Anthropometric indices in predicting 10-year cardiovascular risk among males and females aged 40-74 years in south and southeast Asia: analysis of 12 WHO STEPS survey data.

Background: The relevance of anthropometric indices in predicting cardiovascular disease (CVD) or CVD risk factors is established across different countries, particularly in the high-income countries. However, past studies severely lacked representation from the south and southeast Asian countries. The main aim of this study was to determine the performance of conventional and new anthropometric indices to best predict 10-year cardiovascular disease (CVD) risk in south Asian and southeast Asian populations. Methods: The present study examined data from 14,532 participants in three south Asian and 13,846 participants (all aged between 40 and 74 years) in six southeast Asian countries, drawn from twelve cross-sectional studies (WHO STEPwise approaches to NCD risk factor surveillance [STEPS] survey data from 2008 to 2019). A Predictive performance of ten anthropometric indices were examined for predicting 10-year CVD risk ≥ 10% (CVD-R ≥ 10%). The 10-year CVD-R ≥ 10% was calculated by utilising the WHO CVD risk non-laboratory-based charts. Receiver operating characteristic (ROC) curve analysis was used to identify the optimal anthropometric index. Findings: Among the ten anthropometric indices, a body shape index (ABSI), body adiposity index (BAI), body roundness index (BRI), hip index (HI), and waist-height ratio (WHtR) performed best in predicting 10-year CVD risk among south Asian males and females. Improved performances were found for ABSI, BRI, conicity index (CI), WHtR, and waist-hip ratio (WHR) for 10-year CVD-R ≥ 10% predictions among southeast Asian males. Contrastingly, among southeast Asian females, ABSI and CI demonstrated optimal performance in predicting 10-year CVD-R ≥ 10%. Interpretation: The performance of anthropometric indices in predicting CVD risk varies across countries. ABSI, BAI, BRI, HI, and WHtR showed better predictions in south Asians, whereas ABSI, BRI, CI, WHtR, and WHR displayed enhanced predictions in southeast Asians. Funding: None.

A diagnostic prediction model for the early detection of heart failure following primary percutaneous coronary intervention in patients with ST-elevation myocardial infarction.

BACKGROUND: In this study, we aimed to construct a robust diagnostic model that can predict the early onset of heart failure in patients with ST-elevation myocardial infarction (STEMI) following a primary percutaneous coronary intervention (PCI). This diagnostic model can facilitate the early stratification of high-risk patients, thereby optimizing therapeutic management. METHODS: We performed a retrospective analysis of 664 patients with STEMI who underwent their inaugural PCI. We performed logistic regression along with optimal subset regression and identified important risk factors associated with the early onset of heart failure during the time of admission. Based on these determinants, we constructed a predictive model and confirmed its diagnostic precision using a receiver operating characteristic (ROC) curve. RESULTS: The logistic and optimal subset regression analyses revealed the following three salient risk factors crucial for the early onset of heart failure: the Killip classification, the presence of renal insufficiency, and increased troponin T levels. The constructed prognostic model exhibited excellent discriminative ability, which was indicated by an area under the curve value of 0.847. The model's 95% confidence interval following 200 Bootstrap iterations was found to be between 0.767 and 0.925. The Hosmer-Lemeshow test revealed a chi-square value of 3.553 and a p-value of 0.938. Notably, the calibration of the model remained stable even after 500 Bootstrap evaluations. Furthermore, decision curve analysis revealed a substantial net benefit of the model. CONCLUSION: We have successfully constructed a diagnostic prediction model to predict the incipient stages of heart failure in patients with STEMI following primary PCI. This diagnostic model can revolutionize patient care, allowing clinicians to quickly identify and create individualized interventions for patients at a higher risk.

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.

Integrating clinical and biochemical markers: a novel nomogram for predicting lacunes in cerebral small vessel disease.

Background: Lacunes, a characteristic feature of cerebral small vessel disease (CSVD), are critical public health concerns, especially in the aging population. Traditional neuroimaging techniques often fall short in early lacune detection, prompting the need for more precise predictive models. Methods: In this retrospective study, 587 patients from the Neurology Department of the Affiliated Hospital of Hebei University who underwent cranial MRI were assessed. A nomogram for predicting lacune incidence was developed using LASSO regression and binary logistic regression analysis for variable selection. The nomogram's performance was quantitatively assessed using AUC-ROC, calibration plots, and decision curve analysis (DCA) in both training (n = 412) and testing (n = 175) cohorts. Results: Independent predictors identified included age, gender, history of stroke, carotid atherosclerosis, hypertension, creatinine, and homocysteine levels. The nomogram showed an AUC-ROC of 0.814 (95% CI: 0.791-0.870) for the training set and 0.805 (95% CI: 0.782-0.843) for the testing set. Calibration and DCA corroborated the model's clinical value. Conclusion: This study introduces a clinically useful nomogram, derived from binary logistic regression, that significantly enhances the prediction of lacunes in patients undergoing brain MRI for various indications, potentially advancing early diagnosis and intervention. While promising, its retrospective design and single-center context are limitations that warrant further research, including multi-center validation.

Establishing minimal clinically important differences for the quality of life scale QLICD-SLE for patients with systemic lupus erythematosus based on ROC curve.

The minimal clinically important difference (MCID) is an important concept with big appeal in a field struggling to interpret quality of life (QOL) and other patient-reported outcomes (PRO), is also a bridge between statistics and clinical medicine. This study uses the ROC curve to formulate the MCID value of the Quality of Life Instruments for Chronic Diseases of Systemic lupus erythematosus (QLICD-SLE V2.0) scale. Using the representative item "In general, would you say your health is" of the MOS item short form health survey(SF-36) as an anchor, the questionnaire of QLICD-SLE V2.0 and the anchor item were used to investigate the patients on the first day of hospitalization, and the day before the patient was discharged. 279 patients with lupus erythematosus were participated in this longitudinal follow-up study. The ROC curve was constructed by using the classification based on the anchor item as the gold standard and the difference score of the scale as the test variable. The cut-off point corresponding to the maximum value of the Youden index in the ROC curve is taken as the minimum clinical importance difference (MCID) value of the QLICD-SLE (V2.0) scale. The Results showed that the MCID of physical domain, psychological domain, social domain, general module, specific module and QLICD-SLE (V2.0) total scale are 8.3, 2.3, 2.5, 2.7, 9.2 and 3.2, respectively. Area under the ROC curve of QLICD-SLE (V2.0) is 0.898, P (Area = 0.5) < 0.001, the sensitivity is 100%, the specificity is 66.9%. It concluded that if the total scores after treatments changes at least 3.2 points positively, the treatment intervention can be considered as clinically significant. It is more convincing to use the corresponding cut-off point as the MCID for ROC curve method can visualize the sensitivity and specificity.

Analysis of risk factors for papillary thyroid carcinoma and the association with thyroid function indicators.

Objective: This study aims to analyze the relationship between papillary thyroid carcinoma (PTC) and various factors. Methods: The study involved two groups-PTC patients and non-PTC controls. We utilized binary logistic regression and Least Absolute Shrinkage and Selection Operator (Lasso) regression for variable selection and risk factor analysis. Correlation analysis was performed using Spearman's rank correlation. The diagnostic value of thyroid stimulating hormone (TSH) levels for PTC was assessed using Receiver Operating Characteristic (ROC) curves. Results: PTC patients exhibited higher body mass index (BMI) (23.71 vs. 22.66, p<0.05) and TSH levels (3.38 vs. 1.59, p<0.05). Urinary iodine concentration (UIC) was an independent predictor of PTC (OR=1.005, p<0.05). The optimal TSH threshold for PTC diagnosis was 2.4 mIU/L [The Area Under the Curve (AUC)=67.3%, specificity=71.4%, sensitivity=70.1%]. TSH levels positively correlated with BMI (r=0.593, p<0.05) and UIC (r=0.737, p<0.05). Conclusions: UIC may be an independent predictor of PTC, and TSH levels have some diagnostic value for identifying PTC.

Impact of Long-Term Non-Communicable Diseases on SARS-COV-2 Hospitalized Patients Supported by Radiological Imaging in Southern Pakistan.

COVID-19 patients with already existing chronic medical conditions are more likely to develop severe complications and, ultimately, a higher risk of mortality. This study analyzes the impacts of pre-existing chronic illnesses such as diabetes (DM), hypertension, and cardiovascular diseases (CVDs) on COVID-19 cases by using radiological chest imaging. The data of laboratory-confirmed COVID-19-infected hospitalized patients were analyzed from March 2020 to December 2020. Chest X-ray images were included to further identify the differences in X-ray patterns of patients with co-morbid conditions and without any co-morbidity. The Pearson chi-square test checks the significance of the association between co-morbidities and mortality. The magnitude and dimension of the association were calibrated by the odds ratio (OR) at a 95% confidence interval (95% CI) over the patients' status (mortality and discharged cases). A univariate binary logistic regression model was applied to examine the impact of co-morbidities on death cases independently. A multivariate binary logistic regression model was applied for the adjusted effects of possible confounders. For the sensitivity analysis of the model, receiver operating characteristic (ROC) was applied. Patients with different comorbidities, including diabetes (OR = 33.4, 95% CI: 20.31-54.78, p < 0.001), cardiovascular conditions (OR = 24.14, 95% CI: 10.18-57.73, p < 0.001), and hypertension (OR = 16.9, 95% CI: 10.20-27.33, p < 0.001), showed strong and significant associations. The opacities present in various zones of the lungs clearly show that COVID-19 patients with chronic illnesses such as diabetes, hypertension, cardiovascular disease, and obesity experience significantly worse outcomes, as evidenced by chest X-rays showing increased pneumonia and deterioration. Therefore, stringent precautions and a global public health campaign are crucial to reducing mortality in these high-risk groups.

Can clinical presentation predict radiologically confirmed cauda equina syndrome: retrospective case review of 530 cases at a tertiary emergency department.

PURPOSE: Cauda equina syndrome (CES) may have significant individual consequences if diagnostic delays occur. Our aim was to evaluate the presenting subjective and objective features of patients with suspected CES in comparison to those with radiologically confirmed cauda equina compression (CEC).. METHODS: This was a retrospective analysis of all cases presenting with suspected CES to a tertiary emergency care unit over a two-year period. CEC was defined as radiological confirmation of CEC by Consultant Musculoskeletal (MSK) Radiologist report (MSK-CEC) and by measured canal occupancy due to an acute disc extrusion (> 75%)[measured by a Senior Spinal Surgeon (SP-CEC)]. Routine data collection was compared between categories. Chi square, multivariate regression analyses and ROC analysis of multiple predictors was performed. RESULTS: 530 patients were included in this analysis, 60 (11.3%) had MSK-CEC, and 470 had NO- CEC. Only 43/60 (71.7%) had emergent surgery. Those with MSK-CEC and SP-CEC were statistically more likely to present with bilateral leg pain [(MSK-CEC OR 2.6, 95%CI 1.2, 5.8; p = 0.02)(SP-CEC OR 4.7, 95%CI 1.7, 12.8; p = 0.003)]; and absent bilateral ankle reflexes [(MSK-CEC OR 4.3; 95% CI 2.0, 9.6; p < 0.001)(SP CEC OR 2.5; 95%CI 1.0, 6.19; p = 0.05)] on multivariate analysis. The ROC curve analysis acceptable diagnostic utility of having SP-CEC when both are present [Area under the curve 0.72 (95%CI 0.61, 0.83); p < 0.0001]. CONCLUSION: This study suggests that in those presenting with CES symptoms, the presence of both bilateral leg pain and absent ankle reflexes pose an acceptable diagnostic tool to predict a large acute disc herniation on MRI scan..

Acquisition Time for Resting-State HbO/Hb Coupling Measured by Functional Near-Infrared Spectroscopy in Assessing Autism.

Functional near-infrared spectroscopy was used to record spontaneous hemodynamic fluctuations form the bilateral temporal lobes in 25 children with autism spectrum disorder (ASD) and 22 typically developing (TD) children. The coupling between oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) was calculated by Pearson correlation coefficient, showing significant difference between ASD and TD, thus the coupling could be a characteristic feature for ASD. To evaluate the discrimination ability of the feature obtained in different acquisition times, the receiver operating characteristic curve (ROC) was constructed and the area under curve (AUC) was calculated. The results showed AUC > 0.8 when the time duration was longer than 1.5 min, but longer than 4 min, AUC value (~0.87) hardly varied, implying the maximal discrimination ability reached. This study demonstrated the coupling could be one of characteristic features for ASD even acquired in a short measurement time.

Prognostic Role of Clinicopathological Characteristics and Serum Markers in Metastatic Melanoma Patients Treated with BRAF and MEK Inhibitors.

Prognostic studies can provide important information about disease biology and improve the use of biomarkers to optimize treatment decisions. METHODS: A total of 199 patients with advanced melanoma treated with BRAF + MEK inhibitors were included in our single-center retrospective study. We analyzed the risk of progression and death using multivariate Cox proportional hazard models. The predictive effect of prognostic factors on progression-free survival (PFS) was evaluated in ROC analysis. RESULTS: We found that primary tumor localization, Clark level, pT category, baseline M stage and baseline serum S100B are independent and significant prognostic factors for PFS. The discriminative power of the combination of these factors was excellent for predicting 18 month PFS (AUC 0.822 [95% CI 0.727; 0.916], p < 0.001). Primary tumor localization on the extremities, Clark level V, baseline M1c stage or M1d stage, and elevated baseline serum S100B and LDH levels were independently and significantly associated with unfavorable overall survival (OS). CONCLUSION: Baseline M stage and serum S100B appear to be independent prognostic factors for both PFS and OS in melanoma patients treated with BRAF + MEK inhibitors. We newly identified significant and independent prognostic effects of primary tumor localization and Clark level on survival that warrant further investigation.

A discrete time-to-event model for the meta-analysis of full ROC curves.

The development of new statistical models for the meta-analysis of diagnostic test accuracy studies is still an ongoing field of research, especially with respect to summary receiver operating characteristic (ROC) curves. In the recently published updated version of the "Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy", the authors point to the challenges of this kind of meta-analysis and propose two approaches. However, both of them come with some disadvantages, such as the nonstraightforward choice of priors in Bayesian models or the requirement of a two-step approach where parameters are estimated for the individual studies, followed by summarizing the results. As an alternative, we propose a novel model by applying methods from time-to-event analysis. To this task we use the discrete proportional hazard approach to treat the different diagnostic thresholds, that provide means to estimate sensitivity and specificity and are reported by the single studies, as categorical variables in a generalized linear mixed model, using both the logit- and the asymmetric cloglog-link. This leads to a model specification with threshold-specific discrete hazards, avoiding a linear dependency between thresholds, discrete hazard, and sensitivity/specificity and thus increasing model flexibility. We compare the resulting models to approaches from the literature in a simulation study. While the estimated area under the summary ROC curve is estimated comparably well in most approaches, the results depict substantial differences in the estimated sensitivities and specificities. We also show the practical applicability of the models to data from a meta-analysis for the screening of type 2 diabetes.

The relationship of early expressed milk quantity and later full breastmilk feeding after very preterm birth: A cohort study.

When infants cannot directly breastfeed after birth, mothers are advised to initiate lactation through mechanical expression. Families are recommended to target an expression volume of at least 500-750 mL by Day 14 after birth, as this is considered a 'critical window' to establish milk supply. This is challenging for many mothers after a very preterm birth. This article explores the relationship of early milk quantity and later full breastmilk feeding as a 'gold standard' outcome, using statistical techniques designed for diagnostic tests. A cohort of 132 mothers of infants born at 23 + 0 to 31 + 6 weeks' gestational age submitted expressing logs on Day 4, 14 and 21 after birth and provided later feeding outcome. Using receiver operating characteristic (ROC) analysis, the following 24-h milk quantities were identified as associated with high probability of full breastmilk at 36 weeks' post-menstrual age (PMA): on Day 4, ≥250 g (specificity 88%; positive predictive value 88%) and on Day 21 ≥650 g (specificity 88%; positive predictive value 91%). The following values were identified as associated with low probability of full breastmilk at 36 weeks' PMA: on Day 4 <50 g (sensitivity 92%; negative predictive value 72%) and on Day 21 <250 g (sensitivity 90%; negative predictive value 70%). Participants exceeding the high thresholds had 3-4 times increased likelihood of full breastmilk, whereas those below the low thresholds had 3-5 times lower likelihood. These thresholds have potential as targets for families, to provide individualised prognostic information and to help clinicians target more intensive lactation support.

The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis.

PURPOSE: To assess the accuracy of deep learning models for the diagnosis of maxillary fungal ball rhinosinusitis (MFB) and to compare the accuracy, sensitivity, specificity, precision, and F1-score with a rhinologist. METHODS: Data from 1539 adult chronic rhinosinusitis (CRS) patients who underwent paranasal sinus computed tomography (CT) were collected. The overall dataset consisted of 254 MFB cases and 1285 non-MFB cases. The CT images were constructed and labeled to form the deep learning models. Seventy percent of the images were used for training the deep-learning models, and 30% were used for testing. Whole image analysis and instance segmentation analysis were performed using three different architectures: MobileNetv3, ResNet50, and ResNet101 for whole image analysis, and YOLOv5X-SEG, YOLOv8X-SEG, and YOLOv9-C-SEG for instance segmentation analysis. The ROC curve was assessed. Accuracy, sensitivity (recall), specificity, precision, and F1-score were compared between the models and a rhinologist. Kappa agreement was evaluated. RESULTS: Whole image analysis showed lower precision, recall, and F1-score compared to instance segmentation. The models exhibited an area under the ROC curve of 0.86 for whole image analysis and 0.88 for instance segmentation. In the testing dataset for whole images, the MobileNet V3 model showed 81.00% accuracy, 47.40% sensitivity, 87.90% specificity, 66.80% precision, and a 67.20% F1 score. Instance segmentation yielded the best evaluation with YOLOv8X-SEG showing 94.10% accuracy, 85.90% sensitivity, 95.80% specificity, 88.90% precision, and an 89.80% F1-score. The rhinologist achieved 93.5% accuracy, 84.6% sensitivity, 95.3% specificity, 78.6% precision, and an 81.5% F1-score. CONCLUSION: Utilizing paranasal sinus CT imaging with enhanced localization and constructive instance segmentation in deep learning models can be the practical promising deep learning system in assisting physicians for diagnosing maxillary fungal ball.

Corrigendum: Analysis of factors associated with positive surgical margins and the five-year survival rate after prostate cancer resection and predictive modeling.

[This corrects the article DOI: 10.3389/fonc.2024.1360404.].

Patient-centric thresholding of Cobas® EGFR mutation Test v2 for surveillance of EGFR-mutated metastatic non-small cell lung cancer.

Cobas EGFR mutation Test v2 was FDA-approved as qualitative liquid biopsy for actionable EGFR variants in non-small cell lung cancer (NSCLC). It generates semiquantitative index (SQI) values that correlate with mutant allele levels, but decision thresholds for clinical use in NSCLC surveillance are lacking. We conducted long-term ctDNA monitoring in 20 subjects with EGFR-mutated NSCLC; resulting in a 155 on-treatment samples. We defined optimal SQI intervals to predict/rule-out progression within 12 weeks from sampling and performed orthogonal calibration versus deep-sequencing and digital PCR. SQI showed significant diagnostic power (AUC 0.848, 95% CI 0.782-0.901). SQI below 5 (63% of samples) had 93% (95% CI 87-96%) NPV, while SQI above 10 (25% of samples) had 69% (95% CI 56-80%) PPV. Cobas EGFR showed perfect agreement with sequencing (Kappa 0.860; 95% CI 0.674-1.00) and digital PCR. SQI values strongly (r: 0.910, 95% 0.821-0.956) correlated to mutant allele concentrations with SQI of 5 and 10 corresponding to 6-9 (0.2-0.3%) and 64-105 (1.1-1.6%) mutant allele copies/mL (VAF) respectively. Our dual-threshold classifier of SQI 0/5/10 yielded informative results in 88% of blood draws with high NPV and good overall clinical utility for patient-centric surveillance of metastatic NSCLC.

Immature platelet fraction levels predict the development of prolonged thrombocytopenia after haematopoietic stem cell transplantation.

Prolonged thrombocytopenia (PT) is a serious complication after haematopoietic stem cell transplantation (HSCT). PT has been suggested to be associated with an increased platelet transfusion requirement and poor outcomes after transplantation. Due to the complex mechanism of PT development, it is difficult to diagnose in the early post-transplant period. Our study aimed to identify an early predictive marker for PT after HSCT. Previous studies showed that the clinical utility of immature platelet fraction (IPF) predicts platelet recovery after chemotherapy and successful engraftment. However, the relationship between IPF and PT after HSCT remains unclear. Fifty-two patients with malignant haematological diseases who underwent HSCT were included in the study. We observed the kinetics of recovery of haematological parameters after transplantation and performed receiver operating characteristics (ROC) curve analysis using data from the 52 HSCT patients. The days to rise and peak of IPF, absolute IPF count (A-IPF) and highly fluorescent IPF (H-IPF) were almost synchronised in all patients, at day 10 and day 15, respectively. The begin to rise levels of IPF, H-IPF and A-IPF were all significantly lower in the PT group than in the good engraftment (GE) group (p=0.0016, p=0.0094, p=0.0086, respectively). The peak levels of IPF were significantly lower in the PT group than the GE group (p=0.0036). However, the peaks of H-IPF and A-IPF were not statistically significant between the two groups (p=0.3383, p=0.0887, respectively). The area under the ROC curve (AUC) of IPF rise was 0.739 (95% CI 0.583-0.896; p<0.05) and the cut-off value was 3.5%, while the AUC of IPF peak was 0.800 (95% CI 0.637-0.962; p<0.01) and the cut-off value was 8.0%. In conclusion, early low levels of IPF predict the development of PT after HSCT. These findings may help improve the management and treatment strategies for PT after HSCT.

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.

Continuous Monitoring of Heart Rate Variability and Respiration for the Remote Diagnosis of Chronic Obstructive Pulmonary Disease: Prospective Observational Study.

BACKGROUND: Conventional daytime monitoring in a single day may be influenced by factors such as motion artifacts and emotions, and continuous monitoring of nighttime heart rate variability (HRV) and respiration to assist in chronic obstructive pulmonary disease (COPD) diagnosis has not been reported yet. OBJECTIVE: The aim of this study was to explore and compare the effects of continuously monitored HRV, heart rate (HR), and respiration during night sleep on the remote diagnosis of COPD. METHODS: We recruited patients with different severities of COPD and healthy controls between January 2021 and November 2022. Vital signs such as HRV, HR, and respiration were recorded using noncontact bed sensors from 10 PM to 8 AM of the following day, and the recordings of each patient lasted for at least 30 days. We obtained statistical means of HRV, HR, and respiration over time periods of 7, 14, and 30 days by continuous monitoring. Additionally, the effects that the statistical means of HRV, HR, and respiration had on COPD diagnosis were evaluated at different times of recordings. RESULTS: In this study, 146 individuals were enrolled: 37 patients with COPD in the case group and 109 participants in the control group. The median number of continuous night-sleep monitoring days per person was 56.5 (IQR 32.0-113.0) days. Using the features regarding the statistical means of HRV, HR, and respiration over 1, 7, 14, and 30 days, binary logistic regression classification of COPD yielded an accuracy, Youden index, and area under the receiver operating characteristic curve of 0.958, 0.904, and 0.989, respectively. The classification performance for COPD diagnosis was directionally proportional to the monitoring duration of vital signs at night. The importance of the features for diagnosis was determined by the statistical means of respiration, HRV, and HR, which followed the order of respiration > HRV > HR. Specifically, the statistical means of the duration of respiration rate faster than 21 times/min (RRF), high frequency band power of 0.15-0.40 Hz (HF), and respiration rate (RR) were identified as the top 3 most significant features for classification, corresponding to cutoff values of 0.1 minute, 1316.3 nU, and 16.3 times/min, respectively. CONCLUSIONS: Continuous monitoring of nocturnal vital signs has significant potential for the remote diagnosis of COPD. As the duration of night-sleep monitoring increased from 1 to 30 days, the statistical means of HRV, HR, and respiration showed a better reflection of an individual's health condition compared to monitoring the vital signs in a single day or night, and better was the classification performance for COPD diagnosis. Further, the statistical means of RRF, HF, and RR are crucial features for diagnosing COPD, demonstrating the importance of monitoring HRV and respiration during night sleep.

Expression levels and clinical significance of serum miR-19a/CCL20 in patients with acute cerebral infarction.

Acute cerebral infarction (ACI) is a lethal disease whose early diagnosis is critical for treatment. microRNA (miR)-19a targets CC chemokine ligand 20 (CCL20) in myocardial infarction. We investigated the expression patterns of serum miR-19a and CCL20 of ACI patients and assessed their clinical values. Serum samples of 50 healthy subjects and110 ACI patients were collected. Serum levels of miR-19a, CCL20 mRNA, and biochemical indexes were assessed. miR-19a downstream target gene and the binding relationship between miR-19a and CCL20 were predicted and verified. miR-19a and CCL20 mRNA were subjected to correlation and diagnostic efficiency analysis. miR-19a was poorly expressed in the serum of ACI patients, especially in patients with unstable plaque and large infarction. tumor necrosis factor-α, low-density lipoprotein, and platelet/lymphocyte ratio negatively correlated with serum miR-19a level and positively correlated with CCL20. Dual-luciferase assay revealed that miR-19a could negatively regulate CCL20 expression. CCL20 was highly expressed in the serum of ACI patients. The area under receiver-operating characteristic curve of miR-19a combined with CCL20 was 0.9741 (98.00% specificity, 90.91% sensitivity), higher than their single diagnosis. Collectively, miR-19a had high diagnostic value for ACI and could target to restrain CCL20. The combination of miR-19a and CCL20 improved diagnostic value for ACI.