Identifying the risk factors for pancreatic fistula after laparoscopic pancreaticoduodenectomy in patients with pancreatic cancer.

BACKGROUND: Laparoscopic pancreaticoduodenectomy (LPD) is a surgical procedure for treating pancreatic cancer; however, the risk of complications remains high owing to the wide range of organs involved during the surgery and the difficulty of anastomosis. Pancreatic fistula (PF) is a major complication that not only increases the risk of postoperative infection and abdominal hemorrhage but may also cause multi-organ failure, which is a serious threat to the patient's life. This study hypothesized the risk factors for PF after LPD. AIM: To identify the risk factors for PF after laparoscopic pancreatoduodenectomy in patients with pancreatic cancer. METHODS: We retrospectively analyzed the data of 201 patients admitted to the Fudan University Shanghai Cancer Center between August 2022 and August 2023 who underwent LPD for pancreatic cancer. On the basis of the PF's incidence (grades B and C), patients were categorized into the PF (n = 15) and non-PF groups (n = 186). Differences in general data, preoperative laboratory indicators, and surgery-related factors between the two groups were compared and analyzed using multifactorial logistic regression and receiver-operating characteristic (ROC) curve analyses. RESULTS: The proportions of males, combined hypertension, soft pancreatic texture, and pancreatic duct diameter ≤ 3 mm; surgery time; body mass index (BMI); and amylase (Am) level in the drainage fluid on the first postoperative day (Am > 1069 U/L) were greater in the PF group than in the non-PF group (P < 0.05), whereas the preoperative monocyte count in the PF group was lower than that in the non-PF group (all P < 0.05). The logistic regression analysis revealed that BMI > 24.91 kg/m² [odds ratio (OR) =13.978, 95% confidence interval (CI): 1.886-103.581], hypertension (OR = 8.484, 95%CI: 1.22-58.994), soft pancreatic texture (OR = 42.015, 95%CI: 5.698-309.782), and operation time > 414 min (OR = 15.41, 95%CI: 1.63-145.674) were risk factors for the development of PF after LPD for pancreatic cancer (all P < 0.05). The areas under the ROC curve for BMI, hypertension, soft pancreatic texture, and time prediction of PF surgery were 0.655, 0.661, 0.873, and 0.758, respectively. CONCLUSION: BMI (> 24.91 kg/m²), hypertension, soft pancreatic texture, and operation time (> 414 min) are considered to be the risk factors for postoperative PF.

Discriminative validity and interpretability of the mood and feelings questionnaire.

BACKGROUND: Using the Mood and Feelings Questionnaire (MFQ) to differentiate between depression severity levels remains unexplored. We explored the discriminative validity of the MFQ to identify an optimal cut-off MFQ score to distinguish between subthreshold-to-mild and moderate-to-severe depression severity levels. METHODS: An observational cross-sectional design was used in a sample (N = 67) of help-seeking youth (ages 13 to 18, inclusive) experiencing depressive symptoms. The MFQ was administered verbatim to youth by a research analyst over the phone. Youth were then grouped into subthreshold-to-mild or moderate-to-severe depression severity categories based on scores received on the Kiddie Schedule for Affective Disorders and Schizophrenia-Depression Rating Scale. Receiver Operating Characteristic curve analyses were conducted, with area under the curve (AUC) and Youden Index (J) as primary indices. We hypothesized that the lower limit of the 95 % confidence interval for the area under the curve would be ≥0.70. RESULTS: The primary analysis yielded an AUC of 0.85 (95 % CI: 0.763-0.947) and an optimal cut-off of ≥43 (J = 0.60, positive predictive value = 91.4 %, negative predictive value = 62.5 %, sensitivity = 72.7 %, specificity = 87.0 %). LIMITATIONS: Our study collected a small sample, and as such cannot identify how subgroup classification (e.g., based on race or gender) may moderate outcomes. Further, unknown measurement error of the predictor and reference variable measures can bias the estimates. CONCLUSIONS: Our preliminary findings highlight the potential for the MFQ to support clinical decision-making relevant to adolescents experiencing varying severities of depressive symptoms in secondary care settings.

Establishment and validation of a predictive nomogram for polyuria during general anesthesia in thoracic surgery.

BACKGROUND: To develop and evaluate a predictive nomogram for polyuria during general anesthesia in thoracic surgery. METHODS: A retrospective study was designed and performed. The whole dataset was used to develop the predictive nomogram and used a stepwise algorithm to screen variables. The stepwise algorithm was based on Akaike's information criterion (AIC). Multivariable logistic regression analysis was used to develop the nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the model's discrimination ability. The Hosmer-Lemeshow (HL) test was performed to check if the model was well calibrated. Decision curve analysis (DCA) was performed to measure the nomogram's clinical usefulness and net benefits. P < 0.05 was considered to indicate statistical significance. RESULTS: The sample included 529 subjects who had undergone thoracic surgery. Fentanyl use, gender, the difference between mean arterial pressure at admission and before the operation, operation type, total amount of fluids and blood products transfused, blood loss, vasopressor, and cisatracurium use were identified as predictors and incorporated into the nomogram. The nomogram showed good discrimination ability on the receiver operating characteristic curve (0.6937) and is well calibrated using the Hosmer-Lemeshow test. Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS: Individualized and precise prediction of intraoperative polyuria allows for better anesthesia management and early prevention optimization.

[Predictive Value of the Huaxi Emotional Index in Assessing and Identifying High Suicide Risk Among Inpatients With Depression]. / åŽè¥¿å¿ƒæ™´æŒ‡æ•°åœ¨æŠ‘éƒç—‡ä½é™¢æ‚£è€ é«˜è‡ªæ€é£Žé™©è¯„ä¼°ä¸­çš„é¢„æµ‹ä½œç”¨.

Objective: This study aims to investigate the agreement between the Huaxi Emotional Index (HEI) and the Nurses' Global Assessment of Suicide Risk (NGASR) in assessing high suicide risk and to explore the predictive value of HEI in identifying high suicide risk among patients with depression. Methods: Convenience sampling was used and 386 inpatients with depression were included in this cross-sectional study. All patients were admitted to the Mental Health Center, West China Hospital between June and December 2023. The inclusion criteria were as follows, a diagnosis of depression according to the International Classification of Diseases, Tenth Revision (ICD-10), age over 18, and completion of both NGASR and HEI assessments. According to the exclusion criteria, depression patients who had other comorbid mental disorders or those who had severe cognitive impairments and were unable to communicate effectively were excluded. The study was approved by the Biomedical Ethics Review Committee of West China Hospital (Approval No. 647, 2021). Demographic data such as age, sex, ethnicity, marital status, and educational attainment were collected using a self-designed questionnaire. Both the HEI and NGASR were applied to evaluate the patients. We conducted statistical analyses with SPSS 27, employing Spearman's rank correlation for correlation analysis, Kappa tests for consistency between the two instruments, and receiver operating characteristic (ROC) curves for evaluating the predictive performance of HEI scores for high suicide risk, with the optimal HEI cutoff value determined on the basis of the Youden Index. Results: The study included 386 depression inpatients with an average age of 32 years and an average length-of-stay of 14 days. Of these participants, 252 were female (65.3%) and 134 were male (34.7%). Regarding ethnicity, most of the participants were Han Chinese (89.4%), Tibetans accounted for 7.3%, and other minorities, 3.3%. Regarding marital status, 51.3% of the participants were married, 41.2% single, 6.5% divorced, and 1.0% widowed. Regarding educational attainment, 26.2% had an undergraduate or graduate education, 20.7% had junior college education, 24.8% had high school or secondary technical school education, and 28.2% had middle school education or less. The NGASR identified 57.3% of the participants as being at high suicide risk, while the HEI identified 53.6% as having severe emotional distress. There was a moderate agreement between the HEI and the NGASR scores, with a Kappa value of 0.518 ( P<0.001), indicating statistically significant differences. At an HEI score of 17, the Youden Index peaked at 0.52, predicting high suicide risk with a specificity of 76.36%, a sensitivity of 76.02%, and an area under the ROC curve of 0.829 (95% CI: 0.787-0.871), demonstrating statistically significant differences. Conclusion: HEI and NGASR demonstrate moderate agreement in assessing high suicide risk among depression patients. The HEI questionnaire effectively predicts high suicide risk in patients with depression, with 17 being the optimal cutoff value for assessing high suicide risk.

Evaluating prognostic biomarkers for survival outcomes subject to informative censoring.

Prognostic biomarkers for survival outcomes are widely used in clinical research and practice. Such biomarkers are often evaluated using a C-index as well as quantities based on time-dependent receiver operating characteristic curves. Existing methods for their evaluation generally assume that censoring is uninformative in the sense that the censoring time is independent of the failure time with or without conditioning on the biomarker under evaluation. With focus on the C-index and the area under a particular receiver operating characteristic curve, we describe and compare three estimation methods that account for informative censoring based on observed baseline covariates. Two of them are straightforward extensions of existing plug-in and inverse probability weighting methods for uninformative censoring. By appealing to semiparametric theory, we also develop a doubly robust, locally efficient method that is more robust than the plug-in and inverse probability weighting methods and typically more efficient than the inverse probability weighting method. The methods are evaluated and compared in a simulation study, and applied to real data from studies of breast cancer and heart failure.

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

Background: This study analyzed the risk factors associated with positive surgical margins (PSM) and five-year survival after prostate cancer resection to construct a positive margin prediction model. Methods: We retrospectively analyzed the clinical data of 148 patients treated with prostatectomy. The patients were divided into PSM group and Negative surgical margins (NSM) group. Several parameters were compared between the groups. All patients were followed up for 60 months. The risk factors for PSM and five-year survival were evaluated by univariate analysis, followed by multifactorial dichotomous logistic regression analysis. Finally, ROC curves were plotted for the risk factors to establish a predictive model for PSM after prostate cancer resection. Results: (1) Serum PSA, percentage of positive puncture stitches, clinical stage, surgical approach, Gleason score on puncture biopsy, and perineural invasion were significantly associated with the risk of PSM (P < 0.05). Serum PSA, perineural invasion, Gleason score on puncture biopsy, and percentage of positive puncture stitches were independent risk factors for PSM. (2) Total prostate-specific antigen (tPSA) by puncture, nutritional status, lymph node metastasis, bone metastasis, and seminal vesicle invasion may be risk factors for five-year survival. Lymph node metastasis and nutritional status were the main risk factors for the five-year survival of patients with prostate cancer. (3) After plotting the ROC curve, the area under the curve (AUC) [AUC: 0.776, 95%, confidence interval (CI): 0.725 to 0.854] was found to be a valid predictor of PSM; the AUC [AUC: 0.664, 95%, confidence interval (CI): 0.576 to 0.753] was also a valid predictor of five-year survival (P < 0.05). (4) The scoring system had a standard error of 0.02 and a cut-off value of 6. It predicted PSM after prostate cancer resection with moderate efficacy. Conclusions: Serum PSA, perineural invasion, puncture biopsy Gleason score, and percentage of positive puncture stitches were independent risk factors for positive surgical margins (PSM). Also, lymph node metastasis and nutritional status were the main risk factors for the five-year survival of patients with prostate cancer. Overall, the prediction efficacy of this scoring system concerning the risk of PSM after prostate cancer resection was moderate.

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.

Study on plasma metabolomics profiling of depression in Chinese community-dwelling older adults based on untargeted LC/GC‒MS.

Depression is a serious psychiatric illness that causes great inconvenience to the lives of elderly individuals. However, the diagnosis of depression is somewhat subjective. Nontargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) was used to study the plasma metabolic profile and identify objective markers for depression and metabolic pathway variation. We recruited 379 Chinese community-dwelling individuals aged ≥ 65. Plasma samples were collected and detected by GC/LC‒MS. Orthogonal partial least squares discriminant analysis and a heatmap were utilized to distinguish the metabolites. Receiver operating characteristic curves were constructed to evaluate the diagnostic value of these differential metabolites. Additionally, metabolic pathway enrichment was performed to reveal metabolic pathway variation. According to our standard, 49 people were included in the depression cohort (DC), and 49 people age- and sex-matched individuals were included in the non-depression cohort (NDC). 64 metabolites identified via GC‒MS and 73 metabolites identified via LC‒MS had significant contributions to the differentiation between the DC and NDC, with VIP values > 1 and p values < 0.05. Three substances were detected by both methods: hypoxanthine, phytosphingosine, and xanthine. Furthermore, 1-(sn-glycero-3-phospho)-1D-myo-inositol had the largest area under the curve (AUC) value (AUC = 0.842). The purine metabolic pathway is the most important change in metabolic pathways. These findings show that there were differences in plasma metabolites between the depression cohort and the non-depression cohort. These identified differential metabolites may be markers of depression and can be used to study the changes in depression metabolic pathways.

Exploring Salivary Iodine Concentration as a Biomarker for Iodine Status and Thyroid Nodules in Females From Different Water Iodine Areas: a Cross-sectional Study.

BACKGROUND: It is unclear whether salivary iodine concentration (SIC) can assess iodine status in females from different water iodine regions. OBJECTIVES: Through a cross-sectional study, we explored the feasibility of SIC as a biomarker to assess iodine status in females and develop optimal cutoff values. METHODS: A total of 1991 females were analyzed in this cross-sectional study from the coastal iodine-deficient areas (CIDAs), inland iodine-deficient areas (IIDAs), iodine-adequate areas (IAAs), iodine-excess areas (IEAs), and iodine extra-high areas (IEHAs). SIC, spot urine iodine concentration (SUIC), and daily total iodine intake (TII) were assessed, and ultrasonography was performed in all subjects. RESULTS: There was a positive correlation between SIC and SUIC (r = 0.67; 95% CI: 0.64, 0.69; P < 0.001), and TII (r = 0.47; 95% CI: 0.43, 0.50; P < 0.001). The prevalence of thyroid nodules (TN) showed an upward trend with SIC increasing (Z = -2.83; P-trend = 0.005). The area under the receiver-operating characteristic (ROC) curve for SIC to assess iodine deficiency was 0.62 (95% CI: 0.60, 0.65; P < 0.001) and 0.75 (95% CI: 0.73, 0.77; P < 0.001) for iodine excess. The cutoff values were as follows: SIC < 93.32 µg/L, iodine deficiency; 93.32-224.60 µg/L, iodine adequacy; and >224.60 µg/L, iodine excess. When SIC > 224.60 µg/L, the odds ratio (OR) for UIC > 300 µg/L, excessive TII, and the prevalence of TN were 6.44, 3.68, and 1.27 (95% CI: 4.98, 8.31; 2.83, 4.79; and 1.02, 1.56, respectively; P < 0.05); when SIC < 93.32 µg/L, the OR for UIC < 100 µg/L and insufficient TII were 2.34 and 1.94 (95% CI: 1.73, 3.14 and 1.33, 2.83, respectively; P < 0.05). CONCLUSIONS: Using SIC as a biomarker, females in CIDA exhibited mild iodine deficiency, those in IIDA and IAA demonstrated moderate iodine deficiency, and those in IEA and IEHA exhibited an excess of iodine, consistent with SUIC to assess iodine status. SIC can be used as a good biomarker to evaluate the iodine status in population.

Automated detection of otosclerosis with interpretable deep learning using temporal bone computed tomography images.

Objective: This study aimed to develop an automated detection schema for otosclerosis with interpretable deep learning using temporal bone computed tomography images. Methods: With approval from the institutional review board, we retrospectively analyzed high-resolution computed tomography scans of the temporal bone of 182 participants with otosclerosis (67 male subjects and 115 female subjects; average age, 36.42 years) and 157 participants without otosclerosis (52 male subjects and 102 female subjects; average age, 30.61 years) using deep learning. Transfer learning with the pretrained VGG19, Mask RCNN, and EfficientNet models was used. In addition, 3 clinical experts compared the system's performance by reading the same computed tomography images for a subset of 35 unseen subjects. An area under the receiver operating characteristic curve and a saliency map were used to further evaluate the diagnostic performance. Results: In prospective unseen test data, the diagnostic performance of the automatically interpretable otosclerosis detection system at the optimal threshold was 0.97 and 0.98 for sensitivity and specificity, respectively. In comparison with the clinical acumen of otolaryngologists at P < 0.05, the proposed system was not significantly different. Moreover, the area under the receiver operating characteristic curve for the proposed system was 0.99, indicating satisfactory diagnostic accuracy. Conclusion: Our research develops and evaluates a deep learning system that detects otosclerosis at a level comparable with clinical otolaryngologists. Our system is an effective schema for the differential diagnosis of otosclerosis in computed tomography examinations.

Neutrophil CD64 index as a potential blood biomarker for the diagnosis of neurosyphilis in secondary and tertiary syphilis: A retrospective study.

Objective: To examine the correlation of neutrophil CD64 (nCD64) index with neurosyphilis (NS) across different stages of syphilis. Methods: A total of 1243 syphilis patients at different stages (344 of primary, 385 of secondary, and 514 of tertiary) included in this study were divided into NS and non-NS (NNS). Correlations of nCD64 index with currently used syphilis biomarkers were explored using Spearman correlation test. Relationships between nCD64 index and NS at different stages were investigated by stratified analysis and restricted cubic spline model. The diagnostic performance of nCD64 index for NS was assessed by receiver operating characteristic (ROC) curve. Results: Significant statistical correlations of nCD64 index with cerebrospinal fluid (CSF) NS indicators were found in secondary and tertiary syphilis. Increased nCD64 index was associated with increased risk of NS in secondary and tertiary syphilis. ROC analysis values further confirmed the diagnostic potential of nCD64 index for NS. Marked decrease of nCD64 index was observed in NS patients after effective antisyphilitic treatments. Conclusions: The nCD64 index may help to the diagnosis of NS in secondary and tertiary syphilis.

[Relationship between the percentage of energy intake from macronutrients and obesity among adult residents in 15 provinces of China in 1991-2018].

OBJECTIVE: To explore the relationship between the percentage of energy intake from macronutrients and obesity in Chinese adult residents, and analyze the cut-off values of macronutrients for predicting obesity. METHODS: Data was collected in China Health and Nutrition Survey(CHNS)in 1991-2018. Adults who participated in at least two waves of the surveys and were not obese at baseline were selected as the study subjects. Obesity was defined as body mass index(BMI)≥28.0 kg/m~2. Generalized estimating equation was used to analyze the relationship between the percentage of energy intake from macronutrients and BMI and obesity, and receiver operating characteristic curve(ROC) was used to analyze the cut-off values of percentage of energy intake from macronutrients to predict obesity. RESULTS: The percentage of energy intake from protein and fat of adult residents in 15 provinces(autonomous regions and municipalities) in China showed an increasing trend(P<0.01), and the percentage of energy intake from carbohydrate showed a decreasing trend(P<0.01) between 1991 and 2018. After adjusting for covariates, the group of percentage of energy intake from fat in 20%～30%(ß=0.05, 95%CI 0.01-0.08)and ≥30%(ß=0.15, 95%CI 0.11-0.18)were positively correlated with BMI compared with the group of percentage of energy intake from fat <20%, and the risk of obesity in 20%-30% and ≥ 30% was increased by 17%(OR=1.17, 95%CI 1.04-1.31)and 6%(OR=1.06, 95%CI 1.24-1.56), respectively. Compared with the group of the percentage of energy intake from carbohydrate < 50%, the group of 50% to 65%(ß=-0.08, 95% CI-0.11--0.05) and ≥ 65%(ß=-0.17, 95%CI-0.20--0.13) was negatively correlated with BMI, and the percentage of energy intake from carbohydrate ≥ 65% reduced the risk of obesity(OR=0.71, 95%CI 0.63-0.80). CONCLUSION: Carbohydrate intake was inversely correlated with the risk of obesity, and fat intake was positively correlated with the risk of obesity. Moderate intake of carbohydrates and reduced fat intake can prevent obesity.

Data mining approaches to pneumothorax detection: Integrating mask-RCNN and medical transfer learning techniques.

With the medical condition of pneumothorax, also known as collapsed lung, air builds up in the pleural cavity and causes the lung to collapse. It is a critical disorder that needs to be identified and treated right as it can cause breathing difficulties, low blood oxygen levels, and, in extreme circumstances, death. Chest X-rays are frequently used to diagnose pneumothorax. Using the Mask R-CNN model and medical transfer learning, the proposed work offers•A novel method for pneumothorax segmentation from chest X-rays.•A method that takes advantage of the Mask R-CNN architecture's for object recognition and segmentation.•A modified model to address the issue of segmenting pneumothoraxes and then polish it using a sizable dataset of chest X-rays. The proposed method is tested against other pneumothorax segmentation techniques using a dataset of 'chest X-rays' with 'pneumothorax annotations. The test findings demonstrate that proposed method outperforms other cutting-edge techniques in terms of segmentation accuracy and speed. The proposed method could lead to better patient outcomes by increasing the precision and effectiveness of pneumothorax diagnosis and therapy. Proposed method also benefits other medical imaging activities by using the medical transfer learning approaches which increases the precision of computer-aided diagnosis and treatment planning.

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.

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.

Prognostic value of preoperative radiographic perinephric fat features in renal cell carcinoma patients undergoing surgery.

BACKGROUND: We aimed to assess the prognostic importance of perinephric fat features in images of patients with non-metastatic renal cell carcinoma (RCC) undergoing surgery. METHODS: We enrolled RCC patients who underwent surgical treatment between 2011 and 2019. Two characteristics, including perinephric fat thickness and perinephric fat stranding, were evaluated using preoperative computed tomography or magnetic resonance images. The association between perinephric fat characteristics and disease progression was examined by Kaplan-Meier survival analysis and Cox regression model. RESULTS: In a multivariate Cox proportional hazards model adjusting for tumor stage, intratumoral necrosis, and neutrophil-to-lymphocyte ratio, we found that patients in the thin perinephric fat group (<1 cm) had a poorer progression-free survival (PFS) compared to the thick perinephric fat group (≥1 cm) (HR 2.8; 95% CI 1.175-6.674, p = 0.02). Additionally, the fat stranding group had a poorer PFS than the non-stranding group (HR 3.852; 95% CI 1.082-13.704, p = 0.037). The non-stranding with thick perinephric fat group exhibits the highest cumulative PFS while the stranding with thin perinephric fat group has the lowest cumulative PFS. In receiver operating characteristic curve analysis, combing these two perinephric fat characteristics with tumor stage can achieve a better discriminatory power than tumor stage alone. CONCLUSIONS: Our study indicates that the evaluation of image-based perinephric fat features is a simple, straightforward, reproducible tool for predicting RCC prognosis and may assist in preoperative risk stratification.

Can the ROX index predict high-flow nasal cannula failure in children under 2 with lower respiratory tract infection?

OBJECTIVE: This study evaluates the ROX index's accuracy in predicting the success or failure of high-flow nasal cannula (HFNC) therapy in children under 2 years with acute respiratory failure (ARF) from lower respiratory tract infections. METHODS: From January 2018 to 2021 we conducted this multicenter retrospective cohort study, which included patients aged 2-24 months. We aimed to assess HFNC therapy outcomes as either success or failure. The analysis covered patient demographics, diagnoses, vital signs, and ROX index values at intervals from 0 to 48 h after initiating HFNC. We used bivariate analysis, repeated measures ANOVA, multivariate logistic regression, and the area under the receiver operating characteristic (AUC-ROC) curve for statistical analysis. RESULTS: The study involved 529 patients from six centers, with 198 females (37%) and a median age of 9 months (IQR: 3-15 months). HFNC therapy failed in 38% of cases. We observed significant variability in failure rates across different centers and physicians (p < .001). The ROX index was significantly associated with HFNC outcomes at all time points, showing an increasing trend in success cases over time (p < .001), but not in HFNC failure cases. Its predictive ability is limited, with AUC-ROC values ranging from 0.56 at the start to 0.67 at 48 h. CONCLUSION: While the ROX index is associated with HFNC outcomes in children under 2 years, its predictive ability is modest, impacted by significant variability among patients, physicians, and centers. These findings emphasize the need for more reliable predictive tools for HFNC therapy in this patient population.

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.

Cumulative Exposure to Oxidized Low-density Lipoprotein is a Potential Predictor for Prognosis in Acute Ischemic Stroke: A Cohort Study.

BACKGROUND: Oxidized Low-Density Lipoprotein (ox-LDL) is crucial in the recrudescence and prognosis of acute ischemic stroke (AIS). We aimed to probe into the influence of cumulative ox-LDL exposure on the 90-day prognosis of AIS. METHODS: Patients with AIS were recruited in this research. AIS severity at admission was estimated with infarct volumes and National Institute of Health Stroke Scale (NIHSS) scores. AIS prognosis was assessed using Modified Rankin Scale (mRS) scores at 90 days and the change in NIHSS scores from admission to discharge. Cumulative ox-LDL exposure was defined as ox-LDL level (pg/mL) multiplied by age(y). Multivariate logistic regression analysis was employed to reveal the correlation between exposure factors and the prognosis of AIS. The prognostic prediction ability of cumulative ox-LDL exposure was compared with cumulative LDL exposure by the receiver operating characteristic curve (ROC). RESULTS: Higher cumulative ox-LDL exposure was related to worse prognosis, including neurological worsening at discharge (NIHSS increasing more than 2 points) (OR = 3.02, 95% CI, 1.30-6.98, P = 0.01) and poor functional prognosis at 90 days (mRS ≥ 3) (OR = 21.21, 95% CI, 4.72-95.36, P < 0.001). As multivariate regression analysis showed, significantly increased cumulative ox-LDL exposure was relevant to poor functional prognosis at 90 days (OR = 9.92, 95% CI, 1.23-79.76, P = 0.031), but not with neurological worsening at discharge (P = 0.414). ROC curve revealed that cumulative ox-LDL exposure had a higher predictive value (AUC = 0.843, P < 0.001) for functional prognosis of AIS than cumulative LDL exposure (AUC = 0.629, P = 0.023). CONCLUSION: Cumulative ox-LDL exposure has a positive correlation with poor prognosis at 90 days of AIS, and has a more accurate predictive ability than cumulative LDL exposure.

Relationships between triglyceride-glucose index and incident gestational diabetes mellitus: a prospective cohort study of a Korean population using publicly available data.

Background: The connection between the triglyceride-glucose index (TyG index) and gestational diabetes mellitus (GDM) is currently debated. Our study aimed to investigate the connection between the TyG index and GDM within the Korean population. Methods: Using publically accessible data in Korea, we performed a secondary study on a sample of 589 pregnant women who were carrying a single fetus. The analysis employed a binary logistic regression model, some sensitivity analyses, and subgroup analysis to investigate the association between the TyG index and the occurrence of GDM. To assess the TyG index's potential to predict GDM, a receiver operating characteristic (ROC) study was also carried out. Results: The mean age of the pregnant women was 32.065 ± 3.798 years old, while the mean TyG index was 8.352 ± 0.400. The prevalence rate of GDM was found to be 6.112%. Upon adjusting for potential confounding variables, a positive association was detected between the TyG index and incident GDM (OR = 12.923, 95%CI: 3.581-46.632, p = 0.00009). The validity of this connection was further confirmed by subgroup analysis and sensitivity analyses. With an area under the ROC curve of 0.807 (95%CI: 0.734-0.879), the TyG index showed strong predictive power for GDM. The TyG index's ideal cutoff value for detecting GDM was found to be 8.632, with a sensitivity of 78.7% and a specificity of 72.2%. Conclusion: The findings of our study provide evidence that an increased TyG index is significantly associated with the occurrence of GDM. Utilizing the TyG index during the 10-14 week gestational period may be a valuable tool in identifying pregnant individuals at a heightened risk for developing GDM. Early detection enables timely and efficacious interventions, thereby enhancing the prognosis of affected individuals.