OBJECTIVE: To compare the ability of the prehospital GCS and GCS-M to predict 30-day mortality and severe disability in trauma patients.
DESIGN: We used the Pan-Asia Trauma Outcomes Study registry to enroll all trauma patients >18 years of age who presented to hospitals via emergency medical services from 1 January 2016 to November 30, 2018.
SETTINGS AND PARTICIPANTS: A total of 16,218 patients were included in the analysis of 30-day mortality and 11 653 patients in the analysis of functional outcomes.
OUTCOME MEASURES AND ANALYSIS: The primary outcome was 30-day mortality after injury, and the secondary outcome was severe disability at discharge defined as a Modified Rankin Scale (MRS) score ≥4. Areas under the receiver operating characteristic curve (AUROCs) were compared between GCS and GCS-M for these outcomes. Patients with and without traumatic brain injury (TBI) were analyzed separately. The predictive discrimination ability of logistic regression models for outcomes (30-day mortality and MRS) between GCS and GCS-M is illustrated using AUROCs.
MAIN RESULTS: The primary outcome for 30-day mortality was 1.04% and the AUROCs and 95% confidence intervals for prediction were GCS: 0.917 (0.887-0.946) vs. GCS-M:0.907 (0.875-0.938), P = 0.155. The secondary outcome for poor functional outcome (MRS ≥ 4) was 12.4% and the AUROCs and 95% confidence intervals for prediction were GCS: 0.617 (0.597-0.637) vs. GCS-M: 0.613 (0.593-0.633), P = 0.616. The subgroup analyses of patients with and without TBI demonstrated consistent discrimination ability between the GCS and GCS-M. The AUROC values of the GCS vs. GCS-M models for 30-day mortality and poor functional outcome were 0.92 (0.821-1.0) vs. 0.92 (0.824-1.0) ( P = 0.64) and 0.75 (0.72-0.78) vs. 0.74 (0.717-0.758) ( P = 0.21), respectively.
CONCLUSION: In the prehospital setting, on-scene GCS-M was comparable to GCS in predicting 30-day mortality and poor functional outcomes among patients with trauma, whether or not there was a TBI.
MATERIALS AND METHODS: This was a retrospective study using computed tomography (CT) scans from 3 hospitals. Inclusion criteria were scans with 1-5 nodules of diameter ≥5 mm; exclusion criteria were poor-quality scans or those with nodules measuring <5mm in diameter. In the lesion detection phase, 2,147 nodules from 219 scans were used to develop and train the deep learning 3D-CNN to detect lesions. The 3D-CNN was validated with 235 scans (354 lesions) for sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) analysis. In the path planning phase, Bayesian optimization was used to propose possible needle trajectories for lesion biopsy while avoiding vital structures. Software-proposed needle trajectories were compared with actual biopsy path trajectories from intraprocedural CT scans in 150 patients, with a match defined as an angular deviation of <5° between the 2 trajectories.
RESULTS: The model achieved an overall AUC of 97.4% (95% CI, 96.3%-98.2%) for lesion detection, with mean sensitivity of 93.5% and mean specificity of 93.2%. Among the software-proposed needle trajectories, 85.3% were feasible, with 82% matching actual paths and similar performance between supine and prone/oblique patient orientations (P = .311). The mean angular deviation between matching trajectories was 2.30° (SD ± 1.22); the mean path deviation was 2.94 mm (SD ± 1.60).
CONCLUSIONS: Segmentation, lesion detection, and path planning for CT-guided lung biopsy using an AI-guided software showed promising results. Future integration with automated robotic systems may pave the way toward fully automated biopsy procedures.
AIMS: This study aimed to examine the influence of vessel volume on bolus thermodilution measurements.
METHODS: We prospectively included patients with angina with non-obstructive coronary arteries (ANOCA) undergoing bolus and continuous thermodilution assessments. All patients underwent coronary CT angiography to extract vessel volume. Coronary microvascular dysfunction was defined as coronary flow reserve (CFR)
DESIGN AND STUDY SAMPLE: Study 1 compared the FS measure obtained with MOL and 2IFC procedure at two centre frequencies (CFs) (1 and 4 kHz) in 21 normal-hearing listeners. Study 2 determined the FS measure using MOL at five CFs (0.5-8 kHz) in 32 normal-hearing and nine sensorineural hearing loss listeners and compared them with their thresholds in quiet.
RESULTS: FS measurements with MOL and 2IFC methods were highly correlated and had statistically comparable intra-subject test-retest reliability. FS measures determined with MOL were reduced in the hearing-impaired compared to normal-hearing listeners at the CF corresponding to their hearing loss. Linear regression analysis showed significant relationship between FS deterioration and quiet threshold loss (p
METHODS: This was a prospective observational cohort study, performed at Mater Mother's Hospital in Brisbane, Queensland, Australia, from May 2022 to June 2023, of pregnancies complicated by FGR and appropriate-for-gestational-age (AGA) pregnancies. Maternal serum PlGF levels, sFlt-1/PlGF ratio, UA-PI and UtA-PI were measured at 2-4-weekly intervals from recruitment until delivery. Harrell's concordance statistic (Harrell's C) was used to evaluate multivariable Cox proportional hazards regression models featuring various combinations of placental biomarkers and fetoplacental Doppler indices to ascertain the best combination to predict PTB ( 95th centile or UtA-PI > 95th centile alone (Harrell's C, 0.82, 0.75 and 0.76, respectively). Predictive utility for PTB was best when PlGF 95th centile and UtA-PI > 95th centile were combined (Harrell's C, 0.88) (hazard ratio, 32.99; 95% CI, 10.74-101.32).
CONCLUSIONS: Low maternal serum PlGF level ( 95th centile and UtA-PI > 95th centile) in combination have the greatest predictive utility for PTB in pregnancies complicated by FGR. Their assessment may help guide clinical management of these complex pregnancies. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
OBJECTIVE: To investigate the accuracy of anthropometric indices as a screening tool for predicting MetS among apparently healthy individuals in Karachi, Pakistan.
METHODS: A community-based cross-sectional survey was conducted in Karachi, Pakistan, from February 2022 to August 2022. A total of 1,065 apparently healthy individuals aged 25 years and above were included. MetS was diagnosed using International Diabetes Federation guidelines. Anthropometric indices were defined based on body mass index (BMI), neck circumference (NC), mid-upper arm circumference (MUAC), waist circumference (WC), waist to height ratio (WHtR), conicity index, reciprocal ponderal index (RPI), body shape index (BSI), and visceral adiposity index (VAI). The analysis involved the utilization of Pearson's correlation test and independent t-test to examine inferential statistics. The receiver operating characteristic (ROC) analysis was also applied to evaluate the predictive capacities of various anthropometric indices regarding metabolic risk factors. Moreover, the area under the curve (AUC) was computed, and the chosen anthropometric indices' optimal cutoff values were determined.
RESULTS: All anthropometric indices, except for RPI in males and BSI in females, were significantly higher in MetS than those without MetS. VAI [AUC 0.820 (95% CI 0.78-0.86)], WC [AUC 0.751 (95% CI 0.72-0.79)], WHtR [AUC 0.732 (95% CI 0.69-0.77)], and BMI [AUC 0.708 (95% CI 0.66-0.75)] had significantly higher AUC for predicting MetS in males, whereas VAI [AUC 0.693 (95% CI 0.64-0.75)], WHtR [AUC 0.649 (95% CI 0.59-0.70)], WC [AUC 0.646 (95% CI 0.59-0.61)], BMI [AUC 0.641 (95% CI 0.59-0.69)], and MUAC [AUC 0.626 (95% CI 0.57-0.68)] had significantly higher AUC for predicting MetS in females. The AUC of NC for males was 0.656 (95% CI 0.61-0.70), while that for females was 0.580 (95% CI 0.52-0.64). The optimal cutoff points for all anthropometric indices exhibited a high degree of sensitivity and specificity in predicting the onset of MetS.
CONCLUSION: BMI, WC, WHtR, and VAI were the most important anthropometric predictors for MetS in apparently healthy individuals of Pakistan, while BSI was found to be the weakest indicator.
METHODS: We searched the Tufts Cost-Effectiveness Analysis Registry and PubMed for cost-per-QALY or cost-per-life-year-saved studies of CMR to detect significant CAD. We also developed a linear regression meta-model (CMR Cost-Effectiveness Calculator) based on a larger CMR cost-effectiveness simulation model that can approximate CMR lifetime discount cost, QALY, and cost effectiveness compared to relevant comparators [such as single-photon emission computed tomography (SPECT), coronary computed tomography angiography (CCTA)] or invasive coronary angiography.
RESULTS: CMR was cost-effective for evaluation of significant CAD (either health-improving and cost saving or having a cost-per-QALY or cost-per-life-year result lower than the cost-effectiveness threshold) versus its relevant comparator in 10 out of 15 studies, with 3 studies reporting uncertain cost effectiveness, and 2 studies showing CCTA was optimal. Our cost-effectiveness calculator showed that CCTA was not cost-effective in the US compared to CMR when the most recent publications on imaging performance were included in the model.
CONCLUSIONS: Based on current world-wide evidence in the literature, CMR usually represents a cost-effective option compared to relevant comparators to assess for significant CAD.
METHODS: ALS patients were prospectively recruited. Muscle fasciculation (≥2 over 30-seconds, examined in biceps brachii-brachialis (BB), brachioradialis, tibialis anterior and vastus medialis) and nerve cross-sectional area (CSA) (median, ulnar, tibial, fibular nerve) were evaluated through NMUS. Ultrasound parameters were correlated with clinical data, including revised ALS Functional Rating Scale (ALSFRS-R) progression at one year. A predictive model was constructed to differentiate fast progressors (ALSFRS-R decline ≥ 1/month) from non-fast progressors.
RESULTS: 40 ALS patients were recruited. Three parameters emerged as strong predictors of fast progressors: (i) ALSFRS-R slope at time of NMUS (p = 0.041), (ii) BB fasciculation count (p = 0.027) and (iii) proximal to distal median nerve CSA ratio
METHODS: Consecutive patients with established CKD and estimated glomerular filtration rate (eGFR)
METHODS: Prospectively collected data of ACLF patients from APASL-ACLF Research Consortium (AARC) was analyzed for 30-day outcomes. The models evaluated at days 0, 4, and 7 of presentation for 30-day mortality were: AARC (model and score), CLIF-C (ACLF score, and OF score), NACSELD-ACLF (model and binary), SOFA, APACHE-II, MELD, MELD-Lactate, and CTP. Evaluation parameters were discrimination (c-indices), calibration [accuracy, sensitivity, specificity, and positive/negative predictive values (PPV/NPV)], Akaike/Bayesian Information Criteria (AIC/BIC), Nagelkerke-R2, relative prediction errors, and odds ratios.
RESULTS: Thirty-day survival of the cohort (n = 2864) was 64.9% and was lowest for final-AARC-grade-III (32.8%) ACLF. Performance parameters of all models were best at day 7 than at day 4 or day 0 (p 12 had the lowest 30-day survival (5.7%).
CONCLUSIONS: APASL-ACLF is often a progressive disease, and models assessed up to day 7 of presentation reliably predict 30-day mortality. Day-7 AARC model is a statistically robust tool for classifying risk of death and accurately predicting 30-day outcomes with relatively lower prediction errors. Day-7 AARC score > 12 may be used as a futility criterion in APASL-ACLF patients.
METHODS: mRNA was extracted from 44 fibroadenomas and 36 giant fibroadenomas, and transcriptomic profiling was performed to identify up- and down-regulated genes in the giant fibroadenomas as compared to the fibroadenomas.
RESULTS: A total of 40 genes were significantly up-regulated and 18 genes were significantly down-regulated in the giant fibroadenomas as compared to the fibroadenomas of the breast. The top 5 up-regulated genes were FN1, IL3, CDC6, FGF8 and BMP8A. The top 5 down-regulated genes were TNR, CDKN2A, COL5A1, THBS4 and BMPR1B. The differentially expressed genes (DEGs) were found to be associated with 5 major canonical pathways involved in cell growth (PI3K-AKT, cell cycle regulation, WNT, and RAS signalling) and immune response (JAK-STAT signalling). Further analyses using 3 supervised learning algorithms identified an 8-gene signature (FN1, CDC6, IL23A, CCNA1, MCM4, FLT1, FGF22 and COL5A1) that could distinguish giant fibroadenomas from fibroadenomas with high predictive accuracy.
CONCLUSION: Our findings demonstrated that the giant fibroadenomas are biologically distinct to fibroadenomas of the breast with overexpression of genes involved in the regulation of cell growth and immune response.
MATERIALS AND METHOD: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.
RESULTS: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.
CONCLUSIONS: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.
STUDY DESIGN: Systematic Review of Literature.
METHODS: PubMed, the Cochrane Library, and SCOPUS databases were searched through November 2019.
RESULTS: Eight studies (1,924 patients) met criteria (age range: 28-70.9 years, body mass index range: 21.9-37 kg/m2 , and AHI range: 0.5-62 events/hour). Five studies compared ODI and AHI simultaneously, and three had a week to months between assessments. Sensitivities ranged from 32% to 98.5%, whereas specificities ranged from 47.7% to 98%. Significant heterogeneity was present; however, for studies reporting data for a 4% ODI ≥ 15 events/hour, the specificity for diagnosing OSA ranged from 75% to 98%, and only one study reported the positive predictive value, which was 97%. Direct ODI and AHI comparisons were not made because of different hypopnea scoring, different oxygen desaturation categories, and different criteria for grading OSA severity.
CONCLUSION: Significant heterogeneity exists in studies comparing ODI and AHI. Based on currently published studies, consideration should be given for diagnosing adult OSA with a 4% ODI of ≥ 15 events/hour and for recommending further evaluation for diagnosing OSA with a 4% ODI ≥ 10 events/hour. Screening with oximetry may be indicated for the detection of OSA in select patients. Further study is needed before a definitive recommendation can be made. Laryngoscope, 131:440-447, 2021.
METHODS: SARS-CoV-2 antigens were immobilized on nitrocellulose membrane to capture human IgG, which was then detected with anti-human IgG conjugated gold nanoparticle (hIgG-AuNP). A total of 181 samples were analyzed in-house. Within which 35 were further evaluated in US FDA-approved CLIA Elecsys SARS-CoV-2 assay. The positive panel consisted of RT-qPCR positive samples from patients with both <14 days and >14 days from the onset of clinical symptoms. The negative panel contained samples collected from the pre-pandemic era dengue patients and healthy donors during the pandemic. Moreover, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of FT-DBA were evaluated against RT-qPCR positive sera. However, the overall efficacies were assessed with sera that seroconverted against either nucleocapsid (NCP) or receptor-binding domain (RBD).
RESULTS: In-house ELISA selected a total of 81 true seropositive and 100 seronegative samples. The sensitivity of samples with <14 days using FT-DBA was 94.7%, increasing to 100% for samples >14 days. The overall detection sensitivity and specificity were 98.8% and 98%, respectively, whereas the overall PPV and NPV were 99.6% and 99%. Moreover, comparative analysis between in-house ELISA assays and FT-DBA revealed clinical agreement of Cohen's Kappa value of 0.944. The FT-DBA showed sensitivity and specificity of 100% when compared with commercial CLIA kits.
CONCLUSION: The assay can confirm past SARS-CoV-2 infection with high accuracy within 2 minutes compared to commercial CLIA or in-house ELISA. It can help track SARS-CoV-2 disease progression, population screening, and vaccination response. The ease of use of the assay without requiring any instruments while being semi-quantitative provides the avenue of its implementation in remote areas around the globe, where conventional serodiagnosis is not feasible.
METHOD: The maternal fasting level of adipocytokines of 53 subjects with GDM and 43 normal pregnant (NGDM) was measured using multiplex immunoassay at 24-28 weeks, before delivery, immediate postpartum, and 2-6 months postpuerperium.
RESULTS: Higher levels of AFABP were associated with a 3.7-fold higher risk of GDM. Low chemerin levels were associated with a 3.6-fold higher risk of GDM. Interleukin-10 (IL-10) was inversely associated with the risk of GDM. SPARC had no association with GDM. AFABP was directly correlated to interleukin-6 (r = 0.50), insulin resistance index (r = 0.26), and body mass index (r = 0.28) and inversely correlated to C-reactive protein (r = -0.27). Chemerin levels were directly and strongly correlated with IL-10 (r = 0.41) and interleukin-4 (r = 0.50) and inversely correlated to insulin resistance index (r = -0.23) in GDM but not NGDM. In the longitudinal assessment, there were no significant differences in AFABP and chemerin concentrations of both studied groups.
CONCLUSION: AFABP and chemerin were associated with a higher risk of GDM. These adipocytokines were related to insulin resistance, body mass index, and inflammation in pregnant women diagnosed with GDM.