Clinical Risk Factors Research Articles

Psychiatric diagnoses are common after liver transplantation and associated with increased healthcare utilization and patient financial burden.

Psychiatric disorders after liver transplantation (LT) are associated with worse patient and graft outcomes, which may be amplified by inadequate treatment. We aimed to characterize the burden of psychiatric disorders, treatment patterns, and associated financial burden among LT recipients (LTRs). IQVIA PharMetrics® Plus for Academics-a large health plan claims database representative of the commercially insured U.S. population-was used to identify psychiatric diagnoses among adult LTRs and assess treatment. Multivariable logistic regression analysis identified factors associated with post-LT psychiatric diagnoses and receipt of pharmacotherapy. Patient financial liability was estimated using adjudicated medical/pharmacy claims for LTRs with and without psychiatric diagnoses. Post-LT psychiatric diagnoses were identified in 395 (29.5%) of 1,338 LTRs, of which 106 (26.8%) were incident cases. Treatment varied, with 67.3% receiving pharmacotherapy, 32.1% psychotherapy, 21.0% combination therapy, and 21.5% no treatment. Among 340 LTRs on psychotropic medications before transplant, 24% did not continue them post-LT. Post-LT psychiatric diagnoses were independently associated with female sex, alcohol-associated liver disease (ALD), prolonged LT hospitalization (>2 weeks), and pre-LT psychiatric diagnosis. Incident psychiatric diagnoses were associated with female sex, ALD, and prolonged LT hospitalization. Patients with a post-LT psychiatric diagnosis had higher rates of hospitalization (89.6% vs 81.5%, p<0.001) and financial liability (median $5.5K vs $4.6K USD, p=0.006). Having a psychiatric diagnosis post-LT was independently associated with experiencing high financial liability >$5K. Over 1 in 4 LTRs had a psychiatric diagnosis in a large national cohort, yet nearly a quarter received no treatment. LTRs with psychiatric diagnoses experienced increased healthcare utilization and higher financial liability. Sociodemographic and clinical risk factors could inform high-risk subgroups who may benefit from screening and mitigation strategies.

Enhanced fracture risk prediction: a novel multi-trait genetic approach integrating polygenic scores of fracture-related traits.

Current polygenic scores (PGS) have limited predictive power for fracture risk. To improve genetic prediction, we developed and evaluated a novel metaPGS combining genetic information from multiple fracture-related traits. We derived individual PGS from genome-wide association studies of 16 fracture-related traits and employed an elastic-net logistic regression model to examine the association between the 16 PGSs and fractures. An optimal metaPGS was constructed by combining 11 significant individual PGSs selected by the elastic regularized regression model. We evaluated the predictive power of the metaPGS alone and in combination with clinical risk factors recommended by guidelines. The discrimination ability of metaPGS was assessed using the concordance index. Reclassification was assessed using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). The metaPGS had a significant association with incident fractures (HR 1.21, 95% CI 1.18-1.25 per standard deviation of metaPGS), which was stronger than previously developed bone mineral density (BMD)-related individual PGSs. Models with PGS_FNBMD, PGS_TBBMD, and metaPGS had slightly higher but statistically non-significant c-index than the base model (0.640, 0.644, 0.644 vs. 0.638). However, the reclassification analysis showed that compared to the base model, the model with metaPGS improves the reclassification of fracture. The metaPGS is a promising approach for stratifying fracture risk in the European population, improving fracture risk prediction by combining genetic information from multiple fracture-related traits.

Association between retinal microvascular abnormalities and late-life brain amyloid-β deposition: the ARIC-PET study

BackgroundRetinal microvascular signs are accessible measures of early alterations in microvascular dysregulation and have been associated with dementia; it is unclear if they are associated with AD (Alzheimer’s disease) pathogenesis as a potential mechanistic link. This study aimed to test the association of retinal microvascular abnormalities in mid and late life and late life cerebral amyloid.MethodsParticipants from the ARIC‐PET (Atherosclerosis Risk in Communities‐Positron Emission Tomography) study with a valid retinal measure (N = 285) were included. The associations of mid- and late-life retinal signs with late-life amyloid-β (Aβ) by florbetapir PET were tested. Two different measures of Aβ burden were included: (1) elevated amyloid (SUVR > 1.2) and (2) continuous amyloid SUVR. The retinal measures’ association with Aβ burden was assessed using logistic and robust linear regression models. A newly created retinal score, incorporating multiple markers of retinal abnormalities, was also evaluated in association with greater Aβ burden.ResultsRetinopathy in midlife (OR (95% CI) = 0.36 (0.08, 1.40)) was not significantly associated with elevated amyloid burden. In late life, retinopathy was associated with increased continuous amyloid standardized value uptake ratio (SUVR) (β (95%CI) = 0.16 (0.02, 0.32)) but not elevated amyloid burden (OR (95%CI) = 2.37 (0.66, 9.88)) when accounting for demographic, genetic and clinical risk factors. A high retinal score in late life, indicating a higher burden of retinal abnormalities, was also significantly associated with increased continuous amyloid SUVR (β (95% CI) = 0.16 (0.04, 0.32)) independent of vascular risk factors.ConclusionsRetinopathy in late life may be an easily obtainable marker to help evaluate the mechanistic vascular pathway between retinal measures and dementia, perhaps acting via AD pathogenesis. Well-powered future studies with a greater number of retinal features and other microvascular signs are needed to test these findings.

A first trimester prediction model and nomogram for gestational diabetes mellitus based on maternal clinical risk factors in a resource-poor setting

BackgroundThe implementation of universal screening for Gestational Diabetes Mellitus (GDM) is challenged by several factors key amongst which is limited resources, hence the continued reliance on risk factor-based screening. Effective identification of high-risk women early in pregnancy may enable preventive intervention. This study aimed at developing a GDM prediction model based on maternal clinical risk factors that are easily assessable in the first trimester of pregnancy in a population of Nigerian women.MethodsThis was a multi-hospital prospective observational cohort study of 253 consecutively selected pregnant women from which maternal clinical data was collected at 8–12 weeks gestational age. Diagnosis of GDM was made via a one-step 75-gram Oral Glucose Tolerance Test (OGTT) at 24–28 weeks of gestation. A GDM prediction model and nomogram based on selected maternal clinical risk factors was developed using multiple logistic regression analysis, and its performance was assessed by Receiver Operator Curve (ROC) analysis. Data analysis was carried out using Statistical Package for Social Sciences (SPSS) version 25 and Python programming language (version 3.0).ResultsIncreasing maternal age, higher body mass index (BMI), a family history of diabetes mellitus in first-degree relative and previous history of foetal macrosomia were the major predictors of GDM. The model equation was: LogitP = 6.358 − 0.066 × Age − 0.075 × First trimester BMI − 1.879 × First-degree relative with diabetes mellitus − 0.522 × History of foetal macrosomia. It had an area under the receiver operator characteristic (ROC) curve (AUC) of 0.814 (95% CI: 0.751–0.877; p-value < 0.001), and at a predicted probability threshold of 0.745, it had a sensitivity of 79.2% and specificity of 74.5%.ConclusionThis first trimester prediction model reliably identifies women at high risk for GDM development in the first trimester, and the nomogram enhances its practical applicability, contributing to improved clinical outcomes in the study population.

Incidence and risk factors of delirium in the intensive care unit of SIUT Karachi.

Objective: To estimate the incidence and risk factors of delirium in intensive care unit (ICU) of Sindh Institute of Urology and Transplantation (SIUT), Karachi. Study Design: Cross-sectional study. Setting: The ICU of SIUT, Karachi, Pakistan. Period: February 2023 to August, 2023. Methods: Patients over 18 years of age who were admitted to the ICU for more than 24 hours were analyzed. Patient with Richmond agitation sedation score (RASS &gt;-2) were assessed for delirium using the confusion assessment method (CAM)-ICU method. Delirium subtypes were also evaluated. Domographic and clinical risk factors were evaluated for possible relationship with the existence of delirium. Results: Total 96 patient were enrolled in the study, 64 (66.7%) were male and 32 (33.3%) female. The mean age was 49.1±17.3 years. Mean RASS and CAM-ICU scores were 0.53±1.31 and 1.0±1.42 respectively. Delirium was present in 32 (33.3%) patients. It was noted that 45.4% patients had hypoactive delirium, 31.8% hyperactive delirium, and 22.7% mixed type. Patients aged 50 years or above had higher odds of delirium (OR: 0.41 [0.17-0.98], p=0.045), On multivariable regression model, BMI &lt;25 Kg/m2, coexistence of ischemic heart disease and chronic kidney disease, patients with septic shock, post-operative patients, need of sedation were significantly associated with higher odds of delirium. Conclusion: Delirium is a frequent condition in ICU patients, with a higher occurrence of hypoactive delirium. The leading risk factors associated with delirium were older age, ischemic heart disease, chronic kidney disease and use of sedative drugs.

Clinical and histopathological characteristics of acute kidney injury in a cohort of brain death donors with procurement biopsies.

Kidney biopsies are routinely used for diagnostic and prognostic purposes but their utility in the intensive care unit (ICU) setting is limited. We investigated the associations of clinical and histopathological risk factors with ICU-acute kidney injury (AKI) in donors with brain death (DBD) with kidneys of lower quality and procurement biopsies. Overall,221 donors with brain death, 239 biopsies and 197 recipients were included. The biopsies were reread and scored according to the Banff recommendations. Clinical and histopathological data were compared between donors with and without AKI defined by serum creatinine and by urine output. Logistic regression analysis was applied to identify independent clinical and histopathological risk factors for both phenotypes. Lastly, the impact of each AKI phenotype on outcome was explored.AKI was diagnosedbased on the RIFLE (Risk, Injury, Failure, Loss of function, End-stage kidney disease)AKIN (Acute Kidney Injury Network) orKDIGO (Kidney Disease Improving Global Outcomes) criteria. Acute kidney injury occurred in 65% of donorsbased both uponserum creatinine and by urine output. Serum creatinine was able to better discriminate AKI. Multiorgan failure and severe AKI were captured by serum creatinine, and hemodynamic instability by urine output. Donors with serum creatinine-AKI showed lower chronic macrovascular scores, while donors with urine output-AKI had higher chronic microvascular and tubulointerstitial scores. Tubular injury was similar between the subgroups. Except for delayed graft function and one-year death-censored graft survival, the other short-term recipient outcomes were similar for both AKI phenotypes. Serum creatinine is more suitable than urine output for defining AKI in donors with brain death. There are distinct clinical risk factors for each AKI-ICU phenotype. Donor AKI phenotype does not predict the recipient´s prognosis. Kidney biopsies do not seem to confer any tangible benefit in defining AKI indonors with brain death.

Abstract PO3-08-01: Decision curve analysis to compare breast cancer risk predictions for a polygenic integrated clinical risk model with those of a gold standard

Abstract Risk-based guidelines have been developed to provide advice on options that are available to women who are at increased risk of breast cancer. We and others are focused on improving risk prediction models for general population use. For breast cancer, polygenic risk has been identified as important for risk prediction, especially when combined with clinical risk factors such as family history, anthropometric measures, breast imaging measures, and hormonal and reproductive risk factors. When comparing the performance of risk prediction models, the AUC is most often used but it focuses on performance in terms of distinguishing between affected and unaffected individuals and does not take into account differences in the specificity (true negative rate) and sensitivity (true positive rate) of the models being compared. In contrast, decision curve analysis takes sensitivity and specificity into account and enables the comparison of the net benefit of risk prediction tools at the clinical risk thresholds that are used to guide clinical management decisions. Herein, we use decision curve analysis to compare breast cancer risk prediction guidelines for BRISK and the Breast Cancer Risk Assessment Tool (BCRAT) in the UK Biobank, and for BRISK and IBIS version 7 in the Nurses’ Health Study. We evaluated the net benefit at the 5-year risk thresholds (1.67% and 3%) that are used to guide clinical management decisions around risk-reducing medication and at the remaining lifetime risk of 20% that is used to guide supplemental MRI surveillance. In the UK Biobank, BRISK showed a net benefit over BCRAT at the 5-year risk thresholds of 1.67% and 3.0%. The AUCs were 0.649 (95% CI = 0.640, 0.695) for BRISK and 0.567 (95% CI = 0.556, 0.577) for BCRAT and showed that, overall, BRISK discriminated between affected and unaffected women better than BCRAT (P &amp;lt; 0.001). In the Nurses’ Health Study, BRISK showed a net benefit over IBIS at the remaining lifetime risk threshold of 20%. The AUCs were 0.647; 95% CI = 0.627, 0.668 for BRISK and 0.571; 95% CI = 0.546, 0.595 for IBIS, a statistically significant improvement for BRISK over IBIS (P &amp;lt; 0.0001). We also looked at sensitivity, specificity, positive predictive value and negative predictive value even though these risk models are one-step removed from breast cancer diagnosis by screening/risk-reduction options. The positive predictive values are higher for the BRISK (27.1%) model compared to BCRAT (5.5%) in the UK Biobank dataset and higher for BRISK (15.4%) compared to IBISv7 (14.0%) in the Nurses’ dataset. We have shown the application of a statistical tool that can be used to directly compare risk models at clinically relevant thresholds. We have shown that BRISK, which comprises polygenic risk and clinical risk factors, shows higher net benefit at both 1.67% and 3% thresholds compared to BCRAT and at 20% remaining lifetime risk for IBIS version 7. In breast cancer risk prediction, a tool with high specificity (and therefore a necessary trade-off for low sensitivity) is preferred because we do not want to incorrectly classify women as not being at high risk and deny them the opportunity for intervention in the form of more frequent screening, alternative modes of screening or risk-reducing medication. This improvement in general population risk stratification that BRISK can provide has the potential to have a clinically significant effect on women’s health. Citation Format: Erika Spaeth, Bernard A Rosner, Gill Dite. Decision curve analysis to compare breast cancer risk predictions for a polygenic integrated clinical risk model with those of a gold standard [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-08-01.

Abstract PO5-08-10: Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study

Abstract Introduction: Breast cancer is the most common cancer among women in Taiwan and the incidence rate of breast cancer has been increasing steadily over the past 30 years. To address this, a nationwide screening program with biennial mammography for women aged 40-69 was implemented in 2004, which has contributed to a significant reduction in breast cancer mortality. Asian women are more likely to have dense breast tissue, and studies have shown a correlation between breast density and the risk of developing breast cancer, with higher breast density values associated with increased risk. Polygenic risk scores (PRS) are calculated based on an individual's genetic information and can estimate their risk of developing a particular disease. Although PRS can identify low-penetrance risk variants, its use in breast cancer risk estimation is typically limited to gene-only models, and integration with breast cancer screening databases is rare. Hence, the aim of this specific study was to evaluate the predictive capacity of PRS in women with dense breast tissue by exploring potential genetic markers and constructing a PRS to investigate the genetic factors associated with clinical characteristics and the risk of developing breast cancer. Methods: The PRS was developed using genetic information from the Taiwan Precision Medicine Initiative genotyping data. A total of 6335 patients were enrolled, and 101 single nucleotide polymorphisms (SNPs) were selected as candidate markers based on the PGS Catalog (PGS000001). Clinical characteristics of the study cohort were summarised using median and range, or frequency and percentage. The distribution of characteristics between breast cancer and controls was estimated using an independent two-sample t-test, chi-square, and Fisher’s exact test. The association of the PRS and related characteristics with breast cancer risk in the study cohort was estimated using univariate and multivariate binomial logistic regression. Harrel’s C-index was reported to demonstrate the predictive performance of PRS in both univariate and multivariate models. All p-values were two-sided, and a p less than .05 is considered statistically significant. All analyses were performed using R 4.1.2 (R core team, 2023) Results: The results showed that breast cancer patients constituted a higher proportion of individuals in PRS Q4 (37.8% vs 24.8% in controls). The model's predictive performance for breast cancer risk increased from 0.565 (95% CI = 0.520-0.611) to 0.699 (95% CI = 0.644-0.755) by adding related clinical characteristics compared to the PRS-only model. Patients characterized with benign breast disease (OR = 0.50, 95% CI = 0.31-0.79, P = 0.004) showed a decreased breast cancer risk, and patients with mastalgia or palpable breast lesion (OR = 6.87, 95% CI = 4.29-10.8, P &amp;lt; 0.001) predicted significant greater breast cancer risk. Subgroup analysis of patients without breast symptoms was conducted as they may be overlooked or underestimated for breast cancer screening. For dense breast patients without symptoms, the high PRS group (Q4) consistently showed a significantly elevated breast cancer risk compared to the low PRS group (Q1-Q3) in both univariate (OR = 2.25, 95% CI = 1.43-3.50, P &amp;lt; 0.001) and multivariate analyses (OR = 2.22, 95% CI = 1.41-3.46, P &amp;lt; 0.001). Conclusion: Breast cancer screening has undergone a shift from a general approach to a personalized, risk-based approach. Besides breast cancer screening using family history as the only risk factor, our proposed model identifies both genetic and clinical risk factors using big data analyses including the EHR, cancer screening, and registry from a single institute. The study suggests that integrating PRS into personalized screening strategies could improve risk prediction for Taiwanese females with dense breasts without prominent symptoms. Table 1. Clinical characteristics of study cohort (n=6335). Table 2. Association of the PRS quartiles in breast cancer risk. Table 3. Predictive performance of PRS for breast cancer risk. Citation Format: Chih Yean Lum, Chih Chiang Hung, Chi-Cheng Huang, Tzu-Hung Hsiao, Sin-Hua Moi. Breast cancer risk prediction performance of polygenic risk score in Taiwanese female with dense breast: A nested case-control study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-08-10.

Abstract PO2-28-06: An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients

Abstract Probability of malignancy (POM) for Breast Imaging Reporting and Data System (BI-RADS) category 4 designated breast lesions ranges from 2% – 95% and contributes to a high unnecessary biopsy rate. This is as most clinicians often stick to the biopsy option to rule in or out breast cancer early; withholding biopsy could be risky, and biopsies of BI-RADS 4 lesions serve as a quality metric and performance standard. At 21.1%, biopsy-proven positive predictive value (PPV3) rates for BI-RADS 4 have not improved for decades, translating to high false-positive rates of mammography. Unnecessary biopsies are a big issue in the management of BI-RADS 4 lesions with negative implications including increased medical costs, healthcare wastes, unnecessary psychological burdens to the patients, and potential complications and risks. Objectives 1. Optimize the precision breast cancer risk assessment tool, iBRISK, that utilizes artificial intelligence (AI) technologies, including natural language processing (NLP), image processing, and deep learning, with clinical risk factors and imaging features. 2. Develop a user-friendly web interface for iBRISK to facilitate clinicians or insurers in estimating cancer risk and making informed biopsy decisions for BI-RADS 4 lesions. Methods Our intelligent-augmented breast cancer risk calculator (iBRISK) model was trained on multimodal data collected from 10,778 patients, including demographic factors, historical and clinical characteristics, mammographic features, and pathologic signatures. We validated iBRISK using 4,200 patients from multiple leading hospitals, including Houston Methodist Neal Cancer Center, the University of Texas MD Anderson Cancer Center, and the University of Texas Health San Antonio MD Anderson Mays Cancer Center. The iBRISK was connected to a backend server which is linked to a frontend web user interface using technologies like Hypertext Preprocessor (PHP) for the backend application running on an APACHE HTTP server and React-JavaScript for the front-end web application that communicates with the backend using Representational State Transfer Application Programming Interface (RESTful API) and JavaScript Object Notation (JSON) format. The communication was secured using Secure Sockets Layer (SSL) encryption. Results The iBRISK model demonstrated high sensitivity in malignancy prediction and achieved an accuracy of 89.5%, area under the receiver operating characteristic curve of 0.93 (95% CI: 0.92-0.95), sensitivity of 100%, and specificity of 81%. Only 0.16% of lesions determined to have low POM by the model were malignant. Our multi-center study shows that iBRISK achieves at least 50% reduction in unnecessary biopsies of BI-RADS 4 cases. Data elements for the required 20 features are entered into the interface using a variety of imputation methods including direct text, dropdown menus and radio button selections. The user-friendly web interface provides risk scores (0 – 1), risk levels (low, medium, and high), and associated biopsy recommendations. Conclusion The user-friendly iBRISK web interface is proposed as an adjunct to the BI-RADS system, enhancing the precision of BI-RADS 4 lesion cancer risk stratification. It is expected to reduce unnecessary biopsies, lower health costs, and enhance the quality of health care. This approach aims to tackle a critical issue in breast cancer diagnosis by leveraging advanced AI technologies and big data and providing clinicians with a tool to make more informed and precise biopsy decisions for BI-RADS 4 lesions. Citation Format: Chika Ezeana, Xiaohui Yu, Zhihao Wan, Tiancheng He, Tejal Patel, Virginia Kaklamani, Maryam Elmi, Erika Brigmon, Pamela Otto, Kenneth Kist, Lin Wang, Joe Ensor, Heather Speck, Ya-Chen Shih, Bumyang Kim, I-Wen Pan, David Spak, Wei Yang, Jenny Chang, Stephen Wong. An on-line deep learning decision support tool, iBRISK, aimed at improving breast cancer risk estimation and reducing unnecessary biopsies for BI-RADS 4 patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-06.

Abstract PO1-02-03: Correlative analysis of Breast Cancer Index with CTS5 for prediction of extended endocrine benefit in the BCI Registry study

Abstract Background Hormone receptor-positive (HR+) breast cancer patients face a prolonged risk of late distant recurrence even after 5 years of primary adjuvant endocrine therapy and derive only a modest benefit from extended endocrine therapy (EET). The Breast Cancer Index (BCI) is a validated genomic assay that provides the risk of late (post 5 years) and overall (0-10 years) distant recurrence and predicts EET benefit in patients with HR+ disease. The predictive component of BCI, HOXB13/IL17BR ratio [BCI (H/I)], stratifies patients as Low- and High-Likelihood to benefit from EET and has been validated in the MA.17, Trans-aTTom, IDEAL, and B-42 trials. The Clinical Treatment Score post-5 years (CTS5) is a prognostic tool incorporating standard clinical risk factors such as age at diagnosis, tumor size, tumor grade, and nodal involvement to inform prognosis of late distant recurrence. A previous analysis in the IDEAL trial showed that CTS5 was not predictive of EET benefit. Here, we examine the relationship between BCI (H/I) and CTS5 in a large cohort from the BCI Registry study. Methods The BCI Registry study is an ongoing prospective study to evaluate the long-term clinical outcome, decision impact and medication adherence in HR+ early-stage breast cancer patients receiving BCI testing as part of routine clinical care. BCI (H/I)-Low and BCI (H/I)-High groups were determined using pre-specified cut-points. CTS5 scores were calculated using clinical information from the ClinCapture electronic data capture system and stratified into Low-, Intermediate-, and High-risk groups using published cut-points. Pearson’s correlation coefficient (r) was used to estimate the correlation between BCI (H/I) and CTS5 as continuous variables. Descriptive statistics were used to summarize the distribution of BCI (H/I) groups across all CTS5 risk categories. Results BCI and CTS5 results were reported for 1520 patients (75.1% T1; 53.4% grade II; 80.3% N0). BCI (H/I) classified 934 patients (61.4%) as BCI (H/I)-Low and 586 patients (38.6%) as BCI (H/I)-High. BCI Prognostic classified 705 patients (46.4%) as BCI-Low risk for late distant recurrence and 815 patients (53.6%) as BCI-High risk. CTS5 classified 945 patients (62.2%) as CST5-Low, 400 patients (26.3%) as CTS5-Intermediate, and 175 patients (11.5%) as CTS5-High. When analyzed as continuous variables, BCI (H/I) showed a weak correlation with CTS5 (r=0.18). When stratifying CTS5 risk categories by BCI (H/I) groups, 65.2% (N=616) of CTS5-Low patients were classified as BCI (H/I)-Low, while 34.8% (N=329) of CTS5-High Low patients were classified as BCI (H/I)-High. In addition, 57.8% (N=231) and 42.2% (N=169) of CTS5-Intermediate patients were classified as BCI (H/I)-Low and BCI (H/I)-High, respectively. Finally, 49.7% (N=87) and 50.3% (N=88) of CTS5-High patients were classified as BCI (H/I)-Low and BCI (H/I)-High, respectively. Conclusion BCI (H/I) consistently stratified CTS5 risk categories into separate BCI (H/I)-Low and BCI (H/I)-High groups, indicating that risk prognostication does not equate to prediction of benefit from EET. These results confirm previous findings in the IDEAL study demonstrating that CTS5 is not predictive of EET benefit and further substantiate the clinical utility of BCI as the only predictive biomarker for extended endocrine therapy benefit in patients with HR+ early-stage breast cancer. Citation Format: Naomi Dempsey, Jenna Wong, Natalia Siuliukina, Yi Zhang, Kai Treuner, Reshma Mahtani. Correlative analysis of Breast Cancer Index with CTS5 for prediction of extended endocrine benefit in the BCI Registry study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-02-03.

Abstract PO2-12-08: Increased burden of genetic variants in idiopathic cardiomyopathy genes is associated with cancer therapy-related cardiomyopathy

Abstract Background: Breast cancer (BC) patients are at significant risk of therapy-related cardiotoxicity, but prediction of which patients will experience cardiotoxicity and the severity of the outcome are lacking. We hypothesized that germline genetic variants are potential risk factors for cancer treatment-related cardiomyopathy (CTRC) and in this study, we focused on rare non-synonymous variants within 57 genes deemed causative of familial and sporadic forms of idiopathic cardiomyopathies. Methods: We developed a clinical database and biorepository for patients receiving any potentially cardiotoxic cancer therapy at Mayo Clinic Florida. Enrollment is ongoing and includes consent to access external records. Patient data is extracted from records at baseline, completion of treatment and then once per year. The clinical database includes the following datasets: demographics, pathology, chemotherapy, radiation, echocardiography, cardiac MRI, PET/CT scan, blood pressure, BMI, labs, treatment complications. Treatment complications includes pre-existing conditions (smoking, alcohol and drug use history, hypertension, diabetes, coronary artery disease, hyperlipidemia, prior stroke or myocardial infarction, peripheral vascular disease, renal failure); cardiac medications at baseline; cardiac side-effects; time to cardiotoxicity; cardiotoxicity interventions and outcomes. Exome sequencing was performed by the Mayo Clinic NGS core on the first 86 patients in the registry, average of ~120x read depth following Illumina’s standard protocol for the NovaSeq 6000 and S4 flow cell. Genomic variants were extracted for 57 genes known to be causative of dilated, hypertrophic and arrhythmogenic right ventricular cardiomyopathies and channelopathies. Putative risk variants in known cardiomyopathy genes were defined as non-synonymous variants with a frequency of &amp;lt; 0.0004 in the GnomAD database. We compared the number of patients with putative risk variants in those groups with and without CTRC. Results: 84/86 patients had BC diagnosis. Key characteristics are described in the table below. 60/86 (70%) patients had at least one putative risk variant in one of 31 genes (there were no risk variants identified in 27/57 genes). The average number of putative risk variants per patient within the CTRC group was higher than that observed in patients without CTRC (2.06 vs 1.62). Post-hoc analysis suggested this difference is largely from patients with &amp;gt;2 risk variants in the group who experienced CTRC (N=7/17, 41%) compared to those who did not (N=14/69, 20%), p=0.023. Conclusions: Rare missense variants in the causative genes of idiopathic cardiomyopathies are potential risk factors for CTRC and risk may be increased in patients with a higher burden of variants. Analysis of the genetic data from patients that continue to be accrued in this study, including effects of additional clinical risk factors will be undertaken to improve prediction of cardiotoxicity. Table 1 Distribution of putative cardiomyopathy genetic variants in 86 cancer patients with and without treatment-related cardiomyopathy, separated by treatment regimen Citation Format: Nadine Norton, Thien Nguyen, Joseph Reddy, Jinky Harvey, Morgan Weidner, Amanda Arnold, Lily Evans, Laura Vallow, Pooja Advani. Increased burden of genetic variants in idiopathic cardiomyopathy genes is associated with cancer therapy-related cardiomyopathy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-12-08.

Abstract PO4-07-03: Machine learning breast cancer risk prediction using sequential past mammograms - a pilot study

Abstract Background and Aim Some studies have reported the use of mammogram AI deep learning algorithms to accurately predict the risk of future breast cancer development in women. In these studies, random and unrelated mammograms were independently used for training of the AI model. We propose a novel deep learning system using sequential past mammograms instead of random studies and aimed to determine if it can further improve risk prediction in a pilot study. Methods Pre-training of the backbone of a Swin-transformer machine learning model was performed by applying region-of-interest lesional masks of benign and malignant on a training set of 1644 mammograms derived from the open-access Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset. To further develop the model, deep learning training and validation were performed on the Cohort of Screen-age Women (CSAW) open-access screening mammogram set which was derived from the long term data of 873 women who developed breast cancer for the first time and 7850 non-cancer women. We then compared the diagnostic accuracy of a model using sequential past mammograms for training against the standard, non-sequential model. For the sequential model, imaging segmentation of the potential cancer site on mammography was performed on all past, pre-cancer mammograms obtained within 60 to 2555 days of cancer diagnosis in the pre-processing stage for breast cancer cases. AI training was then carried out by combining the pre-cancer mammograms belonging to the same woman in a temporal sequence using a transformer encoder. The learning rate started from 1e-6 and decayed 10 times every 10 epochs. For the non-sequential model, we removed the temporal encoder and used random, unrelated mammogram studies for training. Validation was performed on a dataset consisting of 75 pre-cancer mammograms from breast cancer cases and 69 mammograms from non-cancer cases. Results Sensitivity, specificity and overall accuracy on the validation set were 77.3% (58/75), 68.1% (47/69) and (105/144) 72.9% respectively for the sequential model compared to 69.3% (52/75), 69.6% (48/69) and 69.4% (100/144) respectively for the non-sequential model. Conclusion The study showed that deep learning breast cancer risk prediction can be further improved by using sequential past mammograms instead of random mammograms for AI model training. This can also potentially enhance other AI risk prediction models that employ combined mammogram and traditional breast cancer clinical risk factors. A larger validation study will be useful. Contact information: email Lester Leong at lester.leong.c.h@singhealth.com.sg ROC curve for breast cancer risk prediction using AI deep learning training with sequential past mammograms. Citation Format: Lester Leong, Mingjie Xu, Weimin Huang, Erli Zhang, Engracia Loh, Sze Yiun Teo, Geok Hoon Lim, Veronique Kiak Mien Tan, Yirong Sim, Fredrik Strand, Ryan Tan. Machine learning breast cancer risk prediction using sequential past mammograms - a pilot study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-03.

Abstract PO1-07-11: Radiogenomic-based imaging intratumor heterogeneity model predicts breast cancer prognosis and unveils therapeutic targets

Abstract Background: Intratumor heterogeneity (ITH) refers to variations observed among cancer cells within a single tumor, posing significant challenges in clinical practice due to its contribution to treatment resistance and unfavorable outcome. However, the inconvenience of multi-region biopsy and limitations of genomic sequencing based on limited tissue impede the practical detection of ITH. Consequently, there is an urgent need for a non-invasive method that comprehensively captures ITH from a holistic perspective of the entire tumor. Methods: We utilized a large multicenter dataset comprising dynamic contrast-enhanced magnetic resonance images from breast cancer patients (n = 1423) along with matched multiomics data (n = 468) to develop a non-invasive machine learning approach for measuring ITH. We extracted quantitative radiomic features from both the entire tumor and peritumor regions, with a specific focus on features associated with imaging heterogeneity. Using these radiomic features, we established an imaging ITH (IITH) model. The robustness of IITH evaluation was determined by assessing its correlation with genomic ITH (employing the mutant-allele tumor heterogeneity [MATH] algorithm) and pathological cellular ITH (analyzing variation in quantitative nuclear features extracted through digital pathology). Prognostic power was evaluated using Kaplan-Meier and multivariate Cox proportional hazards analyses. Integrated multiomics analyses were performed to investigate the molecular basis of different IITH subgroups. Results: We developed a non-invasive radiomic signature to quantify imaging ITH (IITH) in the FUSCC cohort. Breast cancer patients were retrospectively categorized into high and low IITH groups. Multivariate Cox analysis identified high IITH as an independent predictor of poor prognosis in breast cancer patients, even after adjusting for clinical risk factors such as tumor size, positive lymph nodes, clinical subtype and lymphovascular invasion status. These findings were further validated in the DUKE cohort, confirming the prognostic value of IITH. Moreover, we substantiated the robustness of IITH by demonstrating its association with genomic and pathological ITH. Multiomics analysis revealed the activation of oncogenic pathways and metabolic dysregulation in high-IITH tumors. Intriguingly, our investigation also highlighted ferroptosis as a vulnerability and potential therapeutic target in high-IITH tumors, which was further supported by evidence from the TCGA cohort. Conclusion: Radiomic-based assessment of ITH provides a non-invasive approach to comprehensively capture ITH and predict the prognosis of breast cancer patients. Targeting ferroptosis may hold promise as a treatment strategy for patients with high IITH. Citation Format: Guan-Hua Su, Yi Xiao, Chao You, Ren-Cheng Zheng, Shen Zhao, He Wang, Yi-Zhou Jiang, Ya-Jia Gu, Zhi-Ming Shao. Radiogenomic-based imaging intratumor heterogeneity model predicts breast cancer prognosis and unveils therapeutic targets [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-11.

Abstract PO2-01-10: 21-Gene Recurrence Score Index (ONCOTYPE DX) as a predictive biomarker for neoadjuvant chemotherapy response and outcome in patients with HR+HER2- breast cancer

Abstract Background: Hormone receptor positive (HR+), HER2- early stage breast cancer (EBC) has shown lower response to neoadjuvant chemotherapy (NCT) compared with other clinicopathologic subtypes. ONCOTYPE DX may help inform systemic treatment decisions for EBC and predicting likelihood of pathological complete response (pCR) or chemosensitivity. We analyze the pathologic response according ONCOTYPE RS and their prognostic value. Methods: We included a serie of 139 consecutive patients with high clinical risk factor with criteria to received NCT than have performed an ONCOTYPE DX test previous to start NCT. The median age was 51 years with 65 patients (47%) less than 50 years. Median initial tumoral size was 25 mm (9-97), T1: 35%, T2: 51% and T3: 14%. 70 patients (50%) have initial axillary node involvement. Median ONCOTYPE DX score was 29 (12 – 76) , RS &amp;lt; 25 in 36% and RS &amp;lt; 30 in 54%. Complete pathologic response (pCR) was found in 35 patients (25%), and by RCB was: RCB:0 23%, RCB-1 18%, RCB-2 24% and RCB-3 34%. Results: pCR score was correlated with ONCOTYPE DX (pearson : p:0,001) , patients with ONCOTYPE RS score superior to 30 has a total of 37% of pCR in contrast to a 14% in patients with ONCOTYPE RS score less than 30. Median follow-up was 32 months (5 -100). The rate of recurrence was similar independently the ONCOTYPE RS score (12% &amp;lt; 30 ; 15% &amp;gt;30) , but patients with ONCOTYPE RS &amp;lt; 30 with achieve pCR had better prognostic compared to patients that not achieve pCR (0% vs 14%). Patients with ONCOTYPE RS &amp;gt; 30 tumors that had a pCR the recurrence rate was 8% compared to 20% in patients with non pCR. Conclusions: These data suggest ONCOTYPE DX predicts pCR in HR+HER2- BC patients, especially in patients with a RS superior to 30 with an high rate of pCR of 37%. Patients with ONCOTYPE DX less than 30 that achieved pCR had the better outcomes and patients with ONCOTYPE DX superior to 30 that not achieved pCR had the worst outcomes with a total of 20% of recurrence. ONCOTYPE DX predicts the possibility of achieve a pCR and patients with a high ONCOTYPE DX RS score with residual disease have a high recurrence despite receiving treatment with chemotherapy, which requires a better adjuvant treatment, possibly with CDK 4/6 cyclin inhibitors. Citation Format: Ariadna Gasol Cudós, Serafin Morales Murillo, Noemí Tuset Der-Abrain, Felip Vilardell Villellas, Laura Arbones Cid, Jordi Melé Olivé, Carles Canosa Morales. 21-Gene Recurrence Score Index (ONCOTYPE DX) as a predictive biomarker for neoadjuvant chemotherapy response and outcome in patients with HR+HER2- breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-01-10.

Abstract PS11-02: Clinical risk factors of Central Nervous System (CNS)-related death in patients with HER2-positive metastatic breast cancer

Abstract Background: Central nervous system (CNS) relapse represents a challenge in the management of patients with HER2-positive (+) metastatic breast cancer. Data regarding the impact of brain metastases (BrM) and CNS involvement on mortality are sparse. In this study we sought to determine the proportion of HER2+ MBC patients in which CNS disease is the cause of death and identify risk factors associated with CNS-related mortality. Methods: We reviewed medical records of 294 consecutive patients with HER2+ MBC and diagnosis of CNS disease including parenchymal BrM, leptomeningeal disease (LMD) or dural metastases (DM), treated at Memorial Sloan Kettering Cancer Center between August 2010 and April 2022. HER2-positivity was assessed at the first diagnosis of metastatic disease (any site). Clinicopathologic characteristics including disease burden at presentation, timing of CNS disease and extracranial metastases (ECM), neurologic complications, and local and systemic treatments were collected. CNS-related death was defined as any death caused by BrM or LMD or DM progression or CNS treatment-associated complications. CNS-related death was estimated using cumulative incidence in the competing risks setting (with death due to other causes as a competing event) and overall survival (OS) was estimated using Kaplan Meier methodology. Risk factors associated with CNS-related death were assessed using sub-distribution hazards regression modeling. Treatments given after CNS disease diagnosis were treated as time-varying variables. Tests were two-sided with statistical significance &amp;lt; 0.05. Results: After excluding 19 patients (11 for discordant HER2 status, 5 for concomitant second solid tumor, and 3 for missing data), 275 patients were included in these analyses (258 patients with parenchymal BrM +/- LMD, 8 patients with DM only, 2 patients with DM concomitantly with LMD, and 6 patients with LMD only). Overall, 63/275 (23%) presented with CNS as first and only site of metastasis, 210 (76%) patients developed CNS disease synchronously with or following ECM, and 2 had unknown timing. 125/275 (45%) had de novo MBC, and nearly all patients were treated with CNS local therapies (92% ≥1 radiation treatment and 28% underwent ≥1 BrM resection). The median number of lines of systemic therapy after CNS disease diagnosis was 1 (range: 0-14); 105/275 (38%) patients received a HER2 tyrosine kinase inhibitor (lapatinib: 21%, neratinib: 4.4%, and tucatinib: 12%). The median follow-up was 3.6 years for survivors (range: 0.22 years-12 years). 193/275 (70%) patients died, of whom 105 (54%) died of CNS-related cause. The 3-year OS rate was 40% (95% CI=34%-46%), which varied for patients with BrM only at the diagnosis of metastatic disease [56% (95% CI=43%-69%] compared to patients with ECM +/- BrM [35% (95% CI=28%-42%] (p=0.05). The cumulative incidence of CNS-related death at 3 years was 33% (95% CI=27%-39%). Upon univariable modeling, LMD and CNS radiation treatment (RT) were associated with CNS-related death, and these associations remained statistically significant in a multivariable model [LMD: HR=2.49 (95% CI=1.62-3.83), p&amp;lt; 0.0001, and RT: HR=2.91 (95% CI=1.27-6.64), p=0.01]. Conclusions: Greater than half of patients with HER2+ with CNS involvement suffered of CNS-related death, with greatest risk among patients with LMD. We hypothesize that the association between radiotherapy and CNS-related death may reflect upfront response to systemic cancer-directed treatments of those in patients with DM only or limited CNS disease extension. CNS-only relapse at metastatic presentation portended improved survival than intra- plus extracranial progression, supporting an approach of aggressive local therapy for selected patients. Multimodality approaches using HER2-directed agents effective against parenchymal BrM, LMD along with extracranial metastases, in combination with local CNS-directed therapies need to be optimized. Citation Format: Emanuela Ferraro, Rabih Bou Nassif, Anne Reiner, Samantha Brown, Umberto Tosi, Katherine Panageas, Chau Dang, Andrew D Seidman, Nelson Moss. Clinical risk factors of Central Nervous System (CNS)-related death in patients with HER2-positive metastatic breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS11-02.

Risk factors for asthma-related hospital and intensive care admissions in children, adolescents and adults: a cohort study using primary and secondary care data

BackgroundAsthma remains a common cause of hospital admissions across the life course. We estimated the contribution of key risk factors to asthma-related hospital and intensive care unit (ICU) admissions in...

The Effect of Socioeconomic Status and Race/Ethnicity on the Risk of Presenting With Advanced Stage at Diagnosis in Embryonal Tumors.

We evaluated whether socioeconomic status (SES), race/ethnicity, and their interaction were associated with the presentation of advanced stage at diagnosis in embryonal tumors. Children 0 to 19 years of age diagnosed with embryonal tumors between 2006 and 2018 were identified from the US Surveillance, Epidemiology, and End Results program database specialized with Census Tract SES/Rurality. SES quintile was derived from a composite index for census tracts. We performed logistic regression to estimate odds ratios (ORs) and 95% confidence intervals by SES and race/ethnicity, adjusting for sex, age, and diagnosis year. Overall, no significant associations were found between either SES or race/ethnicity and the risk of presenting with advanced stage at diagnosis, although patterns of risk reductions were observed in atypical teratoid/rhabdoid tumors and embryonal rhabdomyosarcoma with increasing SES. In the stratified analysis, decreased odds of presenting with advanced-stage embryonal rhabdomyosarcoma were observed for Hispanics with higher SES (OR: 0.24, 95% Confidence Interval: 0.08-0.75) compared with Hispanics with lower SES. Future studies incorporating individual-level SES, cancer-specific staging information, and potential demographic, clinical, epidemiological, and genetic risk factors are warranted to confirm our findings.

Potential value of CT-based comprehensive nomogram in predicting occult lymph node metastasis of esophageal squamous cell paralaryngeal nerves: a two-center study

PurposeThe aim of this study is to construct a combined model that integrates radiomics, clinical risk factors and machine learning algorithms to predict para-laryngeal lymph node metastasis in esophageal squamous cell carcinoma.MethodsA retrospective study included 361 patients with esophageal squamous cell carcinoma from 2 centers. Radiomics features were extracted from the computed tomography scans. Logistic regression, k nearest neighbor, multilayer perceptron, light Gradient Boosting Machine, support vector machine, random forest algorithms were used to construct radiomics models. The receiver operating characteristic curve and The Hosmer–Lemeshow test were employed to select the better-performing model. Clinical risk factors were identified through univariate logistic regression analysis and multivariate logistic regression analysis and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis.ResultsA total of 1024 radiomics features were extracted. Among the radiomics models, the KNN model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.84 in the training cohort and 0.62 in the internal test cohort. Furthermore, the combined model exhibited an AUC of 0.97 in the training cohort and 0.86 in the internal test cohort.ConclusionA clinical-radiomics integrated nomogram can predict occult para-laryngeal lymph node metastasis in esophageal squamous cell carcinoma and provide guidance for personalized treatment.

The prevalence, consequences, and outcome of perinatal asphyxia in term and late preterm live-born neonatesA A case-control study from Iran

Background: Despite several investigations reporting the prevalence of asphyxia in developed and developing countries, very few studies from Iran have demonstrated the rate of neonatal asphyxia and its affecting factors. Objectives: This study aimed to determine the prevalence of asphyxia, its consequences, and neonatal outcomes among Iranian term and late preterm live-born neonates. Methods: A case-control study was conducted in three hospitals; Vali-e-Asr, Yas, and Ziaean from April 2019 to March 2020. The case group included neonates with a history of perinatal asphyxia. Twice gestational age-matched subjects without such a history also entered the study as the control group. All maternal and neonatal data were collected from participants’ medical records. Determining the prevalence of neonatal asphyxia was the primary outcome of the present study. Results: Totally 48 subjects as the case and control groups were included and 16 asphyxiated cases were observed equal to 3.3 per 1000 live-born neonates. Concerning the type of delivery, cesarean section in the control group and vaginal delivery in the case group were the most frequent types of delivery (p=0.041). Comparing neonatal variables, the results showed a significant difference regarding the groups’ genders (p=0.036). Apgar scores in the case group were also significantly lower than the control group (p=0.0001). More cases also required PPV compared to the controls (12 vs. 0; p=0.0001). Hypoxic-ischemic encephalopathy (HIE) grade 1 was also significantly more frequent in the case group than the controls (p=0.0001). The means of pH and base excess in the case group were significantly lower than the control group (p=0.0001). Conclusion: The results showed the prevalence rate of asphyxia among term and late preterm participants was 3.3 cases per 1000 live-born neonates. Apgar scores&lt;5 accompanied with metabolic acidosis, HIE, and requirement of PPV could be predictive variables for perinatal asphyxia. Observing such clinical risk factors may indicate the need of early diagnosis and timely interventions to improve neonatal outcomes.

Epidemiologic and Clinical Characteristics and Risk Factors for Severe Community-Acquired Clostridium Difficile Infection in Children.

Although children most frequently suffer from milder forms of community-acquired Clostridium difficile infection, severe cases and fatal outcomes have been recorded. In this study we identified older children (>12 years of age) and patients who had surgery up to 6 months before Clostridium difficile infection onset as a subgroup at somewhat higher risk for severe community-acquired Clostridium difficile infection.