Estimation of standardized real-time fatality rate for ongoing epidemics.

BACKGROUND: The fatality rate is a crucial metric for guiding public health policies during an ongoing epidemic. For COVID-19, the age structure of the confirmed cases changes over time, bringing a substantial impact on the real-time estimation of fatality. A 'spurious decrease' in fatality rate can be caused by a shift in confirmed cases towards younger ages even if the fatalities remain unchanged across different ages. METHODS: To address this issue, we propose a standardized real-time fatality rate estimator. A simulation study is conducted to evaluate the performance of the estimator. The proposed method is applied for real-time fatality rate estimation of COVID-19 in Germany from March 2020 to May 2022. FINDINGS: The simulation results suggest that the proposed estimator can provide an accurate trend of disease fatality in all cases, while the existing estimator may convey a misleading signal of the actual situation when the changes in temporal age distribution take place. The application to Germany data shows that there was an increment in the fatality rate at the implementation of the 'live with COVID' strategy. CONCLUSIONS: As many countries have chosen to coexist with the coronavirus, frequent examination of the fatality rate is of paramount importance.

Prognostic Utility of Left Atrial Strain From MRI Feature Tracking in Ischemic and Nonischemic Dilated Cardiomyopathy: A Multicenter Study.

BACKGROUND. MRI-based prognostic evaluation in patients with dilated cardiomyopathy (DCM) has historically used markers of late gadolinium enhancement (LGE) and feature tracking (FT)-derived left ventricular global longitudinal strain (LVGLS). Early data indicate that FT-derived left atrial strain (LAS) parameters, including reservoir, conduit, and booster, may also have prognostic roles in such patients. OBJECTIVE. The purpose of our study was to evaluate the prognostic utility of LAS parameters, derived from MRI FT, in patients with ischemic or nonischemic DCM, including in comparison with the traditional parameters of LGE and LVGLS. METHODS. This retrospective study included 811 patients with ischemic or nonischemic DCM (median age, 60 years; 640 men, 171 women) who underwent cardiac MRI at any of five centers. FT-derived LAS parameters and LVGLS were measured using two- and four-chamber cine images. LGE percentage was quantified. Patients were assessed for a composite outcome of all-cause mortality or heart failure hospitalization. Multivariable Cox regression analyses including demographic characteristics, cardiovascular risk factors, medications used, and a wide range of cardiac MRI parameters were performed. Kaplan-Meier analyses with log-rank tests were also performed. RESULTS. A total of 419 patients experienced the composite outcome. Patients who did, versus those who did not, experience the composite outcome had larger LVGLS (-6.7% vs -8.3%, respectively; p < .001) as well as a smaller LAS reservoir (13.3% vs 19.3%, p < .001), LAS conduit (4.7% vs 8.0%, p < .001), and LAS booster (8.1% vs 10.3%, p < .001) but no significant difference in LGE (10.1% vs 11.3%, p = .51). In multivariable Cox regression analyses, significant independent predictors of the composite outcome included LAS reservoir (HR = 0.96, p < .001) and LAS conduit (HR = 0.91, p < .001). LAS booster and LGE were not significant independent predictors in the models. LVGLS was a significant independent predictor only in a model that initially included LAS booster but not the other LAS parameters. In Kaplan-Meier analysis, all three LAS parameters were significantly associated with the composite outcome (p < .001). CONCLUSION. In this multicenter study, LAS reservoir and LAS conduit were significant independent prognostic markers in patients with ischemic or nonischemic DCM, showing greater prognostic utility than the currently applied markers of LVGLS and LGE. CLINICAL IMPACT. FT-derived LAS analysis provides incremental prognostic information in patients with DCM.

Partly linear single-index cure models with a nonparametric incidence link function.

In cancer studies, it is commonplace that a fraction of patients participating in the study are cured, such that not all of them will experience a recurrence, or death due to cancer. Also, it is plausible that some covariates, such as the treatment assigned to the patients or demographic characteristics, could affect both the patients' survival rates and cure/incidence rates. A common approach to accommodate these features in survival analysis is to consider a mixture cure survival model with the incidence rate modeled by a logistic regression model and latency part modeled by the Cox proportional hazards model. These modeling assumptions, though typical, restrict the structure of covariate effects on both the incidence and latency components. As a plausible recourse to attain flexibility, we study a class of semiparametric mixture cure models in this article, which incorporates two single-index functions for modeling the two regression components. A hybrid nonparametric maximum likelihood estimation method is proposed, where the cumulative baseline hazard function for uncured subjects is estimated nonparametrically, and the two single-index functions are estimated via Bernstein polynomials. Parameter estimation is carried out via a curated expectation-maximization algorithm. We also conducted a large-scale simulation study to assess the finite-sample performance of the estimator. The proposed methodology is illustrated via application to two cancer datasets.

A semiparametric joint model for cluster size and subunit-specific interval-censored outcomes.

Clustered data frequently arise in biomedical studies, where observations, or subunits, measured within a cluster are associated. The cluster size is said to be informative, if the outcome variable is associated with the number of subunits in a cluster. In most existing work, the informative cluster size issue is handled by marginal approaches based on within-cluster resampling, or cluster-weighted generalized estimating equations. Although these approaches yield consistent estimation of the marginal models, they do not allow estimation of within-cluster associations and are generally inefficient. In this paper, we propose a semiparametric joint model for clustered interval-censored event time data with informative cluster size. We use a random effect to account for the association among event times of the same cluster as well as the association between event times and the cluster size. For estimation, we propose a sieve maximum likelihood approach and devise a computationally-efficient expectation-maximization algorithm for implementation. The estimators are shown to be strongly consistent, with the Euclidean components being asymptotically normal and achieving semiparametric efficiency. Extensive simulation studies are conducted to evaluate the finite-sample performance, efficiency and robustness of the proposed method. We also illustrate our method via application to a motivating periodontal disease dataset.

Prognostic Utility of Cardiac MRI Myocardial Strain Parameters in Patients With Ischemic and Nonischemic Dilated Cardiomyopathy: A Multicenter Study.

BACKGROUND. Prior small single-center studies have yielded conflicting results regarding the prognostic significance of myocardial strain parameters derived from feature tracking (FT) on cardiac MRI in patients with dilated cardiomyopathy (DCM). OBJECTIVE. The purpose of this study was to evaluate the prognostic utility of FT parameters on cardiac MRI in patients with ischemic and nonischemic DCM and to determine the optimal strain parameter for outcome prediction. METHODS. This retrospective study included 471 patients (median age, 61 years; 365 men, 106 women) with ischemic (n = 233) or nonischemic (n = 238) DCM and left ventricular (LV) ejection fraction (EF) less than 50% who underwent cardiac MRI at any of four centers from January 2011 to December 2019. Cardiac MRI parameters were determined by manual contouring. In addition, software-based FT was used to calculate six myocardial strain parameters (LV and right ventricular [RV] global radial strain, global circumferential strain, and global longitudinal strain [GLS]). Late gadolinium enhancement (LGE) was also evaluated. Patients were assessed for a composite outcome of all-cause mortality and/or heart-failure hospitalization. Cox regression models were used to determine associations between strain parameters and the composite outcome. RESULTS. Mean LV EF was 27.5% and mean LV GLS was -6.9%. The median follow-up period was 1328 days. The composite outcome occurred in 220 patients (125 deaths, 95 heart-failure hospitalizations). All six myocardial strain parameters were significant independent predictors of the composite outcome (hazard ratio [HR] = 0.92-1.16; all p < .05). In multivariable models that included age, corrected LV and RV end-diastolic volume, LV and RV EF, and presence of LGE, the only strain parameter that was a significant independent predictor of the composite outcome was LV GLS (HR = 1.13, p = .006); LV EF and presence of LGE were not independent predictors of the composite outcome in the models (p > .05). A LV GLS threshold of -6.8% had sensitivity of 62.6% and specificity of 62.6% in predicting the composite outcome rate at 4.0 years. CONCLUSION. LV GLS, derived from FT on cardiac MRI, is a significant independent predictor of adverse outcomes in patients with DCM. CLINICAL IMPACT. This study strengthens the body of evidence supporting the clinical implementation of FT when performing cardiac MRI in patients with DCM.

A novel method to monitor COVID-19 fatality rate in real-time, a key metric to guide public health policy.

An accurate estimator of the real-time fatality rate is warranted to monitor the progress of ongoing epidemics, hence facilitating the policy-making process. However, most of the existing estimators fail to capture the time-varying nature of the fatality rate and are often biased in practice. A simple real-time fatality rate estimator with adjustment for reporting delays is proposed in this paper using the fused lasso technique. This approach is easy to use and can be broadly applied to public health practice as only basic epidemiological data are required. A large-scale simulation study suggests that the proposed estimator is a reliable benchmark for formulating public health policies during an epidemic with high accuracy and sensitivity in capturing the changes in the fatality rate over time, while the other two commonly-used case fatality rate estimators may convey delayed or even misleading signals of the true situation. The application to the COVID-19 data in Germany between January 2020 and January 2022 demonstrates the importance of the social restrictions in the early phase of the pandemic when vaccines were not available, and the beneficial effects of vaccination in suppressing the fatality rate to a low level since August 2021 irrespective of the rebound in infections driven by the more infectious Delta and Omicron variants during the fourth wave.

Analysis of clustered interval-censored data using a class of semiparametric partly linear frailty transformation models.

A flexible class of semiparametric partly linear frailty transformation models is considered for analyzing clustered interval-censored data, which arise naturally in complex diseases and dental research. This class of models features two nonparametric components, resulting in a nonparametric baseline survival function and a potential nonlinear effect of a continuous covariate. The dependence among failure times within a cluster is induced by a shared, unobserved frailty term. A sieve maximum likelihood estimation method based on piecewise linear functions is proposed. The proposed estimators of the regression, dependence, and transformation parameters are shown to be strongly consistent and asymptotically normal, whereas the estimators of the two nonparametric functions are strongly consistent with optimal rates of convergence. An extensive simulation study is conducted to study the finite-sample performance of the proposed estimators. We provide an application to a dental study for illustration.

Nested logistic regression models and ΔAUC applications: Change-point analysis.

The area under the receiver operating characteristic curve (AUC) is one of the most popular measures for evaluating the performance of a predictive model. In nested models, the change in AUC (ΔAUC) can be a discriminatory measure of whether the newly added predictors provide significant improvement in terms of predictive accuracy. Recently, several authors have shown rigorously that ΔAUC can be degenerate and its asymptotic distribution is no longer normal when the reduced model is true, but it could be the distribution of a linear combination of some χ12 random variables [1,2]. Hence, the normality assumption and existing variance estimate cannot be applied directly for developing a statistical test under the nested models. In this paper, we first provide a brief review on the use of ΔAUC for comparing nested logistic models and the difficulty of retrieving the reference distribution behind. Then, we present a special case of the nested logistic regression models that the newly added predictor to the reduced model contains a change-point in its effects. A new test statistic based on ΔAUC is proposed in this setting. A simple resampling scheme is proposed to approximate the critical values for the test statistic. The inference of the change-point parameter is done via m-out-of-n bootstrap. Large-scale simulation is conducted to evaluate the finite-sample performance of the ΔAUC test for the change-point model. The proposed method is applied to two real-life datasets for illustration.

Marginal analysis of current status data with informative cluster size using a class of semiparametric transformation cure models.

This research is motivated by a periodontal disease dataset that possesses certain special features. The dataset consists of clustered current status time-to-event observations with large and varying cluster sizes, where the cluster size is associated with the disease outcome. Also, heavy censoring is present in the data even with long follow-up time, suggesting the presence of a cured subpopulation. In this paper, we propose a computationally efficient marginal approach, namely the cluster-weighted generalized estimating equation approach, to analyze the data based on a class of semiparametric transformation cure models. The parametric and nonparametric components of the model are estimated using a Bernstein-polynomial based sieve maximum pseudo-likelihood approach. The asymptotic properties of the proposed estimators are studied. Simulation studies are conducted to evaluate the performance of the proposed estimators in scenarios with different degree of informative clustering and within-cluster dependence. The proposed method is applied to the motivating periodontal disease data for illustration.

Heterogeneous toroidal spiral particles for islet encapsulation.

Transplantable cell encapsulation systems present a promising approach to deliver a therapeutic solution from hormone-producing cells for the treatment of endocrine diseases like type 1 diabetes. However, the development of a broadly effective and safe transplantation system has been challenging. While some current micro-sized capsules have been optimized for adequate nutrient and metabolic transport, they lack the robustness and retrievability for the clinical safety translation that macro-devices may offer. An existing challenge to be addressed in the current macro-devices is their configuration which may lead to unsatisfactory mass transfer. Here, we design and characterize a millimeter-size particle system of poly-ethylene glycol (PEG) featuring internal toroidal spiral channels, called toroidal spiral particles (TSPs). The characteristic internal structure of the TSPs allows for large encapsulation capacity and large surface area available to all the encapsulated cell mass for effective molecular diffusion. The polymeric matrix renders the particle flexible yet robust for safe transplantation and retrieval. We demonstrate the feasibility of fabricating these particles with various polymer compositions, while optimizing their mechanical properties as well as glucose and insulin permeability. Encapsulation of islets of Langerhans is achieved with high loading capacity (∼160 IEQ per TSP) and excellent cell viability. TSP-encapsulated islets showed similar glucose-stimulated insulin secretion to the naked islets. Preliminary biocompatibility of the TSPs on naïve C57BL/6 mice shows minimal inflammatory response after 4-week transplantation into the intraperitoneal (IP) space. Long-term therapeutic efficacy of encapsulated islets needs to be confirmed in diabetic rodent models in the future, while determining minimal mass required to reverse diabetes. However, we believe from the in vitro favorable results and the TSPs' unique design that TSPs may provide a safe, effective method to transplant and retrieve therapeutic cells for type 1 diabetes treatment and may also be applicable for other cell therapies.

Survival analysis with change-points in covariate effects.

We apply a maximal likelihood ratio test for the presence of multiple change-points in the covariate effects based on the Cox regression model. The covariate effect is assumed to change smoothly at one or more unknown change-points. The number of change-points is inferred by a sequential approach. Confidence intervals for the regression and change-point parameters are constructed by a bootstrap method based on Bernstein polynomials conditionally on the number of change-points. The methods are assessed by simulations and are applied to two datasets.

Type B Interrupted Aortic Arch with a Patent Ductus Arteriosus in an Adult Presenting with Secondary Polycythaemia.

Interrupted aortic arch (IAA) is an extremely rare congenital cyanotic heart disease characterized by complete disruption between the ascending and descending aorta. A patent ductus arteriosus (PDA) or other collateral pathways provide blood flow to the distal descending aorta. Mortality is extremely high at early infancy, particularly after closure of the ductus arteriosus. Survival and presentation in adulthood are extremely rare. Here, we illustrate a rare case of type B IAA in an adult who presented with secondary polycythaemia. The blood supply to the descending aorta and beyond was almost solely via a PDA. This case demonstrates the value of multimodality imaging, including CT and MRI, for diagnosis and treatment planning in these patients. LEARNING POINTS: The presence of secondary polycythaemia, as a result of chronic hypoxia, should prompt a search for underlying cyanotic heart disease even in previously undiagnosed adults.Most previous case reports of IAA in adults feature type A; type B IAA in an adult is far less frequently described.MRI has incremental value compared to CT in intracardiac assessment (aortic valve assessment, RV and LV functional assessment, flow measurement) for these patients; in addition, it provides an excellent depiction of the vascular anatomy of the aorta and great vessels.

Testing for change-point in the covariate effects based on the Cox regression model.

Models with change-point in covariates have wide applications in cancer research with the response being the time to a certain event. A Cox model with change-point in covariate is considered at which the pattern of the change-point effects can be flexibly specified. To test for the existence of the change-point effects, three statistical tests, namely, the maximal score, maximal normalized score, and maximal Wald tests are proposed. The asymptotic properties of the test statistics are established. Monte Carlo approaches to simulate the critical values are suggested. A large-scale simulation study is carried out to study the finite sample performance of the proposed test statistics under the null hypothesis of no change-points and various alternative hypothesis settings. Each of the proposed methods provides a natural estimate for the location of the change-point, but it is found that the performance of the maximal score test can be sensitive to the true location of the change-point in some cases, while the performance of the maximal Wald test is very satisfactory in general even in cases with moderate sample size. For illustration, the proposed methods are applied to two medical datasets concerning patients with primary biliary cirrhosis and breast cancer, respectively.

Heterogeneous Polymeric Particles Encapsulating Human T cells for Controlled Activation, Proliferation, and Delivery.

We report a particulate cell delivery platform, toroidal spiral particles (TSPs), for continuous cell activation, expansion, and local sustained release. Biocompatible TSPs, generated by a self-assembly process of polymeric droplet sedimentation in an aqueous solution and subsequent polymer solidification, possess many engineering design flexibilities to manipulate the microenvironment of the cells to control cell proliferation, migration, and release kinetics. These millimeter-size particles with desired mechanical and physicochemical properties may be potentially used for adoptive cellular therapy (ACT) delivery by a minimally invasive procedure to the tumor mass.

Clinical implications of recent exodontia before diagnosis of gingival squamous cell carcinoma: A new classification.

BACKGROUND: The purpose of this study was to clarify the clinical implications of cases with recent dental extractions to establish a new classification of gingival squamous cell carcinoma (SCC). METHODS: A total of 156 patients were enrolled in this study. The subjects were divided into 3 groups: type I (dentate; n = 46), type II (edentulous; n = 55), and type III (dental extraction; n = 55). Continuous clinical and treatment variables were analyzed by 1-way analysis of variance (ANOVA) or t test, and categorical variables were evaluated by chi-square tests. Assessment of 5-year survival rates were carried out by the Kaplan-Meier analysis, and the influence of related factors was evaluated by the log-rank test. RESULT: The 55 type III patients showed a high probability of bony invasion (80%) and a lower 5-year survival rate (48%) than the other 2 groups. CONCLUSION: Our proposed classification may help clinicians to identify patients with gingival SCC who present with more advanced disease status.