Receiver Operating Characteristic Curve Research Articles

Guiding adjuvant radiotherapy in stage III endometrial cancer: a prognostic model based on SEER

BackgroundThe effect of overall survival (OS) with adjuvant radiotherapy in stage III endometrial cancer (EC) remains controversial, and the adverse invents were unignorable.MethodsA total of 4,064 stage III EC patients who underwent adjuvant chemotherapy post-operatively were selected from Surveillance, Epidemiology, and End Results (SEER) Program. Independent risk factors were identified through Cox regression models. A nomogram was developed accordingly to predict OS. The concordance index (C-index), calibration, and Receiver Operating Characteristic (ROC) curves were applied to assess the model. Patients were divided into the low- and high-risk groups based on the optimal risk cutoff. Stratified analysis was conducted by radiation in both groups, and interactions between radiation and the risk groups were conducted to explore if any benefit less from adjuvant radiotherapy.ResultsA total of five candidate factors were identified from the model showing good calibration and consistency discriminative power in the training (C-index: 0.73; 95% CI: 0.70–0.75), testing (C-index: 0.73; 95% CI: 0.69–0.77), and external validation cohorts (C-index: 0.88, 95% CI, 0.78–0.97). Patients were categorized into the low- and high-risk groups based on the optimal risk cutoff of 2.1048630. The women in the high-risk group experience significantly less (42% vs. 63% reduction) or none (0 vs. 63%) benefit (p-interaction = 0.049 vs. 0.016 in training and testing cohorts, respectively).ConclusionA nomogram incorporating five variables was established to predict OS in stage III EC patients with adjuvant chemotherapy. The high-risk groups benefit less or none from adjuvant radiotherapy, which may serve as a useful reference for better guidance of radiotherapy in stage III EC patients.

Potential utility of anterior segment optical coherence tomography and biometry in differentiating plateau iris configuration from pupillary block

ABSTRACT Clinical relevance Angle closure glaucoma is one of the most common blinding diseases encountered mainly in older age groups, although it may also occur at a younger age. Identifying the underlying cause of angle closure helps in designing specific treatment strategies essential for effective disease management. Background Primary angle closure disease, caused due to pupillary block (PB) and plateau iris configuration (PIC), necessitates different management strategies. This study assessed the potential utility of anterior segment optical coherence tomography (ASOCT) and optical biometry in distinguishing PIC from PB in primary angle closure disease of the young (PACDy). Methods Patients aged 20–40 years with PACDy and age-matched healthy controls were recruited. Ophthalmic examinations included gonioscopy, ultrasound biomicroscopy, ASOCT, and biometry. Anterior chamber depth, lens thickness, axial length, central corneal thickness, lens position, relative lens position, vitreous cavity length, lens vault, relative lens vault and angle opening distance, trabecular iris space area, and trabecular iris angle were measured. Receiver operating characteristics curve analysis evaluated the discriminative ability of these parameters. Results Of the 280 eyes, 128 were normal and 152 had PACDy. Of 128 PACDy, 88 had PIC, and 64 had PB. Both PIC and PB had significantly smaller ASOCT and biometric parameters than normal eyes. However, PIC had intermediate biometric values that fell between normal eyes and PB. All the aforementioned parameters, except central corneal thickness, showed excellent discriminating ability of PIC and PB from normal eyes; however, no single parameter can strongly differentiate PB from PIC. Axial length and relative lens vault had the highest, although weak, power for discriminating PB from PIC. Conclusion ASOCT and biometry effectively distinguish PIC and PB from normal eyes, but no single parameter reliably differentiates PIC from PB. Comprehensive gonioscopy and ultrasound biomicroscopy may be necessary for accurate diagnosis in PACDy.

The Impact of Systemic Inflammation on Recurrence in Patients with Congenital Nasolacrimal Duct Obstruction

Background/Objective: Congenital nasolacrimal duct obstruction (CNLDO) is commonly treated by probing, but recurrence remains a clinical issue. This study investigates the potential role of inflammatory biomarkers, such as the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR), in predicting recurrence after probing in children with CNLDO. Methods: This retrospective cohort study included 172 patients who underwent initial probing for unilateral CNLDO. The patients were then categorized into two groups: those who experienced the complete resolution of symptoms after primary probing, and those who required Ritleng tube intubation due to recurrence following primary probing. Blood samples for calculating inflammatory biomarkers in all subjects were collected during general anesthesia preparation prior to initial probing. NLR, MLR, and PLR values were compared between the groups using the independent samples t-test. The predictive performance of the inflammatory biomarkers for recurrence was assessed using Receiver Operating Characteristic (ROC) curve analysis. Results: A total of 110 patients were included in the probing group, while 62 patients were in the recurrence group. The mean age at the time of the initial probing procedure was 15 ± 4.06 months in the probing group and 15.83 ± 4.02 months in the recurrence group. There was no difference in the duration of the probing procedure between the groups. The mean age at the time of Ritleng tube intubation in the recurrence group was 37.80 ± 13.34 months. The recurrence group exhibited significantly higher values in all analyzed inflammatory markers compared to the probing group, including the NLR (1.12 ± 0.56 vs. 0.86 ± 0.39, p = 0.002), MLR (0.16 ± 0.06 vs. 0.14 ± 0.06, p = 0.005), and PLR (95.13 ± 24.34 vs. 82.23 ± 22.77, p < 0.001). ROC curve analysis indicated that these inflammatory biomarkers demonstrated moderate performance in predicting recurrence. Conclusions: Recurrence following probing in children with CNLDO was associated with complete blood cell count-derived inflammatory biomarkers. The preoperative assessment of these biomarkers may aid in the individualization of disease management and inform the development of new therapeutic strategies.

A novel platelets-related gene signature for predicting prognosis, immune features and drug sensitivity in gastric cancer

BackgroundPlatelets can dynamically regulate tumor development and progression. Nevertheless, research on the predictive value and specific roles of platelets in gastric cancer (GC) is limited. This research aims to establish a predictive platelets-related gene signature in GC with prognostic and therapeutic implications.MethodsWe downloaded the transcriptome data and clinical materials of GC patients (n=378) from The Cancer Genome Atlas (TCGA) database. Prognostic platelets-related genes screened by univariate Cox regression were included in Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk model. Kaplan-Meier curves and receiver operating characteristic curves (ROCs) were performed in the TCGA cohort and three independent validation cohorts. A nomogram integrating the risk score and clinicopathological features was constructed. Functional enrichment and tumor microenvironment (TME) analyses were performed. Drug sensitivity prediction was conducted through The Cancer Therapeutics Response Portal (CTRP) database. Finally, the expression of ten signature genes was validated by quantitative real-time PCR (qRT-PCR).ResultsA ten-gene (SERPINE1, ANXA5, DGKQ, PTPN6, F5, DGKB, PCDH7, GNG11, APOA1, and TF) predictive risk model was finally constructed. Patients were categorized as high- or low-risk using median risk score as the threshold. The area under the ROC curve (AUC) values for the 1-, 2-, and 3-year overall survival (OS) in the training cohort were 0.670, 0.695, and 0.707, respectively. Survival analysis showed a better OS in low-risk patients in the training and validation cohorts. The AUCs of the nomogram for predicting 1-, 2-, and 3-year OS were 0.708, 0.763, and 0.742, respectively. TME analyses revealed a higher M2 macrophage infiltration and an immunosuppressive TME in the high-risk group. Furthermore, High-risk patients tended to be more sensitive to thalidomide, MK-0752, and BRD-K17060750.ConclusionThe novel platelets-related genes signature we identified could be used for prognosis and treatment prediction in GC.

Corneal asymmetry contributes decentration in both spherical and toric orthokeratology lenses

AbstractPurposeTo discuss the characteristics of anterior corneal elevation asymmetry in myopic eyes and clarify which kind of asymmetry most influenced lens position.MethodsIn this retrospective study of 199 consecutive myopic participants, corneal topography was used to analyse asymmetry in anterior corneal elevation. Amongst them, 65 participants (65 eyes) who underwent orthokeratology (31 and 34 with spherical and toric lenses, respectively) were re‐evaluated. Stepwise multiple linear regression analysis was used to identify the contributing factors that influenced lens decentration. Receiver operating characteristic curve (ROC) analysis was employed to assess how the corneal asymmetry vector could predict decentration.ResultsThere were no significant differences in treatment zone decentration (TZDec) between participants wearing toric and spherical lenses (p = 0.60 and 0.64 for 1 week and 1 month of wear, respectively). Amongst the underlying factors, the magnitude of TZDec was only correlated with the amount of corneal asymmetry vector (standardised β = 0.44, 0.48, p < 0.001 for all) after 1 week and 1 month of wear, and the direction of TZDec after 1 month of lens wear was associated with the angle of the asymmetry vector (r = 0.25, p = 0.04). ROC analysis showed that the magnitude of corneal asymmetry vector produced accurate discrimination between non‐severe and severe decentration for 1 week and 1 month of wear (area under the curve was 0.93 ± 0.04 and 0.89 ± 0.05, respectively, p < 0.001). Amongst participants whose corneal asymmetry vector exceeded 41.06 μm and was oriented inferiorly, 35.29% showed severe decentration after 1 month of lens wear.ConclusionsIn myopic participants, corneal asymmetry existed in the 8.0 mm chord diameter. If the asymmetry vector >41.06 μm and the direction was oriented inferiorly, then practitioners must be vigilant about severe decentration which would not be alleviated by a toric design.

Abstract A009: Integrative multiomic analysis of extracellular vesicle transcriptomics profiling combined with cfDNA methylation reveals improved stratification of low-risk and high- risk prostate cancer patients in urine-based liquid biopsy

Abstract Introduction: Prostate cancer (PCa) ranks as the second most frequently diagnosed cancer and the sixth most prevalent cause of cancer-related mortality in men globally. In this study, we employ a multiomic approach that utilizes both urine cfDNA and extracellular vesicle (EV) RNA-derived analytes to improve the risk stratification of individuals with prostate cancer and compare this to multi-parametric MRI performance as well as risk calculators in the same cohort. Materials and Methods: Urine was collected from 95 individuals comprising 51 benign or low-risk (Grade Group (GG) <=1), 10 intermediate-risk (GG2) and 44 high-risk PCa patients (GG 3-5). EV-RNA and cfDNA were concurrently isolated from each urine specimen to exploit complementary information. We developed an EV-RNA sequencing platform targeting mRNAs and lncRNAs and targeted 50 million reads per sample; and cfDNA methylome profiling reached an equivalent sequencing depth. Expression of EV-RNA and splice variant Differential Transcript Usage (DTU) and cfDNA methylation patterns were analyzed using Bio-Techne’s multiomic platform. Machine learning-based feature selection algorithms identified biomarker signatures from each analyte. Receiver-operator characteristic curves (ROC) were generated utilizing leave-one-out cross-validation of naïve Bayes classifier models to compute the area under the curve (AUC). Individual signatures were integrated to create a multiomic classifier. Results: Differential gene expression (DEx) analysis identified 18 DEx genes between low and high-risk patients with both known and novel implications in PCa. Splice variant analysis unveiled 60 genes previously implicated in PCa with DTU. Over 18,000 differentially methylated bases were detected between high and low-risk PCa patients. Feature selection analysis identified the top ten features of EV-RNA expression, splice variants, and cfDNA methylation, resulting in an integrative multi-analyte signature. The multiomic signature demonstrated an AUC of 0.92, surpassing any individual analyte’s performance, as well as the performance achieved by PSA (AUC = 0.52) and MRI PI RADS(AUC=0.65). Conclusions: While biomarker signatures obtained from each analyte in this study resulted in effective stratification of PCa risk, the multiomic signature further improved the discriminatory power—underscoring the complementary nature of the signals. Thus, a multiomic biomarker discovery strategy leveraging cfDNA and EV cargo exhibits significant potential as a risk assessment tool for high-grade prostate cancer. This approach can facilitate more informed decision-making in the context of biopsy procedures. Citation Format: Sudipto K Chakrabortty, Dulaney Miller, Kyle Manning, Rikky Xing, Jeff Cole, Christopher Benway, Sinead Nguyen, Allan George, Yiyuan Yao, Elena M Cortizas, Christian Ray, Siva Gowrisankar, Seth Yu, Allan Pollack, Sandra M Gaston, Sanoj Punnen, Johan Skog. Integrative multiomic analysis of extracellular vesicle transcriptomics profiling combined with cfDNA methylation reveals improved stratification of low-risk and high- risk prostate cancer patients in urine-based liquid biopsy [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A009.

Longitudinal FDG-PET Radiomics for Early Prediction of Treatment Response to Chemoradiation in Locally Advanced Cervical Cancer: A Pilot Study

Objectives: This study aimed to assess the capacity of longitudinal FDG-PET radiomics for early distinguishing between locally advanced cervical cancer (LACC) patients who responded to treatment and those who did not. Methods: FDG-PET scans were obtained before and midway through concurrent chemoradiation for a study cohort of patients with LACC. Radiomics features related to image textures were extracted from the primary tumor volumes and stratified for relevance to treatment response status with the aid of random forest recursive feature elimination. Predictive models based on the k-nearest neighbors time series classifier were developed using the top-selected features to differentiate between responders and non-responders. The performance of the developed models was evaluated using receiver operating characteristic (ROC) curve analysis and n-fold cross-validation. Results: The top radiomics features extracted from scans taken midway through treatment showed significant differences between the two responder groups (p-values < 0.0005). In contrast, those from pretreatment scans did not exhibit significant differences. The AUC of the mean ROC curve for the predictive model based on the top features from pretreatment scans was 0.8529, while it reached 0.9420 for those derived midway through treatment scans. Conclusions: The study highlights the potential of longitudinal FDG-PET radiomics extracted midway through treatment for predicting response to chemoradiation in LACC patients and emphasizes that interim PET scans could be crucial in personalized medicine, ultimately enhancing therapeutic outcomes for LACC.

Novel perspectives on early diagnosis of acute compartment syndrome: the role of admission blood tests

Abstract Purpose The role of admission blood indicators in patients with acute compartment syndrome (ACS) remains debated. Our primary purpose was to observe variations of admission blood indicators in patients with ACS, while our secondary goal was to explore potential biomarkers related to ACS. Methods We collected information on patients with tibial fracture between January 2013 and July 2023, and divided them into ACS and non-ACS groups. Propensity score matching (PSM) analysis was performed to lower the impact of potential confounding variables such as demographics and comorbidities. Admission blood indicators were analyzed using univariate, logistic regression, and receiver operating characteristic (ROC) curve analyses. Then, we established a nomogram prediction model by using R language software. Results After propensity PSM analysis, 127 patients were included in each group. Although numerous blood indicators were found to be relevant to ACS on univariate analysis, logistic regression analysis showed that monocytes (MON, p = 0.015), systemic immune-inflammation index (SII, p = 0.011), and creatine kinase myocardial band (CKMB, p < 0.0001) were risk factors for ACS. Furthermore, ROC curve analysis identified 0.79 × 109/L, 1082.55, and 20.99 U/L as the cut-off values to differentiate ACS patients from patients with tibial fracture. We also found that this combination had the highest diagnostic accuracy. Then, we constructed a nomogram prediction model with AUC of 0.869 for the prediction model, with good consistency in the correction curve and good clinical practicality by decision curve analysis. Conclusions We found that the levels of MON, SII, and CKMB were related to ACS and may be potential biomarkers. We also identified their cut-off values to separate patients with ACS from those with tibial fracture, helping orthopedists promptly evaluate and take early measures. We established a nomogram prediction model that can efficiently predict ACS in patients with tibial fracture.

Gestational diabetes mellitus (GDM): diagnosis using biochemical parameters and anthropometric measurements during the first trimester in the Indian population

Abstract Objectives The objective of the study was to use anthropometric measurements (age, BMI, and subcutaneous fat) in conjunction with biochemical parameters (sex hormone-binding globulin (SHBG), homeostasis model assessment-insulin resistance (HOMA-IR), fasting glucose, serum insulin, and total cholesterol) to predict the probability of gestational diabetes mellitus (GDM) in the first trimester. Methods The study enrolled 48 pregnant women with GDM and 64 high-risk pregnant women without GDM. During the first-trimester examination, maternal blood samples were collected to measure SHBG, fasting blood glucose, serum insulin, and total cholesterol levels. Regression model analysis was used to examine the variables that showed statistically significant differences between the groups and were independent predictors of GDM. Receiver operating characteristic (ROC) curve analysis was employed to determine the risk of developing GDM based on cut-off values. Results The levels of SHBG, HOMA-IR, serum insulin, fasting glucose, and total cholesterol were identified as significant independent markers for predicting GDM. Meanwhile, age, body mass index, and subcutaneous fat values were found to be non-independent predictors of GDM. The areas under the ROC curve were calculated to determine the predictive accuracy of total cholesterol, HOMA-IR, SHBG, and subcutaneous fat for developing into GDM, and were 0.869, 0.977, 0.868, and 0.822 respectively. The sensitivities for a false positive rate of 5 % for predicting GDM were 68.7 , 91.67, 91.7, and 97.9 % for total cholesterol, HOMA-IR, SHBG, and subcutaneous fat, respectively. Conclusions The independent predictors for the subsequent development of GDM in high-risk pregnancies are HOMA-IR, SHBG, Total cholesterol, and subcutaneous fat (SC) levels. These parameters can be used to create a regression model to predict the occurrence of GDM.

Preoperative total parathyroid volume is an independent marker to predict recurrence for secondary hyperparathyroidism

Purpose This study aimed to explore the value of preoperative total parathyroid volume (TPV) as a marker for predict recurrence of renal secondary hyperparathyroidism (SHPT). Methods We identified 28 patients with recurrence and 128 without recurrence who underwent total parathyroidectomy with autotransplantation (tPTX + AT) at our institution between 2015 and 2022. The TPV and postoperative recurrence information of the patients were recorded. Within the intergroup comparison, data obtained from the recurrence and non-recurrence groups were evaluated using the t-test. Univariate and multivariate analyses were performed according to the regression model to determine factors that were significant in predicting postoperative recurrence. The cutoff value of TPV was determined using a receiver operating characteristic (ROC) curve. Results The mean TPV of recurrence and no-recurrence groups were 2.99 ± 1.52 cm3 and 1.73 ± 1.19 cm3, respectively (p = 0.007). In univariate analysis, female sex, total parathyroid volume > 1.99 cm3, serum PTH > 928.37 pg/mL and p > 1.59 mmol/L were independent factors for SHPT recurrence. In multivariable analysis, TPV > 1.99 cm3, serum parathyroid hormone (PTH) > 928.37 pg/mL and p > 1.59 mmol/L were independent factors for SHPT recurrence. The ability of TPV to distinguish between recurrence and non-recurrence was evaluated using the ROC curve. The cutoff value of TPV was estimated as 2.65 cm3. With this value, sensitivity was found as 60.70%, specificity was 89.80%, and AUC was 0.80 (p < 0.001, confidence interval =0.719–0.882). Conclusion According to the data in this study, it can be said that TPV can be used to distinguish recurrence from no-recurrence. Most importantly, TPV can be used to identify SHPT recurrence.

Abstract 4117438: Machine Learning Predicts Successful Transcatheter Mitral Valve Edge to Edge Repair: A Systematic Review and Meta-Analysis

Introduction: Transcatheter Mitral Valve Edge to Edge Repair (TEER) is an established percutaneous treatment for patients with severe symptomatic Mitral Regurgitation (MR). The current AHA/ACC guidelines recommend TEER for inoperable patients with severe primary MR or patients with symptomatic severe secondary MR despite medical therapy. Machine learning (ML) has emerged as a tool for TEER risk stratification due to the paucity of established risk scores. To address the lack of consensus on its efficacy, we conducted a systematic review and meta-analysis of studies that utilized ML to predict the success of TEER. Methods: Electronic databases, including Embase, MEDLINE, and the Cochrane Library, were searched from inception through April 2024. We included studies that used TEER and employed at least one ML model to predict the success of TEER. The Area Under the Receiver Operating Characteristic Curve (AUC) was used to measure the accuracy of ML risk stratification algorithms. Results: 102 publications were screened, with seven eventually included in this analysis. Two studies employed clustering techniques, two utilized extreme gradient boosting, and three used multiple ML algorithms to predict outcomes. Of the four studies that compared the accuracy of ML with traditional Cox regression, all four demonstrated higher accuracy with ML, and this difference was statistically significant in three of the four studies. The mean AUC of the aggregated ML data was 0.737 [95% CI: 0.717, 0.758], compared to 0.627 [95% CI: 0.600, 0.653] for the pooled traditional methods. Conclusions: To our knowledge, we conducted the first systematic review and meta-analysis of ML methods for prediction of TEER success. ML outperformed established risk scores, demonstrating promising potential. Future ML models, trained on larger patient datasets, may further improve predictive accuracy in this patient population.

Abstract Su804: Use of Machine Learning Models to Predict Neurologically Intact Survival for Advanced Age Adults Following Out-of-hospital Cardiac Arrest

Background: We recently published a multivariable logistic regression model that successfully predicted neurologic outcomes in advanced age adults (≥ 65 years old) who achieve return of spontaneous circulation following out-of-hospital cardiac arrest (OHCA). Prior to externally validating this model, we sought to compare the predictive performance of our logistic regression model against common machine learning (ML) algorithms using the same dataset. Methods: We performed a retrospective observational analysis of the Cardiac Arrest Registry to Enhance Survival database from 2013-2021. All non-traumatic OHCA occurring in adults (≥65 years) who survived to hospital admission were included. The primary outcome measure was neurologically intact survival defined as a cerebral performance category (CPC) score of 1 or 2 at hospital discharge. Our original logistic regression model was compared to Boosted Trees and Decision Tree ML algorithms and assessed using accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC) and Area Under the Precision-Recall Curve (AUC-PR). The model variables included age, gender, race/ethnicity, location, witnessed status, who initiated cardiopulmonary resuscitation, whether an automated external defibrillator was applied prior to emergency medical services arrival, and first monitored rhythm. Results: A total of 83,561 OHCA were included. Median age was 75 years (IQR 69-82), 58.9% were male, 53% were White, 67% experienced an OHCA at home, 52% were witnessed by a bystander, 44% received bystander CPR, and 34% were found in shockable rhythm. Neurologically intact survival (CPC 1 or 2) occurred in 23% of patients. The logistic regression model had an accuracy of 79.6%, AUC-PR 0.542, and AUC-ROC of 0.774. The Boosted Trees model had an accuracy of 79.2%, AUC-PR 0.555, and AUC-ROC of 0.774. The Decision Tree Model had an accuracy of 79.3%, AUC-PR 0.490, and AUC-ROC of 0.717. Conclusion: Boosted Trees and Decision Tree ML models performed comparably to the logistic regression model in predicting favorable versus unfavorable neurological outcomes following successful resuscitation from OHCA in advanced-age adults. Given their similar performance, logistic regression may be preferred for its simplicity and interpretability over alternative ML models. Future efforts to improve and ultimately externally validate the model are warranted.

Abstract 4137958: Transthoracic Echocardiography-based Differentiation of Hypertrophic Cardiomyopathy and Cardiac Amyloidosis

Introduction: Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) have similar features on transthoracic echocardiography (TTE) which may make the two difficult to distinguish. However, it is recognized that CA presents at an older age compared to HCM. Our aim was to determine optimal TTE parameters for differentiating HCM from CA and the incremental value of age at the time of diagnosis for differentiation of these two disorders. Methods: Patients with CA (N=121) and HCM (N=227)—diagnosed in accordance with guidelines—who underwent TTE were retrospectively identified. TTE parameters—including left ventricular global longitudinal strain (LVGLS), apical sparing ratio (ASR), AMYLoidosis Index (AMYLI), relative wall thickness (RWT) and ejection fraction to strain ratio (EFSR)—were calculated using artificial intelligence software (US2.AI) and validated by a level III echocardiography reader. Univariate and multivariable logistic regressions were used to compare TTE parameters between CA and HCM patients. Receiver operating characteristic curve (ROC) analysis was conducted to determine performance of echo parameters in distinguishing CA and HCM. Findings: CA patients were older compared to HCM ( Figure 1A ). With respect to TTE parameters, CA was associated with lower magnitude of LVGLS and higher value of ASR, AMYLI, and EFSR. LVGLS (AUC 0.865, 95% CI 0.81-0.91) outperformed AMYLI, RWT, and EFSR for differentiation of HCM from CA ( Figure 1B ). For all five TTE parameters, age provided incremental predictive value for LVGLS (p&lt;0.001), ASR (p&lt;0.001), AMYLI (p=0.007), RWT (p&lt;0.001), and EFSR (p&lt;0.001), Figure 2A-C ). LVGLS &lt;-15.8% was 85% sensitive and 79% specific for CA. Conclusion: LVGLS is an optimal echo parameter for differentiating CA from HCM, outperforming all other traditional CA diagnostic markers. Age provides incremental diagnostic value in the differentiation of these two groups with TTE.

Abstract 4136085: The Prognostic Impact of Thrombocytopenia at 6 months after Transcatheter Aortic Valve Replacement: A Retrospective Multicenter Study

Background: Transient thrombocytopenia after transcatheter aortic valve replacement (TAVR) is a common phenomenon. Thrombocytopenia after TAVR during the perioperative period has been reported to impact prognosis. However, the impact of thrombocytopenia in the mid-term post-TAVR period is currently unknown. Methods: A retrospective observational study was conducted on 1405 patients who underwent TAVR from May 2016 to February 2023 at four institutions. Of these, 850 patients were analyzed. Patients who underwent dialysis (67), converted to an open chest surgery (8), had a Valve-in-Valve procedure (30), underwent a direct aortic approach (16), had a malignant tumor (66), and patients whose platelet levels could not be measured at 6 months after TAVR due to death or dropout were excluded from the analysis. The clinical follow-up occurred at 1, 6, and 12 months after the procedure. Laboratory testing was conducted before TAVR, during the perioperative period, 1 month post-procedure, and 6 months later. The decrease in platelet count (DPC) 6 months after TAVR was calculated by the following formula: [(Baseline platelet – 6 months platelet)/baseline platelet *100]. A receiver operating characteristics curve was plotted for all-cause mortality at 1 year and DPC at 6 months. The calculated cutoff value for the DPC at 6 months (28.68%) was utilized to classify and compare the two groups. Multivariate logistic regression models were also used to examine the association between the DPC at 6 months and all-cause mortality at 1 year. Adjusted for propensity score matching, the DPC was compared at 6 months in self-expandable and balloon-expandable prosthetic valves. Result: Of the 850 eligible patients (age: 85.5 ± 4.9 years), 31 (3.4%) died between 6 months and 1 year after TAVR. The DPC at 6 months after TAVR was 11.9 ± 22.5%. Multivariate logistic regression analysis including the DPC at 6 months, age, gender, and STS score showed that the DPC at 6 months was independently associated with all-cause mortality at 1 year (odds ratio: 1.03, 95% confidence interval: 1.005-1.046, p=0.01). After adjustment for propensity score matching, the balloon-expandable prosthetic valve had a higher rate of thrombocytopenia at 6 months (15.0% vs. 9.6%, p=0.04). Conclusion: The DPC at 6 months after TAVR may predict all-cause mortality at 1 year.

Abstract 4122284: HEART Score Classification of Major Adverse Cardiovascular Events in Patients with Diabetes Mellitus

Introduction: Risk stratifying diabetes mellitus (DM) patients with chest pain in the emergency department (ED) can pose significant challenges because of atypical symptoms, non-specific ECG changes, and elevated baseline troponin levels. This study aims to evaluate HEART Score performance in DM versus non-DM patients. Methods: A prospective observational cohort study included chest pain patients transported by Emergency Medical Services to University of Pittsburgh Medical Center-affiliated hospitals. Data collection involved extracting data from electronic health records, including both DM and non-DM patients with chest pain. The HEART Score was retrospectively calculated by impartial assessors.The primary endpoint was major adverse cardiac events (MACE) within 30 days. This analysis included the classification performance of bivariate comparisons between the HEART Score and ACS outcomes using the area under the receiver-operator characteristic curve (AUC). Results: 4,058 patients were included. 52.7% were male, 42% were white, there was a mean age of 59.24 years (16.13 SD), 28.63% had DM, and 22.1% had MACE. MACE prevalence was 19.7% in non-DM patients and 28% in patients with DM. The AUC demonstrated more robust discriminatory ability of the HEART Score in non-DM (AUC = 0.81) (95% CI 0.79-0.83) versus DM patients (AUC = 0.74) (95% CI 0.71-0.78) (p 0.001). The performance of HEART Score components, including history, age, and risk factors, exhibited better significant predictive capability for MACE in non-DM patients compared to DM patients. Conversely, the HEART-ECG and troponin components were not different between the DM and non-DM groups. The HEART Score demonstrates better performance in White individuals and females when contrasted with males and Black individuals. Nevertheless, these disparities do not reach statistical significance. Conclusion: The HEART Score performs differently in DM versus non-DM patients, with stronger discrimination observed in non-DM individuals. The troponin and ECG components exhibit poorer performance in diabetes mellitus (DM) patients within the context of the HEART Score. This underscores the complexity of risk assessment in DM patients with chest pain and suggests the necessity for further exploration and refinement of risk stratification strategies in this population.

Abstract 4138217: A Machine Learning-Based Novel Risk Score Model for Spontaneous Coronary Artery Dissection

Background: Spontaneous coronary artery dissection (SCAD) remains a poorly understood differential diagnosis of acute coronary syndrome (ACS). We aimed to develop a machine learning-based risk score model to predict in-hospital mortality in patients with SCAD. Methods: The National Inpatient Sample (NIS) 2016-2020 database was used to identify all adult patients (≥18 years of age) with SCAD, identified using ICD-10 code I25.42. Relevant clinical characteristics and outcomes were extracted. The primary endpoint was in-hospital all-cause mortality. The dataset was randomly split into 3 subsets: training, validation, and testing, with a ratio of 0.7, 0.2, and 0.1, respectively. The risk score was generated using R software’s Autoscore package, which is a machine learning-based automatic clinical score generator, and performance was evaluated using the area under receiver-operative characteristics curve (AUC) with 95% confidence intervals (95%CI). Results: Among the 8,260 SCAD patients identified [age 60.9±14.4 years, female 4,708 (57%)], there were 480 (5.8%) primary outcome events. Out of the top 20 covariates seen in the Parsimony plot, we used six for our risk score (0–100): age, comorbidity burden, cardiogenic shock, balloon pump, extracorporeal membrane oxygenation, and race (Figure 1A). The AUC for the derivation and validation cohorts was 0.862 (0.817–0.907) and 0.833 (0.788–0.879), respectively (Figures 1B and 1C). Conclusion: We developed and internally validated a machine learning-based risk score model to predict outcomes in patients with SCAD with a high AUC of 0.833 (0.788–0.879). Risk score models guide decision-making, and future studies are needed for external validation of our findings.

Abstract 4138237: Clinical Utility of T2 Mapping for the Assessment of Myocardial Inflammation in Patients With Cardiac Sarcoidosis

Introduction: 18 F-FDG PET has been used for the evaluation of active inflammatory lesions in cardiac sarcoidosis (CS), but it is burdensome for patients to perform as a routine follow-up. Late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) that can detect myocardial fibrosis is limited in its ability to assess active inflammation. T2 mapping has been shown to detect myocardial inflammation in myocardial diseases without radiation exposure and contrast agents (Figure). However, the ability of T2 mapping for discriminating inflammatory myocardium has not been fully clarified in patients with CS. Hypothesis: We assessed the hypothesis that T2 mapping has potential for the discrimination of inflammatory myocardium in patients with CS. Methods: Twenty-seven subjects who underwent CMR at 3T and 18 F-FDG PET to diagnose CS were enrolled. Fourteen subjects were diagnosed as CS due to LGE and myocardial FDG uptake on 18 F-FDG PET (PET-positive group). Thirteen subjects showed no myocardial FDG uptake (PET-negative group). NativeT1, T2, and extracellular volume fraction (ECV) were measured using a 16-segment model. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate T1 and T2 mapping in discriminating between active and inactive inflammatory segments in the PET-positive group. Results: Mean native T1, native T2 and ECV showed no significant differences between the PET-positive and the PET-negative groups. However, maximum T2 values, which were highest ones in the 16 segments, were significantly higher in the PET-positive group than the PET-negative group (51.3 ± 7.7 ms vs 45.7 ± 3.6 ms, p = 0.014). In the PET-positive group, ROC analysis showed AUC of 0.72 in native T2, 0.61 in native T1, and 0.68 in ECV for discriminating inflammatory myocardium. The optimal cutoff value of native T2 was 45.7 ms, and the diagnostic performance had a sensitivity of 44.4 %, specificity of 93.3 % , positive predictive value of 88.1 %, and negative predictive value of 60.1 %. Conclusion: T2 mapping is a promising approach for the discrimination of inflammatory myocardium without radiation exposure and contrast agents in patients with cardiac sarcoidosis.

Association between estimated glucose disposal rate and female infertility: a cross-sectional study

BackgroundInsulin resistance (IR) can lead to infertility in women. The primary objective of this research was to examine how estimated glucose disposal rate (eGDR) correlates with infertility in women, assessing its validity as an indicator of IR.MethodsData from the National Health and Nutrition Examination Survey spanning 2013 to 2018 were analyzed in this study. In order to investigate the correlation between eGDR and the prevalence of female infertility, this study used a combination of weighted multivariate regression analysis, restricted cubic spline (RCS) analysis, subgroup analyses, sensitive analysis, and receiver operating characteristic (ROC) curves.ResultsThis study enrolled 2541 women, with an average age of (32.52 ± 0.23) years. The overall infertility rate was 14.27%. A negative relationship was observed between eGDR levels and female infertility. Each increment of one unit in eGDR was linked to a 14% reduction in infertility incidence (OR = 0.86, 95% CI 0.80–0.94). RCS analysis revealed a nonlinear, inverse correlation between eGDR and female infertility. Subgroup analyses indicated that age influenced the association between eGDR and female infertility. The ROC curve suggested that eGDR was significantly better than HOMA-IR in predicting infertility [eGDR: 0.632 (95% CI: 0.603, 0.660) vs. HOMA-IR: 0.543 (95% CI: 0.514, 0.572)].ConclusionThere was an observed association where lower eGDR levels were linked with higher rates of female infertility. These results emphasize the significance of implementing measures to manage IR to protect women’s reproductive health.

Early dFLC response by C1D7 predicts complete hematologic response in systemic AL amyloidosis

AbstractDaratumumab and bortezomib, the first-line drugs for AL amyloidosis, typically yield a complete hematologic response (CHR) rate of nearly 60% when used in combinations. An early achievement of CHR is crucial in amyloidosis. We retrospectively evaluated the relationship between dFLC (the difference between free light chain) reduction by Day 7 in Cycle 1 (C1D7) and CHR, organ response, and survival in 48 newly diagnosed AL amyloidosis patients receiving daratumumab, bortezomib, and dexamethasone. The CHR rate within six months was 66.7%. Using Receiver Operating Characteristic Curve curve analysis, we predicted CHR based on a dFLC reduction in C1D7 (67.0% change, optimal sensitivity 87.5%, specificity 81.3%). We introduce the novel concept of “rapid hematologic dFLC response”, defined as a reduction in dFLC levels ≥ 67% in C1D7. The CHR rate in rapid responders’ groups was higher than that in slow responders’ group (90.3% vs. 23.5%, P&lt;0.01). After a median follow-up of 19 months (range: 0.3–57), the renal response rate in rapid responders was higher than that in slow responders (72.0% vs. 27.5%, P = 0.025). The median major organ deterioration event-free survival in the rapid responders’ group (not reached) was significantly superior to that in the slow responders’ group (19 m, 95% CI: 1.79–23.14 m, P = 0.048). In conclusion, early dFLC reduction in C1D7 indicates a high possibility of CHR and organ response and may allow for early modification of therapy in selected patients.

Assessing the predictive potential of ADAM8 for disease control in chronic rhinosinusitis with nasal polyps

BackgroundA disintegrin and metalloproteinase 8 (ADAM8) has been implicated in eosinophilic inflammation; however, its role in chronic rhinosinusitis with nasal polyps (CRSwNP) remains to be elucidated. This study aimed to investigate the predictive significance of ADAM8 levels in nasal secretions for the endotypes and disease control status of CRSwNP.MethodsA cohort comprising 120 CRSwNP patients and 45 healthy controls (HCs) was assembled, delineating 53 non-eosinophilic CRSwNP (neCRSwNP) and 67 eosinophilic CRSwNP (eCRSwNP) patients. Immunohistochemistry and enzyme-linked immunosorbent assay (ELISA) were utilized to measure ADAM8 levels in nasal mucosal tissues and secretions from all participants. The receiver operating characteristic (ROC) curves and Pearson correlation analysis were employed to assess the predictive capability of ADAM8 levels in predictiving CRSwNP endotypes and disease control status.ResultsADAM8 levels in nasal secretions were elevated in CRSwNP patients compared to HCs, with a more pronounced increase observed in eCRSwNP patients. Elevated ADAM8 concentrations in nasal secretions were positively correlated with peripheral blood eosinophil counts and percentages, tissue eosinophil counts, serum total IgE, Lund–Mackay scores, and Lund-Kennedy scores. Ultimately, 103 CRSwNP patients completed the follow-up protocol, with 72 classified as the controlled group and 31 as the uncontrolled group. Uncontrolled CRSwNP patients exhibited significantly higher ADAM8 levels in nasal secretions compared to the controlled group. The ROC curves indicated that ADAM8 in nasal secretions exhibits robust discriminatory capacity for eCRSwNP and postoperative disease control status.ConclusionADAM8 in nasal secretions emerges as a potential novel biomarker for the prognostication of CRSwNP endotypes and the postoperative disease control status.