Bivariate Mixed Model Research Articles

Diagnosing osteomyelitis in diabetic foot by diffusion-weighted imaging and dynamic contrast material-enhanced magnetic resonance imaging: a systematic review and meta-analysis

To evaluate the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast enhanced (DCE), for diagnosing osteomyelitis in the diabetic foot. A thorough search was carried out to identify suitable studies published up to September 2023. The quality of the studies involved was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The diagnostic sensitivity and specificity of each imaging modality/method for each specific cut point were summarized. The summary receiver operating characteristic (SROC) curve was calculated using bivariate mixed effects models. Five studies investigating 187 patients and 234 bone lesions with 110 diagnosed osteomyelitis were enrolled. Four studies used DWI (172 lesions), three studies used DCE techniques (140 lesions) and two studies presented results of conventional MRI (66 lesions). The sensitivity ranges using conventional MRI, DWI and DCE were 65%-100%, 65%-100% and 64%-100%, respectively. The specificity ranges were 50%-61%, 56%-95%, and 66%-93%, respectively. The SROC curve of DWI and DCE was 0.89 (95% CI, 0.86-0.92) and 0.90 (95% CI, 0.87-0.92), respectively. Combining DWI and DCE methods, alongside conventional MRI, can improve the reliability and accuracy of diabetic foot osteomyelitis diagnosis. However, the study recognizes result variability due to varying protocols and emphasizes the need for well-designed studies with standardized approaches. To optimize diagnostic performance, the study recommends considering low ADC values, Ktrans or rapid wash-in rate from DCE such as iAUC60, along with using large ROIs that cover the entire lesion while excluding normal bone marrow.

Diagnostic performance of ADC and ADCratio in MRI-based prostate cancer assessment: Asystematic review and meta-analysis.

To identify factors influencing the diagnostic performance of the quantitative imaging biomarkers ADC and ADCratio in prostate cancer (PCa) detection. A systematic literature search was conducted in Embase, Medline and Web of Science, for studies evaluating ADC values and ADCratio for PCa diagnosis, using the same patient cohorts and using histopathological references as ground truth. Pooled sensitivities, specificities, summary ROC curves and AUCs were calculated from constructed contingency data tables. Diagnostic performance (AUC) was quantitatively pooled using a bivariate mixed effects model. For identifying influencing factors, subgroup analysis, publication bias and heterogeneity assessment were investigated. Thirteen studies, involving 1038 patients and 1441 lesions, were included. For ADC, the pooled sensitivity and specificity was 80% (95% CI: 74-85%) and 78% (95% CI: 70-85%), respectively. For ADCratio pooled sensitivity and specificity was 80% (95% CI: 74-84%) and 80% (95% CI: 71-87%). Summary ROC analysis revealed AUCs of 0.86 (95% CI: 0.83-0.89) and 0.86 (95% CI: 0.83-0.89), respectively. Meta-regression showed heterogeneity between both imaging biomarkers. Subgroup analysis showed that ADCratio improved diagnostic performance in comparison to ADC when including both peripheral and transitional zone lesions (AUC: 0.87 [95% CI: 0.84-0.90] and 0.82 [95% CI: 0.79-0.85], respectively). Both ADC and ADCratio imaging biomarkers showed good and comparable diagnostic performance in PCa diagnosis. However, ADCratio shows better diagnostic performance than ADC in diagnosing transition zone cancers. In quantitative MRI-based PCa diagnosis, the imaging biomarker ADCratio is useful in challenging MRI readings of lesions. Understanding the performance of quantitative imaging biomarkers better can aid diagnostic MRI protocols, enhancing the precision of PCa assessments. MRI diffusion-weighted imaging-based ADC and ADCratio have comparable diagnostic performance in PCa assessment. In contrast to ADC, the ADCratio improves diagnostic performance, when assessing whole gland lesions. Compared to ADCratio, the ADC demonstrates enhanced diagnostic performance when evaluating peripheral zone lesions.

The effects of psychotherapy for anhedonia on subcortical brain volumes measured with ultra-high field MRI

BackgroundAnhedonia is a transdiagnostic symptom often resistant to treatment. The identification of biomarkers sensitive to anhedonia treatment will aid in the evaluation of novel anhedonia interventions. MethodsThis is an exploratory analysis of changes in subcortical brain volumes accompanying psychotherapy in a transdiagnostic anhedonic sample using ultra-high field (7-Tesla) MRI. Outpatients with clinically impairing anhedonia (n = 116) received Behavioral Activation Treatment for Anhedonia, a novel psychotherapy, or Mindfulness-Based Cognitive Therapy (ClinicalTrials.gov Identifiers NCT02874534 and NCT04036136). Subcortical brain volumes were estimated via the MultisegPipeline, and regions of interest were the amygdala, caudate nucleus, hippocampus, pallidum, putamen, and thalamus. Bivariate mixed effects models estimated pre-treatment relations between anhedonia severity and subcortical brain volumes, change over time in subcortical brain volumes, and associations between changes in subcortical brain volumes and changes in anhedonia symptoms. ResultsAs reported previously (Cernasov et al., 2023), both forms of psychotherapy resulted in equivalent and significant reductions in anhedonia symptoms. Pre-treatment anhedonia severity and subcortical brain volumes were not related. No changes in subcortical brain volumes were observed over the course of treatment. Additionally, no relations were observed between changes in subcortical brain volumes and changes in anhedonia severity over the course of treatment. LimitationsThis trial included a modest sample size and did not have a waitlist-control condition or a non-anhedonic comparison group. ConclusionsIn this exploratory analysis, psychotherapy for anhedonia was not accompanied by changes in subcortical brain volumes, suggesting that subcortical brain volumes may not be a candidate biomarker sensitive to response to psychotherapy.

The efficacy of modified contrast-enhanced ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) using Sonazoid in diagnosis of hepatocellular carcinoma: a systematic review and meta-analysis.

The contrast-enhanced ultrasound Liver Imaging Reporting and Data System (CEUS LI-RADS) is an algorithm for the diagnosis of hepatocellular carcinoma (HCC) in high-risk populations. Previous studies have shown the algorithm to have high specificity and moderate sensitivity. Nevertheless, it is designated for utilization solely with blood pool contrast agents. Sonazoid, a contrast agent that combines blood pools and Kupffer cells properties, has recently gained approval for marketing in an increased number of countries. Enhanced sensitivity in diagnosing HCC may be achieved through the distinctive Kupffer phase (KP) exhibited by Sonazoid. Certain academics have suggested the modified CEUS LI-RADS using Sonazoid. The main criteria of mild and late (≥60 seconds) washout in CEUS LI-RADS LR-5 were replaced by KP (>10 minutes) defects as the primary criteria. The purpose of this research was to evaluate the effectiveness of the modified CEUS LI-RADS using Sonazoid in diagnosing HCC. Original studies on Sonazoid and CEUS LI-RADS were searched in the PubMed, Embase, Cochrane Library, and Web of Science databases until 13 July 2023, with no restrictions on language. We enrolled studies that applied Sonazoid for CEUS in patients at high risk of HCC and modified CEUS LI-RADS for the diagnosis of intrahepatic nodules. Meta-analyses, evaluations, case studies, correspondences, remarks, and summaries of conferences were excluded. Additionally, studies that fell outside the scope of this study and contained data on the same patients were also excluded. We evaluated the quality of research by employing the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A bivariate mixed effects model was utilized to conduct a meta-analysis, summarizing the sensitivity and specificity in the diagnosis of HCC. The investigation of potential factors contributing to study heterogeneity was conducted using meta-regression analysis. Out of the 103 studies screened, 6 studies (835 lesions) were included in the final results. Modified CEUS LR-5 exhibited a sensitivity of 0.77 [95% confidence interval (CI): 0.70-0.82; I2=71.98%; P=0.00] and a specificity of 0.88 (95% CI: 0.83-0.92; I2=0.00; P=0.47) for HCC diagnosis, with heterogeneity in sensitivity. The presence of heterogeneity in the study was found to have a significant association with factors such as the study design, the number of image reviewers, the proportion of cirrhosis, the proportion of other non-HCC malignancies (OM) cases, and the type of reference standard (P≤0.05). The modified CEUS LI-RADS LR-5 categorization demonstrates a reasonable level of sensitivity 0.77, but an insufficient level of specificity 0.88 when diagnosing HCC. KP defects cannot be used as a primary feature in the diagnosis of HCC by CEUS LI-RADS, perhaps as an ancillary feature.

Characterization of anti-drug antibody dynamics using a bivariate mixed hidden-markov model by nonlinear-mixed effects approach

Biological therapies may act as immunogenic triggers leading to the formation of anti-drug antibodies (ADAs). Population pharmacokinetic (PK) models can be used to characterize the relationship between ADA and drug disposition but often rely on the ADA bioassay results, which may not be sufficiently sensitive to inform on this characterization.In this work, a methodology that could help to further elucidate the underlying ADA production and impact on the drug disposition was explored. A mixed hidden-Markov model (MHMM) was developed to characterize the underlying (hidden) formation of ADA against the biologic, using certolizumab pegol (CZP), as a test drug. CZP is a PEGylated Fc free TNF-inhibitor used in the treatment of rheumatoid arthritis and other chronic inflammatory diseases.The bivariate MHMM used information from plasma drug concentrations and ADA measurements, from six clinical studies (n = 845), that were correlated through a bivariate Gaussian function to infer about two hidden states; production and no-production of ADA influencing PK. Estimation of inter-individual variability was not supported in this case. Parameters associated with the observed part of the model were reasonably well estimated while parameters associated with the hidden part were less precise. Individual state sequences obtained using a Viterbi algorithm suggested that the model was able to determine the start of ADA production for each individual, being a more assay-independent methodology than traditional population PK. The model serves as a basis for identification of covariates influencing the ADA formation, and thus has the potential to identify aspects that minimize its impact on PK and/or efficacy.

Which cut-off value of the Montreal Cognitive Assessment should be used for post-stroke cognitive impairment? A systematic review and meta-analysis of studies on diagnostic test accuracy.

Post-stroke cognitive impairment (PSCI) is one of the serious complications of stroke. The Montreal Cognitive Assessment (MoCA), as a brief cognitive impairment screening tool, is widely used in stroke survivors. However, some studies have suggested that the use of the universal cut-off value of 26 may be inappropriate for detecting cognitive impairments in stroke settings. We conducted this study to identify the optimal cut-off value of the MoCA in screening for PSCI. PubMed, CINAHL, Embase, the Cochrane Library, and Web of Science were searched for eligible studies until March 23, 2023. All studies were screened by two independent researchers. The quality of each article was evaluated by the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate mixed effects model was used to pool sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the summary receiver operating characteristic curve. 24 studies with a total of 4,231 patients were included in this review. Despite the lack of evidence of publication bias, a high degree of heterogeneity was observed. Meta-analysis revealed that a cut-off value of 21/22 yielded the best diagnostic accuracy. The optimal cut-off varied in different regions, stroke types, and stroke phases as well. The optimal cut-off of MoCA was 21/22 for stroke populations rather than the initially recommended cut-off of 26. A revised (lower) cut-off should be considered for stroke survivors.

The diagnostic value of electrocardiogram-based machine learning in long QT syndrome: a systematic review and meta-analysis.

To perform a meta-analysis to discover the performance of ML algorithms in identifying Congenital long QT syndrome (LQTS). The searched databases included Cochrane, EMBASE, Web of Science, and PubMed. Our study considered all English-language studies that reported the detection of LQTS using ML algorithms. Quality was assessed using QUADAS-2 and QUADAS-AI tools. The bivariate mixed effects models were used in our study. Based on genotype data for LQTS, we performed a subgroup analysis. Out of 536 studies, 8 met all inclusion criteria. The pooled area under the receiving operating curve (SAUROC) for detecting LQTS was 0.95 (95% CI: 0.31-1.00); sensitivity was 0.87 (95% CI: 0.83-0.90), and specificity was 0.91 (95% CI: 0.88-0.93). Additionally, diagnostic odd ratio (DOR) was 65 (95% CI: 39-109). The positive likelihood ratio (PLR) was 9.3 (95% CI: 7.0-12.3) and the negative likelihood ratio (NLR) was 0.14 (95% CI: 0.11-0.20), with very low heterogeneity (I2 = 16%). We found that machine learning can be used to detect features of rare cardiovascular disease like LQTS, thus increasing our understanding of intelligent interpretation of ECG. To improve ML performance in the classification of LQTS subtypes, further research is required. identifier PROSPERO CRD42022360122.

Protocol for a systematic review and meta-analysis of the diagnostic test accuracy of host and HPV DNA methylation in cervical cancer screening and management

IntroductionHuman papillomavirus (HPV) is necessary but not sufficient for cervical cancer development. During cervical carcinogenesis, methylation levels increase across host and HPV DNA. DNA methylation has been proposed as a...

Heritability and Genetic Parameters for Semen Traits in Australian Sheep.

Semen characteristics including volume, gross motility, spermatozoal concentration, and percent post-thaw motility are routinely assessed to determine the quality and quantity of an ejaculate prior to use in artificial breeding programs. Currently, artificial breeding programs in sheep place relatively little emphasis on ram-side factors, such as the fertilising potential of an ejaculate, which may contribute to variability in conception outcomes. Estimating genetic parameters for ejaculate quality and quantity traits could provide insights into whether selective breeding can be used to improve such ram-side traits, improving ovine reproductive performance and farm profitability. Therefore, in this study, a total of 11,470 ejaculate records, including data for ejaculate volume, gross motility, spermatozoal concentration, and percent post-thaw motility, collected over a 20-year period was used to estimate genetic parameters in sheep. Univariate and bivariate mixed model analysis was performed including a variety of fixed effects such as breed, age at collection, centre of collection, collection number, season of collection, and method of collection; and the permanent environmental effects associated with each ram, stud and year of collection, and the breeding value of rams included as random effects. The heritability for ejaculate volume, gross motility, concentration, and percent post-thaw motility was estimated to be 0.161, 0.170, 0.089, and 0.081. Repeatability estimates were moderate, ranging between 0.4126 and 0.5265. Overall, results indicate that semen traits are lowly heritable and moderately repeatable, indicating that these traits are significantly influenced by environmental variables.

Exploring the eligibility of all reported lipoarabinomannan-testing assays in different clinical situations: a systematic review and meta-analysis of 97 articles

ObjectivesHow to choose proper lipoarabinomannan-testing assays for diagnosing tuberculosis (TB) in different populations baffles clinicians. This work assessed all reported lipoarabinomannan assays’ performance and aimed to identify the eligibility of each assay and offer guidance for clinicians. MethodsWe searched PubMed, Embase, and Web of Science until August 23, 2020. The risk of bias was evaluated by QADAS-2. Heterogeneity was evaluated by the Cochran Q test and I2. Sensitivity and specificity were pooled by a bivariate mixed model (register number: CRD42021270506). ResultsA total of 97 articles, covering 144 trials, 16 assays, 45,679 participants, and eight sample types, were divided into five groups. Electrochemiluminescence (ECL) had a sensitivity of 65%, specificity of 92%, and an area under curve (AUC) of 0.85 in diagnosing pulmonary TB in adults. ECL showed a promising diagnostic ability (sensitivity: 78%; specificity: 88%; AUC: 0.88) in patients with HIV, especially for urine detection (sensitivity: 90%; specificity: 89%; AUC: 0.95). The enzyme-linked immune assay showed a preference for diagnosing TB in Asians and Africans, especially in Africans who were smear-positive (sensitivity: 80%; specificity: 88%; AUC: 0.91). ConclusionECL was recommended for diagnosing pulmonary TB in adults, especially for TB/HIV co-infection. Taking urine as a sample further enhanced ECL's diagnostic performance. Enzyme-linked immune assay was recommended as an additional TB-related detection for smear-positive Africans.

Spatiotemporal variation and covariation of heartwood color in planted teak wood from Ghana

Heartwood color is a complex trait that affects the economic and aesthetic value of the wood but is highly variable. How the color of the heartwood varies spatially and temporally is poorly understood. To illustrate how heartwood color varies within a tree, two opposite aspects of wood within the same tree, representing differential growth rate, were used to model the long-short axis system jointly. The color of the heartwood on the long and the short axis was considered to be two different traits. By jointly modeling the long and short axes, the correlation was examined between aspect (spatial) and contemporaneous correlations (within aspect). Spatial and temporal correlations and their interactions describe the indirect physiological, genetic, and environmental changes in wood formation with time and position in the trunk. Spatial correlations were consistently lower than temporal correlations but were positive and significant. Between the heartwood color parameters, b* showed a relatively higher spatial correlation. The results suggest that there is a spatial correlation in the long-short axis for all color parameters and in the two surfaces. Variations between aspects were not statistically significant for any color parameter. The bivariate mixed model method revealed hidden physics behind heartwood color formation. Models need to be developed to account for both spatial and temporal dependence in studies of wood property change.

The effect of the participatory heat education and awareness tools (HEAT) intervention on agricultural worker physiological heat strain: results from a parallel, comparison, group randomized study

BackgroundFarmworkers are at risk of heat-related illness (HRI). We sought to: 1) evaluate the effectiveness of farmworker Spanish/English participatory heat education and a supervisor decision-support mobile application (HEAT intervention) on physiological heat strain; and 2) describe factors associated with HRI symptoms reporting.MethodsWe conducted a parallel, comparison group intervention study from May–September of 2019 in Central/Eastern Washington State, USA. We used convenience sampling to recruit adult outdoor farmworkers and allocated participating crews to intervention (n = 37 participants) and alternative-training comparison (n = 38 participants) groups. We measured heat strain monthly using heart rate and estimated core body temperature to compute the maximum work-shift physiological strain index (PSImax) and assessed self-reported HRI symptoms using a weekly survey. Multivariable linear mixed effects models were used to assess associations of the HEAT intervention with PSImax, and bivariate mixed models were used to describe factors associated with HRI symptoms reported (0, 1, 2+ symptoms), with random effects for workers.ResultsWe observed larger decreases in PSImax in the intervention versus comparison group for higher work exertion levels (categorized as low, low/medium-low, and high effort), after adjustment for maximum work-shift ambient Heat Index (HImax), but this was not statistically significant (interaction − 0.91 for high versus low/medium-low effort, t = − 1.60, p = 0.11). We observed a higher PSImax with high versus low/medium-low effort (main effect 1.96, t = 3.81, p < 0.001) and a lower PSImax with older age (− 0.03, t = − 2.95, p = 0.004), after covariate adjustment. There was no clear relationship between PSImax and the number of HRI symptoms reported. Reporting more symptoms was associated with older age, higher HImax, 10+ years agricultural work, not being an H-2A guest worker, and walking > 3 min to get to the toilet at work.ConclusionsEffort level should be addressed in heat management plans, for example through work/rest cycles, rotation, and pacing, in addition to education and other factors that influence heat stress. Both symptoms and indicators of physiological heat strain should be monitored, if possible, during periods of high heat stress to increase the sensitivity of early HRI detection and prevention. Structural barriers to HRI prevention must also be addressed.Trial registrationClinicalTrials.gov Registration Number: NCT04234802, date first posted 21/01/2020.

Diagnostic and Prognostic Roles of Thrombospondin-2 in Digestive System Cancers.

Background Cancers of digestive system have high case-fatality rate. It is important to find more appropriate methods in diagnosing and predicting gastrointestinal malignances. And thrombospondin-2 (TSP-2) was reported to have the functions, although results were not identical. So we performed this meta-analysis to clarify the significance of TSP-2 in this area. Methods PubMed, Embase, Web of Science, Cochrane Library, and Clinicaltrial.gov were searched for relevant studies. Data were extracted from these involved records. For the meta-analysis of diagnostic test, bivariate mixed effect model was used to estimate diagnostic accuracy. For prognosis part, HRs and their 95% CIs were pooled to compare the overall survival (OS) and disease-free survival (DFS) between patients with high TSP-2 and low TSP-2. Results Nine records were eligible for the analysis of diagnostic test. Pooled results were as follows: sensitivity 0.60 (0.52, 0.68), specificity 0.96 (0.91, 0.98), positive likelihood ratio (PLR) 15.4 (7.3, 32.2), negative likelihood ratio (NLR) 0.42 (0.34, 0.50), and diagnostic odds ratio (DOR) 37 (18, 76). While in prognosis part, 10 articles were included. Patients with increased TSP-2 had shorter OS (HR = 1.64, 95% CI = 1.21-2.22); however, no difference was found in DFS between TSP-2 high and low groups (HR = 1.44, 95% CI = 0.28-7.33). Conclusions TSP-2, as a diagnostic marker, has a high specificity but a moderate sensitivity. Meanwhile, it plays a role in predicting OS. Therefore, making TSP-2 a routine assay could be beneficial to high-risk individuals and patients with digestive malignances.

Diagnostic accuracy of DNA-based SDC2 methylation test in colorectal cancer screening: a meta-analysis

BackgroundA growing body of research suggests that methylated genes can be used as early diagnostic markers for cancer. Some studies on methylated Syndecan 2 (SDC2) have shown that it has a great diagnostic ability in colorectal cancer. This meta-analysis was aimed to estimate the diagnostic performance of methylated SDC2 as a potential novel biomarker to screen for the colorectal cancer.MethodsTwo independent researchers conducted a comprehensive literature search to identify all relevant studies on SDC2 methylation for the diagnosis of colorectal cancer from inception to March 1, 2021. By using STATA and Revman software, the data were analyzed using a Bivariate mixed model. The quality of each study was also evaluated.ResultsA total of 12 studies comprised of 1574 colorectal cancer patients and 1945 healthy people were included in our meta-analysis. Bivariate analysis showed a pooled sensitivity of 0.81 [95% confidence interval (CI) 0.74–0.86], specificity of 0.95 (95% CI 0.93–0.96), positive likelihood ratio of 15.29 (95% CI 10.83–21.60), and negative likelihood ratio of 0.21 (95% CI 0.15–0.27). The diagnostic odds ratio and the area under the summary ROC curve for diagnosing colorectal cancer were 74.42 (95% CI45.44–121.89) and 0.96 (95% CI 0.94–0.97), respectively. For adenomas, the pooled sensitivity and specificity were 0.47 (95% CI 0.34–0.61) and 0.95 (95% CI 0.92–0.97), respectively.ConclusionsOur analysis revealed that methylated SDC2 could be considered as a potential novel biomarker to screen for colorectal cancer.

EM Estimation for the Bivariate Mixed Exponential Regression Model

In this paper, we present a new family of bivariate mixed exponential regression models for taking into account the positive correlation between the cost of claims from motor third party liability bodily injury and property damage in a versatile manner. Furthermore, we demonstrate how maximum likelihood estimation of the model parameters can be achieved via a novel Expectation-Maximization algorithm. The implementation of two members of this family, namely the bivariate Pareto or, Exponential-Inverse Gamma, and bivariate Exponential-Inverse Gaussian regression models is illustrated by a real data application which involves fitting motor insurance data from a European motor insurance company.

Meta-analysis of downregulated E-cadherin as a diagnostic biomarker for cervical cancer.

Downregulation of E-cadherin function or expression has been implicated in the progression of cervical cancer. This meta-analysis of updated publications was performed to assess the association of expression alteration of E-cadherin with disease severity and then to determine the diagnostic accuracy of E-cadherin in discriminating cervical lesions including cervical intraepithelial neoplasia (CIN) grade 1 (CIN1), CIN grade 2 (CIN2), CIN grade 3 (CIN3), and cervical cancer. The articles published from inception to January 2021 were searched in PubMed, EBSCO, CNKI, and WanFang Database and then evaluated according to the criteria of meta-analysis. The eligible studies were retrieved and further analyzed. A bivariate mixed effects binary regression model was applied to determine pooled effect estimates. 16 studies with 2436 subjects from 7 countries were eligible for this meta-analysis. When compared with CIN1 control, the pooled odds ratios (ORs) with 95% confidence interval (CI) for the association of E-cadherin positivity with CIN2, CIN3, and cervical cancer were 0.34 (95% CI 0.23-0.51), 0.23 (95% CI 0.10-0.54), and 0.10 (95% CI 0.07-0.14), respectively. The pooled sensitivity and specificity for CIN3 or worse were 0.60 (95% CI 0.48-0.70) and 0.82 (95% CI 0.73-0.88) respectively, with the AUC of 0.78 (95% CI 0.74-0.82). Similar performance was found in CIN2 or worse. These findings demonstrated that the loss of E-cadherin protein was associated with worsened cervical lesions. E-cadherin might serve as a promising diagnostic biomarker to facilitate the discrimination of precancerous and cancerous lesions.

Total energy expenditure is repeatable in adults but not associated with short-term changes in body composition

Low total energy expenditure (TEE, MJ/d) has been a hypothesized risk factor for weight gain, but repeatability of TEE, a critical variable in longitudinal studies of energy balance, is understudied. We examine repeated doubly labeled water (DLW) measurements of TEE in 348 adults and 47 children from the IAEA DLW Database (mean ± SD time interval: 1.9 ± 2.9 y) to assess repeatability of TEE, and to examine if TEE adjusted for age, sex, fat-free mass, and fat mass is associated with changes in weight or body composition. Here, we report that repeatability of TEE is high for adults, but not children. Bivariate Bayesian mixed models show no among or within-individual correlation between body composition (fat mass or percentage) and unadjusted TEE in adults. For adults aged 20–60 y (N = 267; time interval: 7.4 ± 12.2 weeks), increases in adjusted TEE are associated with weight gain but not with changes in body composition; results are similar for subjects with intervals >4 weeks (N = 53; 29.1 ± 12.8 weeks). This suggests low TEE is not a risk factor for, and high TEE is not protective against, weight or body fat gain over the time intervals tested.

1214. Household Transmission of Febrile Illness Measured by Smartphone-Connected Thermometers, United States, 2016-2021

BackgroundUnderstanding household transmission dynamics of infectious diseases can help develop mitigation strategies. Traditional methods of population-level disease surveillance do not capture household transmission. Data collected from smartphone-connected thermometers that can differentiate among individuals in a household can be used to study these characteristics. Using this technology, we estimated the household secondary attack rate (SAR) of febrile illness, assessed its correlation with CDC-reported influenza-like illness (ILI) and COVID-19 case incidence, and identified risk factors for secondary transmission.MethodsWe conducted a retrospective cohort study among 596,096 febrile illness index cases recorded from August 1, 2016 to January 20, 2021 in households with two or more individuals in all 50 states. Fevers were measured using the Kinsa Smart Thermometer and mobile device app. Secondary cases were defined as household members who recorded a fever 1–10 days after an index case. We calculated SAR prior to and during the COVID-19 pandemic within the study period, and assessed correlation to ILI and COVID-19 case incidence using Spearman’s rank correlation coefficient. Bivariate and multivariable mixed logistic regression models were used to identify risk factors for secondary transmission.ResultsSAR in the pre-COVID-19 period was 5.9% (95% CI: 5.8%–6.0%) during flu season (November to April), and 3.7% (95% CI: 3.6%–3.7%) in flu off-season, and weekly SAR was significantly correlated with ILI reported from CDC (ρ=0.84, p< 0.001). Secondary transmission was 40% more likely to occur in households where the index case’s initial temperature was ≥ 39.1°C. During the COVID-19 period, SAR was 3.3% (95% CI: 3.3%–3.4%), and daily SAR was significantly correlated with national daily COVID-19 incidence rates (ρ=0.86, p< 0.001). Households in census tracts with >50% essential workforce were 50% more likely to experience secondary transmission.ConclusionHousehold SAR was highly correlated with ILI and COVID-19 cases. Capturing household transmission of febrile illness through routine public health surveillance may identify risk factors for infectious disease transmission, allowing for more targeted interventions.Disclosures Danielle Bloch, MPH, Kinsa Health (Employee, Shareholder) John Zicker, MS, Kinsa Health (Employee, Shareholder) Hannah Somhegyi, PhD, Kinsa Health (Employee, Shareholder) Patrick Philips, n/a, Kinsa Health (Employee, Shareholder) Inder Singh, n/a, Kinsa Health (Board Member, Employee, Shareholder) Amy Daitch, PhD, Kinsa Health (Employee, Shareholder)

Volatile organic compounds as a potential screening tool for neoplasm of the digestive system: a meta-analysis

This meta-analysis was aimed to estimate the diagnostic performance of volatile organic compounds (VOCs) as a potential novel tool to screen for the neoplasm of the digestive system. An integrated literature search was performed by two independent investigators to identify all relevant studies investigating VOCs in diagnosing neoplasm of the digestive system from inception to 7th December 2020. STATA and Revman software were used for data analysis. The methodological quality of each study was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool. A bivariate mixed model was used and meta-regression and subgroup analysis were performed to identify possible sources of heterogeneity. A total of 36 studies comprised of 1712 cases of neoplasm and 3215 controls were included in our meta-analysis. Bivariate analysis showed a pooled sensitivity of 0.87 (95% confidence interval (CI) 0.83–0.90), specificity of 0.86 (95% CI 0.82–0.89), a positive likelihood ratio of 6.18 (95% CI 4.68–8.17), and a negative likelihood ratio of 0.15 (95% CI 0.12–0.20). The diagnostic odds ratio and the area under the summary ROC curve for diagnosing neoplasm of the digestive system were 40.61 (95% CI 24.77–66.57) and 0.93 (95% CI 0.90–0.95), respectively. Our analyses revealed that VOCs analysis could be considered as a potential novel tool to screen for malignant diseases of the digestive system.

Bivariate Mixed Poisson Regression Models with Varying Dispersion

The main purpose of this article is to present a new class of bivariate mixed Poisson regression models with varying dispersion that offers sufficient flexibility for accommodating overdispersion and accounting for the positive correlation between the number of claims from third-party liability bodily injury and property damage. Maximum likelihood estimation for this family of models is achieved through an expectation-maximization algorithm that is shown to have a satisfactory performance when three members of this family, namely, the bivariate negative binomial, bivariate Poisson–inverse Gaussian, and bivariate Poisson–Lognormal distributions with regression specifications on every parameter are fitted on two-dimensional motor insurance data from a European motor insurer. The a posteriori, or bonus-malus, premium rates that are determined by these models are calculated via the expected value and variance principles and are compared to those based only on the a posteriori criteria. Finally, we present an extension of the proposed approach with varying dispersion by developing a bivariate Normal copula-based mixed Poisson regression model with varying dispersion and dependence parameters. This approach allows us to consider the influence of individual and coverage-specific risk factors on the mean, dispersion, and copula parameters when modeling different types of claims from different types of coverage. For expository purposes, the Normal copula paired with negative binomial distributions for marginals and regressors on the mean, dispersion, and copula parameters is fitted on a simulated dataset via maximum likelihood.