UCSF Research Articles

Seven-year performance of a clinical metagenomic next-generation sequencing test for diagnosis of central nervous system infections.

Metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) is an agnostic method for broad-based diagnosis of central nervous system (CNS) infections. Here we analyzed the 7-year performance of clinical CSF mNGS testing of 4,828 samples from June 2016 to April 2023 performed by the University of California, San Francisco (UCSF) clinical microbiology laboratory. Overall, mNGS testing detected 797 organisms from 697 (14.4%) of 4,828 samples, consisting of 363 (45.5%) DNA viruses, 211 (26.4%) RNA viruses, 132 (16.6%) bacteria, 68 (8.5%) fungi and 23 (2.9%) parasites. We also extracted clinical and laboratory metadata from a subset of the samples (n = 1,164) from 1,053 UCSF patients. Among the 220 infectious diagnoses in this subset, 48 (21.8%) were identified by mNGS alone. The sensitivity, specificity and accuracy of mNGS testing for CNS infections were 63.1%, 99.6% and 92.9%, respectively. mNGS testing exhibited higher sensitivity (63.1%) than indirect serologic testing (28.8%) and direct detection testing from both CSF (45.9%) and non-CSF (15.0%) samples (P < 0.001 for all three comparisons). When only considering diagnoses made by CSF direct detection testing, the sensitivity of mNGS testing increased to 86%. These results justify the routine use of diagnostic mNGS testing for hospitalized patients with suspected CNS infection.

Machine learning for forecasting initial seizure onset in neonatal hypoxic-ischemic encephalopathy.

This study was undertaken to develop a machine learning (ML) model to forecast initial seizure onset in neonatal hypoxic-ischemic encephalopathy (HIE) utilizing clinical and quantitative electroencephalogram (QEEG) features. We developed a gradient boosting ML model (Neo-GB) that utilizes clinical features and QEEG to forecast time-dependent seizure risk. Clinical variables included cord blood gas values, Apgar scores, gestational age at birth, postmenstrual age (PMA), postnatal age, and birth weight. QEEG features included statistical moments, spectral power, and recurrence quantification analysis (RQA) features. We trained and evaluated Neo-GB on a University of California, San Francisco (UCSF) neonatal HIE dataset, augmenting training with publicly available neonatal electroencephalogram (EEG) datasets from Cork University and Helsinki University Hospitals. We assessed the performance of Neo-GB at providing dynamic and static forecasts with diagnostic performance metrics and incident/dynamic area under the receiver operating characteristic curve (iAUC) analyses. Model explanations were performed to assess contributions of QEEG features and channels to model predictions. The UCSF dataset included 60 neonates with HIE (30 with seizures). In subject-level static forecasting at 30 min after EEG initiation, baseline Neo-GB without time-dependent features had an area under the receiver operating characteristic curve (AUROC) of .76 and Neo-GB with time-dependent features had an AUROC of .89. In time-dependent evaluation of the initial seizure onset within a 24-h seizure occurrence period, dynamic forecast with Neo-GB demonstrated median iAUC = .79 (interquartile range [IQR] .75-.82) and concordance index (C-index) = .82, whereas baseline static forecast at 30 min demonstrated median iAUC = .75 (IQR .72-.76) and C-index = .69. Model explanation analysis revealed that spectral power, PMA, RQA, and cord blood gas values made the strongest contributions in driving Neo-GB predictions. Within the most influential EEG channels, as the preictal period advanced toward eventual seizure, there was an upward trend in broadband spectral power. This study demonstrates an ML model that combines QEEG with clinical features to forecast time-dependent risk of initial seizure onset in neonatal HIE. Spectral power evolution is an early EEG marker of seizure risk in neonatal HIE.

Automatic Enrollment in Patient Portal Systems Mitigates the Digital Divide in Healthcare: An Interrupted Time Series Analysis of an Autoenrollment Workflow Intervention

PurposeRacial and ethnic healthcare disparities require innovative solutions. Patient portals enable online access to health records and clinician communication and are associated with improved health outcomes. Nevertheless, a digital divide in access to such portals persist, especially among people of minoritized race and non-English-speakers. This study assesses the impact of automatic enrollment (autoenrollment) on patient portal activation rates among adult patients at the University of California, San Francisco (UCSF), with a focus on disparities by race, ethnicity, and primary language.Materials and methodsStarting March 2020, autoenrollment offers for patient portals were sent to UCSF adult patients aged 18 or older via text message. Analysis considered patient portal activation before and after the intervention, examining variations by race, ethnicity, and primary language. Descriptive statistics and an interrupted time series analysis were used to assess the intervention’s impact.ResultsAutoenrollment increased patient portal activation rates among all adult patients and patients of minoritized races saw greater increases in activation rates than White patients. While initially not statistically significant, by the end of the surveillance period, we observed statistically significant increases in activation rates in Latinx (3.5-fold, p = < 0.001), Black (3.2-fold, p = 0.003), and Asian (3.1-fold, p = 0.002) patient populations when compared with White patients. Increased activation rates over time in patients with a preferred language other than English (13-fold) were also statistically significant (p = < 0.001) when compared with the increase in English preferred language patients.ConclusionAn organization-based workflow intervention that provided autoenrollment in patient portals via text message was associated with statistically significant mitigation of racial, ethnic, and language-based disparities in patient portal activation rates. Although promising, the autoenrollment intervention did not eliminate disparities in portal enrollment. More work must be done to close the digital divide in access to healthcare technology.

Large Language Models Outperform Traditional Natural Language Processing Methods in Extracting Patient-Reported Outcomes in Inflammatory Bowel Disease

Background and AimsPatient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text information in the electronic health record, making it more available for research and quality improvement. This study aimed to compare traditional natural language processing (tNLP) and large language models (LLMs) in extracting three IBD PROs (abdominal pain, diarrhea, fecal blood) from clinical notes across two institutions. MethodsClinic notes were annotated for each PRO using preset protocols. Models were developed and internally tested at the University of California San Francisco (UCSF), and then externally validated at Stanford University. We compared tNLP and LLM-based models on accuracy, sensitivity, specificity, positive and negative predictive value. Additionally, we conducted fairness and error assessments. ResultsInter-rater reliability between annotators was >90%. On the UCSF test set (n=50), the top-performing tNLP models showcased accuracies of 92% (abdominal pain), 82% (diarrhea) and 80% (fecal blood), comparable to GPT-4, which was 96%, 88%, and 90% accurate, respectively. On external validation at Stanford (n=250), tNLP models failed to generalize (61-62% accuracy) while GPT-4 maintained accuracies >90%. PaLM-2 and GPT-4 showed similar performance. No biases were detected based on demographics or diagnosis. ConclusionLLMs are accurate and generalizable methods for extracting PROs. They maintain excellent accuracy across institutions, despite heterogeneity in note templates and authors. Widespread adoption of such tools has the potential to enhance IBD research and patient care.

Abstract B109: Results from colorectal cancer and clinical trials health education in racially/ethnically diverse populations

Abstract Introduction: The University of California, San Francisco (UCSF), a National Cancer Institute (NCI) designated comprehensive cancer center implemented NCI’s Colorectal cancer (CRC) and Clinical Trials (CT) Education Initiative to foster awareness, knowledge and intent to uptake CRC screening or participate in CT. Racial/ethnic groups have higher death rates and shorter survival rates for most cancers and face additional disparities and inequities. CRC is the third most commonly diagnosed cancer. CRC screening reduces incidence and mortality, however, it is underutilized. CT are studies that evaluate methods to understand prevent, find and treat cancer plus assess safety &amp; effectiveness. Racial/ethnic populations CT participation is low. More diverse enrollment is needed to guide future cancer prevention, control, care and improve health equity. Methods: UCSF Community Health Educators engaged with community partners and the public to conduct CRC &amp; CT education. The education topics included understanding the colon and rectum, colorectal health promotion, risk factors, what is CRC and screening, access to screening, what are clinical trials, how they advance medical progress, diversity &amp; inclusion, types and phases of CT, informed consent, myths and facts, mistrust/other concerns, barriers, and how to find and enroll in CT. Education sessions included administering pre/post surveys- 19-item for CRC and 17-item for CT to measure knowledge and intention to get screened for CRC or participate in CT. Results: There were 772 total surveys (N=411 CRC completed pre/post, 361 completed CT pre/post). CRC respondents: 70% female, 30% male; 54 average age; 53% African American, 0.3% American Indian, 23% Asian, 13% Latino, 4% Pacific Islander, 3% White &amp; 3% Multiracial. Overall (from pre to post education) CRC knowledge significantly increased, as well as intent to get screened, and to ask healthcare provider about CRC. Knowledge improved pre to post by 14% (76% vs 91%). Post-education intent, respondents strongly agreed or agreed: 91% talk to healthcare provider, 92% get screened for CRC, 89% talk about CRC with family/friends, 95% eat healthier, 95% increase physical activity. CT respondents: 77% female, 23% male; 51 average age; 64% African American, 1.2% American Indian, 10% Asian, 17% Latino, 2% Pacific Islander, 4% White &amp; 2% Multiracial. Post CT education knowledge, intent to seek out CT information and intent to join significantly increased. CT knowledge improved pre to post by 7% (83% vs 90%). Post-education intent respondents strongly agreed or agreed: 69% seek out CT information, 59% search for CT, 75% talk to a healthcare provider about CT; 78% importance of people of color CT participation; 48% intent to join a CT and 70% talk to family/friends about joining a CT. Conclusion: CRC and CT education demonstrated efficacy in increasing awareness and knowledge and advanced intent to get colorectal cancer screening and enroll in clinical trials. Citation Format: Michelle Moseley, Eva Pardo, Kim Rhoads, Ghila Andemeskel, Nanci Bocanegra, Tontantzin Rodriguez, Yushan Zhang. Results from colorectal cancer and clinical trials health education in racially/ethnically diverse populations [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B109.

Bridging Language Barriers in COVID-19 Research: Descriptive Study of AccesoCovid.com's Reach and User Engagement.

The COVID-19 pandemic underscored the challenge of swiftly disseminating research findings to a global audience. Language barriers further exacerbated disparities in access to timely scientific information, particularly for non-English speaking communities. The majority of COVID-19 research was published in English, limiting accessibility for Spanish-speaking populations. This paper aims to assess the reach and effectiveness of AccesoCovid.com, a platform designed to disseminate up-to-date COVID-19 research in both English and Spanish, addressing the language gap in scientific communication. AccesoCovid.com was developed through a partnership between the University of California, San Francisco (UCSF) and Universidad Nacional Autónoma de México (UNAM). The website's performance and user engagement were evaluated using Google Analytics over a span of 2 years. Key metrics included user language preference, geographical distribution, and site traffic. The website summarized and translated 1032 articles on various COVID-19 topics, such as "Pharmaceutical Interventions and Vaccines." From February 2021 to February 2023, the platform attracted 57,000 users. Of the 43,000 unique new visitors, 84.2% (n=36,219) hailed from Spanish-speaking regions. The majority accessed the site organically through search engines, with 88.4% (n=38,000) of users arriving this way, while 5000 (11.6%) users accessed the site directly. Most users (n=30,894, 72.1%) preferred the Spanish version of the site. The website's most accessed category was "Pharmaceutical Interventions and Vaccines," followed by "Clinical Presentation and Management" and "Mental Health." Regarding language distribution, 72.1% (n=30,894) of users primarily used Spanish; 21.4% (n=9215) used English; and 6.7% (n=2891) spoke other languages, including Portuguese, Chinese, and German. Geographically, the website attracted visitors from 179 countries, with the highest visitor counts from Mexico (n=12,342, 28.7%), Spain (n=6405, 14.9%), the United States (n=4416, 10.3%), and Peru (n=3821, 8.9%). AccesoCovid.com successfully bridged a critical language gap in the dissemination of COVID-19 research. Its success underscores the pressing need for multilingual scientific resources. The platform demonstrated significant user engagement and reach, particularly in Spanish-speaking countries. This highlights the potential for similar platforms to ensure equitable access to scientific knowledge across diverse linguistic communities. Future efforts should focus on expanding to other languages and conducting formal evaluations to enhance user satisfaction and impact.

Large language models outperform traditional natural language processing methods in extracting patient-reported outcomes in IBD.

Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text information in the electronic health record, making it more available for research and quality improvement. This study aimed to compare traditional natural language processing (tNLP) and large language models (LLMs) in extracting three IBD PROs (abdominal pain, diarrhea, fecal blood) from clinical notes across two institutions. Clinic notes were annotated for each PRO using preset protocols. Models were developed and internally tested at the University of California San Francisco (UCSF), and then externally validated at Stanford University. We compared tNLP and LLM-based models on accuracy, sensitivity, specificity, positive and negative predictive value. Additionally, we conducted fairness and error assessments. Inter-rater reliability between annotators was >90%. On the UCSF test set (n=50), the top-performing tNLP models showcased accuracies of 92% (abdominal pain), 82% (diarrhea) and 80% (fecal blood), comparable to GPT-4, which was 96%, 88%, and 90% accurate, respectively. On external validation at Stanford (n=250), tNLP models failed to generalize (61-62% accuracy) while GPT-4 maintained accuracies >90%. PaLM-2 and GPT-4 showed similar performance. No biases were detected based on demographics or diagnosis. LLMs are accurate and generalizable methods for extracting PROs. They maintain excellent accuracy across institutions, despite heterogeneity in note templates and authors. Widespread adoption of such tools has the potential to enhance IBD research and patient care.

Can silver diamine fluoride reduce invasive treatments with general anesthesia?

This study aims to retrospectively evaluate the differences in dental treatments rendered in general anesthesia (GA) for patients who did or did not receive application of silver diamine fluoride (SDF) prior to GA. 1559 patients (≤6 years) who completed treatment with GA for caries at University of California San Francisco (UCSF) between 2015 and 2019 were included in the study. At baseline patients' electronic health record was reviewed to collect planned treatment, complete dental treatment and demographics. Patients were identified as SDF group (N = 335, 21.49%) or comparison (N = 1224, 78.51%). Dental treatments rendered were compared between the SDF and comparison group with multi-variable regression, including variables for demographics and clinical findings at baseline. The initial analysis identified variations in gender, age, dental pain, pulp involvement, and initial treatment plans between the SDF and comparison groups at baseline. In an unadjusted analysis, the SDF group displayed a statistically significant increase in the number of crown procedures but a notable decrease in the number of pulp therapy and extraction treatments completed (p < 0.05). An adjusted multivariable model affirmed the inverse relationship between SDF application and completion of pulp therapy and extractions at the time of GA (p < 0.05). No significant association was identified with the total number of crowns needed and SDF. The model further indicated a positive correlation between the total count of pulp therapy and extractions completed with patient age and the wait-time for GA. In conclusion, pre-GA application of SDF to carious primary teeth is negatively correlated with completed pulp therapy and extraction. SDF application prior to dental treatment with GA may be a valuable tool to reduce invasive dental procedures in GA.

Conversion Therapy to Transplant or Surgical Resection in Patients with Unresectable Hepatocellular Carcinoma Treated with Boosted Dose of Yttrium-90 Radiation Segmentectomy.

The aim of this study was to assess the efficacy of boosted dose yttrium-90 radioembolization (TARE) as a modality for conversion therapy to transplant or surgical resection in patients with unresectable hepatocellular carcinoma (HCC). In this single-center retrospective study, all patients with a diagnosis of HCC who were treated with boosted dose TARE (>190 Gy) between January 2013 and December 2023 were reviewed. Treatment response and decrease in tumor size were assessed with the RECIST v1.1 and mRECIST criteria. Milan and University of California, San Francisco (UCSF), criteria were used to determine transplant eligibility, and Barcelona Clinic Liver Cancer (BCLC) surgical resection recommendations were used to evaluate tumor resectability. Thirty-eight patients with primary HCC who were treated with boosted dose TARE were retrospectively analyzed. The majority of the patients were Child-Pugh A (n = 35; 92.1%), BCLC C (n = 17; 44.7%), and ECOG performance status 0 (n = 25; 65.8%). The mean sum of the target lesions was 6.0 cm (standard deviation; SD = 4.0). The objective response rate (ORR) was 31.6% by RECIST and 84.2% by mRECIST. The disease control rate (DCR) was 94.7% by both RECIST and mRECIST. Among patients outside of Milan or UCSF, 13/25 (52.0%, Milan) and 9/19 (47.4%, UCSF) patients were successfully converted to within transplant criteria. Of patients who were initially unresectable, conversion was successful in 7/26 (26.9%) patients. This study provides further real-world data demonstrating that boosted-dose TARE is an effective modality for conversion of patients with unresectable HCC to transplant or resection.

Withdrawal pain following patients discontinuing Trk inhibitors.

This article aims to expand on the existing literature regarding the incidence of withdrawal pain following discontinuation of Trk inhibitors and to explore strategies that mitigate this withdrawal pain. A retrospective observational study was conducted among patients who were at least 18 years-old or older and had documentation of starting larotrectinib or entrectinib at University of California, San Francisco (UCSF) between November 2018 and November 2022. Data were collected from electronic records and pharmacy databases and a total of 21 patients were identified in this study. Of the 21 patients included in this study, five patients (24%) experienced pain during temporary or permanent discontinuation of Trk inhibitor with the onset of withdrawal pain ranging from a few hours to three days following discontinuation. Various strategies were implemented to manage this pain including restarting of Trk inhibitor, tapering of Trk inhibitor on discontinuation, minimizing dose interruptions and use of prescription pain medications. This article illustrates the presence of withdrawal pain syndrome in patients stopping a Trk inhibitor treatment and highlight the need for patient education to avoid missing any doses and for development of a guideline for Trk inhibitor discontinuation.

Application of artificial intelligence in rheumatic disease: a bibliometric analysis

The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the prediction of patient outcomes, expanded treatment options, and facilitated the provision of individualized medical solutions. The research in this field has been progressively growing in recent years. Consequently, there is a need for bibliometric analysis to elucidate the current state of advancement and predominant research foci in AI applications within rheumatic diseases. Additionally, it is crucial to identify key contributors and their interrelations in this field. This study aimed to conduct a bibliometric analysis to investigate the current research hotspots and collaborative networks in the application of AI in rheumatic disease in recent years. A comprehensive search was conducted in Web of Science for articles on artificial intelligence in rheumatic diseases, published in SSCI and SCI-EXPANDED until January 1, 2024. Utilizing software tools like VOSviewers and CiteSpace, we analyzed various parameters including publication year, journal, country, institution, and authorship. This analysis extended to examining cited authors, generating reference and citation network graphs, and creating co-citation network and keyword maps. Additionally, research hotspots and trends in this domain were evaluated. As of January 1, 2024, a total of 3508 articles have been published on the application of artificial intelligence (AI) in rheumatic disease, exhibiting a steady rise in both the annual publication frequency and rate. “Scientific Reports” emerged as the leading journal in terms of relevant publications. The United States stood out as the predominant country in terms of the volume of published papers, with the University of California, San Francisco (UCSF) being the most prolific and frequently cited institution. Among authors, Young Ho Lee and Valentina Pedoia were noted for their significant contributions, with Pedoia achieving the highest average citation count per publication. Machine learning emerged as a prominent and central keyword. The trend indicates a growing interest in AI research within rheumatologic diseases, with its role expected to become increasingly pivotal in the field. This study presents a comprehensive summary of research trends and developments in the application of artificial intelligence (AI) in rheumatic diseases. It offers insights into potential collaborations and prospects for future research, clarifying the research frontiers and emerging directions in recent years. The findings of this study serve as a valuable reference for scholars studying rheumatology and immunology.

Implementation and impact of an electronic patient reported outcomes system in a phase II multi-site adaptive platform clinical trial for early-stage breast cancer.

We describe the development and implementation of a system for monitoring patient-reported adverse events and quality of life using electronic Patient Reported Outcome (ePRO) instruments in the I-SPY2 Trial, a phase II clinical trial for locally advanced breast cancer. We describe the administration of technological, workflow, and behavior change interventions and their associated impact on questionnaire completion. Using the OpenClinica electronic data capture system, we developed rules-based logic to build automated ePRO surveys, customized to the I-SPY2 treatment schedule. We piloted ePROs at the University of California, San Francisco (UCSF) to optimize workflow in the context of trial treatment scenarios and staggered rollout of the ePRO system to 26 sites to ensure effective implementation of the technology. Increasing ePRO completion requires workflow solutions and research staff engagement. Over two years, we increased baseline survey completion from 25% to 80%. The majority of patients completed between 30% and 75% of the questionnaires they received, with no statistically significant variation in survey completion by age, race or ethnicity. Patients who completed the screening timepoint questionnaire were significantly more likely to complete more of the surveys they received at later timepoints (mean completion of 74.1% vs 35.5%, P < .0001). Baseline PROMIS social functioning and grade 2 or more PRO-CTCAE interference of Abdominal Pain, Decreased Appetite, Dizziness and Shortness of Breath was associated with lower survey completion rates. By implementing ePROs, we have the potential to increase efficiency and accuracy of patient-reported clinical trial data collection, while improving quality of care, patient safety, and health outcomes. Our method is accessible across demographics and facilitates an ease of data collection and sharing across nationwide sites. We identify predictors of decreased completion that can optimize resource allocation by better targeting efforts such as in-person outreach, staff engagement, a robust technical workflow, and increased monitoring to improve overall completion rates. https://clinicaltrials.gov/study/NCT01042379.

Sex Disparities in Opioid Prescription and Administration on a Hospital Medicine Service.

Decisions to prescribe opioids to patients depend on many factors, including illness severity, pain assessment, and patient age, race, ethnicity, and gender. Gender and sex disparities have been documented in many healthcare settings, but are understudied in inpatient general medicine hospital settings. We assessed for differences in opioid administration and prescription patterns by legal sex in adult patient hospitalizations from the general medicine service at a large urban academic center. This study included all adult patient hospitalizations discharged from the acute care inpatient general medicine services at the University of California, San Francisco (UCSF) Helen Diller Medical Center at Parnassus Heights from 1/1/2013 to 9/30/2021. The primary outcomes were (1) average daily inpatient opioids received and (2) days of opioids prescribed on discharge. For both outcomes, we first performed logistic regression to assess differences in whether or not any opioids were administered or prescribed. Then, we performed negative binomial regression to assess differences in the amount of opioids given. We also performed all analyses on a subgroup of hospitalizations with pain-related diagnoses. Our study cohort included 48,745 hospitalizations involving 27,777 patients. Of these, 24,398 (50.1%) hospitalizations were female patients and 24,347 (49.9%) were male. Controlling for demographic, clinical, and hospitalization-level variables, female patients were less likely to receive inpatient opioids compared to male patents (adjusted OR 0.87; 95% CI 0.82, 0.92) and received 27.5 fewer morphine milligram equivalents per day on average (95% CI - 39.0, - 16.0). When considering discharge opioids, no significant differences were found between sexes. In the subgroup analysis of pain-related diagnoses, female patients received fewer inpatient opioids. Female patients were less likely to receive inpatient opioids and received fewer opioids when prescribed. Future work to promote equity should identify strategies to ensure all patients receive adequate pain management.

Measurable residual disease (MRD) dynamics in multiple myeloma and the influence of clonal diversity analyzed by artificial intelligence

Minimal residual disease (MRD) assessment is a known surrogate marker for survival in multiple myeloma (MM). Here, we present a single institution’s experience assessing MRD by NGS of Ig genes and the long-term impact of depth of response as well as clonal diversity on the clinical outcome of a large population of MM patients; 482 MM patients at the University of California, San Francisco (UCSF) diagnosed from 2008 to 2020 were analyzed retrospectively. MRD assessment was performed by NGS. PFS curves were plotted by the Kaplan–Meier method. In the newly diagnosed group, 119 of 304, achieved MRD negativity at the level of 10−6 at least once. These patients had a prolonged PFS versus patients who were persistently MRD positive at different levels (p > 0.0001). In the relapsed disease group, 64 of 178 achieved MRD negativity at 10−6, and PFS was prolonged versus patients who remained MRD positive (p = 0.03). Three categories of MRD dynamics were defined by artificial intelligence: (A) patients with ≥3 consistently MRD negative samples, (B) patients with continuously declining but detectable clones, and (C) patients with either increasing or a stable number of clones. Groups A and B had a more prolonged PFS than group C (p < 10−7). Patients who were MRD positive and had not yet relapsed had a higher clonal diversity than those patients who were MRD positive and had relapsed. MRD dynamics can accurately predict disease evolution and drive clinical decision-making. Clonal Diversity could complement MRD assessment in the prediction of outcomes in MM.

Evaluating Access and Outcomes in Gender-affirming Breast Augmentation: A Comparative Study of a County Hospital and an Academic Center.

Research on the diverse patient population undergoing gender-affirming breast augmentation remains scarce. We compared patients undergoing this procedure at San Francisco General Hospital (ZSFG), a county hospital, and the University of California, San Francisco (UCSF), an academic medical center. This was a retrospective cohort study of patients who underwent primary gender-affirming breast augmentation at ZSFG (August 2019 to June 2023) and UCSF (March 2015 to June 2023). Differences in sociodemographic characteristics, surgical access, and outcomes between sites were assessed. Of 195 patients, 122 patients had surgery at UCSF and 73 patients at ZSFG. ZSFG patients were more likely to be unstably housed (P < 0.001), Spanish-speaking (P = 0.001), and to have obesity (P = 0.011) and HIV (P = 0.004). Patients at ZSFG took hormones for longer before surgical consultation (P < 0.001) but had shorter referral-to-surgery intervals (P = 0.024). Patients at ZSFG more frequently underwent a subglandular approach (P = 0.003) with longer operative times (P < 0.001). Major surgical complications were uncommon (2.1%) with no differences between sites. Aesthetically, implant malposition/rotation occurred more often in patients at UCSF (P = 0.031), but revision rates were similar at both sites. Patients at UCSF had longer follow-up periods (P = 0.008). County hospital patients seeking gender-affirming breast augmentation have distinct sociodemographic profiles and more comorbidities than academic medical center patients. County patients might experience greater barriers that delay surgical eligibility, such as stable housing. Nevertheless, this procedure can be safely and effectively performed in both patient populations.

Impact of response bias in three surveys on primary care providers' experiences with electronic health records.

To identify impacts of different survey methodologies assessing primary care physicians' (PCPs') experiences with electronic health records (EHRs), we compared three surveys: the 2022 Continuous Certification Questionnaire (CCQ) from the American Board of Family Medicine, the 2022 University of California San Francisco (UCSF) Physician Health IT Survey, and the 2021 National Electronic Health Records Survey (NEHRS). We evaluated differences between survey pairs using Rao-Scott corrected chi-square tests, which account for weighting. CCQ received 3991 responses from PCPs (100% response rate), UCSF received 1375 (3.6% response rate), and NEHRS received 858 (18.2% response rate). Substantial, statistically significant differences in demographics were detected across the surveys. CCQ respondents were younger and more likely to work in a health system; NEHRS respondents were more likely to work in private practice; and UCSF respondents disproportionately practiced in larger academic settings. Many EHR experience indicators were similar between CCQ and NEHRS, but CCQ respondents reported higher documentation burden. The UCSF approach is unlikely to supply reliable data. Significant demographic differences between CCQ and NEHRS raise response bias concerns, and while there were similarities in some reported EHR experiences, there were important, significant differences. Federal EHR policy monitoring and maintenance require reliable data. This test of existing and alternative sources suggest that diversified data sources are necessary to understand physicians' experiences with EHRs and interoperability. Comprehensive surveys administered by specialty boards have the potential to contribute to these efforts, since they are likely to be free of response bias.

Stereotactic Body Radiotherapy and Liver Transplant for Liver Cancer

Whether stereotactic body radiotherapy (SBRT) as a bridge to liver transplant for hepatocellular carcinoma (HCC) is effective and safe is still unknown. To investigate the feasibility of SBRT before deceased donor liver transplant (DDLT) for previously untreated unresectable HCC. In this phase 2 nonrandomized controlled trial conducted between June 1, 2015, and October 18, 2019, 32 eligible patients within UCSF (University of California, San Francisco) criteria underwent dual-tracer (18F-fluorodeoxyglucose and 11C-acetate [ACC]) positron emission tomography with computed tomography (PET-CT) and magnetic resonance imaging (MRI) with gadoxetate followed by SBRT of 35 to 50 Gy in 5 fractions, and the same imaging afterward while awaiting DDLT. Statistical analysis was performed on an intention-to-treat basis between October 1 and 31, 2023. Patients received SBRT followed by DDLT when matched deceased donor grafts were available. Coprimary end points were progression-free survival (PFS) and objective response rates (ORRs) by the Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST 1.1), modified RECIST (mRECIST), and PET Response Criteria in Solid Tumors (PERCIST). Secondary end points were local control rate, overall survival (OS), and safety. A total of 32 patients (median age, 59 years [IQR, 54-63 years]; 22 men [68.8%]) with 56 lesions received SBRT. After a median follow-up of 74.6 months (IQR, 40.1-102.9 months), the median PFS was 17.6 months (95% CI, 6.6-28.6 months), and the median OS was 60.5 months (95% CI, 29.7-91.2 months). The 5-year PFS was 39.9% (95% CI, 19.9%-59.9%), and the 5-year OS was 51.3% (95% CI, 31.7%-70.9%). In terms of number of patients, ORRs were 62.5% ([n = 20] 95% CI, 54.2%-68.7%) by RECIST 1.1, 71.9% ([n = 23] 95% CI, 63.7%-79.0%) by mRECIST, and 78.1% ([n = 25] 95% CI, 73.2%-86.7%) by PERCIST. In terms of number of lesions, ORRs were 75.0% ([n = 42] 95% CI, 61.6%-80.8%) by RECIST 1.1, 83.9% ([n = 47] 95% CI, 74.7%-90.6%) by mRECIST, and 87.5% ([n = 49] 95% CI, 81.3%-98.6%) by PERCIST. Twenty patients with 36 lesions received DDLT, of whom 15 patients (75.0%) with 21 lesions (58.3%) exhibited pathologic complete response. Multivariable analyses revealed that pretreatment metabolic tumor volume (MTV) based on ACC (hazard ratio [HR], 1.06 [95% CI, 1.01-1.10]; P = .01) and complete metabolic response (CMR) by PERCIST (HR, 0.31 [95% CI, 0.10-0.96]; P = .04) were associated with PFS, while pretreatment MTV based on ACC (HR, 1.07 [95% CI, 1.03-1.16]; P = .01), total lesion activity based on ACC (HR, 1.01 [95% CI, 1.00-1.02]; P = .02), and CMR by PERCIST (HR, 0.21 [95% CI, 0.07-0.73]; P = .01) were associated with OS. Toxic effects associated with SBRT were reported for 9 patients (28.1%), with 1 grade 3 event. This phase 2 nonrandomized controlled trial demonstrated promising survival and safety outcomes of SBRT before DDLT for unresectable HCC. Future randomized clinical trials are warranted.

Genetic risk for trait aggression and alcohol use predict unique facets of alcohol-related aggression.

A propensity for aggression or alcohol use may be associated with alcohol-related aggression. Previous research has shown genetic overlap between alcohol use and aggression but has not looked at how alcohol-related aggression may be uniquely influenced by genetic risk for aggression or alcohol use. The present study examined the associations of genetic risk for trait aggression, alcohol use, and alcohol use disorder (AUD) with alcohol-related aggression using a polygenic risk score (PRS) approach. Using genome-wide association study summary statistics, PRSs were created for trait aggression, alcohol consumption, and AUD. These PRSs were used to predict the phenotype of alcohol-related aggression among drinkers in two independent samples: the University of California at San Francisco (UCSF) Family Alcoholism Study (n = 1,162) and the National Longitudinal Study of Adolescent to Adult Health (Add Health; n = 4,291). There were significant associations between the AUD PRS and lifetime alcohol-related aggression in the UCSF study sample. Additionally, the trait aggression PRS was associated with three or more experiences of hitting anyone else and getting into physical fights while under the influence of alcohol, along with a composite score of three or more experiences of alcohol-related aggression, in the UCSF study sample. No significant associations were observed in the Add Health sample. Limited sex-specific genetic effects were observed. These results provide preliminary evidence that genetic influences underlying alcohol use and aggression are uniquely associated with alcohol-related aggression and suggest that these associations may differ by type and frequency of alcohol-related aggression incidents. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Expanding Selection Criteria in Deceased Donor Liver Transplantation for Hepatocellular Carcinoma: Long-term Follow-up of a National Registry and 2 Transplant Centers.

This study compares selection criteria for liver transplant (LT) for hepatocellular carcinoma (HCC) for inclusivity and predictive ability to identify the most permissive criteria that maintain patient outcomes. The Scientific Registry of Transplant Recipients (SRTR) database was queried for deceased donor LT's for HCC (2003-2020) with 3-y follow-up; these data were compared with a 2-center experience. Milan, University of California, San Francisco (UCSF), 5-5-500, Up-to-seven (U7), HALT-HCC, and Metroticket 2.0 scores were calculated. Nationally, 26 409 patients were included, and 547 at the 2 institutions. Median SRTR-follow-up was 6.8 y (interquartile range 3.9-10.1). Three criteria allowed the expansion of candidacy versus Milan: UCSF (7.7%, n = 1898), Metroticket 2.0 (4.2%, n = 1037), and U7 (3.5%, n = 828). The absolute difference in 3-y overall survival (OS) between scores was 1.5%. HALT-HCC (area under the curve [AUC] = 0.559, 0.551-0.567) best predicted 3-y OS although AUC was notably similar between criteria (0.506 < AUC < 0.527, Mila n = 0.513, UCSF = 0.506, 5-5-500 = 0.522, U7 = 0.511, HALT-HCC = 0.559, and Metroticket 2.0 = 0.520), as was Harrall's c-statistic (0.507 < c-statistic < 0.532). All scores predicted survival to P < 0.001 on competing risk analysis. Median follow-up in our enterprise was 9.8 y (interquartile range 7.1-13.3). U7 (13.0%, n = 58), UCSF (11.1%, n = 50), HALT-HCC (6.4%, n = 29), and Metroticket 2.0 (6.3%, n = 28) allowed candidate expansion. HALT-HCC (AUC = 0.768, 0.713-0.823) and Metroticket 2.0 (AUC = 0.739, 0.677-0.801) were the most predictive of recurrence. All scores predicted recurrence and survival to P < 0.001 using competing risk analysis. Less restrictive criteria such as Metroticket 2.0, UCSF, or U7 allow broader application of transplants for HCC without sacrificing outcomes. Thus, the criteria for Model for End-stage Liver Disease-exception points for HCC should be expanded to allow more patients to receive life-saving transplantation.

A novel nomogram to predict the recurrence of hepatocellular carcinoma after liver transplantation using extended selection criteria

BackgroundLiver transplantations (LTs) with extended criteria have produced surgical results comparable to those obtained with traditional standards. However, it is not sufficient to predict hepatocellular carcinoma (HCC) recurrence after LT according to morphological criteria alone. The present study aimed to construct a nomogram for predicting HCC recurrence after LT using extended selection criteria. MethodsRetrospective data on patients with HCC, including pathology, serological markers and follow-up data, were collected from January 2015 to April 2020 at Huashan Hospital, Fudan University, Shanghai, China. Logistic least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were performed to identify and construct the prognostic nomogram. Receiver operating characteristic (ROC) curves, Kaplan-Meier curves, decision curve analyses (DCAs), calibration diagrams, net reclassification indices (NRIs) and integrated discrimination improvement (IDI) values were used to assess the prognostic capacity of the nomogram. ResultsA total of 301 patients with HCC who underwent LT were enrolled in the study. The nomogram was constructed, and the ROC curve showed good performance in predicting survival in both the development set (2/3) and the validation set (1/3) (the area under the curve reached 0.748 and 0.716, respectively). According to the median value of the risk score, the patients were categorized into the high- and low-risk groups, which had significantly different recurrence-free survival (RFS) rates (P < 0.01). Compared with the Milan criteria and University of California San Francisco (UCSF) criteria, DCA revealed that the new nomogram model had the best net benefit in predicting 1-, 3- and 5-year RFS. The nomogram performed well for calibration, NRI and IDI improvement. ConclusionsThe nomogram, based on the Milan criteria and serological markers, showed good accuracy in predicting the recurrence of HCC after LT using extended selection criteria.