scholarly journals Relationship Between Baseline Influenza-like Illness Rates And Healthcare Settings

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Dino Rumoro ◽  
Shital Shah ◽  
Gillian Gibbs ◽  
Marilyn Hallock ◽  
Gordon Trenholme ◽  
...  
Keyword(s):  
Medical Center ◽  
Academic Medical Center ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Free Text ◽  
Patient Acuity ◽  
Influenza Like Illness ◽  
Healthcare Settings ◽  
Standard Deviations ◽  
The Guardian

ObjectiveTo examine the baseline influenza-like illness (ILI) rates in theemergency departments (ED) of a large academic medical center(AMC), community hospital (CH), and neighboring adult andpediatric primary care clinics.IntroductionThe primary goal of syndromic surveillance is early recognitionof disease trends, in order to identify and control infectious diseaseoutbreaks, such as influenza. For surveillance of influenza-like illness(ILI), public health departments receive data from multiple sourceswith varying degrees of patient acuity, including outpatient clinicsand emergency departments. However, the lack of standardization ofthese data sources may lead to varying baseline levels of ILI activitywithin a local area.MethodsGeographic Utilization of Artificial Intelligence in Real-Timefor Disease Identification and Alert Notification (GUARDIAN) – asyndromic surveillance program – was used to automate ILI detectionusing free text chief complaint/reason for visit fields and vital signsfor a large AMC - ED, CH - ED, and neighboring outpatient clinicsduring the summer (June 15, 2016 to August 18, 2016) in order tocreate a baseline. The GUARDIAN system defined ILI as fever(temperature≥100°F) and cough and/or sore throat. Descriptiveanalysis of the observed ILI rates along with bivariate ANOVA withpost hoc Bonferroni and t-test were utilized to examine the differencewithin the settings.ResultsThe average ILI rate for EDs is higher than the clinics by at least0.39%. The CH- ED had 4.23% baseline ILI rate as compared to1.35% for AMC-ED. While the AMC – Clinics have 0.96% baselineILI rate as compared to 0.25% for CH – Clinics. The CH- ED andAMC – Clinics represented higher variations. Based on bivariate test,CH – ED was significantly different than AMC – ED, AMC - Clinics,and CH – Clinics (F= 10.58, df = 1238, p<0.05). For the AMC –Clinics, the average ILI rate for clinics providing services to adultpatients was 0.66% (SD: 4.5%) as compared to 2.03% (SD: 10.81%)for pediatric clinics, which was not statistically significant.ConclusionsThe CH - ED has higher baseline ILI rates compared to othersettings, as well as the CDC Region 5’s baseline (1.9% for 2015-2016). Based on previous studies1, this is likely due to providers’use of chief complaint free text fields. Thus, the CH – ED will havehigher thresholds for widespread ILI activity. In addition, differencesin baseline ILI rates between AMC - ED, AMC - Clinics, and CH -Clinics may result in different thresholds for widespread ILI activity(i.e., Average + 3 Standard Deviations). The CH – ED and AMC –Clinics had higher baseline standard deviations, indicting variationsin underlying patient populations. In addition, pediatric clinics havehigher baseline ILI activity but also higher variations, indicating theunique characteristics of pediatric patients. Thus, due to the abovefindings, there is a need to closely monitor the ILI rates at varioushealthcare sites for both timing of onset, as well as the intensity ofILI activity.

scholarly journals Utility of Natural Language Processing for Clinical Quality Measures Reporting

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Dino P. Rumoro ◽  
Shital C. Shah ◽  
Gillian S. Gibbs ◽  
Marilyn M. Hallock ◽  
Gordon M. Trenholme ◽  
...  
Keyword(s):  
Health Care ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Medical Center ◽  
Academic Medical Center ◽  
Quality Measures ◽  
Surveillance Program ◽  
Free Text ◽  
Influenza Immunization

ObjectiveTo explain the utility of using an automated syndromic surveillanceprogram with advanced natural language processing (NLP) to improveclinical quality measures reporting for influenza immunization.IntroductionClinical quality measures (CQMs) are tools that help measure andtrack the quality of health care services. Measuring and reportingCQMs helps to ensure that our health care system is deliveringeffective, safe, efficient, patient-centered, equitable, and timely care.The CQM for influenza immunization measures the percentage ofpatients aged 6 months and older seen for a visit between October1 and March 31 who received (or reports previous receipt of) aninfluenza immunization. Centers for Disease Control and Preventionrecommends that everyone 6 months of age and older receive aninfluenza immunization every season, which can reduce influenza-related morbidity and mortality and hospitalizations.MethodsPatients at a large academic medical center who had a visit toan affiliated outpatient clinic during June 1 - 8, 2016 were initiallyidentified using their electronic medical record (EMR). The 2,543patients who were selected did not have documentation of influenzaimmunization in a discrete field of the EMR. All free text notes forthese patients between August 1, 2015 and March 31, 2016 wereretrieved and analyzed using the sophisticated NLP built withinGeographic Utilization of Artificial Intelligence in Real-Timefor Disease Identification and Alert Notification (GUARDIAN)– a syndromic surveillance program – to identify any mention ofinfluenza immunization. The goal was to identify additional cases thatmet the CQM measure for influenza immunization and to distinguishdocumented exceptions. The patients with influenza immunizationmentioned were further categorized by GUARDIAN NLP intoReceived, Recommended, Refused, Allergic, and Unavailable.If more than one category was applicable for a patient, they wereindependently counted in their respective categories. A descriptiveanalysis was conducted, along with manual review of a sample ofcases per each category.ResultsFor the 2,543 patients who did not have influenza immunizationdocumentation in a discrete field of the EMR, a total of 78,642 freetext notes were processed using GUARDIAN. Four hundred fiftythree (17.8%) patients had some mention of influenza immunizationwithin the notes, which could potentially be utilized to meet the CQMinfluenza immunization requirement. Twenty two percent (n=101)of patients mentioned already having received the immunizationwhile 34.7% (n=157) patients refused it during the study time frame.There were 27 patients with the mention of influenza immunization,who could not be differentiated into a specific category. The numberof patients placed into a single category of influenza immunizationwas 351 (77.5%), while 75 (16.6%) were classified into more thanone category. See Table 1.ConclusionsUsing GUARDIAN’s NLP can identify additional patients whomay meet the CQM measure for influenza immunization or whomay be exempt. This tool can be used to improve CQM reportingand improve overall influenza immunization coverage by using it toalert providers. Next steps involve further refinement of influenzaimmunization categories, automating the process of using the NLPto identify and report additional cases, as well as using the NLP forother CQMs.Table 1. Categorization of influenza immunization documentation within freetext notes of 453 patients using NLP

Clinical Predictors for Laboratory-Confirmed Influenza Infections: Exploring Case Definitions for Influenza-Like Illness

2015 ◽  
Vol 36 (3) ◽  
pp. 241-248 ◽  
Author(s):  
Shital C. Shah ◽  
Dino P. Rumoro ◽  
Marilyn M. Hallock ◽  
Gordon M. Trenholme ◽  
Gillian S. Gibbs ◽  
...  
Keyword(s):  
Influenza A ◽  
Medical Center ◽  
Academic Medical Center ◽  
Clinical Signs ◽  
Case Definition ◽  
Chief Complaint ◽  
Nasopharyngeal Swab ◽  
Clinical Predictors ◽  
Case Definitions ◽  
Influenza Like Illness

OBJECTIVETo identify clinical signs and symptoms (ie, “terms”) that accurately predict laboratory-confirmed influenza cases and thereafter generate and evaluate various influenza-like illness (ILI) case definitions for detecting influenza. A secondary objective explored whether surveillance of data beyond the chief complaint improves the accuracy of predicting influenza.DESIGNRetrospective, cross-sectional study.SETTINGLarge urban academic medical center hospital.PARTICIPANTSA total of 1,581 emergency department (ED) patients who received a nasopharyngeal swab followed by rRT-PCR testing between August 30, 2009, and January 2, 2010, and between November 28, 2010, and March 26, 2011.METHODSAn electronic surveillance system (GUARDIAN) scanned the entire electronic medical record (EMR) and identified cases containing 29 clinical terms relevant to influenza. Analyses were conducted using logistic regressions, diagnostic odds ratio (DOR), sensitivity, and specificity.RESULTSThe best predictive model for identifying influenza for all ages consisted of cough (DOR=5.87), fever (DOR=4.49), rhinorrhea (DOR=1.98), and myalgias (DOR=1.44). The 3 best case definitions that included combinations of some or all of these 4 symptoms had comparable performance (ie, sensitivity=89%–92% and specificity=38%–44%). For children <5 years of age, the addition of rhinorrhea to the fever and cough case definition achieved a better balance between sensitivity (85%) and specificity (47%). For the fever and cough ILI case definition, using the entire EMR, GUARDIAN identified 37.1% more influenza cases than it did using only the chief complaint data.CONCLUSIONSA simplified case definition of fever and cough may be suitable for implementation for all ages, while inclusion of rhinorrhea may further improve influenza detection for the 0–4-year-old age group. Finally, ILI surveillance based on the entire EMR is recommended.Infect Control Hosp Epidemiol 2015;00(0): 1–8

scholarly journals Healthcare personnel absenteeism, presenteeism, and staffing challenges during epidemics

2020 ◽  
pp. 1-4
Author(s):  
Douglas W. Challener ◽  
Laura E. Breeher ◽  
JoEllen Frain ◽  
Melanie D. Swift ◽  
Pritish K. Tosh ◽  
...  
Keyword(s):  
Seasonal Influenza ◽  
Medical Center ◽  
Academic Medical Center ◽  
The United States ◽  
Healthcare Personnel ◽  
Linear Regression Models ◽  
Influenza Like Illness ◽  
Healthcare Workforce ◽  
Academic Medical ◽  
Hourly Employees

Abstract: Objective: Presenteeism is an expensive and challenging problem in the healthcare industry. In anticipation of the staffing challenges expected with the COVID-19 pandemic, we examined a decade of payroll data for a healthcare workforce. We aimed to determine the effect of seasonal influenza-like illness (ILI) on absences to support COVID-19 staffing plans. Design: Retrospective cohort study. Setting: Large academic medical center in the United States. Participants: Employees of the academic medical center who were on payroll between the years of 2009 and 2019. Methods: Biweekly institutional payroll data was evaluated for unscheduled absences as a marker for acute illness-related work absences. Linear regression models, stratified by payroll status (salaried vs hourly employees) were developed for unscheduled absences as a function of local ILI. Results: Both hours worked and unscheduled absences were significantly related to the community prevalence of influenza-like illness in our cohort. These effects were stronger in hourly employees. Conclusions: Organizations should target their messaging at encouraging salaried staff to stay home when ill.

Documenting presence: A descriptive study of chaplain notes in the intensive care unit

2016 ◽  
Vol 15 (2) ◽  
pp. 190-196 ◽  
Author(s):  
Brittany M. Lee ◽  
Farr A. Curlin ◽  
Philip J. Choi
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Patient Care ◽  
Medical Record ◽  
Medical Center ◽  
Academic Medical Center ◽  
Retrospective Chart Review ◽  
Relevant Information ◽  
Patient Care Team ◽  
Free Text

AbstractObjective:To clarify and record their role in the care of patients, hospital chaplains are increasingly called on to document their work in the medical record. Chaplains' documentation, however, varies widely, even within single institutions. Little has been known, however, about the forms that documentation takes in different settings or about how clinicians interpret chaplain documentation. This study aims to examine how chaplains record their encounters in an intensive care unit (ICU).Method:We performed a retrospective chart review of the chaplain notes filed on patients in the adult ICUs at a major academic medical center over a six-month period. We used an iterative process of qualitative textual analysis to code and analyze chaplains' free-text entries for emergent themes.Results:Four primary themes emerged from chaplain documentation. First, chaplains frequently used “code language,” such as “compassionate presence,” to recapitulate interventions already documented elsewhere in a checklist of ministry interventions. Second, chaplains typically described what they observed rather than interpreting its clinical significance. Third, chaplains indicated passive follow-up plans, waiting for patients or family members to request further interaction. Fourth, chaplains sometimes provided insights into particular relationship dynamics.Significance of results:As members of the patient care team, chaplains access the medical record to communicate clinically relevant information. The present study suggests that recent emphasis on evidence-based practice may be leading chaplains, at least in the medical center we studied, to use a reduced, mechanical language insufficient for illuminating patients' individual stories. We hope that our study will promote further consideration of how chaplain documentation can enhance patient care and convey the unique value that chaplains add to the clinical team.

Defining Legacy: The Perceptions of Pediatric Health Care Providers

2020 ◽  
Vol 59 (11) ◽  
pp. 1004-1010
Author(s):  
Jessika Boles ◽  
Maile Jones ◽  
Jenna Dunbar ◽  
Jessica Cook
Keyword(s):  
Health Care ◽  
Health Care Providers ◽  
Family Relationships ◽  
Medical Center ◽  
Academic Medical Center ◽  
Free Text ◽  
Care Providers ◽  
Pediatric Health ◽  
Pediatric Health Care ◽  
Free Text Response

Legacy building interventions like plaster hand molds are offered in most children’s hospitals, yet little is known about how the concept of legacy is understood and described by pediatric health care providers. Therefore, this study explored pediatric health care providers’ perceptions of legacy at an academic medical center to ensure that future legacy interventions are evidence-informed and theoretically grounded. An electronic survey featuring three open-ended questions and two multiple-choice questions with an option for free text response was completed by 172 medical and psychosocial health care providers. Analysis yielded four themes: (1) legacy is intergenerational, enduring, and typically associated with end-of-life; (2) legacies articulate the impacts on others for which one is known and remembered; (3) legacies can be expressed through tangible items or intangible qualities; and (4) legacies are informed and generated by family relationships and work experiences. By understanding legacy as a personally and professionally contextualized experience, health care providers can better assess and meet the legacy needs of hospitalized pediatric patients and families.

scholarly journals Developing and Validating a Fireworks-Related Syndrome Definition in Kansas

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Zachary M Stein
Keyword(s):  
Syndromic Surveillance ◽  
Public Information ◽  
False Positives ◽  
Chief Complaint ◽  
Surveillance Program ◽  
Anatomical Location ◽  
Free Text ◽  
Data Set ◽  
Ed Visits ◽  
Key Terms

ObjectiveTo develop a syndrome definition and analyze syndromic surveillance data usefulness in surveillance of firework-related emergency department visits in Kansas. Introduction Across the U.S.A., multiple people seek treatment for fireworks-related injuries around the July 4th holiday. Syndromic surveillance in Kansas allows for near real-time analysis of the injuries occurring during the firework selling season. During the 2017 July 4thholiday, the Kansas Syndromic Surveillance Program (KSSP) production data feed received data from 88 EDs at excellent quality and timeliness. Previous and current firework safety messaging in Kansas is dependent on voluntary reporting from hospitals across the state. With widespread coverage of EDs by KSSP, data can be more complete and timely to better drive analysis and public information Methods:KSSP data was queried through the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) v.1.20 provided by the National Syndromic Surveillance Program. Data between June 12, 2017 and August 13, 2017 were queried. The first query (Query A, Table 1.) searched the Discharge Diagnosis History field for the “W39” ICD-10 Diagnosis code, “Discharge of firework.” These records were searched for common firework terms contained in the Chief Complaint History field. These firework-related free text terms (Query B, Table 1.) were then combined with other potential firework-related terms to create a preliminary free text query (Query C, Table 1.). This preliminary query was run on the Chief Complaint History field. Data were then searched for false positive cases and appropriate negation terms were included to accommodate this. The new query with negation terms (Query D, Table 1.) was run on the Chief Complaint History field, combined with the results from the Discharge Diagnosis History field, and then combined records were de-duplicated based on a unique visit identifier. The final data set was then classified by the anatomical location of the injury and the gender and age group of the patient. Results:The initial query (Query A, Table 1.) for the diagnosis code “W39” returned 101 unique ED visits. Of these 101 unique ED visits, the following terms were identified in the Chief Complaint History field: shell, artillery, bomb, sparkler, grenade, fire cracker, firework, and firework show. These key terms were translated into Query B, Table 1. Other key terms deemed likely to capture specific firework-related exposures were then included into Query C, Table 1. , including roman, candle, lighter, M80, and punk. Query C was then used to query the Chief Complaint History field, returning 144 unique ED visits. Cases captured by Query C were then reviewed by hand for false positives and the negation terms, lighter fluid, fish, nut, and pistachio, were incorporated the Query D, Table 1. The previous process for Query C was then repeated on Query D, leaving a remaining 136 unique cases. Query A’s 101 unique ED visits was then combined with the 136 unique ED visits captured by Query D and de-duplicated. The de-duplicated data set contained 170 unique ED visits which were then reviewed by hand for false positives. The final removal of false positives from the combined and de-duplicated data set left a remaining 154 unique ED visits for firework-related injuries during this time period.For these data, the most common victims of firework injuries were males, accounting for 65.5% of all firework related ED visits and children ages 0 to 19 accounting for 44.2% of these visits. At every age breakout, male injuries exceeded female injuries. The most common anatomical location of the injury was one or both hands with 38.3% of all injuries mentioned hands as their primary injury. Injuries to the eyes, face, and head accounted for the second most injuries (28.6% of all patients). Conclusions: The selling of fireworks will be a yearly occurrence of a specific exposure that can potentially lead to injuries. Utilizing syndromic surveillance to review the holiday firework injuries is a very rapid method to assess the impact of these injuries and may allow for future direction of public information during the holiday. Having a syndrome definition that builds on knowledge from previous years will allow for quicker case identification as well.State public information regarding firework safety can be significantly bolstered by accurate and rapid data assessment. Developing a firework injury syndrome definition that is accurate and returns information rapidly has allowed for increased buy-in to the Kansas Syndromic Surveillance Program from public information offices, fire marshal’s offices, and other program fields.

Abstract WP324: Stroke Recognition Among Inpatient Staff at an Academic Medical Center

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Eric E Adelman ◽  
William J Meurer ◽  
Dorinda K Nance ◽  
Mary Jo Kocan ◽  
Kate E Maddox ◽  
...  
Keyword(s):  
Primary Outcome ◽  
Medical Center ◽  
Academic Medical Center ◽  
Signs And Symptoms ◽  
Rapid Identification ◽  
Warning Signs ◽  
Free Text ◽  
Staff Members ◽  
Academic Medical ◽  
Stroke Knowledge

Background About 10% of all strokes occur in hospitalized patients. The goal of this work was to evaluate the knowledge of stroke signs and to determine predictors of that knowledge among inpatient staff at an academic medical center. Methods Stroke education was the topic of a mandatory in-service for all adult inpatient medical, surgical, and ICU nursing unit clinical staff; including nurses, techs, and aides. The staff members anonymously completed an optional web-survey which included free text responses for stroke signs and symptoms, along with additional multiple choice questions regarding experience and training. The primary outcome was stroke knowledge which was defined as correct naming of 2 or more stroke warning signs or symptoms. Logistic regression was used to determine predictors of the primary outcome. Results The survey was offered to 1,593 staff members and 875 (55%) completed the survey. Eighty-seven percent of inpatient staff members correctly identified 2 or more stroke warning signs or symptoms while 31% identified 3 stroke warning signs or symptoms. Individual level predictors of stroke knowledge are shown in the Table. Greater self-reported confidence in identifying stroke symptoms and higher ratings for the importance of rapid identification of stroke symptoms were associated with stroke knowledge. Clinical experience, educational experience, work location, and personal knowledge of a stroke patient were not associated with stroke knowledge. Conclusion More than 80% of adult clinical inpatient staff members have knowledge of two or more stroke signs and symptoms. Future nursing education should emphasize the importance of rapid identification of stroke signs and symptoms and increasing confidence in knowledge of stroke symptoms.

The Milieu Manager: A Nursing Staffing Strategy to Reduce Observer Use in the Acute Psychiatric Inpatient Setting

2017 ◽  
Vol 23 (6) ◽  
pp. 422-430 ◽  
Author(s):  
Patrick Triplett ◽  
Sandra Dearholt ◽  
Mary Cooper ◽  
John Herzke ◽  
Erin Johnson ◽  
...  
Keyword(s):  
Patient Safety ◽  
Medical Center ◽  
Academic Medical Center ◽  
Psychiatric Inpatient ◽  
Cost Effective ◽  
Cost Of Care ◽  
Inpatient Setting ◽  
Patient Acuity ◽  
Psychiatric Units ◽  
Nursing Cost

BACKGROUND: Rising acuity levels in inpatient settings have led to growing reliance on observers and increased the cost of care. OBJECTIVES: Minimizing use of observers, maintaining quality and safety of care, and improving bed access, without increasing cost. DESIGN: Nursing staff on two inpatient psychiatric units at an academic medical center pilot-tested the use of a “milieu manager” to address rising patient acuity and growing reliance on observers. Nursing cost, occupancy, discharge volume, unit closures, observer expense, and incremental nursing costs were tracked. Staff satisfaction and reported patient behavioral/safety events were assessed. RESULTS: The pilot initiatives ran for 8 months. Unit/bed closures fell to zero on both units. Occupancy, patient days, and discharges increased. Incremental nursing cost was offset by reduction in observer expense and by revenue from increases in occupancy and patient days. Staff work satisfaction improved and measures of patient safety were unchanged. CONCLUSIONS: The intervention was effective in reducing observation expense and improved occupancy and patient days while maintaining patient safety, representing a cost-effective and safe approach for management of acuity on inpatient psychiatric units.

Integration of genomic and clinical data augments surveillance of healthcare-acquired infections

2019 ◽  
Vol 40 (6) ◽  
pp. 649-655 ◽  
Author(s):  
Doyle V. Ward ◽  
Andrew G. Hoss ◽  
Raivo Kolde ◽  
Helen C. van Aggelen ◽  
Joshua Loving ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Infection Control ◽  
Clinical Data ◽  
Medical Center ◽  
Academic Medical Center ◽  
Whole Genome Sequence ◽  
False Alarms ◽  
Whole Genome ◽  
Healthcare Settings ◽  
Cross Transmission

AbstractBackground:Determining infectious cross-transmission events in healthcare settings involves manual surveillance of case clusters by infection control personnel, followed by strain typing of clinical/environmental isolates suspected in said clusters. Recent advances in genomic sequencing and cloud computing now allow for the rapid molecular typing of infecting isolates.Objective:To facilitate rapid recognition of transmission clusters, we aimed to assess infection control surveillance using whole-genome sequencing (WGS) of microbial pathogens to identify cross-transmission events for epidemiologic review.Methods:Clinical isolates ofStaphylococcus aureus,Enterococcus faecium,Pseudomonas aeruginosa, andKlebsiella pneumoniaewere obtained prospectively at an academic medical center, from September 1, 2016, to September 30, 2017. Isolate genomes were sequenced, followed by single-nucleotide variant analysis; a cloud-computing platform was used for whole-genome sequence analysis and cluster identification.Results:Most strains of the 4 studied pathogens were unrelated, and 34 potential transmission clusters were present. The characteristics of the potential clusters were complex and likely not identifiable by traditional surveillance alone. Notably, only 1 cluster had been suspected by routine manual surveillance.Conclusions:Our work supports the assertion that integration of genomic and clinical epidemiologic data can augment infection control surveillance for both the identification of cross-transmission events and the inclusion of missed and exclusion of misidentified outbreaks (ie, false alarms). The integration of clinical data is essential to prioritize suspect clusters for investigation, and for existing infections, a timely review of both the clinical and WGS results can hold promise to reduce HAIs. A richer understanding of cross-transmission events within healthcare settings will require the expansion of current surveillance approaches.

scholarly journals Clinical Presentation of Hepatocellular Carcinoma in African Americans vs. Caucasians: A Retrospective Analysis

2021 ◽  
Vol 28 (3) ◽  
pp. 387-399
Author(s):  
Hrishikesh Samant ◽  
Kapil Kohli ◽  
Krunal Patel ◽  
Runhua Shi ◽  
Paul Jordan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Medical Center ◽  
Academic Medical Center ◽  
Ethnic Disparities ◽  
Surveillance Program ◽  
Chi Square ◽  
Vein Thrombosis ◽  
Disease Characteristics ◽  
Academic Medical

Hepatocellular carcinoma (HCC) remains an important form of cancer-related morbidity and mortality in the U.S. and worldwide. Previous U.S.-based studies on survival suggest ethnic disparities in HCC patients, but the complex interplay of multiple factors that contribute are still incompletely understood. Here we considered the influences of risk factors contributing towards HCC survival, including ethnic background, over ten years at a premier academic medical center with a majority (57.20%) African American (AA) population. Retrospective HCC data were collected from 2008–2018 at LSUHSC-Shreveport, an urban tertiary medical center. Data included demographics, comorbidities, liver disease characteristics, and tumor parameters. Statistical analysis was performed using Chi Square and one-way ANOVA. Results: 229 HCC patients were identified (male 78.6%). The mean HCC age at diagnosis was 61 years (SD = 7.3). Compared to non-Hispanic Caucasians (42.7%), AA patients (57.2% of total) were older at presentation, had more frequent diabetes/dyslipidemia/NAFLD (45 (34.3%) compared with 19 (19.3%) in non-Hispanic Caucasians, p = 0.02), and had a larger HCC burden at diagnosis. We conclude that compared to white patients, despite having similar BMI and MELD scores and rates of portal vein thrombosis, AA patients with HCC in our cohort were older at presentation, had a significantly increased incidence of modifiable metabolic risk factors including diabetes, higher AFP values, increased incidence of gallstones, and larger sized HCCs, and were more likely to be outside Milan criteria. These findings have important prognostic and diagnostic implications for developing a more targeted HCC surveillance program.

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