5,343 research outputs found
Recommended from our members
Noninvasive Urinary Monitoring of Progression in IgA Nephropathy.
Standard methods for detecting and monitoring of IgA nephropathy (IgAN) have conventionally required kidney biopsies or suffer from poor sensitivity and specificity. The Kidney Injury Test (KIT) Assay of urinary biomarkers has previously been shown to distinguish between various kidney pathologies, including chronic kidney disease, nephrolithiasis, and transplant rejection. This validation study uses the KIT Assay to investigate the clinical utility of the non-invasive detection of IgAN and predicting the progression of renal damage over time. The study design benefits from longitudinally collected urine samples from an investigator-initiated, multicenter, prospective study, evaluating the efficacy of corticosteroids versus Rituximab for preventing progressive IgAN. A total of 131 urine samples were processed for this study; 64 urine samples were collected from 34 IgAN patients, and urine samples from 64 demographically matched healthy controls were also collected; multiple urinary biomarkers consisting of cell-free DNA, methylated cell-free DNA, DMAIMO, MAMIMO, total protein, clusterin, creatinine, and CXCL10 were measured by the microwell-based KIT Assay. An IgA risk score (KIT-IgA) was significantly higher in IgAN patients as compared to healthy control (87.76 vs. 14.03, p < 0.0001) and performed better than proteinuria in discriminating between the two groups. The KIT Assay biomarkers, measured on a spot random urine sample at study entry could distinguish patients likely to have progressive renal dysfunction a year later. These data support the pursuit of larger prospective studies to evaluate the predictive performance of the KIT-IgA score in both screening for non-invasive diagnosis of IgAN, and for predicting risk of progressive renal disease from IgA and utilizing the KIT score for potentially evaluating the efficacy of IgAN-targeted therapies
Rates and predictors of recurrent work disability due to common mental health disorders in the United States.
ContextDespite the high prevalence of work disability due to common mental disorders (CMD), no information exists on the rates and predictors of recurrence in a United States population.ObjectiveTo estimate recurrent work disability statistics and evaluate factors associated with recurrence due to CMDs including adjustment, anxiety, bipolar, and depressive disorders.MethodsRecurrent work disability statistics were calculated using a nationwide database of disability claims. For the CMDs, univariate and multiple variable analyses were used to examine demographic factors and comorbidities associated with the time to recurrence.ResultsOf the CMDs, cases with bipolar (n = 3,017) and depressive disorders (n = 20,058) had the highest recurrence densities, 98.7 and 70.9 per 1000 person-years, respectively. These rates were more than three times higher than recurrence rates for other chronic disorders (e.g., diabetes, asthma; n = 105,558) and non-chronic disorders (e.g., injury, acute illnesses; n = 153,786). Individuals with CMD were also more likely to have a subsequent disability distinct from their mental health condition. Risk factors for recurrent CMD disability included being younger, being an hourly employee, living in a geographic area with more college graduates, having more previous psychiatric visits, having a previous work leave, and the type of work industry.ConclusionsResults indicate that CMD patients may benefit from additional care and disability management both during and after their work absence to help prevent subsequent CMD and non-CMD related leaves
Policy Relevant Heterogeneity in the Value of Statistical Life: New Evidence from Panel Data Quantile Regressions
We examine differences in the value of statistical life (VSL) across potential wage levels in panel data using quantile regressions with intercept heterogeneity. Latent heterogeneity is econometrically important and affects the estimated VSL. Our findings indicate that a reasonable average cost per expected life saved cut-off for health and safety regulations is 8 million per life saved, but the VSL varies considerably across the labor force. Our results reconcile the previous discrepancies between hedonic VSL estimates and the values implied by theories linked to the coefficient of relative risk aversion. Because the VSL varies elastically with income, regulatory agencies should regularly update the VSL used in benefit assessments, increasing the VSL proportionally with changes in income over time.panel data, quantile regression, VSL, value of statistical life, fixed effects, PSID, fatality risk, CFOI
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
Quantitative extraction of high-dimensional mineable data from medical images
is a process known as radiomics. Radiomics is foreseen as an essential
prognostic tool for cancer risk assessment and the quantification of
intratumoural heterogeneity. In this work, 1615 radiomic features (quantifying
tumour image intensity, shape, texture) extracted from pre-treatment FDG-PET
and CT images of 300 patients from four different cohorts were analyzed for the
risk assessment of locoregional recurrences (LR) and distant metastases (DM) in
head-and-neck cancer. Prediction models combining radiomic and clinical
variables were constructed via random forests and imbalance-adjustment
strategies using two of the four cohorts. Independent validation of the
prediction and prognostic performance of the models was carried out on the
other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88).
Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the
potential of radiomics for assessing the risk of specific tumour outcomes using
multiple stratification groups. This could have important clinical impact,
notably by allowing for a better personalization of chemo-radiation treatments
for head-and-neck cancer patients from different risk groups.Comment: (1) Paper: 33 pages, 4 figures, 1 table; (2) SUPP info: 41 pages, 7
figures, 8 table
Association between 5-Year clinical outcome in patients with nonmedically evacuated mild blast traumatic brain injury and clinical measures collected within 7 days postinjury in combat
Importance: Although previous work has examined clinical outcomes in combat-deployed veterans, questions remain regarding how symptoms evolve or resolve following mild blast traumatic brain injury (TBI) treated in theater and their association with long-term outcomes.
Objective: To characterize 5-year outcome in patients with nonmedically evacuated blast concussion compared with combat-deployed controls and understand what clinical measures collected acutely in theater are associated with 5-year outcome.
Design, Setting, and Participants: A prospective, longitudinal cohort study including 45 service members with mild blast TBI within 7 days of injury (mean 4 days) and 45 combat deployed nonconcussed controls was carried out. Enrollment occurred in Afghanistan at the point of injury with evaluation of 5-year outcome in the United States. The enrollment occurred from March to September 2012 with 5-year follow up completed from April 2017 to May 2018. Data analysis was completed from June to July 2018.
Exposures: Concussive blast TBI. All patients were treated in theater, and none required medical evacuation.
Main Outcomes and Measures: Clinical measures collected in theater included measures for concussion symptoms, posttraumatic stress disorder (PTSD) symptoms, depression symptoms, balance performance, combat exposure intensity, cognitive performance, and demographics. Five-year outcome evaluation included measures for global disability, neurobehavioral impairment, PTSD symptoms, depression symptoms, and 10 domains of cognitive function. Forward selection multivariate regression was used to determine predictors of 5-year outcome for global disability, neurobehavior impairment, PTSD, and cognitive function.
Results: Nonmedically evacuated patients with concussive blast injury (n = 45; 44 men, mean [SD] age, 31 [5] years) fared poorly at 5-year follow-up compared with combat-deployed controls (n = 45; 35 men; mean [SD] age, 34 [7] years) on global disability, neurobehavioral impairment, and psychiatric symptoms, whereas cognitive changes were unremarkable. Acute predictors of 5-year outcome consistently identified TBI diagnosis with contribution from acute concussion and mental health symptoms and select measures of cognitive performance depending on the model for 5-year global disability (area under the curve following bootstrap validation [AUCBV] = 0.79), neurobehavioral impairment (correlation following bootstrap validation [RBV] = 0.60), PTSD severity (RBV = 0.36), or cognitive performance (RBV = 0.34).
Conclusions and Relevance: Service members with concussive blast injuries fared poorly at 5-year outcome. The results support a more focused acute screening of mental health following TBI diagnosis as strong indicators of poor long-term outcome. This extends prior work examining outcome in patients with concussive blast injury to the larger nonmedically evacuated population
Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study
Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared.
Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests.
Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively).
Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa
Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury.
For a continuous treatment, the generalised propensity score (GPS) is defined as the conditional density of the treatment, given covariates. GPS adjustment may be implemented by including it as a covariate in an outcome regression. Here, the unbiased estimation of the dose-response function assumes correct specification of both the GPS and the outcome-treatment relationship. This paper introduces a machine learning method, the 'Super Learner', to address model selection in this context. In the two-stage estimation approach proposed, the Super Learner selects a GPS and then a dose-response function conditional on the GPS, as the convex combination of candidate prediction algorithms. We compare this approach with parametric implementations of the GPS and to regression methods. We contrast the methods in the Risk Adjustment in Neurocritical care cohort study, in which we estimate the marginal effects of increasing transfer time from emergency departments to specialised neuroscience centres, for patients with acute traumatic brain injury. With parametric models for the outcome, we find that dose-response curves differ according to choice of specification. With the Super Learner approach to both regression and the GPS, we find that transfer time does not have a statistically significant marginal effect on the outcomes
Identification of miRNA signatures associated with radiation-induced late lung injury in mice.
Acute radiation exposure of the thorax can lead to late serious, and even life-threatening, pulmonary and cardiac damage. Sporadic in nature, late complications tend to be difficult to predict, which prompted this investigation into identifying non-invasive, tissue-specific biomarkers for the early detection of late radiation injury. Levels of circulating microRNA (miRNA) were measured in C3H and C57Bl/6 mice after whole thorax irradiation at doses yielding approximately 70% mortality in 120 or 180 days, respectively (LD70/120 or 180). Within the first two weeks after exposure, weight gain slowed compared to sham treated mice along with a temporary drop in white blood cell counts. 52% of C3H (33 of 64) and 72% of C57Bl/6 (46 of 64) irradiated mice died due to late radiation injury. Lung and heart damage, as assessed by computed tomography (CT) and histology at 150 (C3H mice) and 180 (C57Bl/6 mice) days, correlated well with the appearance of a local, miRNA signature in the lung and heart tissue of irradiated animals, consistent with inherent differences in the C3H and C57Bl/6 strains in their propensity for developing radiation-induced pneumonitis or fibrosis, respectively. Radiation-induced changes in the circulating miRNA profile were most prominent within the first 30 days after exposure and included miRNA known to regulate inflammation and fibrosis. Importantly, early changes in plasma miRNA expression predicted survival with reasonable accuracy (88-92%). The miRNA signature that predicted survival in C3H mice, including miR-34a-5p, -100-5p, and -150-5p, were associated with pro-inflammatory NF-κB-mediated signaling pathways, whereas the signature identified in C57Bl/6 mice (miR-34b-3p, -96-5p, and -802-5p) was associated with TGF-β/SMAD signaling. This study supports the hypothesis that plasma miRNA profiles could be used to identify individuals at high risk of organ-specific late radiation damage, with applications for radiation oncology clinical practice or in the context of a radiological incident
Personnel Airdrop Risk Assessment Using Bootstrap Sampling
Previous work on personnel airdrop problems involving jumpers has been (1) event-oriented entanglement rates, (2) number of canopy bumps, (3) landing injuries, and (4) deaths. The thesis expands this area of research by developing cumulative distribution functions of maximum possible chute entanglement risk for the C-17 using bootstrap techniques. By comparing the effects of various C-17 aircraft configurations on the entanglement CDF, this thesis shows that under certain configurations the risk of centerline entanglement for the C-17 is less than for the C-141
- …