Sex-Specific Developmental Scales for Surveillance.

BACKGROUND: Developmental surveillance, conducted routinely worldwide, is fundamental for early detection of children at risk for developmental delay. We aimed to explore sex-related difference in attainment rates of developmental milestones and to evaluate the clinical need for separate sex-specific scales. METHODS: This is a cross-sectional, natiowide retrospective study, utilizing data from a national child surveillance program of ∼1000 maternal child health clinics. The main cohort, used for constructing sex-specific developmental scales, included all children born between January 2014 to September 2020, who visited maternal child health clinics from birth to 6 years of age (n = 839 574). Children with abnormal developmental potential were excluded (n = 195 616). A validation cohort included all visits between 2020 and 2021 (n = 309 181). The sex-differences in normative attainment age of 59 developmental milestones from 4 domains were evaluated. The milestones with a significant gap between males and females were identified, and the projected error rates when conducting unified versus sex-specific surveillance were calculated. RESULTS: A new sex-specific developmental scale was constructed. In total, females preceded males in most milestones of all developmental domains, mainly at older ages. Conducting routine developmental surveillance using a unified scale, compared with sex-specific scales, resulted in potential missing of females at risk for developmental delay (19.3% of failed assessments) and over-diagnosis of males not requiring further evaluation (5.9% of failed assessments). CONCLUSIONS: There are sex-related differences in the normative attainment rates of developmental milestones, indicating possible distortion of the currently used unified scales. These findings suggest that using sex-specific scales may improve the accuracy of early childhood developmental surveillance.

Data-driven dissection of the fever effect in autism spectrum disorder.

Some individuals with autism spectrum disorder (ASD) demonstrate marked behavioral improvements during febrile episodes, in what is perhaps the only present-day means of modulating the core ASD phenotype. Understanding the nature of this so-called fever effect is therefore essential for leveraging this natural temporary relief of symptoms to a sustained efficacious intervention. Toward this goal, we used machine learning to analyze the rich clinical data of the Simons Simplex Collection, in which one out of every six children with ASD was reported to improve during febrile episodes, across multiple ASD domains. Reported behavioral improvements during febrile episodes were associated with maternal infection in pregnancy (OR = 1.7, 95% CI = [1.42, 2.03], P = 4.24 × 10-4 ) and gastrointestinal (GI) dysfunction (OR = 1.46, 95% CI = [1.15, 1.81], P = 1.94 × 10-3 ). Family members of children reported to improve when febrile have an increased prevalence of autoimmune disorders (OR = 1.43, 95% CI = [1.23, 1.67], P = 3.0 × 10-6 ), language disorders (OR = 1.63, 95% CI = [1.29, 2.04], P = 2.5 × 10-5 ), and neuropsychiatric disorders (OR = 1.59, 95% CI = [1.34, 1.89], P < 1 × 10-6 ). Since both GI abnormalities and maternal immune activation have been linked to ASD via proinflammatory cytokines, these results might suggest a possible involvement of immune dysregulation in the fever effect, consistent with findings in mouse models. This work advances our understanding of the fever-responsive ASD subtype and motivates the future studies to directly test the link between proinflammatory cytokines and behavioral modifications in individuals with ASD.

Trends in the Prevalence of Chronic Medication Use Among Children in Israel Between 2010 and 2019: Protocol for a Retrospective Cohort Study.

BACKGROUND: Prescription of psychostimulants has significantly increased in most countries worldwide for both preschool and school-aged children. Understanding the trends of chronic medication use among children in different age groups and from different sociodemographic backgrounds is essential. It is essential to distinguish between selected therapy areas to help decision-makers evaluate not only the relevant expected medication costs but also the specific services related to these areas. OBJECTIVE: This study will analyze differences in trends regarding medications considered psychobehavioral treatments and medications considered nonpsychobehavioral treatments and will identify risk factors and predictors for chronic medication use among children. METHODS: This is a retrospective study. Data will be extracted from the Clalit Health Services data warehouse. For each year between 2010 and 2019, there are approximately 1,500,000 children aged 0-18 years. All medication classes will be identified using the Anatomical Therapeutic Chemical code. A time-trend analysis will be performed to investigate if there is a significant difference between the trends of children's psychobehavioral and nonpsychobehavioral medication prescriptions. A logistic regression combined with machine learning models will be developed to identify variables that may increase the risk for specific chronic medication types and identify children likely to get such treatment. RESULTS: The project was funded in 2019. Data analysis is currently underway, and the results are expected to be submitted for publication in 2022. Understanding trends regarding medications considered psychobehavioral treatments and medications considered nonpsychobehavioral treatments will support the identification of risk factors and predictors for chronic medication use among children. CONCLUSIONS: Analyzing the response of the patient (and their parents or caregivers) population over time will hopefully help improve policies for prescriptions and follow-up of chronic treatments in children. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36756.

Improving Cardiovascular Disease Prediction Using Automated Coronary Artery Calcium Scoring from Existing Chest CTs.

Cardiovascular disease (CVD) prediction models are widely used in modern medicine and are incorporated into prominent guidelines. Coronary artery calcium (CAC) is a marker of coronary atherosclerotic disease and has proven utility for predicting cardiovascular disease. Despite this, current guidelines recommend against including CAC scores in CVD prediction models due to the medical and financial costs of acquiring it, and the insufficient evidence concerning its ability to improve existing models. Modern machine learning models are capable of automatically extracting coronary calcium scores from existing chest computed tomography (CT) scans, negating these costs. To determine whether the inclusion of CAC scores, automatically extracted using a machine learning algorithm from chest CTs performed for any reason, improves the performance of the American Heart Association/American College of Cardiology 2013 pooled cohort equations (PCE). A retrospective cohort of patients with available chest CTs prior to an index date (2012) was used to compare the performance of the PCE model and an augmented-PCE model which utilizes the CT-based CAC scores on top of the existing model. The PCE and the augmented-PCE predictions were calculated as of an index date (2012) using data from the electronic health record and existing chest CTs. The performance of both models was evaluated by comparing their predictions to cardiovascular events that occurred during a 5-year follow-up period (until 2017). A total of 14,135 patients aged 40-79 years were included in the study, of whom 470 (3.3%) had documented CVD events during the follow-up. The augmented-PCE model showed a significant improvement in c-statistic (0.64 ≥ 0.69, Δ = 0.05, 95% CI: 0.03 to 0.06), sensitivity (53% ≥ 57%, Δ = 4.7%, 95% CI: 0-9.0%), specificity (67% ≥ 70%, Δ = 2.8%, 95% CI: 0.9-5.1%), in positive predictive value (5% ≥ 6%, Δ = 0.9%, 95% CI: 0.4 to 1.4%), negative predictive value (97.7% ≥ 97.9%, Δ = 0.3%, 95% CI: 0.1 to 0.5%), and in the categorical net reclassification index (7.4%, 95% CI: 2.4 to 12.1%). Automatically generated CAC scores from existing CTs can aid in CVD risk determination, improving model performance when used on top of existing predictors. Use of existing CTs avoids most pitfalls currently cited against the routine use of CAC in CVD predictions (e.g., additional radiation exposure), and thus affords a net gain in predictive accuracy.

Standardization of a Developmental Milestone Scale Using Data From Children in Israel.

Importance: Routine developmental screening tests for children are used worldwide for early detection of developmental delays. However, assessment of developmental milestone norms lacks strong normative data, and there are inconsistencies among different screening tools. Objective: To establish milestone norms and build an updated developmental scale. Design, Setting, and Participants: This is a cross-sectional, population-based study conducted between 2014 and 2020. Developmental assessments were conducted by trained public health nurses, documented in national maternal child health clinics, known as Tipat Halav, which serve all children in Israel. Participants included all children born between January 2014 and September 2020, who were followed at the maternal child health clinics from birth to age 6 years. Exclusion criteria were preterm birth, missing gestational age, low birth weight (<2.5 kg), abnormal weight measurement (<3% according to standardized child growth charts), abnormal head circumference measurement (<3% or >97% according to standardized child growth charts), and visits without developmental data or without the child's age. Data analysis was performed from September 2020 to June 2021. Exposures: In total, 59 milestones in 4 developmental domains were evaluated, and the achievement rate per child's age was calculated for each milestone. Main Outcomes and Measures: A contemporary developmental scale, the Tipat Halav Israel Screening (THIS) Developmental Scale, was built, presenting the 75%, 90%, and 95% achievement rates for each milestone. The THIS scale was compared with other commonly used screening tests, including the Denver Developmental Screening Test II (Denver II), the Alberta Infant Motor Scale (AIMS), and the Centers for Disease Control and Prevention (CDC) Developmental Assessment. Results: A total of 839 574 children were followed in the maternal child health clinics between January 2014 and September 2020 in Israel, and 195 616 children were excluded. A total of 3 774 517 developmental assessments were performed for the remaining 643 958 children aged 0 to 6 years (319 562 female children [49.6%]), resulting in the establishment of new developmental norms. In terms of the comparable milestones, THIS milestones had a match of 18 of 27 (67%) with the Denver II, 7 of 7 (100%) with AIMS, and 10 of 19 (53%) with the CDC Developmental Assessment. The remaining unmatched milestones were achieved earlier in the THIS scale compared with other screening tools. Conclusions and Relevance: The THIS developmental scale is based on the largest population evaluated to date for developmental performance, representing the heterogeneous, multicultural population comprising this cohort. It is recommended for further evaluation worldwide.

Lorentzian geometry and variability reduction in airplane boarding: Slow passengers first outperforms random boarding.

Airlines use different boarding policies to organize the queue of passengers waiting to enter the airplane. We analyze three policies in the many-passenger limit by a geometric representation of the queue position and row designation of each passenger and apply a Lorentzian metric to calculate the total boarding time. The boarding time is governed by the time each passenger needs to clear the aisle, and the added time is determined by the aisle-clearing time distribution through an effective aisle-clearing time parameter. The nonorganized queues under the common random boarding policy are characterized by large effective aisle-clearing time. We show that, subject to a mathematical assumption which we have verified by extensive numerical computations in all realistic cases, the average total boarding time is always reduced when slow passengers are separated from faster passengers and the slow group is allowed to enter the airplane first. This is a universal result that holds for any combination of the three main governing parameters: the ratio between effective aisle-clearing times of the fast and the slow groups, the fraction of slow passengers, and the congestion of passengers in the aisle. Separation into groups based on aisle-clearing time allows for more synchronized seating, but the result is nontrivial, as the similar fast-first policy-where the two groups enter the airplane in reverse order-is inferior to random boarding for a range of parameter settings. The asymptotic results conform well with discrete-event simulations with realistic numbers of passengers. Parameters based on empirical data, with hand luggage as criteria for separating passengers into the slow and fast groups, give an 8% reduction in total boarding time for slow first compared to random boarding.

Size-Based Routing Policies: Non-Asymptotic Analysis and Design of Decentralized Systems.

Size-based routing policies are known to perform well when the variance of the distribution of the job size is very high. We consider two size-based policies in this paper: Task Assignment with Guessing Size (TAGS) and Size Interval Task Assignment (SITA). The latter assumes that the size of jobs is known, whereas the former does not. Recently, it has been shown by our previous work that when the ratio of the largest to shortest job tends to infinity and the system load is fixed and low, the average waiting time of SITA is, at most, two times less than that of TAGS. In this article, we first analyze the ratio between the mean waiting time of TAGS and the mean waiting time of SITA in a non-asymptotic regime, and we show that for two servers, and when the job size distribution is Bounded Pareto with parameter α=1, this ratio is unbounded from above. We then consider a system with an arbitrary number of servers and we compare the mean waiting time of TAGS with that of Size Interval Task Assignment with Equal load (SITA-E), which is a SITA policy where the load of all the servers are equal. We show that in the light traffic regime, the performance ratio under consideration is unbounded from above when (i) the job size distribution is Bounded Pareto with parameter α=1 and an arbitrary number of servers as well as (ii) for Bounded Pareto distributed job sizes with α∈(0,2)\{1} and the number of servers tends to infinity. Finally, we use the result of our previous work to show how to design decentralized systems with quality of service constraints.

Addressing bias in prediction models by improving subpopulation calibration.

OBJECTIVE: To illustrate the problem of subpopulation miscalibration, to adapt an algorithm for recalibration of the predictions, and to validate its performance. MATERIALS AND METHODS: In this retrospective cohort study, we evaluated the calibration of predictions based on the Pooled Cohort Equations (PCE) and the fracture risk assessment tool (FRAX) in the overall population and in subpopulations defined by the intersection of age, sex, ethnicity, socioeconomic status, and immigration history. We next applied the recalibration algorithm and assessed the change in calibration metrics, including calibration-in-the-large. RESULTS: 1 021 041 patients were included in the PCE population, and 1 116 324 patients were included in the FRAX population. Baseline overall model calibration of the 2 tested models was good, but calibration in a substantial portion of the subpopulations was poor. After applying the algorithm, subpopulation calibration statistics were greatly improved, with the variance of the calibration-in-the-large values across all subpopulations reduced by 98.8% and 94.3% in the PCE and FRAX models, respectively. DISCUSSION: Prediction models in medicine are increasingly common. Calibration, the agreement between predicted and observed risks, is commonly poor for subpopulations that were underrepresented in the development set of the models, resulting in bias and reduced performance for these subpopulations. In this work, we empirically evaluated an adapted version of the fairness algorithm designed by Hebert-Johnson et al. (2017) and demonstrated its use in improving subpopulation miscalibration. CONCLUSION: A postprocessing and model-independent fairness algorithm for recalibration of predictive models greatly decreases the bias of subpopulation miscalibration and thus increases fairness and equality.

Cross-lingual Unified Medical Language System entity linking in online health communities.

OBJECTIVE: In Hebrew online health communities, participants commonly write medical terms that appear as transliterated forms of a source term in English. Such transliterations introduce high variability in text and challenge text-analytics methods. To reduce their variability, medical terms must be normalized, such as linking them to Unified Medical Language System (UMLS) concepts. We present a method to identify both transliterated and translated Hebrew medical terms and link them with UMLS entities. MATERIALS AND METHODS: We investigate the effect of linking terms in Camoni, a popular Israeli online health community in Hebrew. Our method, MDTEL (Medical Deep Transliteration Entity Linking), includes (1) an attention-based recurrent neural network encoder-decoder to transliterate words and mapping UMLS from English to Hebrew, (2) an unsupervised method for creating a transliteration dataset in any language without manually labeled data, and (3) an efficient way to identify and link medical entities in the Hebrew corpus to UMLS concepts, by producing a high-recall list of candidate medical terms in the corpus, and then filtering the candidates to relevant medical terms. RESULTS: We carry out experiments on 3 disease-specific communities: diabetes, multiple sclerosis, and depression. MDTEL tagging and normalizing on Camoni posts achieved 99% accuracy, 92% recall, and 87% precision. When tagging and normalizing terms in queries from the Camoni search logs, UMLS-normalized queries improved search results in 46% of the cases. CONCLUSIONS: Cross-lingual UMLS entity linking from Hebrew is possible and improves search performance across communities. Annotated datasets, annotation guidelines, and code are made available online (https://github.com/yonatanbitton/mdtel).

Developing a COVID-19 mortality risk prediction model when individual-level data are not available.

At the COVID-19 pandemic onset, when individual-level data of COVID-19 patients were not yet available, there was already a need for risk predictors to support prevention and treatment decisions. Here, we report a hybrid strategy to create such a predictor, combining the development of a baseline severe respiratory infection risk predictor and a post-processing method to calibrate the predictions to reported COVID-19 case-fatality rates. With the accumulation of a COVID-19 patient cohort, this predictor is validated to have good discrimination (area under the receiver-operating characteristics curve of 0.943) and calibration (markedly improved compared to that of the baseline predictor). At a 5% risk threshold, 15% of patients are marked as high-risk, achieving a sensitivity of 88%. We thus demonstrate that even at the onset of a pandemic, shrouded in epidemiologic fog of war, it is possible to provide a useful risk predictor, now widely used in a large healthcare organization.

Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization.

Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA)1,2 and risk predictors like the Fracture Risk Assessment Tool (FRAX)3-6, are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans. A CT-based predictor was created using three automatically generated bone imaging biomarkers (vertebral compression fractures (VCFs), simulated DXA T-scores and lumbar trabecular density) and CT metadata of age and sex. A cohort of 48,227 individuals (51.8% women) aged 50-90 with available CTs before 2012 (index date) were assessed for 5-year fracture risk using FRAX with no bone mineral density (BMD) input (FRAXnb) and the CT-based predictor. Predictions were compared to outcomes of major osteoporotic fractures and hip fractures during 2012-2017 (follow-up period). Compared with FRAXnb, the major osteoporotic fracture CT-based predictor presented better receiver operating characteristic area under curve (AUC), sensitivity and positive predictive value (PPV) (+1.9%, +2.4% and +0.7%, respectively). The AUC, sensitivity and PPV measures of the hip fracture CT-based predictor were noninferior to FRAXnb at a noninferiority margin of 1%. When FRAXnb inputs are not available, the initial evaluation of fracture risk can be done completely automatically based on a single abdomen or chest CT, which is often available for screening candidates7,8.

Translating clinical trial results into personalized recommendations by considering multiple outcomes and subjective views.

Currently, clinicians rely mostly on population-level treatment effects from RCTs, usually considering the treatment's benefits. This study proposes a process, focused on practical usability, for translating RCT data into personalized treatment recommendations that weighs benefits against harms and integrates subjective perceptions of relative severity. Intensive blood pressure treatment (IBPT) was selected as the test case to demonstrate the suggested process, which was divided into three phases: (1) Prediction models were developed using the Systolic Blood-Pressure Intervention Trial (SPRINT) data for benefits and adverse events of IBPT. The models were externally validated using retrospective Clalit Health Services (CHS) data; (2) Predicted risk reductions and increases from these models were used to create a yes/no IBPT recommendation by calculating a severity-weighted benefit-to-harm ratio; (3) Analysis outputs were summarized in a decision support tool. Based on the individual benefit-to-harm ratios, 62 and 84% of the SPRINT and CHS populations, respectively, would theoretically be recommended IBPT. The original SPRINT trial results of significant decrease in cardiovascular outcomes following IBPT persisted only in the group that received a "yes-treatment" recommendation by the suggested process, while the rate of serious adverse events was slightly higher in the "no-treatment" recommendation group. This process can be used to translate RCT data into individualized recommendations by identifying patients for whom the treatment's benefits outweigh the harms, while considering subjective views of perceived severity of the different outcomes. The proposed approach emphasizes clinical practicality by mimicking physicians' clinical decision-making process and integrating all recommendation outputs into a usable decision support tool.

Lorentzian-geometry-based analysis of airplane boarding policies highlights "slow passengers first" as better.

We study airplane boarding in the limit of a large number of passengers using geometric optics in a Lorentzian metric. The airplane boarding problem is naturally embedded in a (1+1)-dimensional space-time with a flat Lorentzian metric. The duration of the boarding process can be calculated based on a representation of the one-dimensional queue of passengers attempting to reach their seats in a two-dimensional space-time diagram. The ability of a passenger to delay other passengers depends on their queue positions and row designations. This is equivalent to the causal relationship between two events in space-time, whereas two passengers are timelike separated if one is blocking the other and spacelike if both can be seated simultaneously. Geodesics in this geometry can be utilized to compute the asymptotic boarding time, since space-time geometry is the many-particle (passengers) limit of airplane boarding. Our approach naturally leads to the introduction of an effective refractive index that enables an analytical calculation of the average boarding time for groups of passengers with different aisle-clearing time distribution. In the past, airline companies attempted to shorten the boarding times by trying boarding policies that allow either slow or fast passengers to board first. Our analytical calculations, backed by discrete-event simulations, support the counterintuitive result that the total boarding time is shorter with the slow passengers boarding before the fast passengers. This is a universal result, valid for any combination of the parameters that characterize the problem: the percentage of slow passengers, the ratio between aisle-clearing times of the fast and the slow group, and the density of passengers along the aisle. We find an improvement of up to 28% compared with the fast-first boarding policy. Our approach opens up the possibility to unify numerous simulation-based case studies under one framework.

Optimal back-to-front airplane boarding.

The problem of finding an optimal back-to-front airplane boarding policy is explored, using a mathematical model that is related to the 1+1 polynuclear growth model with concave boundary conditions and to causal sets in gravity. We study all airplane configurations and boarding group sizes. Optimal boarding policies for various airplane configurations are presented. Detailed calculations are provided along with simulations that support the main conclusions of the theory. We show that the effectiveness of back-to-front policies undergoes a phase transition when passing from lightly congested airplanes to heavily congested airplanes. The phase transition also affects the nature of the optimal or near-optimal policies. Under what we consider to be realistic conditions, optimal back-to-front policies lead to a modest 8-12% improvement in boarding time over random (no policy) boarding, using two boarding groups. Having more than two groups is not effective.

Unrooted unordered homeomorphic subtree alignment of RNA trees.

: We generalize some current approaches for RNA tree alignment, which are traditionally confined to ordered rooted mappings, to also consider unordered unrooted mappings. We define the Homeomorphic Subtree Alignment problem (HSA), and present a new algorithm which applies to several modes, combining global or local, ordered or unordered, and rooted or unrooted tree alignments. Our algorithm generalizes previous algorithms that either solved the problem in an asymmetric manner, or were restricted to the rooted and/or ordered cases. Focusing here on the most general unrooted unordered case, we show that for input trees T and S, our algorithm has an O(nTnS + min(dT,dS)LTLS) time complexity, where nT,LT and dT are the number of nodes, the number of leaves, and the maximum node degree in T, respectively (satisfying dT ≤ LT ≤ nT), and similarly for nS,LS and dS with respect to the tree S. This improves the time complexity of previous algorithms for less general variants of the problem.In order to obtain this time bound for HSA, we developed new algorithms for a generalized variant of the Min-Cost Bipartite Matching problem (MCM), as well as to two derivatives of this problem, entitled All-Cavity-MCM and All-Pairs-Cavity-MCM. For two input sets of size n and m, where n ≤ m, MCM and both its cavity derivatives are solved in O(n3 + nm) time, without the usage of priority queues (e.g. Fibonacci heaps) or other complex data structures. This gives the first cubic time algorithm for All-Pairs-Cavity-MCM, and improves the running times of MCM and All-Cavity-MCM problems in the unbalanced case where n ≪ m.We implemented the algorithm (in all modes mentioned above) as a graphical software tool which computes and displays similarities between secondary structures of RNA given as input, and employed it to a preliminary experiment in which we ran all-against-all inter-family pairwise alignments of RNAse P and Hammerhead RNA family members, exposing new similarities which could not be detected by the traditional rooted ordered alignment approaches. The results demonstrate that our approach can be used to expose structural similarity between some RNAs with higher sensitivity than the traditional rooted ordered alignment approaches. Source code and web-interface for our tool can be found in http://www.cs.bgu.ac.il/\~negevcb/FRUUT.