Integrative analysis of an endoplasmic reticulum stress-related signature in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a malignancy in which plasma cells proliferate abnormally, and it remains incurable. The cells are characterized by high levels of endoplasmic reticulum stress (ERS) and depend on the ERS response for survival. Thus, we aim to find an ERS-related signature of MM and assess its diagnostic value. METHODS: We downloaded three datasets of MM from the Gene Expression Omnibus database. After identifying ERS-related differentially expressed genes (ERDEGs), we analyzed them using Gene Ontology enrichment analysis. A protein-protein interaction network, a transcription factor-mRNA network, a miRNA-mRNA network and a drug-mRNA network were constructed to explore the ERDEGs. The clinical application of these genes was identified by calculating the infiltration of immune cells and using receiver operating characteistic analyses. Finally, qPCR was performed to further confirm the roles of ERDEGs. RESULTS: We obtained nine ERDEGs of MM. Gene Ontology enrichment indicated that the ERDEGs played a role in the endoplasmic reticulum membrane. Additionally, the protein-protein interaction network showed interaction among the ERDEGs, and there were 20 proteins, 107 transcription factors, 42 drugs or molecular compounds and 51 miRNAs which were likely to interact with the nine genes. In addition, immune cell infiltration analyses showed that there was a strong correlation between the nine genes and immune cells, and these potential biomarkers exhibited good diagnostic values. Finally, the expression of ERDEGs in MM cells was different from that in healthy donor samples. CONCLUSION: The nine ERS-related genes, CR2, DHCR7, DNAJC3, KDELR2, LPL, OSBPL3, PINK1, VCAM1 and XBP1 are potential biomarkers of MM, and this supports further clinical development of the diagnosis and treatment of MM.

Two-phase stratified sampling and analysis for predicting binary outcomes.

The two-phase study design is a cost-efficient sampling strategy when certain data elements are expensive and, thus, can only be collected on a sub-sample of subjects. To date guidance on how best to allocate resources within the design has assumed that primary interest lies in estimating association parameters. When primary interest lies in the development and evaluation of a risk prediction tool, however, such guidance may, in fact, be detrimental. To resolve this, we propose a novel strategy for resource allocation based on oversampling cases and subjects who have more extreme risk estimates according to a preliminary model developed using fully observed predictors. Key to the proposed strategy is that it focuses on enhancing efficiency regarding estimation of measures of predictive accuracy, rather than on efficiency regarding association parameters which is the standard paradigm. Towards valid estimation and inference for accuracy measures using the resultant data, we extend an existing semiparametric maximum likelihood ethod for estimating odds ratio association parameters to accommodate the biased sampling scheme and data incompleteness. Motivated by our sampling design, we additionally propose a general post-stratification scheme for analyzing general two-phase data for estimating predictive accuracy measures. Through theoretical calculations and simulation studies, we show that the proposed sampling strategy and post-stratification scheme achieve the promised efficiency improvement. Finally, we apply the proposed methods to develop and evaluate a preliminary model for predicting the risk of hospital readmission after cardiac surgery using data from the Pennsylvania Health Care Cost Containment Council.

Identification and bioinformatic characterization of a serum miRNA signature for early detection of laryngeal squamous cell carcinoma.

BACKGROUND: The growing understanding of cancer biology and the establishment of new treatment modalities has not yielded the expected results in terms of survival for Laryngeal Squamous Cell Cancer (LSCC). Early diagnosis, as well as prompt identification of patients with high risk of relapse would ensure greater chance of therapeutic success. However, this goal remains a challenge due to the absence of specific biomarkers for this neoplasm. METHODS: Serum samples from 45 LSCC patients and 23 healthy donors were collected for miRNA expression profiling by TaqMan Array analysis. Additional 20 patients and 42 healthy volunteers were included for the validation set, reaching an equal number of clinical samples for each group. The potential diagnostic ability of the such identified three-miRNA signature was confirmed by ROC analysis. Moreover, each miRNA was analyzed for the possible correlation with HNSCC patients' survival and TNM status by online databases Kaplan-Meier (KM) plotter and OncomiR. In silico analysis of common candidate targets and their network relevance to predict shared biological functions was finally performed by PANTHER and GeneMANIA software. RESULTS: We characterized serum miRNA profile of LSCC patients identifying a novel molecular signature, including miR-223, miR-93 and miR-532, as circulating marker endowed with high selectivity and specificity. The oncogenic effect and the prognostic significance of each miRNA was investigated by bioinformatic analysis, denoting significant correlation with OS. To analyse the molecular basis underlying the pro-tumorigenic role of the signature, we focused on the simultaneously regulated gene targets-IL6ST, GTDC1, MAP1B, CPEB3, PRKACB, NFIB, PURB, ATP2B1, ZNF148, PSD3, TBC1D15, PURA, KLF12-found by prediction tools and deepened for their functional role by pathway enrichment analysis. The results showed the involvement of 7 different biological processes, among which inflammation, proliferation, migration, apoptosis and angiogenesis. CONCLUSIONS: In conclusion, we have identified a possible miRNA signature for early LSCC diagnosis and we assumed that miR-93, miR-223 and miR-532 could orchestrate the regulation of multiple cancer-related processes. These findings encourage the possibility to deepen the molecular mechanisms underlying their oncogenic role, for the desirable development of novel therapeutic opportunities based on the use of short single-stranded oligonucleotides acting as non-coding RNA antagonists in cancer.

Chemokine receptor CXCR4 based positron emission tomography imaging in systemic sclerosis-related interstitial lung disease.

OBJECTIVE: To assess chemokine receptor CXCR4 expression in lung parenchyma and on peripheral immune cells in systemic sclerosis-related interstitial lung disease (SSc-ILD) patients. METHODS: SSc-ILD patients underwent 68Ga- CPCR4 Trifluoroacetate positron emission tomography (PET) scan, SUVmean in different lung regions and architecturally abnormal areas, and receiver operating characteristic (ROC) curves were analyzed. CXCR4 expression on peripheral immune cells using flow cytometer was studied and correlated with the different lung regions. In addition, subset analysis of CXCR4 expression by clinical subset (early, progressive, stable), ILD pattern and anti-Scl-70 positivity were done. RESULTS: On PET, SSc-ILD patients showed higher median SUVmean uptake of CXCR4 in the whole lung (0.56; p< 0.0001), different lung regions and architecturally abnormal areas than controls. Highest area under curve (AUC) were observed in dorsobasal regions (AUC-0.91; p< 0.0001) and reticular with architecturally distorted areas (AUC-0.95; p< 0.0001). Progressive subset had higher whole lung median SUVmean (0.73) than early (0.49; p< 0.0001) and stable (0.45; p< 0.0001) subsets, and AUC than early and stable subsets. Usual interstitial pneumonia pattern ILD showed higher CXCR4 uptake compared with non-specific interstitial pneumonia (p= 0.0032). Additionally, a trend for higher uptake was noted in anti-Scl70 positive patients as compared with anti-Scl70 negative ones. SSc-ILD patients had higher CD4+CXCR4+T cells (p= 0.0003), and CD8+CXCR4+T cells (p= 0.0013), and showed moderate to strong association on correlation with the lung parenchymal regions. CONCLUSION: In SSc-ILD, CXCR4 expression is upregulated in both lung parenchyma and peripheral T cells, significantly in progressive and UIP subsets. CXCR4 expression is a potential tool for activity assessment and prognostication.

Association of serum trefoil factor 3 and leptin levels with obesity: A case-control study.

BACKGROUND: Obesity has a detrimental impact on individuals, communities, and healthcare systems. Trefoil factor 3 is a secretory protein involved in metabolic processes related to weight regulation. However, its relation with obesity is not fully understood. OBJECTIVE: We aimed to assess the serum trefoil factor 3 level and to immunohistochemical detect the leptin in obese patients to evaluate their relation to obesity pathogenesis. METHODS: As a case-control study, we enrolled 83 non-obese persons as a control group with a BMI (18.5-24.9) and 83 obese persons as a patient group with a BMI > 30. All the study volunteers are subjected to anthropometric measurements, glucose, and lipid profile analysis by colorimetric methods. Serum trefoil factor 3 level was estimated by ELISA and leptin hormone was detected immunohistochemically in the blood using cell block technique. RESULTS: ROC curve analysis for TFF3 showed a good relation with obesity with an AUC of 0.891 and a cut-off value of > 96 ng/ml. There was a significant positive correlation between TFF3 and fasting blood sugar, total cholesterol, and triglycerides. The logistic regression analysis showed that TFF3 is a good risk factor for obesity incidence [p = 0.008; OR = 1.117; (95 % CI): 1.029-1.213]. This was confirmed by multiple linear regression that gave an equation for the possibility of predicting BMI using several factors including TFF3 [BMI = 0.821 + 0.051 × TFF3 + 0.044 × FBS + 0.85 × TC]. The more surprising was the ability of the immunohistochemistry cell block technique to detect leptin antigens associated with an obese person blood not only adipose tissue or serum. CONCLUSION: Leptin hormone and TFF3 could be good indicators for obesity incidence. Further research with a larger sample size and in different populations could completely approve our results.

Investigating the expression level of miR-17-3p, miR-101-3p, miR-335-3p, and miR-296-3p in the peripheral blood of patients with acute myocardial infarction.

The role of inflammation has been proven in acute myocardial infarction (AMI) pathogenesis. Due to the effect of NLRP3 gene expression in the inflammation process of MI, we aimed to explore the expression changes and diagnostic power of four inflammation-related miRNAs including miR-17-3p, miR-101-3p, miR-335-3p, miR-296-3p and their potential target, NLRP3, in ST-segment elevation myocardial infarction (STEMI), and non-STEMI (NSTEMI) patients as two major classes of AMI. The expression level of these genes were evaluated in 300 participants equally divided into three groups of STEMI, NSTEMI, and control using quantitative real-time PCR. The expression level of NLRP3 was upregulated in STEMI and NSTEMI patients compared to control subjects. Besides, the expression levels of miR-17-3p, miR-101-3p, and miR-296-3p were significantly downregulated in STEMI and NSTEMI patients compared to controls. The increased expression of NLRP3 had a very strong inverse correlation with miR-17-3p in patients with STEMI and with miR-101-3p in the STEMI and NSTEMI patients. ROC curve analysis showed that the expression level of miR-17-3p had the highest diagnostic power for discrimination between STEMI patients and controls. Remarkably, the combination of all markers resulted in a higher AUC. In summary, there is a significant association between the expression levels of miR-17-3p, miR-101-3p, miR-335-3p, miR-296-3p, and NLRP3 and the incidence of AMI. Although the miR-17-3p expression level has the highest diagnostic power to distinguish between STEMI patients and control subjects, the combination of these miRNAs and NLRP3 could serve as a novel potential diagnostic biomarker of STEMI.

ISTH bleeding assessment tool and platelet function analyzer in children with mild inherited platelet function disorders.

OBJECTIVES: To evaluate the diagnostic performance of platelet function analyzer (PFA) and The International Society on Thrombosis and Hemostasis bleeding-assessment-tool (ISTH-BAT) in detecting mild inherited platelet function disorders (IPFDs) in children with suspected bleeding disorders. METHODS: Prospective single-center diagnostic study including consecutive patients <18 years with suspected bleeding disorder and performing a standardized workup for platelet function defects including ISTH-BAT, PFA, platelet aggregation testing, blood smear-based immunofluorescence, and next-generation sequencing-based genetic screening for IPFDs. RESULTS: We studied 97 patients, of which 34 von Willebrand disease (VWD, 22 type-1, 11 type-2), 29 IPFDs (including delta-/alpha-storage pool disease, Glanzmann thrombasthenia, Hermansky-Pudlak syndrome) and 34 with no diagnosis. In a model combining PFA-adenosine diphosphate (ADP), PFA-epinephrine (EPI), and ISTH-BAT overall performance to diagnose IPFDs was low with area under the curves of 0.56 (95% CI 0.44, 0.69) compared with 0.84 (95% CI 0.76, 0.92) for VWD. Correlation of PFA-EPI/-ADP and ISTH-BAT was low with 0.25/0.39 Spearman's correlation coefficients. PFA were significantly prolonged in patients with VWD and Glanzmann thrombasthenia. ISTH-BAT-scores were only positive in severe bleeding disorders, but not in children with mild IPFDs or VWD. CONCLUSION: Neither ISTH-BAT nor PFA or the combination of both help diagnosing mild IPFDs in children. PFA is suited to exclude severe IPFDs or VWD and is in this regard superior to ISTH-BAT in children.

Nonparametric receiver operating characteristic curve analysis with an imperfect gold standard.

This article addresses the challenge of estimating receiver operating characteristic (ROC) curves and the areas under these curves (AUC) in the context of an imperfect gold standard, a common issue in diagnostic accuracy studies. We delve into the nonparametric identification and estimation of ROC curves and AUCs when the reference standard for disease status is prone to error. Our approach hinges on the known or estimable accuracy of this imperfect reference standard and the conditional independent assumption, under which we demonstrate the identifiability of ROC curves and propose a nonparametric estimation method. In cases where the accuracy of the imperfect reference standard remains unknown, we establish that while ROC curves are unidentifiable, the sign of the difference between two AUCs is identifiable. This insight leads us to develop a hypothesis-testing method for assessing the relative superiority of AUCs. Compared to the existing methods, the proposed methods are nonparametric so that they do not rely on the parametric model assumptions. In addition, they are applicable to both the ROC/AUC analysis of continuous biomarkers and the AUC analysis of ordinal biomarkers. Our theoretical results and simulation studies validate the proposed methods, which we further illustrate through application in two real-world diagnostic studies.

Nonparametric estimation of linear personalized diagnostics rules via efficient grid algorithm.

Many diseases are heterogeneous, comprised of multiple disease subgroups. It is of great interest but highly unlikely to find a single biomarker that can accurately detect such heterogeneous diseases across different subgroups. In this article, we propose to estimate a personalized diagnostic rule (PDR) to tailor more effective biomarkers to each individual according to a linear combination of his or her profiles. A standard grid search algorithm can be used to estimate the optimal linear PDR that maximizes the area under the receiver operating characteristics curve (AUC) among all the linear PDRs, but it is time-consuming especially when the number of variables is large. Alternatively, we developed an efficient grid rotation algorithm that provides a nearly suboptimal solution and studied its variation to find the optimal solution. We implemented the cross-validated forward variable selection method to find a subset of useful variables while avoid overfitting. Extensive simulations show that our proposed method reduces bias and variance. Analysis of a gastric cancer biomarker study and censored survival outcome data illustrates the practical utility of our proposed method. The proposed method is implemented in the open-source R package persDx.

Likelihood ratio combination of multiple biomarkers via smoothing spline estimated densities.

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research.

Investigating the diagnostic potential of IL-1ß, IL-10, and IL-36γ in gingival crevicular fluid in patients with different periodontal conditions.

BACKGROUND: The study aimed to analyze cytokine levels, including interleukin (IL)-1ß, IL-10, and IL-36γ, to investigate the link between pro- and anti-inflammatory responses in periodontal conditions and assess their potential as diagnostic biomarkers for distinguishing between different types of periodontal conditions. METHODS: 80 systemically healthy non-smokers (25 periodontally healthy, 25 with gingivitis, 30 with periodontitis) were included. Clinical periodontal parameters were recorded, and gingival crevicular fluid (GCF) samples were obtained. Receiver operating characteristic (ROC) curve analysis was applied to determine the diagnostic value of cytokines. RESULTS: IL-36γ had the highest sensitivity for diagnosing periodontitis, although its specificity for identifying those without periodontitis was relatively low. The combination of IL-1ß and IL-36γ was the most effective in differentiating periodontitis from periodontal health. IL-10 was found to be an acceptable discriminator for distinguishing gingivitis from healthy conditions. However, its sensitivity and specificity for identifying gingivitis were lower. The combination of the three cytokines showed the highest ability to distinguish between periodontitis and gingivitis. CONCLUSION: The levels of IL-1ß, IL-10, and IL-36γ in GCF may provide insights into periodontal health and disease status. Further studies are needed to validate these results and explore the potential of these cytokines in periodontal disease management.

All three of these cytokines exhibit exceptional diagnostic accuracy, particularly in distinguishing between chronic periodontitis and periodontal health.Moreover, the combination of IL-1ß and IL-36γ stands out as the most accurate diagnostic indicator for periodontitis. This combination could serve as a robust biomarker panel for the early detection and monitoring of periodontal disease, potentially allowing for timely interventions to prevent disease progression.

Distributed non-disclosive validation of predictive models by a modified ROC-GLM.

BACKGROUND: Distributed statistical analyses provide a promising approach for privacy protection when analyzing data distributed over several databases. Instead of directly operating on data, the analyst receives anonymous summary statistics, which are combined into an aggregated result. Further, in discrimination model (prognosis, diagnosis, etc.) development, it is key to evaluate a trained model w.r.t. to its prognostic or predictive performance on new independent data. For binary classification, quantifying discrimination uses the receiver operating characteristics (ROC) and its area under the curve (AUC) as aggregation measure. We are interested to calculate both as well as basic indicators of calibration-in-the-large for a binary classification task using a distributed and privacy-preserving approach. METHODS: We employ DataSHIELD as the technology to carry out distributed analyses, and we use a newly developed algorithm to validate the prediction score by conducting distributed and privacy-preserving ROC analysis. Calibration curves are constructed from mean values over sites. The determination of ROC and its AUC is based on a generalized linear model (GLM) approximation of the true ROC curve, the ROC-GLM, as well as on ideas of differential privacy (DP). DP adds noise (quantified by the ℓ 2 sensitivity Δ 2 ( f ^ ) ) to the data and enables a global handling of placement numbers. The impact of DP parameters was studied by simulations. RESULTS: In our simulation scenario, the true and distributed AUC measures differ by Δ AUC < 0.01 depending heavily on the choice of the differential privacy parameters. It is recommended to check the accuracy of the distributed AUC estimator in specific simulation scenarios along with a reasonable choice of DP parameters. Here, the accuracy of the distributed AUC estimator may be impaired by too much artificial noise added from DP. CONCLUSIONS: The applicability of our algorithms depends on the ℓ 2 sensitivity Δ 2 ( f ^ ) of the underlying statistical/predictive model. The simulations carried out have shown that the approximation error is acceptable for the majority of simulated cases. For models with high Δ 2 ( f ^ ) , the privacy parameters must be set accordingly higher to ensure sufficient privacy protection, which affects the approximation error. This work shows that complex measures, as the AUC, are applicable for validation in distributed setups while preserving an individual's privacy.

Neurotrophin growth factors and their receptors as promising blood biomarkers for Alzheimer's Disease: a gene expression analysis study.

BACKGROUND: Alzheimer's disease (AD) is a multifaceted neurological ailment affecting more than 50 million individuals globally, distinguished by a deterioration in memory and cognitive abilities. Investigating neurotrophin growth factors could offer significant contributions to understanding AD progression and prospective therapeutic interventions. METHODS AND RESULTS: The present investigation collected blood samples from 50 patients diagnosed with AD and 50 healthy individuals serving as controls. The mRNA expression levels of neurotrophin growth factors and their receptors were measured using quantitative PCR. A Bayesian regression model was used in the research to assess the relationship between gene expression levels and demographic characteristics such as age and gender. The correlations between variables were analyzed using Spearman correlation coefficients, and the diagnostic potential was assessed using a Receiver Operating Characteristic curve. NTRK2, TrkA, TrkC, and BDNF expression levels were found to be considerably lower (p-value < 0.05) in the blood samples of AD patients compared to the control group. The expression of BDNF exhibited the most substantial decrease in comparison to other neurotrophin growth factors. Correlation analysis indicates a statistically significant positive association between the genes. The ROC analysis showed that BDNF exhibited the greatest Area Under the Curve (AUC) value of 0.76, accompanied by a sensitivity of 70% and specificity of 66%. TrkC, TrkA, and NTRK2 demonstrated considerable diagnostic potential in distinguishing between cases and controls. CONCLUSION: The observed decrease in the expression levels of NTRK2, TrkA, TrkC, and BDNF in AD patients, along with the identified associations between specific genes and their diagnostic capacity, indicate that these expressions have the potential to function as biomarkers for the diagnosis and treatment of AD.

Assessment of ID family proteins expression in colorectal cancer of Iraqi patients.

BACKGROUND: Colorectal cancer (CRC) is the second most deathly worldwide and third most common cancer, CRC is a very heterogeneous disease where tumors can form by both environmental and genetic risk factors and includes epigenetic and genetic alternations. Inhibitors of DNA binding proteins (ID) are a class of helix-loop-helix transcription regulatory factors; these proteins are considered a family of four highly preserved transcriptional regulators (ID1-4), shown to play significant roles in many processes that are associated with tumor development. ID family plays as negatively dominant antagonists of other essential HLH proteins, concluding the creation of non-functional heterodimers and regulation of the transcription process. MATERIALS AND METHODS: 120 Fresh tissue and blood samples Forty (40) samples of fresh tissue and blood were collected from patients diagnosed with CRC, twenty (20) samples were collected from a patient diagnosed as healthy. The (qRT-PCR) method is a sensitive technique for the quantifying of steady-state mRNA levels that used to evaluation the expression levels of ID (1-4) gene. RESULTS: The findings indicate downregulation in ID1 in tissue with a highly significant change between patients and control groups, where upregulation in the ID1 gene is shown in blood samples.ID2 gene also demonstrated high significant change where show upregulation in tissue and downregulation in blood sample. ID3 and ID4 genes show downregulation in tissue and blood samples with a significant change in ID3 blood samples between patient and blood groups. CONCLUSION: Because of the regulation function of the ID family in many processes, the up or down regulation of IDs genes in tumors Proves how important its tumor development, and therefore those proteins can be used as an indicator for CRC.

Development and validation of a lung metastases-predicting nomogram for intermediate- to high-risk differentiated thyroid carcinoma patients.

Aim: This research aimed to construct a clinical model for forecasting the likelihood of lung metastases in differentiated thyroid carcinoma (DTC) with intermediate- to high-risk.Methods: In this study, 375 DTC patients at intermediate to high risk were included. They were randomly divided into a training set (70%) and a validation set (30%). A nomogram was created using the training group and then validated in the validation set using calibration, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve.Results: The calibration curves demonstrated excellent consistency between the predicted and the actual probability. ROC analysis showed that the area under the curve in the training cohort was 0.865 and 0.845 in the validation cohort. Also, the DCA curve indicated that this nomogram had good clinical utility.Conclusion: A user-friendly nomogram was constructed to predict the lung metastases probability with a high net benefit.

[Box: see text].

Anchor-based minimal important difference values are often sensitive to the distribution of the change score.

PURPOSE: Anchor-based studies are today the most popular approach to determine a minimal important difference value for an outcome variable. However, a variety of construction methods for such values do exist. This constitutes a challenge to the field. In order to distinguish between more or less adequate construction methods, meaningful minimal requirements can be helpful. For example, minimal important difference values should not reflect the intervention(s) the patients are exposed to in the study used for construction, as they should later allow to compare interventions. This requires that they are not sensitive to the distribution of the change score observed. This study aims at investigating to which degree established construction methods fulfil this minimal requirement. METHODS: Six constructions methods were considered, covering very popular and recently suggested methods. The sensitivity of MID values to the distribution of the change score was investigated in a simulation study for these six construction methods. RESULTS: Five out of six construction methods turned out to yield MID values which are sensitive to the distribution of the change score to a degree that questions their usefulness. Insensitivity can be obtained by using construction methods based solely on an estimate of the conditional distribution of the anchor variable given the change score. CONCLUSION: In future the computation of MID values should be based on construction methods avoiding sensitivity to the distribution of the change score.

Developing a prediction model to identify people with severe mental illness without regular contact to their GP - a study based on data from the Danish national registers.

INTRODUCTION: People with severe mental illness (SMI) face a higher risk of premature mortality due to physical morbidity compared to the general population. Establishing regular contact with a general practitioner (GP) can mitigate this risk, yet barriers to healthcare access persist. Population initiatives to overcome these barriers require efficient identification of those persons in need. OBJECTIVE: To develop a predictive model to identify persons with SMI not attending a GP regularly. METHOD: For individuals with psychotic disorder, bipolar disorder, or severe depression between 2011 and 2016 (n = 48,804), GP contacts from 2016 to 2018 were retrieved. Two logistic regression models using demographic and clinical data from Danish national registers predicted severe mental illness without GP contact. Model 1 retained significant main effect variables, while Model 2 included significant bivariate interactions. Goodness-of-fit and discriminating ability were evaluated using Hosmer-Lemeshow (HL) test and area under the receiver operating characteristic curve (AUC), respectively, via cross-validation. RESULTS: The simple model retained 11 main effects, while the expanded model included 13 main effects and 10 bivariate interactions after backward elimination. HL tests were non-significant for both models (p = 0.50 for the simple model and p = 0.68 for the extended model). Their respective AUC values were 0.789 and 0.790. CONCLUSION: Leveraging Danish national register data, we developed two predictive models to identify SMI individuals without GP contact. The extended model had slightly better model performance than the simple model. Our study may help to identify persons with SMI not engaging with primary care which could enhance health and treatment outcomes in this group.

The diagnostic accuracy of quality control rules.

Internal quality control in clinical chemistry laboratories are based on analyzing samples of stable control materials among the patient samples. The control results are interpreted by using quality control rules that usually are designed to detect systematic errors. The best rules have a high probability of error detection (Ped), i.e. to detect the maximal allowable (critical) systematic error and a low probability of false rejection (Pfr, false alarm). In this work we show that quality control rules can be represented by points on a ROC curve which appears when Ped is plotted against Pfr and only the control limit is varied. Further, we introduce a new method for choosing the optimal control limit, analogous to choosing the optimal operating point on the ROC curve of a diagnostic test. This decision needs knowledge of the pretest probability of a critical systematic error, the benefit of detecting it when it occurs and the cost of false alarm. The ROC curve analysis showed that if rules based on N = 2 are used, mean rules outperform Westgard rules because the ROC curve of the mean rules was lying above the ROC curves of the Westgard rules. A mean rule also had a lower maximum expected increase in the number of unacceptable patient results reported during the presence of an out-of-control error condition (Max E(NUF)) than comparable Westgard rules.

Optimal two-phase sampling for comparing correlated areas under the ROC curves of two screening tests in the presence of verification bias.

The accuracy of a screening test is often measured by the area under the receiver characteristic (ROC) curve (AUC) of a screening test. Two-phase designs have been widely used in diagnostic studies for estimating one single AUC and comparing two AUCs where the screening test results are measured for a large sample (Phase one sample) while the disease status is only verified for a subset of Phase one sample (Phase two sample) by a gold standard. In this paper, we consider the optimal two-phase sampling design for comparing the performance of two ordinal screening tests in classifying disease status. Specifically, we derive an analytical variance formula for the AUC difference estimator and use it to find the optimal sampling probabilities that minimize the variance formula for the AUC difference estimator. According to the proposed optimal two-phase design, the strata with the levels of two tests far apart from each other should be over-sampled while the strata with the levels of two tests close to each other should be under-sampled. Simulation results indicate that two-phase sampling under optimal allocation (OA) achieves a substantial amount of variance reduction, compared with two-phase sampling under proportional allocation (PA). Furthermore, in comparison with a one-phase random sampling, two-phase sampling under OA or PA has a clear advantage in reducing the variance of AUC difference estimator when the variances of the two screening test results in the disease population differ greatly from their counterparts in non-disease population.

Validation of three health literacy screening questions compared with S-TOFHLA in a low-income diverse English- and Spanish-Speaking population.

BACKGROUND: Clinicians need a tool to gauge patients' ability to understand health conditions and treatment options. The Short-form Test of Functional Health Literacy in Adults (S-TOFHLA) is the gold standard for this, but its length is prohibitive for use in clinical settings. This study seeks to validate a novel three-item question set for predicting health literacy. METHODS: This cross-sectional study utilized an in-person questionnaire alongside the S-TOFHLA. The sample included 2027 English- and Spanish-speaking adults (≥18 years) recruited from primary care practices serving a low-income eastern Pennsylvania community. Most patients (57.7%) identified as Hispanic. Diagnostic accuracy of each question and aggregated scores were assessed against the validated survey by calculating the area under the receiver operating characteristic (AUROC) curve. RESULTS: Questions in the 'Problems Learning' and 'Help Reading' domains (AUROC 0.66 for each) performed better than the 'Confident Forms' question (AUROC 0.64). Summing all three scores resulted in an even higher AUROC curve (0.71). Cronbach's alpha of the combined items was 0.696. CONCLUSIONS: Study results suggest that any of the three questions are viable options for screening health literacy levels of diverse patients in primary care clinical settings. However, they perform better as a summed score than when used individually.