Calculation Of Confidence Intervals Research Articles

Meaningful cognitive change for the Minimal Assessment of Cognitive Function in Multiple Sclerosis

Background: There is limited information on interpretation of cognitive changes over time in multiple sclerosis (MS). Objective: This study aimed to provide normative data for the assessment of statistically meaningful change in all tests of the Minimal Assessment of Cognitive Function in MS (MACFIMS). Methods: We applied the reliable change methodology to a healthy Italian cohort, assessed with two alternate versions of the MACFIMS 1 year apart. We calculated confidence intervals of retest score variance using the reliable change index (RCI). Moreover, multivariable linear regression models adjusted for age, sex, education, and baseline score were built to calculate the regression-based change index (RB-CI). Results: Overall, 200 healthy individuals were enrolled. Thresholds for interpreting change in each test were calculated. In the multivariable models, baseline score was associated with retest score in all tests (B from 0.439 to 0.760; p < 0.001). RB-CI can be calculated with data of the multivariable models. Conclusion: We provide normative data for reliable cognitive change evaluation for all the tests of the MACFIMS, which includes the Symbol Digit Modalities Test and Brief International Cognitive Assessment in MS, two widely used tools for screening and monitoring cognition in MS. Our findings can significantly improve the interpretation of cognitive changes in MS.

Abstract RF02-01: A Multicenter, Phase I/II Trial of Anastrozole, Palbociclib, Trastuzumab, and Pertuzumab in Hormone Receptor (HR)-Positive, HER2-Positive Metastatic Breast Cancer (ASPIRE)

Abstract Background: Among patients (pts) with HR-positive and HER2-positive breast cancer, crosstalk between HER2 and estrogen receptor (ER) signaling pathways may contribute to endocrine resistance but anti-HER2 agents in combination with endocrine therapy can restore endocrine sensitivity. Mechanistically, HER2/HER3 signaling promotes survival by way of PI3K-AKT activation whereas ER-CDK4/6-Rb signaling promotes cell cycle progression. The combination of anti-HER2 therapy with an aromatase inhibitor (AI) and a CDK 4/6 inhibitor would allow for blockade of both pathways and provide a novel, all biologic, and chemotherapy-free approach to the treatment of HR-positive, HER2-positive metastatic breast cancer (MBC). Methods: We conducted a phase I/II multi-institution trial in pts with previously untreated, HR-positive and HER2-positive MBC. In the Phase I portion, pts received escalating doses of palbociclib (100mg, 125mg) in conjunction with trastuzumab, pertuzumab and an AI anastrozole, using a 3+3 dose escalation trial design. In the phase II portion, pts received palbociclib at the maximum tolerated dose (MTD), anastrozole, trastuzumab, and pertuzumab. The primary endpoints of the Phase I and II portions were MTD and clinical benefit rate (CBR) defined as the sum of complete response, partial response, and stable disease for &amp;gt;/= 6 months, respectively. Secondary endpoints included progression free survival (PFS), objective response rate (ORR) and safety. The Phase II portion of this study was powered with 30 pts to show efficacy of palbociclib administered at the MTD in combination with anastrozole, trastuzumab and pertuzumab if the CBR achieved at 6 months exceeded 58%. The Clopper-Pearson method was used to calculate confidence intervals for ORR and CBR. The PFS distribution was estimated using the Kaplan-Meier method. Results: In the Phase I portion, a total of 9 pts were enrolled. No DLTs were observed at the 100mg dose (N=3) or the 125mg dose (N=6) level, and thus, 125mg was established as the MTD. An additional 24 pts were enrolled to the Phase II portion at the MTD, with a total of 30 pts in the modified intention-to-treat population included in the efficacy analysis. The median age of the population was 57.6 years (range 50.5-63.8) and 27% of pts were premenopausal and received ovarian function suppression as part of treatment. As shown in Table 1, the primary endpoint, CBR, was 97% (95% CI: 0.83-1.0, p&amp;lt;0.0001). ORR was 70% (95% CI: 0.51-0.85). Median time to objective response was 2.8 months with earliest response at 3 months. Median duration of response was not reached with range of 5.1-42.2 months. Median PFS was also not reached with range of 8-44.8 months. Safety data were consistent with known toxicity profiles of agents. Most common adverse events included diarrhea (80%), neutropenia (77%), leukopenia (70%), anemia (67%), and fatigue (60%). Grade 3-4 events occurred in 63% (19/30) of pts and included neutropenia (68%), leukopenia (32%), decrease in absolute neutrophil count (21%), and anemia (21%). Conclusions: The combination of anastrozole, palbociclib, trastuzumab, and pertuzumab was well tolerated and effective with a clinical benefit rate of 97% in pts with previously untreated HR-positive, HER2-positive MBC. The combination provides a chemotherapy-free alternative for pts with triple positive breast cancers. Further follow up will determine impact on PFS. Table 1. Results in Patients on Anastrozole, Palbociclib, Trastuzumab, and Pertuzumab Citation Format: Rima Patel, Krystal Cascetta, Paula Klein, Erin Moshier, Maryann Kwa, Julie Fasano, Anupama Goel, Melissa Accordino, Charles Shapiro, Rita Vaccaro, Gargi Atul Joshi, Joseph Sparano, Amy Tiersten. A Multicenter, Phase I/II Trial of Anastrozole, Palbociclib, Trastuzumab, and Pertuzumab in Hormone Receptor (HR)-Positive, HER2-Positive Metastatic Breast Cancer (ASPIRE) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr RF02-01.

Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T

PurposeFurther acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b-value images and associated bias in quantitative ADC values. Deep learning-based reconstruction and denoising may provide a solution to address this challenge. MethodsThe effects of SNR reduction on ADC bias and variability were investigated using a commercial diffusion phantom and numerical simulations. In the phantom, performance of different reconstruction methods, including conventional parallel (SENSE) imaging, compressed sensing (C-SENSE), and compressed SENSE acceleration with an artificial intelligence deep learning-based technique (C-SENSE AI), was compared at different acceleration factors and flip angles using ROI-based analysis. ADC bias was assessed by Lin's Concordance correlation coefficient (CCC) followed by bootstrapping to calculate confidence intervals (CI). ADC random measurement error (RME) was assessed by the mean coefficient of variation (CV¯) and non-parametric statistical tests. ResultsThe simulations predicted increasingly negative bias and loss of precision towards lower SNR. These effects were confirmed in phantom measurements of increasing acceleration, for which CCC decreased from 0.947 to 0.279 and CV¯ increased from 0.043 to 0.439, and of decreasing flip angle, for which CCC decreased from 0.990 to 0.063 and CV¯ increased from 0.037 to 0.508. At high acceleration and low flip angle, C-SENSE AI reconstruction yielded best denoised ADC maps. For the lowest investigated flip angle, CCC = {0.630, 0.771 and 0.987} and CV¯={0.508, 0.426 and 0.254} were obtained for {SENSE, C-SENSE, C-SENSE AI}, the improvement by C-SENSE AI being significant as compared to the other methods (CV: p = 0.033 for C-SENSE AI vs. C-SENSE and p < 0.001 for C-SENSE AI vs. SENSE; CCC: non-overlapping CI between reconstruction methods). For the highest investigated acceleration factor, CCC = {0.479,0.926,0.960} and CV¯={0.519,0.119,0.118} were found, confirming the reduction of bias and RME by C-SENSE AI as compared to C-SENSE (by trend) and to SENSE (CV: p < 0.001; CCC: non-overlapping CI). ConclusionADC bias and random measurement error in DWI at low SNR, typically associated with scan acceleration, can be effectively reduced by deep-learning based C-SENSE AI reconstruction.

Psychological distress among Brazilian workers during the initial stage of the COVID-19 pandemic: a descriptive study.

COVID-19 pandemic imposed drastic and abrupt changes to working environment and organization and that might have caused additional negative effects on mental health. Thus, this study aimed to quantify and assess the severity of psychological distress experienced by Brazilian essential and nonessential workers during the first months of the COVID-19 pandemic. This descriptive study included 2,903 participants who answered an online questionnaire between April and May 2020. The research questionnaire was translated and culturally adapted to the Brazilian population from a questionnaire developed and validated for the Spanish population. Variables were analyzed using simple and cumulative percentage distributions and measures of central tendency and dispersion. The Wilson score interval was used to calculate confidence interval (CI) for the main outcome, psychological distress. It was observed a high prevalence (72.6%) of psychological distress among the study's participants. They also presented a median risk perception score of 60 (out of a maximum of 90), and their greatest concern was transmitting the virus to family members, close contacts or patients. Furthermore, it was found a lower sense of coherence and work engagement among the participants than those observed in previous studies conducted in other countries. Almost three quarters of the study's participants were classified as presenting psychological distress. Thus, it is imperative to provide mental health remotely delivered interventions to workers during public health events that require prolonged social distancing measures.

Real life retrospective study of cannabidiol therapy in alternating hemiplegia of childhood

BackgroundMany alternating hemiplegia of childhood (AHC) patients have received Cannabidiol (CBD) but, to our knowledge, there are no published data available. GoalsTest the hypothesis that CBD has favorable effects on AHC spells. MethodsRetrospective review of available data of AHC patients who received CBD. Primary analysis: Clinical Global Impression Scale of Improvement (CGI-I) score for response of AHC spells to CBD with calculation of 95% confidence interval (CI) for rejection of the null hypothesis. Secondary analyses, performed to achieve an understanding of the effect of CBD as compared to flunarizine, were CGI-I scores of 1) epileptic seizures to CBD, 2) AHC spells to flunarizine, 3) epileptic seizures to flunarizine. Also, Mann-Whitney test was done for comparison of CGI-I scores of CBD and flunarizine to both AHC spells and seizures. ResultsWe studied 16 AHC patients seen at Duke University and University of Lyon. CI of CGI-I scores for AHC spells in response to CBD and to flunarizine, each separately, indicated a positive response to each of these two medications: neither overlapped with the null hypothesis score, 4, indicating significant positive responses with p < 0.05 for both. These two scores also did not differ (p = 0.84) suggesting similar efficacy of both: CBD score was 2 ± 1.1 with a 95% CI of 1.5–2.6 and flunarizine score was 2.3 ± 1.3 with a 95% CI of 1.7–3.1. In patients who had seizures, CI calculations indicated a positive effect of CBD on seizure CGI scores but not of flunarizine on seizure scores. CBD was well tolerated with no patients discontinuing it due to side effects and with some reporting positive behavioral changes. ConclusionOur study indicates a real-life positive effect of CBD on AHC type spells.

Inference for Generalized Inverted Exponential Distribution UnderProgressive Type-I Interval Censored Data

This article discusses the estimation methods for parameters of a generalized inverted exponential distribution with different estimation methods by using Progressive type-I interval censored data. In addition to conventional maximum likelihood estimation, the mid-point method, probability plot method and method of moments are suggested for parameter estimation. To get maximum likelihood estimates, we utilize the Newton-Raphson, expectation -maximization and stochastic expectation-maximization methods. Furthermore, the approximate confidence intervals for the parameters are obtained via the inverse of the observed information matrix. The Monte Carlo simulations are used to introduce numerical comparisons of the proposed estimators. In addition, we use the percentile bootstrapping technique that is used to calculate confidence intervals. The proposed methodology in a real-life using the survival times of guinea pigs inoculated with different doses of tubercle bacilli data are considered to offer the applicability of the suggested methods.

Reliability generalization meta-analysis: comparing different statistical methods

AbstractReliability generalization (RG) is a kind of meta-analysis that aims to characterize how reliability varies from one test application to the next. A wide variety of statistical methods have typically been applied in RG meta-analyses, regarding statistical model (ordinary least squares, fixed-effect, random effects, varying-coefficient models), weighting scheme (inverse variance, sample size, not weighting), and transformation method (raw, Fisher’s Z, Hakstian and Whalen’s and Bonett’s transformation) of reliability coefficients. This variety of methods compromise the comparability of RG meta-analyses results and their reproducibility. With the purpose of examining the influence of the different statistical methods applied, a methodological review was conducted on 138 published RG meta-analyses of psychological tests, amounting to a total of 4,350 internal consistency coefficients. Among all combinations of procedures that made theoretical sense, we compared thirteen strategies for calculating the average coefficient, eighteen for calculating the confidence intervals of the average coefficient and calculated the heterogeneity indices for the different transformations of the coefficients. Our findings showed that transformation methods of the reliability coefficients improved the normality adjustment of the coefficient distribution. Regarding the average reliability coefficient and the width of confidence intervals, clear differences among methods were found. The largest discrepancies were found between the different strategies for calculating confidence intervals. Our findings point towards the need for the meta-analyst to justify the statistical model assumed, as well as the transformation method of the reliability coefficients and the weighting scheme.

Measures of Agreement with Multiple Raters: Fréchet Variances and Inference.

Most measures of agreement are chance-corrected. They differ in three dimensions: their definition of chance agreement, their choice of disagreement function, and how they handle multiple raters. Chance agreement is usually defined in a pairwise manner, following either Cohen's kappa or Fleiss's kappa. The disagreement function is usually a nominal, quadratic, or absolute value function. But how to handle multiple raters is contentious, with the main contenders being Fleiss's kappa, Conger's kappa, and Hubert's kappa, the variant of Fleiss's kappa where agreement is said to occur only if every rater agrees. More generally, multi-rater agreement coefficients can be defined in a g-wise way, where the disagreement weighting function uses g raters instead of two. This paper contains two main contributions. (a) We propose using Fréchet variances to handle the case of multiple raters. The Fréchet variances are intuitive disagreement measures and turn out to generalize the nominal, quadratic, and absolute value functions to the case of more than two raters. (b) We derive the limit theory of g-wise weighted agreement coefficients, with chance agreement of the Cohen-type or Fleiss-type, for the case where every item is rated by the same number of raters. Trying out three confidence interval constructions, we end up recommending calculating confidence intervals using the arcsine transform or the Fisher transform.

Fusion of transfer learning models with LSTM for detection of breast cancer using ultrasound images

Breast Cancer (BC) is one of the top reasons for fatality in women worldwide. As a result, timely identification is critical for successful therapy and excellent survival rates. Transfer Learning (TL) approaches have recently shown promise in aiding in the early recognition of BC. In this work, three TL models, MobileNetV2, ResNet50, and VGG16, were combined with LSTM to extract the features from Ultrasound Images (USIs). Furthermore, the Synthetic Minority Over-sampling Technique (SMOTE) with Tomek (SMOTETomek) was employed to balance the extracted features. The proposed method with VGG16 achieved an F1 score of 99.0 %, Matthews Correlation Coefficient (MCC) and Kappa Coefficient of 98.9 % with an Area Under Curve (AUC) of 1.0. The K-fold method was applied for cross-validation and achieved an average F1 score of 96 %.Moreover, the Gradient-weighted Class Activation Mapping (Grad-CAM) method was applied for visualization, and the Local Interpretable Model-agnostic Explanations (LIME) method was applied for interpretability. The Normal Approximation Interval (NAI) and bootstrapping methods were used to calculate Confidence Intervals (CIs). The proposed method achieved a Lower CI (LCI), Upper CI (UCI), and Mean CI (MCI) of 96.50 %, 99.75 %, and 98.13 %, respectively, with the NAI, while 95 % LCI of 93.81 %, an UCI of 96.00 %, and a bootstrap mean of 94.90 % with the bootstrap method. Furthermore, the performance of the six state-of-the-art (SOTA) TL models, such as Xception, NASNetMobile, InceptionResNetV2, MobileNetV2, ResNet50, and VGG16, were compared with the proposed method.

Structurally enhanced perimetric examination

Aims/Purpose: To assess the use of structural data to guide perimetric examination.Methods: Two perimetric strategies were tested on ten healthy participants: standard ZEST with population‐based prior distributions and Structural‐ZEST (S‐ZEST), enhanced with OCT data to determine the starting parameters. Both strategies were tested twice, in random order. The Compass Automated Perimeter (CMP, iCare) was used to collect the perimetric data through the Open Perimetry Interface and bespoke Shiny user interface (UI). The UI was used to import the Spectralis SD‐OCT scans (Heidelberg Engineering, Germany) and align the OCT data with the fundus image captured by the CMP. A 10–2 grid was centred on the fovea of each participant, aligned with the fovea‐disc axis. The UI calculated the Ganglion Cell Layer (GCL) thickness corresponding to each test location, after accounting for ganglion cell displacement. The starting priors for S‐ZEST were modelled using an independent dataset of glaucoma and healthy subjects. Bland–Altman plots were used to quantify test–retest variability. Bootstrap was used to calculate confidence intervals (CIs) and p‐values to compare the widths of the 95%‐Limits of Repeatability (LoR). Correlation between GCL thickness and sensitivity (dB) and the difference in test duration was calculated using linear mixed effect models. Agreement was quantified with the mean difference between the results of the two strategies.Results: 95%‐LoRs for S‐ZEST of the two devices were similar (p = 0.829). The structure–function correlation was significant for both (p &lt; 0.001), and very similar between S‐ZEST (R2 = 0.24) and ZEST (R2 = 0.23). There was a small but statistically significant mean difference in sensitivity between S‐ZEST and ZEST (0.56 [0.35, 0.77] dB, p = 0.034). S‐ZEST was 2.7 [1.2, 3.5] min faster than ZEST (p &lt; 0.001).Conclusions: S‐ZEST significantly shortened testing time without reducing repeatability in healthy observers.

Establishment of Reference Range for Leucine Arylamidase in Pregnant Women in China.

Leucine arylamidase (LAP) is a type of proteinase. During pregnancy, the LAP value of women will continue to rise, but there is currently no established normal range for this value. A total of 26,208 records of LAP information from pregnant women who visited Qingdao Municipal Hospital between 2016 and 2022 were selected, including 25,582 records of healthy pregnant women. The study used the AU5800 Series Clinical Chemistry Analyzer as the detection equipment. The Wilcoxon method was appropriate for calculating confidence intervals in this study since LAP values in pregnant women have a skewed distribution. The reference ranges obtained were 64.00 - 70.50 U/L, 161.00 - 166.00 U/L, and 174.50 - 182.50 U/L for the first trimester, second trimester, and third trimester, respectively. LAP can rise significantly during pregnancy, so it is essential to establish an appropriate reference range for pregnant women. Abnormal values of LAP carry a risk of developing various pregnancy disorders.

The Clinical Diagnosis-Based Nationwide Epidemiology of Metabolic Dysfunction-Associated Liver Disease in Korea.

Although most epidemiological studies have been conducted using a relatively small population or subjects who had medical screenings, the present study aimed to investigate the incidence and prevalence of MASLD (formerly NAFLD) in Korea using nationwide registry data provided by the Health Insurance Review and Assessment Service (HIRA). Using nationwide medical records provided by HIRA, we analyzed the entire dataset of patients with MASL (KCD10-K76.0) and MASH (KCD10-K75.8) from 2010 to 2021 and calculated the incidence and prevalence by year, age, and gender. The prevalence and incidence rates were calculated by analyzing the HIRA data covering almost the entire population of Korea for 12 years, from 2010 to 2021, with an average population of 50,856,244 during this period. Statistical analyses included calculating confidence intervals using Ulm's formula and conducting sex- and age-specific analyses with a Cochran-Armitage test for trends. The annual incidence of MASL/MASH increased significantly from 9.71/0.37 in 2010 to 13.95/5.52 per 1000 persons in 2021 (p < 0.01). The annual prevalence of MASL increased from 15.69 in 2010 to 34.23 per 1000 persons in 2021, while the annual prevalence of MASH increased from 0.49 to 9.79 per 1000 persons between 2010 and 2021 (p < 0.01). Regarding the sex-dimorphic feature of MASLD, there was a male predominance in those < 50 years old but a female predominance in those ≥ 50 years old for the incidence and prevalence of MASL and the incidence of MASH. The incidence of MASL increased by 3% to 4% every year, while the incidence of MASH increased 14.91-fold from 2010 to 2021. The increasing trend is noteworthy compared with previous reports.

Higher-order asymptotic corrections and their application to the Gamma Variance Model

We present improved methods for calculating confidence intervals and p values in situations where standard asymptotic approaches fail due to small sample sizes. We apply these techniques to a specific class of statistical model that can incorporate uncertainties in parameters that themselves represent uncertainties (informally, “errors on errors”) called the Gamma Variance Model. This model contains fixed parameters, generically denoted by ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon $$\\end{document}, that represent the relative uncertainties in estimates of standard deviations of Gaussian distributed measurements. If the ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon $$\\end{document} parameters are small, one can construct confidence intervals and p values using standard asymptotic methods. This is formally similar to the familiar situation of a large data sample, in which estimators for all adjustable parameters have Gaussian distributions. Here we address the important case where the ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon $$\\end{document} parameters are not small and as a consequence the first-order asymptotic distributions do not represent a good approximation. We investigate improved test statistics based on the technology of higher-order asymptotics (modified likelihood root and Bartlett correction). The effective application of higher-order corrections removes an important computational barrier to the use of the Gamma Variance Model.

Improved Outcomes with Heavy Silicone Oil in Complex Primary Retinal Detachment: A Large Multicenter Matched Cohort Study

PurposeTo establish whether Densiron® 68, a heavier-than-water endotamponade agent, is an effective alternative to conventional light silicone oil in primary rhegmatogenous retinal detachment surgery for eyes with inferior breaks in the detached retina and severe proliferative vitreoretinopathy. DesignCohort study of routinely collected data from the European Society of Retina Specialists (EURETINA) and British and Eire Association of Vitreoretinal Surgeons (BEAVRS) vitreoretinal database between 2015 and 2022. ParticipantsAll consecutive eyes that underwent primary rhegmatogenous retinal detachment surgery using either Densiron® 68 or light silicone oil as an internal tamponade agent. MethodsTo minimize confounding bias, we undertook 2:1 nearest neighbor matching on inferior breaks, large inferior rhegmatogenous retinal detachments, proliferative vitreoretinopathy, and, for visual analyses, baseline visual acuity between treatment groups. We fit regression models including prognostically relevant covariates, treatment-covariate interactions, and matching weights. We used g-computation with cluster-robust methods to estimate marginal effects. For nonlinear models, we calculated confidence intervals using bias-corrected cluster bootstrapping with 9 999 replications. Main Outcome MeasuresPresence of a fully attached retina and visual acuity at least two months after oil removal. ResultsOf 1061 eyes enrolled, 426 and 239 were included in our matched samples for anatomical and visual outcome analyses, respectively. The primary success rate was higher in the Densiron® 68 group (113 of 142; 80%) compared to the light silicone oil group (180 of 284; 63%), with an adjusted odds ratio of 1.90 (95% CI 1.63 to 2.23, P < 0.001). We also observed a significant improvement favoring Densiron® 68 of -0.26 logMAR in postoperative visual acuity between the two groups (95% CI -0.43 to -0.10, P = 0.002). The anatomical benefit of using Densiron® 68 in eyes with inferior retinal breaks and large detachments was more pronounced among eyes with proliferative vitreoretinopathy Grade C. We found no evidence of visual effect moderation by anatomical outcome or foveal attachment. ConclusionsDensiron achieved higher anatomical success rates and improved visual outcomes compared to conventional light silicone oil in eyes with inferior retinal pathology and severe proliferative vitreoretinopathy.

Validation of a Composite Real-World Mortality Variable Among Patients with Hematologic Malignancies Treated in the United States

Introduction: Real-world data (RWD) is being increasingly used to support clinical and regulatory decisions where utilization of clinical trial data is difficult or impractical. With this expanded utilization comes a need to ensure RWD is of sufficient quality and completeness to adequately answer relevant research questions. Given the importance of overall survival (OS) for oncology research, treatment and care, complete and accurate real-world mortality data capture is of critical importance. Currently the industry standard practice aggregates mortality data across available data sources to maximize data capture. This study sought to validate and benchmark a composite real-world mortality variable against the gold standard data source from the National Death Index (NDI). Methods: This observational study was conducted using the COTA real-world database, a de-identified database derived from the electronic health records (EHRs) of partnered healthcare providers in the United States. Patients diagnosed with select cancers between January 1st, 2015 and December 31st, 2020 were included. Cancer types included in this study were: acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), diffuse large B-cell lymphoma (DLBCL), follicular lymphoma (FL), marginal zone B-cell lymphoma (MZL), multiple myeloma (MM), and myelodysplastic syndrome (MDS). COTA's composite mortality variable utilizes structured and unstructured data from the EHRs of COTA partnered healthcare provider sites and commercially available obituary data including the Social Security Administration death master file. To conduct the validation study, COTA records were matched to NDI records using an NDI-developed matching algorithm. Patients without an NDI match were considered alive according to the NDI. Validation metrics were calculated in the overall population and by subgroup including cancer type and key demographic characteristics. Metrics assessed included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and date concordance (exact, ± 7 days, ± 15 days, ± 30 days). Corresponding 95% confidence intervals were estimated using the Wilson method for calculating confidence intervals for proportions. Results: The final study population included 21,567 patients across 7 cancer types (AML N=2638, CLL N=2502, DLBCL N=6066, FL N=2835, MM N=4234, MDS N=2295, and MZL N=997). Within the overall study population, validation analysis comparing the real-world composite mortality variable to the NDI demonstrated high sensitivity (87.8), specificity (95.7), PPV (90.9) and NPV (94.1). (Table 1) Some variability in validation metrics by disease was observed with MM having the highest sensitivity (90.4) and FL having the highest specificity (99.0). Sensitivity and specificity across all cancer types were greater than 80%. Patients 18-29 years of age had the lowest sensitivity (75.0), while patients 90 years of age or greater had the highest sensitivity (92.7), but lowest specificity (77.1). Validation metrics were similar across sex, race, and year of diagnosis subgroups. Date concordance between the real-world composite mortality variable and NDI dates of death was high. In the study population (N=21,567), exact date concordance was observed in 88.0% of patients, and concordance rates for 7-, 15- and 30-day intervals were 93.1%, 93.8%, and 94.3%, respectively. (Table 2) Rates of concordance were consistent across disease subtypes, subgroups, and date intervals. Conclusions: This study found that a composite real-world mortality variable yielded strong performance in comparison to the gold-standard NDI database, as measured by the validation metrics of sensitivity, specificity, PPV and NPV. Additionally, high rates of date concordance were observed across the exact, 7-, 15- and 30-day intervals. These findings are critical to ensure the reliability of results generated using RWD, and further establish the use of a composite real-world mortality variable as best practice. Future research will expand this validation methodology to a cohort of patients with solid tumor cancers, as well as investigate the performance of overall survival using a composite real-world mortality variable.

Exact confidence intervals for population growth rate, longevity and generation time

By quantifying key life history parameters in populations, such as growth rate, longevity, and generation time, researchers and administrators can obtain valuable insights into its dynamics. Although point estimates of demographic parameters have been available since the inception of demography as a scientific discipline, the construction of confidence intervals has typically relied on approximations through series expansions or computationally intensive techniques. This study introduces the first mathematical expression for calculating confidence intervals for the aforementioned life history traits when individuals are unidentifiable and data are presented as a life table. The key finding is the accurate estimation of the confidence interval for r, the instantaneous growth rate, which is tested using Monte Carlo simulations with four arbitrary discrete distributions. In comparison to the bootstrap method, the proposed interval construction method proves more efficient, particularly for experiments with a total offspring size below 400. We discuss handling cases where data are organized in extended life tables or as a matrix of vital rates. We have developed and provided accompanying code to facilitate these computations.

Association between circulating micro-ribonucleic acids and metabolic syndrome in older adults from a population-based study

Aging is a major factor in development of chronic non-communicable diseases (NCD). Epigenetic causes are risk factors in NCD development since studies indicate that the expression of micro-ribonucleic acids (miRs) is altered under different clinical conditions. This study aimed to analyze the expression profile of circulating miRs and investigate their association with biomarkers of cardiometabolic risk in older adults living in São Paulo municipality, Brazil. A cross-sectional study was conducted based on the analysis of data from 200 older adults, with a mean age of 69.1 (0.5) years old participating in the ISA-Nutrition. The expression profiles of 21 plasma miRs related to glycemic and lipid metabolism, adiposity, and inflammation were evaluated in relation to cardiometabolic risk. Individuals were distributed into groups according to diagnosis of metabolic syndrome (MetS). The Stata Somersd module was used to calculate confidence intervals for Kendall's tau-a to estimate the correlations among variables. Differences in the plasma expression were observed in two of the 21 miRs evaluated according to the MetS presence in participants. Individuals with MetS showed higher expression of miR-30a and miR-122 than individuals without MetS. Considering that miR-30, and miR-122 were altered due to MetS, these miRs may be potential biomarkers for MetS in older adults.

Pilot Study on the Molecular Pathogenesis of Pyeloureteral Junction Obstruction: Underdevelopment or Fibrosis?

Background and Objectives: Congenital ureteral stenosis is one of the leading causes of impaired urinary drainage and subsequent dilatation of the urinary collecting system, known as hydronephrosis or ureterohydronephrosis. The mechanism that leads to obstruction is not clearly known. Multiple studies in rat models have shown increased angiotensin II and TGFβ levels in obstructed ureteral tissue. The aim of the study is to investigate the expression of fibrosis-related genes in obstructive and normal ureteral tissue. Material and Methods: It is a monocentric pilot study in which nineteen patients were selected prospectively. 17 patients underwent Hynes-Anderson pyeloplasty due to the PUJO; two patients underwent ureteroneocystostomy due to ureterovesical junction obstruction (UVJO); and six patients were chosen for the control group: five underwent nephrectomies due to the kidney tumor and one underwent upper pole heminephrectomy due to the duplex kidney with normal pyeloureteric junctions in all. Tissue RNA was chemically extracted after freezing the biopsy samples in liquid nitrogen, with cDNA synthesis performed immediately after nucleic acid isolation. qPCR was performed to evaluate the relative expression of Tgfb1, Mmp1, Timp1, Pai1, Ctgf, and Vegfa. Expression levels of the Gapdh and Gpi genes (geometric average) were used to calculate the relative expression of the investigated genes. Outliers were removed prior to calculating confidence intervals for the experimental groups, and a Wilcoxon rank-sum test was performed to determine the statistical significance of the differences. Results: Significant differences between healthy and stenotic tissue samples in Ctgf gene expression levels were observed, with the samples from afflicted tissue showing lower expression. No statistical difference in expression levels of Tgfb1, Timp1, Vegfa, Mmp1, and Pai1 was found. Conclusions: These findings suggest that tissue fibrosis, similar to other tissues and organs, is not the leading cause of stenosis, at least at the moment of surgery. Decreased CTGF expression is indicative of the developmental origin of obstruction.

The intragenerational mobility of the top income earners during financial crises, a story of a cohort

In this study, we explore the dynamics of the intragenerational mobility of the top income earners during financial crises. We analyze panel data on the income levels of a cohort consisting of 22,601 individuals in Israel born between 1963 and 1973, for the period between 1995 and 2013. Studying a specific cohort allows us to focus on the changes caused by period effects, rather than cohort replacement distortions. We use common intragenerational mobility measurements before, during, and after two major recessions- the Dot.com crisis and the Global Financial Crisis of 2008- which occurred during the analyzed period. However, since these are usually descriptive, we adopt a methodology that enables us to calculate confidence intervals of these measurements and thus test for changes over time. Our results show if the two crises had any effect on the intragenerational mobility of the top income earners of the analyzed cohort, it was a minor and transitory effect.

297-OR: Systematic Review of Immune Interventions Trials in Recent Onset Type 1 and Effect on HbA1c

There has been no meta-analysis that addressed the essential issue of efficacy of immunomodulatory agents on HbA1c in patients with recent onset type 1 diabetes (T1D). The aim of this study was to conduct a systematic review and meta-analysis to estimate the treatment effect of different immunotherapeutic strategies vs. placebo on HbA1c in subjects with T1D over the first 2 years of disease. We searched PubMed and Clinicaltrials.gov from 1990 until November 2022 to identify randomized clinical trials (RCTs) and observational investigations that compared the efficacy of immunomodulatory agents in adult and children with recent onset T1D. The study included 35 trials with a total of 2987 participants, 2086 in the treatment groups and 901 in the control groups. We extracted information about year of publication, study and exposure duration, immunotherapy type, no. patients, age, sex, weight, and BMI. HbA1c was evaluated at baseline, and at 3, 6, 9, 12 and 24 months. Funnel plots with confidence regions were used to explore small-study and publication biases. A random-effects model to pool effect sizes Knapp-Hartung adjustments was used to calculate confidence intervals. The evaluation outlined a significantly higher HbA1c in the treatment group at baseline (MD = 0.26 %, 95% confidence interval (CI) 0.0025 to 0.51). Heterogeneity was detected (I2 = 67%; p&amp;lt;0.01; prediction interval -0.71 to 1.23). Small trials with higher HbA1c in the treatment group seem to be underrepresented, however, most of the studies provided non-significant results. In analysis at 12 months (12 trials) results showed a significantly higher HbA1c in the treatment group (MD = 0.13 %, 95% confidence interval (CI) 0.09 to 0.16). This result is almost driven by one study. Results of the metanalysis indicate that overall, in the first 2 years of the disease and irrespective of the immunointervention agent tested, no effect on HbA1c levels could be detected vs. placebo. A few exceptions were observed in the data analysis with some agents. Disclosure P.Pozzilli: None. S.Fallucca: Consultant; Dompé. S.Pieralice: None. M.Marelli: Employee; Dompé. L.Mancini: None.