Wensheng Guo

In the supplementary material, we provide detailed calculations for the EM algorithm and the likelihood ratio test. Additionally, we include further results from the application analysis and simulation studies.

Some patients with COVID-19 show changes in signs and symptoms such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asymptomatic. It is important to identify these subgroups and to understand what biological and clinical predictors are related to these subgroups. This information will provide insights into how the immune system may respond differently to infection and can further be used to identify infected individuals. We propose a flexible nonparametric mixed-effects mixture model that identifies risk factors and classifies patients with biological changes. We model the latent probability of biological changes using a logistic regression model and trajectories in the latent groups using smoothing splines. We developed an EM algorithm to maximize the penalized likelihood for estimating all parameters and mean functions. We evaluate our methods by simulations and apply the proposed model to investigate changes in temperature in a cohort of COVID-19-infected hemodialysis patients.

Prediction modeling for clinical decision making is of great importance and needed to be updated frequently with the changes of patient population and clinical practice. Existing methods are either done in an ad hoc fashion, such as model recalibration or focus on studying the relationship between predictors and outcome and less so for the purpose of prediction. In this article, we propose a dynamic logistic state space model to continuously update the parameters whenever new information becomes available. The proposed model allows for both time‐varying and time‐invariant coefficients. The varying coefficients are modeled using smoothing splines to account for their smooth trends over time. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at prespecified time intervals, which allows for better approximation of the underlying …

In this chapter, we present an approach to analyzing longitudinal data using state space models (SSMs) as the building blocks at both the group level and the subject level. The resultant hierarchical SSMs can characterize complex temporal dynamics. Their mixed-effects model representation enables a likelihood-based method for parameter estimation, hypothesis testing, and model selection, such as restricted maximum likelihood, likelihood ratio test, and information criteria. Best linear unbiased predictions can be straightforwardly calculated. The Kalman filtering and smoothing algorithms lead to efficient computations. We further present bivariate hierarchical SSMs for studying relationships between the outcomes and nonlinear and non-Gaussian hierarchical SSMs. Numeric examples are provided for the illustration of the methods described.

INTRODUCTION Intervention of Alzheimer's dementia hinges on early diagnosis and advanced planning. This work utilizes the cognitive clock, a novel indicator of brain health, to develop a dementia prediction model that can be easily applied in broad settings. METHODS Data came from over 3000 community‐dwelling older adults. Cognitive age was estimated by aligning Mini‐Mental State Examination (MMSE) scores to a clock that represents the typical cognitive aging profile. We identified a mean cognitive age at Alzheimer's dementia onset and predicted the corresponding chronological age at person‐specific level. RESULTS The mean chronological age at baseline was 78 years. A total of 881 (28%) participants developed Alzheimer's dementia. The mean cognitive age at onset was 91 years. The predicted chronological age at onset had a mean (standard deviation) of 87.6 (6.7) years. The model's …

In observational studies, the time origin of interest for time-to-event analysis is often unknown, such as the time of disease onset. Existing approaches to estimating the time origins are commonly built on extrapolating a parametric longitudinal model, which rely on rigid assumptions that can lead to biased inferences. In this paper, we introduce a flexible semiparametric curve registration model. It assumes the longitudinal trajectories follow a flexible common shape function with person-specific disease progression pattern characterized by a random curve registration function, which is further used to model the unknown time origin as a random start time. This random time is used as a link to jointly model the longitudinal and survival data where the unknown time origins are integrated out in the joint likelihood function, which facilitates unbiased and consistent estimation. Since the disease progression pattern naturally …

Background The COVID-19 pandemic has created more devastation to dialysis patients than to the general population. Patient-level prediction models for SARS-CoV-2 infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether previously built prediction models are still sufficiently effective. Methods We developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after three or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID- 19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors. Result From April 2020 to August 2020, our machine learning model achieved an AUC of 0.75, an improvement over 0.07 from a previously developed machine learning model published on Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUC of 0.6 in December 2021 and January 2022. Over the whole study period from April 2020 to February 2022, fixing the false positive rate at 20%, our model can detect 40% of the positive patients. We found that features derived from local infection information reported by CDC are the most …

The Brier score has been a popular measure of prediction accuracy for binary outcomes. However, it is not straightforward to interpret the Brier score for a prediction model since its value depends on the outcome prevalence. We decompose the Brier score into two components, the mean squares between the estimated and true underlying binary probabilities, and the variance of the binary outcome that is not reflective of the model performance. We then propose to modify the Brier score by removing the variance of the binary outcome, estimated via a general sliding window approach. We show that the new proposed measure is more sensitive for comparing different models through simulation. A standardized performance improvement measure is also proposed based on the new criterion to quantify the improvement of prediction performance. We apply the new measures to the data from the Breast Cancer …

By clustering patients with the urologic chronic pelvic pain syndromes (UCPPS) into homogeneous subgroups and associating these subgroups with baseline covariates and other clinical outcomes, we provide opportunities to investigate different potential elements of pathogenesis, which may also guide us in selection of appropriate therapeutic targets. Motivated by the longitudinal urologic symptom data with extensive subject heterogeneity and differential variability of trajectories, we propose a functional clustering procedure where each subgroup is modeled by a functional mixed effects model, and the posterior probability is used to iteratively classify each subject into different subgroups. The classification takes into account both group-average trajectories and between-subject variabilities. We develop an equivalent state-space model for efficient computation. We also propose a cross-validation based Kullback …

Patients with obstructive sleep apnea (OSA) are at elevated risk of developing systemic vascular disease and cognitive dysfunction. Here, cerebral oxygen metabolism was assessed in patients with OSA by means of a magnetic resonance-based method involving simultaneous measurements of cerebral blood flow rate and venous oxygen saturation in the superior sagittal sinus for a period of 10 minutes at an effective temporal resolution of 1.3 seconds before, during, and after repeated 24-second breath-holds mimicking spontaneous apneas, yielding, along with pulse oximetry-derived arterial saturation, whole-brain CMRO2 via Fick’s Principle. Enrolled subjects were classified based on their apnea-hypopnea indices into OSA (N = 31) and non-sleep apnea reference subjects (NSA = 21), and further compared with young healthy subjects (YH, N = 10). OSA and NSA subjects were matched for age and body …

Neurologic injury is a leading cause of morbidity and mortality following pediatric cardiac arrest. In this study, we assess the feasibility of quantitative, non-invasive, frequency-domain diffuse optical spectroscopy (FD-DOS) neuromonitoring during cardiopulmonary resuscitation (CPR), and its predictive utility for return of spontaneous circulation (ROSC) in an established pediatric swine model of cardiac arrest. Cerebral tissue optical properties, oxy- and deoxy-hemoglobin concentration ([HbO2], [Hb]), oxygen saturation (StO2) and total hemoglobin concentration (THC) were measured by a FD-DOS probe placed on the forehead in 1-month-old swine (8–11 kg; n = 52) during seven minutes of asphyxiation followed by twenty minutes of CPR. ROSC prediction and time-dependent performance of prediction throughout early CPR (< 10 min), were assessed by the weighted Youden index (Jw, w = 0.1) with tenfold …

This study was designed to investigate the acute effects of nonnicotinized e-cigarette (e-cig) aerosol inhalation in nonsmokers both in terms of blood-based markers of inflammation and oxidative stress and evaluate their association with hemodynamic-metabolic MRI parameters quantifying peripheral vascular reactivity, cerebrovascular reactivity, and aortic stiffness. Thirty-one healthy nonsmokers were subjected to two blood draws and two identical MRI protocols, each one before and after a standardized e-cig vaping session. After vaping, the serum levels of C-reactive protein, soluble intercellular adhesion molecule, and the danger signal machinery high-mobility group box 1 (HMGB1) and its downstream effector and the NLR family pyrin domain containing 3 (NLRP3) inflammasome (as monitored by its adaptor protein ASC) increased significantly relative to the respective baseline (prevaping) values. Moreover …

While adolescent–adult connections have been shown to be protective against violence perpetration and victimization, mechanisms through which these connections confer protection from violence are poorly understood. We assessed whether adolescent–adult connections protected youth in lower resource urban neighborhoods from exposure to environmental risk factors for violence during daily activities. We overlaid on the city landscape minute-by-minute activity paths from 274 randomly sampled predominantly African American male youth, ages 10 to 24, enrolled in a population-based study of daily activities in Philadelphia, PA, to calculate environmental exposures and to compare exposures along actual versus shortest potential travel routes. Adolescent–adult connections were defined using brief survey questions and detailed family genograms. Analyses demonstrated that youth’s selected travel routes …

Although photodynamic therapy (PDT) is an established modality for cancer treatment, current dosimetric quantities, such as light fluence and PDT dose, do not account for the differences in PDT oxygen consumption for different fluence rates (ϕ). A macroscopic model was adopted to calculate reactive oxygen species concentration ([ROS]_rx) to predict Photofrin-PDT outcome in mice bearing radiation-induced fibrosarcoma (RIF) tumors. Singlet oxygen is the primary cytotoxic species for ROS, which is responsible for cell death in type II PDT, although other type I ROS is included in the parameters used in our model. Using a combination of fluences (50-250 J∕cm^2) and ϕ (75 or 150 mW∕cm^2), tumor regrowth rate, k, was determined for each condition by fitting the tumor volume versus time to V0*exp(k*t). Treatment was delivered with a collimated laser beam of 1 cm diameter at 630 nm. Explicit dosimetry of light …

Obstructive sleep apnea (OSA) is characterized by intermittent obstruction of the airways during sleep. Cerebrovascular reactivity (CVR) is an index of cerebral vessels' ability to respond to a vasoactive stimulus, such as increased CO2. We hypothesized that OSA alters CVR, expressed as a breath-hold index (BHI) defined as the rate of change in CBF or BOLD signal during a controlled breath-hold stimulus mimicking spontaneous apneas by being both hypercapnic and hypoxic. In 37 OSA and 23 matched non sleep apnea (NSA) subjects, we obtained high temporal resolution CBF and BOLD MRI data before, during, and between five consecutive BH stimuli of 24 s, each averaged to yield a single BHI value. Greater BHI was observed in OSA relative to NSA as derived from whole-brain CBF (78.6 ± 29.6 vs. 60.0 ± 20.0 mL/min2/100 g, P = 0.010) as well as from flow velocity in the superior sagittal sinus (0 …

Since the Covid-19 outbreak, researchers have been predicting how the epidemic will evolve, especially the number in each country, through using parametric extrapolations based on the history. In reality, the epidemic progressing in a particular country depends largely on its policy responses and interventions. Since the outbreaks in some countries are earlier than United States, the prediction of US cases can benefit from incorporating the similarity in their trajectories. We propose an empirical Bayesian time series framework to predict US cases using different countries as prior reference. The resultant forecast is based on observed US data and prior information from the reference country while accounting for different population sizes. When Italy is used as prior in the prediction, which the US data resemble the most, the cases in the US will exceed 300,000 by the beginning of April unless strong measures are adopted.