The Association between Long-term PM2.5 Exposure and Risk for Pancreatic Cancer: An Application of Social Informatics.

There is a profound need to identify modifiable risk factors to screen and prevent pancreatic cancer. Air pollution, including fine particulate matter (PM2.5), is increasingly recognized as a risk factor for cancer. We conducted a case-control study using data from the electronic health record (EHR) of Duke University Health System, 15-year residential history, NASA satellite fine particulate matter (PM2.5) and neighborhood socioeconomic data. Using deterministic and probabilistic linkage algorithms, we linked residential history and EHR data to quantify long term PM2.5 exposure. Logistic regression models quantified the association between a one interquartile range (IQR) increase in PM2.5 concentration and pancreatic cancer risk. The study included 203 cases and 5027 controls (median age of 59 years, 62% female, 26% Black). Individuals with pancreatic cancer had higher average annual exposure (9.4 µg/m3) as compared to IQR increase in average annual PM2.5 was associated with greater odds of pancreatic cancer (OR=1.20; 95% CI: 1.00-1.44). These findings highlight the link between elevated PM2.5 exposure and increased pancreatic cancer risk. They may inform screening strategies for high-risk populations and guide air pollution policies to mitigate exposure.

Regression-Assisted Bayesian Record Linkage for Causal Inference in Observational Studies with Covariates Spread Over Two Files.

We consider causal inference for observational studies with data spread over two files. One file includes the treatment, outcome, and some covariates measured on a set of individuals, and the other file includes additional causally-relevant covariates measured on a partially overlapping set of individuals. By linking records in the two databases, the analyst can control for more covariates, thereby reducing the risk of bias compared to using only one file alone. When analysts do not have access to a unique identifier that enables perfect, error-free linkages, they typically rely on probabilistic record linkage to construct a single linked data set, and estimate causal effects using these linked data. This typical practice does not propagate uncertainty from imperfect linkages to the causal inferences. Further, it does not take advantage of relationships among the variables to improve the linkage quality. We address these shortcomings by fusing regression-assisted, Bayesian probabilistic record linkage with causal inference. The Markov chain Monte Carlo sampler generates multiple plausible linked data files as byproducts that analysts can use for multiple imputation inferences. Here, we show results for two causal estimators based on propensity score overlap weights. Using simulations and data from the Italy Survey on Household Income and Wealth, we show that our approach can improve the accuracy of estimated treatment effects.

Evaluating cut-off levels for progesterone, ß human chorionic gonadotropin and ß human chorionic gonadotropin ratio to exclude pregnancy viability in women with a pregnancy of unknown location: A prospective multicenter cohort study.

INTRODUCTION: There is no global agreement on how to best determine pregnancy of unknown location viability and location using biomarkers. Measurements of progesterone and ß human chorionic gonadotropin (ßhCG) are still used in clinical practice to exclude the possibility of a viable intrauterine pregnancy (VIUP). We evaluate the predictive value of progesterone, ßhCG, and ßhCG ratio cut-off levels to exclude a VIUP in women with a pregnancy of unknown location. MATERIAL AND METHODS: This was a secondary analysis of prospective multicenter study data of consecutive women with a pregnancy of unknown location between January 2015 and 2017 collected from dedicated early pregnancy assessment units of eight hospitals. Single progesterone and serial ßhCG measurements were taken. Women were followed up until final pregnancy outcome between 11 and 14 weeks of gestation was confirmed using transvaginal ultrasonography: (1) VIUP, (2) non-viable intrauterine pregnancy or failed pregnancy of unknown location, and (3) ectopic pregnancy or persisting pregnancy of unknown location. The predictive value of cut-off levels for ruling out VIUP were evaluated across a range of values likely to be encountered clinically for progesterone, ßhCG, and ßhCG ratio. RESULTS: Data from 2507 of 3272 (76.6%) women were suitable for analysis. All had data for ßhCG levels, 2248 (89.7%) had progesterone levels, and 1809 (72.2%) had ßhCG ratio. The likelihood of viability falls with the progesterone level. Although the median progesterone level associated with viability was 59 nmol/L, VIUP were identified with levels as low as 5 nmol/L. No single ßhCG cut-off reliably ruled out the presence of viability with certainty, even when the level was more than 3000 IU/L, there were 39/358 (11%) women who had a VIUP. The probability of viability decreases with the ßhCG ratio. Although the median ßhCG ratio associated with viability was 2.26, VIUP were identified with ratios as low as 1.02. A progesterone level below 2 nmol/L and ßhCG ratio below 0.87 were unlikely to be associated with viability but were not definitive when considering multiple imputation. CONCLUSIONS: Cut-off levels for ßhCG, ßhCG ratio, and progesterone are not safe to be used clinically to exclude viability in early pregnancy. Although ßhCG ratio and progesterone have slightly better performance in comparison, single ßhCG used in this manner is highly unreliable.

A Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control.

This article seeks to investigate the impact of aging on functional connectivity across different cognitive control scenarios, particularly emphasizing the identification of brain regions significantly associated with early aging. By conceptualizing functional connectivity within each cognitive control scenario as a graph, with brain regions as nodes, the statistical challenge revolves around devising a regression framework to predict a binary scalar outcome (aging or normal) using multiple graph predictors. Popular regression methods utilizing multiplex graph predictors often face limitations in effectively harnessing information within and across graph layers, leading to potentially less accurate inference and predictive accuracy, especially for smaller sample sizes. To address this challenge, we propose the Bayesian Multiplex Graph Classifier (BMGC). Accounting for multiplex graph topology, our method models edge coefficients at each graph layer using bilinear interactions between the latent effects associated with the two nodes connected by the edge. This approach also employs a variable selection framework on node-specific latent effects from all graph layers to identify influential nodes linked to observed outcomes. Crucially, the proposed framework is computationally efficient and quantifies the uncertainty in node identification, coefficient estimation, and binary outcome prediction. BMGC outperforms alternative methods in terms of the aforementioned metrics in simulation studies. An additional BMGC validation was completed using an fMRI study of brain networks in adults. The proposed BMGC technique identified that sensory motor brain network obeys certain lateral symmetries, whereas the default mode network exhibits significant brain asymmetries associated with early aging.

A multicenter prospective external validation of the diagnostic performance of IOTA simple descriptors and rules to characterize ovarian masses.

OBJECTIVES: To evaluate the diagnostic performance of the IOTA (International Ovarian Tumor Analysis group) (clinically oriented three-step strategy for preoperative characterization of ovarian masses when ultrasonography is performed by examiners with different background training and experience. METHODS: A 27-month prospective multicenter cross-sectional study was performed. 36 level II ultrasound examiners contributed in three UK hospitals. Transvaginal ultrasonography was performed using a standardized approach. Step one uses simple descriptors (SD), step two ultrasound simple rules (SR) and step three subjective assessment of ultrasound images (SA) by examiners. The final outcome was findings at surgery and the histological diagnosis of surgically removed masses. RESULTS: 1165 women with adnexal masses underwent transvaginal ultrasonography, 301 had surgery. Prevalence of malignancy was 31% (n=92). SD were able to classify 46% of the masses into benign or malignant (step one), with a sensitivity of 93% and specificity of 97%. Applying SD followed by SR to residual unclassified masses by SD enabled 89% of all masses (n=268) to be classified with a sensitivity 95% of and specificity of 95%. SA was then used to evaluate the rest of the masses. Compared to the risk of malignancy index (RMI), the sensitivity and specificity for the three-step (SD+SR+SA) strategy were 93% (95% CI: 86-97%) and 92% (95% CI: 87-95%) vs. 72% (95% CI: 62-80%) and 95% (95% CI: 91-97%) for RMI, respectively. CONCLUSION: The IOTA three-step strategy shows good test performance on external validation in the hands of ultrasonography examiners with different background training and experience. This performance is considerably better than the RMI.

Tubo-ovarian abscess: A proposed new scoring system to guide clinical management.

OBJECTIVE: To create a risk scoring system comprised of clinical and radiological characteristics that can predict the likelihood of antibiotic treatment failure of tubo-ovarian abscesses. The score should guide clinicians in identifying patients to whom early intervention should be offered instead of a prolonged trial of antibiotics. METHODS: A multicenter, retrospective cohort study carried out between January 1, 2013 and September 30, 2019, identified consecutive patients with tubo-ovarian abscess. Using a chronological split, patients were allocated to two groups for the development and subsequent validation of the postulated scoring system. Univariate and bivariate analyses were performed to identify statistically significant variables for the failure of intravenous antibiotic treatment. RESULTS: In total, 214 consecutive patients with tubo-ovarian abscesses were identified. Data from the first 150 patients were used for the development of the postulated scoring system; data from the subsequent 64 patients were used for validation. Statistically significant clinical features between those having successful and unsuccessful management were: temperature (median = 37.1℃ vs 38.2℃, P = 0.0001), C-reactive protein (151 mg/L vs 243 mg/L, P = 0.0001), and tubo-ovarian abscess diameter (6.0 cm vs 8.0 cm, P = 0.0001). These parameters were used to create a risk prediction score. A score of four or more was predictive of requiring surgical/radiological intervention of tubo-ovarian abscess (P < 0.001). The score had a sensitivity of 69% and a specificity of 88%, with area under the curve (AUC) = 0.859. CONCLUSION: Currently, there is no guidance for clinicians on when to operate on a tubo-ovarian abscess. Our prediction score is simple, using only three easily obtained clinical characteristics.

Validation of a virtual reality simulator for the use of transvaginal ultrasonography in gynaecology and early pregnancy.

INTRODUCTION: The objective was to validate a virtual reality simulation ultrasound model as a tool for training in the use of transvaginal ultrasonography in gynaecology and early pregnancy. METHODS: Three separate groups consisting of novice and intermediate level residents as well as expert ultrasound examiners were recruited to the study. All were asked to answer a questionnaire regarding demographic data and ultrasound experience. They subsequently completed two modules: basic gynaecology and early pregnancy, followed by corresponding assessments using a high-fidelity simulator (Scantrainer; Medaphor™, Cardiff, UK). Finally, the expert group completed an additional questionnaire about various elements of the simulator using a 5-point Likert scale. RESULTS: Each group consisted of eight participants. Overall, the participants agreed that simulation played a role in training (Novices: 75% (n = 6); Intermediates: 100% (n = 8); Experts: 75% (n = 6)). For the degree of realism of the target objects in the gynaecology and early pregnancy module environments compared to a real-patient environment, the simulator was rated satisfactory or very satisfactory by 88% (n = 7) and 75% (n = 6) of experts, respectively. All experts rated the overall usefulness of the content of the simulator for learning fundamental ultrasound technical skills compared to current training methods to be at least satisfactory. When reviewing the assessment scores, experts scored higher than non-experts in gynaecology (P = 0.002) and early pregnancy modules (P = 0.03). DISCUSSION: Face, content and construct validity were demonstrated by the virtual reality ultrasound simulator, suggesting it may be an effective method for training ultrasound skills in gynaecology and early pregnancy to non-expert residents.

Weighted Score Tests Implementing Model-Averaging Schemes in Detection of Rare Variants in Case-Control Studies.

Multi-locus effect modeling is a powerful approach for detection of genes influencing a complex disease. Especially for rare variants, we need to analyze multiple variants together to achieve adequate power for detection. In this paper, we propose several parsimonious branching model techniques to assess the joint effect of a group of rare variants in a case-control study. These models implement a data reduction strategy within a likelihood framework and use a weighted score test to assess the statistical significance of the effect of the group of variants on the disease. The primary advantage of the proposed approach is that it performs model-averaging over a substantially smaller set of models supported by the data and thus gains power to detect multi-locus effects. We illustrate these proposed approaches on simulated and real data and study their performance compared to several existing rare variant detection approaches. The primary goal of this paper is to assess if there is any gain in power to detect association by averaging over a number of models instead of selecting the best model. Extensive simulations and real data application demonstrate the advantage the proposed approach in presence of causal variants with opposite directional effects along with a moderate number of null variants in linkage disequilibrium.

Study of drotaverine on first stage of labour and pregnancy outcome.

A prospective randomised study of 200 women with spontaneous onset of labour was carried out in 100 women who were given 40mg of drotaverine hydrochloride intravenously at > or = 3cm dilatation of the cervix, the other 100 were taken as control. The effects of the drug on the progress and outcome of labour were noted. The mean durations of active phase of labour in primigravida and multigravida were 148.9 minutes and 99.5 minutes in drotaverine group whereas in control group were 331.6 minutes and 227.9 minutes respectively. It was concluded that drotaverine is highly effective in reducing the duration of active phase of labour by hastening cervical dilatation, more effective when given in more dilated cervix than with less dilatation and more effective in multigravida than in primigravida. There was no interference with uterine contractility and no increase in operative delivery. It reduces the incidence of traumatic postpartum haemorrhage by reducing the incidence of cervical tear. It is a safe drug for the mother as well as for the baby.