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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]

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–Leibler information criterion to choose the optimal number of subgroups. The performance of the proposed method is assessed through a simulation study. We apply our methods to longitudinal bi-weekly measures of a primary urological urinary symptoms score from a UCPPS longitudinal cohort study, and identify four subgroups ranging from moderate decline, mild decline, stable and mild increasing. The resulting clusters are also associated with the one-year changes in several clinically important outcomes, and are also related to several clinically relevant baseline predictors, such as sleep disturbance score, physical quality of life and painful urgency. 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]

Purpose/objectives: The clinical effects of radiation dose-intensification in locally advanced non-small cell lung (NSCLCa) and other cancers are challenging to predict and are ideally studied in randomized trials. The purpose of this study was to assess the use of dose-escalated radiation for locally advanced NSCLCa in the U.S., 2004-2013, a period in which there were no published level 1 studies on dose-escalation.Materials/methods: We performed analyses on two cancer registry databases with complementary strengths and weaknesses: the National Oncology Data Alliance (NODA) 2004-2013 and the National Cancer Database (NCDB) 2004-2012. We classified locally advanced patients according to the use of dose-escalation (>70 Gy). We used adjusted logistic regression to assess the association of year of treatment with dose-escalated radiation use in two periods representing time before and after the closure of a cooperative group trial (RTOG 0617) on dose-escalation: 2004-2010 and 2010-2013. To determine the year in which a significant change in dose could have been detected had dose been prospectively monitored within the NODA network, we compared the average annual radiation dose per year with the forecasted dose (average of the prior 3 years) adjusted for patient age and comorbidities.Results: Within both the NODA and NCDB, use of dose-escalation increased from 2004 to 2010 (p < 0.0001) and decreased from 2010 to 2013 (p = 0.0018), even after controlling for potential confounders. Had the NODA network been monitoring radiation dose in this cohort, significant changes in average annual dose would have been detected at the end of 2008 and 2012.Conclusions: Patterns of radiation dosing in locally advanced NSCLCa changed in the U.S. in the absence of level 1 evidence. Monitoring radiation dose is feasible using an existing national cancer registry data collection infrastructure. [ABSTRACT FROM AUTHOR]

Photodynamic therapy (PDT) is a well-established treatment modality for cancer and other malignant diseases; however, quantities such as light fluence and PDT dose do not fully account for all of the dynamic interactions between the key components involved. In particular, fluence rate (ϕ) effects, which impact the photochemical oxygen consumption rate, are not accounted for. In this preclinical study, reacted reactive oxygen species ([ROS]rx) was investigated as a dosimetric quantity for PDT outcome. The ability of [ROS]rx to predict the cure index (CI) of tumor growth, CI = 1 − k / kctr, where k and kctr are the growth rate of tumor under PDT study and the control tumor without PDT, respectively, for benzoporphyrin derivative (BPD)-mediated PDT, was examined. Mice bearing radiation-induced fibrosarcoma (RIF) tumors were treated with different in-air fluences (Φ = 22.5 to 166.7 J / cm2) and in-air fluence rates (ϕair = 75 to 250 mW / cm2) with a BPD dose of 1 mg / kg and a drug-light interval (DLI) of 15 min. Treatment was delivered with a collimated laser beam of 1-cm-diameter at 690 nm. Explicit measurements of in-air light fluence rate, tissue oxygen concentration, and BPD concentration were used to calculate for [ROS]rx. Light fluence rate at 3-mm depth (ϕ3 mm), determined based on Monte-Carlo simulations, was used in the calculation of [ROS]rx at the base of tumor. CI was used as an endpoint for three dose metrics: light fluence, PDT dose, and [ROS]rx. PDT dose was defined as the product of the time-integral of photosensitizer concentration and ϕ3 mm. Preliminary studies show that [ROS]rx best correlates with CI and is an effective dosimetric quantity that can predict treatment outcome. The threshold dose for [ROS]rx for vascular BPD-mediated PDT using DLI of 15 min is determined to be 0.26 mM and is about 3.8 times smaller than the corresponding value for conventional BPD-mediated PDT using DLI of 3 h. [ABSTRACT FROM AUTHOR]

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 prediction accuracy was supported by multiple testing statistics., Discussion: Our model offers an easy-to-use tool for predicting person-specific age at Alzheimer's dementia onset., (© 2023 the Alzheimer's Association.)

Background: The coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (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 or not 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 3 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 area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination., Conclusion: As found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors PK and HZ declared that they were an editorial board member of Frontiers at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Duan, Li, Ma, Zhang, Lasky, Monaghan, Chaudhuri, Usvyat, Gu, Guo, Kotanko and Wang.)

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 CMRO 2 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. CMRO 2 was lower in OSA than in the YH group during normal breathing (105.6 ± 14.1 versus 123.7 ± 22.8 μmol O 2 /min/100g, P = 0.01). Further, the fractional change in CMRO 2 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 CMRO 2 between OSA and NSA subjects. The data suggest altered brain oxygen metabolism in OSA and possibly in NSA as well.

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, nitric oxide metabolites and reactive oxygen species production decreased and increased, respectively. These observations were paralleled by impaired peripheral vascular reactivity (with reduced flow-mediated dilation and attenuated hyperemic response after a cuff-occlusion test) and metabolic alterations expressed by decreased venous oxygen saturation, postvaping. The current results suggest propagation of inflammation signaling via activation of the danger signaling axis (HMGB1-NLRP3). The findings indicate that a single episode of vaping has adverse impacts on vascular inflammation and function. NEW & NOTWORTHY Endothelial cell signaling and blood biomarkers were found to correlate with functional vascular changes in a single episode e-cigarettes inhalation in healthy adults. This is indicative of the potential of e-cigarettes (even when inhaled acutely) to lead of vascular dysfunction.

The effects of e-cigarette (e-cig) aerosol inhalation by nonsmokers have not been examined to date. The present study was designed to evaluate the acute response to aerosol inhalation of non-nicotinized e-cigarettes in terms of oxidative stress and indices of endothelial activation in human pulmonary microvascular endothelial cells (HPMVEC). Ten smoking-naïve healthy subjects (mean age ± SD = 28.7 ± 5.5 yr) were subjected to an e-cig challenge, following which their serum was monitored for markers of inflammation [C-reactive protein (CRP) and soluble intercellular adhesion molecule (sICAM)] and nitric oxide metabolites (NOx). The oxidative stress and inflammation burden of the circulating serum on the vascular network was also assessed by measuring reactive oxygen species (ROS) production and induction of ICAM-1 expression on HPMVEC. Our results show that serum indices of oxidative stress and inflammation increased significantly ( P < 0.05 as compared with baseline), reaching a peak at approximately 1-2 h post-e-cig aerosol inhalation and returning to baseline levels at 6 h. The circulatory burden of the serum (ICAM-1 and ROS) increased significantly at 2 h and returned to baseline values 6 h post-e-cig challenge. ROS production by HPMVEC was found to occur via activation of the NADPH oxidase 2 (NOX2) pathways. These findings suggest that even in the absence of nicotine, acute e-cig aerosol inhalation leads to a transient increase in oxidative stress and inflammation. This can adversely affect the vascular endothelial network by promoting oxidative stress and immune cell adhesion. Thus e-cig inhalation has the potential to drive the onset of vascular pathologies.

Background and Objectives: Despite colistin's longstanding reported association with nephrotoxicity, the attributable risk and timing of toxicity onset are still unknown. Whether substantial toxicity occurs during the initial 72 hours of exposure has important implications for early treatment decisions. The objective of this study was to compare colistin-exposed patients with a matched control group given other broad spectrum antibiotics., Design, Setting, Participants, & Measurements: We conducted a retrospective cohort study in patients treated for multidrug-resistant Pseudomonas , Klebsiella , or Acinetobacter spp. Colistin-exposed patients were matched to unexposed controls using propensity scores. AKI was defined according to the Kidney Disease Improving Global Outcomes creatinine criteria. Incidence rate ratios and risk differences of AKI in the matched cohort were estimated with the generalized estimating equation Poisson regression model. Risk factors for AKI were tested for effect modification in the matched cohort., Results: The study included 150 propensity-matched pairs with similar types of infection, similar delays to effective treatment, and similar baseline characteristics. Incidence of AKI was 77 of 150 (51%) in the colistin group versus 33 of 150 (22%) in matched controls (risk difference, 29%; 95% confidence interval, 19 to 39), corresponding to a number needed to harm of 3.5. Early toxicity was apparent, because AKI risk was higher in colistin-exposed patients at 72 hours of exposure (incidence rate ratio, 1.9; 95% confidence interval, 1.1 to 3.5). In both groups, hospital mortality in patients who experienced AKI was lower if kidney function returned to baseline during hospitalization. The effect of colistin exposure on AKI risk varied inversely according to baseline hemoglobin concentration., Conclusions: Colistin is associated with substantial excess AKI that is apparent within the first 72 hours of treatment. Colistin's toxicity varied according to baseline hemoglobin concentration., Podcast: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2018&#95;03&#95;15&#95;CJASNPodcast&#95;18&#95;4&#95;M.mp3., (Copyright © 2018 by the American Society of Nephrology.)

Background: Both age and smoking promote endothelial dysfunction and impair vascular reactivity. Here, we tested this hypothesis by quantifying new cardiovascular magnetic resonance (CMR)-based biomarkers in smokers and nonsmokers., Methods: Study population: young non-smokers (YNS: N = 45, mean age = 30.2 ± 0.7 years), young smokers (YS: N = 39 mean age 32.1 ± 0.7 years), older non-smokers (ONS: N = 45, mean age = 57.8 ± 0.6 years), and older smokers (OS: N = 40, mean age = 56.3 ± 0.6 years), all without overt cardiovascular disease. Vascular reactivity was evaluated following cuff-induced hyperemia via time-resolved blood flow velocity and oxygenation (SvO2) in the femoral artery and vein, respectively. SvO2 dynamics yielded washout time (time to minimum SvO2), resaturation rate (upslope) and maximum change from baseline (overshoot). Arterial parameters included pulse ratio (PR), hyperemic index (HI) and duration of hyperemia (TFF). Pulse-wave velocity (PWV) was assessed in aortic arch, thoracoabdominal aorta and iliofemoral arteries. Ultrasound-based carotid intimal-medial thickness (IMT) and brachial flow-mediated dilation were measured for comparison., Results: Age and smoking status were independent for all parameters. Smokers had reduced upslope (-28.4%, P < 0.001), increased washout time (+15.3%, P < 0.01), and reduced HI (-19.5%, P < 0.01). Among non-smokers, older subjects had lower upslope (-22.7%, P < 0.01) and overshoot (-29.4%, P < 0.01), elevated baseline pulse ratio (+14.9%, P < 0.01), central and peripheral PWV (all P < 0.05). Relative to YNS, YS had lower upslope (-23.6%, P < 0.01) and longer washout time (13.5%, P < 0.05). Relative to ONS, OS had lower upslope (-33.0%, P < 0.01). IMT was greater in ONS than in YNS (+45.6%, P < 0.001), and also in YS compared to YNS (+14.7%, P < 0.05)., Conclusions: Results suggest CMR biomarkers of endothelial function to be sensitive to age and smoking independent of each other.