Multicentric Assessment of a Multimorbidity-Adjusted Disability Score to Stratify Depression-Related Risks Using Temporal Disease Maps: Instrument Validation Study.

BACKGROUND: Comprehensive management of multimorbidity can significantly benefit from advanced health risk assessment tools that facilitate value-based interventions, allowing for the assessment and prediction of disease progression. Our study proposes a novel methodology, the Multimorbidity-Adjusted Disability Score (MADS), which integrates disease trajectory methodologies with advanced techniques for assessing interdependencies among concurrent diseases. This approach is designed to better assess the clinical burden of clusters of interrelated diseases and enhance our ability to anticipate disease progression, thereby potentially informing targeted preventive care interventions. OBJECTIVE: This study aims to evaluate the effectiveness of the MADS in stratifying patients into clinically relevant risk groups based on their multimorbidity profiles, which accurately reflect their clinical complexity and the probabilities of developing new associated disease conditions. METHODS: In a retrospective multicentric cohort study, we developed the MADS by analyzing disease trajectories and applying Bayesian statistics to determine disease-disease probabilities combined with well-established disability weights. We used major depressive disorder (MDD) as a primary case study for this evaluation. We stratified patients into different risk levels corresponding to different percentiles of MADS distribution. We statistically assessed the association of MADS risk strata with mortality, health care resource use, and disease progression across 1 million individuals from Spain, the United Kingdom, and Finland. RESULTS: The results revealed significantly different distributions of the assessed outcomes across the MADS risk tiers, including mortality rates; primary care visits; specialized care outpatient consultations; visits in mental health specialized centers; emergency room visits; hospitalizations; pharmacological and nonpharmacological expenditures; and dispensation of antipsychotics, anxiolytics, sedatives, and antidepressants (P<.001 in all cases). Moreover, the results of the pairwise comparisons between adjacent risk tiers illustrate a substantial and gradual pattern of increased mortality rate, heightened health care use, increased health care expenditures, and a raised pharmacological burden as individuals progress from lower MADS risk tiers to higher-risk tiers. The analysis also revealed an augmented risk of multimorbidity progression within the high-risk groups, aligned with a higher incidence of new onsets of MDD-related diseases. CONCLUSIONS: The MADS seems to be a promising approach for predicting health risks associated with multimorbidity. It might complement current risk assessment state-of-the-art tools by providing valuable insights for tailored epidemiological impact analyses of clusters of interrelated diseases and by accurately assessing multimorbidity progression risks. This study paves the way for innovative digital developments to support advanced health risk assessment strategies. Further validation is required to generalize its use beyond the initial case study of MDD.

Analysing disease trajectories in a cohort of 71,849 Patients: A visual analytics and statistical approach.

BACKGROUND: Disease trajectories have become increasingly relevant within the context of an aging population and the rising prevalence of chronic illnesses. Understanding the temporal progression of diseases is crucial for enhancing patient care, preventive measures, and effective management. OBJECTIVE: The objective of this study is to propose and validate a novel methodology for trajectory impact analysis and interactive visualization of disease trajectories over a cohort of 71,849 patients. METHODS: This article introduces an innovative comprehensive approach for analysis and interactive visualization of disease trajectories. First, Risk Increase (RI) index is defined that assesses the impact of the initial disease diagnosis on the development of subsequent illnesses. Secondly, visual graphics methods are used to represent cohort trajectories, ensuring a clear and semantically rich presentation that facilitates easy data interpretation. RESULTS: The proposed approach is demonstrated over the disease trajectories of a cohort comprising 71,849 patients from Tolosaldea, Spain. The study finds several clinically relevant trajectories in this cohort, such as that after suffering a cerebral ischemic stroke, the probability of suffering dementia increases 10.77 times. The clinical relevance of the study outcomes have been assessed by an in-depth analysis conducted by expert clinicians. The identified disease trajectories are in agreement with the latest advancements in the field. CONCLUSION: The proposed approach for trajectory impact analysis and interactive visualization offers valuable graphs for the comprehensive study of disease trajectories for improved clinical decision-making. The simplicity and interpretability of our methods make them valuable approach for healthcare professionals.

Population-based study of disease trajectory after radical treatment for high-risk prostate cancer.

OBJECTIVES: To investigate long-term disease trajectories among men with high-risk localized or locally advanced prostate cancer (HRLPC) treated with radical radiotherapy (RT) or radical prostatectomy (RP). MATERIAL AND METHODS: Men diagnosed with HRLPC in 2006-2020, who received primary RT or RP, were identified from the Prostate Cancer data Base Sweden (PCBaSe) 5.0. Follow-up ended on 30 June 2021. Treatment trajectories and risk of death from prostate cancer (PCa) or other causes were assessed by competing risk analyses using cumulative incidence for each event. RESULTS: In total, 8317 men received RT and 4923 men underwent RP. The median (interquartile range) follow-up was 6.2 (3.6-9.5) years. After RT, the 10-year risk of PCa-related death was 0.13 (95% confidence interval [CI] 0.12-0.14) and the risk of death from all causes was 0.32 (95% CI 0.31-0.34). After RP, the 10-year risk of PCa-related death was 0.09 (95% CI 0.08-0.10) and the risk of death from all causes was 0.19 (95% CI 0.18-0.21). The 10-year risks of androgen deprivation therapy (ADT) as secondary treatment were 0.42 (95% CI 0.41-0.44) and 0.21 (95% CI 0.20-0.23) after RT and RP, respectively. Among men who received ADT as secondary treatment, the risk of PCa-related death at 10 years after initiation of ADT was 0.33 (95% CI 030-0.36) after RT and 0.27 (95% CI 0.24-0.30) after RP. CONCLUSION: Approximately one in 10 men with HRLPC who received primary RT or RP had died from PCa 10 years after diagnosis. Approximately one in three men who received secondary ADT, an indication of PCa progression, died from PCa 10 years after the start of ADT. Early identification and aggressive treatment of men with high risk of progression after radical treatment are warranted.

A latent class linear mixed model for monotonic continuous processes measured with error.

Motivated by measurement errors in radiographic diagnosis of osteoarthritis, we propose a Bayesian approach to identify latent classes in a model with continuous response subject to a monotonic, that is, non-decreasing or non-increasing, process with measurement error. A latent class linear mixed model has been introduced to consider measurement error while the monotonic process is accounted for via truncated normal distributions. The main purpose is to classify the response trajectories through the latent classes to better describe the disease progression within homogeneous subpopulations.