Outcomes of Echocardiography‐Detected Rheumatic Heart Disease: Validating a Simplified Score in Cohorts From Different Countries

Background The natural history of latent rheumatic heart disease (RHD) detected by echocardiography remains unclear. We aimed to assess the accuracy of a simplified score based on the 2012 World Heart Federation criteria in predicting mid‐term RHD echocardiography outcomes in children from 4 different countries. Methods and Results Patient‐level baseline and follow‐up data of children with latent RHD from 4 countries (Australia, n=62; Brazil, n=197; Malawi, n=40; New Zealand, n=94) were combined. A simplified echocardiographic scoring system previously developed from Brazilian and Ugandan cohorts, consisting of 5 point‐based variables with respective weights, was applied: mitral valveanterior leaflet thickening (weight=3), excessive leaflet tip motion (3), regurgitation jet length ≥2 cm (6), aortic valve focal thickening (4), and any regurgitation (5). Unfavorable outcome was defined as worsening diagnostic category, persistent definite RHD or development/worsening of valve regurgitation/stenosis. The score model was updated using methods for recalibration. 393 patients (314 borderline, 79 definite RHD) with median follow‐up of 36 (interquartile range, 25–48) months were included. Median age was 14 (interquartile range, 11–16) years and secondary prophylaxis was prescribed to 16%. The echocardiographic score model applied to this external population showed significant association with unfavorable outcome (hazard ratio, 1.10; 95% CI, 1.04–1.16; P =0.001). Unfavorable outcome rates in low (≤5 points), intermediate (6–9), and high‐risk (≥10) children at 3‐year follow‐up were 14.3%, 20.8%, and 38.5% respectively ( P <0.001). The updated score model showed good performance in predicting unfavorable outcome. Conclusions The echocardiographic score model for predicting RHD outcome was updated and validated for different latent RHD populations. It has potential utility in the clinical and screening setting for risk stratification of latent RHD.

EXTERNAL VALIDATION OF A NOVEL DIGITAL SIGNATURE IN CONTINUOUS CARDIORESPIRATORY MONITORING TO DETECT EARLY RESPIRATORY DETERIORATION OF ICU PATIENTS

The goal of predictive analytics monitoring is the early detection of patients at high risk of subacute potentially catastrophic illnesses. A good example of a target illness is respiratory failure leading to urgent unplanned intubation, where early detection might lead to interventions that improve patient outcome. Previously, we identified signatures of this illness in the continuous cardiorespiratory monitoring data of Intensive Care Unit patients and devised algorithms to identify patients at rising risk. Here, we externally validated 3 logistic regression models to estimate risk of emergency intubation that were developed in Medical and Surgical ICUs at the University of Virginia. We calculated the model outputs for more than 8000 patients in University of California San Francisco ICUs, 240 of whom underwent emergency intubation as determined by individual chart review. We found that the AUC of the models exceeded 0.75 in this external population, and that the risk rose appreciably over the 12 hours prior to the event. We conclude that abnormal signatures of respiratory failure in the continuous cardiorespiratory monitoring are a generalizable phenomenon.

Are Breast Cancer Nomograms Still Valid to Predict the Need for Axillary Dissection?

Background: Nomograms can help in estimating the nodal status among clinically node-negative patients. Yet their validity in external cohorts over time is unknown. If the nodal stage can be estimated preoperatively, the need for axillary dissection can be decided. Objectives: The aim of this study was to validate three existing nomograms predicting 4 or more axillary lymph node metastases. Method: The risk for ≥4 lymph node metastases was calculated for n = 529 eligible breast cancer patients using the nomograms of Chagpar et al. [Ann Surg Oncol. 2007;14:670–7], Katz et al. [J Clin Oncol. 2008;26(13):2093–8], and Meretoja et al. [Breast Cancer Res Treat. 2013;138(3):817–27]. Discrimination and calibration were calculated for each nomogram to determine their validity. Results: In this cohort, the AUC values for the Chagpar, Katz, and Meretoja models were 0.79 (95% CI 0.74–0.83), 0.87 (95% CI 0.83–0.91), and 0.82 (95% CI 0.76–0.86), respectively, showing good discrimination between patients with and without high nodal burdens. Conclusion: This study presents support for the use of older breast cancer nomograms and confirms their current validity in an external population.

A Simplified Hypervolume-Based Evolutionary Algorithm for Many-Objective Optimization

Evolutionary algorithms based on hypervolume have demonstrated good performance for solving many-objective optimization problems. However, hypervolume needs prohibitively expensive computational effort. This paper proposes a simplified hypervolume calculation method which can be used to roughly evaluate the convergence and diversity of solutions. The main idea is to use the nearest neighbors of a particular solution to calculate the volume as the solution’s hypervolume value. Moreover, this paper improves the selection operator and the update strategy of external population according to the simplified hypervolume. Then, the proposed algorithm (SHEA) is compared with some state-of-the-art algorithms on fifteen test functions of CEC2018 MaOP competition, and the experimental results prove the feasibility of the proposed algorithm.

Dosimetry of external population exposure: a comparison of the Chernobyl and Fukushima accidents

The paper is devoted to comparing the models and the doses of the population external exposure from radioactive fallout after the accident at the Chernobyl and Fukushima-1 NPPs estimated with their help. In the case of the Fukushima-1 accident, the model proposed by the UNSCEAR was used. Both the values of the doses of gamma radiation in the environment and the values of the effective doses of external exposure of comparable population groups normalized to the same surface activity of radionuclides were close for both accidents. The reasons for this are both the similarity of the isotopic compositions of the radioactive fallout and the fact that the “Japanese” model of external exposure was based on the “Chernobyl” model up to using the same numerical values for some parameters, due to the lack of specific Japanese post-accident data for the moment of the first dose estimates for the inhabitants of Japan. For a more accurate comparison of the external exposure of residents after two accidents it is necessary to verify the values of the parameters of the Japanese model using the results of measurements of gamma radiation dose rates in the environment and individual external doses of the residents after the accident at the Fukushima-1 NPP.

Comparing Alternatives for Estimation from Nonprobability Samples

Three approaches to estimation from nonprobability samples are quasi-randomization, superpopulation modeling, and doubly robust estimation. In the first, the sample is treated as if it were obtained via a probability mechanism, but unlike in probability sampling, that mechanism is unknown. Pseudo selection probabilities of being in the sample are estimated by using the sample in combination with some external data set that covers the desired population. In the superpopulation approach, observed values of analysis variables are treated as if they had been generated by some model. The model is estimated from the sample and, along with external population control data, is used to project the sample to the population. The specific techniques are the same or similar to ones commonly employed for estimation from probability samples and include binary regression, regression trees, and calibration. When quasi-randomization and superpopulation modeling are combined, this is referred to as doubly robust estimation. This article reviews some of the estimation options and compares them in a series of simulation studies.