[Value of histological activity in predicting endoscopic relapse among patients of ulcerative colitis in endoscopic remission].

Objective: To investigate the value of histological evaluation in predicting endoscopic relapse among patients with ulcerative colitis (UC) who were in endoscopic remission, and to compare the usefulness of various histological scoring systems. Methods: Histological sections from 61 patients with UC who were in endoscopic remission were retrospectively analyzed, at Peking University Third Hospital, Beijing, China from January 2015 to June 2021. They were subdivided into endoscopic persistent remission group (remission group, n=31, Mayo endoscopic score 0) and endoscopic relapse group (relapse group, n=30, Mayo endoscopic score≥1) according to the results of the first endoscopic reexamination after the biopsy. Histological evaluation was performed using the Geboes score (GS) and its simplified version (SGS), the Nancy index (NI) and the Robarts histopathological index (RHI). The median and maximum histological scores for each case in all biopsies were recorded. Univariate comparisons were performed using chi-squares and multivariate analysis using binary logistic regression. The values of four histological evaluation systems for predicting endoscopic relapse among UC patients in endoscopic remission were analyzed using receiver operating characteristic (ROC) curves. Results: Significant differences were observed between the remission and relapse groups. The differences were more pronounced in the maximum histological scores; the mean and highest results of area under the ROC curve scores (AUC) for GS, SGS, NI, and RHI were 0.657, 0.668, 0.682, 0.691, and 0.866, 0.863, 0.864, 0.869, respectively. The differences were statistically significant (P<0.05). The corresponding best cut-offs were GS≥2B.1, SGS≥2B.1, NI≥2, and RHI≥2.5, respectively, which meant mild active inflammation histologically, while there was no statistical difference of AUC among the four histological scoring indices (P>0.05). Univariate and multivariate analyses revealed statistically significant differences in the number of neutrophils in the epithelium and lamina propria (P<0.05). Conclusions: Biopsies from UC patients in endoscopic remission may still have histological active inflammation which appears to correlate with endoscopic relapse. Four commonly used histological scoring systems can be used to assess the risk of endoscopic relapse among UC patients in endoscopic remission. The patients who more likely have endoscopic relapse seem to have a histological score greater than the cut-off value (i.e., mild histological activity). The maximum histological scores can accurately predict the risk of endoscopic relapse, while the presence of epithelial and laminar propria neutrophil infiltrates can independently predict the endoscopic relapse in these patients. Considering the utility and convenience in routine practice, NI is recommended for evaluating histological inflammatory activity.

Biochemical and Anthropometric Indices of Insulin Resistance in Obese and Overweight Children with Metabolic Dysfunction-Associated Fatty Liver Disease.

BACKGROUND The prevalence of metabolic (dysfunction)-associated fatty liver disease (MAFLD) increases together with the epidemic of childhood obesity. An important mechanism in the phenomenon appears to be insulin resistance (IR), the assessment of which in children is problematic. The homeostatic model assessment of IR (HOMA-IR), commonly used for this, is not standardized and appears not to correlate with IR in the pediatric population. Therefore, our study aimed to evaluate potential substitute indices of IR, including the triglyceride-glucose index (TyG), triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C), modified TyG indices: TyG-waist circumference (TyG-WC) and TyG-body mass index (TyG-BMI) as surrogate markers of MAFLD in obese children suspected to have liver disease. MATERIAL AND METHODS The retrospective study included 264 obese children admitted to the Department to diagnose suspected liver disease. MAFLD was diagnosed according to the International Expert Consensus Statement. Anthropometric measurements and laboratory tests were made and the indices were calculated. Receiver operating characteristics analysis was performed to calculate the power of the indices. RESULTS MAFLD was diagnosed in 184 patients (70%). Obese children with MAFLD showed significantly higher activity of liver enzymes and concentration of total cholesterol, TG, WC, and waist-to-hip ratio compared to non-hepatopathic obese controls (n=80). The most important indices in identifying MAFLD were: TyG (AUC=0.641, p<0.001, cut-off =8.41, sensitivity=57.4%, specificity=68.8%), and TG/HDL-C (AUC=0.638, p<0.001, cut-off=2.5, sensitivity=48.6%, specificity=76.3%). TyG-BMI and HOMA-IR were not useful predictors. CONCLUSIONS TyG and TG/HDL-C can be considered as potential surrogate biomarkers in predicting MAFLD in obese children.

Predictability of the Delta Neutrophil Index and Other Blood Parameters on Perinatal Outcomes in Pregnant Women With Systemic Lupus Erythematosus.

PROBLEM: This study aimed to evaluate the predictive value of delta neutrophil index (DNI), a peripheral blood parameter, on perinatal outcomes in pregnant women with systemic lupus erythematosus (SLE). METHOD OF STUDY: One hundred eighty-one participants, 78 pregnant women with SLE, and 103 healthy pregnant women were included in this retrospective study. Peripheral blood parameters including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and DNI taken in the first trimester were compared between groups. RESULTS: NLR, PLR, and DNI were significantly higher in the SLE group (p = 0.027, p = 0.007, p = 0.0001, respectively). The same parameters were not found to be significant in determining disease activity in pregnant women with SLE (p > 0.05). When the predictive value of DNI for SGA in pregnancies with SLE was evaluated by receiver operating characteristic curve (ROC), the area under the ROC curve (AUC) was 0.666 (95% CI; 0.544-0.788, p = 0.018) with 84.6% sensitivity, 53.8% specificity, 56.0% PPV, and 78.1% NPV at a cut-off value of 0.16. The predictive value of DNI according to ROC for stillbirth in pregnancies with SLE was AUC 0.731 (95% CI: 0.539-0.923, p = 0.019) with a cut-off value of 0.17, sensitivity of 90%, specificity of 51.5%, PPV of 58.5%, and NPV of 87.2%. CONCLUSIONS: Although DNI's prediction of SGA and stillbirth in pregnant women with SLE is encouraging, it needs more evidence from prospective studies with larger series.

Association of Severe Vitamin D Deficiency with Hospitalization in the Previous Year in Hospitalized Exacerbated COPD Patients.

Purpose: Vitamin D deficiency (VDD, 25-hydroxyvitamin D < 20 ng/mL) has been reported associated with exacerbation of chronic obstructive pulmonary disease (COPD) but sometimes controversial. Research on severe vitamin D deficiency (SVDD, 25-hydroxyvitamin D < 10 ng/mL) in exacerbation of COPD is limited. Patients and Methods: We performed a retrospective observational study in 134 hospitalized exacerbated COPD patients. 25-hydroxyvitamin D was modeled as a continuous or dichotomized (cutoff value: 10 or 20 ng/mL) variable to evaluate the association of SVDD with hospitalization in the previous year. Receiver operator characteristic (ROC) analysis was performed to find the optimal cut-off value of 25-hydroxyvitamin D. Results: In total 23% of the patients had SVDD. SVDD was more prevalent in women, and SVDD group tended to have lower blood eosinophils counts. 25-hydroxyvitamin D level was significantly lower in patients who were hospitalized in the previous year (13.6 vs 16.7 ng/mL, P = 0.044), and the prevalence of SVDD was higher (38.0% vs 14.3%, P = 0.002). SVDD was independently associated with hospitalization in the previous year [odds ratio (OR) 4.34, 95% CI 1.61-11.72, P = 0.004] in hospitalized exacerbated COPD patients, whereas continuous 25-hydroxyvitamin D and VDD were not (P = 0.1, P = 0.9, separately). The ROC curve yielded an area under the curve of 0.60 (95% CI 0.50-0.71) with an optimal 25-hydroxyvitamin D cutoff of 10.4 ng/mL. Conclusion: SVDD probably showed a more stable association with hospitalization in the previous year in hospitalized exacerbated COPD patients. Reasons for lower eosinophil counts in SVDD group needed further exploration.

Peripheral PD-1+NK cells could predict the 28-day mortality in sepsis patients.

Background: Unbalanced inflammatory response is a critical feature of sepsis, a life-threatening condition with significant global health burdens. Immune dysfunction, particularly that involving different immune cells in peripheral blood, plays a crucial pathophysiological role and shows early warning signs in sepsis. The objective is to explore the relationship between sepsis and immune subpopulations in peripheral blood, and to identify patients with a higher risk of 28-day mortality based on immunological subtypes with machine-learning (ML) model. Methods: Patients were enrolled according to the sepsis-3 criteria in this retrospective observational study, along with age- and sex-matched healthy controls (HCs). Data on clinical characteristics, laboratory tests, and lymphocyte immunophenotyping were collected. XGBoost and k-means clustering as ML approaches, were employed to analyze the immune profiles and stratify septic patients based on their immunological subtypes. Cox regression survival analysis was used to identify potential biomarkers and to assess their association with 28-day mortality. The accuracy of biomarkers for mortality was determined by the area under the receiver operating characteristic (ROC) curve (AUC) analysis. Results: The study enrolled 100 septic patients and 89 HCs, revealing distinct lymphocyte profiles between the two groups. The XGBoost model discriminated sepsis from HCs with an area under the receiver operating characteristic curve of 1.0 and 0.99 in the training and testing set, respectively. Within the model, the top three highest important contributions were the percentage of CD38+CD8+T cells, PD-1+NK cells, HLA-DR+CD8+T cells. Two clusters of peripheral immunophenotyping of septic patients by k-means clustering were conducted. Cluster 1 featured higher proportions of PD1+ NK cells, while cluster 2 featured higher proportions of naïve CD4+T cells. Furthermore, the level of PD-1+NK cells was significantly higher in the non-survivors than the survivors (15.1% vs 8.6%, P<0.01). Moreover, the levels of PD1+ NK cells combined with SOFA score showed good performance in predicting the 28-day mortality in sepsis (AUC=0.91,95%CI 0.82-0.99), which is superior to PD1+ NK cells only(AUC=0.69, sensitivity 0.74, specificity 0.64, cut-off value of 11.25%). In the multivariate Cox regression, high expression of PD1+ NK cells proportion was related to 28-day mortality (aHR=1.34, 95%CI 1.19 to 1.50; P<0.001). Conclusion: The study provides novel insights into the association between PD1+NK cell profiles and prognosis of sepsis. Peripheral immunophenotyping could potentially stratify the septic patients and identify those with a high risk of 28-day mortality.

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.

BACKGROUND: Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data. AIM: To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients. METHODS: Data of patients treated for colorectal cancer (n = 2044) at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected. Patients were divided into an experimental group (n = 60) and a control group (n = 1984) according to unplanned reoperation occurrence. Patients were also divided into a training group and a validation group (7:3 ratio). We used three different machine learning methods to screen characteristic variables. A nomogram was created based on multifactor logistic regression, and the model performance was assessed using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, and decision curve analysis. The risk scores of the two groups were calculated and compared to validate the model. RESULTS: More patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation (P < 0.05): Prognostic Nutritional Index value, history of laparotomy, hypertension, or stroke, hypoproteinemia, age, tumor-node-metastasis staging, surgical time, gender, and American Society of Anesthesiologists classification. Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility. CONCLUSION: This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.

Lobe-Based Hepatic Uptake Index of Gd-EOB-DTPA on Contrast-Enhanced MRI to Quantitatively Discriminate between Compensated and Decompensated Hepatitis B-Related Cirrhosis.

Purpose: To use hepatic uptake index (HUI) of liver lobes on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) to discriminate between patients with hepatitis B-related cirrhosis in compensated and decompensated statuses. Methods: Forty-four consecutive patients with hepatitis B-related cirrhosis who underwent Gd-EOB-DTPA-enhanced MRI were divided into compensated and decompensated statuses based on clinical evaluation. Volume and signal intensity of individual lobes were retrospectively measured to calculate HUI of the right liver lobe (RHUI), medial (MHUI) and lateral (LHUI) left liver lobes, and caudate lobe (CHUI). Spearman's rank correlation analyses were performed to evaluate relationships of lobe-based HUI with Child-Pugh and model for end-stage liver disease (MELD) scoring system scores in compensated and decompensated statuses. The Mann-Whitney U-test was used to compare the lobe-based HUI between compensated and decompensated statuses. The performance of lobe-based HUI in distinguishing cirrhosis was evaluated using receiver operating characteristic (ROC) analysis, and the area under the ROC curve (AUC) was calculated as a measure of accuracy. Delong's method was used for statistical analysis to elucidate which HUI is optimal. Results: Compensated and decompensated liver cirrhosis were confirmed in 25 (56.82%) and 19 (43.18%) patients, respectively. According to Spearman's rank correlation analysis, RHUI, MHUI, LHUI, and CHUI were all significantly associated with Child-Pugh and MELD scores (all P values <0.05). Receiver operating characteristic analysis demonstrated that among all lobe-based HUI parameters, RHUI could best perform the previous discrimination with a cut-off of 485.73 and obtain an AUC of 0.867. The AUC of RHUI improved and was significantly different from that of MHUI, LHUI, and CHUI (P = 0.03, P = 0.007, and P < 0.001, respectively, Delong's test). Conclusions: The RHUI could help quantitatively discriminate hepatitis B-related cirrhosis between compensated and decompensated statuses.

The potential role of serum amyloid A as biomarker of rheumatic diseases: a systematic review and meta-analysis.

The identification of novel, robust biomarkers for the diagnosis of rheumatic diseases (RDs) and the presence of active disease might facilitate early treatment and the achievement of favourable long-term outcomes. We conducted a systematic review and meta-analysis of studies investigating the acute phase reactant, serum amyloid A (SAA), in RD patients and healthy controls to appraise its potential as diagnostic biomarker. We searched PubMed, Scopus, and Web of Science from inception to 10 April 2024 for relevant studies. We evaluated the risk of bias and the certainty of evidence using the JBI Critical Appraisal Checklist and GRADE, respectively (PROSPERO registration number: CRD42024537418). In 32 studies selected for analysis, SAA concentrations were significantly higher in RD patients compared to controls (SMD = 1.61, 95% CI 1.24-1.98, p < 0.001) and in RD patients with active disease compared to those in remission (SMD = 2.17, 95% CI 1.21-3.13, p < 0.001). Summary receiving characteristics curve analysis showed a good diagnostic accuracy of SAA for the presence of RDs (area under the curve = 0.81, 95% CI 0.78-0.84). The effect size of the differences in SAA concentrations between RD patients and controls was significantly associated with sex, body mass index, type of RD, and study country. Pending the conduct of prospective studies in different types of RDs, the results of this systematic review and meta-analysis suggest that SAA is a promising biomarker for the diagnosis of RDs and active disease.

Re-examining the factor structure of the Insomnia Severity Index (ISI) and defining the meaningful within-individual change (MWIC) for subjects with insomnia disorder in two phase III clinical trials of the efficacy of lemborexant.

BACKGROUND: The Insomnia Severity Index (ISI) is a widely used measure of insomnia severity. Various ISI research findings suggest different factor solutions and meaningful within-individual change (MWIC) to detect treatment response in patients with insomnia. This study examined an ISI factor solution and psychometric indices to define MWIC in a robust patient sample from clinical trial settings. METHODS: We endeavored to improve upon previous validation of ISI by examining structural components of confirmatory factor analysis (CFA) models using two large, placebo-controlled clinical trials of lemborexant for insomnia. Using the best-fitting two-factor solution, we evaluated anchor-based, distribution-based and receiver operating characteristic (ROC) curve methods to derive an estimate of the MWIC. RESULTS: The model structure for the 7-item scale proposed in other research did not fit the observed data from our two lemborexant clinical trials (N = 1956) as well as a two-factor solution based on 6 items did. Using triangulation of anchor-based, distribution-based, and ROC methods, we determined that a 5-point reduction using 6 items best represented a clinically meaningful improvement in individuals with insomnia in our patient sample. CONCLUSIONS: A 6-item two-factor scale had better psychometric properties than the 7-item scale in this patient sample. On the 6-item scale, a reduction of 5 points in the ISI total score represented the MWIC. Generalizability of the proposed MWIC may be limited to patient populations with similar demographic and clinical characteristics.

Caries lesions diagnosis with deep convolutional neural network in intraoral QLF images by handheld device.

OBJECTIVES: This study investigated the effectiveness of a deep convolutional neural network (CNN) in diagnosing and staging caries lesions in quantitative light-induced fluorescence (QLF) images taken by a self-manufactured handheld device. METHODS: A small toothbrush-like device consisting of a 400 nm UV light-emitting lamp with a 470 nm filter was manufactured for intraoral imaging. A total of 133 cases with 9,478 QLF images of teeth were included for caries lesion evaluation using a CNN model. The database was divided into development, validation, and testing cohorts at a 7:2:1 ratio. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated for model performance. RESULTS: The overall caries prevalence was 19.59%. The CNN model achieved an AUC of 0.88, an accuracy of 0.88, a specificity of 0.94, and a sensitivity of 0.64 in the validation cohort. They achieved an overall accuracy of 0.92, a sensitivity of 0.95 and a specificity of 0.55 in the testing cohort. The model can distinguish different stages of caries well, with the best performance in detecting deep caries followed by intermediate and superficial lesions. CONCLUSIONS: Caries lesions have typical characteristics in QLF images and can be detected by CNNs. A QLF-based device with CNNs can assist in caries screening in the clinic or at home. TRIAL REGISTRATION: The clinical trial was registered in the Chinese Clinical Trial Registry (No. ChiCTR2300073487, Date: 12/07/2023).

Sex hormone-binding globulin is a valuable diagnostic indicator of gestational diabetes mellitus.

Objective: To assess the performance of the Sex Hormone-Binding Globulin (SHBG) assay as a diagnostic indicator of Gestational Diabetes Mellitus (GDM) in the study population. Design: Analytical cross-sectional study. Setting: Hospital-based, Benue State University Teaching Hospital (BSUTH), Makurdi, Nigeria. Participants: Women with singleton pregnancies at 24 to 28 weeks gestational age attending Antenatal care at BSUTH, Makurdi. Intervention: Serum SHBG levels were assayed by ELISA during a diagnostic 75-gram Oral Glucose Tolerance Test (OGTT) for assessment of GDM in the cohort of consecutively selected participants who met the inclusion criteria. Main Outcome Measures: Serum levels of SHBG and presence of GDM in the participants. Result: Serum SHBG was significantly negatively correlated (rpb = - 0.534, p-value < 0.001) with the presence of GDM. It had an area under the ROC curve of 0.897 (95% Confidence Interval = 0.858-0.935; p-value < 0.001). A cut-off value of 452.0 nmol/L indicative of GDM had a diagnostic odds ratio of 21.4 in the study population. Conclusion: SHBG is a valuable diagnostic indicator for GDM in the study population. Funding: None declared.

A risk prediction model of gestational diabetes mellitus based on traditional and genetic factors.

BACKGROUND: Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication during pregnancy. We aimed to evaluate a risk prediction model of GDM based on traditional and genetic factors. METHODS: A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to gather general data. Serum test results were collected from the laboratory information system. Independent risk factors for GDM were identified using univariate and multivariate logistic regression analyses. A GDM risk prediction model was constructed and evaluated with the Hosmer-Lemeshow goodness-of-fit test, goodness-of-fit calibration plot, receiver operating characteristic curve and area under the curve. RESULTS: Among traditional factors, age ≥30 years, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms (SNPs) (rs2779116, rs5215, rs11605924, rs7072268, rs7172432, rs10811661, rs2191349, rs10830963, rs174550, rs13266634 and rs11071657) were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk. CONCLUSIONS: Both genetic and traditional factors contribute to the risk of GDM in women, operating through diverse mechanisms. Strengthening the risk prediction of SNPs for postpartum DM among women with GDM history is crucial for maternal and child health protection.

We aimed to evaluate a risk prediction model of gestational diabetes mellitus (GDM) based on traditional and genetic factors. A total of 2744 eligible pregnant women were included. Face-to-face questionnaire surveys were conducted to collect general data. Among traditional factors, age ≥30 years old, family history, GDM history, impaired glucose tolerance history, systolic blood pressure ≥116.22 mmHg, diastolic blood pressure ≥74.52 mmHg, fasting plasma glucose ≥5.0 mmol/L, 1-hour postprandial blood glucose ≥8.8 mmol/L, 2-h postprandial blood glucose ≥7.9 mmol/L, total cholesterol ≥4.50 mmol/L, low-density lipoprotein ≥2.09 mmol/L and insulin ≥11.5 mIU/L were independent risk factors for GDM. Among genetic factors, 11 single nucleotide polymorphisms were identified as potential predictors of the risk of postpartum DM among women with GDM history, collectively accounting for 3.6% of the genetic risk. Both genetic and traditional factors increase the risk of GDM in women.

MITOCHONDRIAL VITIATION CONGRUENTLY APTLY WITH AUTISM SPECTRUM DISORDER.

Mitochondrial dysfunction in autism leads to impair the mitochondria's ability to synthesis adenosine triphosphate (ATP) by impairment citric acid cycle as well as increase anaerobic glycolysis. Aim - measuring and evaluating the levels of mitochondrial markers; including glutamate oxaloacetate transaminase (GOT), glutamate pyruvate transaminase (GPT), malate dehydrogenase, and pyruvate kinase) in the autistic group and knowing the possibility of using these markers to diagnose children with autism spectrum disorder. A case-control study was done in the Al-Zahraa Teaching Hospital (Kut City, Iraq) on 100 Iraqi children (male and female), between (April 2023 and January 2024). Their ages ranged between 3 and 9 years. Among them were 50 patients enrolled as autistic group and 50 healthy enrolled as control group. Blood samples were collected and bioassays for GOT, GPT, pyruvate kinase, and malate dehydrogenase were measured by ELISA technique. The autistic group showed that the urine GOT, urine GPT, serum malate, and serum pyruvate levels in the ASD group was significantly higher (P<0.001) than the control group. The ROC analysis showed that urine GOT, urine GOT, serum malate and serum pyruvate had an accuracy level of (81%,71%,77%, and 80 %) and the area under the curve (AUC) was > 0.7 (0.8),0.7, 0.7(0.76), and 0.7(0.8) thus urine GOT, urine GPT, serum, malate, and serum pyruvate are a valid diagnostic marker. There was a significant difference in the mean urine and serum concentrations of mitochondrial markers (GOT, GPT, malate dehydrogenase, and pyruvate kinase) between autistic children and the control group due to mitochondrial dysfunction.

Age-appropriate BMI cut-offs for malnutrition among older adults in India.

With the increasing prevalence of obesity in India, body mass index (BMI) has garnered importance as a disease predictor. The current World Health Organization (WHO) body mass index (BMI) cut-offs may not accurately portray these health risks in older adults aged 60 years and above. This study aims to define age-appropriate cut-offs for older adults (60-74 years and 75 years and above) and compare the performance of these cut-offs with the WHO BMI cut-offs using cardio-metabolic conditions as outcomes. Using baseline data from the Longitudinal Ageing Study in India (LASI), classification and regression tree (CART) cross-sectional analysis was conducted to obtain age-appropriate BMI cut-offs based on cardio-metabolic conditions as outcomes. Logistic regression models were estimated to compare the association of the two sets of cut-offs with cardio-metabolic outcomes. The area under the receiver operating characteristic curve (AUC), sensitivity and specificity were estimated. Agreement with waist circumference, an alternate measure of adiposity, was conducted. For older adults aged 60-74 years and 75 years and above, the cut-off for underweight reduced from < 18.5 to < 17.4 and < 13.3 respectively. The thresholds for overweight and obese increased for older adults aged 60-74 years old from > = 25 to > 28.8 and > = 30 to > 33.7 respectively. For older adults aged 75 years and above, the thresholds decreased for both categories. The largest improvement in AUC was observed in older adults aged 75 years and above. The newly derived cut-offs also demonstrated higher sensitivity and specificity among all age-sex stratifications. There is a need to adopt greater rigidity in defining overweight/obesity among older adults aged 75 years and above, as opposed to older adults aged 60-74 years old among whom the thresholds need to be less conservative. Further stratification in the low risk category could also improve BMI classification among older adults. These age-specific thresholds may act as improved alternatives of the current WHO BMI thresholds and improve classification among older adults in India.

Construction of a novel prognostic scoring model for HBV-ACLF liver failure based on dynamic data.

Early prognostic assessment of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is important for guiding clinical management and reducing mortality. The aim of this study was to dynamically monitor the clinical characteristics of HBV-ACLF patients, thereby allowing the construction of a novel prognostic scoring model to predict the outcome of HBV-ACLF patients. Clinical data was prospectively collected for 518 patients with HBV-ACLF and randomly divided into training and validation sets. We constructed day-1, day-2, and day-(1 + 3) prognostic score models based on dynamic time points. The prognostic risk score constructed for day-3 was found to have the best predictive ability. The factors included in this scoring system, referred to as DSM-ACLF-D3, were age, hepatic encephalopathy, alkaline phosphatase, total bilirubin, triglycerides, very low-density lipoprotein, blood glucose, neutrophil count, fibrin, and INR. ROC analysis revealed the area under the curve predicted by DSM-ACLF-D3 for 28-day and 90-day mortality (0.901 and 0.889, respectively) was significantly better than those of five other scoring systems: COSSH-ACLF IIs (0.882 and 0.836), COSSH-ACLFs (0.863 and 0.832), CLIF-C ACLF (0.838 and 0.766), MELD (0.782 and 0.762) and MELD-Na (0.756 and 0.731). Dynamic monitoring of the changes in clinical factors can therefore significantly improve the accuracy of scoring models. Evaluation of the probability density function and risk stratification by DSM-ACLF-D3 also resulted in the best predictive values for mortality. The novel DSM-ACLF-D3 prognostic scoring model based on dynamic data can improve early warning, prediction and clinical management of HBV-ACLF patients.

Development and validation of an intraoperative hypothermia nomograph model for patients undergoing video-assisted thoracoscopic lobectomy: a retrospective study.

This study aimed to develop and internally validate a nomogram model for assessing the risk of intraoperative hypothermia in patients undergoing video-assisted thoracoscopic (VATS) lobectomy. This study is a retrospective study. A total of 530 patients who undergoing VATS lobectomy from January 2022 to December 2023 in a tertiary hospital in Wuhan were selected. Patients were divided into hypothermia group (n = 346) and non-hypothermia group (n = 184) according to whether hypothermia occurred during the operation. Lasso regression was used to screen the independent variables. Logistic regression was used to analyze the risk factors of hypothermia during operation, and a nomogram model was established. Bootstrap method was used to internally verify the nomogram model. Receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the model. Calibration curve and Hosmer Lemeshow test were used to evaluate the accuracy of the model. Decision curve analysis (DCA) was used to evaluate the clinical utility of the model. Intraoperative hypothermia occurred in 346 of 530 patients undergoing VATS lobectomy (65.28%). Logistic regression analysis showed that age, serum total bilirubin, inhaled desflurane, anesthesia duration, intraoperative infusion volume, intraoperative blood loss and body mass index were risk factors for intraoperative hypothermia in patients undergoing VATS lobectomy (P < 0.05). The area under ROC curve was 0.757, 95% CI (0.714-0.799). The optimal cutoff value was 0.635, the sensitivity was 0.717, and the specificity was 0.658. These results suggested that the model was well discriminated. Calibration curve has shown that the actual values are generally in agreement with the predicted values. Hosmer-Lemeshow test showed that χ2 = 5.588, P = 0.693, indicating that the model has a good accuracy. The DCA results confirmed that the model had high clinical utility. The nomogram model constructed in this study showed good discrimination, accuracy and clinical utility in predicting patients with intraoperative hypothermia, which can provide reference for medical staff to screen high-risk of intraoperative hypothermia in patients undergoing VATS lobectomy.

A clinical tool to predict overall survival of elderly patients with soft tissue sarcoma after surgical resection.

With the aging world population, the incidence of soft tissue sarcoma (STS) in the elderly gradually increases and the prognosis is poor. The primary goal of this research was to analyze the relevant risk factors affecting the postoperative overall survival in elderly STS patients and to provide some guidance and assistance in clinical treatment. The study included 2,353 elderly STS patients from the Surveillance, Epidemiology, and End Results database. To find independent predictive variables, we employed the Cox proportional risk regression model. R software was used to develop and validate the nomogram model to predict postoperative overall survival. The performance and practical value of the nomogram were evaluated using calibration curves, the area under the curve, and decision curve analysis. Age, tumor primary site, disease stage, tumor size, tumor grade, N stage, and marital status, are the risk variables of postoperative overall survival, and the prognostic model was constructed on this basis. In the two sets, both calibration curves and receiver operating characteristic curves showed that the nomogram had high predictive accuracy and discriminative power, while decision curve analysis demonstrated that the model had good clinical usefulness. A predictive nomogram was designed and tested to evaluate postoperative overall survival in elderly STS patients. The nomogram allows clinical practitioners to more accurately evaluate the prognosis of individual patients, facilitates the progress of individualized treatment, and provides clinical guidance.

Understanding the value of monocyte distribution width (MDW) in acutely ill medical patients presenting to the emergency department: a prospective single center evaluation.

The monocyte distribution width (MDW) has emerged as a promising biomarker for accurate and early identification of patients with potentially life-threatening infections. Here we tested the diagnostic performance of MDW in adult patients requiring hospital admission for community-acquired infections and sepsis, evaluated sources of heterogeneity in the estimates of diagnostic accuracy, and assessed the meaning of MDW in a patient population presenting to the emergency department (ED) for acute non-infectious conditions. 1925 consecutive patients were categorized into three groups: non-infection (n = 1507), infection (n = 316), and sepsis/septic shock (n = 102). Diagnostic performance for infection or sepsis of MDW alone or in combination with components of SOFA was tested using AUC of ROC curves, sensitivity, and specificity. The relationship between MDW and different pathogens as well as the impact of non-infectious conditions on MDW values were explored. For the prediction of infection, the AUC/ROC of MDW (0.84) was nearly overlapping that of procalcitonin (0.83), and C-reactive protein (0.89). Statistical optimal cut-off value for MDW was 21 for predicting infection (sensitivity 73%, specificity 82%) and 22 for predicting sepsis (sensitivity 79%, specificity 83%). The best threshold to rule out infection was MDW ≤ 17 (NPV 96.9, 95% CI 88.3-100.0), and ≤ 18 (NPV 99.5, 95% CI 98.3-100.0) to rule out sepsis. The combination of MDW with markers of organ dysfunction (creatinine, bilirubin, platelets) substantially improved the AUC (0.96 (95% CI 0.94-0.97); specificity and sensitivity of 88% and 94%, respectively). In conclusion, MDW has a good diagnostic performance in diagnosing infection and sepsis in patients presenting in ED. Its use as an infection marker even increases when combined with other markers of organ dysfunction. Understanding the impact of interactions of non-infectious conditions and comorbidities on MDW and its diagnostic accuracy requires further elucidation.

Risk factors and predictive models for frozen shoulder.

This study aims to explore the risk factors associated with frozen shoulder (FS) and develop a predictive model for diagnosing FS, in order to facilitate early detection of the condition. A total of 103 patients diagnosed with FS and admitted to the Department of Joint Surgery at Suining Central Hospital between October 2021 and October 2023 were consecutively included in the study. Additionally, 309 individuals without shoulder joint diseases, matched for age and gender, who visited the department during the same time, were included as the control group.The complete recording of clinical data for all patients was followed by the utilization of statistical tests such as the Mann-Whitney U test, sample t test, and chi-square test to compare different groups. Additionally, multivariate binary logistic regression analysis was employed to identify risk factors associated with the occurrence of FS in patients, leading to the establishment of a prediction model and derivation of a simplified equation. The diagnostic effectiveness of individual indicators and prediction models was assessed through the use of receiver operating characteristic (ROC) curve analysis. In the sample of 103 individuals, 35 were identified as male and 68 as female, with an average age range of 40-70 years (mean age: 54.20 ± 6.82 years). The analysis conducted between different groups revealed that individuals with a low body mass index (BMI), in conjunction with other factors such as diabetes, cervical spondylosis, atherosclerosis, and hyperlipidemia, were more susceptible to developing FS. Logistic regression analysis further indicated that low BMI, diabetes, cervical spondylosis, and hyperlipidemia were significant risk factors for the occurrence of FS. These variables were subsequently incorporated into a predictive model, resulting in the creation of a simplified equation.The ROC curve demonstrated that the combined indicators in the predictive model exhibited superior diagnostic efficacy compared to single indicators, as evidenced by an area under the curve of 0.787, sensitivity of 62.1%, and specificity of 82.2%. Low BMI, diabetes, cervical spondylosis, and hyperlipidemia are significant risk factors associated with the occurrence of FS. Moreover, the utilization of a prediction model has demonstrated superior capability in forecasting the likelihood of FS compared to relying solely on individual indicators. This finding holds potential in offering valuable insights for the early diagnosis of FS.

Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images.

This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9) expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms associated with pathomics. Here, we included 127 GBM patients, 78 of whom were randomly allocated to the training and test cohorts for pathomics modeling. The prognostic significance of MMP9 was assessed using Kaplan-Meier and Cox regression analyses. PyRadiomics was used to extract the features of H&E-stained whole slide images. Feature selection was performed using the maximum relevance and minimum redundancy (mRMR) and recursive feature elimination (RFE) algorithms. Prediction models were created using support vector machines (SVM) and logistic regression (LR). The performance was assessed using ROC analysis, calibration curve assessment, and decision curve analysis. MMP9 expression was elevated in patients with GBM. This was an independent prognostic factor for GBM. Six features were selected for the pathomics model. The area under the curves (AUCs) of the training and test subsets were 0.828 and 0.808, respectively, for the SVM model and 0.778 and 0.754, respectively, for the LR model. The C-index and calibration plots exhibited effective estimation abilities. The pathomics score calculated using the SVM model was highly correlated with overall survival time. These findings indicate that MMP9 plays a crucial role in GBM development and prognosis. Our pathomics model demonstrated high efficacy for predicting MMP9 expression levels and prognosis of patients with GBM.