Objective As electronic medical record (EMR) data are increasingly used in HIV clinical and epidemiologic research, accurately identifying people with HIV (PWH) from EMR data is paramount. We sought to evaluate EMR data types and compare EMR algorithms for identifying PWH in a multicenter EMR database. Materials and Methods We collected EMR data from 7 healthcare systems in the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) including diagnosis codes, anti-retroviral therapy (ART), and laboratory test results. Results In total, 13 935 patients had a positive laboratory test for HIV; 33 412 patients had a diagnosis code for HIV; and 17 725 patients were on ART. Only 8576 patients had evidence of HIV-positive status for all 3 data types (laboratory results, diagnosis code, and ART). A previously validated combination algorithm identified 22 411 patients as PWH. Conclusion EMR algorithms that combine laboratory results, administrative data, and ART can be applied to multicenter EMR data to identify PWH.

IntroductionThe COVID-19 pandemic has had profound effects on access to care, including outpatient sexually transmitted infection (STI) testing and treatment. Many vulnerable populations already relied on the emergency department (ED) for much of their care prior to the pandemic. This study examines trends in STI testing and positivity before and during the pandemic at a large urban medical center and evaluates the role of the ED in providing STI care.MethodsThis is a retrospective review of all gonorrhea, chlamydia, and trichomonas tests from November 1, 2018, through July 31, 2021. Demographic information and location and results of STI testing were extracted from the electronic medical record. Trends in STI testing and positivity were examined for 16 month periods before and after the COVID-19 pandemic started (March 15, 2020), with the latter divided into the early pandemic period (EPP: March 15 -July 31, 2020) and late pandemic period (LPP: August 1, 2020 - July 31, 2021).ResultsTests per month decreased by 42.4% during the EPP, but rebounded by July 2020. During the EPP, the proportion of all STI testing originating in the ED increased from 21.4% pre-pandemic to 29.3%, and among pregnant women from 45.2% to 51.5%. Overall STI positivity rate increased from 4.4% pre-pandemic to 6.2% in the EPP. Parallel trends were observed for gonorrhea and chlamydia individually. The ED represented 50.5% of overall positive tests, and as much as 63.1% of positive testing during the EPP. The ED was the source of 73.4% of positive tests among pregnant women, which increased to 82.1% during the EPP.ConclusionsSTI trends from this large urban medical center paralleled national trends, with an early decrease in positive cases followed by a rebound by the end of May 2020. The ED represented an important source of testing for all patients, and especially for pregnant patients, throughout the study period, but even more so early in the pandemic. This suggests that more resources should be directed towards STI testing, education, and prevention in the ED, as well as to support linkage to outpatient primary and obstetric care during the ED visit.

It is unknown how the COVID-19 pandemic impacted traditional measures of retention in HIV care. We calculated six different retention measures at an urban HIV care clinic for two time periods: pre-pandemic, and during the first year of the COVID-19 pandemic, with and without inclusion of telehealth appointments. Spearman rank correlation was used to assess correlation between different measures of retention. For both the pre-pandemic and pandemic time periods, there was strong correlation among measures of missed visits (range 0.857–0.957). More patients were considered retained in care during the pandemic when telehealth appointments were included in the analysis.

Introduction Researchers in the United States have created several models to predict persons most at risk for HIV. Many of these predictive models use data from all persons newly diagnosed with HIV, the majority of whom are men, and specifically men who have sex with men (MSM). Consequently, risk factors identified by these models are biased toward features that apply only to men or capture sexual behaviours of MSM. We sought to create a predictive model for women using cohort data from two major hospitals in Chicago with large opt-out HIV screening programs. Methods We matched 48 newly diagnosed women to 192 HIV-negative women based on number of previous encounters at University of Chicago or Rush University hospitals. We examined data for each woman for the two years prior to either their HIV diagnosis or their last encounter. We assessed risk factors including demographic characteristics and clinical diagnoses taken from patient electronic medical records (EMR) using odds ratios and 95% confidence intervals. We created a multivariable logistic regression model and measured predictive power with the area under the curve (AUC). In the multivariable model, age group, race, and ethnicity were included a priori due to increased risk for HIV among specific demographic groups. Results The following clinical diagnoses were significant at the bivariate level and were included in the model: pregnancy (OR 1.96 (1.00, 3.84)), hepatitis C (OR 5.73 (1.24, 26.51)), substance use (OR 3.12 (1.12, 8.65)) and sexually transmitted infections (STIs) chlamydia, gonorrhoea, or syphilis. We also a priori included demographic factors that are associated with HIV. Our final model had an AUC of 0.74 and included healthcare site, age group, race, ethnicity, pregnancy, hepatitis C, substance use, and STI diagnosis. Conclusions Our predictive model showed acceptable discrimination between those who were and were not newly diagnosed with HIV. We identified risk factors such as recent pregnancy, recent hepatitis C diagnosis, and substance use in addition to the traditionally used recent STI diagnosis that can be incorporated by health systems to detect women who are vulnerable to HIV and would benefit from preexposure prophylaxis (PrEP).

UNSTRUCTURED Epic Systems is a major provider of health information technology in the United States, providing electronic medical records (EMR) for more than 250 million patients. Epic’s platform includes predictive models for patient care, including a model that predicts a patient’s probability of being a no-show for an outpatient healthcare appointment. However, the model has not been externally validated in certain groups of patients, including people with HIV (PwH). Regular medical care for PwH is of utmost importance, missed medical appointments among PwH are independently associated with increased mortality. We conducted an external validation of Epic’s no-show model in PwH using encounter data from the University of Chicago Medicine between January 21 to March 30, 2022. We compared Epic’s predicted no-show probability at the time of the encounter to the actual outcome of these appointments. We also examined the performance of the Epic model among PwH for only HIV care appointments in the Infectious Diseases department. We further compared the no-show model among PwH for HIV care appointments to an alternate random forest model we created using a subset of 7 readily accessible features used in the Epic model and four additional features related to HIV clinical care or demographics. We identified 674 PwH who contributed 1,406 total scheduled in-person appointments during the study period. The performance of the Epic model among PwH for all appointments in any outpatient clinic had an AUC of 0.65 (0.63-0.66). When we restricted the data to include only HIV care clinic appointments, we identified 331 PwH who contributed 440 infectious disease appointments. The AUC of the Epic model in for HIV care appointments among PwH was 0.63 (0.59-0.67), there was no significant difference in performance compared to the model that included all appointments (P=0.36). The alternate model we created for PwH attending HIV care appointments had an AUC of 0.78 (0.75-0.82) a significant improvement over the Epic model restricted to HIV care appointments (P<0.001). Model performance among PwH was significantly lower than reported by Epic. We found that a model that incorporated a subset of the features used in the original Epic model along with demographic and HIV clinical information performed substantially better among PwH attending HIV care appointments. The inclusion of demographic factors seemed to improve the model performance substantially, indicating that among populations suffering from extreme disparities, such as PwH, inclusion of demographic information may be key to enhance the predicting prediction of difficulties in appointment attendance.