Risk Stratification System Research Articles

A Comparison of Open Ventral Hernia Repair Risk Stratification Systems: A Call for Consensus

Background/Objectives: Ventral hernia repair (VHR) is a common surgical intervention linked to specific surgical site complications. In such occurrences, the related morbidity is often substantial. Although known risk factors have long been recognized, their systematic inclusion in risk stratification systems lacks universal validation. This study evaluates the effectiveness and correspondence of three risk assessment tools—CeDAR, VHWG, and the modified VHWG—in predicting postoperative wound complications in VHR patients. Methods: We analyzed data from 203 patients who underwent VHR for incisional midline or lateral wall hernia across two surgical departments between 2019 and 2023. Each patient was scored using CeDAR, VHWG, and the modified VHWG systems. Outcomes were assessed based on surgical site occurrences (SSOs) such as seroma formation, wound infections, and recurrences. Results: The incidence of SSOs was 8.9%, with two recorded deaths (0.89%). CeDAR scores showed a statistically significant relationship with SSOs but failed to accurately predict complication rates across subgroups. The VHWG grading system effectively predicted higher complication rates for grades III and IV compared to grades I and II, though its modified version did not show significant predictive improvements. Secondary outcomes indicated a higher SSO rate in patients requiring posterior component separation (TAR) and those with larger hernia defects, though the differences were not statistically significant. Major preoperative risk factors, including smoking, diabetes, and obesity, did not show significant correlations with SSO rates in this study. Conclusions: Current risk estimation tools inadequately predict SSOs in VHR. Enhancing prediction accuracy will require incorporating both patient-specific and surgical factors, potentially through advanced algorithms and large-scale studies.

Individualised prediction of chemotherapy benefit in early-onset colorectal cancer

PurposesWhether patients with early-onset colorectal cancer (EOCRC) receive chemotherapy has been controversial, so our study aimed to screen patients with EOCRC who benefit from chemotherapy.MethodsA total of 2166 EOCRC patients were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were divided into chemotherapy and non-chemotherapy groups, propensity score matching (PSM) was performed to balance the differences between the groups, and the Kaplan–Meier method was used to calculate the cancer-specific survival (CSS) of EOCRC patients. Multifactorial COX regression analysis was used to identify independent prognostic factors for CSS and to construct a nomogram for predicting CSS in EOCRC patients. The overall risk score was calculated based on Nomogram, and EOCRC patients were classified into high-risk and low-risk groups to assess further chemotherapy's therapeutic effect on patients with different risk stratification.ResultsBefore PSM, patients in the chemotherapy group had poorer CSS (p < 0.001). After PSM, there was no significant difference in patient CSS between the two groups (p = 0.057). Independent prognostic factors (Race, Grade, Pathology, AJCC.N, AJCC.M, CEA, Marital. Status) were screened according to multifactorial COX regression analyses and included in the Nomogram predicting CSS in EOCRC patients. A risk stratification system for EOCRC patients was further developed, and the results showed that chemotherapy had no significant effect on CSS in the low-risk group of patients, but in the high-risk group, chemotherapy significantly improved CSS in EOCRC patients.ConclusionsWe developed a clinical risk model by combining different risk factors, which can accurately screen those with high-risk EOCRC for benefit from chemotherapy. For low-risk EOCRC patients, our results did not observe a better survival benefit from chemotherapy, and more prospective studies are needed in the future to prove our conclusions.

Integrating Contrast-enhanced US to O-RADS US for Classification of Adnexal Lesions with Solid Components: Time-intensity Curve Analysis versus Visual Assessment.

Purpose To compare the diagnostic performance of time-intensity curve (TIC) analysis and subjective visual assessment of contrast-enhanced US (CEUS) when integrated with the Ovarian-Adnexal Reporting and Data System (O-RADS) US risk stratification system for characterizing adnexal lesions with solid components. Materials and Methods In this prospective multicenter study conducted from September 2021 to December 2022, female individuals with suspected adnexal lesions containing solid components detected at routine US were enrolled. All participants underwent preoperative CEUS examinations. Histopathologic findings were used as the reference standard for diagnosis. Lesions were classified according to the O-RADS US system. Enhancement of solid tissue compared with the outer myometrium was evaluated using both TIC analysis and subjective visual assessment. The diagnostic performance of O-RADS alone and each CEUS assessment method when integrated with the O-RADS US system was assessed and compared using receiver operating characteristic curve analysis. Results A total of 180 lesions (80 malignant and 100 benign histopathologic outcomes) in 175 participants (median age, 47 years [IQR, 33-56]) were analyzed. Incorporating CEUS (assessed through both TIC analysis and subjective visual assessment) with O-RADS US showed significantly improved diagnostic performance over O-RADS US alone, with an area under the receiver operating characteristic curve (AUC) of 0.86 (95% CI: 0.80, 0.91) compared with 0.78 (95% CI: 0.71, 0.84). No evidence of a difference was observed between the AUCs of TIC analysis and subjective visual assessment in the enhancement evaluation of solid tissue with CEUS for adnexal malignancy categorization (P = .83). Conclusion Subjective visual assessment and TIC analysis of CEUS features when integrated with the O-RADS US scoring system showed comparable diagnostic performance in assigning adnexal malignancy risk. Keywords: Adnexal Lesions, Contrast-enhanced US, O-RADS, Time-intensity Curve Analysis Supplemental material is available for this article. © RSNA, 2024.

Prognostic Nomogram for Predicting Survival in Asian Patients With Small‐Cell Lung Cancer: A Comprehensive Population‐Based Study and External Verification

ABSTRACTBackgroundThe incidence of small cell lung cancer (SCLC) among Asian patients is on the rise. Nevertheless, there remains a deficiency in precise prognostic models tailored to the specific needs of this patient population. It is imperative to develop a novel nomogram aimed at forecasting the prognosis of Asian SCLC patients.MethodsThe SEER database supplied data on 661 Asian SCLC patients, who were then divided into training and internal validation sets through a random selection process. In addition, we identified 212 patients from a Chinese medical institution for the purpose of creating an external validation cohort. To forecast survival, we employed both univariate and multivariate analyses. The performance of our nomogram was assessed through calibration plots, the concordance index (C‐index), and decision curve analysis (DCA).ResultsFive independent prognostic factors were determined and integrated into the nomogram. C‐index values for the training and internal validation cohorts were 0.774 (95% confidence interval [CI] = 0.751–0.797) and 0.731 (95%CI = 0.690–0.772), respectively. In the external validation cohort, the C‐index is 0.712 (95% CI = 0.655–0.7692). Calibration curves demonstrated highly accurate predictions. When compared to the AJCC staging system, our model exhibited improved net benefits in DCA. Furthermore, the risk stratification system effectively differentiated patients with varying survival risks.ConclusionWe have created a novel nomogram for predicting the survival of Asian patients with SCLC. This nomogram has been subjected to external validation and has shown its superiority over the conventional TNM staging system. It offers a more precise and reliable means of forecasting the prognosis of Asian SCLC patients.

A practical nomogram and risk stratification system for predicting in-hospital mortality of patients with non-acute myocardial infarction related cardiogenic shock

Abstract Background The morbidity and mortality of Cardiogenic Shock(CS) remain high despite the advance in management these years1.As acute myocardial infarction (AMI) is considered to be the most common reason for cardiogenic shock,Non-AMI related CS is excluded from the majority of CS studies and remains largely understudied2. Considering its inherent complexity and hemodynamically multiplicity, it is needed to focusing on these patients to identify specific risk factors, which may be conducive to the promotion of critically CS patients with early decision and timely intervention. Purpose In this study,we sought to investigate the variables which are predictive of in hospital mortality in non AMI related CS patients, developed and validated a practical nomogram for risk quantification. Methods 1298 patients and 548 patients with cardiogenic shock from the MIMIC-IV (version 1.0) and MIMIC-III(version1.4) databaes were included in the study after excluding acute myocardial infarction. We used in-hospital death and 30 day survival as the end points. Lasson and logistic regression analysis were used to identify statistically significant predictors which were finally involved in the nomogram. The predictive performance of the nomogram was evaluated by Harrell’s concordance index (C-index) and calibration after comparing with commonly used ICU scores for in-hospital death.Lastly, we plotted Kaplan-Meier curves of three subgroup classified by the identified cutoff values of the nomogram to further assess the discriminative ability of the prognosis. Results The in-hospital mortality of patients with non-AMI related CS is about 36.6%.After the analysis of regression, age, heart rate, wbc, albumin, lactic acid, GCS score, urine volume, using vasopressor were identified as the most critical factors for the in-hospital death. Based on the these results, a nomogram for predicting in-hospital death was established.The calibration plot also shows good consistency between nomogram prediction and actual in-hospital mortality.In addition, compared with other scores, the AUC value of nomogram was 0.806 (95% CI = 0.799-0.831) in the training queue, 0.814 (95% CI = 0.771-0.852) in the internal validation queue and 0.730 (95% CI = 0.690-0.767) in the external validation set, which was significantly higher than other commonly used ICU scoring systems(SAPSII, APSIII and SOFA).Also, three risk subgroups were created using two boundary points (303 and 339) of the nomogram score and the survival curve shows that there were significant differences in the 30 day survival of each subgroup. Conclusion A prognostic nomogram and risk stratification system of non-AMI related CS were established and verified, which can provide a practical tool for individualized clinical management of patients.Workflow of the study.Nomogram for non-AMI related CS patients

A novel 12-lead standard ECG derived scoring system for risk stratification in the emergency department

Abstract Background/Aims The ECG represents a routinely used diagnostic tool in the assessment of patients in the emergency department. Beside specific alterations within the ECG indicative of underlying pathologies, such as ST-elevation in myocardial infarction, several other parameters have been developed over the past decades that proofed themselves as strong and independent predictors of worsened outcome independent of a specific underlying pathology. These include basic metrics such as heart rate and QRS duration, but also more advanced biomarkers such as QRS microfragmentation (QRSµf) [1] and total cosine R to T (TCRT) [2] . In this work, we aimed to summarize all these markers derived from 12-lead standard ECGs into one novel risk score and evaluate its performance as mortality predictor in the emergency department. Methods We retrospectively collected 10- second 12- lead ECG recordings unselected patients presenting to the emergency department of a large university hospital between September 2019 and June 2022. For each patient a risk score was calculated from the first retrieved ECG using the criteria displayed in Figure 1. Calculation of all parameters was done as previously published. Patients with a total score of 0-1 points, 2-3 points, 4-6 points and 7 points were considered as low, intermediate, high and very high risk, respectively. The performance of the risk score was evaluated using Cox regression analysis and Kaplan-Meier curves with all-cause mortality within one year as primary endpoint. Results In total, 10 781 patients were included in our analyses. Median age was 69 (IQR 54-80) years, 5 588 (52%) were male and 5 193 (48%) were female. 6 966 (65%) patients were considered as low risk, 2 656 (25%) patients as intermediate risk, 1 098 (10%) patients as high risk and 61 (1%) patients as very high risk according to the ECG risk score with one year mortality rates of 8%, 17%, 26% and 43%, respectively (p &amp;lt; 0.001 for difference). In cox regression analysis the novel ECG risk score was a strong and independent predictor of one-year all-cause mortality with a hazard ratio of 1.35 (95% CI 1.31-1.39, p &amp;lt; 0.001). Kaplan-Meier survival curves for different risk categories are displayed in Figure 2. Conclusion In an unselected population of patients presenting to the emergency department of a large university hospital a novel risk score calculated from standard 12-lead ECGs recorded during clinical routine was a strong and independent mortality predictor. Further studies are needed to evaluate how incorporation of the novel score into existing triage systems can improve patient care.Figure 1:Calculation of ECG risk scoreFigure 2:Kaplan-Meier survival curves

Reduction of aortic stiffness in newly diagnosed persons with HIV-infection: modifiable vascular dysfunction after antiretroviral therapy?

Abstract Background People living with HIV (PLWH) are at an increased risk of cardiovascular disease (CVD), which is underestimated by contemporary risk stratification systems[1]. Subclinical cardiovascular abnormalities may be present at the time of HIV diagnosis and serve as potential substrate for future CVD. Aortic stiffness, as measured with pulse wave velocity (PWV), is a validated predictor of CVD [2,3] and has been shown to be elevated in PLWH at the time of HIV diagnosis compared to HIV-uninfected persons[4]. Purpose To prospectively assess the influence of contemporary antiretroviral therapy (ART) on aortic stiffness in a group of newly diagnosed PLWH in a low- to middle-income setting. Methods A group of ART naïve PLWH with modest cardiovascular risk and free from known CVD, was recruited at the time of HIV diagnosis from the Western Cape Province of South Africa. Aortic stiffness was measured using carotid-femoral PWV on a commercially available device that simultaneously measured the carotid and femoral waveforms on a supine patient in a temperature-controlled room utilising established methodology[4]. The algorithmically-calculated time delay between the upstroke of the carotid and femoral wave-forms provided the transit time (TT). Carotid femoral path distance (path length) was measured using surface anatomy. PWV was calculated as direct path length divided by TT. Aortic distensibility was expressed according to the Bramwell-Hill equation as 3.57/PWV(squared)[5]. ART was initiated and PWV was re-evaluated 9 months later. Results Seventy-three participants completed follow-up and were included. The cohort was relatively young (mean age 32±7 years) with good female representation (n=33, 45%). Most received tenofovir/lamivudine/dolutegravir (n=71, 97%) Half of the group smoked cigarettes. Two participants were controlled hypertensives, and two participants had metabolic syndrome. A significant decrease in PWV (with scaling factor of 0.8) was observed at 9 months of ART (5.93 ±1.0 vs. 5.69 ±1.0 m/s; p=0.01). Aortic distensibility increased significantly as well (0.20 [IQR: 0.16 to 0.26] vs. 0.21 [IQR: 0.19 to 0.27] mmHg-1; p=0.02). Conclusion Improvement in aortic stiffness was observed in a group of newly diagnosed PLWH after 9 months of ART-initiation. This provides evidence of a modifiable component of vascular dysfunction and, in turn, cardiovascular risk with the use of contemporary ART at 9 months. It is unknown if this effect will be sustained long-term. The use of aortic stiffness (alone or in combination with other parameters) as a predictor of cardiovascular outcomes in the ART era requires further study.Abstract figureAbstract table

Temporal Trends in Thyroid Nodule Size on Ultrasonography

In recent years, concern has grown around the overdetection of thyroid cancer. Changes to thyroid nodule risk stratification systems and guidelines were made to improve diagnostic yield. It is not known how these advancements have affected the size of thyroid nodules reported on ultrasonography over time. To evaluate change in reported nodule size since 1990, particularly between studies of thyroid ultrasonography obtained for diagnostic vs screening purposes. The systematic review included original research studies that reported thyroid nodule size in adults undergoing their first thyroid ultrasonography. Excluded studies were those that included patients with known thyroid disease, prior thyroid ultrasonography, nodules identified through other imaging modalities, and/or that had constraints on nodule size and/or characteristics. PubMed, SCOPUS, CENTRAL, and CINAHL were reviewed from January 1990 to March 2021. Study characteristics, patient demographic characteristics, nodule size, and ultrasonography techniques were independently extracted by multiple observers. The size of thyroid nodules reported via ultrasonography over time. Mixed-effects meta-regression models were used to evaluate mean nodule size (1) overall, (2) in studies that used ultrasonography diagnostically, and (3) in studies that used ultrasonography for screening. A total of 11 963 patients were included; the mean (SD) age was 47.6 (5.2) years. A total of 1097 studies were identified; of these, 395 full-text articles were assessed, and 18 studies met inclusion criteria. All were done at academic institutions. Altogether, these studies had 11 963 patients who underwent a first thyroid ultrasonography. Reported mean nodule size increased 0.52 mm each year from 1990 to 2021 (95% CI, 0.2-0.81). Diagnostic subgroup mean nodule size increased 0.57 mm each year from 1990 to 2021 (95% CI, 0.21-0.93). Screening subgroup mean nodule size decreased by 0.23 mm each year up to 2012 (95% CI, -0.40 to -0.07). The results of this systematic review and meta-analysis suggest that thyroid nodule size reported on diagnostic ultrasonography has increased over time in conjunction with changes in risk stratification systems, nodule guidelines, and radiology practice patterns. Conversely, a decrease in size reported in asymptomatic, ultrasonography-screened populations was observed. Findings from screening studies show that subcentimeter nodules are prevalent and easily identified with ultrasonography, but clinical relevance is questionable. Altogether, these results may provide insight into how ultrasonography guidelines and practice patterns have changed thyroid nodule reporting over time and can inform future guidelines and policies associated with thyroid nodule management.

Validity of the Johns Hopkins Adjusted Clinical Groups system on the utilisation of healthcare services in Norway: a retrospective cross-sectional study

BackgroundThe Adjusted Clinical Groups (ACG) System is a validated electronic risk stratification system. However, there is a lack of studies on the association between different ACG risk scores and the utilisation of different healthcare services using different sources of input data. The aim of this study was therefore to assess the validity of the association between five different ACG risk scores and the utilisation of a range of different healthcare services using input data from either general practitioners (GPs) or hospitals.MethodsRegistry-based study of all adult inhabitants in four Norwegian municipalities that received somatic healthcare in one year (N = 168 285). The ACG risk scores resource utilisation band, unscaled ACG concurrent risk, unscaled concurrent risk, frailty flag and chronic condition count were calculated using age, sex and diagnosis codes from GPs and a hospital, respectively. Healthcare utilisation covered GP, municipal and hospital services. Areas under the receiver operating curve (AUC) were calculated and compared to the AUC of a model using only age and sex.ResultsUtilisation of all healthcare services increased with increasing scores in the “resource utilisation band” (RUB) and all other investigated ACG risk scores. The risk scores overall distinguished well between levels of utilisation of GP visits (AUC up to 0.84), hospitalisation (AUC up to 0.8) and specialist outpatient visits (AUC up to 0.72), but not out-of-hours GP visits (AUC up to 0.62). The score “unscaled ACG concurrent risk” overall performed best. Risk scores based on data from either GPs or hospitals performed better for the classification of healthcare services in their respective domains. The model based on age and sex performed better for distinguishing between levels of utilisation of municipal services (AUC 0.83–0.90 compared to 0.46–0.79).ConclusionsRisk scores from the ACG system is valid for classifying GP visits, hospitalisation and specialist outpatient visits. It does not outperform simpler models in the classification of utilisation of municipal services such as nursing homes and home services and outpatient emergency care in primary healthcare. The ACG system can be applied in Norway using administrative data from either GPs or hospitals.

A machine learning-based workflow for predicting transplant outcomes in patients with sickle cell disease.

Allogeneic haematopoietic cell transplantation (HCT) with HLA-matched sibling donor remains the most established curative therapeutic option for patients with sickle cell disease (SCD). However, it is not without risks, highlighting the need for a risk stratification system. Utilizing a machine learning (ML) approach that combines clinical and imaging variables, we identified red cell distribution width and renal organ damage as important risk factors for patients undergoing HCT. This ML-based algorithm, similar to an approach previously reported for predicting mortality in patients with SCD, should be applicable to risk factor discovery in similar studies.

A tri-light warning system for hospitalized COVID-19 patients: Credibility-based risk stratification for future pandemic preparedness

PurposeThe novel coronavirus pneumonia (COVID-19) has continually spread and mutated, requiring a patient risk stratification system to optimize medical resources and improve pandemic response. We aimed to develop a conformal prediction-based tri-light warning system for stratifying COVID-19 patients, applicable to both original and emerging variants. MethodsWe retrospectively collected data from 3646 patients across multiple centers in China. The dataset was divided into a training set (n = 1451), a validation set (n = 662), an external test set from Huoshenshan Field Hospital (n = 1263), and a specific test set for Delta and Omicron variants (n = 544). The tri-light warning system extracts radiomic features from CT (computed tomography) and integrates clinical records to classify patients into high-risk (red), uncertain-risk (yellow), and low-risk (green) categories. Models were built to predict ICU (intensive care unit) admissions (adverse cases in training/validation/Huoshenshan/variant test sets: n = 39/21/262/11) and were evaluated using AUROC ((area under the receiver operating characteristic curve)) and AUPRC ((area under the precision-recall curve)) metrics. ResultsThe dataset included 1830 men (50.2 %) and 1816 women (50.8 %), with a median age of 53.7 years (IQR [interquartile range]: 42–65 years). The system demonstrated strong performance under data distribution shifts, with AUROC of 0.89 and AUPRC of 0.42 for original strains, and AUROC of 0.77–0.85 and AUPRC of 0.51–0.60 for variants. ConclusionThe tri-light warning system can enhance pandemic responses by effectively stratifying COVID-19 patients under varying conditions and data shifts.

Assessment of the Diagnostic Performance of a Commercially Available Artificial Intelligence Algorithm for Risk Stratification of Thyroid Nodules on Ultrasound.

Background: Thyroid nodules are challenging to accurately characterize on ultrasound (US), though the emergence of risk stratification systems and more recently artificial intelligence (AI) algorithms has improved nodule classification. The purpose of this study was to evaluate the performance of a recent Food and Drug Administration (FDA)-cleared AI tool for detection of malignancy in thyroid nodules on US. Methods: One year of consecutive thyroid US with ≥1 nodule from Duke University Hospital and its affiliate community hospital (649 nodules from 347 patients) were retrospectively evaluated. Included nodules had ground truth diagnoses by surgical pathology, fine needle aspiration (FNA), or three-year follow-up US showing stability. An FDA-cleared AI tool (Koios DS Thyroid) analyzed each nodule to generate (i) American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) descriptors, scores, and follow-up recommendations and (ii) an AI-adapter score to further adjust risk assessments and recommendations. Four groups were then compared: (i) Koios with AI-adapter, (ii) Koios without AI-adapter, (iii) clinical radiology report, and (iv) radiology report combined with AI-adapter. Performance of the final recommendations (FNA or no FNA) was determined based on ground truth, and comparison between the four groups was made using sensitivity, specificity, and receiver-operating-curve analysis. Results: Of 649 nodules, 32 were malignant and 617 were benign. Performance of Koios with AI-adapter enabled was similar to radiologists (area under the curve [AUC] 0.70 for both, [CI 0.60-0.81] and [0.60-0.79], respectively). Koios with AI-adapter had improved specificity compared to radiologists (0.63 [CI: 0.59-0.67] versus 0.43 [CI: 0.38-0.48]) but decreased sensitivity (0.69 [CI: 0.50-0.83) versus 0.81 [CI: 0.61, 0.92]). Highest performance was seen when the radiology interpretation was combined with the AI-adapter (AUC 0.76, [CI: 0.67-0.85]). Combined with the AI-adapter, radiologist specificity improved from 0.43 ([CI: 0.38-0.48]) to 0.53 ([CI: 0.49-0.58]) (McNemar's test p < 0.001), resulting in 17% fewer FNA recommendations, with unchanged sensitivity (0.81, p = 1). Conclusion: Koios DS demonstrated standalone performance similar to radiologists, though with lower sensitivity and higher specificity. Performance was best when radiologist interpretations were combined with the AI-adapter component, with improved specificity and reduced unnecessary FNA recommendations.

Development of a Mitochondrial Membrane Permeability Transition Prognosis System in Colon Adenocarcinoma: Risk Stratification and Therapeutic Target Identification.

Explore the role of mitochondrial membrane permeability transition (MPT) in colon adenocarcinoma (COAD). Further exploration of risk stratification for COAD prognostic assessment has important clinical value. MPT-related pathways play a key role in the pathogenesis of many human diseases, including tumorigenesis. Its impact on COAD is still unknown. Bioinformatics analysis was conducted by analyzing the GEO database and TCGA database, and the bioinformatics results were verified by in vitro experiments. Through the analysis of the transcriptome data of 1008 COAD samples in the GEO database and TCGA database, the differential expressions of MPT-related genes in COAD were explored, followed by molecular subtype analysis based on MPT characteristics by univariate Cox algorithm analysis and the consensus clustering algorithm. The gene signature associated with MPT molecular subtypes was further identified and the MPT scoring system was established by the LASSO-univariate Cox analysis algorithm. After evaluating the prognostic value of the MPT scoring system in COAD patients via nomogram establishment, the clinical value of the MPT scoring system was comprehensively analyzed through somatic mutation characteristics analysis, immunotherapy response analysis, immunoinfiltration analysis, and drug sensitivity analysis. CCK-8, WB, PCR, colony formation method, and Transwell method were used to verify the effect of the screened target on the proliferation and invasion of COAD cells. We successfully established a scoring system related to MPT and validated the prognostic value of COAD patients. The potential clinical value of the MPT scoring system was also analyzed. VSIG4 was selected for further in vitro experiments to verify the effect of the screened targets on the proliferation and invasion ability of COAD cells. We established an MPT scoring system for effective risk stratification of COAD patients, demonstrating the impact of MPT on the development of COAD and its potential value as an intervention factor.

Antenatal Risk of Coarctation for Newborns at Hartford Hospital (ARCH) Pathway: A Predictor of Postnatal Management Strategy.

The diagnosis of coarctation of the aorta (CoA) prior to birth can be challenging due to the physiologic changes during postnatal transition. Prenatal risk stratification can standardize postnatal management and improve outcome. CT Children's Fetal Cardiology created the Antenatal Risk of CoA in Hartford (ARCH) clinical pathway defining four distinct postnatal order sets based on degree of suspicion for ductal dependency on fetal evaluation: low, low-moderate, moderate-high, and high risk. This study aims to evaluate safety and efficacy of the ARCH pathway in neonates with suspected CoA. This study was a single-center, retrospective chart review evaluating maternal-infant dyads with findings concerning for CoA between July 2004 and July 2021, before and after ARCH pathway implementation. Neonates were evaluated for the presence or absence of critical CoA and postnatal clinical data were collected. Statistical analysis was performed using chi square and Fisher's exact test. There were 108 maternal-infant dyads studied, comprising 53 non-pathway patients and 55 ARCH pathway participants. Thirty-three neonates had critical CoA, comprising 23 non-pathway and 10 ARCH pathway subjects.Patients categorized in the high-risk group were highly associated with critical CoA (P = 0.003). Non-pathway neonates with CoA demonstrated higher likelihood of hospital transfer compared to ARCH pathway neonates (56.5% vs. 10.0%, P = 0.021).NICU admission, prostaglandin administration, and intubation were not significantly different between before and after ARCH implementation (P < 0.05). More echocardiograms were performed in ARCH pathway neonates without CoA than their non-pathway counterparts (1.586 vs. 2.133, P = 0.049). The ARCH pathway is a safe, reliable prenatal risk stratification system to help guide management of patients with critical CoA. These results identify effective targets of modification to the pathway to reduce resource utilization without compromising safety.

8549 A Pre-ablative Stimulated Thyroglobulin Value That Predicts ATA High Risk Patients and Their Treatment Response

Abstract Disclosure: H. Gondaliya: None. N. Nangadda: None. D. Bhat: None. K.S. Khadilkar: None. S. Kumbenahalli Siddegowda: None. B.G. Sooragonda: None. A. Shetty: None. S. Kannan: None. In ATA risk stratification system for differentiated thyroid cancer (DTC), one of the variables placing patients in ATA high risk category is thyroglobulin (Tg) suggestive of distant metastases. However there is no defined cut-off Tg level for guiding the clinicians. We aimed to determine pre-ablative stimulated Tg (psTg) value that predicts ATA high risk patients (based on non-Tg parameters) and their treatment response on follow up. This was an ambispective observational study of 285 patients with DTC treated at our centre from April 2017 to November 2022. psTg was measured in all patients 3-4 weeks after surgery and they were subjected to RAIA depending on clinician’s discretion and followed up using the ATA dynamic risk stratification every 6 months. The cohort was divided into two groups: ATA high risk and ATA non-high risk (Low and intermediate risk). Patients among high risk group were divided based on response to treatment into structural incomplete group and non-structural incomplete group (Excellent, Indeterminate and Biochemically incomplete response). The mean age of the study population was 41±15 years and 68% (n=194) were female and 87% (n=249) were papillary thyroid cancer (PTC) and 13% (n=36) were Follicular Thyroid Cancer (FTC). Median duration of follow up was 2.95 years (IQR: 1.42 - 5.12). Among those with PTC, 75% were Classic, 15% Follicular variant and 10% aggressive variants that included tall cell, hobnail, diffuse sclerosing and columnar variant. On ATA risk stratification, 81.75% (n=233) patients were in non-high risk group with median psTg 3.4 ng/ml (IQR 0.5-12.2) and 18.25% (n=52) were in high-risk group with median psTg 150.5 ng/ml (IQR 7.15-439.5). On ROC analysis of all the patients, a psTg value &amp;gt;53.3 ng/ml was predictive of ATA high risk group with PPV of 64%; NPV of 93.5% and AUC 0.81. In ATA high-risk group, there were 73% (n=38) of patients with structural incomplete response on follow up and their median psTg was 287 ng/ml (IQR 95-500) and there were 27% (n=14) with non-structural incomplete response and their median psTg level of 6.2 ng/ml (IQR 2.3-110). A psTg value &amp;gt;53 ng/ml discriminated structural incomplete from non-structural incomplete response with PPV of 77%; NPV 87% and AUC 0.83. A psTg value &amp;gt;53 ng/ml not only predicts ATA high risk patients but also indicates those who are likely to remain with structural residual disease at follow up of 3 years. Presentation: 6/1/2024

8172 Retrospective Analysis of mRNA Expression Based Signatures of Thyroid Tumor Invasion and Metastases

Abstract Disclosure: S. Ahmadi: None. E. Namiranian: None. M. Alshalalfa: Employee; Self; Veracyte, Inc.. Stock Owner; Self; Veracyte, Inc. R. Jiang: Employee; Self; Veracyte, Inc.. Stock Owner; Self; Veracyte, Inc. Y. Hao: Employee; Self; Veracyte, Inc.. Stock Owner; Self; Veracyte, Inc. J.P. Klopper: Employee; Self; Veracyte, Inc.. Stock Owner; Self; Veracyte, Inc. R.T. kloos: Employee; Self; Veracyte, Inc.. Stock Owner; Self; Veracyte, Inc.. E. Marqusee: None. The American Thyroid Association thyroid cancer risk stratification system is driven primarily by the extent of vascular and extrathyroidal extension, as well as lymph node metastases. Minimizing surgical intervention for thyroid tumors from indeterminate thyroid nodules (ITN - Bethesda III and IV cytology) is often preferred to mitigate surgical complications and lessen the need for thyroid hormone supplementation post-operatively if clinical outcomes are not worsened. By leveraging the whole transcriptome derived Afirma Genomic Sequencing Classifier (GSC) thyroid nodule molecular testing platform, mRNA expression-based signatures were developed to predict thyroid tumors with a low risk of clinically significant invasion and metastases with a &amp;gt; 95% negative predictive value as part of the Afirma Genomics Resource for Intelligent Discovery (GRID). The objective of this study was to analyze the performance of these signatures on a retrospective cohort of patients with ITN who had Afirma GSC suspicious findings and thyroid surgery. Two hundred three subjects were evaluated, and pathology reports were scored for the extent of thyroid tumor invasion or metastases. Tumors with extensive vascular invasion or extrathyroidal extension are labeled as high risk (and otherwise are low risk). Tumors with lymph node metastases that either have documented &amp;gt; 2mm tumor deposits, &amp;gt; 40% of central nodes involved, or lateral lymph node metastases are labeled as high risk (and otherwise are low risk). The Afirma GRID invasion and metastases signatures were applied to correlate with the pathology scoring. The cohort was comprised of 144 (70%) female and 59 (30%) male patients with median age of 54 [IQR 40-65]. The cohort was mostly Bethesda III (n= 152, 75%). Fifty three percent (n=109) of the samples were malignant upon histology review. Two samples were scored as high risk for lymph node metastases (LNM) and 4 samples were scored as high risk for invasion (INV) based on final pathology. The LNM signature ruled out 55% of samples with 100% NPV and 55% specificity. No samples with high risk LNM scores were erroneously ruled out by the LNM GRID signature. The rule out % was similar in female (58%) and males (46%) (chi-squared test p=0.1). For the invasion model, 57% of samples were ruled out with 99% NPV and 57% specificity. One sample with extra-thyroidal invasion had a false negative GRID INV signature. The INV rule out % was similar in female (57%) and males (54%) (chi-squared test p=0.19). In summary, the Afirma GRID INV and LNM signatures ruled out clinically significant thyroid tumor features in &amp;gt; 50% of the studied cohort with &amp;gt; 95% NPV. Prospective studies should be performed to confirm the potential for Afirma GRID tumor behavior signatures to optimize and individualize surgical decision-making, decreasing the burden of unnecessary extent of thyroid surgery while maintaining outstanding clinical outcomes. Presentation: 6/3/2024

Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns in Breast Multiplexed Digital Pathology.

The tumor microenvironment is widely recognized for its central role in driving cancer progression and influencing prognostic outcomes. There have been increasing efforts dedicated to characterizing this complex and heterogeneous environment, including developing potential prognostic tools by leveraging modern deep learning methods. However, the identification of generalizable data-driven biomarkers has been limited, in part due to the inability to interpret the complex, black-box predictions made by these models. In this study, we introduce a data-driven yet interpretable approach for identifying patterns of cell organizations in the tumor microenvironment that are associated with patient prognoses. Our methodology relies on the construction of a bi-level graph model: (i) a cellular graph, which models the intricate tumor microenvironment, and (ii) a population graph that captures inter-patient similarities, given their respective cellular graphs, by means of a soft Weisfeiler-Lehman subtree kernel. This systematic integration of information across different scales enables us to identify patient subgroups exhibiting unique prognoses while unveiling tumor microenvironment patterns that characterize them. We demonstrate our approach in a cohort of breast cancer patients and show that the identified tumor microenvironment patterns result in a risk stratification system that provides new complementary information with respect to standard stratification systems. Our results, which are validated in two independent cohorts, allow for new insights into the prognostic implications of the breast tumor microenvironment. This methodology could be applied to other cancer types more generally, providing insights into the cellular patterns of organization associated with different outcomes.

B-220 Clinical Application of the “Clg-Fish” Technique for the Diagnosis of Multiple Myeloma

Abstract Background The international standard system [R2-ISS] for Multiple Myeloma (MM) includes genetic characteristics of malignant cells by fluorescent in situ hybridization -t(4;14), del(17p) and t(14;16) at diagnosis; We included interphase fluorescent in situ hybridization (cIg-FISH) of purified CD138 cells in this evaluation. The aim of this study is to evaluate the implementation of cIg-FISH in MM for disease diagnosis and stratification. Methods oBone marrow [BM] from patients with MM diagnosed by the European Myeloma Network (REM) criteria were evaluated. BM mononuclear cells obtained by density gradient centrifugation for 30 minutes, with Ficoll-Hypaque density medium; followed by immunoassay with anti-Kappa/anti-Lambda antibodies labeled with FISH probes. We detected the following abnormalities: t(4;14), t(14;16) and deletion of 17p13. The t(11;14) and the amplification/deletion of 1q21-q22 (CKS1B)/1p32.3 (CDKN2C). We used the probes: TP53, IGH::FGFR3, IGH::CCND1, IGH:MAF and CKS1B/CDKN2C, one hundred cells per probe. Signals exceeding the cutoff recommended by REM were considered abnormal. The viability of the method was tested on samples 24, 48 and 72 hours after collection. Results Genetic aberrations seen in 24 and 48 hs plasma cells, a specific rearrangement of 14q32.33 t(4;14) in the 48 hs sample; Samples processed for 72 hours resulted in cells that were unviable for analysis. Genetic aberrations were detected by cIg-FISH in cells from 40% of patients. We detected the following abnormalities t(4;14) (Fig.1A), del(17p) (Fig. 1B); deletion of 1p32.3 amplification (CDKN2C) 1q21 (Fig. 1 C-D). The cIg-FISH study detected genetic aberrations in MM as those recommended by the European Myelomat Network - t(4;14), t(14;16) and del(17p), by FISH analysis for risk stratification for MM. Conclusions The inclusion of cIg-FISH in risk stratification systems has clinical relevance because cIg-FISH is a standard tool for detecting genetic abnormalities and prognosing the disease.

Risk Stratification of Thyroid Nodules Diagnosed as Bethesda Category III by Ultrasound, Size, and Cytology.

This study aimed to evaluate the performance of an integrated risk stratification system (RSS) based on ultrasound (US) RSSs, nodule size, and cytology subcategory for diagnosing malignancy in thyroid nodules initially identified as Bethesda category III on fine-needle aspiration. This retrospective study was conducted at two institutions and included consecutive patients with Bethesda category III nodules, and final diagnoses confirmed by repeat biopsy or surgery. A total of 320 Bethesda category III nodules (≥1 cm) from 309 patients (223 female and 86 male; mean age, 50.9 ± 12.0 years) were included. The malignancy risk of Bethesda category III nodules and predictors of malignancy were assessed according to US RSSs, nodule size, and cytology subcategory. The diagnostic performances of US-size cytology (USC) RSS and US RSS alone for malignancy were compared. The intermediate or high suspicion US category independently increased the malignancy risk in all US RSSs (P ≤ 0.001). Large nodule size (≥3 cm) independently increased the malignancy risk of low- or intermediate suspicion US category nodules. Additionally, the atypia of undetermined significance cytology subcategory independently increased the malignancy risk of low suspicion US category nodules in most US RSSs. The area under the receiver operating characteristic curve of the USC RSSs was greater than that of the US RSSs alone (P < 0.048). Malignancy was not found in the very low risk category of USC RSS. The diagnostic performance of USC RSS for malignancy was superior to that of US RSS alone in Bethesda category III nodules. Malignancy can be ruled out in the very low-risk category of USC RSS.

Lymph node ratio–based model for predicting survival and assessing the benefit of adjuvant chemotherapy in postoperative duodenal adenocarcinoma

BackgroundThis study aimed to evaluate the prognostic significance of lymph node ratio in postoperative duodenal adenocarcinoma and develop a nomogram-based model for prognosis assessment and treatment optimization. MethodsClinical information of patients with duodenal adenocarcinoma were retrieved from the Surveillance, Epidemiology, and End Results database, and prognostic factors were identified by univariate and multivariable analyses. Prognostic factors influencing patient outcomes were identified using univariate and multivariable Cox analyses. Subsequently, a novel nomogram and risk stratification system were developed based on these identified factors. ResultsA total of 943 eligible patients were included, with 656 in the training cohort and 287 in the validation cohort. Lymph node ratio ≥0.12 were associated with poorer overall survival (hazard ratio 1.562, 95% confidence interval 1.195–2.041, and P = .001 for lymph node ratio = 0.12–0.30; hazard ratio 2.431, 95% confidence interval 1.847–3.199, and P < .001 for lymph node ratio >0.30). Prognostic factors including age at diagnosis, race, T stage, lymph node ratio, and tumor size were integrated into the nomogram. Patients in the low-risk group demonstrated significantly better overall survival compared with those in the high-risk group. Additionally, adjuvant chemotherapy significantly improved overall survival in the high-risk subgroup, whereas low-risk patients might be exempt from adjuvant chemotherapy. ConclusionsThis study represented the pioneering endeavor in introducing a lymph node ratio–based nomogram model for prognosis stratification and adjuvant chemotherapy decision-making protocol for patients with duodenal adenocarcinoma, thereby guiding personalized treatment strategies and minimizing overtreatment.