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Hepatocellular carcinoma (HCC), constituting the predominant manifestation of liver cancer, stands as a formidable medical challenge. The prognosis subsequent to surgical intervention, particularly for individuals presenting with a solitary tumor, relies heavily on the degree of invasiveness. The decision-making process surrounding therapeutic modalities in such cases assumes paramount importance. This case report illuminates a rather unusual clinical scenario. Here, we encounter a patient who, following a disease-free interval, manifested an atypical presentation of HCC, specifically, a solitary cardiac metastasis. The temporal interval of remission adds an additional layer of complexity to the case. Through a multidisciplinary planning process, the decision was made for surgical removal of the metastatic tumor.

Introduction: Use of validated risk stratification tools for work up of suspected pulmonary embolism (PE) is an endorsed recommendation of the American Society of Hematology and the American College of Emergency Physicians. Due to variations in clinical practice, electronic health record (EHR) systems, and medical provider trainings, the use, documentation, and implementation of these tools vary widely. Current processes do not allow for electronic capture and quality reporting at most institutions. Methods: We sought to design and implement clinical pre-test probability (PTP) tools at three large healthcare institutions for use in emergency departments (ED). At each site, a hematologist partnered with ED physician leadership and local information technology experts to design and implement PTP tools in clinical workflows. Overall, 38 EDs (high volume tertiary EDs and smaller regional EDs) located in the United States along the East Coast and Midwest regions were involved. A major goal of implementation was integration into clinical workflow and automatic documentation. After design and implementation, awareness and education of the new EHR tool was distributed electronically to ED providers. After implementation, use of PTP tools was analyzed between September 12, 2022 and January 11, 2023. PTP use was examined as percent of visits for which patients underwent CT pulmonary angiography (CTPA). Results: Each institution chose the 3 tier Wells' Score for implementation while one site designed an integrated Wells'/PERC or YEARS score calculator. Two sites implemented the tool as an optional flowsheet and one site implemented the tool as mandatory parameters within the orders for PE imaging studies. The mandatory tool could be bypassed with prespecified selections or if a D-dimer existed within 48 hours . Complete details of the site-specific implementation are shown in Table 1. Over the 4-month evaluation timeframe, there was a total of 270,214 ED encounters. Uptake and utilization of the PTP tools are shown in Table 2. Use of PTP was highest at the site with forced PTP documentation which ranged from 49-53% of ordered CTPAs, compared to Site 2 where use was 2-3%, and Site 1 where use ranged from 1-3%. At Site 1, use of PTP increased slightly over the study period with signs that PE yield on imaging was increasing as well (3.4% to 5.9%). At Site 2, PE yield on imaging was overall high (9-10%), and remained similar with similar use of PTP tools over the study period. Use of PTP and PE yield (6-8%) on imaging also remained similar throughout the study at Site 3. Conclusion: Forced use of PTP within the orders for CTPA led to the highest use of PTP (Site 3) but this did not result in meaningfully higher PE yield on CTPA compared to the other sites. PE yield at each site was at or above the 5% yield for PE on CTPA which has been previously reported. Given that PTP uptake was relatively stable over the measurement period at Site 2 and 3, this indicates the process had mostly stabilized and that other strategies are needed to improve uptake of PTP, reduce utilization of CTPA, and further increase yield on CTPA. Increasing PTP use and rising PE yield at Site 1 demonstrated ongoing potential for improvements past this study timeframe.

Background: Waldenström Macroglobulinemia (WM) is an indolent lymphoma with heterogeneous outcomes. The recently developed, Revised International Prognostic Scoring System for WM (rIPSS-WM) did not examine the prognostic impact of MYD88L265P, a somatic alteration present in most patients with WM. Moreover, rIPSS-WM could only be partially validated in an external cohort. Here, we report the performance of rIPSS-WM and propose a refined prognostic model with external validation. Methods: We reviewed medical records of treatment-naïve patients with active WM consecutively seen at Mayo Clinic, Rochester (MCR) between 01/01/1996 and 12/31/2017. We applied the rIPSS-WM model in patients with data available for all required parameters, including age, serum beta-2 microglobulin (β2M), serum lactate dehydrogenase (LDH) and serum albumin at diagnosis to assess the prognostic utility of this model. We then identified the dichotomized clinical and laboratory parameters prognostic for overall survival (OS) by univariate analyses (UVA) in our cohort. The significant parameters were analyzed to independently predict OS in a multivariable analysis (MVA) and to create a prognostic model, which was subsequently validated in an independent multi-institutional cohort. Results: We identified 889 patients with active WM at MCR, with a median follow-up of 8.2 [95% confidence interval (CI): 7.5-9] years. The estimated median OS for this cohort was 10.6 (95%CI: 9.7-11.4) years. Patients with available data in this cohort for the validation of rIPSS-WM (n=241) were risk stratified into very low (n=46), low (n=64), intermediate (n=58), high (n=46) and very-high risk groups (n=27) per rIPSS-WM, with the respective 5-year OS rates of 96%, 76%, 72%, 77% and 32%, demonstrating overlapping survival curves for the low, intermediate and the high-risk groups. Because rIPSS-WM could not be validated, we used the data from the MCR cohort and identified age at diagnosis of active WM, LDH >upper limit of normal (ULN), β2M >3µg/mL and albumin <3.5g/dL as statistically significant prognostic markers for OS on UVA. Notably, the presence of MYD88 L265P did not impact OS [median 11.4 years (95%CI: 10.1-not reached (NR) for MYD88 L265P genotype (n=231) versus 10 years (95% CI: 8.1-NR) for MYD88 WT genotype (n=65); p=0.33]. On MVA, LDH >ULN (HR 2.34), serum albumin <3.5 g/dL (HR 1.5) and age groups 65-75 years (HR 1.4) and >75 years (HR 2.6) were independently prognostic. Based on comparable HRs, we assigned a score of 1 point each to serum albumin <3.5 g/dL and age group 66-75 years and similarly, 2 points each for age >75 years and serum LDH >ULN per their impact on OS. Patients with the composite scores of 0, 1 and 2 were categorized as low risk, low- intermediate risk and intermediate risk, respectively. Due to similar OS for the scores of 3 to 5, these were combined to form the high-risk group of our proposed model: Modified Staging System for WM (MSS-WM). The median OS was 14.6 years [95%CI: 9.1 years-NR] for low-risk (score 0, n=71, 21%), 11.2 years (95% CI: 9.1-15.2 years) for low-intermediate risk (score 1, n=110, 32%), 8.1 years (95% CI: 6.4-12.2) for intermediate risk (score 2, n=81, 24%) and 5.5 years (95 % CI: 3.9-11 years) for the high-risk group (score ≥3, n=79, 23%); p<0.0001. In this derivation cohort, the 5-year OS rate was 93%, 82%, 69% and 55% and 10-year OS rate was 60%, 53%, 45% and 30% for the low, low-intermediate, intermediate, and high-risk cohort, respectively ( Figure 1A). We also validated MSS-WM using competing risk analysis (p=0.001) and in the cohort of rituximab treated patients (p<0.0001). We then used data from an independent series of 335 treatment-naïve symptomatic patients from academic institutions (n=5) across the US and Europe for external validation of the MSS-WM model. The median follow-up for the validation cohort was 6.3 years (95% CI: 5.5-7.2 years). The 5-year OS was 93%, 90%, 75% and 57% and the estimated 10-year OS was 91%, 80%, 64% and 35% for the low risk (n= 86, 26%), low-intermediate (n=107, 32%), intermediate (n=86, 26%) and high-risk (n=59, 18%) cohorts, respectively (p<0.0001, Figure 1B). Conclusions: Our proposed model, MSS-WM, is a simple, clinically useful, externally validated model that reliably risk stratifies previously untreated patients with active WM into four groups that have distinct outcomes based on the composite scores derived from the patients' age, serum albumin and serum LDH at diagnosis.

Introduction: ASH Guidelines for diagnosis of pulmonary embolism (PE) start with risk assessment using a pretest probability tool (PTP) followed by D-dimer testing or CT pulmonary angiography (CTPA) depending on risk. We aimed to develop an electronic clinical quality measure (eCQM) to encourage broader use of a validated PTP scoring tool in emergency departments (EDs). The Centers for Medicare & Medicaid Services require reporting of eCQMs which consist of value sets or lists of standardized codes linked to patient data within the electronic health record. We sought to identify a standardized way to identify CTPA and abnormal D-dimer tests using administrative codes (i.e. CPT, LOINC, SNOMEDCT codes) across three institutions. Methods: Value sets identifying CTPA and D-dimers were reviewed from existing eCQMs. Three institutions representing 38 EDs from across the country submitted data from all ED encounters between September 12, 2022 and January 11, 2023. Imaging types were reviewed from each of the CPT codes and LOINC (Logical Observation Identifiers Names and Codes). We determined the number of ED encounters, pre-test probability tool use, and diagnosis of PE using different codes. We calculated sensitivity, specificity, positive and negative predictive value (PPV and NPV, respectively). Results: Over the 4-month study period, 270,214 encounters were identified across 38 EDs. Each institution identified CTPA with site-specific codes (Table 1). The Full CTPA Value Set identified 55% more encounters than the site-specific codes (Full set 15,054/9679 site-specific codes). LOINC code 88322-3 was only used at one site. CPT code 71275 had the best sensitivity and specificity, but still had false positives (PPV 82%) (Table 2). D-dimer values were identified as LOINC code 48065-7 and 91556-1 at Site 1 and 48067-3 at Site 2. SNOMEDCT codes were not used at any site despite being the way other eCQMs identify elevated D-dimer results. Different D-dimer tests with different normal ranges were used at each site. All sites did not have an electronically extractable positive indicator for D-dimer. Conclusion: Due to persistent false positives using CPT Code 71275 for identification of CTPA, using this code would lead to inclusion of patients for whom PTP was not required or recommended. Additionally, coding for an abnormal D-dimer test result are not standardized across institutions. Therefore, administrative codes cannot be used to develop eCQMs whose aim is to evaluate whether CTPA is ordered appropriately based on the PTP risk level and laboratory testing.

Introduction: Waldenström macroglobulinemia (WM) is a rare B-cell lymphoma that may be preceded by an asymptomatic IgM-monoclonal gammopathy of undetermined significance (MGUS). The mechanisms underlying the progression from IgM-MGUS to WM remain to be fully elucidated; however, the stepwise accumulation of genomic abnormalities has been suggested as a potential driver. Currently, the role of epigenetic regulation of DNA remains underexplored in this disease spectrum. Aberrant DNA methylation is a hallmark of oncogenesis that plays an important role in hematologic malignancies. The aim of this study was to assess differential DNA methylation in WM compared to IgM-MGUS and identify hyper- and hypomethylated pathways that may alter key cellular functions. Methods: This study included 15 patients with WM and 6 with IgM-MGUS. Bone marrow samples were prospectively collected and CD19+ and/or CD138+ B cells were positively selected. Subsequently, genome-wide DNA methylation analysis was performed. CpG methylation ratios were segmented into 200-bp regions and differentially methylated regions (DMRs) comparing WM to IgM-MGUS were identified. DMRs with a Q-value <0.05 and absolute methylation difference of ≥20% were considered significant and utilized for analysis. DMRs were annotated according to genomic regions of interest (promotors, 3'UTR, 5'UTR, introns, exons, CpG islands, CpG shores). Next, genes with significant promotor hyper- or hypomethylation were assessed using the Ingenuity Pathway Analysis. Pathways with a corrected p-value<0.05 were considered statistically significant and a Z-value was calculated based on the degree of differential methylation. Results: Genome-wide DNA methylation analysis revealed a significant number of DMRs (n= 15691, hyper: 8248; hypo: 7443) in WM compared to IgM-MGUS. Assessing genomic regions, promotors (hyper: 1379; hypo: 2403), introns (hyper: 1548; hypo: 2292), CpG shores (hyper: 587; hypo: 849) and 3'UTR (hyper: 66; hypo: 193) regions were observed to have a significantly greater number of hypomethylated DMRs (all comparisons, p<0.001) in WM compared to IgM-MGUS (Figure A). While exons (hyper: 1686; hypo: 1378), CpG islands (hyper: 2128; hypo: 82), and 5'UTR (hyper: 854; hypo: 246) regions had a significantly greater number of hypermethylated DMRs (all comparisons, p<0.001) (Figure A). Next, promotor methylation-based pathway analysis revealed differential methylation of multiple cellular pathways important in WM pathogenesis (Figure B). These included pathways involved in intracellular signaling and the regulation of cellular proliferation, such as the STAT3 and CXCR4 pathways which were hypomethylated, and the PTEN signaling pathway which was hypermethylated. Of interest, we have previously demonstrated the transcription factor, STAT3, to have an increased expression in WM and to be implicated in IgM secretion (Elsawa, 2011). Next, multiple cytokine signaling pathways were observed to be hypomethylated in WM compared to IgM-MGUS including members of the interleukin (IL) family (IL-1, IL-8, IL-15, IL-23), VEGF, and TGF-β. Of relevance, previous studies have demonstrated the elevation of IL-8, IL-15, and increased expression of angiogenic cytokines, including VEGF, in patients with WM (Lewicki, 2010; Anagnostopoulos, 2007). Furthermore, our group has also shown miRNA-based epigenetic regulation of IL-8 and IL-15 in WM compared to IgM-MGUS (Chohan, 2023). Lastly, several pathways critical in the regulation of the cell structure and the extracellular matrix (ECM) were found to be hypomethylated including the FGF, actin cytoskeleton, PAK, and FAK signaling pathways. Of note, the FGF/FGFR axis has been demonstrated to be dysregulated in WM, and its targeting has been shown to halt WM cell growth (Sacco, 2021). Conclusions: This study demonstrates differential DNA methylation of key genomic regions in WM compared to IgM-MGUS. Promotor methylation-based analysis reveals hypomethylation of several important cellular pathways involved in cytokine signaling, cell structure/ECM, and intracellular signaling. These results underscore the influence of epigenetics in the IgM-gammopathy disease spectrum and suggest the potential role of aberrant DNA methylation underlying the malignant progression from IgM-MGUS to WM.

Introduction Pulmonary Embolism (PE) is a major cause of morbidity and mortality worldwide. Diagnosis is essential as untreated PE has a 30% mortality rate. Currently, computer tomography pulmonary angiogram (CTPA) is the standard of care to diagnose PE. However, this modality is expensive, exposes patients to radiation and increases risk of contrast induced nephropathy. Furthermore, only 5% of CTPAs performed are positive for PE signifying that many unnecessary tests are being performed. To address this gap, Pre-Test Probability (PTP) tools have been developed to safely exclude patients with low risk of PE and limit iatrogenic harm from over testing. Despite the tools being developed over 20 years ago, they are not widely available in electronic health record (EHR) systems and even when they are built in, uptake remains low. Methods Three large integrated health systems using Epic EHR systems across five states (MA, WI. MN, AZ, FL) built electronic PTP assessments into their EHR systems to safely exclude patients with low risk of PE and limit over testing. Two sites made the PTP optional, and one site made it a forced function (providers were forced to complete PTP prior to ordering CTPA unless a D-dimer result was present in the prior 48 hours). To assess the usability of these EHR tools, the team conducted a survey with ED clinicians across the three test sites. The survey was created to identify which PE risk stratification methods were employed by ED clinicians (EHR embedded PTP tool, apps on their phone, web applications like MDcalc, calculations from memory, or if they relied on clinical gestalt), and to identify barriers to using the EHR embedded PTP. Results Site 1 had 47 respondents, Site 2 had 94 respondents, and Site 3 had 16 respondents. Two key findings emerged from the survey (Figure 1). First, clinicians used a variety of risk stratification approaches and few of them documented their risk assessments in structured fields in the medical record. Second, clinical gestalt was the most used PE risk stratification approach, and this was true across all three test sites. In addition, the health system with a PTP prompt before clinicians could proceed with CTPA orders (Site 3) did not have a higher percentage of clinicians who reported using the EHR embedded tool than sites that did not prompt clinicians to fill in the PTP. Clinicians cited a variety of different barriers to using the EHR embedded PTP tool (Table 1). Conclusion While many clinicians indicated intermittent use of PTP criteria to aid in the diagnosis of PE, risk factors and scores from phone apps, web tools, and calculations from memory are unlikely to be documented in the patient's medical record, which means that they are not accessible to all members of the ED care team and cannot be used for electronic clinical quality measure. The biggest hurdles to increasing the use of structured PTP assessments in the ED setting to safely exclude PE and limit iatrogenic harm from over testing are (1) obtaining buy-in from ED clinicians that the PTP tools provide value over clinical gestalt, at least for a subset of patients who could most benefit, and (2) identifying best practices for incorporating PTP tools into EHR systems that make the tools easier to access and use 3) developing and implementing effective education and awareness programs around the PTP tools. This survey adds to the body of literature on PTP assessments in the ED setting and can help inform further research that is needed to continue to improve the diagnosis of PE. Funding: This project is funded in part by the Gordon and Betty Moore Foundation through Grant GBMF10778 to the American Society of Hematology.

Abstract Introduction/Objective Ki-67, a nuclear protein present during the active phases of the cell cycle, is an important prognostic and predictive marker for many tumor types, including breast cancer. This study compared the results of breast cancer specimens immunohistochemically (IHC) stained with the FDA-approved vs. Mayo Clinic’s (MC) validated Ki-67 protocol using AI-based image analysis algorithms for quantitative examination at an individual cell level. Methods/Case Report Sixty-two breast cancer specimens were selected with a rough equal representation of Ki-67 percentage in low (<10%), intermediate (10-20%) and high (>20%) categories. Two adjacent sections of each specimen were stained with the FDA and MC protocols, respectively, and scanned to obtain whole slide digital images. Two AI algorithms, one with tumor cell identification and the other without, were used to quantify positive and negative cells and calculate Ki-67 percentages. Results (if a Case Study enter NA) Both algorithms show high concordance between the two staining protocols, with r-squared scores of 0.989 and 0.992, respectively, analyzing with or without tumor identification. AI identified tumor better on MC stained slides vs. FDA stained slides. The results demonstrate a high-performance concordance between the MC and the FDA IHC staining protocols. Conclusion The data also illustrates the accuracy of using AI image analysis algorithms to compare different IHC protocols at an individual cell level. The AI tumor identification difference in slides stained with two IHC protocols reflects the importance of training an algorithm with a specific staining protocol to achieve optimal tumor recognition.

Abstract Background Gram stain (GS) is essential for preliminary bacterial identification. The lack of a standardized quality control method for GS is a significant drawback. By utilizing a deep convolutional neural network (CNN) design, we present a proof-of-concept for a novel solution to GS quality assurance. Using this approach, current manual GS, and potentially automated GS methods, may possess a standardized method of quality control. Methods Twenty-nine GS from positive blood cultures encompassing the spectrum of GS quality (acceptable, over/under decolorization, smear thickness, stain artifact) were selected by two expert microbiologists. A total of 197 images were obtained using a Cognex D-900 camera and Olympus BX-43 microscope at 1000X magnification. Images were classified using deep learning software (Cognex, ViDi Suite 4.1). Eighty-one images were classified as suboptimal and 116 were classified as optimal by expert review. To address background variation due to manual GS, we implemented a screening classification tool to separate images into 5 distinct classes (pale, rose, orange, indigo, variegated). The training and test sets consisted of 100 and 97 images respectively. Using a chaining technique, a second classification tool was added after the screening classification tool. The second tool further divided the 197 images into two dataset classes: optimal and suboptimal. The second tool used 105 images for training and 92 images for the test set. Model performance was compared to expert microbiology review, with GS verified by organism identification using MALDI-TOF. Results Cognex ViDi Suite 4.1 could discriminate between optimal and suboptimal GS images. The chained classification tool was able to be trained with 100% recall and 100% accuracy, with a resulting F-score of 1.00. Figure 1 VisionPro GUI with optimal image (MRSA) Conclusion Deep learning enabled image analysis is an effective method for sorting GS which are acceptable for further interpretation and suboptimal smears which require rework. Additional study is warranted to develop deep learning image analysis tools for the interpretation of GS on multiple specimen types and incorporate into automated GS systems. Disclosures Jane Hata, PhD , D(ABMM), Roche Molecular Systems: Grant/Research Support Kyle Magee, MLS(ASCP), SeluxDx: Employee

Objective: Parents frequently purchase and inquire about smartwatch devices to monitor child behaviors and functioning. This pilot study examined the feasibility and accuracy of using smartwatch monitoring for the prediction of disruptive behaviors. Methods: The study enrolled children ( N = 10) aged 7–10 years hospitalized for the treatment of disruptive behaviors. The study team completed continuous behavioral phenotyping during study participation. The machine learning protocol examined severe behavioral outbursts (operationalized as episodes that preceded physical restraint) for preparing the training data. Supervised machine learning methods were trained with cross-validation to predict three behavior states—calm, playful, and disruptive. Results: The participants had a 90% adherence rate for per protocol smartwatch use. Decision trees derived conditional dependencies of heart rate, sleep, and motor activity to predict behavior. A cross-validation demonstrated 80.89% accuracy of predicting the child's behavior state using these conditional dependencies. Conclusion: This study demonstrated the feasibility of 7-day continuous smartwatch monitoring for children with severe disruptive behaviors. A machine learning approach characterized predictive biomarkers of impending disruptive behaviors. Future validation studies will examine smartwatch physiological biomarkers to enhance behavioral interventions, increase parental engagement in treatment, and demonstrate target engagement in clinical trials of pharmacological agents for young children.