Comparative Effectiveness of the Revised American Joint Committee on Cancer Staging System.

The American Joint Committee on Cancer (AJCC) staging manual is periodically updated. This study aims to examine the staging performances in delineating stage-specific survival curves and to evaluate the reclassification of cases based on the 7th and 8th AJCC editions in Taiwan. Data were sourced from the Taiwan Cancer Registry for cases diagnosed in 2017 (7th edition) and 2018 (8th edition), each with a 2-year follow-up period. Performance was assessed using the area under the receiver operating characteristic curve and the Lorenz curve-derived Gini index, along with 2-year survival rates for evaluating patient prognoses. The 8th edition showed superior staging in four specific cancer types. Oropharyngeal cancer exhibited more variable 2-year survival rates across stages, and liver cancer showed a clearer decline in survival rates with advancing stages. The 8th edition also improved prognostic staging for non-small cell lung cancer and reclassified 26.4% of stage 4 prostate cancer patients under the 7th edition into stages 3 or 4A, showing 2-year survival rates above 90%. Our study highlights the 8th edition's refined capacity for stage-specific survival distinctions and reclassification of cases to enhance prognostication in certain cancers within Taiwan. We recommend a comprehensive evaluation when adopting a new edition or version.

Estimating effects of longitudinal and cumulative exposure to PFAS mixtures on early adolescent body composition.

Few methods have been used to characterize repeatedly measured biomarkers of chemical mixtures. We applied latent profile analysis (LPA) to serum concentrations of 4 perfluoroalkyl and polyfluoroalkyl substances (PFAS) measured at 4 time points from gestation to age 12 years. We evaluated the relationships between profiles and z scores of height, body mass index, fat mass index, and lean body mass index at age 12 years (n = 218). We compared LPA findings with an alternative approach for cumulative PFAS mixtures using g-computation to estimate the effect of simultaneously increasing the area under the receiver operating characteristic curve (AUC) for all PFAS. We identified 2 profiles: a higher PFAS profile (35% of sample) and a lower PFAS profile (relative to each other), based on their average PFAS concentrations at all time points. The higher PFAS profile had generally lower z scores for all outcomes, with somewhat larger effects for males, though all 95% CIs crossed the null. For example, the higher PFAS profile was associated with a 0.50-unit lower (ß = -0.50; 95% CI, -1.07 to 0.08) BMI z score among males but not among females (ß = 0.04; 95% CI, -0.45 to 0.54). We observed similar patterns with AUCs. We found that a higher childhood PFAS profile and higher cumulative PFAS mixtures may be associated with altered growth in early adolescence. This article is part of a Special Collection on Environmental Epidemiology.

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.

Chemical composition alterations in rat brain hypothalamus induced by irisin administration using spectroscopic and machine learning techniques.

This study employed Fourier transform infrared (FTIR) spectroscopy to determine the chemical composition of brain tissues and the changes induced by irisin at doses of 50 mg and 100 mg. Brain tissues were collected from control rats and those administered with irisin, and key vibrational peaks were analyzed. In the 50 mg irisin group, all described vibrations decreased compared to control tissues, while the 100 mg group showed a decrease only in lipid vibrations. Comparatively, the 50 mg group had lower absorbance of phospholipids, amides, and lipid functional groups than the 100 mg group. Lower amounts of these compounds were found in treated tissues compared to controls, with higher levels in the 100 mg group. Ratios between amide peaks revealed significant differences between groups. Principal component analysis (PCA) differentiated control and irisin-treated tissues, primarily using PC1 and PC3. The decision tree model exhibited high classification accuracy, especially in the 800-1800 cm⁻1 range, with high sensitivity and specificity. FTIR spectroscopy effectively highlighted chemical changes in brain tissues due to irisin, demonstrating dose-dependent variations. The combination of PCA, ROC analysis, and decision tree modeling underscored the potential of FTIR spectroscopy for studying the biochemical effects of compounds like irisin.

Semisupervised transfer learning for evaluation of model classification performance.

In many modern machine learning applications, changes in covariate distributions and difficulty in acquiring outcome information have posed challenges to robust model training and evaluation. Numerous transfer learning methods have been developed to robustly adapt the model itself to some unlabeled target populations using existing labeled data in a source population. However, there is a paucity of literature on transferring performance metrics, especially receiver operating characteristic (ROC) parameters, of a trained model. In this paper, we aim to evaluate the performance of a trained binary classifier on unlabeled target population based on ROC analysis. We proposed Semisupervised Transfer lEarning of Accuracy Measures (STEAM), an efficient three-step estimation procedure that employs (1) double-index modeling to construct calibrated density ratio weights and (2) robust imputation to leverage the large amount of unlabeled data to improve estimation efficiency. We establish the consistency and asymptotic normality of the proposed estimator under the correct specification of either the density ratio model or the outcome model. We also correct for potential overfitting bias in the estimators in finite samples with cross-validation. We compare our proposed estimators to existing methods and show reductions in bias and gains in efficiency through simulations. We illustrate the practical utility of the proposed method on evaluating prediction performance of a phenotyping model for rheumatoid arthritis (RA) on a temporally evolving EHR cohort.

Nonparametric worst-case bounds for publication bias on the summary receiver operating characteristic curve.

The summary receiver operating characteristic (SROC) curve has been recommended as one important meta-analytical summary to represent the accuracy of a diagnostic test in the presence of heterogeneous cutoff values. However, selective publication of diagnostic studies for meta-analysis can induce publication bias (PB) on the estimate of the SROC curve. Several sensitivity analysis methods have been developed to quantify PB on the SROC curve, and all these methods utilize parametric selection functions to model the selective publication mechanism. The main contribution of this article is to propose a new sensitivity analysis approach that derives the worst-case bounds for the SROC curve by adopting nonparametric selection functions under minimal assumptions. The estimation procedures of the worst-case bounds use the Monte Carlo method to approximate the bias on the SROC curves along with the corresponding area under the curves, and then the maximum and minimum values of PB under a range of marginal selection probabilities are optimized by nonlinear programming. We apply the proposed method to real-world meta-analyses to show that the worst-case bounds of the SROC curves can provide useful insights for discussing the robustness of meta-analytical findings on diagnostic test accuracy.

Diagnostic performance of shear wave measurement in the detection of hepatic fibrosis: A multicenter prospective study.

AIM: This study aimed to establish the shear wave measurement (SWM) cut-off value for each fibrosis stage using magnetic resonance (MR) elastography values as a reference standard. METHODS: We prospectively analyzed 594 patients with chronic liver disease who underwent SWM and MR elastography. Correlation coefficients (were analyzed, and the diagnostic value was evaluated by the area under the receiver operating characteristic curve. Liver stiffness was categorized by MR elastography as F0 (<2.61 kPa), F1 (≥2.61 kPa, <2.97 kPa, any fibrosis), F2 (≥2.97 kPa, <3.62 kPa, significant fibrosis), F3 (≥3.62 kPa, <4.62 kPa, advanced fibrosis), or F4 (≥4.62 kPa, cirrhosis). RESULTS: The median SWM values increased significantly with increasing fibrosis stage (p < 0.001). The correlation coefficient between SWM and MR elastography values was 0.793 (95% confidence interval 0.761-0.821). The correlation coefficients between SWM and MR elastography values significantly decreased with increasing body mass index and skin-capsular distance; skin-capsular distance values were associated with significant differences in sensitivity, specificity, accuracy, or positive predictive value, whereas body mass index values were not. The best cut-off values for any fibrosis, significant fibrosis, advanced fibrosis, and cirrhosis were 6.18, 7.09, 8.05, and 10.89 kPa, respectively. CONCLUSIONS: This multicenter study in a large number of patients established SWM cut-off values for different degrees of fibrosis in chronic liver diseases using MR elastography as a reference standard. It is expected that these cut-off values will be applied to liver diseases in the future.

A comparative study of hepatic steatosis using two different qualitative ultrasound techniques measured based on magnetic resonance imaging-derived proton density fat fraction.

AIM: This study aimed to evaluate the diagnostic performance of attenuation measurement (ATT; dual-frequency method) and improved algorithm of ATT (iATT; reference method) for the assessment of hepatic steatosis using magnetic resonance imaging (MRI)-derived proton density fat fraction (PDFF) as the reference standard. METHODS: We prospectively analyzed 427 patients with chronic liver disease who underwent ATT, iATT, or MRI-derived PDFF. Correlation coefficients were analyzed, and diagnostic values were evaluated by area under the receiver operating characteristic curve (AUROC). The steatosis grade was categorized as S0 (<5.2%), S1 (≥5.2%, <11.3%), S2 (≥11.3%, <17.1%), and S3 (≥17.1%) according to MRI-derived PDFF values. RESULTS: The median ATT and iATT values were 0.61 dB/cm/MHz (interquartile range 0.55-0.67 dB/cm/MHz) and 0.66 dB/cm/MHz (interquartile range 0.57-0.77 dB/cm/MHz). ATT and iATT values increased significantly as the steatosis grade increased in the order S0, S1, S2, and S3 (p < 0.001). The correlation coefficients between ATT or iATT values and MRI-derived PDFF values were 0.533 (95% confidence interval [CI] 0.477-0.610) and 0.803 (95% CI 0.766-0.834), with a significant difference between them (p < 0.001). For the detection of hepatic steatosis of ≥S1, ≥S2, and ≥S3, iATT yielded AUROCs of 0.926 (95% CI 0.901-0.951), 0.913 (95% CI 0.885-0.941), and 0.902 (95% CI 0.869-0.935), with significantly higher AUROC values than for ATT (p < 0.001, p < 0.001, p = 0.001). CONCLUSION: iATT showed excellent diagnostic performance for hepatic steatosis, and was strongly correlated with MRI-derived PDFF, with AUROCs of ≥0.900.

The evaluation value of atherogenic index of plasma and high-sensitivity C-reactive protein for the degree of coronary artery lesion in premature coronary artery disease.

BACKGROUND: Premature coronary artery disease (PCAD) is prevailing. We aimed to investigate the evaluation value of atherogenic index of plasma (AIP) and high-sensitivity C-reactive protein (hs-CRP) for the occurrence and severity of coronary artery lesion in PCAD patients. METHODS: PCAD (PACD group)/non-PCAD (control group) patients were enrolled. The coronary artery lesion degree was evaluated using Gensini score (GS). PCAD patients were allocated into the low/medium/high GS groups, with general clinical baseline data analyzed. Plasma hs-CRP/AIP levels were compared in PCAD patients with different disease degree. Correlations between plasma hs-CRP/AIP with Gensini score, independent risk factors affecting the occurrence of PCAD, and the predictive value of hs-CRP/AIP/their combination for the occurrence and degree of PCAD were evaluated by Spearman correlation analysis/Logistic multivariate regression/receiver operating characteristic (ROC) curve. The differences in the area under the curve (AUC) were compared using MedCalc-Comparison of ROC curves. RESULTS: Plasma hs-CRP/AIP levels in the PCAD group were increased. Plasma hs-CRP/AIP levels varied significantly among PCAD patients with different disease degree. Plasma hs-CRP/AIP levels were markedly positively correlated with the Gensini score. Smoking history/homocysteine/fasting blood-glucose/hs-CRP/AIP were all independent risk factors affecting PCAD occurrence. The AUC of hs-CRP and AIP combination predicting the occurrence of PCAD was 0.950 (90.80% sensitivity/93.33% specificity). hs-CRP/AIP combination assisted in predicting the disease degree in PCAD patients. CONCLUSIONS: AIP and hs-CRP are independent risk factors for the occurrence of PCAD, and their combination has high predictive value for PCAD occurrence and disease degree, which are both positively correlated with coronary artery lesion degree.

A modified analysis protocol for the PhNR test.

PURPOSE: Several studies have reported that glaucoma patients have abnormal photopic negative response (PhNR) results compared to reference control subjects. The International Society for Clinical Electrophysiology of Vision (ISCEV) released an extended protocol for PhNR (I-PhNR) in 2018. The purpose of this study was to compare the I-PhNR protocol to a similar protocol modified (M-PhNR) to enhance the performance of the method in detecting glaucomatous damage. METHODS: Thirty subjects were enrolled in this study (12 glaucoma patients, 10 glaucoma suspects, 8 normal controls). PhNR tests were conducted with a Diagnosys E3 mobile system (Diagnosys LLC, Lowell, MA). I-PhNR tests utilized all parameters specified by the ISCEV requirement. M-PhNR tests used the same parameters as the ISCEV tests with the exceptions of a 5-45 Hz bandpass filter and a novel, objective sweep-selection parameter. According to the ISCEV protocol, the PhNR relative to baseline (i.e., BT), a-wave and b-wave response amplitudes and BT/b-wave amplitude ratios were measured. Coefficients of variation, receiver operating characteristic (ROC) curves, and t-tests were used to assess the data from one randomly chosen eye per subject. RESULTS: The M-PhNR protocol resulted in a decrease in the intra-subject repeat test coefficient of variation and a decrease in the average inter-subject coefficient of variation for the glaucoma subjects. The ROC curves demonstrated an increase in the area under the curve (AUC) for the M-PhNR compared to the I-PhNR protocol. The sensitivity and specificity were also greater for the M-PhNR protocol. CONCLUSIONS: The M-PhNR protocol resulted in a decrease in intra-subject and inter-subject data variability which resulted in a significant increase in the ROC AUC, sensitivity, and specificity for glaucoma. Thus, the M-PhNR protocol shows promise as a better diagnostic tool than the I-PhNR protocol for detecting glaucoma.

Linear associations of Chinese visceral adiposity index and its change with hyperuricemia: A prospective cohort study.

BACKGROUND AND AIMS: This study aims to investigate the association of Chinese visceral adiposity index (CVAI) with incident hyperuricemia (HUA). METHODS AND RESULTS: We included 5186 adults aged ≥45 years from China Health and Retirement Longitudinal Study. Modified Poisson regression model was used to estimate the relative risks (RRs) of incident HUA associated with baseline CVAI, and logistic model was used to estimate the odds ratios (ORs) of HUA for CVAI change. Restricted cubic splines analysis was adopted to model the dose-response associations. The area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the predictive value of CVAI. During 4-year follow-up, a total of 510 (9.8%) HUA cases were identified. The RRs (95%CIs) of incident HUA were 3.75 (2.85-4.93) for quartile 4 versus quartile 1 and 1.56 (1.45-1.69) for per-standard deviation increase in baseline CVAI. For the analyses of CVAI change, compared with stable group, participants in decreased group had 34% lower risk (OR 0.66, 95%CI 0.49-0.87) and those in increased group had 35% (1.35, 1.03-1.78) higher risk of HUA. Linear associations of baseline CVAI and its change with HUA were observed (Pnonlinear >0.05). Besides, the AUC value for HUA was 0.654 (0.629-0.679), which was higher than other five obesity indices. CONCLUSIONS: Our study found linear associations between baseline CVAI and its change and risk of HUA. CVAI had the best predictive performance in predicting incident HUA. These findings suggest CVAI as a reliable obesity index to identify individuals with higher HUA risk.

Association of traditional and novel obesity indicators with stroke risk: Findings from the Rural Chinese cohort study.

BACKGROUND AND AIMS: Few researchers have compared the effectiveness of traditional and novel obesity indicators in predicting stroke incidence. We aimed to evaluate the associations between six obesity indices and stroke risk, and to further identify the optimal indicator. METHODS AND RESULTS: A total of 14,539 individuals from the Rural Chinese Cohort Study were included in the analyses. We used the Cox proportional hazards regression models to evaluate the association between six obesity indices (including body mass index [BMI], waist circumference [WC], conicity index [C-index], lipid accumulation product [LAP], visceral adiposity index [VAI], and Chinese visceral adiposity index [CVAI]) and stroke risk. Receiver operating characteristic curves were employed to compare their predictive ability on stroke risk. During a median follow-up period of 11.13 years, a total of 1257 cases of stroke occurred. In the multiple-adjusted Cox regression model, WC, BMI, C-index, and CVAI were positively associated with ischemic stroke (P < 0.01) rather than hemorrhagic stroke risk. Dose-response analyses showed a linear correlation of WC, BMI, C-index, and LAP (Poverall <0.05, and Pnonlinear >0.05), but a non-linear correlation of CVAI (Poverall <0.05, and Pnonlinear <0.05) with the risk of ischemic stroke. CVAI demonstrates the highest areas under the curves (AUC: 0.661, 95% CI: 0.653-0.668), indicating a superior predictive ability for ischemic stroke occurrence compared to other five indices (P < 0.001). CONCLUSION: WC, BMI, C-index, LAP, and CVAI were all positively related to the risk of ischemic stroke, among which CVAI exhibited stronger predictive ability for ischemic stroke.

The potential presence of infection may be indicated through non-invasive prediction of procalcitonin and C-reactive protein levels within the initial three days after cervical cerclage: a retrospective case-control study.

PURPOSE: To identify which non-invasive infection indicators could better predict post-cervical cerclage (CC) infections, and on which days after CC infection indicators should be closely monitored. METHODS: The retrospective, single-center study included 619 single-pregnancy patients from January 2021 to December 2022. Patients were categorized into infected and uninfected groups based on physicians' judgments of post-CC infections. Registered information included patient characteristics, cervical insufficiency history, gestational age at CC, surgical method (McDonald/Shirodkar), purpose of CC, mid-pregnancy miscarriage/preterm birth, infection history or risk factors, and infection indices on days 1, 3, 5, and 7 after CC. Propensity score matching (PSM) was applied to reduce patient characteristic bias. Statistical analysis of C-reactive protein (CRP), white blood cell (WBC), neutrophil count (NEU), percentage of neutrophil count (NEU_P), interleukin-6 (IL-6), and procalcitonin (PCT) in the infected group compared with the uninfected group was performed using chi-square tests and t-tests. Receiver operating characteristic (ROC) curves were used to further assess the diagnostic value of CRP, PCT, and CRP-PCT in combination. RESULTS: Among the 619 included patients, 206 patients were matched using PSM and subsequently assessed. PCT values on day 1 and day 3 after CC exhibited significant differences between the two groups in two statistical ways (P < 0.01, P < 0.05). The CRP levels on day 1 were significantly higher in the infected group compared to the uninfected group in two statistical ways (P < 0.05). On day 3, the mean CRP value was significantly elevated in the infected group compared to the uninfected group (P < 0.05). Analyses of IL-6, WBC, NEU, and NEU_P did not yield clinically significant results. The area under the ROC curves for CRP, PCT, and CRP-PCT on day 1 and day 3 were all below 0.7. In the preventive CC group, the AUC values of CRP and CRP-PCT obtained on d1 were found to be higher than 0.7, indicating moderate diagnostic accuracy. CONCLUSION: For women after CC surgery, especially of preventive aim, increased serum CRP and PCT levels from post-CC day 1 to day 3 may signal a potential postoperative infection, warranting close monitoring.

Predictive value of maternal serum placental growth factor levels for discordant fetal growth in twins: a retrospective cohort study.

BACKGROUND: Accurate prenatal recognition of discordant fetal growth in twins is critical for deciding suitable management strategies. We explored the predictive value of the level of maternal second-trimester placental growth factor (PLGF) as a novel indicator of discordant fetal growth. METHODS: A total of 860 women pregnant with twins were enrolled, including 168 women with monochorionic twins (31 cases of discordant fetal growth and 137 without) and 692 with dichorionic twins (79 cases of discordant fetal growth and 613 without). Maternal second-trimester PLGF concentrations were measured via immunofluorescence. RESULTS: Maternal second-trimester PLGF levels were significantly lower in women pregnant with twins who subsequently developed discordant fetal growth than in those who did not (monochorionic twin pregnancy: P < 0.001; dichorionic twin pregnancy: P < 0.001). A 3-4 fold difference in median PLGF concentrations was detected between the two groups with both monochorionic and dichorionic twin pregnancies. Maternal second-trimester PLGF levels were significantly correlated with birth weight differences (monochorionic twin pregnancy: r = - 0.331, P < 0.001; dichorionic twin pregnancy: r = - 0.234, P < 0.001). A receiver operating characteristic curve was used to evaluate the predictive efficiency. In monochorionic twin pregnancies, the area under the curve (AUC) was 0.751 (95% confidence interval [CI]: 0.649-0.852), and the cutoff value was 187.5 pg/mL with a sensitivity of 77.4% and specificity of 71.0%. In dichorionic twin pregnancies, the AUC was 0.716 (95% CI; 0.655-0.777), and the cutoff value was 252.5 pg/mL with a sensitivity of 65.1% and specificity of 69.6%. Based on the above cutoff values, univariate and multivariate logistic regression analyses were performed to calculate the odds ratios (OR) for the PLGF levels. After adjustment for potential confounding factors, low PLGF concentrations still significantly increased the risk of discordant fetal growth (monochorionic twin pregnancy: adjusted OR: 7.039, 95% CI: 2.798-17.710, P < 0.001; dichorionic twin pregnancy: adjusted OR: 4.279, 95% CI: 2.572-7.120, P < 0.001). CONCLUSIONS: A low maternal second-trimester PLGF level is considered a remarkable risk factor and potential predictor of discordant fetal growth. This finding provides a complementary screening strategy for the prediction of discordant fetal growth and offers a unique perspective for the subsequent research in this field.

Diagnostic value of antibody concentration ratio for treatment-refractory myasthenia gravis.

OBJECTIVE: This study aimed to assess the diagnostic potential of the Antibody concentration ratio in identifying treatment-refractory myasthenia gravis (MG). METHODS: A retrospective analysis was conducted on 116 MG patients who underwent antibody detection at least twice between June 1, 2015, and June 1, 2023. Demographic and clinical characteristics were collated to ascertain their association with refractory MG. The Antibody Concentration Ratio was applied to determine treatment response, using the International Consensus Guidance criteria as the reference standard. The area under nonparametric receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy were calculated to assess the diagnostic efficacy of the Antibody concentration ratio following consecutive immunotherapy relative to initial antibody concentrations for refractory MG. RESULTS: 19 out of 116 patients were unequivocally diagnosed with refractory MG. A significant correlation was found between the Antibody Concentration Ratio and refractory MG status in treatment-refractory and treatment-responsive patients. Subsequently, the AUC demonstrated the robust diagnostic capability of the Antibody concentration ratio for refractory MG, with an AUC of 0.8709 (95% CI: 0.7995-0.9422, p < 0.0001). The optimal cut-off value stood at 0.8903, exhibiting a sensitivity of 94.74% (95% CI: 75.36%-99.73%), a specificity of 68.04% (95% CI: 58.23%-76.48%), and accuracy of 72.41% (95% CI: 64.28%-80.54%). CONCLUSION: Elevated Antibody Concentration Ratio is intrinsically linked with refractory MG and exhibits potential as an diagnostic biomarker for the condition.

Cut-off values of Geriatric Nutritional Risk Index for cardiovascular events in Japanese patients with acute myocardial infarction.

The Geriatric Nutritional Risk Index (GNRI) is a straightforward nutritional risk assessment tool with an established relationship with poor prognosis in patients with heart failure. However, the utility of the GNRI in patients with acute myocardial infarction (AMI) remains unclear given the time-dependent changes in the pathophysiology of AMI and the selected endpoints. Accordingly, we aimed to evaluate the optimal cut-off values of the GNRI for cardiovascular events in patients with AMI. We used time-dependent receiver operating characteristic analysis to identify the optimal cut-off values for two endpoints, all-cause death and major adverse cardiac events (MACE: all-cause death, non-fatal myocardial infarction, hospitalization for heart failure, and stroke), over 4 years in 360 patients with AMI between 2012 and 2020. The cumulative incidence of MACE was 11.6%. The cut-off value of the GNRI for all-cause death was 82.7 (area under the curve [AUC], 0.834) at 3 months and 90.3 (AUC 0.854) at 4 years. The cut-off value of the GNRI for MACE was 83.0 (AUC 0.841) at 3 months and 95.3 (AUC 0.821) at 4 years. The GNRI demonstrated consistently high reliability relative to other indicators of AMI. Our findings indicated that the optimal cut-off value and reliability of the GNRI for cardiovascular events varied according to the endpoints and observation periods. GNRI emerges as a crucial predictor of prognosis for patients with AMI.

Three-dimensional soft tissue reconstruction and volume measurement used for the diagnosis of dysthyroid optic neuropathy.

PURPOSE: Dysthyroid optic neuropathy (DON) leads to vision loss. This study aimed to investigate a new method that can directly evaluate the change in muscle cone inner volume (MCIV) and distinguish DON orbits from non-DONs. MATERIALS AND METHODS: This study included 54 patients (108 orbits) who were diagnosed with thyroid eye disease and treated at the Beijing Tongren Hospital between December 2019 and September 2021. The extraocular muscle volume (EOMV), orbital fat volume (OFV), and bony orbit volume (BOV) of the patients were measured using three-dimensional reconstruction. MCIV was measured using artificially defined boundaries. The associations between these volumes and clinical indicators were studied, and the diagnostic efficacy of these volumes for DON was described using receiver operating characteristic (ROC) curves. RESULTS: The ROC curve showed that the area under the curve of MCIV/BOV (%) combined with EOMV/BOV (%) reached 0.862 (p < 0.001), with a sensitivity of 85.7% and a specificity of 76.1%. CONCLUSION: The combination of MCIV/BOV (%) and EOMV/BOV (%) is a good indicator for the diagnosis of DON, which aids in the early detection and intervention of DON.

Association between the triglyceride glucose index and obstructive sleep apnea and its symptoms: results from the NHANES.

BACKGROUND: Certain studies have indicated a link between obstructive sleep apnea and insulin resistance in specific populations. To gain more clarity, extensive research involving a broad sample of the overall population is essential. The primary objective of this study was to investigate this correlation by utilizing data from the National Health and Nutrition Examination Survey database. METHODS: The analysis incorporated data from the National Health and Nutrition Examination Survey database spanning the time periods from 2005 to 2008 and from 2015 to 2018, with a focus on American adults aged 18 years and older after applying weight adjustments. Key variables such as obstructive sleep apnea, triglyceride glucose index, and various confounding factors were considered. A generalized linear logistic regression model was used to investigate the association between obstructive sleep apnea and the triglyceride glucose index, with additional exploration of the consistency of the results through hierarchical analysis and other techniques. RESULTS: The study included participants aged between 18 and 90 years, with an average age of 46.75 years. Among the total sample, 50.76% were male. The triglyceride glucose index demonstrated a diagnostic capability for obstructive sleep apnea, with an AUC of 0.701 (95% CI: 0.6619-0.688). According to the fully adjusted model, individuals in the fourth quartile of the triglyceride glucose index showed an increased likelihood of having obstructive sleep apnea compared to those in the first quartile (OR: 1.45; 95% CI: 1.02-2.06; P < 0.05). Subgroup analysis indicated that male sex (OR: 2.09; 95% CI: 1.76-2.45; P < 0.05), younger age (OR: 2.83; 95% CI: 2.02-3.96; P < 0.05), white ethnicity (OR: 2.29; 95% CI: 1.93-2.73; P < 0.05), and obesity (OR: 1.54; 95% CI: 1.28-1.85; P < 0.05) were correlated with an elevated risk of OSA. CONCLUSIONS: This study demonstrated a strong association between an elevated TG index and OSA. Additionally, the triglyceride glucose index could serve as an independent predictor of obstructive sleep apnea.

Associations between serum ferritin baselines and trajectories and the incidence of metabolic dysfunction-associated steatotic liver disease: a prospective cohort study.

BACKGROUND AND AIMS: Evidence from prospective cohort studies on the relationship between metabolic dysfunction-associated steatotic liver disease (MASLD) and longitudinal changes in serum ferritin (SF) still limited. This study aimed to investigate the associations of SF baselines and trajectories with new-onset MASLD and to present a MASLD discriminant model. METHODS: A total of 1895 participants who attended health examinations at least three times in a hospital in Dalian City between 2015 and 2022 were included. The main outcome was the incidence of MASLD. The associations between SF baselines and trajectories with the risk of MASLD were analyzed by Cox proportional hazards regression, restricted cubic spline (RCS) analysis and time-dependent receiver operating characteristic (ROC) curve analysis. In addition, a MASLD discrimination model was established using logistic regression analyses. RESULTS: Among the 1895 participants, 492 developed MASLD during follow-up. Kaplan-Meier analysis indicated that participants in the low-stable trajectory group had a longer MASLD-free time compared with participants in other groups. Compared with those in the low-stable trajectory group, the adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) for the risk of new-onset MASLD in the medium-high, high-stable and high-high trajectory groups were 1.54(1.18-2.00), 1.77(1.35-2.32) and 1.55(1.07-2.26), respectively (Ptrend < 0.001). The results were robust in subgroup and sensitivity analyses. Multivariate Cox proportional regression showed that SF was an independent risk factor of MASLD (HR = 1.002, 95%CI: 1.000-1.003, P = 0.003). The restricted cubic spline demonstrated a nonlinear relationship between SF and the risk of MASLD. The 8-variable model had high discriminative performance, good accuracy and clinical effectiveness. The ROC curve results showed that AUC was greater than that of the FLI, HSI and ZJU models (all P < 0.01). CONCLUSIONS: Not only a higher baseline SF but also SF changing trajectory are significantly associated with risk of new-onset MASLD. SF could be a predictor of the occurrence of MASLD.

Different view of the diagnostics test accuracy measures and optimal cut-off point selection procedure under tree or umbrella ordering.

In the realm of medical diagnostic testing, diagnostic tests can assume either binary forms, distinguishing between diseased and non-diseased states, or ordinal forms, categorizing states from non-diseased to various stages (1 to K). Another significant classification scheme for multi-class scenarios is tree or umbrella ordering, which entails several unordered sub-classes (subtypes) within a biomarker. Within tree or umbrella ordering, the classifier assesses whether the marker measurement for one class surpasses or falls below those for the other classes. Although Receiver Operating Characteristic (ROC) curves and summary measures have been adapted to accommodate tree and umbrella ordering, these approaches often yield cut-off points that generate highly sensitive tests for certain disease subtypes while compromising specificity for others. This may not be ideal for all diseases. Hence, in this investigation, we explore diverse measures of diagnostic test accuracy and optimal cut-off point selection procedures under tree or umbrella ordering to foster more specific tests. We present numerical examples and simulation studies and demonstrate the approach using real data on lung cancer.