Comparison of algorithms for identifying people with HIV from electronic medical records in a large, multi-site database

Ridgway, Jessica P; Mason, Joseph A.; Friedman, Eleanor E.; Devlin, Samantha; Zhou, Junlan; Meltzer, David O.; Schneider, John A.

doi:10.1093/jamiaopen/ooac033

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, ICD-9/10 codes provide a ubiquitous way for researchers to build predictive models using easily accessible data even if they lack the resources to carry out a review of medical records for every patient. Previous studies have shown that using ICD-9/10 codes can be effective at scaling up the rapid identification of people living with HIV [ 14 , 34 ]. Future studies should include social history variables and unstructured fields or free-text notes to see if they improve overall model performance.…”

Section: Discussionmentioning

confidence: 99%

“…Data contained sociodemographic information, encounter information, diagnosis codes, laboratory data, and antiretroviral therapy (ART) medication or prescription drug orders. We used the following criteria to determine if an individual was HIV-positive: (1) a positive HIV laboratory test (confirmatory HIV antibody, p24 antigen, or HIV viral load > 20 copies/mL), (2) an HIV viral load test performed (regardless of results) and prescription for ART, excluding pre-exposure prophylaxis prescriptions, (3) an HIV diagnosis code (International Classification of Diseases [ICD] version 9 [ICD-9] codes 42, 079.53, 795.71, and V08; ICD version 10 [ICD-10] codes B20, R75, and Z21) and a prescription for ART, excluding pre-exposure prophylaxis, or (4) an HIV diagnosis code and 2 HIV viral load tests performed [ 14 ]. Individuals were only included in the study if they met one (or more) of the above criteria and had at least two HIV care encounters.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation

Mason¹,

Friedman²,

Devlin³

et al. 2023

JMIR Public Health Surveill

Self Cite

View full text Add to dashboard Cite

Background Reducing care lapses for people living with HIV is critical to ending the HIV epidemic and beneficial for their health. Predictive modeling can identify clinical factors associated with HIV care lapses. Previous studies have identified these factors within a single clinic or using a national network of clinics, but public health strategies to improve retention in care in the United States often occur within a regional jurisdiction (eg, a city or county). Objective We sought to build predictive models of HIV care lapses using a large, multisite, noncurated database of electronic health records (EHRs) in Chicago, Illinois. Methods We used 2011-2019 data from the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), a database including multiple health systems, covering the majority of 23,580 people with an HIV diagnosis living in Chicago. CAPriCORN uses a hash-based data deduplication method to follow people across multiple Chicago health care systems with different EHRs, providing a unique citywide view of retention in HIV care. From the database, we used diagnosis codes, medications, laboratory tests, demographics, and encounter information to build predictive models. Our primary outcome was lapses in HIV care, defined as having more than 12 months between subsequent HIV care encounters. We built logistic regression, random forest, elastic net logistic regression, and XGBoost models using all variables and compared their performance to a baseline logistic regression model containing only demographics and retention history. Results We included people living with HIV with at least 2 HIV care encounters in the database, yielding 16,930 people living with HIV with 191,492 encounters. All models outperformed the baseline logistic regression model, with the most improvement from the XGBoost model (area under the receiver operating characteristic curve 0.776, 95% CI 0.768-0.784 vs 0.674, 95% CI 0.664-0.683; P<.001). Top predictors included the history of care lapses, being seen by an infectious disease provider (vs a primary care provider), site of care, Hispanic ethnicity, and previous HIV laboratory testing. The random forest model (area under the receiver operating characteristic curve 0.751, 95% CI 0.742-0.759) revealed age, insurance type, and chronic comorbidities (eg, hypertension), as important variables in predicting a care lapse. Conclusions We used a real-world approach to leverage the full scope of data available in modern EHRs to predict HIV care lapses. Our findings reinforce previously known factors, such as the history of prior care lapses, while also showing the importance of laboratory testing, chronic comorbidities, sociodemographic characteristics, and clinic-specific factors for predicting care lapses for people living with HIV in Chicago. We provide a framework for others to use data from multiple different health care systems within a single city to examine lapses in care using EHR data, which will aid in jurisdictional efforts to improve retention in HIV care.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation

Mason¹,

Friedman²,

Devlin³

et al. 2023

JMIR Public Health Surveill

Self Cite

View full text Add to dashboard Cite

show abstract

Early diagnosis of HIV cases by means of text mining and machine learning models on clinical notes

Morales-Sánchez,

Montalvo,

Riaño

et al. 2024

Computers in Biology and Medicine

View full text Add to dashboard Cite

Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation (Preprint)

Mason¹,

Friedman²,

Devlin³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

BACKGROUND Reducing lapses in care for people living with HIV (PLWH) is critical to ending the HIV epidemic and beneficial for the individual health of PLWH. Predictive modeling can identify clinical factors that are associated with lapses in HIV care. Previous studies have identified clinical factors that are associated with lapses in HIV care at the clinic level or national level, but public health strategies to improve retention in care in the U.S. often occur at the city or county level. OBJECTIVE We sought to build predictive models of lapses in HIV care using a large, multi-site, non-curated database of electronic health records (EHR) in Chicago. METHODS We used data between 2011 to 2019 from the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), a database that includes 11 health systems containing 12.8 million patients, covering the majority of PLWH in Chicago. CAPriCORN uses a hash-based data deduplication method to follow people across multiple Chicago healthcare systems with different EHRs, providing a unique city-wide view on retention in care for PLWH. From the database, we utilized diagnosis codes, medications, laboratory tests, demographics, and encounter information to build predictive models. Our primary outcome was lapses in HIV care, which we defined as having more than 12 months between subsequent HIV care encounters. We built logistic regression, random forest, and elastic net logistic regression models using all variables and compared their performance to a baseline logistic regression model containing only demographics and retention history. RESULTS We included PLWH with at least two HIV care encounters in the database, resulting in 16,930 PLWH with a total of 191,492 encounters. All models outperformed the baseline logistic regression model, with the most improvement from the elastic net logistic regression model (AUC 0.754 [0.746 - 0.762] vs 0.674 [0.664-0.683], P<.001). Top predictors included retention history, being seen by an Infectious Disease provider (vs. primary care provider), site of care, Hispanic ethnicity, and laboratory testing for gonorrhea and chlamydia. The random forest model (AUC 0.751 [0.742-0.759]) revealed age, insurance type, and chronic comorbidities such as hypertension as important in predicting a lapse in care. CONCLUSIONS We used a real-world approach to leverage the full scope of data available in modern EHRs to predict lapses in HIV care. Our findings reinforce previously known factors, such as history of prior lapses in care, while also showing the importance of laboratory testing, chronic comorbidities, sociodemographic characteristics, and individual clinic-specific factors for predicting lapses in care for PLWH in Chicago. This work provides a framework for others to use data from multiple different healthcare systems within a single city to examine lapses in care using EHR data.

show abstract

Comparison of algorithms for identifying people with HIV from electronic medical records in a large, multi-site database

Cited by 3 publications

References 16 publications

Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation

Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation

Early diagnosis of HIV cases by means of text mining and machine learning models on clinical notes

Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation (Preprint)

Contact Info

Product

Resources

About