Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study.

BACKGROUND: The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. UOU impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19. OBJECTIVE: We applied machine learning techniques to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity. METHODS: This retrospective, cross-sectional cohort study was conducted based on data from 4110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. The inclusion criterion was an unplanned admission of a patient aged ≥18 years; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or 2 COVID-19 International Classification of Disease, Tenth Revision codes. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for 2 subgroups: encounters with UOU and COVID-19 and those with no UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with 3 utilization metrics: low-severity unplanned admission, medium-severity unplanned admission and receiving mechanical ventilation, and high-severity unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and BMI. RESULTS: Topic modeling yielded 10 topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (eg, HIV) and no UOU and COVID-19 (eg, diabetes). In the regression analysis, each incremental increase in the classifier's predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29; P=.009). CONCLUSIONS: Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

Screening for PTSD during pregnancy: a missed opportunity.

BACKGROUND: Prenatal posttraumatic stress disorder (PTSD) is often overlooked in obstetric care, despite evidence that untreated PTSD negatively impacts both mother and baby. OB-GYN clinics commonly screen for depression in pregnant patients; however, prenatal PTSD screening is rare. Although the lack of PTSD screening likely leaves a significant portion of pregnant patients with unaddressed mental health needs, the size of this care gap has not been previously investigated. METHODS: This retrospective chart review study included data from 1,402 adult, pregnant patients who completed PTSD (PTSD Checklist-2; PCL) and depression (Edinburgh Postnatal Depression Survey; EPDS) screenings during a routine prenatal care visit. Descriptive statistics identified screening rates for PTSD and depression, and logistic regression analyses identified demographic variables associated with screening outcomes and assessed whether screening results (+ PCL/ + EPDS, + PCL/-EPDS, -PCL/ + EPDS, -PCL/-EPDS) were associated with different provider intervention recommendations. RESULTS: 11.1% of participants screened positive for PTSD alone, 3.8% for depression alone, and 5.4% for both depression and PTSD. Black (OR = 2.24, 95% CI [1.41,3.54]) and Latinx (OR = 1.64, 95% CI [1.01,2.66]) patients were more likely to screen positive for PTSD compared to White patients, while those on public insurance were 1.64 times (95% CI [1.21,2.22]) more likely to screen positive compared to those with private insurance. Patients who screened positive for both depression and PTSD were most likely to receive referrals for behavioral health services (44.6%), followed by -PCL/ + EPDS (32.6%), + PCL/-EPDS (10.5%), and -PCL/-EPDS (3.6%). A similar pattern emerged for psychotropic medication prescriptions. CONCLUSIONS: Over ten percent of pregnant patients in the current study screened positive for PTSD without depression, highlighting a critical mental health need left unaddressed by current obstetric standards of care. Routine PTSD screening during prenatal care alongside strategies aimed at increasing referral resources and access to mental health services are recommended.