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PurposeWe examined baseline clinical and psychosocial characteristics that predict 12-month symptom change in men and women with urological chronic pelvic pain syndromes.Materials and methodsA total of 221 female and 176 male patients with urological chronic pelvic pain syndromes were recruited from 6 academic medical centers in the United States and evaluated at baseline with a comprehensive battery of symptom, psychosocial and illness-impact measures. Based on biweekly symptom reports, a functional clustering procedure classified participant outcome as worse, stable or improved on pain and urinary symptom severity. Cumulative logistic modeling was used to examine individual predictors associated with symptom change as well as multiple predictor combinations and interactions.ResultsAbout 60% of participants had stable symptoms with smaller numbers (13% to 22%) showing clear symptom worsening or improvement. For pain and urinary outcomes the extent of widespread pain, amount of nonurological symptoms and poorer overall health were predictive of worsening outcomes. Anxiety, depression and general mental health were not significant predictors of outcomes but pain catastrophizing and self-reported stress were associated with pain outcome. Prediction models did not differ between men and women and for the most part they were independent of symptom duration and age.ConclusionsThese results demonstrate for the first time in a large multisite prospective study that presence of widespread pain, nonurological symptoms and poorer general health are risk factors for poorer pain and urinary outcomes in men and women. The results point to the importance of broad based assessment for urological chronic pelvic pain syndromes and future studies of the mechanisms that underlie these findings.

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 predicts time-to-event, we further propose a new functional survival model using the registration function as a predictor of the time-to-event. The asymptotic consistency and semiparametric efficiency of the proposed models are proved. Simulation studies and two real data applications demonstrate the effectiveness of this new approach. for this article are available online. [ABSTRACT FROM AUTHOR]

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 mass index. CMRO2 was lower in OSA than in the YH group during normal breathing (105.6 ± 14.1 versus 123.7 ± 22.8 μmol O2/min/100g, P = 0.01). Further, the fractional change in CMRO2 in response to a breath-hold challenge was larger in OSA than in the YH group (15.2 ± 9.2 versus 8.5 ± 3.4%, P = 0.04). However, there was no significant difference in CMRO2 between OSA and NSA subjects. The data suggest altered brain oxygen metabolism in OSA and possibly in NSA as well. [ABSTRACT FROM AUTHOR]

A state space representation for smoothing spline ANOVA (SS ANOVA) that addresses the problems of heavy computational burden of the process is presented. Such a representation not only leads to high computational efficiency, but also allows the algorithm to be implemented in an online setting.

In this paper, we apply a new time-frequency spectral estimation method for multichannel data to epileptiform electroencephalography (EEG). The method is based on the smooth localized complex exponentials (SLEX) functions which are time-frequency localized versions of the Fourier functions and, hence, are ideal for analyzing nonstationary signals whose spectral properties evolve over time. The SLEX functions are simultaneously orthogonal and localized in time and frequency because they are obtained by applying a projection operator rather than a window or taper. In this paper, we present the Auto-SLEX method which is a statistical method that 1) computes the periodogram using the SLEX transform, 2) automatically segments the signal into approximately stationary segments using an objective criterion that is based on log energy, and 3) automatically selects the optimal bandwidth of the spectral smoothing window. The method is applied to the intracranial EEG from a patient with temporal lobe epilepsy. This analysis reveals a reduction in average duration of stationarity in preseizure epochs of data compared to baseline. These changes begin up to hours prior to electrical seizure onset in this patient. Index Terms--Electroencephalography, spectral analysis, stochastic processes, time-frequency analysis.