Key Clinical Variables Research Articles

Genomic, Transcriptional, and Immunological Validation of Distinct Molecular Subtypes of Classic Hodgkin Lymphoma through Tissue-Based and Noninvasive Methods

Introduction: The scarcity of malignant Hodgkin and Reed-Sternberg (HRS) cells has hampered comprehensive genomic profiling of classic Hodgkin lymphoma (cHL) from tumor tissue. Multiple recent studies have demonstrated that plasma cfDNA profiling facilitates molecular characterization of cHL. Leveraging noninvasive genotypes and Latent Dirichlet Allocation, we recently defined 2 genetic cHL subtypes in a large international cohort comprising 366 patients including pediatric and adult patients of all ages (Alig et al., ASH 2022 and ICML 2023). Cluster H1 comprised ~2/3 of cases and was dominated by a high somatic mutational burden, and non-silent mutations in genes canonically involved in NF-κB, JAK/STAT and PI3K signaling as well as in B2M. Conversely, cluster H2 (~1/3 of cases) was primarily characterized by recurrent somatic copy number aberrations as well as mutations in TP53 and KMT2D. Herein, we validate these previously identified genetic subtypes in external datasets as well as through orthogonal methods including tissue-based and noninvasive transcriptional and immune profiling. Methods: To validate genetic cHL subtypes, we first leveraged public cHL genotypes from 61 patients obtained through whole genome/exome sequencing (WGS/WES) of flow-sorted HRS cells (Maura et al, Blood Cancer Disc. 2023). Using our previously locked down probabilistic classifier, we assigned the H1/H2 subtype, and then correlated cluster assignments with clinical variables. To explore transcriptional differences between genetic subtypes, we profiled baseline plasma samples (n=113) from the larger plasma genotyping cohort (n=293) using EPIC-Seq (Esfahani et al, Nat Biotechnol 2022), which allows for noninvasive gene expression profiling from cfDNA fragmentation patterns at transcription start sites. Further, we used SABER (Sworder, Cancer Cell 2023) to enumerate T-cell receptor (TCR) rearrangements in cfDNA (cfTCR, n=292). Finally, to assess subtype-specific immune infiltrate patterns, we profiled cHL tumor specimens using RNA-Seq (n=64), and applied CIBERSORTx. Results: Similar to observations in our cfDNA discovery cohort, H1 was found to be the more prevalent subtype in the external validation set comprising 56% of tumors, while 44% were classified as H2. Recurrence frequencies of genetic features were comparable to and significantly correlated with those from our plasma discovery cohort ( R S=0.59 [H1] and R S=0.63 [H2], P<0.001 each). Of note, when considering the whole genome space, the higher mutational burden of H1 tumors could be confirmed ( P<0.01), and this association was independent of the tumor EBV status. In agreement with the discovery cohort, the bimodal age distribution and increased EBV positivity of the H2 subtype could also be recapitulated (31% vs 6% EBV+, P<0.01). To explore transcriptional differences between H1/H2 subtypes, we took advantage of the plasma enrichment of HRS cell ctDNA and utilized EPIC-Seq to noninvasively infer expression of 1,676 targeted genes. Tracking signatures derived from scRNA-Sequencing in plasma samples, we found that both HRS cells and the cHL tumor microenvironment can be successfully profiled by EPIC-Seq. Strikingly, we found substantial enrichment of a cytokine response signature in H1 tumors, while T-cell activation was among the top upregulated signatures in H2 tumors ( Fig. A). Importantly, the T-cell signature found in H2 was accompanied by a higher abundance of T-cell clones as quantified by SABER in baseline plasma samples ( P<0.001, Fig. B). Notably, cfTCR fragment length profiles resembled the mutant ctDNA profiles, strongly suggesting a tumor origin of the TCR rearrangements detected in plasma. Lastly, immune cell deconvolution of bulk RNA-Seq specimens indicated a higher abundance of CD8+ T-cells in H2 tumors ( P<0.01), further confirming our prior observations. Conclusions: Collectively, these results serve to validate H1 and H2 as distinct cHL subtypes, to confirm the characteristic genotypes defining them, and recapitulate their distinctive associations with key clinical and pathological variables including age and EBV status. We further validate the subtypes using orthogonal methods revealing dominant cytokine driven signaling in H1. Conversely, H2 tumors, which largely lack B2M mutations, despite their lower mutational burden, are rather immunogenic triggering T-cell responses.

FRI029 Chronic Free Fatty Acid Elevation Is Associated With An Overcompensatory Increase In Hepatic Mitochondrial Function In Patients With Overweight Or Obesity

Abstract Disclosure: F. Bril: None. S. Kalavalapalli: None. K. Cusi: None. The obesity epidemic has resulted in a significant increase in the prevalence of nonalcoholic fatty liver disease (NAFLD). Some patients with NAFLD progress to more severe forms of the disease, such as nonalcoholic steatohepatitis (NASH) and cirrhosis. Our central hypothesis is that the inability of hepatic mitochondria to enhance nutrient oxidation in the setting of nutrient oversupply plays a key role in the progression of NAFLD. Specifically, the aim of this study was to assess if free fatty acid (FFA) excess contributes to hepatic mitochondrial dysfunction in patients with overweight or obesity. Patients with BMI ≥ 25kg/m2, without diabetes were included in the study. Patients underwent a 2-hour oral glucose tolerance test (OGTT) and a liver proton magnetic resonance spectroscopy (1H-MRS) to measure liver fat. In vivo hepatic mitochondrial ATP levels were measured by phosphorus (31P)-MRS every 30 minutes during a 2-hour oral fructose challenge (OFC). Due to unregulated phosphorylation of fructose upon entering hepatocytes, the OFC provides a dynamic measurement of hepatic ATP consumption and re-synthesis. Hepatic ATP levels were estimated as β-ATP/total phosphorus. A total of 37 patients were recruited for this study (age: 54 ± 11 years; sex: 43% male/57% female; BMI: 34.4 ± 6.3 kg/m2; A1c: 5.7 ± 0.4%; NAFLD: 51%). No differences were found in hepatic ATP levels in patients with or without NAFLD (0.019 ± 0.015 vs. 0.016 ± 0.012, p=0.44). Patients were then divided in three groups (tertiles) based on fasting FFA levels. We did not observe any significant differences among these three groups in key clinical variables, such as age, sex, BMI, A1c or prevalence of NAFLD. At baseline (pre OFC), we did not observe any difference in hepatic ATP levels (i.e., β-ATP/total phosphorus) among FFA tertiles (0.020 ± 0.015, 0.020 ± 0.012, and 0.012 ± 0.012, p=0.25). During the OFC, we estimated changes over time in hepatic ATP levels as the decremental area under the curve (AUROC) of β-ATP/total phosphorus. We observed that the decremental AUROC of hepatic ATP levels during OFC was significantly different among groups (0.57 ± 0.86, 0.14 ± 0.59, -0.40 ± 0.54, p=0.006). Patients in the highest tertile of FFAs showed no consumption of ATP after the OFC, but rather an overall increase in ATP AUROC over time. Plasma FFA levels correlated well with hepatic ATP AUROC during the OFC (-0.47, p =0.007). Conclusion: Chronic elevations in FFA levels are associated with changes in hepatic mitochondrial ATP synthesis, which were unmasked after an oral fructose challenge. While long-term, FFA elevations may impair mitochondrial function, in our study we observed an overcompensatory response in patients with high plasma FFA, characterized by an increase in hepatic ATP levels over time after fructose consumption. At what point this compensation is overwhelmed leading to inflammation and cellular lipotoxicity remains to be determined. Presentation: Friday, June 16, 2023

The evolution of antiseizure medication therapy selection in adults: Is artificial intelligence -assisted antiseizure medication selection ready for prime time?

Antiseizure medications (ASMs) are the mainstay of symptomatic epilepsy treatment. The primary goal of pharmacotherapy with ASMs in epilepsy is to achieve complete seizure remission while minimizing therapy-related adverse events. Over the years, more ASMs have been introduced, with approximately 30 now in everyday use. With such a wide variety, much guidance is needed in choosing ASMs for initial therapy, subsequent replacement monotherapy, or adjunctive therapy. The specific ASMs are typically tailored by the patient's related factors, including epilepsy syndrome, age, sex, comorbidities, and ASM characteristics, including the spectrum of efficacy, pharmacokinetic properties, safety, and tolerability. Weighing these key clinical variables requires experience and expertise that may be limited. Furthermore, with this approach, patients may endure multiple trials of ineffective treatments before the most appropriate ASM is found. A more reliable way to predict response to different ASMs is needed so that the most effective and tolerated ASM can be selected. Soon, alternative approaches, such as deep machine learning (ML), could aid the individualized selection of the first and subsequent ASMs. The recognition of epilepsy as a network disorder and the integration of personalized epilepsy networks in future ML platforms can also facilitate the prediction of ASM response. Augmenting the conventional approach with artificial intelligence (AI) opens the door to personalized pharmacotherapy in epilepsy. However, more work is needed before these models are ready for primetime clinical practice.

A global neuronopathic gaucher disease registry (GARDIAN): a patient-led initiative

BackgroundGaucher disease (GD) is a rare autosomal recessive lysosomal storage disorder. GD types 2 and 3 are known as neuronopathic Gaucher disease (nGD) because they have brain involvement that progresses over time. Implementing a systematic approach to the collection of real-world clinical and patient-relevant outcomes data in nGD presents an opportunity to fill critical knowledge gaps and ultimately help healthcare providers in the management of this patient population. This paper summarizes the development of a patient-initiated Gaucher Registry for Development Innovation and Analysis of Neuronopathic Disease (GARDIAN).MethodsThe International Gaucher Alliance led the GARDIAN planning, including governance, scope, stakeholder involvement, platform, and reporting. Registry element input was determined in a series of meetings with clinical experts, patients, and caregivers, who identified key clinical variables and the draft content of nGD patient-reported outcomes (PRO) and observer-reported outcomes (ObsRO) focusing on symptoms, patient physical and emotional functioning. These were then tested in cognitive interviews with patients with nGD (> 12 years of age) and caregivers.ResultsCore registry data elements (n = 138) were identified by seven global clinical experts from Egypt, Germany, Israel, Japan, United Kingdom (UK), and United State (US) and reviewed via online Delphi method by 14 additional clinicians with experience of nGD from six countries and three pharmaceutical representatives. The elements were consistent with those identified via interviews with 10 patients/caregivers with nGD from Japan, Sweden, UK, and US. Key domains identified were demographics, diagnostic information, health status, clinical symptomatology, laboratory testing, treatment, healthcare resource utilization, aids/home improvements, and patient/caregiver burden and quality of life, specifically physical functioning, self-care, daily and social activities, emotional impacts, support services, and caregiver-specific impacts. Nine caregivers and six patients from the US, UK, China, Mexico, Egypt, and Japan participated in the cognitive interviews that informed revisions to ensure that all items are understandable and interpreted as intended.ConclusionsThe comprehensive set of clinical and patient relevant outcomes data, developed collaboratively among all stakeholders, to be reported using GARDIAN will bridge the many gaps in the understanding of nGD and align with regulatory frameworks on real-world data needs.

Prevalence of hypercalcemia in primary hypoadrenocorticism in dogs: Multicenter, retrospective study.

Hypoadrenocorticism is an important differential for hypercalcemia. The etiology of hypercalcemia in hypoadrenocorticism in dogs is unclear. To review the prevalence of hypercalcemia and use statistical models to identify clinical, demographic, and biochemical variables associated with hypercalcemia in dogs with primary hypoadrenocorticism. One hundred ten dogs with primary hypoadrenocorticism; 107 with recorded total calcium (TCa), 43 recorded ionized calcium (iCa). Multicenter retrospective observational study at 4 UK referral hospitals. Univariable logistic regression analyses were performed to assess the association between independent variables of signalment, hypoadrenocorticism type (glucocorticoid only deficient hypoadrenocorticism [GHoC] vs glucocorticoid and mineralocorticoid deficient hypoadrenocorticism [GMHoC]), clinicopathological variables and hypercalcemia. Hypercalcemia was defined as elevated TCa, an elevated iCa, or both elevated TCa and iCa (Model 1) or as elevated iCa (Model 2). Overall prevalence of hypercalcemia was 34.5% (38/110). The odds of hypercalcemia (Model 1) were increased (P < .05) in dogs with GMHoC ([vs GHoC], OR [odds ratio] = 3.86, 95% confidence interval [CI] 1.105-13.463), higher serum creatinine (OR = 1.512, 95% CI 1.041-2.197), and higher serum albumin (OR = 4.187, 95% CI 1.744-10.048). The odds of ionized hypercalcemia (Model 2) were increased (P < .05) with reduced serum potassium concentration (OR = 0.401, 95% CI 0.184-0.876) and younger age (OR = 0.737, 95% CI 0.558-0.974). This study identified several key clinical and biochemical variables associated with hypercalcemia in dogs with primary hypoadrenocorticism. These findings aid understanding of the pathophysiology and etiology of hypercalcemia in dogs with primary hypoadrenocorticism.

Development of natural language processing (NLP) models for extracting key features from unstructured notes to create real-world data (RWD) assets for clinical research at scale.

6607 Background: RWD derived from Electronic Health Records (EHR) has detailed clinical information about patient journeys that can assist in clinical research, trial design, safety assessments etc. However, much of the vital information is locked away in unstructured clinical texts and needs to be converted to structured format to be useful for downstream applications. We demonstrate how this can be achieved at scale with a high degree of accuracy through NLP. Methods: NLP models were developed to extract data for 11 clinical variables from unstructured notes of ~98k lung cancer patients and merged with the structured data into a common data model (Table). These models were a combination of domain knowledge, rule-based models, machine learning models, and deep learning models. The increase in fill rate per variable over structured data only was used to quantify the improvement by NLP. The accuracy of the models was assessed against a manually curated dataset comprising of 752 patients. Results: The NLP models significantly improved the fill rate of key clinical variables and were able to extract the information from clinical notes with high accuracy (Table). For some variables such as NSCLC/SCLC status, surgery, tumor grade and histology, all or most of the data was extracted via NLP. Metastatic status via NLP included distant metastasis, locally advanced disease and no metastasis whereas in the structured data, only data for distant metastasis was present. In the case of Performance Status (PS), even though a significant number of patients had at least 1 PS recorded in the structured data, NLP significantly increased longitudinal capture, thus increasing the density of this variable per patient. Conclusions: NLP models can be developed and used to enrich structured RWD data by extracting information from unstructured documents thus significantly improving the utility of this data for downstream applications. Given the high accuracy of these models and the scale at which they can be run, this can be a good alternative to human curation or can augment human curation enabling the creation of very large-scale datasets for clinical research. [Table: see text]

Factors Influencing Admission Decisions in Skilled Nursing Facilities: Retrospective Quantitative Study.

Occupancy rates within skilled nursing facilities (SNFs) in the United States have reached a record low. Understanding drivers of occupancy, including admission decisions, is critical for assessing the recovery of the long-term care sector as a whole. We provide the first comprehensive analysis of financial, clinical, and operational factors that impact whether a patient referral to an SNF is accepted or denied, using a large health informatics database. Our key objectives were to describe the distribution of referrals sent to SNFs in terms of key referral- and facility-level features; analyze key financial, clinical, and operational variables and their relationship to admission decisions; and identify the key potential reasons behind referral decisions in the context of learning health systems. We extracted and cleaned referral data from 627 SNFs from January 2020 to March 2022, including information on SNF daily operations (occupancy and nursing hours), referral-level factors (insurance type and primary diagnosis), and facility-level factors (overall 5-star rating and urban versus rural status). We computed descriptive statistics and applied regression modeling to identify and describe the relationships between these factors and referral decisions, considering them individually and controlling for other factors to understand their impact on the decision-making process. When analyzing daily operation values, no significant relationship between SNF occupancy or nursing hours and referral acceptance was observed (P>.05). By analyzing referral-level factors, we found that the primary diagnosis category and insurance type of the patient were significantly related to referral acceptance (P<.05). Referrals with primary diagnoses within the category "Diseases of the Musculoskeletal System" are least often denied whereas those with diagnoses within the "Mental Illness" category are most often denied (compared with other diagnosis categories). Furthermore, private insurance holders are least often denied whereas "medicaid" holders are most often denied (compared with other insurance types). When analyzing facility-level factors, we found that the overall 5-star rating and urban versus rural status of an SNF are significantly related to referral acceptance (P<.05). We found a positive but nonmonotonic relationship between the 5-star rating and referral acceptance rates, with the highest acceptance rates found among 5-star facilities. In addition, we found that SNFs in urban areas have lower acceptance rates than their rural counterparts. While many factors may influence a referral acceptance, care challenges associated with individual diagnoses and financial challenges associated with different remuneration types were found to be the strongest drivers. Understanding these drivers is essential in being more intentional in the process of accepting or denying referrals. We have interpreted our results using an adaptive leadership framework and suggested how SNFs can be more purposeful with their decisions while striving to achieve appropriate occupancy levels in ways that meet their goals and patients' needs.

A Novel Clinical Prognostic Model for Breast Cancer Patients with Malignant Pleural Effusion: Avoiding Chemotherapy in Low-Risk Groups?

Malignant pleural effusion (MPE) is a severe complication in patients with advanced cancer that is associated with a poor prognosis. Breast cancer is the second leading cause of MPE after lung cancer. We therefore aim to describe clinical characteristics of the patients with MPE combined with breast cancer and construct a machine learning-based model for predicting the prognosis of such patients. This study is a retrospective and observational study. Least absolute shrinkage and selection operator (LASSO) and univariate Cox regression analyses were applied to identify eight key clinical variables, and a nomogram model was established. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses. 196 patients with both MPE and breast cancer (143 in the training group and 53 in the ex-ternal validation group) were analyzed in this study. The median overall survival in two cohorts was 16.20 months and 11.37 months. Based on the ROC curves for 3-, 6-, and 12-month survival, the areas under the curves were 0.824, 0.824, and 0.818 in the training set and 0.777, 0.790, and 0.715 in the validation set, respectively. In the follow-up analysis, both systemic and intrapleural chemotherapy significantly increased survival in the high-risk group compared to the low-risk group. Collectively, MPE confers a poor prognosis in breast cancer patients. We have developed a first-ever survival prediction model for breast cancer patients with newly diagnosed MPE and validated the model using an independent cohort.

Benchmarking of survival outcomes following Haematopoietic Stem Cell Transplantation (HSCT): an update of the ongoing project of the European Society for Blood and Marrow Transplantation (EBMT) and Joint Accreditation Committee of ISCT and EBMT (JACIE)

From 2016 EBMT and JACIE developed an international risk-adapted benchmarking program of haematopoietic stem cell transplant (HSCT) outcome to provide individual EBMT Centers with a means of quality-assuring the HSCT process and meeting FACT-JACIE accreditation requirements relating to 1-year survival outcomes. Informed by previous experience from Europe, North America and Australasia, the Clinical Outcomes Group (COG) established criteria for patient and Center selection, and a set of key clinical variables within a dedicated statistical model adapted to the capabilities of the EBMT Registry. The first phase of the project was launched in 2019 to test the acceptability of the benchmarking model through assessment of Centers’ performance for 1-year data completeness and survival outcomes of autologous and allogeneic HSCT covering 2013–2016. A second phase was delivered in July 2021 covering 2015–2019 and including survival outcomes. Reports of individual Center performance were shared directly with local principal investigators and their responses were assimilated. The experience thus far has supported the feasibility, acceptability and reliability of the system as well as identifying its limitations. We provide a summary of experience and learning so far in this ‘work in progress’, as well as highlighting future challenges of delivering a modern, robust, data-complete, risk-adapted benchmarking program across new EBMT Registry systems.

Development of a simplified model and nomogram in preoperative diagnosis of pediatric chronic cholangitis with pancreaticobiliary maljunction using clinical variables and MRI radiomics

ObjectiveThe aim of this study was to develop a model that combines clinically relevant features with radiomics signature based on magnetic-resonance imaging (MRI) for diagnosis of chronic cholangitis in pancreaticobiliary maljunction (PBM) children.MethodsA total of 144 subjects from two institutions confirmed PBM were included in this study. Clinical characteristics and MRI features were evaluated to build a clinical model. Radiomics features were extracted from the region of interest manually delineated on T2-weighted imaging. A radiomics signature was developed by the selected radiomics features using the least absolute shrinkage and selection operator and then a radiomics score (Rad-score) was calculated. We constructed a combined model incorporating clinical factors and Rad-score by multivariate logistic regression analysis. The combined model was visualized as a radiomics nomogram to achieve model visualization and provide clinical utility. Receiver operating curve analysis and decision curve analysis (DCA) were used to evaluate the diagnostic performance.ResultsJaundice, protein plug, and ascites were selected as key clinical variables. Eight radiomics features were combined to construct the radiomics signature. The combined model showed superior predictive performance compared with the clinical model alone (AUC in the training cohort: 0.891 vs. 0.767, the validation cohort: 0.858 vs. 0.731), and the difference was significant (p = 0.002, 0.028) in the both cohorts. DCA confirmed the clinical utility of the radiomics nomogram.ConclusionThe proposed model that combines key clinical variables and radiomics signature is helpful in the diagnosis of chronic cholangitis in PBM children.

The need for risk stratification in type 2 diabetes and chronic kidney disease: Proposed clinical value of KidneyIntelX

Abstract Chronic kidney disease (CKD) develops in &gt; 40% of people living with diabetes and affects &gt; 7 million people in the United States. Of the 15 million individuals with type 2 diabetes and CKD in the United States, &gt; 90% are in the “early stages of CKD” (stages G1–G3). Standard risk stratification tools for progression of kidney disease have limitations, and lack precision at an individual level. Individualized risk tools, such as KidneyIntelX™, that incorporate well-validated prognostic protein biomarkers integrated with key clinical variables and are integrated into the electronic health record (EHR) can help address these challenges. KidneyIntelX can identify patients earlier in their disease course when intervention would be most impactful. Herein, 4 case studies are presented to demonstrate how 3 different physicians utilized KidneyIntelX to make clinical decisions and optimize the management of patients with type 2 diabetes and CKD.

Abstract P2-14-07: Omission of Sentinel Lymph Node Biopsy in Patients with Early Stage Breast Cancer: Looking Beyond the Choosing Wisely Guidelines for Age &amp;lt; 70

Abstract Background: The Surgical Society of Oncology Choosing Wisely Campaign for breast cancer advocates against the routine use of sentinel lymph node biopsy (SLNB) for women ≥ 70 years with early stage estrogen positive (ER+), clinically node negative (cN0) disease, given the low likelihood of axillary involvement and axillary recurrence risk, absence of survival benefit and greater reliance on genomic testing for therapeutic decisions. We hypothesize that this practice may be extended to a younger cohort of patients. In this proof-of-concept feasibility study, we first sought to determine the incidence of node positive (N+) disease in our health system using natural language understanding (NLU) technology to extract relevant data from the electronic medical record (EMR). NLU of the clinical narrative has been proven to aid clinical decision support by extracting relevant information and can populate clinical databases to facilitate optimal population management strategies. The advantage of NLU over a cancer registry is the speed and efficiency of data extraction for a large number of patients in real time, plus the capture of data points not conventionally included in a registry. Methods: All patients with early stage ER+, cN0 breast cancer who had SLNB from January 2015-December 2017 were identified in an integrated academic health network comprised of 15 hospitals in Western Pennsylvania. Patitent clinical data were abstracted from the EMR using Realyze Intelligence™ NLU technology. The Realyze NLU pipeline uses a combination of machine learning algorithms and standard terminologies to create a breast cancer patient model that includes genomic, phenotypic, and clinical data. The pipeline gathers information from all data sources – structured and unstructured – and normalizes the information to create a complete model of patient clinical criteria. Realyze Information Models use clinical data formatting flexible enough to represent clinical disorders on a concept level as well as the encounter, patient, and population levels. A breast cancer model with focus on the lymph node identification, pathological as well as clinical tumor and node classification were developed and mapped to standard terminology. A Semantic Reasoning layer is provided by different mechanisms including a rule-based layer to render answers to the questions posed in this hypothesis. NLU performance was validated by manually verifying key clinical variables (i.e., clinical stage, pathologic stage, and nodal positivity) on a subset of patients. Statistical analysis to determine any difference in N+ rates by age was performed using Chi-square testing with significance set at p &amp;lt; 0.05. Results: We identified 602 pts with early stage ER+, cN0 breast cancer over this period who underwent SLNB. Average age was 59.6 years old. As a whole group, there was an increase in N+ rates as the stage increased (Table 1). When comparing incidence of N+ disease stratified by age (&amp;lt; 70 or &amp;gt;70), there was no difference in N+ rates across all stages. In addition, equally low rates of SLN positivity were seen for patients specifically with stage T1a and T1b disease. Conclusions: These data suggest that the Choosing Wisely recommendation to omit SLNB may be extended to a younger cohort of pts with ER+, cN0 disease, specifically those with stage T1a or T1b tumors. With low rates of N+ disease, and less reliance on axillary stage for treatment decision making, the harms of surgical axillary staging may outweigh the benefits. Future validation is needed with a larger sample size. Table 1 Citation Format: Neil Carleton, Gilan Saadawi, Steffi Oesterreich, ADRIAN V. LEE, Emilia Diego. Omission of Sentinel Lymph Node Biopsy in Patients with Early Stage Breast Cancer: Looking Beyond the Choosing Wisely Guidelines for Age &amp;lt; 70 [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-14-07.

Profiles of circumscribed interests in autistic youth.

Circumscribed interests (CI) encompass a range of different interests and related behaviors that can be characterized by either a high intensity but otherwise usual topic [referred to as restricted interests (RI)] or by a focus on topics that are not salient outside of autism [referred to as unusual interests (UI)]. Previous research has suggested that there is pronounced variability across individuals in terms of the endorsement of different interests, however, this variability has not been quantified using formal subtyping approaches. Therefore, using Latent Profile Analysis in a sample of 1,892 autistic youth (Mage = 10.82, SDage = 4.14; 420 females), this study aimed to identify subgroups based on the RU and UI profiles. Three profiles of autistic individuals were identified. They were characterized as Low CI, Predominantly RI, and Predominantly UI. Importantly, profiles differed on several key demographic and clinical variables, including age, sex composition, IQ, language level, social and communication abilities, anxiety, and obsessive-compulsive behaviors. Although replication across other samples is needed, the profiles identified in this study are potentially promising for future research given their distinct profiles of RI and UI and unique patterns of associations with key cognitive and clinical variables. Therefore, this study represents an important initial step towards more individualized assessment and support for diverse presentations of CI in autistic youth.

Levels of immunoglobulin isotypes in serum and respiratory samples of patients with chronic obstructive pulmonary disease: protocol for a systematic review and meta-analysis

IntroductionChronic obstructive pulmonary disease (COPD) is an inflammatory respiratory disorder characterised by the progressive worsening of lung function. Acute exacerbation of COPD (AECOPD) is a leading contributor to patient morbidity,...

Evaluation of Maternal Near Miss Events at Tibebe Ghion Specialized Hospital in Bahir Dar, Ethiopia

Objective To estimate: 1) the prevalence of maternal mortality and severe morbidity (Maternal Near Miss (MNM)); and 2) associated risk factors, at a tertiary referral hospital in Bahir Dar, Ethiopia. Methods Prospective data collection of 658 consecutive delivering pregnant patients and their neonates at Tibebe Ghion Specialized Hospital (TGSH) in Bahir Dar, Ethiopia, from February 26-June 10, 2020. Demographic and outcome data were collected as part of a quality improvement initiative using the REDCap mobile app. Univariable and multivariable logistic regression were used to investigate the relationship between the key demographic and clinical variables with MNM. Results There were no maternal deaths and 70 MNM during the study period. The median patient age was 26 years (IQR 23-30); 49% of women were nulliparous, and 56% delivered vaginally. Patients had higher odds of experiencing MNM if living in a rural area (OR=3.71, p&lt;0.01) or with a hypertensive disorder of pregnancy (OR=2.27, p=0.03). The postpartum hemorrhage rate was 1.7%. Conclusion The MNM and mortality rates at TGSH were less than elsewhere in the region. Living in a rural area and having hypertensive disorders of pregnancy were major predictors of MNM events. Multiple areas were identified where quality improvement projects could be developed.

TPD52 is a prognostic biomarker and potential therapeutic target for ER+/PR+/HER2+ breast cancer.

Breast cancer (BRCA) represents a significant health challenge for women globally, with refractory cases showing resistance to current therapeutic strategies. The discovery of novel molecular markers and therapeutic targets is critical for improving outcomes in these patients. The primary aim of this study is to elucidate the role of tumor protein D52 (TPD52) as a novel molecular marker and potential therapeutic target to improve outcomes for BRCA patients. Using bioinformatics methods, we screened and evaluated the expression, prognosis, and associated mechanisms of TPD52 in BRCA. Two hundred and thirty-eight BRCA cases and 19 control cases were collected from Shanghai First Maternity and Infant Hospital, and the protein expression of TPD52 was detected by immunohistochemistry, and the correlation between TPD52 and the prognosis of BRCA was analyzed. The expression of TPD52 in BRCA tissues was significantly higher than that in the control (P<0.001), displaying a strong association with key clinical variables, concurrently indicating an unfavorable prognostic implication. The survival analysis revealed high TPD52 expression was an independent adverse prognostic factor for overall (P=0.008) and disease-specific survival (P=0.005). Gene set enrichment analysis showed that TPD52 negatively correlated with estradiol, AMP-activated protein kinase, and other responses. Immune infiltration analysis showed that TPD52 was associated with immune cell infiltration, Th-1/Th-2 cell balance, and immune defense cells such as dendritic and plasmacytoid dendritic cells. It is further found that high TPD52 expression is associated with progression-free and disease-free survival from the analysis of immunohistochemical data of the patients at our hospital. In summary, TPD52 may serve as an independent prognostic biomarker for BRCA, which may represent a promising novel therapeutic target, particularly for the refractory estrogen receptor-positive (ER+)/progesterone receptor-positive (PR+)/human epidermal growth factor receptor 2-positive (HER2+) BRCA cases that have failed endocrine therapy and targeted treatment.

The impact of daylight and window views on length of stay among patients with heart disease: A retrospective study in a cardiac intensive care unit

BackgroundHeart disease is the leading cause of death in the United States. The length of stay (LOS) is a well-established parameter used to evaluate health outcomes among critically ill patients with heart disease in cardiac intensive care units (CICUs). While evidence suggests that the presence of daylight and window views can positively influence patients’ LOS, no studies to date have differentiated the impact of daylight from window views on heart disease patients. Also, existing research studies on the impact of daylight and window views have failed to account for key clinical and demographic variables that can impact the benefit of such interventions in CICUs. MethodsThis retrospective study investigated the impact of access to daylight vs. window views on CICU patients’ LOS. The study CICU is located in a hospital in the southeast United States and has rooms of the same size with different types of access to daylight and window views, including rooms with daylight and window views (with the patient bed located parallel to full-height, south-facing windows), rooms with daylight and no window views (with the patient bed located perpendicular to the windows), and windowless rooms. Data from electronic health records (EHRs) for the time-period September 2015 to September 2019 (n=2936) were analyzed to investigate the impact of room type on patients’ CICU LOS. Linear regression models were developed for the outcome of interest, controlling for potential confounding variables. ResultsUltimately, 2319 patients were finally included in the study analysis. Findings indicated that patients receiving mechanical ventilation in rooms with access to daylight and window views had shorter LOS durations (16.8 h) than those in windowless rooms. Sensitivity analysis for a subset of patients with LOS ≤3 days revealed that parallel bed placement to the windows and providing access to both daylight and window views significantly reduced their LOS compared to windowless rooms in the unit (P=0.007). Also, parallel bed placement to the window significantly reduced LOS in this patient subset for those with an experience of delirium (P=0.019), dementia (P=0.008), anxiety history (P=0.009), obesity (P=0.003), and those receiving palliative care (P=0.006) or mechanical ventilation (P=0.033). ConclusionsFindings from this study could help architects make design decisions and determine optimal CICU room layouts. Identifying the patients who benefit most from direct access to daylight and window views may also help CICU stakeholders with patient assignments and hospital training programs.

Potentially preventable hospitalizations and super-utilization of inpatient services among patients with chronic kidney disease in Hawaiʻi

BackgroundChronic kidney disease (CKD) is linked to high morbidity and mortality and increased hospitalization burden. If appropriately managed in the outpatient setting, ambulatory care-sensitive conditions (ACSCs) do not lead to hospitalization. Hospitalizations due to ACSCs are considered as potentially preventable hospitalizations. Patients with recurrent hospitalizations are considered as super-utilizers of inpatient services. The aim of this study is to determine prevalence of potentially preventable hospitalizations and super-utilization of inpatient services among patients with CKD in Hawaiʻi.MethodsHawaiʻi statewide inpatient data (2015–2017) were used to identify adult CKD patients with hospitalizations during a 12-month period from the first recorded date of CKD. The associations between the potentially preventable hospitalizations and super-utilization and other key patient demographic and clinical variables (sex, age, ethnicity, insurance type, Charlson comorbidity index (CCI), county of residence, and homelessness indicator) were analyzed using bivariate analysis. Multivariable logistic regression was utilized to assess the associations between the potentially preventable hospitalizations and patient variables.ResultsApproximately 2% of patients reported potentially preventable hospitalizations, and a total of 12.3% patients reported super-utilization. Out of all CKD-specific ACSC hospitalizations, 74.2% were due to heart failure and 25.8% were due to hyperkalemia. Patients who reported super-utilization were more likely to report potentially preventable hospitalization (OR: 5.98, 95%CI: 4.50–7.93) than patients who did not report super-utilization.ConclusionThis study showed prevalence of potentially preventable hospitalizations and high inpatient utilization among CKD patients in Hawaiʻi. Heart failure and hyperkalemia were the two major causes of CKD-specific ACSC hospitalizations in this cohort. Effective strategies should be employed to improve the outpatient CKD management to reduce hospitalizations and in turn reduce cost.

Immune Checkpoint Inhibitor (ICI) Genes and Aging in Clear Cell Renal Cell Carcinoma (ccRCC): Clinical and Genomic Study.

Background: It is anticipated that there will be a large rise in the number of tumor diagnoses and mortality in those aged 65 and older over the course of upcoming decades. Immune checkpoint inhibitors, often known as ICIs, boost immune system activity by selectively targeting ICI genes. On the other hand, old age may be connected with unfavorable results. Methods: The Cancer Genome Atlas (TCGA) provided gene expression data from ccRCC tissue and key clinical variables. ICI gene databases were applied and verified using the GEO database. Results: We identified 14 ICI genes as risk gene signatures among 528 ccRCC patients using univariate and multivariable cox hazard models, and the elderly group was linked with poor survival. Then, by utilizing a new nomogram method, the TNFSF15 gene and age predicting values were estimated at one, three, and five years (85%, 81%, and 81%), respectively, and our age-related risk score was significant even after multivariable analysis (HR = 1.518, p = 0.009, CI = 1.1102.076). TNFSF15 gene expression was lower in elderly ccRCC patients (p = 0.0001). A negative connection between age and the TNFSF15 gene expression was discovered by correlation analysis (p = 0.0001). The verification of the gene by utilizing GEO (GSE167093) with 604 patients was obtained as external validation that showed significant differences in the TNFSF15 gene between young and elderly patients (p = 0.007). Additionally, the protein-protein interactions of the TNFSF15 gene with other ICI genes and aging-related genes was determined. In addition, the TNFSF15 expression was significantly correlated with pathological stages (p = 0.018). Furthermore, it was discovered that the biological processes of senescence, cellular senescence, the immune system, and many immune cell infiltration and immune function types are all closely tied. Conclusions: Along with the risk score evaluation, the ICI gene TNFSF15 was identified as a tumor suppressor gene related to inequalities in age survival and is associated with pathological stages and different immunity statuses. The aging responses of ccRCC patients and related gene expression need further investigation in order to identify potential therapeutic targets.

Survival Benefit of Birtamimab in Mayo Stage IV AL Amyloidosis in the Phase 3 VITAL Study Consistent after Adjustment for Key Baseline Variables

Introduction: Amyloid light chain (AL) amyloidosis - a progressive disorder caused by misfolded light chains produced by plasma cells - is associated with high mortality, poor quality of life, and increased healthcare costs, particularly in newly diagnosed patients with advanced disease (Mayo 2012 Stage IV, median overall survival <6 months). Birtamimab is a monoclonal antibody designed to neutralize circulating soluble and deplete deposited insoluble amyloid by promoting phagocytic clearance. In 2018, the Phase 3 VITAL study - a multicenter, global, randomized, double-blind, placebo-controlled study (NCT02312206) conducted in newly diagnosed, treatment-naive patients with AL amyloidosis and cardiac involvement - was terminated based on a futility analysis of the primary endpoint (time to all-cause mortality [ACM] or time to cardiac hospitalization ≥91 days after first study drug infusion); the final hazard ratio (HR) numerically favored birtamimab + standard of care (SOC) over placebo + SOC (0.826 [95% CI: 0.574, 1.189]). Post hoc analysis of ACM over 9 months revealed a significant survival benefit (HR=0.413 [95% CI: 0.191, 0.895]) in patients at high risk for early death (i.e., Mayo 2012 Stage IV). Here we report the results of sensitivity analyses of ACM in the subgroup of patients with Mayo 2012 Stage IV AL amyloidosis, adjusting for key baseline variables. Methods: For ACM, the hazard ratio and 90% two-sided confidence interval were estimated from the semi-parametric Cox Regression model stratified by randomization strata (i.e., Mayo Stage I/II vs III/IV, renal stage I vs II/III, and 6MWT [6-minute walk test] distance). Separately, baseline variables including age, sex, race, ethnicity, age at diagnosis, duration since diagnosis, NT-proBNP (N-terminal pro-brain natriuretic peptide), dFLC (difference between involved and uninvolved free light chain), FLC (free light chain), NYHA (New York Heart Association) class, troponin-T, and 6MWT distance were added to the Cox Regression model to evaluate the impact on the overall survival benefit. All adjudicated deaths prior to 9 months were included in the analysis. Patients who had no events were censored at 9 months. Results: Of the 260 patients enrolled in the VITAL study, 77 (29.6%) were characterized as Mayo 2012 Stage IV at baseline, 38 randomized to birtamimab + SOC and 39 to placebo + SOC. These patients had a median age of 64 years and were primarily white (93.5%) and male (68.8%). Baseline demographic and clinical characteristics were generally balanced between the two treatment groups among these Mayo Stage IV patients. After adjustment for key baseline demographic, clinical, and laboratory variables, the adjusted hazard ratios with each of the baseline variables added separately to the Cox Regression model range from 0.336 to 0.465, with all the upper bounds of the 90% confidence interval less than 1 (Figure). Conclusions: Birtamimab is the only investigational therapeutic that has shown a significant survival benefit in Mayo Stage IV AL amyloidosis patients. The survival benefit of birtamimab was consistent across all key baseline variables, including demographic factors (age, sex, race, ethnicity), clinical characteristics (age at diagnosis, duration since diagnosis, NYHA class, 6MWT distance), and laboratory parameters (NT-proBNP, dFLC, FLC, troponin-T), reinforcing the strength of the survival data in Mayo Stage IV patients. The AFFIRM-AL study, designed to confirm the VITAL study results in Mayo Stage IV AL amyloidosis patients, is currently active and enrolling. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal