Citations

Abstract Hemorrhagic shock is a common emergency that accounts for more than 10% of global mortality and up to 40% of trauma-related mortality. The Advanced Trauma Life Support (ATLS) guidelines outline resuscitation strategies in patients with massive hemorrhage based on clinical trial and observational data. Early intervention with fluids and/or blood products is recommended during resuscitation as key to maintaining vascular patency, stabilizing a patient’s blood pressure, maintaining tissue oxygenation, and limiting shock. From a transfusion perspective, there is wide heterogeneity in the quality of blood products due to donor and manufacturing variation. The optimal transfusion strategy for massive hemorrhage remains unclear. In silico models of resuscitation may offer a means to evaluate the effectiveness and safety of various resuscitation protocols. Here we describe a stochastic multicompartment model of fluid balance and resuscitation that includes (i) cardiovascular hemodynamics, (ii) body fluid compartments and capillary solute exchange, (iii) the ability to alter hemorrhage, resuscitation, and hemostasis parameters, and (iv) tissue oxygenation and capillary-alveolar gas exchange. Building upon deterministic frameworks, this stochastic model more faithfully reflects the clinical heterogeneity of bleeding patients at a level I trauma center. This allows for a proof of concept in silico trial comparing crystalloids, conventional component therapy (CCT), i.e. red cell, platelet and plasma components, to cold-stored low titer group O whole blood (LTOWB). In an in silico cohort of ATLS class III (>30% and ≤40% blood volume lost) hemorrhage, LTOWB resuscitation reduced the time spent in the critical hemostatic window (platelet count <50x109/L, INR≥2, hemoglobin (Hgb) <8 g/dL and fibrinogen <150 mg/dL) compared with CCT (90.59 vs. 147.62 minutes; p=0.04), with no difference in predicted event-free survival (t = 240 minutes; cardiac < 1.5, fluid overload > 10% or SBP > 150% of the starting SBP, or Hgb < 3.0 g/dL). In a separate cohort of ATLS class IV (>40% blood volume lost) hemorrhage, LTOWB yielded a higher predicted event-free survival (74%) versus the CCT arm (69%, p < 0.01). We demonstrate how this in silico platform can function as a digital twin for hemorrhagic trauma enabling precision transfusion strategies, and in parallel, an operational twin to support blood banks to forecast blood product demand and inform massive transfusion protocols and clinical trial design.

Abstract Background Recently, a posterior pathway for fluid drainage from the retina to the meningeal lymphatics in the optic nerve (ON) sheath was identified in rodents using intravitreal imaging tracers directly injected into the ocular-globe. Fluid and solute clearance along this pathway may be associated with many diseases. However, intravitreal tracers are rarely used in clinical imaging. As intravenous Gadolinium-based-contrast-agent (GBCA) can enter the globe via the blood-ocular-barriers, it may provide an alternative approach to image this pathway. Purpose To establish a clinically feasible intravenous GBCA-based MRI approach for tracking fluid and solute transport along the posterior lymphatic pathway in the ocular glymphatic system. Materials&Methods This prospective study was conducted from March 2021 to September 2022 in healthy participants. Dynamic-susceptibility-contrast-in-the-CSF (cDSC) MRI was performed before, immediately and 4 hours after intravenous-GBCA administration to track GBCA distribution in aqueous humor (AH) and cerebrospinal fluid (CSF) in regions-of-interest (ROIs) in the globe (anterior-cavity, vitreous-body), in the intraorbital and extraorbital ON, and in the intracranial CSF space proximal to the ON (chiasmatic-cistern, interpeduncular-cistern). Kruskal–Wallis tests with post-hoc Dunn’s tests were used for group comparisons. Results Sixteen healthy participants (mean age±SD: 51±21 years, 5 men) were recruited. Intravenous-GBCA enhancement was observed in all ROIs immediately after injection. At 4-hour-post-GBCA, the vitreous body showed a trend of smaller enhancement area (55±11% versus 49±11%, P =.14) and lower GBCA-concentration (0.044±0.014 versus 0.028±0.010 mmol/L, P =.07) compared to immediate-post-GBCA. The intraorbital ON showed more widespread enhancement (39±5% versus 59±6%, P =.01) and significantly higher GBCA-concentration (0.023±0.009 versus 0.059±0.015 mmol/L, P <.001) at 4-hour-post-GBCA. Conclusion Dynamic fluid and solute transportation along the posterior lymphatic pathway in the ocular glymphatic system in healthy participants was measured by tracking intravenous-GBCAs entering the globe via the blood-ocular-barriers using cDSC-MRI. Summary Statement Dynamic fluid and solute transportation along the posterior lymphatic pathway in the ocular glymphatic system in healthy participants was measured by tracking intravenous-Gadolinium-based-contrast-agents entering ocular-globe via blood-ocular-barriers using dynamic-susceptibility-contrast-in-the-CSF (cDSC)-MRI. Key Results In this prospective study of sixteen participants, Gadolinium-based-contrast-agent (GBCA)-induced signal changes were detected in the aqueous-humor and cerebrospinal fluid immediately following intravenous administration using dynamic-susceptibility-contrast-in-the-CSF (cDSC)-MRI. At 4-hour-post-GBCA, the vitreous-body showed a trend of smaller enhancement area (55±11% versus 49±11%, P =.14) and lower GBCA-concentration (0.044±0.014 versus 0.028±0.010 mmol/L, P =.07) compared to immediate-post-GBCA. The intraorbital-optic-nerve showed more widespread enhancement (39±5% versus 59±6%, P =.01) and higher GBCA-concentration (0.023±0.009 versus 0.059±0.015 mmol/L, P <.001) at 4-hour-post-GBCA.

PURPOSE Automated curation of breast cancer treatment data with minimal human involvement could accelerate the collection of statewide and nationwide evidence for patient management and assessing the effectiveness of treatment pathways. The primary challenges are the complexity and inconsistency of structured clinical data streams and accurate extraction of this information from free-text clinical narratives. MATERIALS AND METHODS We proposed a hybrid two-phase information extraction framework that combined a Unified Medical Language System parser (phase-1) with a fine-tuned large language model (LLM; phase-2) to extract longitudinal treatment timelines from time-stamped clinical notes. Our framework was developed through end-to-end joint learning as a question-answering model, where the model was trained to simultaneously answer five questions, each corresponding to a specific treatment. RESULTS We fine-tuned and internally validated the model on 26,692 patients with breast cancer (diagnosed between 2013 and 2020) receiving treatment at Mayo Clinic and externally validated the model on 162 randomly selected patients from Stanford Healthcare. Zero-shot LLM (out-of-the-box) had high specificity but low sensitivity, indicating that although these frameworks are useful for generic language understanding, they are lacking in terms of targeted clinical tasks. The proposed model achieved 0.942 average AUROC on the internal and 0.924 on the external data, demonstrating only marginal drop in performance when evaluated on external. The proposed model also achieved better trade-off between sensitivity (average: 79.2%) and specificity (average: 76.2%) compared with rule-based (average sensitivity: 70.5%, average specificity: 68.1%) and structured codes (average sensitivity: 64.1%, average specificity: 83.5%). CONCLUSION The proposed framework can extract temporal information about cancer treatments from various time-stamped clinic notes, regardless of the setting of treatment administration (inpatient or outpatient) or time frame. To support the cancer research community for such data curation and longitudinal analysis, we have packaged the code as a docker image, which needs minimal system reconfiguration and shared with an open-source academic license.

In 2007, the Bárány Society embarked on a project to establish definitions of vestibular syndromes and disorders based on best available evidence, referred to as the International Classification of Vestibular Disorders (ICVD). Since then, numerous publications providing consensus-driven diagnostic criteria for vestibular symptoms, syndromes, and disorders have been published. Here, we reflect on the rationale for developing the ICVD as well as its subsequent achievements, challenges, and outlook. In this summary of the work of the ICVD to date, the authors will focus on practical aspects to help improve the utility and applicability of these diagnostic criteria moving forward.

PURPOSE Patient-centered outcomes (PCOs) are pivotal in cancer treatment, as they directly reflect patients' quality of life. Although multiple studies suggest that factors affecting breast cancer–related morbidity and survival are influenced by treatment side effects and adherence to long-term treatment, such data are generally only available on a smaller scale or from a single center. The primary challenge with collecting these data is that the outcomes are captured as free text in clinical narratives written by clinicians. MATERIALS AND METHODS Given the complexity of PCO documentation in these narratives, computerized methods are necessary to unlock the wealth of information buried in unstructured text notes that often document PCOs. Inspired by the success of large language models (LLMs), we examined the adaptability of three LLMs, GPT-2, BioGPT, and PMC-LLaMA, on PCO tasks across three institutions, Mayo Clinic, Emory University Hospital, and Stanford University. We developed an open-source framework for fine-tuning LLM that can directly extract the five different categories of PCO from the clinic notes. RESULTS We found that these LLMs without fine-tuning (zero-shot) struggle with challenging PCO extraction tasks, displaying almost random performance, even with some task-specific examples (few-shot learning). The performance of our fine-tuned, task-specific models is notably superior compared with their non–fine-tuned LLM models. Moreover, the fine-tuned GPT-2 model has demonstrated a significantly better performance than the other two larger LLMs. CONCLUSION Our discovery indicates that although LLMs serve as effective general-purpose models for tasks across various domains, they require fine-tuning when applied to the clinician domain. Our proposed approach has the potential to lead more efficient, adaptable models for PCO information extraction, reducing reliance on extensive computational resources while still delivering superior performance for specific tasks.

Abstract Background The current global pandemic has created unprecedented challenges in the blood supply network. Given the recent shortages, there must be a civilian plan for massively bleeding patients when there are no blood products on the shelf. Recognizing that the time to death in bleeding patients is less than 2 h, timely resupply from unaffected locations is not possible. One solution is to transfuse emergency untested whole blood (EUWB), similar to the extensive military experience fine‐tuned over the last 19 years. While this concept is anathema in current civilian transfusion practice, it seems prudent to have a vetted plan in place. Methods and Materials During the early stages of the 2020 global pandemic, a multidisciplinary and international group of clinicians with broad experience in transfusion medicine communicated routinely. The result is a planning document that provides both background information and a high‐level guide on how to emergently deliver EUWB for patients who would otherwise die of hemorrhage. Results and Conclusions Similar plans have been utilized in remote locations, both on the battlefield and in civilian practice. The proposed recommendations are designed to provide high‐level guidance for experienced blood bankers, transfusion experts, clinicians, and health authorities. Like with all emergency preparedness, it is always better to have a well‐thought‐out and trained plan in place, rather than trying to develop a hasty plan in the midst of a disaster. We need to prevent the potential for empty shelves and bleeding patients dying for lack of blood.

Abstract Background: Racial disparities in breast cancer outcomes persist in the US, in which black women are more likely to die from breast cancer than white women. Despite levels of mammographic screening comparable to the US, Atlanta has a disproportionate burden of late-stage breast tumors and higher mortality rates, especially among black women. To better understand this disparity, we sought to identify tumor, treatment, and patient characteristics that may contribute to the observed differences in breast cancer mortality between black and white women in the metropolitan Atlanta area. Methods: In this study, patients were identified from the Georgia Cancer Registry. We included 4943 non-Hispanic white and 3580 non-Hispanic black women with an initial diagnosis of stage I-IV primary breast cancer between January 2010 and December 2014 in Atlanta. Cox proportional hazard regression was used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) to compare black vs. white breast cancer mortality by tumor (stage, grade, ER status, and molecular subtype), treatment (surgery type, receipt of chemotherapy, radiation, hormone therapy, and trastuzumab), and patient (insurance, marital status, SES, and location of service) characteristics. We performed a mediation analysis to evaluate the contribution of subtype, stage, and socioeconomic status (SES) as mediators on the path between race and breast cancer mortality. Results: Compared with white breast cancer patients, black women were younger with tumors that were higher stage, higher grade, and more likely to be larger, node positive and triple negative. In fully adjusted models, black women diagnosed with Luminal A subtype were 58% more likely to die of breast cancer compared to their white counterparts (HR=1.58, 95%CI 1.31-2.00). Similarly, black women with private insurance were 60% more likely to die of breast cancer (HR=1.61, 95%CI 1.25-1.98), and black women in the highest SES group were more than twice as likely to die from breast cancer (HR=2.26, 95%CI 1.26-4.06) than white women with comparable SES. The smallest disparities in breast cancer mortality by race were observed among women without insurance, in the lowest SES index, or those diagnosed with triple-negative breast cancer. The mediation analysis showed 84.8% of the effect between race and breast cancer mortality was through stage, subtype, and SES. Conclusion: Our results indicate variation in racial disparities in breast cancer mortality by tumor and patient characteristics. Consistent with previous reports, our mediation results suggest that later stage and triple-negative subtype among black women are major contributors of the observed disparity. However, our results also shed new light on the disparity, suggesting that the largest disparities are observed among women with ER+ tumors amenable to adjuvant therapies and are most pronounced among women of high SES. More research is needed to understand the drivers of disparities in these treatable tumors. Citation Format: Lindsay J. Collin, Renjian Jiang, Kevin C. Ward, Keerthi Gogineni, Preeti D. Subhedar, Mark Sherman, Mia M. Gaudet, Carmen Radecki Breitkopf, Olivia D'Angelo, Sheryl Gabram-Mendola, Jolie Siegel, Rana Aneja, Anne Gaglioti, Lauren E. McCullough. Identification of factors contributing to breast cancer mortality disparities in the metropolitan Atlanta area [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr C053.

e14608 Background: Beyond curative surgery, the optimal treatment for early stage pancreatic cancer is still a matter of debate. We conducted a comparative survival analysis between patients with resectable pancreatic cancer who received adjuvant treatment with either gemcitabine or 5-fluorouracil based chemotherapy and chemoradiation regimens. Methods: The Surveillance, Epidemiology and End Results (SEER)-Medicare database was used to identify patients with pancreatic cancer diagnosed from 1998 to 2005 who received curative surgery and adjuvant chemotherapy with either 5-fluorouracil or gemcitabine. These groups were subdivided by treatment with radiotherapy. Patients were followed until death, study endpoint or a maximum of five years after diagnosis. Results: 359 patients received 5-fluorouracil and 346 received gemcitabine. Compared to chemoradiation with 5-fluorouracil, outcomes for patients who received chemoradiation with gemcitabine did not differ. Patients who received gemcitabine without radiation had increased hazards (hazard ratio (HR) = 1.50 for high grade tumors (HGT), HR = 1.32 for low grade tumors (LGT)). However, outcomes of patients who received 5-fluorouracil without radiation varied with tumor grade. In LGT, patients had better outcomes with 5-fluorouracil when compared with chemoradiation with 5-fluorouracil (HR = 0.43). In HGT, the opposite was true (HR 2.10). Conclusions: Patients with low grade resectable pancreatic cancer may have better outcomes with 5-fluorouracil based chemotherapy without radiation when compared to 5-fluorouracil with radiation.

AbstractBackgroundRacial disparities in breast cancer (BC) outcomes persist where non-Hispanic black (NHB) women are more likely to die from BC than non-Hispanic white (NHW) women, and the extent of this disparity varies geographically. We evaluated tumor, treatment, and patient characteristics that contribute to racial differences in BC mortality in Atlanta, Georgia, where the disparity was previously characterized as especially large.MethodsWe identified 4943 NHW and 3580 NHB women in the Georgia Cancer Registry with stage I–IV BC diagnoses in Atlanta (2010–2014). We used Cox proportional hazard regression to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) comparing NHB vs NHW BC mortality by tumor, treatment, and patient characteristics on the additive and multiplicative scales. We additionally estimated the mediating effects of these characteristics on the association between race and BC mortality.ResultsAt diagnosis, NHB women were younger—with higher stage, node-positive, and triple-negative tumors relative to NHW women. In age-adjusted models, NHB women with luminal A disease had a 2.43 times higher rate of BC mortality compared to their NHW counterparts (95% CI = 1.99 to 2.97). High socioeconomic status (SES) NHB women had more than twice the mortality rates than their white counterparts (HR = 2.67, 95% CI = 1.65 to 4.33). Racial disparities among women without insurance, in the lowest SES index, or diagnosed with triple-negative BC were less pronounced.ConclusionsIn Atlanta, the largest racial disparities are observed in luminal tumors and most pronounced among women of high SES. More research is needed to understand drivers of disparities within these treatable features.

Abstract Background: Patients achieving a pathologic complete response (pCR) following neoadjuvant therapy have significantly improved event-free survival relative to those who do not; and pCR is an FDA-accepted endpoint to support accelerated approval of novel agents/combinations in the neoadjuvant treatment of high risk early stage breast cancer. Previous studies have shown that recurrence risk increased with increasing burden of residual disease (as assessed by the RCB index). As well, these studies suggest that patients with minimum residual disease (RCB-I class) also have favorable outcomes (comparable to those achieving a pCR) within high risk tumor subtypes. In this study, we assess whether integrating RCB with MRI functional tumor volume (FTV), which in itself is prognostic, can improve prediction of distant recurrence free survival (DRFS); and identify a subset of patients with minimal residual disease with comparable DRFS as those who achieved a pCR. Imaging tools can then be used to identify the subset that will do well early and guide the timing of surgical therapy. Method: We performed a pooled analysis of 596 patients from the I-SPY2 TRIAL with RCB, pre-surgical MRI FTV data and known follow-up (median 2.5 years). We first assessed whether FTV predicts residual disease (pCR or pCR/RCB-I) using ROC analysis. We applied a power transformation to normalize the pre-surgical FTV distribution; and assessed its association with DRFS using a bi-variate Cox proportional hazard model adjusting for HR/HER2 subtype. We also fitted a bivariate Cox model of RCB index adjusting for subtype; and assessed whether adding pre-surgical FTV to this model further improves association with DRFS using a likelihood ratio (LR) test. For the Cox modeling, penalized splines approximation of the transformed FTV and RCB index with 2 degrees of freedom was used to allow for non-linear effects of FTV and RCB on DRFS. Result: Pre-surgical MRI FTV is significantly associated with DRFS (Wald p&lt;0.00001), and more effective at predicting pCR/RCB-I than predicting pCR alone (AUC: 0.72 vs. 0.65). Larger pre-surgical FTV remains associated with worse DRFS adjusting for subtype (Wald p &lt;0.00001). The RCB index is also significantly associated with DRFS adjusting for subtype (Wald p&lt;0.00001). Adding FTV to a model containing RCB and subtype further improves association with DRFS (LR p=0.0007). RCB-I patients have excellent DRFS (94% at 3 years compared to 95% in the pCR group). Efforts are underway to identify an optimal threshold for dichotomizing pre-surgical FTV and FTV change measures for use in combination with pCR/RCB-I class to generate integrated RCB (iRCB) groups as a composite predictor of DRFS. Conclusion: Pre-surgical MRI FTV is effective at predicting minimal residual disease (RCB0/I) in the I-SPY 2 TRIAL. Despite the association between FTV and RCB, FTV appears to provide independent added prognostic value (to RCB and subtype), suggesting that integrating MRI volume measures and RCB into a composite predictor may improve DRFS prediction. Citation Format: Hylton NM, Symmans WF, Yau C, Li W, Hatzis C, Isaacs C, Albain KS, Chen Y-Y, Krings G, Wei S, Harada S, Datnow B, Fadare O, Klein M, Pambuccian S, Chen B, Adamson K, Sams S, Mhawech-Fauceglia P, Magliocco A, Feldman M, Rendi M, Sattar H, Zeck J, Ocal I, Tawfik O, Grasso LeBeau L, Sahoo S, Vinh T, Yang S, Adams A, Chien AJ, Ferero-Torres A, Stringer-Reasor E, Wallace A, Boughey JC, Ellis ED, Elias AD, Lang JE, Lu J, Han HS, Clark AS, Korde L, Nanda R, Northfelt DW, Khan QJ, Viscusi RK, Euhus DM, Edmiston KK, Chui SY, Kemmer K, Wood WC, Park JW, Liu MC, Olopade O, Tripathy D, Moulder SL, Rugo HS, Schwab R, Lo S, Helsten T, Beckwith H, Haugen PK, van't Veer LJ, Perlmutter J, Melisko ME, Wilson A, Peterson G, Asare AL, Buxton MB, Paoloni M, Clennell JL, Hirst GL, Singhrao R, Steeg K, Matthews JB, Sanil A, Berry SM, Abe H, Wolverton D, Crane EP, Ward KA, Nelson M, Niell BL, Oh K, Brandt KR, Bang DH, Ojeda-Fournier H, Eghtedari M, Sheth PA, Bernreuter WK, Umphrey H, Rosen MA, Dogan B, Yang W, Joe B, I-SPY 2 TRIAL Consortium, Yee D, Pusztai L, DeMichele A, Asare SM, Berry DA, Esserman LJ. Refining neoadjuvant predictors of three year distant metastasis free survival: Integrating volume change as measured by MRI with residual cancer burden [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P2-07-03.