Ribosomal quality control factors inhibit repeat-associated non-AUG translation from GC-rich repeats.

A GGGGCC (G4C2) hexanucleotide repeat expansion in C9ORF72 causes amyotrophic lateral sclerosis and frontotemporal dementia (C9ALS/FTD), while a CGG trinucleotide repeat expansion in FMR1 leads to the neurodegenerative disorder Fragile X-associated tremor/ataxia syndrome (FXTAS). These GC-rich repeats form RNA secondary structures that support repeat-associated non-AUG (RAN) translation of toxic proteins that contribute to disease pathogenesis. Here we assessed whether these same repeats might trigger stalling and interfere with translational elongation. We find that depletion of ribosome-associated quality control (RQC) factors NEMF, LTN1 and ANKZF1 markedly boost RAN translation product accumulation from both G4C2 and CGG repeats while overexpression of these factors reduces RAN production in both reporter assays and C9ALS/FTD patient iPSC-derived neurons. We also detected partially made products from both G4C2 and CGG repeats whose abundance increased with RQC factor depletion. Repeat RNA sequence, rather than amino acid content, is central to the impact of RQC factor depletion on RAN translation-suggesting a role for RNA secondary structure in these processes. Together, these findings suggest that ribosomal stalling and RQC pathway activation during RAN translation inhibits the generation of toxic RAN products. We propose augmenting RQC activity as a therapeutic strategy in GC-rich repeat expansion disorders.

Dissecting the roles of EIF4G homologs reveals DAP5 as a modifier of CGG repeat-associated toxicity in a Drosophila model of FXTAS.

Neurodegeneration in Fragile X-associated tremor/ataxia syndrome (FXTAS) is caused by a CGG trinucleotide repeat expansion in the 5' UTR of FMR1. Expanded CGG repeat RNAs form stable secondary structures, which in turn support repeat-associated non-AUG (RAN) translation to produce toxic peptides. The parameters that impact RAN translation initiation efficiency are not well understood. Here we used a Drosophila melanogaster model of FXTAS to evaluate the role of the eIF4G family of eukaryotic translation initiation factors (EIF4G1, EIF4GII and EIF4G2/DAP5) in modulating RAN translation and CGG repeat-associated toxicity. DAP5 knockdown robustly suppressed CGG repeat-associated toxicity and inhibited RAN translation. Furthermore, knockdown of initiation factors that preferentially associate with DAP5 (such as EIF2ß, EIF3F and EIF3G) also selectively suppressed CGG repeat-induced eye degeneration. In mammalian cellular reporter assays, DAP5 knockdown exhibited modest and cell-type specific effects on RAN translation. Taken together, these data support a role for DAP5 in CGG repeat associated toxicity possibly through modulation of RAN translation.

Evaluating Resource Utilization with Free or Pedicled Perforator Flaps in Distal Leg Reconstruction.

BACKGROUND: The decision between local and free tissue coverage for distal lower leg defects has long been dictated by the location and size of defects. Recent reports of distal defects treated successfully with pedicled perforator flaps demonstrate equivalent clinical outcomes; however, the complication rate can be high. The goal of this study was to evaluate the cost equivalence of free versus pedicled perforator flap to assist decision-making and guide clinical care. METHODS: The institutional database was searched for patients with acute injury over the distal lower extremity requiring free or pedicled perforator flap. Demographic, clinical, and total resource cost was gathered. Patients were matched to Gustilo-Anderson or Arbeitsgemeinschaft fur Osteosynthesefragen classification as well as size of defect and outcomes, and cost compared. RESULTS: We have included 108 free flaps and 22 pedicled perforator flaps in the study. There was no difference in complication rate between groups. Free flaps had significantly more reoperations, required longer operative time, and had longer intensive care unit (ICU) care with higher cost of surgery and overall cost than pedicled flaps. When controlling for size of defect, surgical cost remained significantly different between groups (p = 0.013), but overall cost did not. Multivariable regression analysis indicated flap type to be the primary driver of cost of surgery, while body mass index elevated the total cost. CONCLUSION: Pedicled perforator flap coverage for small to medium-sized defects (< 70 cm2) is a viable and cost-effective option for distal lower leg soft tissue reconstruction after acute traumatic injury with similar clinical outcomes and shorter operative duration and ICU stay.

The RNA helicase DHX36-G4R1 modulates C9orf72 GGGGCC hexanucleotide repeat-associated translation.

GGGGCC (G4C2) hexanucleotide repeat expansions in the endosomal trafficking gene C9orf72 are the most common genetic cause of ALS and frontotemporal dementia. Repeat-associated non-AUG (RAN) translation of this expansion through near-cognate initiation codon usage and internal ribosomal entry generates toxic proteins that accumulate in patients' brains and contribute to disease pathogenesis. The helicase protein DEAH-box helicase 36 (DHX36-G4R1) plays active roles in RNA and DNA G-quadruplex (G4) resolution in cells. As G4C2 repeats are known to form G4 structures in vitro, we sought to determine the impact of manipulating DHX36 expression on repeat transcription and RAN translation. Using a series of luciferase reporter assays both in cells and in vitro, we found that DHX36 depletion suppresses RAN translation in a repeat length-dependent manner, whereas overexpression of DHX36 enhances RAN translation from G4C2 reporter RNAs. Moreover, upregulation of RAN translation that is typically triggered by integrated stress response activation is prevented by loss of DHX36. These results suggest that DHX36 is active in regulating G4C2 repeat translation, providing potential implications for therapeutic development in nucleotide repeat expansion disorders.

Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study.

BACKGROUND: The use of artificial intelligence (AI) in the medical domain has attracted considerable research interest. Inference applications in the medical domain require energy-efficient AI models. In contrast to other types of data in visual AI, data from medical laboratories usually comprise features with strong signals. Numerous energy optimization techniques have been developed to relieve the burden on the hardware required to deploy a complex learning model. However, the energy efficiency levels of different AI models used for medical applications have not been studied. OBJECTIVE: The aim of this study was to explore and compare the energy efficiency levels of commonly used machine learning algorithms-logistic regression (LR), k-nearest neighbor, support vector machine, random forest (RF), and extreme gradient boosting (XGB) algorithms, as well as four different variants of neural network (NN) algorithms-when applied to clinical laboratory datasets. METHODS: We applied the aforementioned algorithms to two distinct clinical laboratory data sets: a mass spectrometry data set regarding Staphylococcus aureus for predicting methicillin resistance (3338 cases; 268 features) and a urinalysis data set for predicting Trichomonas vaginalis infection (839,164 cases; 9 features). We compared the performance of the nine inference algorithms in terms of accuracy, area under the receiver operating characteristic curve (AUROC), time consumption, and power consumption. The time and power consumption levels were determined using performance counter data from Intel Power Gadget 3.5. RESULTS: The experimental results indicated that the RF and XGB algorithms achieved the two highest AUROC values for both data sets (84.7% and 83.9%, respectively, for the mass spectrometry data set; 91.1% and 91.4%, respectively, for the urinalysis data set). The XGB and LR algorithms exhibited the shortest inference time for both data sets (0.47 milliseconds for both in the mass spectrometry data set; 0.39 and 0.47 milliseconds, respectively, for the urinalysis data set). Compared with the RF algorithm, the XGB and LR algorithms exhibited a 45% and 53%-60% reduction in inference time for the mass spectrometry and urinalysis data sets, respectively. In terms of energy efficiency, the XGB algorithm exhibited the lowest power consumption for the mass spectrometry data set (9.42 Watts) and the LR algorithm exhibited the lowest power consumption for the urinalysis data set (9.98 Watts). Compared with a five-hidden-layer NN, the XGB and LR algorithms achieved 16%-24% and 9%-13% lower power consumption levels for the mass spectrometry and urinalysis data sets, respectively. In all experiments, the XGB algorithm exhibited the best performance in terms of accuracy, run time, and energy efficiency. CONCLUSIONS: The XGB algorithm achieved balanced performance levels in terms of AUROC, run time, and energy efficiency for the two clinical laboratory data sets. Considering the energy constraints in real-world scenarios, the XGB algorithm is ideal for medical AI applications.

Clinical characteristics and outcomes of mixed virus or bacterial infection in children with laboratory-confirmed influenza infection.

BACKGROUND/PURPOSE: This study investigated the demographic characteristics and influenza complications of paediatric patients and explored the association of different influenza virus types and viral and bacterial coinfections with disease severity. METHODS: This retrospective cohort study used data collected in 2010-2016 from the Chang Gung Research Database (CGRD), the largest collection of multi-institutional electronic medical records in Taiwan. Data were retrieved for children aged 0-18 years with laboratory-confirmed influenza. We extracted and analysed the demographic characteristics and the data on clinical features, complications, microbiological information, and advanced therapies of each case. RESULTS: We identified 6193 children with laboratory-confirmed influenza, of whom 1964 (31.7%) were hospitalised. The age of patients with influenza A infection was lower than that of patients with influenza B (4.48 vs. 6.68, p < 0.001). Patients with influenza B infection had a higher incidence of myositis or rhabdomyolysis (4.4%, p < 0.001) and a higher need for advanced therapies (OR, 1.96; 95% CI, 1.32-2.9, p < 0.001). In addition to bacterial (OR, 9.07; 95% CI, 5.29-15.54, p < 0.001) and viral coinfection (OR, 7.73; 95% CI, 5.4-11.07, p < 0.001), dual influenza A and B infection was also a risk factor for influenza complications (OR, 2.13; 95% CI, 1.47-3.09, p < 0.001). CONCLUSION: Dual influenza A and B infection and bacterial coinfection can contribute to influenza complications. Early recognition of any influenza complication is critical for the timely initiation of organ-specific advanced therapies to improve influenza-associated outcomes.

Development and validation of machine learning-based risk prediction models of oral squamous cell carcinoma using salivary autoantibody biomarkers.

INTRODUCTION: The incidence of oral cavity squamous cell carcinoma (OSCC) continues to rise. OSCC is associated with a low average survival rate, and most patients have a poor disease prognosis because of delayed diagnosis. We used machine learning techniques to predict high-risk cases of OSCC by using salivary autoantibody levels and demographic and behavioral data. METHODS: We collected the salivary samples of patients recruited from a teaching hospital between September 2008 and December 2012. Ten salivary autoantibodies, sex, age, smoking, alcohol consumption, and betel nut chewing were used to build prediction models for identifying patients with a high risk of OSCC. The machine learning algorithms applied in the study were logistic regression, random forest, support vector machine with the radial basis function kernel, eXtreme Gradient Boosting (XGBoost), and a stacking model. We evaluated the performance of the models by using the area under the receiver operating characteristic curve (AUC), with simulations conducted 100 times. RESULTS: A total of 337 participants were enrolled in this study. The best predictive model was constructed using a stacking algorithm with original forms of age and logarithmic levels of autoantibodies (AUC = 0.795 ± 0.055). Adding autoantibody levels as a data source significantly improved the prediction capability (from 0.698 ± 0.06 to 0.795 ± 0.055, p < 0.001). CONCLUSIONS: We successfully established a prediction model for high-risk cases of OSCC. This model can be applied clinically through an online calculator to provide additional personalized information for OSCC diagnosis, thereby reducing the disease morbidity and mortality rates.

Value of the high-sensitivity troponin T assay for diagnosis of acute myocardial infarction in patients with and without renal insufficiency.

BACKGROUND: Cardiac troponins are important markers for diagnosis of acute myocardial infarction (AMI) in general population; however, chronically-elevated troponins levels are often seen in patients with renal insufficiency, which reduce their diagnostic accuracy. The aim of our study was to access the diagnostic values of initial high-sensitive cardiac troponin T (hs-cTnT) and relative change of hs-cTnT for AMI in patients with and without renal insufficiency. METHODS: Cardiac care unit patients with elevated hs-cTnT levels in 2017-2018 were enrolled. Receiver operating characteristic (ROC) curves were used to evaluate initial hs-cTnT levels and relative changes after 3 h of enrollment for diagnosis of AMI in patients with estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 (low), and eGFR ≥ 60 mL/min/1.73 m2 (normal). RESULTS: Of 359 patients, 240 patients had low eGFR, and 119 patients had normal eGFR. The area under the ROC curve (AUC) for the initial hs-cTnT levels was 0.58 (95% CI, 0.5-0.65, p = 0.053) among patients with low eGFR and 0.54 (95% CI, 0.4-0.67, p = 0.612) among patients with normal eGFR. AUCs for relative changes of hs-cTnT were 0.82 (95% CI, 0.76-0.88, p < 0.001) in patients with low eGFR and 0.82 (95% CI, 0.71-0.91, p < 0.001) in patients with normal eGFR. Optimal cutoff values for the relative changes in hs-cTnT were 16% and 12% in patients with low eGFR and normal eGFR, respectively. CONCLUSIONS: Relative changes in hs-cTnT levels had better diagnostic accuracy than initial hs-cTnT levels.

Incidence and Patterns of Extended-Course Antibiotic Therapy in Patients Evaluated for Lyme Disease.

BACKGROUND: Most patients with Lyme disease (LD) can be treated effectively with 2-4 weeks of antibiotics. The Infectious Disease Society of America guidelines do not currently recommend extended treatment even in patients with persistent symptoms. METHODS: To estimate the incidence of extended use of antibiotics in patients evaluated for LD, we retrospectively analyzed claims from a nationwide US health insurance plan in 14 high-prevalence states over 2 periods: 2004-2006 and 2010-2012. RESULTS: As measured by payer claims, the incidence of extended antibiotic therapy among patients evaluated for LD was higher in 2010-2012 (14.72 per 100 000 person-years; n = 684) than in 2004-2006 (9.94 per 100 000 person-years; n = 394) (P < .001). Among these patients, 48.8% were treated with ≥2 antibiotics in 2010-2012 and 29.9% in 2004-2006 (P < .001). In each study period, a distinct small group of providers (roughly 3%-4%) made the diagnosis in >20% of the patients who were evaluated for LD and prescribed extended antibiotic treatment. CONCLUSIONS: Insurance claims data suggest that the use of extended courses of antibiotics and multiple antibiotics in the treatment of LD has increased in recent years.

MoCab: A Model Management System Based on FHIR for Clinical Decision Support.

MoCab is a framework that deploys high-accuracy medical models across various health information systems (HISs) using fast healthcare interoperability resources (FHIR). MoCab simplifies the process by importing and configuring stored models and retrieving data for prediction. Two case studies illustrate how MoCab can be used to support decision-making. The proposed framework increases model reusability across EHRs and improves the clinical decision-making process.

MoCab: A framework for the deployment of machine learning models across health information systems.

BACKGROUND AND OBJECTIVE: Machine learning models are vital for enhancing healthcare services. However, integrating them into health information systems (HISs) introduces challenges beyond clinical decision making, such as interoperability and diverse electronic health records (EHR) formats. We proposed Model Cabinet Architecture (MoCab), a framework designed to leverage fast healthcare interoperability resources (FHIR) as the standard for data storage and retrieval when deploying machine learning models across various HISs, addressing the challenges highlighted by platforms such as EPOCH®, ePRISM®, KETOS, and others. METHODS: The MoCab architecture is designed to streamline predictive modeling in healthcare through a structured framework incorporating several specialized parts. The Data Service Center manages patient data retrieval from FHIR servers. These data are then processed by the Knowledge Model Center, where they are formatted and fed into predictive models. The Model Retraining Center is crucial in continuously updating these models to maintain accuracy in dynamic clinical environments. The framework further incorporates Clinical Decision Support (CDS) Hooks for issuing clinical alerts. It uses Substitutable Medical Apps Reusable Technologies (SMART) on FHIR to develop applications for displaying alerts, prediction results, and patient records. RESULTS: The MoCab framework was demonstrated using three types of predictive models: a scoring model (qCSI), a machine learning model (NSTI), and a deep learning model (SPC), applied to synthetic data that mimic a major EHR system. The implementations showed how MoCab integrates predictive models with health data for clinical decision support, utilizing CDS Hooks and SMART on FHIR for seamless HIS integration. The demonstration confirmed the practical utility of MoCab in supporting clinical decision making, validated by its application in various healthcare settings. CONCLUSIONS: We demonstrate MoCab's potential in promoting the interoperability of machine learning models and enhancing its utility across various EHRs. Despite facing challenges like FHIR adoption, MoCab addresses key challenges in adapting machine learning models within healthcare settings, paving the way for further enhancements and broader adoption.

Self-Supervised Learning-Based General Laboratory Progress Pretrained Model for Cardiovascular Event Detection.

OBJECTIVE: Leveraging patient data through machine learning techniques in disease care offers a multitude of substantial benefits. Nonetheless, the inherent nature of patient data poses several challenges. Prevalent cases amass substantial longitudinal data owing to their patient volume and consistent follow-ups, however, longitudinal laboratory data are renowned for their irregularity, temporality, absenteeism, and sparsity; In contrast, recruitment for rare or specific cases is often constrained due to their limited patient size and episodic observations. This study employed self-supervised learning (SSL) to pretrain a generalized laboratory progress (GLP) model that captures the overall progression of six common laboratory markers in prevalent cardiovascular cases, with the intention of transferring this knowledge to aid in the detection of specific cardiovascular event. METHODS AND PROCEDURES: GLP implemented a two-stage training approach, leveraging the information embedded within interpolated data and amplify the performance of SSL. After GLP pretraining, it is transferred for target vessel revascularization (TVR) detection. RESULTS: The proposed two-stage training improved the performance of pure SSL, and the transferability of GLP exhibited distinctiveness. After GLP processing, the classification exhibited a notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated metrics demonstrated substantial superiority ([Formula: see text]) compared to prior GLP processing. CONCLUSION: Our study effectively engages in translational engineering by transferring patient progression of cardiovascular laboratory parameters from one patient group to another, transcending the limitations of data availability. The transferability of disease progression optimized the strategies of examinations and treatments, and improves patient prognosis while using commonly available laboratory parameters. The potential for expanding this approach to encompass other diseases holds great promise. CLINICAL IMPACT: Our study effectively transposes patient progression from one cohort to another, surpassing the constraints of episodic observation. The transferability of disease progression contributed to cardiovascular event assessment.

Web-based newborn screening system for metabolic diseases: machine learning versus clinicians.

BACKGROUND: A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. OBJECTIVE: The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. METHODS: The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. RESULTS: The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. CONCLUSIONS: This SOA Web service-based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically.

Information extraction for tracking liver cancer patients' statuses: from mixture of clinical narrative report types.

OBJECTIVE: To provide an efficient way for tracking patients' condition over long periods of time and to facilitate the collection of clinical data from different types of narrative reports, it is critical to develop an efficient method for smoothly analyzing the clinical data accumulated in narrative reports. MATERIALS AND METHODS: To facilitate liver cancer clinical research, a method was developed for extracting clinical factors from various types of narrative clinical reports, including ultrasound reports, radiology reports, pathology reports, operation notes, admission notes, and discharge summaries. An information extraction (IE) module was developed for tracking disease progression in liver cancer patients over time, and a rule-based classifier was developed for answering whether patients met the clinical research eligibility criteria. The classifier provided the answers and direct/indirect evidence (evidence sentences) for the clinical questions. To evaluate the implemented IE module and the classifier, the gold-standard annotations and answers were developed manually, and the results of the implemented system were compared with the gold standard. RESULTS: The IE model achieved an F-score from 92.40% to 99.59%, and the classifier achieved accuracy from 96.15% to 100%. CONCLUSIONS: The application was successfully applied to the various types of narrative clinical reports. It might be applied to the key extraction for other types of cancer patients.

Rule-based healthcare-associated bloodstream infection classification and surveillance system.

Healthcare-associated infections (HAIs) are a major patient safety issue. These adverse events add to the burden of resource use, promote resistance to antibiotics, and contribute to patient deaths and disability. A rule-based HAI classification and surveillance system was developed for automatic integration, analysis, and interpretation of HAIs and related pathogens. Rule-based classification system was design and implement to facilitate healthcare-associated bloodstream infection (HABSI) surveillance. Electronic medical records from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of HABSI. The detailed information in each HABSI was presented systematically to support infection control personnel decision. The accuracy of HABSI classification was 0.94, and the square of the sample correlation coefficient was 0.99.

lab: an R package for generating analysis-ready data from laboratory records.

Background: Electronic health records (EHRs) play a crucial role in healthcare decision-making by giving physicians insights into disease progression and suitable treatment options. Within EHRs, laboratory test results are frequently utilized for predicting disease progression. However, processing laboratory test results often poses challenges due to variations in units and formats. In addition, leveraging the temporal information in EHRs can improve outcomes, prognoses, and diagnosis predication. Nevertheless, the irregular frequency of the data in these records necessitates data preprocessing, which can add complexity to time-series analyses. Methods: To address these challenges, we developed an open-source R package that facilitates the extraction of temporal information from laboratory records. The proposed lab package generates analysis-ready time series data by segmenting the data into time-series windows and imputing missing values. Moreover, users can map local laboratory codes to the Logical Observation Identifier Names and Codes (LOINC), an international standard. This mapping allows users to incorporate additional information, such as reference ranges and related diseases. Moreover, the reference ranges provided by LOINC enable us to categorize results into normal or abnormal. Finally, the analysis-ready time series data can be further summarized using descriptive statistics and utilized to develop models using machine learning technologies. Results: Using the lab package, we analyzed data from MIMIC-III, focusing on newborns with patent ductus arteriosus (PDA). We extracted time-series laboratory records and compared the differences in test results between patients with and without 30-day in-hospital mortality. We then identified significant variations in several laboratory test results 7 days after PDA diagnosis. Leveraging the time series-analysis-ready data, we trained a prediction model with the long short-term memory algorithm, achieving an area under the receiver operating characteristic curve of 0.83 for predicting 30-day in-hospital mortality in model training. These findings demonstrate the lab package's effectiveness in analyzing disease progression. Conclusions: The proposed lab package simplifies and expedites the workflow involved in laboratory records extraction. This tool is particularly valuable in assisting clinical data analysts in overcoming the obstacles associated with heterogeneous and sparse laboratory records.

Engaging a national-scale cohort of smart thermometer users in participatory surveillance.

Participatory surveillance systems crowdsource individual reports to rapidly assess population health phenomena. The value of these systems increases when more people join and persistently contribute. We examine the level of and factors associated with engagement in participatory surveillance among a retrospective, national-scale cohort of individuals using smartphone-connected thermometers with a companion app that allows them to report demographic and symptom information. Between January 1, 2020 and October 29, 2022, 1,325,845 participants took 20,617,435 temperature readings, yielding 3,529,377 episodes of consecutive readings. There were 1,735,805 (49.2%) episodes with self-reported symptoms (including reports of no symptoms). Compared to before the pandemic, participants were more likely to report their symptoms during pandemic waves, especially after the winter wave began (September 13, 2020) (OR across pandemic periods range from 3.0 to 4.0). Further, symptoms were more likely to be reported during febrile episodes (OR = 2.6, 95% CI = 2.6-2.6), and for new participants, during their first episode (OR = 2.4, 95% CI = 2.4-2.5). Compared with participants aged 50-65 years old, participants over 65 years were less likely to report their symptoms (OR = 0.3, 95% CI = 0.3-0.3). Participants in a household with both adults and children (OR = 1.6 [1.6-1.7]) were more likely to report symptoms. We find that the use of smart thermometers with companion apps facilitates the collection of data on a large, national scale, and provides real time insight into transmissible disease phenomena. Nearly half of individuals using these devices are willing to report their symptoms after taking their temperature, although participation varies among individuals and over pandemic stages.

Smart Thermometer-Based Participatory Surveillance to Discern the Role of Children in Household Viral Transmission During the COVID-19 Pandemic.

Importance: Children's role in spreading virus during the COVID-19 pandemic is yet to be elucidated, and measuring household transmission traditionally requires contact tracing. Objective: To discern children's role in household viral transmission during the pandemic when enveloped viruses were at historic lows and the predominance of viral illnesses were attributed to COVID-19. Design, Setting, and Participants: This cohort study of a voluntary US cohort tracked data from participatory surveillance using commercially available thermometers with a companion smartphone app from October 2019 to October 2022. Eligible participants were individuals with temperature measurements in households with multiple members between October 2019 and October 2022 who opted into data sharing. Main Outcomes and Measures: Proportion of household transmissions with a pediatric index case and changes in transmissions during school breaks were assessed using app and thermometer data. Results: A total of 862 577 individuals from 320 073 households with multiple participants (462 000 female [53.6%] and 463 368 adults [53.7%]) were included. The number of febrile episodes forecast new COVID-19 cases. Within-household transmission was inferred in 54 506 (15.4%) febrile episodes and increased from the fourth pandemic period, March to July 2021 (3263 of 32 294 [10.1%]) to the Omicron BA.1/BA.2 wave (16 516 of 94 316 [17.5%]; P < .001). Among 38 787 transmissions in 166 170 households with adults and children, a median (IQR) 70.4% (61.4%-77.6%) had a pediatric index case; proportions fluctuated weekly from 36.9% to 84.6%. A pediatric index case was 0.6 to 0.8 times less frequent during typical school breaks. The winter break decrease was from 68.4% (95% CI, 57.1%-77.8%) to 41.7% (95% CI, 34.3%-49.5%) at the end of 2020 (P < .001). At the beginning of 2022, it dropped from 80.3% (95% CI, 75.1%-84.6%) to 54.5% (95% CI, 51.3%-57.7%) (P < .001). During summer breaks, rates dropped from 81.4% (95% CI, 74.0%-87.1%) to 62.5% (95% CI, 56.3%-68.3%) by August 2021 (P = .02) and from 83.8% (95% CI, 79.2%-87.5) to 62.8% (95% CI, 57.1%-68.1%) by July 2022 (P < .001). These patterns persisted over 2 school years. Conclusions and Relevance: In this cohort study using participatory surveillance to measure within-household transmission at a national scale, we discerned an important role for children in the spread of viral infection within households during the COVID-19 pandemic, heightened when schools were in session, supporting a role for school attendance in COVID-19 spread.

Using time-course as an essential factor to accurately predict sepsis-associated mortality among patients with suspected sepsis.

BACKGROUND: Biomarker dynamics in different time-courses might be the primary reason why a static measurement of a single biomarker cannot accurately predict sepsis outcomes. Therefore, we conducted this prospective hospital-based cohort study to simultaneously evaluate the performance of several conventional and novel biomarkers of sepsis in predicting sepsis-associated mortality on different days of illness among patients with suspected sepsis. METHODS: We evaluated the performance of 15 novel biomarkers including angiopoietin-2, pentraxin 3, sTREM-1, ICAM-1, VCAM-1, sCD14 and 163, E-selectin, P-selectin, TNF-alpha, interferon-gamma, CD64, IL-6, 8, and 10, along with few conventional markers for predicting sepsis-associated mortality. Patients were grouped into quartiles according to the number of days since symptom onset. Receiver operating characteristic curve (ROC) analysis was used to evaluate the biomarker performance. RESULTS: From 2014 to 2017, 1,483 patients were enrolled, of which 78% fulfilled the systemic inflammatory response syndrome criteria, 62% fulfilled the sepsis-3 criteria, 32% had septic shock, and 3.3% developed sepsis-associated mortality. IL-6, pentraxin 3, sCD163, and the blood gas profile demonstrated better performance in the early days of illness, both before and after adjusting for potential confounders (adjusted area under ROC curve [AUROC]:0.81-0.88). Notably, the Sequential Organ Failure Assessment (SOFA) score was relatively consistent throughout the course of illness (adjusted AUROC:0.70-0.91). CONCLUSION: IL-6, pentraxin 3, sCD163, and the blood gas profile showed excellent predictive accuracy in the early days of illness. The SOFA score was consistently predictive of sepsis-associated mortality throughout the course of illness, with an acceptable performance.

Applying symptom dynamics to accurately predict influenza virus infection: An international multicenter influenza-like illness surveillance study.

BACKGROUND: Public health organizations have recommended various definitions of influenza-like illnesses under the assumption that the symptoms do not change during influenza virus infection. To explore the relationship between symptoms and influenza over time, we analyzed a dataset from an international multicenter prospective emergency department (ED)-based influenza-like illness cohort study. METHODS: We recruited patients in the US and Taiwan between 2015 and 2020 with: (1) flu-like symptoms (fever and cough, headache, or sore throat), (2) absence of any of the respiratory infection symptoms, or (3) positive laboratory test results for influenza from the current ED visit. We evaluated the association between the symptoms and influenza virus infection on different days of illness. The association was evaluated among different subgroups, including different study countries, influenza subtypes, and only patients with influenza. RESULTS: Among the 2471 recruited patients, 45.7% tested positive for influenza virus. Cough was the most predictive symptom throughout the week (odds ratios [OR]: 7.08-11.15). In general, all symptoms were more predictive during the first 2 days (OR: 1.55-10.28). Upper respiratory symptoms, such as sore throat and productive cough, and general symptoms, such as body ache and fatigue, were more predictive in the first half of the week (OR: 1.51-3.25). Lower respiratory symptoms, such as shortness of breath and wheezing, were more predictive in the second half of the week (OR: 1.52-2.52). Similar trends were observed for most symptoms in the different subgroups. CONCLUSIONS: The time course is an important factor to be considered when evaluating the symptoms of influenza virus infection.