Receiver Operating Characteristic Curve Analysis Research Articles

Individualized radiology screening for newly diagnosed nasopharyngeal carcinoma

ObjectivesCurrent guidelines recommend universal PET/CT screening for metastases staging in newly diagnosed nasopharyngeal carcinoma (NPC) despite the low rate of synchronous distant metastasis (SDM). The study aims to achieve individualized screening recommendations of NPC based on the risk of SDM. Methods and materials18 pre-treatment peripheral blood indicators was retrospectively collected from 2271 primary NPC patients. A peripheral blood risk score (PBRS) was constructed by indicators associated with SDM on least absolute shrinkage and selection operator (LASSO) regression. The PBRS-based distant metastases (PBDM) model was developed from features selected by logistic regression analyses in the training cohort and then validated in the validation cohort. Receiver operator characteristic curve analysis, calibration curves, and decision curve analysis were applied to evaluate PBDM model performance. ResultsPre-treatment Epstein–Barr viral DNA copy number, percentage of total lymphocytes, serum lactate dehydrogenase level, and monocyte-to-lymphocyte ratio were most strongly associated with SDM in NPC and used to construct the PBRS. Sex (male), T stage (T3-4), N stage (N2-3), and PBRS (≥1.076) were identified as independent risk factors for SDM and applied in the PBDM model, which showed good performance. Through the model, patients in the training cohort were stratified into low-, medium-, and high-risk groups. Individualized screening recommendations were then developed for patients with differing risk levels. ConclusionThe PBDM model offers individualized recommendations for applying PET/CT for metastases staging in NPC, allowing more targeted screening of patients with greater risk of SDM compared with current recommendations.

Preoperative factors associated with patient satisfaction after modified kinematically aligned total knee arthroplasty in varus knees

PurposeKinematically aligned-total knee arthroplasty (KA-TKA) has recently attracted significant interest because it delivers better clinical outcomes than mechanically aligned (MA)-TKA. However, the optimal preoperative factors that maximize the effectiveness of KA-TKA remain controversial. Therefore, we aimed to determine the preoperative factors that are appropriate for and might benefit from modified KA-TKA, based on patient-reported outcome measures (PROMs). MethodsThe relationships between postoperative satisfaction and other variables in 84 patients with varus-type osteoarthritis of the knee who underwent primary modified KA-TKAs were investigated using stepwise multiple regression analysis. Patients were assigned to groups that were satisfied or dissatisfied with their outcomes after KA-TKA according to the 2011 Knee Society Scores. The cutoff for preoperative advanced activities scores (AASs) for excellent satisfaction were determined by analyzing receiver operating characteristic (ROC) curves. The variables correlated with excellent satisfaction were identified using logistic regression models. ResultsA preoperative variable relevant to patient satisfaction was AAS (β ​= ​0.465). The results of the ROC curve analysis revealed eight optimal cut-offs for preoperative AASs that predicted excellent satisfaction, with a sensitivity and specificity of 0.700 and 0.625, respectively. Logistic regression analysis revealed that AASs significantly contributed to excellent patient satisfaction (odds ratio, 1.181; p ​= ​0.001). ConclusionsIndividuals who were highly active preoperatively were good candidates for KA-TKA. This modified kinematically aligned surgical technique can satisfy the demands of highly active patients.

Defining "High Recurrence" of Depressive Episodes for Predicting Diagnostic Conversion from Major Depressive Disorder to Bipolar Disorder: A 5-year Retrospective Study.

This study determined the threshold for recurrent depressive episodes that predicted conversion from major depressive disorder (MDD) to bipolar disorder (BD). We retrospectively reviewed the medical records of 296 patients diagnosed with MDD for a minimum of 5 years in two university hospitals. We examined their the Diagnostic and Statistical Manual of Mental Disorders, 5th edition diagnoses and detailed clinical information at the initial admission and yearly assessments after discharge to establish the threshold for recurrent depressive episodes indicating a risk of diagnostic conversion from MDD to BD. Optimal cut-offs were derived using receiver operating characteristic (ROC) curves. ROC curve analysis revealed that more than four recurrent depressive episodes was indicative of potential diagnostic conversion from MDD to BD (area under the curve, 0.604; sensitivity, 0.353; specificity, 0.855; positive predictive value, 0.421; negative predictive value, 0.816). These findings suggest that the best predictor of conversion from MDD to BD is more than four recurrent depressive episodes. Our findings have the potential to enhance diagnostic accuracy and treatment efficiency. To validate our results, longitudinal prospective studies are necessary.

Clinical significance of 18F-FDG-PET/CT for detection of incidental pre-malignant and malignant colonic lesions: correlation with colonoscopic and histopathological results

BackgroundIncidental colorectal fluorodeoxyglucose (FDG) uptake, observed during positron emission tomography/computed tomography (PET/CT) scans, attracts particular attention due to its potential to represent both benign and pre-malignant/malignant lesions. Early detection and excision of these lesions are crucial for preventing cancer development and reducing mortality. This research aims to evaluate the correlation between incidental colorectal FDG uptake on PET/CT with colonoscopic and histopathological results.MethodsRetrospective analysis was performed on data from all patients who underwent PET/CT between December 2019 and December 2023 in our hospital. The study included 79 patients with incidental colonic FDG uptake who underwent endoscopy. Patient characteristics, imaging parameters, and the corresponding colonoscopy and histopathological results were studied. A comparative analysis was performed among the findings from each of these modalities. The optimal cut-off value of SUVmax for 18F-FDG PET/CT diagnosis of premalignant and malignant lesions was determined by receiver operating characteristic (ROC) curves. The area under the curve (AUC) of SUVmax and the combined parameters of SUVmax and colonic wall thickening (CWT) were analyzed.ResultsAmong the 79 patients with incidental colorectal FDG uptake, histopathology revealed malignancy in 22 (27.9%) patients and premalignant polyps in 22 (27.9%) patients. Compared to patients with benign lesions, patients with premalignant and malignant lesions were more likely to undergo a PET/CT scan for primary evaluation (p = 0.013), and more likely to have focal GIT uptake (p = 0.001) and CWT (p = 0.001). A ROC curve analysis was made and assesed a cut-off value of 7.66 SUVmax (sensitivity: 64.9% and specificity: 82.4%) to distinguish premalignant and malignant lesions from benign lesions. The AUCs of the SUVmax and the combined parameters of SUVmax and CWT were 0.758 and 0.832 respectively.ConclusionFor patients undergo PET/CT for primary evaluation, imaging features of colorectal focal FDG uptake and CWT were more closely associated with premalignant and malignant lesions. The SUVmax helps determine benign and premalignant/malignant lesions of the colorectum. Moreover, the combination of SUVmax and CWT parameters have higher accuracy in estimating premalignant and malignant lesions than SUVmax.

Deep learning system for malignancy risk prediction in cystic renal lesions: a multicenter study.

To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs). In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model's performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA). From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48-65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52-69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53-69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000). The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility. In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment. The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility.

A novel tRNA-derived fragment tRF-17-18VBY9M works as a potential diagnostic biomarker for gastric cancer

BackgroundGastric cancer (GC) is one of the most prevalent malignant tumors worldwide. The low effectiveness of common biomarkers for the detection of early GC makes it essential to seek new biomarkers to improve diagnostic efficacy. tsRNAs (transfer RNA-derived small RNAs) are related to the growth of malignant tumors. In this article, we focused on whether tsRNAs may be employed as biomarkers for GC.MethodstRF-17-18VBY9M was screened in the tsRFun database as a research object. The methodological efficacy of tRF-17-18VBY9M was evaluated using Sanger sequencing, agarose gel electrophoresis assays, and gradient dilution. The χ2 test was applied to assess the interaction between tRF-17-18VBY9M expression and clinicopathologic characteristics. The receiver operating characteristic (ROC) curve was utilized to investigate the clinical efficiency of tRF-17-18VBY9M in GC.ResultsThe Chi-square test demonstrated that high-expressed tRF-17-18VBY9M was closely associated with the T stage, tumor node metastasis stage (TNM), lymph node metastasis, and neurological/vascular invasion. ROC curve analysis revealed that the diagnostic value of tRF-17-18VBY9M in GC was superior to carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), and carbohydrate antigen 724 (CA724).ConclusiontRF-17-18VBY9M is up-regulated in both GC sera and tissues. Differential tRF-17-18VBY9M expression distinguishes GC patients from healthy donors and gastritis patients, which suggests tRF-17-18VBY9M could act as a diagnostic biomarker in GC.

Systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio in patients with type 2 diabetes at different stages of diabetic retinopathy.

To investigate systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) levels in patients with type 2 diabetes at different stages of diabetic retinopathy (DR). This retrospective study included 141 patients with type 2 diabetes mellitus (DM): 45 without diabetic retinopathy (NDR), 47 with non-proliferative diabetic retinopathy (NPDR), and 49 with proliferative diabetic retinopathy (PDR). Complete blood counts were obtained, and NLR, PLR, and SII were calculated. The study analysed the ability of inflammatory markers to predict DR using receiver operating characteristic (ROC) curves. The relationships between DR stages and SII, PLR, and NLP were assessed using multivariate logistic regression. The average NLR, PLR, and SII were higher in the PDR group than in the NPDR group (P=0.011, 0.043, 0.009, respectively); higher in the NPDR group than in the NDR group (P<0.001 for all); and higher in the PDR group than in the NDR group (P<0.001 for all). In the ROC curve analysis, the NLR, PLR, and SII were significant predictors of DR (P<0.001 for all). The highest area under the curve (AUC) was for the PLR (0.929 for PLR, 0.925 for SII, and 0.821 for NLR). Multivariate regression analysis indicated that NLR, PLR, and SII were statistically significantly positive and independent predictors for the DR stages in patients with DM [odds ratio (OR)=1.122, 95% confidence interval (CI): 0.200-2.043, P<0.05; OR=0.038, 95%CI: 0.018-0.058, P<0.05; OR=0.007, 95%CI: 0.001-0.01, P<0.05, respectively). The NLR, PLR, and SII may be used as predictors of DR.

Dual FDG/PSMA PET imaging to predict lesion-based progression of mCRPC during PSMA-RLT

Candidates for prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) of metastatic castration-resistant prostate cancer (mCRPC) frequently have “mismatch” lesions with pronounced 18-fluorodeoxyglucose ([18F]FDG) but attenuated PSMA ligand uptake on positron emission tomography (PET). However, no quantitative criteria yet exist to identify mismatch lesions and predict their response to RLT. To define such criteria, we retrospectively analyzed 267 randomly-selected glucometabolic mCRPC metastases from 22 patients. On baseline PET, we determined [18F]FDG and [68Ga]Ga-PSMA-11 maximum standardized uptake value (SUVmax), and calculated the [18F]FDG SUVmax/[68Ga]Ga-PSMA-11 SUVmax quotient (FPQ). From follow-up [18F]FDG PET after two lutetium-177-PSMA-617 RLT cycles, we evaluated the treatment response and categorized the lesions into three subgroups (partial remission, stable disease, progression) based on change in [18F]FDG SUVmax. Lastly, we compared the baseline PET variables in progressing versus non-progressing lesions. Variables differing significantly, and a score incorporating them, were assessed via receiver operator characteristic (ROC) curve analysis, regarding ability to predict lesional progression, with area under the curve (AUC) as metric. Cut-offs with optimal sensitivity and specificity were determined using the maximum value of Youden's index. Fifty-one of 267 lesions (19.1%) progressed, 102/267 (38.2%) manifested stable disease, and 114/267 (42.7%) partially responded after two RLT cycles. At baseline, median [68Ga]Ga-PSMA-11 SUVmax was significantly lower (p < 0.001), median FPQ significantly higher (p < 0.001), and median [18F]FDG SUVmax similar in progressing versus non-progressing lesions. [68Ga]Ga-PSMA-11 SUVmax and FPQ showed predictive power regarding progression (AUCs: 0.89, 0.90). An introduced clinical score combining both further improved predictive performance (AUC: 0.94). Optimal cut-offs to foretell progression were: [68Ga]Ga-PSMA-11 SUVmax < 11.09 (88.2% sensitivity, 81.9% specificity), FPQ ≥ 0.92 (90.2% sensitivity, 78.7% specificity), clinical score ≥ 6/9 points (88.2% sensitivity, 87.5% specificity). At baseline, a low [68 Ga]Ga-PSMA-11 SUVmax and a high FPQ predict early lesional progression under RLT; [18F]FDG SUVmax does not. A score combining [68 Ga]Ga-PSMA-11 SUVmax and FPQ predicts early lesional progression even more effectively and might therefore be useful to quantitatively identify mismatch lesions.

Upstroke time is a more useful marker of atherosclerosis than percentage of mean arterial pressure for detecting coronary artery disease in subjects with a normal ankle-brachial index.

Upstroke time (UT) and percentage of mean arterial pressure (%MAP) at the ankle have been shown to serve as atherosclerotic markers. The purpose of this study was to directly compare the diagnostic accuracy of UT with that of %MAP for clinical coronary artery disease (CAD) in subjects with a normal ankle-brachial index (ABI) in both legs. We measured UT and %MAP in 1953 subjects with a normal ABI. The optimal cutoff values of UT and %MAP derived from a receiver operating characteristic (ROC) curve to diagnose CAD were 148 ms and 40.4%, respectively. Multivariable analyses revealed that both UT ≥ 148 ms (odds ratio [OR], 2.72; p < 0.001) and %MAP ≥ 40.4% (OR, 1.28; p = 0.003) were significantly associated with CAD. When the subjects were divided into four groups according to the cutoff values of UT and %MAP, there was no significant difference in the risk of CAD between subjects with UT ≥ 148 ms and %MAP < 40.4% and those with UT ≥ 148 ms and %MAP ≥ 40.4% (OR, 1.45; p = 0.09). ROC curve analyses revealed that the area under the curve value of UT was significantly higher than that of %MAP (0.69 vs. 0.53, p < 0.001). The addition of UT to traditional risk factors significantly improved the diagnostic accuracy for CAD (0.82 to 0.84, p = 0.004), whereas the addition of %MAP to traditional risk factors did not improve the diagnostic accuracy for CAD (0.82 to 0.82, p = 0.84). UT is more useful than %MAP for identifying individuals with CAD among those with a normal ABI.

Blister formation in acute compartment syndrome: Unraveling the underlying predictors.

Blisters are a common complication of orthopedic trauma and can cause surgery delay and increase the risk of infection. This study aims to identify risk factors for blisters in patients with acute compartment syndrome (ACS). Our study collected data from 206 ACS patients admitted to 2 hospitals between November 2013 and January 2021. Patients were divided into 2 groups: the blister group (BG) and the control group (CG), based on the presence or absence of blisters. We conducted univariate analysis, logistic regression analysis, and receiver operating characteristic (ROC) curve analysis to identify any significant differences in demographics, comorbidities, and admission laboratory test results between the 2 groups. Our study found that the incidence of blisters in ACS patients was 21.8% (45 out of 206). Univariate analysis identified several factors that were significantly associated with blister formation. Logistic regression analysis showed that patients who developed ACS in the winter or spring (P = .007, OR = 2.690, 95% CI [1.308-5.534]), patients who received a referral (the process whereby patients are transferred between medical facilities for further evaluation and treatment attempts prior to admission to our hospital) (P = .009, OR = 4.235, 95% CI [1.432-12.527]), and patients with higher PLR (P = .036, OR = 1.005, 95% CI [1.000-1.009]) were independent risk factors for blisters. Additionally, a history of drinking (P = .039, OR = 0.027, 95% CI [0.046-0.927]) was found to be a protective factor for blister formation in these patients. Moreover, ROC curve analysis showed that a PLR value of 138 was the cutoff point for predicting the development of blisters in ACS patients. Our study identified seasonal factors (refer to these months like winter or spring), referral, and patients with higher PLR as independent risk factors, and a history of drinking as a protective factor for blister formation in ACS patients. These findings allow clinicians to individualize the evaluation of blister risk and perform early targeted therapies.

A predictive model in patients with chronic hydrocephalus following aneurysmal subarachnoid hemorrhage: a retrospective cohort study

ObjectiveOur aim was to develop a nomogram that integrates clinical and radiological data obtained from computed tomography (CT) scans, enabling the prediction of chronic hydrocephalus in patients with aneurysmal subarachnoid hemorrhage (aSAH).MethodA total of 318 patients diagnosed with subarachnoid hemorrhage (SAH) and admitted to the Department of Neurosurgery at the Affiliated People’s Hospital of Jiangsu University between January 2020 and December 2022 were enrolled in our study. We collected clinical characteristics from the hospital’s medical record system. To identify risk factors associated with chronic hydrocephalus, we conducted both univariate and LASSO regression models on these clinical characteristics and radiological features, accompanied with penalty parameter adjustments conducted through tenfold cross-validation. All features were then incorporated into multivariate logistic regression analyses. Based on these findings, we developed a clinical-radiological nomogram. To evaluate its discrimination performance, we conducted Receiver Operating Characteristic (ROC) curve analysis and calculated the Area Under the Curve (AUC). Additionally, we employed calibration curves, and utilized Brier scores as an indicator of concordance. Additionally, Decision Curve Analysis (DCA) was performed to determine the clinical utility of our models by estimating net benefits at various threshold probabilities for both training and testing groups.ResultsThe study included 181 patients, with a determined chronic hydrocephalus prevalence of 17.7%. Univariate logistic regression analysis identified 11 potential risk factors, while LASSO regression identified 7 significant risk factors associated with chronic hydrocephalus. Multivariate logistic regression analysis revealed three independent predictors for chronic hydrocephalus following aSAH: Periventricular white matter changes, External lumbar drainage, and Modified Fisher Grade. A nomogram incorporating these factors accurately predicted the risk of chronic hydrocephalus in both the training and testing cohorts. The AUC values were calculated as 0.810 and 0.811 for each cohort respectively, indicating good discriminative ability of the nomogram model. Calibration curves along with Hosmer-Lemeshow tests demonstrated excellent agreement between predicted probabilities and observed outcomes in both cohorts. Furthermore, Brier scores (0.127 for the training and 0.09 for testing groups) further validated the predictive performance of our nomogram model. The DCA confirmed that this nomogram provides superior net benefit across various risk thresholds when predicting chronic hydrocephalus. The decision curve demonstrated that when an individual’s threshold probability ranged from 5 to 62%, this model is more effective in predicting the occurrence of chronic hydrocephalus after aSAH.ConclusionA clinical-radiological nomogram was developed to combine clinical characteristics and radiological features from CT scans, aiming to enhance the accuracy of predicting chronic hydrocephalus in patients with aSAH. This innovative nomogram shows promising potential in assisting clinicians to create personalized and optimal treatment plans by providing precise predictions of chronic hydrocephalus among aSAH patients.

Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model.

Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF). To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model. Thirty mice with carbon tetrachloride (CCl4)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000s/mm2) diffusion-weighted imaging (DWI) on a 3T scanner. DWI data were processed using RSI model (2-5 compartments) with the Bayesian Information Criterion (BIC) determining the optimal model. Conventional ADC value and signal fraction of each compartment in the optimal RSI model were compared across groups. Receiver operating characteristics (ROC) curve analysis was performed to determine the diagnosis performances of different parameters, while Spearman correlation analysis was employed to investigate the correlation between different tissue compartments and the stage of LF. According to BIC results, a 4-compartment RSI model (RSI4) with optimal ADCs of 0.471×10-3, 1.653×10-3, 9.487×10-3, and>30×10-3, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C1 and C3 compartments were observed between LF and control groups (P=0.018 and 0.003, respectively). ROC analysis showed that RSI4-C3 was the most effective single diffusion parameter for characterizing LF (AUC=0.876, P=0.003). Furthermore, the combination of ADC values and RSI4-C3 value increased the diagnosis performance significantly (AUC=0.894, P=0.002). The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI4-C3 showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI4-C3 values further improved the discrimination performance.

Fecal proteolytic profiling of pediatric inflammatory bowel disease: A pilot study.

Colonoscopy is the gold standard for diagnosing inflammatory bowel disease (IBD). However, this invasive procedure has a high burden for pediatric patients. Previous research has shown elevated fecal amino acid concentrations in children with IBD versus controls. We hypothesized that this finding could result from increased proteolytic activity. Therefore, the aim of this study was to investigate whether fecal protease-based profiling was able to discriminate between IBD and controls. Protease activity was measured in fecal samples from patients with IBD (Crohn's disease (CD) n = 19; ulcerative colitis (UC) n = 19) and non-IBD controls (n = 19) using a fluorescence resonance energy transfer (FRET)-peptide library. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic value of each FRET-peptide substrate. Screening the FRET-peptide library revealed an increased total proteolytic activity (TPA), as well as degradation of specific FRET-peptides specifically in fecal samples from IBD patients. Based on level of significance (p < .001) and ROC curve analysis (AUC > 0.85), the fluorogenic substrates W-W, A-A, a-a, F-h, and H-y showed diagnostic potential for CD. The substrates W-W, a-a, T-t, G-v, and H-y showed diagnostic potential for UC based on significance (p < .001) and ROC analysis (AUC > 0.90). None of the FRET-peptide substrates used was able to differentiate between protease activity in fecal samples from CD versus UC. This study showed an increased fecal proteolytic activity in children with newly diagnosed, treatment-naïve, IBD. This could lead to the development of novel, noninvasive biomarkers for screening and diagnostic purposes.

Less Acetabular Coverage Predicts the Failure of Core Decompression for Osteonecrosis of the Femoral Head: A Retrospective Cohort Study.

It is unclear whether less acetabular coverage is associated with the failure of core decompression (CD) for osteonecrosis of the femoral head (ONFH). This study aimed to investigate the clinical outcomes of CD for ONFH with small- or medium-sized pre-collapse lesions, and determine what factors, especially acetabular anatomical parameters, predict the failure of CD. Between January 2010 and December 2022, we retrospectively reviewed 269 consecutive CDs in 188 patients diagnosed with ONFH with small- or medium-sized pre-collapse lesions. The Kaplan-Meier method was used to evaluate the survival rate of CD for ONFH with progression of collapse or conversion to total hip arthroplasty (THA) as the endpoint. Univariate and multivariate logistic regression analyses were conducted to identify the potential risk factors for the failure of CD. Receiver operating characteristic (ROC) curve analysis was further performed with conversion to THA as the endpoint to determine the predictive value of these factors. The overall 5-year survival rate of CD for ONFH with small- or medium-sized pre-collapse lesions was 74.3% (95% confidence interval (CI) 69.0%-81.1%) with progression of collapse as the endpoint and 83.9% (95% CI 79.3%-88.7%) with conversion to THA as the endpoint. Univariate logistic regression analysis showed that bilateral affected hips was significantly associated with progression of collapse, and center-edge angle (CEA), sharp angle, acetabular head index (AHI), as well as acetabular depth ratio (ADR) were significantly associated with both progression of collapse and conversion to THA. Multivariate logistic regression analysis further indicated that CEA and AHI were independent risk factors for both progression of collapse and conversion to THA. ROC curve analysis with conversion to THA as the endpoint revealed that the cutoff values for CEA and AHI were 26.8° (sensitivity = 74.4%, specificity = 78.6%, area under the curve (AUC) = 0.809) and 79.8 (sensitivity = 78.4%, specificity = 73.8%, AUC = 0.818), respectively. CD showed satisfactory clinical outcomes for ONFH with small- or medium-sized pre-collapse lesions where less acetabular coverage with a CEA < 26.8° or AHI < 79.8 was identified as an independent risk factor for the failure of CD.

Stratifying Mortality Risk in Intensive Care: A Comprehensive Analysis Using Cluster Analysis and Classification and Regression Tree Algorithms

Abstract Background Machine learning (ML) can be promising for stratifying patients into homogeneous groups and assessing mortality based on score combination. Using ML, we compared mortality prediction performance for clustered and non-clustered models and tried to develop a simple decision algorithm to predict the patient’s cluster membership with classification and regression trees (CART). Methods Retrospective study involving patients requiring ICU admission (1st January 2011–16th September 2022). Clusters were identified by combining Charlson Comorbidity Index (CCI) plus Simplified Acute Physiology Score II (SAPS II) or Sequential Organ Failure Assessment (SOFA). Intercluster and survival analyses were performed. We analyzed the relationship with mortality with multivariate logistic regressions and receiver operating characteristic curves (ROC) for models with and without clusters. Nested models were compared with Likelihood Ratio Tests (LRT). Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were compared for non-nested models. With the best model, we used CART to build a decision tree for patient’s membership. Results Our sample consisted of 2605 patients (mortality 59.7%). For both score combinations, we identified two clusters (A and B for CCI + SAPS II, α and β for CCI + SOFA). Belonging to cluster B/β was associated with shorter survival times (Peto-Peto p-values &lt; 0.0001) and increased mortality (Odds-ratio 4.65 and 5.44, respectively). According to LRT and ROC analysis, clustered models performed better, and CCI + SOFA showed the lowest AIC and BIC values (AIC = 3021.21, BIC = 3132.65). Using CART (β cluster positive case) the accuracy of the decision tree was 94.8%. Conclusion Clustered models significantly improved mortality prediction. The CCI + SOFA clustered model showed the best balance between complexity and data fit and should be preferred. Developing a user-friendly decision-making algorithm for cluster membership with CART showed high accuracy. Further validation studies are needed to confirm these findings.

Diagnostic performance of F-18 FDG PET/CT in differentiating autoimmune pancreatitis from pancreatic cancer: a systemic review and meta-analysis.

This study aims to evaluate the utility of F-18 FDG PET/CT in the non-invasive diagnosis of autoimmune pancreatitis (AIP) and differentiating it from pancreatic cancer (CaP) based on the amount and pattern of FDG uptake, as well as involvement of extra-pancreatic sites. A systematic search was conducted using PubMed, Scopus, Cochrane Library and Google Scholar. Only those studies that compared the findings of F-18 FDG PET/CT in terms of SUVmax, pattern of FDG uptake and presence of FDG-avid extra-pancreatic sites in both AIP and CaP were included. Studies were qualitatively assessed for risk of bias and publication bias. The diagnostic performance of parameters on PET/CT was examined through pooled sensitivity, specificity, diagnostic odd's ratio (DOR) and summary receiver operator characteristic (SROC) curve analysis. Six studies were included with a total of 580 patients. 178 patients had AIP (Age 18-90years, male, M: female, F ratio-8.4:1) and 402 patients had CaP (Age 22-88years, M:F ratio-1.5:1). Type of AIP was reported in only 3 studies, with the included cases predominantly being type 1 AIP. All studies were retrospective with heterogeneity and a risk on patient selection and index test. The FDG uptake, expressed as SUVmax, was lower in AIP with a weighted mean difference of -3.11 (95% confidence interval, CI: -5.28 to -0.94). To diagnose AIP, the pooled sensitivity, specificity and DOR of diffuse pattern of FDG uptake were 0.59 (95% CI: 0.51-0.66), 0.89 (95% CI: 0.86-0.92) and 21.07 (95% CI: 5.07-88.32), respectively, with an area under curve (AUC) of 0.717 on SROC analysis. The pooled sensitivity, specificity and DOR of FDG-avid extra pancreatic sites were 0.55 (95% CI: 0.45-0.65), 0.58 (95% CI: 0.52-0.64) and 2.33 (95% CI: 1.40-3.89), respectively, with an AUC of 0.632. On F-18 FDG PET/CT, a pancreatic lesion of AIP has a lower SUVmax value than CaP. A diffuse pattern of FDG uptake and presence of an extra-pancreatic FDG-avid site are nearly 21 times and twice more likely in AIP than CaP, respectively.

Neutrophils-derived Parameters as Cost-effective Inflammatory, Disease Activity Marker in Systemic Lupus Erythematosus (SLE) and Role in Prediction of Lupus Nephritis

Introduction: Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disease with various clinical manifestations and approximately 50% of the SLE patients develop lupus nephritis (LN), which increases the risk of renal failure, cardiovascular diseases and overall survival. Objectives: Evaluation of neutrophil-derived parameters (Neut-X, Neut-Y and Neut-Z) as an inflammatory, disease activity marker and as a predictor of nephritis in SLE patients. Material and Methods: In this cross-sectional study, 3 ml K3EDTA blood was taken from 110 SLE patients presented in Department of Clinical Immunology to evaluate neutrophil-derived parameters in Sysmex XT2000i haematology analyser and their correlation with other inflammatory biomarkers like, erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) as well as SLE disease activity index-2000 (SLEDAI-2K). 24-hours urine protein levels were also estimated as a marker of renal involvement. Records of renal biopsy were available in 73 SLE patients, who belonged to different morphologic classification of LN. Results: After performing bivariate Spearman correlation analysis (SPSS software, version 26.0), Neut-X (measures cytoplasmic granularity by side scattering) and Neut-Z (vector sum of Neut-X and Neut-Y) showed a significant positive correlation ( r &gt; 0.200, P &lt; .05) with ESR, CRP and SLEDAI-2K while Neut-Y (measures nucleic acid content by sideward fluorescence) showed a significant negative correlation ( r &gt; 0.200, P &lt; .05) with ESR and CRP. Receiver-operating characteristic curve analysis was used to evaluate diagnostic value of 24-hour urine protein and predictive values of neutrophil-derived parameters for renal involvement in SLE patients. Among neutrophil-derived parameters, Neut-X was found to be the best predictor of renal dysfunction with highest AUC of 0.710 (95% CI: 0.604-0.816; cut-off: 1344.50; sensitivity: 84.7%; specificity: 50%) followed by Neut-Z with AUC of 0.707 (95% CI: 0.600-0.813; cut-off: 1403.67; sensitivity: 87.5%; specificity: 50%). Conclusion: Our study shows that neutrophil-derived parameters may be used as novel cost-effective, non-invasive biomarker of disease activity as well as for predicting renal involvement in SLE patients.

Efficacy of Bedside Index for Severity In Acute Pancreatitis (Bisap) Score As Predictor of In-Hospital Outcome in Acute Pancreatitis

Background: Acute pancreatitis is a potentially life-threatening condition characterized by inflammation of the pancreas. Early identification of patients at risk of severe disease is crucial for devising appropriate management strategies and improving outcomes. The aim of the study was to investigate the efficacy of BISAP score as predictor of in-hospital outcome in patients with acute pancreatitis. Methods: This was a longitudinal study conducted in Department of Medicine, Sir Salimullah Medical College Mitford Hospital, Dhaka from January 2023 to December 2023. After ethical approval, a total 107 subjects were included in this study based on inclusion and exclusion criteria. Severity of the disease was assessed by BISAP score. Theoutcome determinants were length of hospital stay, complete recovery, partial recovery with complication, transfer to ICU and mortality. Chi Square test, Binominal Regression analysis and Receiver operator characteristic (ROC) curve analysis were performed as applicable. p value &lt;0.05 was considered as the level of significance. Results: The mean BISAP score among 107 study participants was 2.00 ± 0.76. Patients with BISAP score ³3 had significantly increased odds of prolonged hospital stay (OR: 11.226; 95% CI: 2.985- 42.222; p&lt;0.001), higher rate of partial recovery with complications (OR: 7.302; 95.325% CI: -20.997; p&lt;0.001). &lt;0.001), and greater likelihood of intensive care unit (ICU) transfer (OR: 1.136; 95% CI: 0.968-1.333; p=0.004). A BISAP score cutoff value of ³3 was associated with increased length of hospital stay (sensitivity 91.3%, specificity 97.6%, AUC=0.945), partial recovery with complications (sensitivity 83.3%, specificity 96.4%, AUC=0.899), and ICU transfer (sensitivity 75%, specificity 80.6%, AUC=0.778).Conclusion: It can be concluded that increased BISAP score can be served as an independent predictor of in-hospital in patients with acute pancreatitis (AP). Bangladesh J Medicine 2024; 35: 88-92

Development and validation of a novel criterion of histologic healing in ulcerative colitis defined by inflammatory cell enumeration in lamina propria mucosae: A multicenter retrospective cohort in China.

Histological healing is closely associated with improved long-term clinical outcomes and lowered relapses in patients with ulcerative colitis (UC). Here, we developed a novel diagnostic criterion for assessing histological healing in UC patients. We conducted a retrospective cohort study in UC patients, whose treatment was iteratively optimized to achieve mucosal healing at Shanghai Tenth People's Hospital of Tongji University from January 2017 to May 2022. We identified an inflammatory cell enumeration index (ICEI) for assessing histological healing based on the proportions of eosinophils, CD177+ neutrophils, and CD40L+ T cells in the colonic lamina propria under high power field (HPF), and the outcomes (risks of symptomatic relapses) of achieving histological remission vs. persistent histological inflammation using Kaplan-Meier curves. Intrareader reliability and inter-reader reliability were evaluated by each reader. The relationships to the changes in the Nancy index and the Geboes score were also assessed for responsiveness. The ICEI was further validated in a new cohort of UC patients from other nine university hospitals. We developed an ICEI for clinical diagnosis of histological healing, i.e., Y=1.701X1 + 0.758X2 + 1.347X3 - 7.745 (X1, X2, and X3 represent the proportions of CD177+ neutrophils, eosinophils, and CD40L+ T cells, respectively, in the colonic lamina propria under HPF). The receiver operating characteristics curve (ROC) analysis revealed that Y <-0.391 was the cutoff value for the diagnosis of histological healing and that an area under the curve (AUC) was 0.942 (95% confidence interval [CI]: 0.905-0.979) with a sensitivity of 92.5% and a specificity of 83.6% (P<0.001). The intraclass correlation coefficient (ICC) for the intrareader reliability was 0.855 (95% CI: 0.781-0.909), and ICEI had good inter-reader reliability of 0.832 (95% CI: 0.748-0.894). During an 18-month follow-up, patients with histological healing had a substantially better outcome compared with those with unachieved histological healing (P<0.001) using ICEI. During a 12-month follow-up from other nine hospitals, patients with histological healing also had a lower risk of relapse than patients with unachieved histological healing. ICEI can be used to predict histological healing and identify patients with a risk of relapse 12 months and 18 months after clinical therapy. Therefore, ICEI provides a promising, simplified approach to monitor histological healing and to predict the prognosis of UC. Chinese Clinical Trial Registry, No. ChiCTR2300077792.

Diagnostic Value of Single LH and LH/FSH Ratio at 60-minute after GnRHa Stimulation Test for Central Precocious Puberty.

To evaluate the diagnostic value of luteinizing hormone (LH) and LH/follicle stimulating hormone (FSH) ratio at 60 min after gonadotropin-releasing hormone analogs (GnRHa) stimulation test for central precocious puberty (CPP) in girls. Two hundred and fifty-seven girls, aged 3 to 7.5 y, suspected of precocious puberty at authors' hospital from April 2020 through November 2023 were enrolled in the study. The blood was taken at 0, 30, 60 min after GnRHa stimulation test, and LH and LH/FSH were detected by chemiluminescence assay. The diagnostic efficacy was analysed by Mann-Whitney U test, spearman's correlation analysis and receiver operating characteristic (ROC) analysis. The proportion of obesity was analysed by Chi-square test. LH and LH/FSH at different times were statistically significantly different (P <0.05) between the CPP and non-CPP groups. Spearman's correlation analysis showed that the level of LH and LH/FSH at 60 min had the strongest consistency with the peak of LH (r = 0.9988, P <0.001) and LH/FSH (r = 0.9981, P <0.001). ROC curve analysis showed that the area under the ROC curves at 60 min of LH and LH/FSH were 0.975 and 0.997 with a cut-off value of 5.70 IU/L and 0.609, respectively. The peak of LH and LH/FSH in the diagnosis of CPP can be determined by LH and LH/FSH at 60 min after the triptorelin acetate is injected. This will reduce the number of blood draws required compared with the traditional stimulation test, which is more effective and acceptable for children.