusters.

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Deterioration in white-matter health plays a role in cognitive ageing. Our goal was to discern heterogeneity of white-matter tract vulnerability in ageing using longitudinal imaging data (two to five imaging and cognitive assessments per participant) from a population-based sample of 553 elderly participants (age ≥60 years). We found that different clusters (healthy white matter, fast white-matter decliners and intermediate white-matter group) were heterogeneous in the spatial distribution of white-matter integrity, systemic health and cognitive trajectories. White-matter health of specific tracts (genu of corpus callosum, posterior corona radiata and anterior internal capsule) informed about cluster assignments. Not surprisingly, brain amyloidosis was not significantly different between clusters. Clusters had differential white-matter tract vulnerability to ageing (commissural fibres > association/brainstem fibres). Identification of vulnerable white-matter tracts is a valuable approach to assessing risk for cognitive decline.

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AIMS: We aimed to identify a 'frequent admitter' phenotype among patients admitted for acute decompensated heart failure (HF). METHODS AND RESULTS: We studied 10 363 patients in a population-based prospective HF registry (2008-2012), segregated into clusters based on their 3-year HF readmission frequency trajectories. Using receiver-operating characteristic analysis, we identified the index year readmission frequency threshold that most accurately predicts HF admission frequency clusters. Two clusters of HF patients were identified: a high frequency cluster (90.9%, mean 2.35 ± 3.68 admissions/year) and a low frequency cluster (9.1%, mean 0.50 ± 0.81 admission/year). An index year threshold of two admissions was optimal for distinguishing between clusters. Based on this threshold, 'frequent admitters', defined as patients with ≥ 2 HF admissions in the index year (n = 2587), were of younger age (68 ± 13 vs 69 ± 13 years), more often male (58% vs. 54%), smokers (38.4% vs. 34.4%) and had lower left ventricular ejection fraction (37 ± 17 vs. 41 ± 17%) compared to 'non-frequent admitters' (< 2 HF admissions in the index year; n = 7776) (all P < 0.001). Despite similar rates of advanced care utilization, frequent admitters had longer length of stay (median 4.3 vs. 4.0 days), higher annual inpatient costs (€ 7015 vs. € 2967) and higher all-cause mortality at 3 years compared to the non-frequent admitters (adjusted odds ratio 2.33, 95% confidence interval 2.11-2.58; P < 0.001). CONCLUSION: 'Frequent admitters' have distinct clinical characteristics and worse outcomes compared to non-frequent admitters. This study may provide a means of anticipating the HF readmission burden and thereby aid in healthcare resource distribution relative to the HF admission frequency phenotype.

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