Synovitis Is Associated With Constant Pain in Knee Osteoarthritis: A Cross-sectional Study of OMERACT Knee Ultrasound Scores.

OBJECTIVE: To examine the association between ultrasound (US)-detected knee inflammation and intermittent and constant pain experiences in patients with knee osteoarthritis (OA). METHODS: Participants with radiographically early-stage (Kellgren-Lawrence arthritis grading scale [KL] ≤ 2) and late-stage (KL ≥ 3) disease and frequent symptoms underwent musculoskeletal US measures of inflammation using the Outcome Measures in Rheumatology (OMERACT) knee US scoring system. Pain experiences were captured using the Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP) tool. We assessed the association between US-synovitis and ICOAP pain experiences using a series of linear, logistic, or multinomial logistic regression models (as appropriate for each variable), while adjusting for age, sex, BMI, and radiographic stage. Secondary analyses were performed similarly by radiographic stage. RESULTS: Pain and synovitis measures from 248 patients (453 knees) were included. Worse synovitis was associated with higher ICOAP constant pain scores (ß 8.05, 95% CI 0.67-15.43), but not intermittent pain scores. Moderate-to-severe synovitis was associated with a 4.73-fold increased relative risk (95% CI 1.06-17.00) of a constant pain pattern. In secondary analyses, moderate-to-severe synovitis in early radiographic OA was associated with 2.70-higher odds (95% CI 1.04-7.02) of any constant pain, 3.28-higher odds (95% CI 1.43-7.52) of any intermittent pain, and with higher intermittent (ß 10.47, 95% CI 1.03-19.91) and constant (ß 12.62, 95% CI 3.02-22.23) pain scores. No associations were identified for synovitis in those with late radiographic OA. CONCLUSION: In patients with knee OA, moderate-to-severe synovitis is most strongly associated with constant pain. Inflammation may play context-specific roles across pain experiences, especially in earlier radiographic stages of knee OA.

Estimation of the basic reproduction number, average incubation time, asymptomatic infection rate, and case fatality rate for COVID-19: Meta-analysis and sensitivity analysis.

The coronavirus disease-2019 (COVID-19) has been found to be caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensive knowledge of COVID-19 remains incomplete and many important features are still unknown. This manuscript conducts a meta-analysis and a sensitivity study to answer the questions: What is the basic reproduction number? How long is the incubation time of the disease on average? What portion of infections are asymptomatic? And ultimately, what is the case fatality rate? Our studies estimate the basic reproduction number to be 3.15 with the 95% CI (2.41-3.90), the average incubation time to be 5.08 days with the 95% CI (4.77-5.39) (in day), the asymptomatic infection rate to be 46% with the 95% CI (18.48%-73.60%), and the case fatality rate to be 2.72% with 95% CI (1.29%-4.16%) where asymptomatic infections are accounted for.

Dynamic prediction of time to a clinical event with sparse and irregularly measured longitudinal biomarkers.

In clinical research and practice, landmark models are commonly used to predict the risk of an adverse future event, using patients' longitudinal biomarker data as predictors. However, these data are often observable only at intermittent visits, making their measurement times irregularly spaced and unsynchronized across different subjects. This poses challenges to conducting dynamic prediction at any post-baseline time. A simple solution is the last-value-carry-forward method, but this may result in bias for the risk model estimation and prediction. Another option is to jointly model the longitudinal and survival processes with a shared random effects model. However, when dealing with multiple biomarkers, this approach often results in high-dimensional integrals without a closed-form solution, and thus the computational burden limits its software development and practical use. In this article, we propose to process the longitudinal data by functional principal component analysis techniques, and then use the processed information as predictors in a class of flexible linear transformation models to predict the distribution of residual time-to-event occurrence. The measurement schemes for multiple biomarkers are allowed to be different within subject and across subjects. Dynamic prediction can be performed in a real-time fashion. The advantages of our proposed method are demonstrated by simulation studies. We apply our approach to the African American Study of Kidney Disease and Hypertension, predicting patients' risk of kidney failure or death by using four important longitudinal biomarkers for renal functions.

Landmark Linear Transformation Model for Dynamic Prediction with Application to a Longitudinal Cohort Study of Chronic Disease.

Dynamic prediction of the risk of a clinical event using longitudinally measured biomarkers or other prognostic information is important in clinical practice. We propose a new class of landmark survival models. The model takes the form of a linear transformation model, but allows all the model parameters to vary with the landmark time. This model includes many published landmark prediction models as special cases. We propose a unified local linear estimation framework to estimate time-varying model parameters. Simulation studies are conducted to evaluate the finite sample performance of the proposed method. We apply the methodology to a dataset from the African American Study of Kidney Disease and Hypertension and predict individual patient's risk of an adverse clinical event.

Risk factors for mortality after respiratory syncytial virus lower respiratory tract infection in adults with hematologic malignancies.

BACKGROUND: Respiratory syncytial virus (RSV) lower respiratory tract infection (LRTI) is associated with high mortality in patients with hematologic malignancies (HM). We sought to determine whether allogeneic hematopoietic cell transplant (allo-HCT) recipients would be at higher risk for 60-day mortality. METHODS: We examined a retrospective cohort of adults with HM with or without HCT treated for RSV LRTI (n = 154) at our institution from 1996-2013. We defined possible RSV LRTI as RSV detected only in the upper respiratory tract with new radiologic infiltrates and proven RSV LRTI as RSV detected in BAL fluid with new radiologic infiltrates. Immunodeficiency Scoring Index (ISI) and Severe Immunodeficiency (SID) criteria were calculated for HCT recipients. Multivariable logistic regression analyses were performed to identify independent risk factors associated with 60-day all-cause mortality. RESULTS: Mortality was high in HM patients (25%), but there was no difference between those without HCT, autologous or allo-HCT recipients in logistic regression models. Separate multivariate models showed that at RSV diagnosis, neutropenia (OR 8.3, 95% CI 2.8-24.2, P = 0.005) and lymphopenia (OR 3.7, 95% CI 1.7-8.2, P = 0.001) were associated with 60-day mortality. Proven LRTI was associated with higher 60-day mortality (neutropenia model: OR 4.7, 95%CI 1.7-13.5; lymphopenia model: OR 3.3, 95% CI 1.2-8.8), and higher ICU admission. In HCT recipients, high ISI and very severe immunodeficiency by SID criteria were associated with higher 60-day all-cause mortality. CONCLUSIONS: Mortality is similarly high among HM patients without HCT and HCT recipients. High-grade immunodeficiency and detection of RSV from BAL fluid are associated with higher 60-day mortality.

Nonparametric analysis of dependently interval-censored failure time data.

Failure time studies based on observational cohorts often have to deal with irregular intermittent observation of individuals, which produces interval-censored failure times. When the observation times depend on factors related to a person's failure time, the failure times may be dependently interval censored. Inverse-intensity-of-visit weighting methods have been developed for irregularly observed longitudinal or repeated measures data and recently extended to parametric failure time analysis. This article develops nonparametric estimation of failure time distributions using weighted generalized estimating equations and monotone smoothing techniques. Simulations are conducted for examination of the finite sample performance of proposed estimators. This research is motivated in part by the Toronto Psoriatic Arthritis Cohort Study, and the proposed methodology is applied to this study.

Estimation of parametric failure time distributions based on interval-censored data with irregular dependent follow-up.

Event history studies based on disease clinic data often face several complications. Specifically, patients may visit the clinic irregularly, and the intermittent observation times could depend on disease-related variables; this can cause a failure time outcome to be dependently interval-censored. We propose a weighted estimating function approach so that dependently interval-censored failure times can be analysed consistently. A so-called inverse-intensity-of-visit weight is employed to adjust for the informative inspection times. Left truncation of failure times can also be easily handled. Additionally, in observational studies, treatment assignments are typically non-randomized and may depend on disease-related variables. An inverse-probability-of-treatment weight is applied to estimating functions to further adjust for measured confounders. Simulation studies are conducted to examine the finite sample performances of the proposed estimators. Finally, the Toronto Psoriatic Arthritis Cohort Study is used for illustration. Copyright © 2017 John Wiley & Sons, Ltd.