V Bobo, William

ABSTRACTObjectivesPrior studies have raised concerns about postpartum hemorrhage (PPH) risk associated with selective serotonin and serotonin‐norepinephrine reuptake inhibitors (SSRIs and SNRIs), although limitations include possible confounding by maternal indications. The current study examined the risk of PPH associated with prenatal S/NRIs accounting for diagnosed maternal depression during pregnancy.MethodsThe current study is a secondary analysis of data from a cohort of 2213 pregnancies (1997–2010) identified from a large electronic medical record‐linkage system. Exposures were identified from electronic prescriptions records and resulted in three main groups: S/NRI‐exposed pregnancies (S/NRI users), antidepressant‐unexposed pregnancies (non‐users), and S/NRI exposure in the year prior to (but not during) pregnancy (former users). Data used to identify PPH occurrences included obstetrician chart diagnoses, diagnosis codes, and recorded estimated blood loss (EBL). The risk of PPH by exposure group was estimated using unadjusted, minimally adjusted (maternal age at delivery, delivery year, parity, and maternal smoking), and depression‐adjusted models (previous covariates and maternal depression). Supplemental analyses included an examination of PPH risk with prenatal bupropion monotherapy.ResultsThe study cohort consisted of 837 S/NRI users, 401 former users, and 786 non‐users, as well as 114 bupropion users identified for additional analyses. The frequency of PPH was 11.6% in the overall cohort, and was 14.7% for S/NRI users, 10.2% for former users, and 8.7% for non‐users. The risk of PPH was significantly higher for S/NRI users than non‐users in unadjusted (OR 1.82 [95% CI 1.33, 2.49]), minimally adjusted (aOR 1.66 [1.19, 2.31]), and depression‐adjusted models (aOR 1.46 [1.02, 2.10]). The risk of PPH was also higher for S/NRI former users than non‐users in unadjusted models (OR 1.54 [1.05, 2.26]) but not minimally adjusted (aOR 1.42 [0.95, 2.11]) or depression‐adjusted models (aOR 1.41 [0.95, 2.10]). No significant differences in PPH risk were observed between S/NRI users and former users, between bupropion users and non‐users, or between S/NRI users and bupropion users.ConclusionsPrenatal S/NRI exposure was associated with an increased risk of PPH after adjusting for maternal depression, although residual confounding and confounding by other factors (including unmeasured factors) may be possible, as the risk of PPH appeared to be similar between S/NRI users and former users. Best‐practice PPH risk management strategies should be adhered to for depressed patients, especially those who require treatment with antidepressants during pregnancy.

ABSTRACT Adherence in digital health studies with extended observation periods (≥ 12 months) is limited, and participant retention considerably reduces with time. The US Food and Drug Administration has issued guidelines for improving participant engagement, adherence, and diversity in digital health studies combined with decentralized procedures. A decentralized digital health study on well‐being was designed with protocolized procedures to study the feasibility of participant engagement and technology support to facilitate adherence (wearing the smartwatch ≥ 70% of time) sustained over a 12‐month period. At the end of the study, participants were asked about their ease of participation and free‐response questions about how wearing the smartwatches impacted their physical wellness. An inductive thematic analysis (ITA) was performed to assess themes of those responses and association with adherence. A total of 298 participants were recruited between 2022 and 2023 ( n = 129 in Cohort A in October 22, n = 169 in Cohort B in April 23), with 23% non‐white participants accrued. Among the 298 participants accrued, 273 (92% of accrued participants) completed the 12‐month study with an average overall adherence of 77.4% (SD = 32.64) wear‐time across 12 months. Median adherence of participants whose responses exemplified an ITA theme encompassing perceived behavior changes in sleep and physical activity was higher than those who did not have a response exemplifying that theme. Conversely, those expressing perceived discomfort or intrusiveness of the smartwatch had a statistically lower adherence. These results highlight the crucial roles of technology support and robust engagement efforts to enable sustained adherence over extended follow‐up periods in decentralized digital health studies.

Introduction: Despite advances in obstetric care, postpartum hemorrhage (PPH) is a leading cause of maternal mortality worldwide. Prior reviews of studies published through 2016 suggest an association of antidepressant use during late pregnancy and increased risk of PPH. However, a causal link between prenatal antidepressants and PPH remains controversial. Objectives: This systematic literature review aimed to synthesize the empirical evidence on the association of antidepressant exposure in late pregnancy with the risk of PPH, including studies published before and after 2016. Methods: A systematic literature search was conducted using PubMed, OVID Medline, EMBASE, SCOPUS, PsycINFO, and CINAHL from inception to September 9, 2023. Original, peer-reviewed studies (published in English) that reported on the frequency or risk of PPH in women with evidence of antidepressant use during pregnancy and included at least one control group were included. Results: Twenty studies (eight published after 2016) met inclusion criteria, most of which focused on the risks of PPH associated with selective serotonin reuptake inhibitors (SSRIs) or serotonin-norepinephrine reuptake inhibitors (SNRIs). The main findings from the individual studies were mixed, but the majority documented statistically significant associations of PPH with late prenatal exposure, especially for exposures occurring within 30 days of delivery, compared with unexposed deliveries. Fourteen studies addressed underlying antidepressant indications or their correlates. Few studies focused on prenatal antidepressants and the risk of well-defined severe PPH or on antidepressant dose changes and general PPH risk. None examined competing risks of antidepressant discontinuation on mental health outcomes. Conclusions: Late pregnancy exposure to antidepressants may be a minor risk factor for PPH, but it is unclear to what extent reported associations are causal in nature, as opposed to correlational (effects related to nonpharmacological factors including maternal indication). For patients needing antidepressants during pregnancy, current evidence does not favor routinely discontinuing antidepressants specifically to reduce the risk of PPH.

Abstract Pharmacogenomic (PGx) biomarkers integrated using machine learning can be embedded within the electronic health record (EHR) to provide clinicians with individualized predictions of drug treatment outcomes. Currently, however, drug alerts in the EHR are largely generic (not patient‐specific) and contribute to increased clinician stress and burnout. Improving the usability of PGx alerts is an urgent need. Therefore, this work aimed to identify principles for optimal PGx alert design through a health‐system‐wide, mixed‐methods study. Clinicians representing multiple practices and care settings ( N = 1062) in urban, rural, and underserved regions were invited to complete an electronic survey comparing the usability of three drug alerts for citalopram, as a case study. Alert 1 contained a generic warning of pharmacogenomic effects on citalopram metabolism. Alerts 2 and 3 provided patient‐specific predictions of citalopram efficacy with varying depth of information. Primary outcomes included the System's Usability Scale score (0–100 points) of each alert, the perceived impact of each alert on stress and decision‐making, and clinicians' suggestions for alert improvement. Secondary outcomes included the assessment of alert preference by clinician age, practice type, and geographic setting. Qualitative information was captured to provide context to quantitative information. The final cohort comprised 305 geographically and clinically diverse clinicians. A simplified, individualized alert (Alert 2) was perceived as beneficial for decision‐making and stress compared with a more detailed version (Alert 3) and the generic alert (Alert 1) regardless of age, practice type, or geographic setting. Findings emphasize the need for clinician‐guided design of PGx alerts in the era of digital medicine.

AbstractObjectivesInterpatient variability in bipolar I depression (BP‐D) symptoms challenges the ability to predict pharmacotherapeutic outcomes. A machine learning workflow was developed to predict remission after 8 weeks of pharmacotherapy (total score of ≤8 on the Montgomery Åsberg Depression Rating Scale [MADRS]).MethodsSupervised machine learning models were trained on data from BP‐D patients treated with olanzapine (N = 168) and were externally validated on patients treated with olanzapine/fluoxetine combination (OFC; N = 131) and lamotrigine (LTG; N = 126). Top predictors were used to develop a prognosis rule informing how many symptoms should change and by how much within 4 weeks to increase the odds of achieving remission.ResultsAn AUC of 0.76 (NIR:0.59; p = 0.17) was established to predict remission in olanzapine‐treated subjects. These trained models achieved AUCs of 0.70 with OFC (NIR:0.52; p < 0.03) and 0.73 with LTG (NIR:0.52; p < 0.003), demonstrating external replication of prediction performance. Week‐4 changes in four MADRS symptoms (reported sadness, reduced sleep, reduced appetite, and concentration difficulties) were top predictors of remission. Across all pharmacotherapies, three or more of these symptoms needed to improve by ≥2 points at Week‐4 to have a 65% chance of achieving remission at 8 weeks (OR: 3.74, 95% CI: 2.45–5.76; p < 9.3E‐11).ConclusionMachine learning strategies achieved cross‐trial and cross‐drug replication in predicting remission after 8 weeks of pharmacotherapy for BP‐D. Interpretable prognoses rules required only a limited number of depressive symptoms, providing a promising foundation for developing simple quantitative decision aids that may, in the future, serve as companions to clinical judgment at the point of care.

Objectives To compare the agreement between percentile ranks from 4 multi-morbidity scores. Design Population-based descriptive study. Setting Olmsted County, Minnesota (USA). Participants We used the medical records-linkage system of the Rochester Epidemiology Project (REP; http://www.rochesterproject.org ) to identify all residents of Olmsted County, Minnesota who reached one or more birthdays between 1 January 2005 and 31 December 2014 (10 years). Methods For each person, we calculated 4 multi-morbidity scores using readily available diagnostic code lists from the US Department of Health and Human Services, the Clinical Classifications Software, and the Elixhauser Comorbidity Index. We calculated scores using diagnostic codes received in the 5 years before the index birthday and fit quantile regression models across age and separately by sex to transform unweighted, simple counts of conditions into percentile ranks as compared to peers of same age and of same sex. We compared the percentile ranks of the 4 multi-morbidity scores using intra-class correlation coefficients (ICCs). Results We assessed agreement in 181,553 persons who reached a total of 1,075,433 birthdays at ages 18 years through 85 years during the study period. In general, the percentile ranks of the 4 multi-morbidity scores exhibited high levels of agreement in 6 score-to-score pairwise comparisons. The agreement increased with older age for all pairwise comparisons, and ICCs were consistently greater than 0.65 at ages 50 years and older. Conclusions The assignment of percentile ranks may be a simple and intuitive way to assess the underlying trait of multi-morbidity across studies that use different measures.

AbstractBackgroundDue to invasive treatments and stressors related to heart health, adults with congenital heart defects (CHDs) may have an increased risk of post‐traumatic stress disorder (PTSD), anxiety, and/or depressive disorders. Our objectives were to estimate the prevalence of these disorders among individuals with CHDs.MethodsUsing IBM® MarketScan® Databases, we identified adults age 18–49 years with ≥2 outpatient anxiety/depressive disorder claims on separate dates or ≥1 inpatient anxiety/depressive disorder claim in 2017. CHDs were defined as ≥2 outpatient CHD claims ≥30 days apart or ≥1 inpatient CHD claim documented in 2007–2017. We used log‐binomial regression to estimate adjusted prevalence ratios (aPR) and 95% confidence intervals (CI) for associations between CHDs and anxiety/depressive disorders.ResultsOf 13,807 adults with CHDs, 12.4% were diagnosed with an anxiety or depressive disorder. Adults with CHDs, compared to the 5,408,094 without CHDs, had higher prevalence of PTSD (0.8% vs. 0.5%; aPR: 1.5 [CI: 1.2–1.8]), anxiety disorders (9.9% vs. 7.5%; aPR: 1.3 [CI: 1.3–1.4]), and depressive disorders (6.3% vs. 4.9%; aPR: 1.3 [CI: 1.2–1.4]). Among individuals with CHDs, female sex (aPR range: 1.6–3.3) and inpatient admission (aPR range 1.1–1.9) were associated with anxiety/depressive disorders.ConclusionOver 1 in 8 adults with CHDs had diagnosed PTSD and/or other anxiety/depressive disorders, 30–50% higher than adults without CHDs. PTSD was rare, but three times more common in women with CHDs than men. Screening and referral for services for these conditions in people with CHDs may be beneficial.

Objective: To determine whether a bi-directional relationship exists between depression and HF within a single population of individuals receiving primary care services, using longitudinal electronic health records (EHRs). Methods: This retrospective cohort study utilized EHRs for adults who received primary care services within a large healthcare system in 2006. Validated EHR-based algorithms identified 10,649 people with depression (depression cohort) and 5,911 people with HF (HF cohort) between January 1, 2006 and December 31, 2018. Each person with depression or HF was matched 1:1 with an unaffected referent on age, sex, and outpatient service use. Each cohort (with their matched referents) was followed up electronically to identify newly diagnosed HF (in the depression cohort) and depression (in the HF cohort) that occurred after the index diagnosis of depression or HF, respectively. The risks of these outcomes were compared (vs. referents) using marginal Cox proportional hazard models adjusted for 16 comorbid chronic conditions. Results: 2,024 occurrences of newly diagnosed HF were observed in the depression cohort and 944 occurrences of newly diagnosed depression were observed in the HF cohort over approximately 4–6 years of follow-up. People with depression had significantly increased risk for developing newly diagnosed HF (HR 2.08, 95% CI 1.89–2.28) and people with HF had a significantly increased risk of newly diagnosed depression (HR 1.34, 95% CI 1.17–1.54) after adjusting for all 16 comorbid chronic conditions. Conclusion: These results provide evidence of a bi-directional relationship between depression and HF independently of age, sex, and multimorbidity from chronic illnesses.

AbstractThe purpose of this study was to estimate the extent of potential antidepressant overprescribing in a geographically defined U.S. population, and to determine the indications and factors that account for it. We conducted a cohort study of new antidepressant prescriptions for elderly residents of Olmsted County, Minnesota, 2005‐2012, using the Rochester Epidemiology Project medical records‐linkage system. Indications for antidepressants were abstracted from health records for all cohort members. Potential antidepressant overprescribing was defined based on regulatory approval, the level of evidence identified from a standardized drug information database, and multidisciplinary expert review. Predictors of potential antidepressant overprescribing were investigated using logistic regression models, stratified by general antidepressant indication (general medical indication, specific psychiatric diagnosis, and non‐specific psychiatric symptoms). Potential antidepressant overprescribing occurred in 24% of 3199 incident antidepressant prescriptions during the study period, and involved primarily newer antidepressants that were prescribed for non‐specific psychiatric symptoms and subthreshold diagnoses. Potential antidepressant overprescribing was associated with nursing home residence, having a higher number of comorbid medical conditions and outpatient prescribers, taking more concomitant medications, having greater use of urgent or acute care services in the year preceding the index antidepressant prescription, and being prescribed antidepressants via telephone, e‐mail, or patient portal. In conclusion, potential antidepressant overprescribing occurred in elderly persons and involved mainly newer antidepressants used for non‐specific psychiatric symptoms and subthreshold diagnoses, and was associated with indicators of higher clinical complexity or severity and with prescribing without face‐to‐face patient contact.