Longitudinal natural history studies based on real-world data in rare diseases: Opportunity and a novel approach.

Growing interest in therapeutic development for rare diseases necessitate a systematic approach to the collection and curation of natural history data that can be applied consistently across this group of heterogenous rare diseases. In this study, we discuss the challenges facing natural history studies for leukodystrophies and detail a novel standardized approach to creating a longitudinal natural history study using existing medical records. Prospective studies are uniquely challenging for rare diseases. Delays in diagnosis and overall rarity limit the timely collection of natural history data. When feasible, prospective studies are often cross-sectional rather than longitudinal and are unlikely to capture pre- or early- symptomatic disease trajectories, limiting their utility in characterizing the full natural history of the disease. Therapeutic development in leukodystrophies is subject to these same obstacles. The Global Leukodystrophy Initiative Clinical Trials Network (GLIA-CTN) comprises of a network of research institutions across the United States, supported by a multi-center biorepository protocol, to map the longitudinal clinical course of disease across leukodystrophies. As part of GLIA-CTN, we developed Standard Operating Procedures (SOPs) that delineated all study processes related to staff training, source documentation, and data sharing. Additionally, the SOP detailed the standardized approach to data extraction including diagnosis, clinical presentation, and medical events, such as age at gastrostomy tube placement. The key variables for extraction were selected through face validity, and common electronic case report forms (eCRF) across leukodystrophies were created to collect analyzable data. To enhance the depth of the data, clinical notes are extracted into "original" and "imputed" encounters, with imputed encounter referring to a historic event (e.g., loss of ambulation 3 months prior). Retrospective Functional Assessments were assigned by child neurologists, using a blinded dual-rater approach and score discrepancies were adjudicated by a third rater. Upon completion of extraction, data source verification is performed. Data missingness was evaluated using statistics. The proposed methodology will enable us to leverage existing medical records to address the persistent gap in natural history data within this unique disease group, allow for assessment of clinical trajectory both pre- and post-formal diagnosis, and promote recruitment of larger cohorts.

Trajectories network analysis of chronic diseases among middle-aged and older adults: evidence from the China Health and Retirement Longitudinal Study (CHARLS).

BACKGROUND: Given the increased risk of chronic diseases and comorbidity among middle-aged and older adults in China, it is pivotal to identify the disease trajectory of developing chronic multimorbidity and address the temporal correlation among chronic diseases. METHOD: The data of 15895 participants from the China Health and Retirement Longitudinal Study (CHARLS 2011 - 2018) were analyzed in the current study. Binomial tests and the conditional logistic regression model were conducted to estimate the associations among 14 chronic diseases, and the disease trajectory network analysis was adopted to visualize the relationships. RESULTS: The analysis showed that hypertension is the most prevalent disease among the 14 chronic conditions, with the highest cumulative incidence among all chronic diseases. In the disease trajectory network, arthritis was found to be the starting point, and digestive diseases, hypertension, heart diseases, and dyslipidemia were at the center, while memory-related disease (MRD), stroke, and diabetes were at the periphery of the network. CONCLUSIONS: With the chronic disease trajectory network analysis, we found that arthritis was prone to the occurrence and development of various other diseases. In addition, patients of heart diseases/hypertension/digestive disease/dyslipidemia were under higher risk of developing other chronic conditions. For patients with multimorbidity, early prevention can preclude them from developing into poorer conditions, such as stroke, MRD, and diabetes. By identifying the trajectory network of chronic disease, the results provided critical insights for developing early prevention and individualized support services to reduce disease burden and improve patients' quality of life.

Impact of Differential Rates of Disease Progression in Amyloid-Positive Early Alzheimer's Disease: Findings from a Longitudinal Cohort Analysis.

BACKGROUND: There is limited literature regarding the impact of differential rates of disease progression on longitudinal outcomes in individuals with early Alzheimer's disease (AD) and confirmed brain amyloid pathology. OBJECTIVES: To describe the underlying characteristics and long-term outcomes associated with different rates of disease progression among amyloid-positive individuals with early symptomatic AD. DESIGN: Retrospective observational study. SETTING: Data from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) in the United States (06/2005-11/2021). PARTICIPANTS: Individuals with a clinical assessment of mild cognitive impairment or dementia and Clinical Dementia Rating® Dementia Staging Instrument Sum of Boxes (CDR-SB) score 0.5-9.0 (inclusive; first visit defined as the index date) and confirmed amyloid positivity. Participants were stratified into No Progression (change ≤0), Slower Progression (0< change <2.0 points), Median Progression (2.0-point change), and Faster Progression (change >2.0 points) cohorts based on the observed distribution of changes in CDR-SB score between the index and first subsequent visit. MEASUREMENTS: For each cohort, the functional and neuropsychiatric outcomes were described at index and each subsequent visit for up to five years, and least-square (LS) mean changes from baseline were estimated using linear mixed-effects models adjusting for baseline demographic and clinical characteristics. RESULTS: Among 1,263 participants included in the analysis, the mean±standard deviation (SD) age at index was 72.7±9.7 years and 55.3% were males. Demographic characteristics and comorbidity profiles at index were similar across cohorts. However, at index, the Faster Progression (N=279) cohort had higher CDR-SB and Functional Assessment Questionnaire (FAQ) scores compared with the No Progression (N=474), Slower Progression (N=297), and Median Progression (N=213) cohorts. Adjusting for baseline characteristics, at year 5 after index the FAQ score increased by 23.6 points for Faster Progression cohort and 10.4, 15.8, and 19.2 points for the No, Slower, and Median Progression cohorts, respectively. The corresponding increases in Neuropsychiatric Inventory Questionnaire (NPI-Q) scores were 6.7 points for the Faster Progression cohort, and by 1.3, 3.1, and 8.3 points, for the No, Slower, and Median Progression cohorts, respectively. CONCLUSIONS: Despite similar demographic and clinical profiles at baseline, amyloid-positive individuals with greater deterioration based on CDR-SB early in the AD trajectory continue to experience worse functional and behavioral outcomes over time than those with more gradual deterioration in this metric.

Estimating motor progression trajectory pursuant to temporal dynamic status of cardiac denervation in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a multifaceted disease that encompasses diverse clinical phenotypes. The diversity of PD could be subtyped based on the temporal dynamic status of cardiac sympathetic innervation; (1) initially, denervated myocardium (peripheral nervous system-predominant; PNS-predominant), (2) preserved myocardium (central nervous system-predominant; CNS-predominant), and (3) preserved myocardium who developed cardiac sympathetic denervation (CSD) on the subsequent imaging (Converter; delayed cardiac denervation). This study assessed how the cardiac denervation could reflect the pathobiology. We investigated whether this phenotyping could help predict the motor progression trajectory of PD. METHODS: Cardiac sympathetic innervation was evaluated using initial and sequential 123I-meta-iodobenzylguanidine myocardial scintigraphy and patients were stratified into three groups as above. Motor severity and progression were evaluated in each patient. The association between subtypes and dopaminergic nigrostriatal degeneration was analyzed. The influence of cardiac denervation on motor progression was also investigated. RESULTS: Among the enrolled 195 patients, 144 PD subjects were defined as PNS-predominant, 16 as Converter, and 35 as CNS-predominant. The most severe nigrostriatal degeneration was observed in the PNS-predominant group and the dopaminergic degeneration was the most asymmetric in the CNS-predominant group. Positive linear trends of nigrostriatal degeneration and its asymmetric degeneration of striatum and globus pallidus were found across the patterns of cardiac sympathetic innervation (PNS-predominant vs. Converter vs. CNS-predominant). It indicated an increasing degree of asymmetric degeneration among the groups. A longitudinal estimation of motor progression revealed distinct cardiac denervation trajectories for each subtype. CONCLUSIONS: These results implicated that the subtypes of CSD might indicate a predominant origin and spreading pattern of pathobiology.

TrajVis: a visual clinical decision support system to translate artificial intelligence trajectory models in the precision management of chronic kidney disease.

OBJECTIVE: Our objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients' longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression. MATERIALS AND METHODS: We first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system. RESULTS: The TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare. DISCUSSION: The TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations. CONCLUSION: TrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.

Disease progression in chronic heart failure is linear: Insights from multistate modelling.

AIMS: Understanding the pattern of disease progression in chronic heart failure (HF) may inform patient care and healthcare system design. We used a four-state Markov model to describe the disease trajectory of patients with HF. METHODS AND RESULTS: Consecutive patients (n = 4918) were enrolled (median age 75 [67-81] years, 61.3% men, 44% with HF and reduced ejection fraction). We generated a model by observing events during the first 2 years of follow-up. The model yielded surprisingly accurate predictions of how a population with HF will behave during subsequent years. As examples, the predicted transition probability from hospitalization to death was 0.11; the observed probabilities were 0.13, 0.14, and 0.16 at 3, 4, and 5 years, respectively. Similarly, the predicted transition intensity for rehospitalization was 0.35; the observed probabilities were 0.38, 0.34, and 0.35 at 3, 4, and 5 years, respectively. A multivariable model including covariates thought to influence outcome did not improve accuracy. Predicted average life expectancy was approximately 10 years for the unadjusted model and 13 years for the multivariable model, consistent with the observed mortality of 41% at 5 years. CONCLUSIONS: A multistate Markov chain model for patients with chronic HF suggests that the proportion of patients transitioning each year from a given state to another remains constant. This finding suggests that the course of HF at a population level is more linear than is commonly supposed and predictable based on current patient status.

Multisystem health comorbidity networks of metabolic dysfunction-associated steatotic liver disease.

BACKGROUND: The global burden of metabolic dysfunction-associated steatotic liver disease (MASLD) is growing, but its subsequent health consequences have not been thoroughly examined. METHODS: A phenome-wide association study was conducted to map the associations of MASLD with 948 unique clinical outcomes among 361,021 Europeans in the UK Biobank. Disease trajectory and comorbidity analyses were applied to visualize the sequential patterns of multiple comorbidities related to the occurrence of MASLD. The associations jointly verified by observational and polygenic phenome-wide analyses were further replicated by two-sample Mendelian randomization analysis using data from the FinnGen study and international consortia. FINDINGS: The observational and polygenic phenome-wide association study revealed the associations of MASLD with 96 intrahepatic and extrahepatic diseases, including circulatory, metabolic, genitourinary, neurological, gastrointestinal, and hematologic diseases. Sequential patterns of MASLD-related extrahepatic comorbidities were primarily found in circulatory, metabolic, and inflammatory diseases. Mendelian randomization analyses supported the causal associations between MASLD and the risk of several intrahepatic disorders, metabolic diseases, cardio-cerebrovascular disease, and ascites but found no associations with neurological diseases. CONCLUSIONS: This study elucidated multisystem comorbidities and health consequences of MASLD, contributing to the development of combination interventions targeting distinct pathways for health promotion among patients with MASLD. FUNDING: X.L. was funded by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001) and the National Nature Science Foundation of China (82204019) and Y.D. was funded by the Key Project of Traditional Chinese Medicine Science and Technology Plan of Zhejiang Province (GZY-ZJ-KJ-24077) and the National Natural Science Foundation of China (82001673 and 82272860).