studies

HEPATOLOGY, VOLUME 62, NUMBER 1 (SUPPL) AASLD ABSTRACTS 497A
Disclosures:
The following authors have nothing to disclose: Jack Masur, Shyam Kottilil, Eleanor
Wilson, Samir R. Shah
579
Successful Implementation of a Community-based
Patient Navigation Program to Increase Screening and
Linkage-to-care in High-Risk Patients with Chronic Hepatitis
B Infection
Jennifer M. Newton 1 , Matt Johnson 2 , Sharon Song 2 , Helen S. Te 1 ,
Edwin Chandrasekar 2 , Karen E. Kim 1 ; 1 Medicine, University of
Chicago Medicine, Chicago, IL; 2 Asian Health Coalition, Chicago,
IL
Background: Chronic hepatitis B virus infection (CHB) is estimated
to affect up to 2.2 million people in the United States,
and disproportionately affects foreign-born Asian and African
immigrants. CHB carries a high morbidity and mortality
due to undertreatment, which largely reflects lack of awareness
of infection and poor linkage to care. Purpose: In this
intervention study, we have implemented a community-based
patient navigation program. Utilizing community-health workers
(CHWs) and patient navigators (PNs), we aim to increase
patient awareness of infection and improve linkage to care
for foreign-born Asian, Pacific Islander and African patients
with CHB. Methods: While CHWs provide culturally competent
education, PNs are essential for care coordination. To improve
community linkage to care, we developed a co-curriculum to
enhance care coordination and community linkage for hepatitis
B surface antigen (HBsAg) positive patients. To strengthen
their partnerships, CHWs and HPNs jointly attend site visits
and training sessions for cultural competency, CHB education,
navigating the healthcare system and community awareness.
Results: In the first 6 months of the intervention, 46 (4.7%) of the
974 individuals screened were HBsAg positive. Asian (674,
69.2%) and Black/African (224, 23%) patients accounted for
the largest ethnic groups screened, while Iraq (228, 23.4%)
and Myanmar (147, 15.1%) represent the largest screenings
by country of origin. 247 patients received all 3 HBV serologic
tests (surface antigen, surface antibody, and core antibody).
Of these patients, 111 (44.9%) were previously vaccinated,
60 (24.3%) were susceptible to infection, and 40 (16.2%) had
resolved infection. 891 (91.5%) of the 974 patients screened
received test results and counseling, including all 46 (100%)
patients with active infection. Among patients with active
HBV infection, 37 (80.4%) have been linked to care, with 31
(67.4%) seeking care through specialty clinics and 6 (13%)
through primary care. 3 (6.5%) patients moved out of state,
and 6 (13%) have been lost to follow-up. Conclusions: To our
knowledge, this is the first community-based hepatitis B patient
navigation program to be instituted in the country. This intervention
has been successful in linking high-risk Asian and African
patients to healthcare for CHB, and is a promising model that
may be implemented nationally to decrease the unnecessary
morbidity and mortality associated with CHB.
Disclosures:
Helen S. Te - Advisory Committees or Review Panels: BMS; Grant/Research
Support: Abbvie, Conatus, BMS
The following authors have nothing to disclose: Jennifer M. Newton, Matt Johnson,
Sharon Song, Edwin Chandrasekar, Karen E. Kim
580
Novel Algorithm to Predict Hospital Readmission in
Patients with Cirrhosis Utilizing Machine Learning Classifiers
Spencer L. James 1 , Shawn Shah 2 , Zilla H. Hussain 2 , Neil Volk 2 ,
Joseph Shatzel 2 , Rolland Dickson 2 ; 1 Medical School, Geisel
School of Medicine at Dartmouth, Lebanon, NH; 2 Department of
Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH
BACKGROUND: Patients with cirrhosis have high rates of hospital
readmission. Current models used to predict readmission
in cirrhotic patients are based on limited data. The aims of this
study are to use machine learning classifiers (MLC), a novel
discipline of computer science focused on predicting outcomes
from complex datasets, to develop a model which can accurately
predict which cirrhotic patients will be readmitted after
discharge. METHODS: We retrospectively reviewed patients
with a primary diagnosis of cirrhosis admitted to a tertiary
care center over a 21-month period (April 2011 to December
2012). Using admission data, we applied various MLC
and a deep learning technique to develop a predictive model
for readmission in cirrhotics. The MLC utilized features of the
patient’s EHR including age, sex, serum sodium, platelet count,
hemoglobin, hepatic biomarkers, albumin, international normalized
ratio, MELD score, use of rifaximin or lactulose, use of
diuretics, use of a beta-blocker, prior TIPS, presence of hepatocellular
carcinoma, etiology of cirrhosis and socioeconomic
factors (such as income and insurance). We then tested the
model’s ability to predict the likelihood of readmission among
cirrhotic patients in the database. RESULTS: During the study
period, 317 unique patients accounting for a total of 498
admissions were included. The average patient age was 57.9
years (range of 14 to 88), and 58% were male. The mean
number of admissions per patient was 2.4 (range of 1 to 9),
with an average time to readmission of 88 days (range of 1
to 411 days). From this sample, we trained machine learning
classifiers on a randomly selected 80% of the data, and
then tested the predictive accuracy on the remaining 20%.
This train/test process was repeated 100 times. The mean
accuracy to correctly identify hospital readmission varied by
classifier, with random forest classification correctly predicting
patient readmission 70% of the time on average (range of
53% to 80%). Random forest demonstrated a mean sensitivity
and specificity of 50% and 85%, respectively, with a PPV and
NPV of 70% and 69%, respectively, in the native distribution
of the dataset. CONCLUSION: Machine learning classifiers
were capable of predicting hospital readmission for patients
with cirrhosis nearly 70% of the time. As a decision support
tool, machine learning is promising to help risk stratify patients
admitted with cirrhosis. With further validation, such classifiers
could be incorporated into EHR systems to help identify highrisk
patients with cirrhosis who are likely to be readmitted, and
prompt appropriate multidisciplinary intervention.
Disclosures:
The following authors have nothing to disclose: Spencer L. James, Shawn Shah,
Zilla H. Hussain, Neil Volk, Joseph Shatzel, Rolland Dickson