Estimation of constant and time-varying dynamic parameters of HIV infection in a nonlinear differential equation model

Modeling viral dynamics in HIV/AIDS studies has resulted in a deep understanding of pathogenesis of HIV infection from which novel antiviral treatment guidance and strategies have been derived. Viral dynamics models based on nonlinear differential equations have been proposed and well developed over the past few decades. However, it is quite challenging to use experimental or clinical data to estimate the unknown parameters (both constant and time-varying parameters) in complex nonlinear differential equation models. Therefore, investigators usually fix some parameter values, from the literature or by experience, to obtain only parameter estimates of interest from clinical or experimental data. However, when such prior information is not available, it is desirable to determine all the parameter estimates from data. In this paper we intend to combine the newly developed approaches, a multi-stage smoothing-based (MSSB) method and the spline-enhanced nonlinear least squares (SNLS) approach, to estimate all HIV viral dynamic parameters in a nonlinear differential equation model. In particular, to the best of our knowledge, this is the first attempt to propose a comparatively thorough procedure, accounting for both efficiency and accuracy, to rigorously estimate all key kinetic parameters in a nonlinear differential equation model of HIV dynamics from clinical data. These parameters include the proliferation rate and death rate of uninfected HIV-targeted cells, the average number of virions produced by an infected cell, and the infection rate which is related to the antiviral treatment effect and is time-varying. To validate the estimation methods, we verified the identifiability of the HIV viral dynamic model and performed simulation studies.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS290 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Sieve estimation of constant and time-varying coefficients in nonlinear ordinary differential equation models by considering both numerical error and measurement error

This article considers estimation of constant and time-varying coefficients in nonlinear ordinary differential equation (ODE) models where analytic closed-form solutions are not available. The numerical solution-based nonlinear least squares (NLS) estimator is investigated in this study. A numerical algorithm such as the Runge--Kutta method is used to approximate the ODE solution. The asymptotic properties are established for the proposed estimators considering both numerical error and measurement error. The B-spline is used to approximate the time-varying coefficients, and the corresponding asymptotic theories in this case are investigated under the framework of the sieve approach. Our results show that if the maximum step size of the $p$-order numerical algorithm goes to zero at a rate faster than $n^{-1/(p\wedge4)}$, the numerical error is negligible compared to the measurement error. This result provides a theoretical guidance in selection of the step size for numerical evaluations of ODEs. Moreover, we have shown that the numerical solution-based NLS estimator and the sieve NLS estimator are strongly consistent. The sieve estimator of constant parameters is asymptotically normal with the same asymptotic co-variance as that of the case where the true ODE solution is exactly known, while the estimator of the time-varying parameter has the optimal convergence rate under some regularity conditions. The theoretical results are also developed for the case when the step size of the ODE numerical solver does not go to zero fast enough or the numerical error is comparable to the measurement error. We illustrate our approach with both simulation studies and clinical data on HIV viral dynamics.Comment: Published in at http://dx.doi.org/10.1214/09-AOS784 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

A dynamic Bayesian nonlinear mixed-effects model of HIV response incorporating medication adherence, drug resistance and covariates

HIV dynamic studies have contributed significantly to the understanding of HIV pathogenesis and antiviral treatment strategies for AIDS patients. Establishing the relationship of virologic responses with clinical factors and covariates during long-term antiretroviral (ARV) therapy is important to the development of effective treatments. Medication adherence is an important predictor of the effectiveness of ARV treatment, but an appropriate determinant of adherence rate based on medication event monitoring system (MEMS) data is critical to predict virologic outcomes. The primary objective of this paper is to investigate the effects of a number of summary determinants of MEMS adherence rates on virologic response measured repeatedly over time in HIV-infected patients. We developed a mechanism-based differential equation model with consideration of drug adherence, interacted by virus susceptibility to drug and baseline characteristics, to characterize the long-term virologic responses after initiation of therapy. This model fully integrates viral load, MEMS adherence, drug resistance and baseline covariates into the data analysis. In this study we employed the proposed model and associated Bayesian nonlinear mixed-effects modeling approach to assess how to efficiently use the MEMS adherence data for prediction of virologic response, and to evaluate the predicting power of each summary metric of the MEMS adherence rates.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS376 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

APOBEC3G levels predict rates of progression to AIDS

BACKGROUND: APOBEC3G (hA3G) is a newly discovered cellular factor of innate immunity that inhibits HIV replication in vitro. Whether hA3G conferrs protection against HIV in vivo is not known. To investigate the possible anti-HIV activity of hA3G in vivo, we examined hA3G mRNA abundance in primary human cells isolated from either HIV-infected or HIV-uninfected individuals, and found that hA3G mRNA levels follow a hierarchical order of long-term nonprogressors>HIV-uninfected>Progressors; and, hA3G mRNA abundance is correlated with surrogates of HIV disease progression: viral load and CD4 count. Another group later confirmed that HIV-infected subjects have lower hA3G mRNA levels than HIV-uninfected controls, but did not find correlations between hA3G mRNA levels and viral load or CD4 count. These conflicing results indicate that a more comprehensive, conclusive investigation of hA3G expression levels in various patient cohorts is urgently needed. PRESENTATION OF THE HYPOTHESIS: For exploring whether hA3G abundance might influence HIV disease progression, we have formulated a hypothesis that inlcudes two parts: a) in vivo, the basal hA3G mRNA expression level per PBMC is a constant – with minor physiologic fluctuations – determined by host genetic and epigenetic elements in a healthy individual; and that the basal hA3G mRNA expression levels in a population follow a Normal (or Gaussian) distribution; b) that although HIV infects randomly, it results in more rapid disease progression in those with lower hA3G mRNA levels, and slower disease progression in those with higher hA3G mRNA levels. TESTING THE HYPOTHESIS: This hypothesis could be tested by a straighforward set of experiments to compare the distribution of hA3G mRNA levels in HIV-uninfected healthy individuals and that in HIV-infected, antiretroviral therapy-naïve subjects who are at early and late stages of infection. IMPLICATION OF THE HYPOTHESIS: Testing this hypothesis will have significant implications for biomedical research. a) It will link hA3G to the mechanisms underlying slower disease progression in long-term nonprogressors. And, b) It may help to establiseh a new prognostic marker, the hA3G abundance measurement, for HIV-infected patients

Neural Generalized Ordinary Differential Equations with Layer-varying Parameters

Deep residual networks (ResNets) have shown state-of-the-art performance in various real-world applications. Recently, the ResNets model was reparameterized and interpreted as solutions to a continuous ordinary differential equation or Neural-ODE model. In this study, we propose a neural generalized ordinary differential equation (Neural-GODE) model with layer-varying parameters to further extend the Neural-ODE to approximate the discrete ResNets. Specifically, we use nonparametric B-spline functions to parameterize the Neural-GODE so that the trade-off between the model complexity and computational efficiency can be easily balanced. It is demonstrated that ResNets and Neural-ODE models are special cases of the proposed Neural-GODE model. Based on two benchmark datasets, MNIST and CIFAR-10, we show that the layer-varying Neural-GODE is more flexible and general than the standard Neural-ODE. Furthermore, the Neural-GODE enjoys the computational and memory benefits while performing comparably to ResNets in prediction accuracy

Machine Learning to Predict Mortality for Aneurysmal Subarachnoid Hemorrhage (aSAH) Using a Large Nationwide EHR Database

Aneurysmal subarachnoid hemorrhage (aSAH) develops quickly once it occurs and threatens the life of patients. We aimed to use machine learning to predict mortality for SAH patients at an early stage which can help doctors make clinical decisions. In our study, we applied different machine learning methods to an aSAH cohort extracted from a national EHR database, the Cerner Health Facts EHR database (2000-2018). The outcome of interest was in-hospital mortality, as either passing away while still in the hospital or being discharged to hospice care. Machine learning-based models were primarily evaluated by the area under the receiver operating characteristic curve (AUC). The population size of the SAH cohort was 6728. The machine learning methods achieved an average of AUCs of 0.805 for predicting mortality with only the initial 24 hours\u27 EHR data. Without losing the prediction power, we used the logistic regression to identify 42 risk factors, -examples include age and serum glucose-that exhibit a significant correlation with the mortality of aSAH patients. Our study illustrates the potential of utilizing machine learning techniques as a practical prognostic tool for predicting aSAH mortality at the bedside

Self-Guided Smartphone Application to Manage Chronic Musculoskeletal Pain: A Randomized, Controlled Pilot Trial

OBJECTIVE: The goal of this study is to evaluate the feasibility and efficacy of an auricular point acupressure smartphone app (mAPA) to self-manage chronic musculoskeletal pain. METHODS: A prospective, longitudinal, randomized, controlled pilot trial was conducted using a three-group design (self-guided mAPA ( RESULTS: After a 4-week APA intervention, participants in the in-person mAPA group had improved physical function of 32% immediately post-intervention and 29% at the 1M follow-up. Participants in the self-guided mAPA group had higher improvement (42% at post-intervention and 48% at the 1M follow-up). Both mAPA groups had similar degrees of pain intensity relief at post-intervention (45% for in-person and 48% for the self-guided group) and the 1M follow-up (42% for in-person and 45% for the self-guided group). Over 50% of the participants in each group reached at least 30% reduced pain intensity at post-intervention, and this was sustained in the mAPA groups at the 1M follow-up. Approximately 80% of the participants in both mAPA groups were satisfied with the treatment outcomes and adhered to the suggested APA practice; however, participants in the self-guided group had higher duration and more frequency in APA use. The attrition rate was 16% at the 1M follow-up. No adverse effects of APA were reported, and participants found APA to be beneficial and the app to be valuable. CONCLUSIONS: The study findings indicate that participants effectively learned APA using a smartphone app, whether they were self-guided or received in-person training. They were able to self-administer APA to successfully manage their pain. Participants found APA to be valuable in their pain self-management and expressed satisfaction with the intervention using the app

On identifiability of nonlinear ODE models and applications in viral dynamics

Ordinary differential equations (ODEs) are a powerful tool for modeling dynamic processes with wide applications in a variety of scientific fields. Over the last two decades, ODEs have also emerged as a prevailing tool in various biomedical research fields, especially in infectious disease modeling. In practice, it is important and necessary to determine unknown parameters in ODE models based on experimental data. Identifiability analysis is the first step in determining unknown parameters in ODE models and such analysis techniques for nonlinear ODE models are still under development. In this article, we review identifiability analysis methodologies for nonlinear ODE models developed in the past couple of decades, including structural identifiability analysis, practical identifiability analysis, and sensitivity-based identifiability analysis. Some advanced topics and ongoing research are also briefly reviewed. Finally, some examples from modeling viral dynamics of HIV and influenza viruses are given to illustrate how to apply these identifiability analysis methods in practice.NIAID/NIH research grants AI055290, AI50020, AI28433, AI078498, RR06555, the University of Rochester Provost Award, and the University of Rochester DCFAR (P30AI078498) Mentoring Award.http://www.siam.org/journals/sirev/53-1/75700.htmlai201

Nurse-Administered Auricular Point Acupressure for Cancer-Related Pain

PURPOSE: The study aimed to (1) examine the feasibility of providing a training course on auricular point acupressure (APA) for clinical oncology nurses to integrate APA into real-world nursing care settings, and (2) examine the effectiveness of APA on cancer-related pain (CRP) under usual inpatient oncology ward conditions. METHODS: This was a 2-phase feasibility study. Phase 1, an in-person, 8 hour training program was provided to oncology nurses. Phase 2, a prospective and feasibility study was conducted to evaluate the integration of APA into nursing care activities to manage CRP. Oncology patients were included if their pain was rated at ≥4 on a 0 to 10 numeric rating scale in the past 24 hours. Patients received 1 APA treatment administered by the nurses and were instructed to stimulate the points for 3 days. Study outcomes (pain intensity, fatigue, and sleep disturbance), pain medication use, and APA practice were measured by a phone survey daily. RESULTS: Ten oncology nurses received APA training in phase 1. APA had been added to the hospital\u27s electronic health records (EHRs) as a pain treatment. In phase 2, 33 oncology patients received APA treatment with a 100% adherence rate (pressing the seeds 3 times per day, 3 minutes per time based on the suggestion). The side effects of APA were minimal (~8%-12% felt tenderness on the ear). After 3 days of APA, patients reported 38% pain relief, 39% less fatigue, and 45% improvement in sleep disturbance; 24% reduced any type of pain medication use and 19% reduced opioid use (10 mg opioids using milligram morphine equivalent). The major barrier to integrating APA into routine nursing practice was time management (how to include APA in a daily workflow). CONCLUSION: It is feasible to provide 8-hour training to oncology nurses for mastering APA skill and then integrating APA into their daily nursing care for patients with CRP. Based on the promising findings (decreased pain, improved fatigue and sleep disturbance, and less opioid use), the next step is to conduct a randomized clinical trial with a larger sample to confirm the efficacy of APA for oncology nurses to treat CRP in real-world practice.ClinicalTrial.gov identifier number: NCT04040140