bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving Hospital Readmission Prediction using Individualized Utility Analysis
Authors: Ko Michael , Chen Emma , Rajpurkar Pranav , Agrawal Ashwin , Avati Anand , Ng Andrew , Basu Sanjay , Shah Nigam H. 
Published: July 2020

Structured AbstractImportanceMachine learning (ML) models for allocating readmission-mitigating interventions are typically selected according to their discriminative ability, which may not necessarily translate into utility in allocation of resources.ObjectiveTo determine whether ML models for allocating readmission-mitigating interventions are ranked differently based on their overall utility and their discriminative ability.DesignA retrospective analysis of ML models using claims data acquired from the Optum Clinformatics Data Mart.SettingHealth plan claims from all 50 states for commercially-insured individuals.Participants513,495 patients who were admitted as inpatients over the period January 2016 through January 2017.Main Outcomes and MeasuresMaximum utility achieved by three machine learning models for allocating readmission-mitigating interventions, determined using cost accrued in the 90 days post-discharge of an index admission and estimated counterfactual cost. Data were analyzed between April 2019 and March 2020.ResultsThe study sample consisted of 513,495 patients (mean [SD] age 69 [19] years; 294,895 [57%] Female) mean 90 day cost of $11,552 for the study period. Allocating readmission-mitigating interventions based on a LightGBM model trained to predict readmissions achieved a maximum utility of $-12,645 per patient, and an AUC of 0.74 (95% CI 0.74, 0.75); allocating interventions based on a model trained to predict cost as a proxy achieved a higher maximum utility of $-12,472 per patient, and an AUC of 0.63 (95% CI 0.62, 0.63). A hybrid model combining both intervention strategies achieved a maximum utility of $-12,472, and an AUC of 0.71 (95% CI 0.71, 0.71), comparable with the best models on either metric.Conclusion and RelevanceWe demonstrate that machine learning models may be ranked differently based on overall utility and discriminative ability. Machine learning models for allocation of limited health resources should consider directly optimizing for utility.Key pointsQuestionDo machine learning models for allocating readmission-mitigating interventions rank differently based on overall utility and discriminative performance?FindingA machine learning model predicting a patient’s future cost of care was able to achieve higher utility than a readmission risk prediction model, even though it had a lower discriminative performance in predicting readmissions.MeaningOur study suggests that machine learning models guiding allocation of limited health resources may consider evaluating and optimizing on utility.
ACE2 Expression is elevated in Airway Epithelial Cells from aged and male donors but reduced in asthma
Authors: Wark Peter AB , Pathinayake Prabuddha S. , Kaiko Gerard , Nichol Kristy , Ali Ayesha , Chen Ling , Sutanto Erika N , Garratt Luke W , Sohal Sukhwinder S. , Lu Wenying , Eapen Mathew S. , Oldmeadow Christopher , Bartlett Nathan , Reid Andrew , Veerati Punnam , C-Y.Hsu Alan , Looi Kevin , Iosifidis Thomas , Stick Stephen M , Hansbro Philip M. , Kicic Anthony 
Published: July 2020

AbstractRationaleCOVID-19 is complicated by acute lung injury, and death in some individuals. It is caused by SARS-CoV-2 that requires the ACE2 receptor and serine proteases to enter airway epithelial cells (AECs).ObjectiveTo determine what factors are associated with ACE2 expression particularly in patients with asthma and chronic obstructive pulmonary disease (COPD).MethodsWe obtained upper and lower AECs from 145 people from two independent cohorts, aged 2-89, Newcastle (n=115), and from Perth (n= 30) Australia. The Newcastle cohort was enriched with people with asthma (n=37) and COPD (n=38). Gene expression for ACE2 and other genes potentially associated with SARS-CoV-2 cell entry were assessed by quantitative PCR, protein expression was confirmed with immunohistochemistry on endobronchial biopsies and cultured AECs.ResultsIncreased gene expression of ACE2 was associated with older age (p=0.02) and male sex (p=0.03), but not pack-years smoked. When we compared gene expression between adults with asthma, COPD and healthy controls, mean ACE2 expression was lower in asthma (p=0.01). Gene expression of furin, a protease that facilitates viral endocytosis, was also lower in asthma (p=0.02), while ADAM-17, a disintegrin that cleaves ACE2 from the surface was increased (p=0.02). ACE2 protein levels were lower in endobronchial biopsies from asthma patients.ConclusionsIncreased ACE2 expression occurs in older people and males. Asthma patients have reduced expression. Altered ACE2 expression in the lower airway may be an important factor in virus tropism and may in part explain susceptibility factors and why asthma patients are not over-represented in those with COVID-19 complications.ImpactACE2 is the primary receptor for SARS-COV-2. We demonstrate that lower airway expression of ACE2 is increased in older adults and males. We also find that lower ACE2 expression in epithelial cells occurs in people with asthma and is associated with reduced Furin expression and increased ADAM-17 expression. This may explain at least in part the relative sparing of people with asthma from severe COVID-19 disease.
Accounting for external factors and early intervention adoption in the design and analysis of stepped-wedge designs: Application to a proposed study design to reduce opioid-related mortality
Authors: Rennert Lior , Heo Moonseong , Litwin Alain H. , De Gruttola Victor 
Published: July 2020

BackgroundStepped-wedge designs (SWDs) are currently being used to investigate interventions to reduce opioid overdose deaths in communities located in several states. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and social distancing orders due to the COVID-19 pandemic. Furthermore, control communities may prematurely adopt components of the proposed intervention as they become widely available. These types of events induce confounding of the intervention effect by time. Such confounding is a well-known limitation of SWDs; a common approach to adjusting for it makes use of a mixed effects modeling framework that includes both fixed and random effects for time. However, these models have several shortcomings when multiple confounding factors are present.MethodsWe discuss the limitations of existing methods based on mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce mortality associated with the opioid epidemic, and propose solutions to accommodate deviations from assumptions that underlie these models. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models under different sources of confounding. We specifically examine the impact of factors external to the study and premature adoption of intervention components.ResultsWhen only external factors are present, our simulation studies show that commonly used mixed effects models can result in unbiased estimates of the intervention effect, but have inflated Type 1 error and result in under coverage of confidence intervals. These models are severely biased when confounding factors differentially impact intervention and control clusters; premature adoption of intervention components is an example of this scenario. In these scenarios, models that incorporate fixed intervention-by-time interaction terms and an unstructured covariance for the intervention-by-cluster-by-time random effects result in unbiased estimates of the intervention effect, reach nominal confidence interval coverage, and preserve Type 1 error, but may reduce power.ConclusionsThe incorporation of fixed and random time effects in mixed effects models require certain assumptions about the impact of confounding by time in SWD. Violations of these assumptions can result in severe bias of the intervention effect estimate, under coverage of confidence intervals, and inflated Type 1 error. Since model choice has considerable impact on study power as well as validity of results, careful consideration needs to be given to choosing an appropriate model that takes into account potential confounding factors.
Non-COVID-19 deaths in the United States during the imposition of sheltering-in-place
Authors: Catalano Ralph , Casey Joan A. , Gemmill Alison , Bruckner Tim A. 
Published: July 2020

ABSTRACTPublic debate over imposed sheltering-in-place in the United States (US) includes the claim that non-COVID-19 deaths increased above those expected from history and from such deaths in Sweden. We test this claim by applying Box-Jenkins transfer function modeling to weekly deaths between December 29, 2013 and May 16, 2020 in the US and Sweden. In the eight weeks of imposed sheltering-in-place in the US – assumed begun in the week of March 22 – an average of 715 fewer non-COVID-19 deaths (95% CI: −1428, −2) occurred per week implying a total of 5720 less than expected from those in Sweden and from history. Results changed little (i.e., 5472 fewer deaths than expected) when we excluded deaths in New York City. Contrary to speculation, non-COVID-19 mortality appeared to decline in the US after shelter-in-place orders.
Persistent heterogeneity not short-term overdispersion determines herd immunity to COVID-19
Authors: Tkachenko Alexei V. , Maslov Sergei , Elbanna Ahmed , Wong George N. , Weiner Zachary J. , Goldenfeld Nigel 
Published: July 2020

It has become increasingly clear that the COVID-19 epidemic is characterized by overdispersion whereby the majority of the transmission is driven by a minority of infected individuals. Such a strong departure from the homogeneity assumptions of the traditional well-mixed compartment model is usually hypothesized to be the result of short-term super-spreader events, such as an individual’s extreme rate of virus shedding at the peak of infectivity while attending a large gathering without appropriate mitigation. However, we demonstrate that the spread of epidemics is primarily sensitive to longterm, orpersistent heterogeneityof individual susceptibility or infectivity. We demonstrate how to incorporate this heterogeneity into a wide class of epidemiological models, and derive a non-linear dependence of the effective reproduction numberReon the susceptible population fractionS. Persistent heterogeneity has three important consequences compared to the effects of short-term overdispersion: (1) It results in a major modification of the early epidemic dynamics; (2) It significantly suppresses the herd immunity threshold; (3) It also significantly reduces the final size of the epidemic. We estimate social and biological contributions to persistent heterogeneity using data on real-life face-to-face contact networks and age variation of the incidence rate during the COVID-19 epidemic. In addition, empirical data from the COVID-19 epidemic in New York City (NYC) and Chicago, as well as 50 US states provide a consistent characterization of the level of heterogeneity. Our estimates suggest that the hardest-hit areas, such as NYC, are close to the heterogeneity-modified herd immunity threshold following the first wave of the epidemic. However, this type of immunity is fragile as it wanes over time if the pattern of social interactions changes substantially.Significance StatementThis study demonstrates how a wide class of epidemiological models can be adapted for applications to heterogeneous populations in the context of the COVID-19 epidemic. It is shown that a persistent heterogeneity, rather than bursty short-term variations in infection transmission is responsible for self-limiting epidemic dynamics. Compact generalizations of the classical results for the herd immunity threshold and the final size of an epidemic are derived. The degree of persistent heterogeneity is estimated from data on real-life face-to-face contact networks, and on age variation of susceptibility to COVID-19. The estimate is further supported by the analysis of the empirical data from the epidemic in NYC and Chicago, as well as in 50 US states. The results suggest that by the end of the first wave of the epidemic, the hardest-hit areas, such as NYC, have been close to the heterogeneity-modified herd immunity, thereby limiting their vulnerability to a potential second wave of the epidemic.
An Interpretable Risk Prediction Model for Healthcare with Pattern Attention
Authors: Kamal Sundreen Asad , Yin Changchang , Qian Buyue , Zhang Ping 
Published: July 2020

AbstractBackgroundThe availability of massive amount of data enables the possibility of clinical predictive tasks. Deep learning methods have achieved promising performance on the tasks. However, most existing methods suffer from three limitations: (i) There are lots of missing value for real value events, many methods impute the missing value and then train their models based on the imputed values, which may introduce imputation bias. The models’ performance is highly dependent on the imputation accuracy. (ii) Lots of existing studies just take Boolean value medical events (e.g. diagnosis code) as inputs, but ignore real value medical events (e.g., lab tests and vital signs), which are more important for acute disease (e.g., sepsis) and mortality prediction. (iii) Existing interpretable models can illustrate which medical events are conducive to the output results, but are not able to give contributions of patterns among medical events.MethodsIn this study, we propose a novel interpretable Pattern Attention model with Value Embedding (PAVE) to predict the risks of certain diseases. PAVE takes the embedding of various medical events, their values and the corresponding occurring time as inputs, leverage self-attention mechanism to attend to meaningful patterns among medical events for risk prediction tasks. Because only the observed values are embedded into vectors, we don’t need to impute the missing values and thus avoids the imputations bias. Moreover, the self-attention mechanism is helpful for the model interpretability, which means the proposed model can output which patterns cause high risks.ResultsWe conduct sepsis onset prediction and mortality prediction experiments on a publicly available dataset MIMIC-III and our proprietary EHR dataset. The experimental results show that PAVE outperforms existing models. Moreover, by analyzing the self-attention weights, our model outputs meaningful medical event patterns related to mortality.ConclusionsPAVE learns effective medical event representation by incorporating the values and occurring time, which can improve the risk prediction performance. Moreover, the presented self-attention mechanism can not only capture patients’ health state information, but also output the contributions of various medical event patterns, which pave the way for interpretable clinical risk predictions.AvailabilityThe code for this paper is available at: https://github.com/yinchangchang/PAVE.
Children with COVID-19 like symptoms in Italian Pediatric Surgeries: the dark side of the coin
Authors: Trapani Gianfranco , Fanos Vassilios , Bertino Enrico , Maiocco Giulia , Jamal Osama Al , Fiore Michele , Bembo Vincenzo , Careddu Domenico , Barberio Lando , Zanino Luisella , Verlato Giuseppe 
Published: July 2020

AbstractBackgroundSymptoms of SARS-CoV-2 infection in children are nonspecific and shared with other common acute viral illnesses (fever, respiratory or gastrointestinal symptoms, and cutaneous signs), thus making clinical differential diagnosis tricky. In Italy, first line management of pediatric care is handed over to Primary Care Pediatricians (PCPs), who were not allowed to directly perform diagnostic tests during the recent COVID-19 outbreak. Without a confirmatory diagnosis, PCPs could only collect information on “COVID-19 like symptoms” rather than identify typical COVID-19 symptoms.AimTo evaluate the prevalence of COVID-19 like symptoms in outpatient children, during Italian lockdown. To provide PCPs a risk score to be used in clinical practice during the differential diagnosis process.MethodsA survey was submitted to 50 PCPs (assisting 47,500 children) from 7 different Italian regions between the 4th of March and the 23rd of May 2020 (total and partial lockdown period). COVID-19 like symptoms in the assisted children were recorded, as well as presence of confirmed/suspected cases in children’s families, which was taken as proxy of COVID-19. Multivariable logistic regression was accomplished to estimate the risk of having suspected/confirmed cases in families, considering symptoms as potential determinants.Results2,300 children (4.8% of overall survey population) fell ill with COVID-19 like symptoms, 3.1% and 1.7% during total and partial lockdown period respectively. The concurrent presence of fatigue, cough, and diarrhea in children, in absence of sore throat/earache and abnormal skin signs, represents the maximum risk level of having a suspected/confirmed case of COVID-19 at home.ConclusionsThe percentage of children presenting COVID-19 like symptoms at home has been remarkable also during the total lockdown period. The present study identified a pattern of symptoms which could help, in a cost-effective perspective, PCPs in daily clinical practice to define priorities in addressing children to the proper diagnostic procedure.
Are Cloth Masks a Substitute to Medical Masks in reducing transmission and contamination? A Systematic Review
Authors: Santos Milena , Torres Darlyane , Cardoso Paula C. , Pandis Nikolaos , Flores-Mir Carlos , Medeiros Rita , Normando David 
Published: July 2020

AbstractDuring the COVID-19 pandemic the use of cloth masks has increased dramatically due to the shortage of medical masks. However, the efficiency of this material is controversial. We aimed to investigate the efficiency of cloth masks in reducing transmission and contamination by droplets and aerosols for the general population and healthcare workers. Electronic databases were searched without year or language restrictions. Clinical and laboratorial studies were included. The risk of bias (RoB) was assessed using an adapted quality checklist for laboratory-based studies. ROBINS-I tool and Cochrane RoB 2.0 were used to evaluate non-randomized (n-RCT) and randomized clinical trials (RCT), respectively. The quality of the evidence was assessed through GRADE tool. From the eleven studies selected, eight were laboratory-based studies, one non-randomized and one RCT supported by laboratory data. Between the evaluated fabrics only three presented a filtration efficiency >90%. Hybrid of cotton/chiffon (95%CI 95.2 to 98.8), hybrid of cotton/silk (95%CI 92.2 to 95.8) and cotton quilt (95%CI 94.2 to 97.8). A meta-analysis was not feasible due to a high methodological heterogeneity. The overall quality of evidence ranged from very low to moderate. Despite the lower efficiency compared to medical masks, laboratorial results may underestimate the efficiency of cloth masks in real life. Cloth mask efficiency is higher when made of hybrid fabrics (cotton/chiffon, cotton/silk) and cotton quilt, mainly with multiple layers. In pandemic situations any measure that can contribute to source control at the population level can have a beneficial effect. However, cloth masks are not recommended for healthcare workers.
Modeling the initial phase of SARS-CoV-2 deposition in the respiratory tract mimicked by the 11C radionuclide
Authors: Evangelista Heitor , Amaral Cesar , Porto Luís Cristóvão , Gonçalves Sergio J. , Sodré Eduardo Delfino , Nogueira Juliana , dos Santos Angela M.G. , Cataldo Marcio 
Published: July 2020

AbstractThe knowledge on the deposition and retention of the viral particle of SARS-CoV-2 in the respiratory tract during the very initial intake from the ambient air is of prime importance to understand the infectious process and COVID-19 initial symptoms. To give some light on that, we propose to use a modified version of a widely tested lung deposition model developed by the ICRP, in the context of the ICRP Publication 66, that provides deposition patterns of microparticles in different lung compartments. In the model, we mimicked the “environmental decay” of the virus, determined by controlled experiments related to normal speeches, by the radionuclide 11C that presents comparable decay rates. Our results confirm clinical observations on the high virus retentions observed in the extrathoracic region and the lesser fraction on the alveolar section (in the order of 5), which relevance is a subject to be investigated.
Evaluation of plasma tau and neurofilament light chain biomarkers in a 12-year clinical cohort of human prion diseases
Authors: Thompson Andrew GB , Anastasiadis Prodromos , Druyeh Ronald , Whitworth Ines , Nayak Annapurna , Nihat Akin , Mok Tze How , Rudge Peter , Wadsworth Jonathan DF , Rohrer Jonathan , Schott Jonathan M , Heslegrave Amanda , Zetterberg Henrik , Collinge John , Jackson Graham S , Mead Simon 
Published: July 2020

ABSTRACTPrion diseases are fatal neurodegenerative conditions with highly accurate CSF and imaging diagnostic markers, but major unmet needs for blood biomarkers. Using ultrasensitive immuno-assays, we measured tau and neurofilament light chain (NfL) protein concentrations in 709 plasma samples taken from 377 individuals with prion disease during a 12-year prospective clinical study, alongside healthy and neurological control groups. This provides an unprecedented opportunity to evaluate their potential as biomarkers. Plasma tau and NfL were increased across all prion disease types. For distinguishing sCJD from control groups including clinically-relevant “CJD mimics”, both show considerable diagnostic value. In sCJD, NfL was substantially elevated in every sample tested, including during early disease with minimal functional impairment and in all follow-up samples. Plasma tau was independently associated with rate of clinical progression in sCJD, while plasma NfL showed independent association with severity of functional impairment. In asymptomatic PRNP mutation carriers, plasma NfL was higher within 2 years of symptom onset than in samples taken earlier. We present biomarker trajectories for 9 individuals healthy at enrolment who developed symptoms during follow-up, showing potential for plasma NfL as a “proximity marker”. We conclude that plasma tau and NfL have potential to fill key unmet needs for biomarkers in prion disease: as an outcome for clinical trials (NfL and tau); for predicting onset in at-risk individuals (NfL); and as an accessible test for earlier identification of patients that may have CJD and require more definitive tests (NfL). Further studies should evaluate their performance directly in these specific roles.