Disease Progression In Models Research Articles

Opportunities and Challenges of Disease Progression Modeling in Drug Development - An IQ Perspective.

Disease progression modeling (DPM) represents an important model-informed drug development framework. The scientific communities support the use of DPM to accelerate and increase efficiency in drug development. This article summarizes International Consortium for Innovation & Quality (IQ) in Pharmaceutical Development mediated survey conducted across multiple biopharmaceutical companies on challenges and opportunities for DPM. Additionally, this summary highlights the viewpoints of IQ from the 2021 workshop hosted by the US Food and Drug Administration (FDA). Sixteen pharmaceutical companies participated in the IQ survey with 36 main questions. The types of questions included single/multiple choice, dichotomous, rank questions, and open-ended or free text. The key results show that DPM has different representation, it encompasses natural disease history, placebo response, standard of care as background therapy, and can even be interpreted as pharmacokinetic/pharmacodynamic modeling. The most common reasons for not implementing DPM as frequently seem to be difficulties in internal cross-functional alignment, lack of knowledge of disease/data, and time constraints. If successfully implemented, DPM can have an impact on dose selection, reduction of sample size, trial read-out support, patient selection/stratification, and supportive evidence for regulatory interactions. The key success factors and key challenges of disease progression models were highlighted in the survey and about 24 case studies across different therapeutic areas were submitted from various survey sponsors. Although DPM is still evolving, its current impact is limited but promising. The success of such models in the future will depend on collaboration, advanced analytics, availability of and access to relevant and adequate-quality data, collaborative regulatory guidance, and published examples of impact.

Partially observable collaborative model for optimizing personalized treatment selection

Precision medicine that enables personalized treatment decision support has become an increasingly important research topic in chronic disease care. The main challenges in designing a treatment algorithm include modeling individual disease progression dynamics and designing adaptive treatment selection strategy. This study aims to develop an adaptive treatment selection framework tailored to an individual patient’s disease progression pattern and treatment response. We propose a Partially Observable Collaborative Model (POCM) to capture the individual variations in a heterogeneous population and optimize treatment outcomes in three stages. The POCM first infers the disease progression models by subgroup patterns using population data in stage one and then fine-tunes the models for individual patients with a small number of treatment trials in stage two. In stage three, we show how the treatment policies based on the Partially Observable Markov Decision Process (POMDP) can be tailored to individual patients by utilizing the disease models learned from the POCM. Using a simulated population of chronic depression patients, we show that the POCM can more accurately estimate the personal disease progression than the traditional method of solving a hidden Markov model. We also compare the POMDP treatment policies with other heuristic policies and demonstrate that the POCM-based policies give the highest net monetary benefits in majority of parameter settings. To conclude, the POCM method is a promising approach to model the chronic disease progression process and recommend a personalized treatment plan for individual patients in a heterogeneous population.

Motor unit number index (MUNIX) loss of 50% occurs in half the time of 50% functional loss according to the D50 disease progression model of ALS

Capturing disease progression in amyotrophic lateral sclerosis (ALS) is challenging and refinement of progression markers is urgently needed. This study introduces new motor unit number index (MUNIX), motor unit size index (MUSIX) and compound muscle action potential (CMAP) parameters called M50, MUSIX200 and CMAP50. M50 and CMAP50 indicate the time in months from symptom onset an ALS patient needs to lose 50% of MUNIX or CMAP in relation to the mean values of controls. MUSIX200 represents the time in months until doubling of the mean MUSIX of controls. We used MUNIX parameters of Musculi abductor pollicis brevis (APB), abductor digiti minimi (ADM) and tibialis anterior (TA) of 222 ALS patients. Embedded in the D50 disease progression model, disease aggressiveness and accumulation were analyzed separately. M50, CMAP50 and MUSIX200 significantly differed among disease aggressiveness subgroups (p < 0.001) regardless of disease accumulation. ALS patients with a low M50 had a significantly shorter survival compared to high M50 (median 32 versus 74 months). M50 preceded the loss of global function (median of about 14 months). M50, CMAP50 and MUSIX200 characterize the disease course in ALS in a new way and may be applied as early measures of disease progression.

Booster Dose Vaccination and Dynamics of COVID-19 Pandemic in the Fifth Wave: An Efficient and Simple Mathematical Model for Disease Progression

Mathematical studies exploring the impact of booster vaccine doses on the recent COVID-19 waves are scarce, leading to ambiguity regarding the significance of booster doses. A mathematical model with seven compartments was used to determine the basic and effective reproduction numbers and the proportion of infected people during the fifth wave of COVID-19. Using the next-generation matrix, we computed the effective reproduction parameter, Rt. During the fifth COVID-19 wave, the basic reproductive number in Thailand was calculated to be R0= 1.018691. Analytical analysis of the model revealed both local and global stability of the disease-free equilibrium and the presence of an endemic equilibrium. A dose-dependent decrease in the percentage of infected individuals was observed in the vaccinated population. The simulation results matched the real-world data of the infected patients, establishing the suitability of the model. Furthermore, our analysis suggested that people who had received vaccinations had a better recovery rate and that the death rate was the lowest among those who received the booster dose. The booster dose reduced the effective reproduction number over time, suggesting a vaccine efficacy rate of 0.92. Our study employed a rigorous analytical approach to accurately describe the dynamics of the COVID-19 fifth wave in Thailand. Our findings demonstrated that administering a booster dose can significantly increase the vaccine efficacy rate, resulting in a lower effective reproduction number and a reduction in the number of infected individuals. These results have important implications for public health policymaking, as they provide useful information for the more effective forecasting of the pandemic and improving the efficiency of public health interventions. Moreover, our study contributes to the ongoing discourse on the effectiveness of booster doses in mitigating the impact of the COVID-19 pandemic. Essentially, our study suggests that administering a booster dose can substantially reduce the spread of the virus, supporting the case for widespread booster dose campaigns.

Abstract P3-01-13: Palbociclib, ribociclib and abemaciclib in real-world data: risk of disease progression on first-line treatment of metastatic breast cancer

Abstract Introduction and objectives: Cyclin dependent kinase (CDK) 4/6 inhibitors along with endocrine therapy (ET) is the standard first-line for endocrine receptor (ER)-positive HER2-negative metastatic breast cancer. Three CDK 4/6 inhibitors have the FDA and EMA approval: abemaciclib, palbociclib and ribociclib. They appear to have notable differences in their pharmacokinetic characteristics and ability to inhibit every cyclin. Nevertheless, the natural history of disease associated with each CDK 4/6 inhibitor remains unclear. The aim of this study is to establish a risk prediction model for disease progression under the different therapeutic regimes and assess whether there are differences in terms of efficacy between them. Methods: This is a retrospective observational study in which patients treated with ET plus abemaciclib, palbociclib or ribociclib as front line for metastatic breast cancer in Virgen del Rocio Hospital between April 2014 and April 2021 were selected. Patients in this population were followed for a period of 36 months. They were studied according to their clinical characteristics and disease outcome using descriptive analysis. The risk of progression was studied by a transversal method using a univariate logistic regression model. Moreover, Kaplan Meier curves were used to estimate progression-free survival (PFS) and the Breslow test to estimate the p value. All statistical analyses were performed with SPSS 28.0 (Statistical Package for the Social Sciences). Results: A total of 189 patients were selected. 55(29.1%) of them received abemaciclib, 83(44%) palbociclib and 51(27%) ribociclib. 50(27%) patients had de novo metastatic disease, while 139(73%) were recurrences of previous disease. Almost half of the patients (45%) received fulvestrant and 104 (55%) patients received aromatase inhibitors. Table 1 represents the proportion of patients with visceral involvement and endocrine resistance in each arm. The univariate logistic regression model showed that patients treated with palbociclib had 2.36(CI95% 1.165-4.765) times the risk of progression compared to abemaciclib, while patients treated with ribociclib had 2.50(CI95% 1.139-5. 475) times the risk of progression compared to abemaciclib. Overall PFS was 30.03 months. A tendency towards best PFS with abemaciclib in comparison with the other CDK 4/6 inhibitors was appreciated, but it did not reach statistical significance (abemaciclib-palbociclib p=0.289; abemaciclib-ribociclib; p= 0.293; palbociclib-ribociclib p=0.979). Conclusions: In our sample of patients with ER-positive HER2-negative metastatic breast cancer treated with the different CDK 4/6 inhibitors as front line therapy, patients with abemaciclib achieved a lower risk of progression and a trend toward longer PFS. Further follow-up will be necessary to determine whether abemaciclib provides greater benefit in PFS. Table 1. Proportion of patients with visceral involvement and endocrine resistance in each arm. Visceral metastases: Liver, Lung, central nervous System; Non-Visceral: Bone, skin, Lymph node. Citation Format: Mónica Cejuela, M. ángeles Castilla, Marta Benavent, Sonia Molina-Pinelo, Maria A Dominguez-Cejudo, Ana Gil, Alejandro Falcon, Francisco Javier Salvador Bofill. Palbociclib, ribociclib and abemaciclib in real-world data: risk of disease progression on first-line treatment of metastatic breast cancer. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-01-13.

Clinical Phenotype Imprints on Brain Atrophy Progression in Parkinson’s Disease

There is much controversy about the link between motor symptom progression and the plethora of reported brain atrophy patterns in idiopathic Parkinson’s disease (PD). The main goal of this study is to provide empirical evidence for unique and common contributions of clinical phenotype characteristics on the dynamic changes of brain structure over time. We analyzed the behavioral and magnetic resonance imaging (MRI) data of PD patients (n = 22) and healthy individuals (n = 21) acquired two years apart through the computational anatomy framework of longitudinal voxel-based morphometry (VBM). This analysis revealed a symmetrical bi-hemispheric pattern of accelerated grey matter decrease in PD extending through the insula, parahippocampal gyrus, medial temporal lobes and the precuneus. We observed a hemisphere-specific correlation between the established scores for motor symptoms severity and the rate of atrophy within motor regions, which was further differentiated by the clinical phenotype characteristics of PD patients. Baseline cerebellum anatomy differences between the tremor-dominant and akineto-rigid PD remained stable over time and can be regarded as trait rather than state-associated features. We interpret the observed pattern of progressive brain anatomy changes as mainly linked to insular areas that determine together with basal ganglia the motor and non-motor phenotype in PD. Our findings provide empirical evidence for the sensitivity of computational anatomy to dynamic changes in PD, offering additional opportunities to establish reliable models of disease progression.

A multidimensional ODE-based model of Alzheimer’s disease progression

Data-driven Alzheimer’s disease (AD) progression models are useful for clinical prediction, disease mechanism understanding, and clinical trial design. Most dynamic models were inspired by the amyloid cascade hypothesis and described AD progression as a linear chain of pathological events. However, the heterogeneity observed in healthy and sporadic AD populations challenged the amyloid hypothesis, and there is a need for more flexible dynamical models that accompany this conceptual shift. We present a statistical model of the temporal evolution of biomarkers and cognitive tests that allows diverse biomarker paths throughout the disease. The model consists of two elements: a multivariate dynamic model of the joint evolution of biomarkers and cognitive tests; and a clinical prediction model. The dynamic model uses a system of ordinary differential equations to jointly model the rate of change of an individual’s biomarkers and cognitive tests. The clinical prediction model is an ordinal logistic model of the diagnostic label. Prognosis and time-to-onset predictions are obtained by computing the clinical label probabilities throughout the forecasted biomarker trajectories. The proposed dynamical model is interpretable, free of one-dimensional progression hypotheses or disease staging paradigms, and can account for the heterogeneous dynamics observed in sporadic AD. We developed the model using longitudinal data from the Alzheimer’s Disease Neuroimaging Initiative. We illustrate the patterns of biomarker rates of change and the model performance to predict the time to conversion from MCI to dementia.

Budget impact analysis of two treatment approaches for hepatitis C in Malaysia through the use of voluntary and compulsory licensing options.

A scaled-up treatment cascade with direct-acting antiviral (DAA) therapy is necessary to achieve global WHO targets for hepatitis C virus (HCV) elimination in Malaysia. Recently, limited access to sofosbuvir/daclatasvir (SOF/DAC) is available through compulsory licensing, with access to sofosbuvir/velpatasvir (SOF/VEL) expected through voluntary licensing due to recent agreements. SOF/VEL has superior clinical outcomes but has higher drug acquisition costs compared to SOF/DAC. A stratified treatment cascade might be the most cost-efficient approach for Malaysia whereby all HCV patients are treated with SOF/DAC except for patients with cirrhosis who are treated with SOF/VEL. This study aimed to conduct a 5-year budget impact analysis of the proposed stratified treatment cascade for HCV treatment in Malaysia. A disease progression model that was developed based on model-predicted HCV epidemiology data was used for the analysis, where all HCV patients in scenario A were treated with SOF/DAC for all disease stages while in scenario B, SOF/DAC was used only for non-cirrhotic patients and SOF/VEL was used for the cirrhotic patients. Healthcare costs associated with DAA therapy and disease stage monitoring were included to estimate the downstream cost implications. The stratified treatment cascade with 109 in Scenario B was found to be cost-saving compared to Scenario A. The cumulative savings for the stratified treatment cascade was USD 1.4 million over 5 years. A stratified treatment cascade with SOF/VEL was expected to be cost-saving and can result in a budget impact reduction in overall healthcare expenditure in Malaysia.

Predictive Analytics for Re-Hospitalization and Disease Prediction

Hospital readmissions can have serious consequences for both patients and healthcare providers. They can lead to increased healthcare costs, lower patient satisfaction, and negative health outcomes. When patients are readmitted to the hospital soon after being discharged, their risk of morbidity and mortality increases. Additionally, hospitals may face penalties for having too many readmissions. To address this issue, researchers are investigating the potential of using machine learning techniques to predict a patient’s risk for readmission and illness progression. A large dataset of electronic health records from a major hospital was analyzed, containing patient information on medical histories, diagnoses, treatments, and personal characteristics. Several machine learning techniques, such as logistic regression, decision trees, random forests, and neural networks, were utilized to detect important risk factors and develop predictive models for re-hospitalization and disease progression.&#x0D; The performance of each machine learning model was assessed by the researchers using diverse metrics, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. The study showed that machine learning techniques have the potential to improve patient outcomes by identifying high-risk individuals who may benefit from targeted interventions.&#x0D; The paper presents a range of approaches to explore the factors that contribute to hospital readmissions and introduces a predictive model that employs machine learning algorithms to identify patients who are at a higher risk of readmission. The research involved patients who were admitted to a tertiary hospital, and the model’s effectiveness was assessed using several metrics. The article discusses the possible implications of the model for clinical practice and future research, emphasizing the potential of machine learning algorithms to predict hospital readmissions and enhance patient outcomes.[1]

The Mean Duration of COVID-19 Found Closely Associated with the Ayurvedic Pathognomic Model Shatkriyakala (SKK): Inference of an Observational Cross-Sectional Exploratory Study

A valid measurement for Corona virus disease is needed for case definition and adopting appropriate therapeutic measures. Identification of epidemiology and etio-pathogenesis is must for case definitions. The incubation period The incubation period is the time elapsed between pathogen exposure and appearance of symptoms. It can be utilized to trace the contact history of population at high risk. Incubation, latent and generation periods may help both in understanding transmission and developing therapeutic measures. The evidenced incubation period of covid-19 infection is found to be 5.6-6.7 days. Ayurveda advocates Shatkriyakala pathognomic model i.e. six stage model of disease progression. Aims and Objective: The present study focused to trace –pathological model in context of covid-19. Material and method: This study was conducted in Government ayurvedic institute, COVID testing centre, New Delhi. The tool of data collection was Questionnaire 1 &amp;2. Result:We report a temporal pattern of COVID-19 pathology which corresponds to six-stages of disease progression.(shadvidh kriyakala).&#x0D; Discussion: Assessment of relationship between the variables (Hetu-dosha and Dushya) responsible for pathogenesis temporal dynamics of covid-19 may serve as an instrument for mapping the pathogenesis.&#x0D; Conclusion: Clinical staging of covid-19 can be potentially mapped by six stage classical pathological model.

Thermoneutral housing shapes hepatic inflammation and damage in mouse models of non-alcoholic fatty liver disease.

Inflammation is a common unifying factor in experimental models of non-alcoholic fatty liver disease (NAFLD) progression. Recent evidence suggests that housing temperature-driven alterations in hepatic inflammation correlate with exacerbated hepatic steatosis, development of hepatic fibrosis, and hepatocellular damage in a model of high fat diet-driven NAFLD. However, the congruency of these findings across other, frequently employed, experimental mouse models of NAFLD has not been studied. Here, we examine the impact of housing temperature on steatosis, hepatocellular damage, hepatic inflammation, and fibrosis in NASH diet, methionine and choline deficient diet, and western diet + carbon tetrachloride experimental models of NAFLD in C57BL/6 mice. We show that differences relevant to NAFLD pathology uncovered by thermoneutral housing include: (i) augmented NASH diet-driven hepatic immune cell accrual, exacerbated serum alanine transaminase levels and increased liver tissue damage as determined by NAFLD activity score; (ii) augmented methionine choline deficient diet-driven hepatic immune cell accrual and increased liver tissue damage as indicated by amplified hepatocellular ballooning, lobular inflammation, fibrosis and overall NAFLD activity score; and (iii) dampened western diet + carbon tetrachloride driven hepatic immune cell accrual and serum alanine aminotransferase levels but similar NAFLD activity score. Collectively, our findings demonstrate that thermoneutral housing has broad but divergent effects on hepatic immune cell inflammation and hepatocellular damage across existing experimental NAFLD models in mice. These insights may serve as a foundation for future mechanistic interrogations focused on immune cell function in shaping NAFLD progression.

Disease progression modeling of the North Star Ambulatory Assessment for Duchenne Muscular Dystrophy.

Duchenne muscular dystrophy (DMD) is a rare genetic disorder caused by decreased or absent dystrophin gene leading to progressive muscle degeneration and weakness in young boys. Disease progression models for the North Star Ambulatory Assessment (NSAA), a functional measurement widely used to assess outcomes in clinical trials, were developed using a longitudinal population modeling approach. The relationship between NSAA total score over time, loss of ambulation, and potential covariates that may influence disease progression were evaluated. Data included individual participant observations from an internal placebo-controlled phase II clinical trial and from the external natural history database for male patients with DMD obtained through the Cooperative International Neuromuscular Research Group (CINRG). A modified indirect response model for NSAA joined to a loss of ambulation (LOA) time-to-event model described the data well. Age was used as the independent variable because ambulatory function is known to vary with age. The NSAA and LOA models were linked using the dissipation rate constant parameter from the NSAA model by including the parameter as a covariate on the hazard equation for LOA. No covariates were identified. The model was then used as a simulation tool to explore various clinical trial design scenarios. This model contributes to the quantitative understanding of disease progression in DMD and may guide model-informed drug development decisions for ongoing and future DMD clinical trials.

Safety and efficacy of first-in-man intrathecal injection of human astrocytes (AstroRx®) in ALS patients: phase I/IIa clinical trial results

BackgroundMalfunction of astrocytes is implicated as one of the pathological factors of ALS. Thus, intrathecal injection of healthy astrocytes in ALS can potentially compensate for the diseased astrocytes. AstroRx® is an allogeneic cell-based product, composed of healthy and functional human astrocytes derived from embryonic stem cells. AstroRx® was shown to clear excessive glutamate, reduce oxidative stress, secrete various neuroprotective factors, and act as an immunomodulator. Intrathecal injection of AstroRx® to animal models of ALS slowed disease progression and extended survival. Here we report the result of a first-in-human clinical study evaluating intrathecal injection of AstroRx® in ALS patients.MethodsWe conducted a phase I/IIa, open-label, dose-escalating clinical trial to evaluate the safety, tolerability, and therapeutic effects of intrathecal injection of AstroRx® in patients with ALS. Five patients were injected intrathecally with a single dose of 100 × 106 AstroRx® cells and 5 patients with 250 × 106 cells (low and high dose, respectively). Safety and efficacy assessments were recorded for 3 months pre-treatment (run-in period) and 12 months post-treatment (follow-up period).ResultsA single administration of AstroRx® at either low or high doses was safe and well tolerated. No adverse events (AEs) related to AstroRx® itself were reported. Transient AEs related to the Intrathecal (IT) procedure were all mild to moderate. The study demonstrated a clinically meaningful effect that was maintained over the first 3 months after treatment, as measured by the pre-post slope change in ALSFRS-R. In the 100 × 106 AstroRx® arm, the ALSFRS-R rate of deterioration was attenuated from − 0.88/month pre-treatment to − 0.30/month in the first 3 months post-treatment (p = 0.039). In the 250 × 106 AstroRx® arm, the ALSFRS-R slope decreased from − 1.43/month to − 0.78/month (p = 0.0023). The effect was even more profound in a rapid progressor subgroup of 5 patients. No statistically significant change was measured in muscle strength using hand-held dynamometry and slow vital capacity continued to deteriorate during the study.ConclusionsOverall, these findings suggest that a single IT administration of AstroRx® to ALS patients at a dose of 100 × 106 or 250 × 106 cells is safe. A signal of beneficial clinical effect was observed for the first 3 months following cell injection. These results support further investigation of repeated intrathecal administrations of AstroRx®, e.g., every 3 months.Trial Registration: NCT03482050.

Development of Novel Murine BRAFV600E-Driven Papillary Thyroid Cancer Cell Lines for Modeling of Disease Progression and Preclinical Evaluation of Therapeutics.

The Cancer Genome Atlas study in thyroid cancer exposed the genomic landscape of ~500 PTCs and revealed BRAFV600E-mutant tumors as having different prognosis, contrasting indolent cases and those with more invasive disease. Here, we describe the generation and characterization of six novel BRAFV600E-driven papillary thyroid cancer (PTC) cell lines established from a BrafV600E+/-/Pten+/-/TPO-Cre mouse model that spontaneously develop thyroid tumors. The novel cell lines were obtained from animals representing a range of developmental stages and both sexes, with the goal of establishing a heterogeneous panel of PTC cell lines sharing a common driver mutation. These cell lines recapitulate the genetics and diverse histopathological features of BRAFV600E-driven PTC, exhibiting differing degrees of growth, differentiation, and invasive potential that may help define mechanisms of pathogenesis underlying the heterogeneity present in the patient population. We demonstrate that these cell lines can be used for a variety of in vitro applications and can maintain the potential for in vivo transplantation into immunocompetent hosts. We believe that these novel cell lines will provide powerful tools for investigating the molecular basis of thyroid cancer progression and will lead to the development of more personalized diagnostic and treatment strategies for BRAFV600E-driven PTC.

Risk Amplifiers for Vascular Disease and CKD in South Asians: When Intrinsic β-Cell Dysfunction Meets a High-Carbohydrate Diet.

South Asians, comprising almost one fourth of the world population, are at higher risk of type 2 diabetes mellitus, hypertension, cardiovascular disease, and CKD compared with other ethnic groups. This has major public health implications in South Asia and in other parts of the world to where South Asians have immigrated. The interplay of various modifiable and nonmodifiable risk factors confers this risk. Traditional models of cardiometabolic disease progression and CKD evaluation may not be applicable in this population with a unique genetic predisposition and phenotype. A wider understanding of dietary and lifestyle influences, genetic and metabolic risk factors, and the pitfalls of conventional equations estimating kidney function in this population are required in providing care for kidney diseases. Targeted screening of this population for metabolic and vascular risk factors and individualized management plan for disease management may be necessary. Addressing unhealthy dietary patterns, promoting physical activity, and medication management that adheres to cultural factors are crucial steps to mitigate the risk of cardiovascular disease and CKD in this population. In South Asian countries, a large rural and urban community-based multipronged approach using polypills and community health workers to decrease the incidence of these diseases may be cost-effective.

Longitudinal Exposure-Response Modeling of Multiple Indicators of Alzheimer's Disease Progression.

Progression in Alzheimer's disease manifests as changes in multiple biomarker, cognitive, and functional endpoints. Disease progression modeling can be used to integrate these multiple measures into a synthesized metric of where a patient lies within the disease spectrum, allowing for a more dynamic measure over the range of the disease. This study aimed to combine modeling techniques from psychometric research (e.g., item response theory) and pharmacometrics (e.g., hierarchical models) to describe the multivariate longitudinal disease progression for patients with mild-to-moderate Alzheimer's disease. Additionally, we aimed to extend the subsequent model to make it suitable for clinical trial simulation, with the inclusion of covariates, to explain variability in latent progression (i.e., disease progression) and to aid in the assessment of enrichment strategies. Multiple longitudinal endpoints in the Alzheimer's Disease Neuroimaging Initiative database were modeled. This model was validated internally using visual predictive checks, and externally by comparing data from the placebo arms of two Phase 2 crenezumab studies, ABBY (NCT01343966) and BLAZE (NCT01397578). The Alzheimer's Disease Neuroimaging Initiative began in 2004: the initial 5-year study (ADNI-1) was extended by 2 years in 2009 by a Grand Opportunities grant (ADNI-GO), and in 2011 and 2016 by further competitive renewals of the ADNI-1 grant (ADNI-2 and ADNI-3, respectively). This work studies natural progression data from patients with confirmed Alzheimer's disease. The Phase 2 ABBY and BLAZE trials evaluated the safety and efficacy of crenezumab in patients with mild-to-moderate Alzheimer's disease. From the Alzheimer's Disease Neuroimaging Initiative database, 305 subjects who had a baseline diagnosis of mild-to-moderate Alzheimer's disease were included in modeling. From the ABBY and BLAZE studies, 158 patients were included from the studies' placebo arms. Longitudinal cognitive and functional assessments modeled included the Clinical Dementia Rating (both as Sum of Boxes and individual item scores), the Mini-Mental State Examination, the Alzheimer's Disease Assessment Scale - Cognitive Subscale, the Functional Activities Questionnaire, the Montreal Cognitive Assessment, and the Rey Auditory Verbal Learning Test. Also included were the imaging variable fluorodeoxyglucose-positron emission tomography and the following magnetic resonance imaging volumetrics: entorhinal, fusiform, hippocampal, intra-cranial, mid-temporal, ventricular, and whole brain. Applying item response theory approaches in this longitudinal setting showed clinical assessments informing a common disease scale in the following order (from early disease to late disease): Rey Auditory Verbal Learning Test, Functional Activities Questionnaire, Montreal Cognitive Assessment, Alzheimer's Disease Assessment Scale - Cognitive Subscale 12, Clinical Dementia Rating - Sum of Boxes, and Mini-Mental State Examination. The Clinical Dementia Rating communication and home-and-hobbies items were most informative at earlier disease stages, while memory, orientation, and personal care informed the disease status at later stages. A clinical trial simulation model was developed and accurately described within-sample longitudinal distribution of endpoints. Simplifying the model to use only baseline age, MMSE, and APOEε4 status as predictors, out-of-sample mean progression of ADAS-Cog and CDR Sum of Boxes in the ABBY and BLAZE placebo arms was accurately described; however, the variability in these endpoints was underpredicted and suggests possibility for further model refinement when extrapolating from the ADNI sample to trial data. Clinical trial simulations were performed to exemplify use of the model to investigate hypothetical disease modification effects on the multivariate, longitudinal progression on the Alzheimer's Disease Assessment Scale - Cognitive Subscale and the Clinical Dementia Rating - Sum of Boxes. The latent variable structure of item response theory can be extended to capture a variety of scales that are common assessments and indicators of disease status in mild-to-moderate Alzheimer's disease. These models are not intended to support causal inferences, but they do successfully characterize the observed correlation between endpoints over time and result in concise numerical indices of disease status that reflect the totality of evidence from considering the endpoints jointly. As such, the models have utility for a variety of tasks in clinical trial design, including simulation of hypothetical drug effects, interpolation of missing data, and assessment of in-sample information.

Personalized Computational Causal Modeling of the Alzheimer Disease Biomarker Cascade.

Mathematical models of complex diseases, such as Alzheimer's disease, have the potential to play a significant role in personalized medicine. Specifically, models can be personalized by fitting parameters with individual data for the purpose of discovering primary underlying disease drivers, predicting natural history, and assessing the effects of theoretical interventions. Previous work in causal/mechanistic modeling of Alzheimer's Disease progression has modeled the disease at the cellular level and on a short time scale, such as minutes to hours. No previous studies have addressed mechanistic modeling on a personalized level using clinically validated biomarkers in individual subjects. This study aimed to investigate the feasibility of personalizing a causal model of Alzheimer's Disease progression using longitudinal biomarker data. We chose the Alzheimer Disease Biomarker Cascade model, a widely-referenced hypothetical model of Alzheimer's Disease based on the amyloid cascade hypothesis, which we had previously implemented mathematically as a mechanistic model. We used available longitudinal demographic and serial biomarker data in over 800 subjects across the cognitive spectrum from the Alzheimer's Disease Neuroimaging Initiative. The data included participants that were cognitively normal, had mild cognitive impairment, or were diagnosed with dementia (probable Alzheimer's Disease). The model consisted of a sparse system of differential equations involving four measurable biomarkers based on cerebrospinal fluid proteins, imaging, and cognitive testing data. Personalization of the Alzheimer Disease Biomarker Cascade model with individual serial biomarker data yielded fourteen personalized parameters in each subject reflecting physiologically meaningful characteristics. These included growth rates, latency values, and carrying capacities of the various biomarkers, most of which demonstrated significant differences across clinical diagnostic groups. The model fits to training data across the entire cohort had a root mean squared error (RMSE) of 0.09 (SD 0.081) on a variable scale between zero and one, and were robust, with over 90% of subjects showing an RMSE of < 0.2. Similarly, in a subset of subjects with data on all four biomarkers in at least one test set, performance was high on the test sets, with a mean RMSE of 0.15 (SD 0.117), with 80% of subjects demonstrating an RMSE < 0.2 in the estimation of future biomarker points. Cluster analysis of parameters revealed two distinct endophenotypic groups, with distinct biomarker profiles and disease trajectories. Results support the feasibility of personalizing mechanistic models based on individual biomarker trajectories and suggest that this approach may be useful for reclassifying subjects on the Alzheimer's clinical spectrum. This computational modeling approach is not limited to the Alzheimer Disease Biomarker Cascade hypothesis, and can be applied to any mechanistic hypothesis of disease progression in the Alzheimer's field that can be monitored with biomarkers. Thus, it offers a computational platform to compare and validate various disease hypotheses, personalize individual biomarker trajectories and predict individual response to theoretical prevention and therapeutic intervention strategies.

Predictive value of magnetic resonance imaging radiomics-based machine learning for disease progression in patients with high-grade glioma

Accurately predicting the prognosis of patients with high-grade glioma (HGG) is potentially important for treatment. However, the predictive value of images of various magnetic resonance imaging (MRI) sequences for prognosis at different time points is unknown. We established predictive machine learning models of HGG disease progression and recurrence using MRI radiomics and explored the factors influencing prediction accuracy. Radiomics features were extracted from T1-weighted (T1WI), contrast-enhanced T1-weighted (CE-T1WI), T2-weighted (T2WI), and fluid-attenuated inversion recovery (FLAIR) images (postoperative radiotherapy planning MRI images) obtained from 162 patients with HGG. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection. Machine learning models were used to build prediction models to estimate disease progression or recurrence. The influence of different MRI sequences, regions of interest (ROIs), and prediction time points was also explored. The receiver operating characteristic (ROC) curve was used to evaluate the discriminative performance of each model, and the DeLong test was employed to compare the ROC curves. Radiomics features from T2WI and FLAIR demonstrated greater predictive value for disease progression compared with T1WI or CE-TIWI. The best predictive models, with areas under the ROC curves (AUCs) of 0.70, 0.68, 0.78, 0.78, and 0.78 for predicting disease progression at the 6th, 9th, 12th, 15th, and 18th month after radiotherapy, respectively, were obtained by combining clinical features with gross tumor volume (GTV) and clinical target volume (CTV) features extracted from T2WI and FLAIR. Structural MRI obtained before radiotherapy can be used to predict the disease progression or posttreatment recurrence of HGG. When using MRI radiomics to predict long-term outcomes as opposed to short-term outcomes, better predictive results may be obtained.

COVID-19 early assessment outcomes on Internet data: A review study

Abstract Background: Severe acute respiratory syndrome coronavirus (SARS-CoV)-2, which is a novel coronavirus from the same family as SARS-CoV and Middle East respiratory syndrome coronavirus, has overrun worldwide leading the World Health Organization to declare a pandemic. Objectives: This study aimed to clarify high-risk factors for coronavirus disease 2019 (COVID-19) with multivariate analysis and establish a predictive model of disease progression to help clinicians better choose a therapeutic strategy. Clinical implications of COVID-19 will push society past this pandemic with the latest in technology and research and further studies into the pathogenesis evaluation. Methods: A comprehensive search of the PubMed, MEDLINE, Uptodate, Natural MEDLINE, Embase, and Web of Science electronic databases was made, using the following search terms: “COVID-19,” “COVID-19 SCORE,” “COVID-19 diagnosis,” “COVID-19 management,” “coronavirus,” and “SARS-CoV-2.” We included scientific publications from December 1, 2019, to April 31, 2021, which focused on clinical characteristics and treatments for SARS-CoV-2 that were eligible for inclusion. We screened all reference lists of relevant studies to identify any missing publications. Results: A total of 40 articles were reviewed. We revealed that the present review emphasizes that the higher risk of comorbidity, age, lymphocyte, and lactate dehydrogenase (CALL) score has a good predictive value for mortality in COVID-19 than the CURB-65 score. The 2021–2022 SARS-CoV-2 Omicron variant is a global concern due to its rapid spread to displace the main Delta and Omicron variants. This scoring system has been designed to categorize based on the systemic disease involvement and, thus, would serve as a reliable indicator for prognostic assessment in patients. Conclusion: This review highlights the higher predictive value of the CALL score for higher risk COVID-19 mortality than the CURB-65 score. The 2021–2022 SARS-CoV-2 Omicron variant is of global concern as its rapid spread has replaced the main Delta and Alpha variants. Scoring systems are designed to categorize disease and, thus, serve as reliable indicators of patient prognosis. Therefore, establishing corresponding standard assessment forms and admission criteria and preparing medical resources for critically ill patients is necessary as much as possible.

Dynamic predictions from longitudinal CD4 count measures and time to death of HIV/AIDS patients using a Bayesian joint model

A Bayesian joint modeling approach to dynamic prediction of HIV progression and mortality allows individualized predictions to be made for HIV patients, based on monitoring of their CD4 counts. This study aims to provide predictions of patient-specific trajectories of HIV disease progression and survival. Longitudinal data on 254 HIV/AIDS patients who received ART between 2009 and 2014, and who had at least one CD4 count observed, were employed in a Bayesian joint model of disease progression. Different forms of association structure that relate the longitudinal CD4 biomarker and time to death were assessed; and predictions were averaged over the different models using Bayesian model averaging. The individual follow-up times ranged from 1 to 120 months, with a median of 22 months and IQR 7–39 months. The estimates of the association structure parameters from two of the three models considered indicated that the HIV mortality hazard at any time point is associated with the rate of change in the underlying value of the CD4 count. Model averaging the dynamic predictions resulted in only one of the hypothesized association structures having non-zero weight in almost all time points for each individual, with the exception of twelve patients, for whom other association structures were preferred at a few time points. The predictions were found to be different when we averaged them over models than when we derived them from the highest posterior weight model alone. The model with highest posterior weight for almost all time points for each individual gave an estimate of the association parameter of −0.02 implying that for a unit increase in the CD4 count, the hazard of HIV mortality decreases by a factor (hazard ratio) of 0.98. Functional status and alcohol intake are important contributing factors that affect the mean square root of CD4 measurements.