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.

Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine

Artificial intelligence (AI) and machine learning (ML) methods are currently widely employed in medicine and healthcare. A PubMed search returns more than 100,000 articles on these topics published between 2018 and 2022 alone. Notwithstanding several recent reviews in various subfields of AI and ML in medicine, we have yet to see a comprehensive review around the methods' use in longitudinal analysis and prediction of an individual patient's health status within a personalized disease pathway. This review seeks to fill that gap. After an overview of the AI and ML methods employed in this field and of specific medical applications of models of this type, the review discusses the strengths and limitations of current studies and looks ahead to future strands of research in this field. We aim to enable interested readers to gain a detailed impression of the research currently available and accordingly plan future work around predictive models for deterioration in health status.

Awareness of Imminent Death: Results From a Mixed Methods Study of Israeli Family Caregivers' Perceptions of Their Awareness and That of the Patients for Whom They Cared

We studied levels of awareness of impending death in older patients and their family caregivers. Using a mixed methods approach, we interviewed 70 family caregivers in Israel. Of the caregivers, 64% reported having been aware of the impending death, 33% were unaware, and 3% uncertain. Caregivers reported their perception that 36% of patients were aware, 27% unaware, and for 37% they were uncertain about the patient's awareness. Mechanisms that increased caregivers' awareness were specific diagnosis, significant deterioration in health, preparation by a health professional, or patient preparations for death. This study clarifies processes which aid awareness, and the relationship between awareness and actual preparation for dying.

A Computational Method for Learning Disease Trajectories From Partially Observable EHR Data

Diseases can show different courses of progression even when patients share the same risk factors. Recent studies have revealed that the use of trajectories, the order in which diseases manifest throughout life, can be predictive of the course of progression. In this study, we propose a novel computational method for learning disease trajectories from EHR data. The proposed method consists of three parts: first, we propose an algorithm for extracting trajectories from EHR data; second, three criteria for filtering trajectories; and third, a likelihood function for assessing the risk of developing a set of outcomes given a trajectory set. We applied our methods to extract a set of disease trajectories from Mayo Clinic EHR data and evaluated it internally based on log-likelihood, which can be interpreted as the trajectories' ability to explain the observed (partial) disease progressions. We then externally evaluated the trajectories on EHR data from an independent health system, M Health Fairview. The proposed algorithm extracted a comprehensive set of disease trajectories that can explain the observed outcomes substantially better than competing methods and the proposed filtering criteria selected a small subset of disease trajectories that are highly interpretable and suffered only a minimal (relative 5%) loss of the ability to explain disease progression in both the internal and external validation.

Lifespan Perspective on Congenital Heart Disease Research: JACC State-of-the-Art Review

More than 90% of patients with congenital heart disease (CHD) are nowadays surviving to adulthood and adults account for over two-thirds of the contemporary CHD population in Western countries. Although outcomes are improved, surgery does not cure CHD. Decades of longitudinal observational data are currently motivating a paradigm shift toward a lifespan perspective and proactive approach to CHD care. The aim of this review is to operationalize these emerging concepts by presenting new constructs in CHD research. These concepts include long-term trajectories and a life course epidemiology framework. Focusing on a precision health, we propose to integrate our current knowledge on the genome, phenome, and environome across the CHD lifespan. We also summarize the potential of technology, especially machine learning, to facilitate longitudinal research by embracing big data and multicenter lifelong data collection.

Decompression of Multimorbidity Along the Disease Trajectories of Diabetes Mellitus Patients

Multimorbidity, the presence of two or more diseases in a patient, is maybe the greatest health challenge for the aging populations of many high-income countries. One of the main drivers of multimorbidity is diabetes mellitus (DM) due to its large number of risk factors and complications. Yet, we currently have very limited understanding of how to quantify multimorbidity beyond a simple counting of diseases and thereby inform prevention and intervention strategies tailored to the needs of elderly DM patients. Here, we conceptualize multimorbidity as typical temporal progression patterns of multiple diseases, so-called trajectories, and develop a framework to perform a matched and sex-specific comparison between DM and non-diabetic patients. We find that these disease trajectories can be organized into a multi-level hierarchy in which DM patients progress from relatively healthy states with low mortality to high-mortality states characterized by cardiovascular diseases, chronic lower respiratory diseases, renal failure, and different combinations thereof. The same disease trajectories can be observed in non-diabetic patients, however, we find that DM patients typically progress at much higher rates along their trajectories. Comparing male and female DM patients, we find a general tendency that females progress faster toward high multimorbidity states than males, in particular along trajectories that involve obesity. Males, on the other hand, appear to progress faster in trajectories that combine heart diseases with cerebrovascular diseases. Our results show that prevention and efficient management of DM are key to achieve a compression of morbidity into higher patient ages. Multidisciplinary efforts involving clinicians as well as experts in machine learning and data visualization are needed to better understand the identified disease trajectories and thereby contribute to solving the current multimorbidity crisis in healthcare.

Transitive Sequencing Medical Records for Mining Predictive and Interpretable Temporal Representations

Electronic health records (EHRs) contain important temporal information about the progression of disease and treatment outcomes. This paper proposes a transitive sequencing approach for constructing temporal representations from EHR observations for downstream machine learning. Using clinical data from a cohort of patients with congestive heart failure, we mined temporal representations by transitive sequencing of EHR medication and diagnosis records for classification and prediction tasks. We compared the classification and prediction performances of the transitive sequential representations (bag-of-sequences approach) with the conventional approach of using aggregated vectors of EHR data (aggregated vector representation) across different classifiers. We found that the transitive sequential representations are better phenotype "differentiators" and predictors than the "atemporal" EHR records. Our results also demonstrated that data representations obtained from transitive sequencing of EHR observations can present novel insights about the progression of the disease that are difficult to discern when clinical data are treated independently of the patient's history.

Age-stratified longitudinal study of Alzheimer's and vascular dementia patients

INTRODUCTION

Similar symptoms, comorbidities and suboptimal diagnostic tests make the distinction between different types of dementia difficult, although this is essential for improved work-up and treatment optimization.

METHODS

We calculated temporal disease trajectories of earlier multi-morbidities in Alzheimer's disease (AD) dementia and vascular dementia (VaD) patients using the Danish National Patient Registry covering all hospital encounters in Denmark (1994 to 2016). Subsequently, we reduced the comorbidity space dimensionality using a non-linear technique, uniform manifold approximation and projection.

RESULTS

We found 49,112 and 24,101 patients that were diagnosed with AD or VaD, respectively. Temporal disease trajectories showed very similar disease patterns before the dementia diagnosis. Stratifying patients by age and reducing the comorbidity space to two dimensions, showed better discrimination between AD and VaD patients in early-onset dementia.

DISCUSSION

Similar age-associated comorbidities, the phenomenon of mixed dementia, and misdiagnosis create great challenges in discriminating between classical subtypes of dementia.