Objectives To define pregnancy episodes and estimate gestational age within electronic health record (EHR) data from the National COVID Cohort Collaborative (N3C). Materials and Methods We developed a comprehensive approach, named Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS), and applied it to EHR data in the N3C (1/1/2018-4/7/2022). HIPPS combines: 1) an extension of a previously published pregnancy episode algorithm, 2) a novel algorithm to detect gestational age-specific signatures of a progressing pregnancy for further episode support, and 3) pregnancy start date inference. Clinicians performed validation of HIPPS on a subset of episodes. We then generated pregnancy cohorts based on gestational age precision and pregnancy outcomes for assessment of accuracy and comparison of COVID-19 and other characteristics. Results We identified 628,165 pregnant persons with 816,471 pregnancy episodes, of which 52.3% were live births, 24.4% were other outcomes (stillbirth, ectopic pregnancy, abortions), and 23.3% had unknown outcomes. Clinician validation agreed 98.8% with HIPPS-identified episodes. We were able to estimate start dates within one week of precision for 475,433 (58.2%) episodes. 62,540 (7.7%) episodes had incident COVID-19 during pregnancy. Discussion HIPPS provides measures of support for pregnancy-related variables such as gestational age and pregnancy outcomes based on N3C data. Gestational age precision allows researchers to find time to events with reasonable confidence. Conclusion We have developed a novel and robust approach for inferring pregnancy episodes and gestational age that addresses data inconsistency and missingness in EHR data. Lay Summary The National COVID Cohort Collaborative (N3C) provides researchers a unique opportunity to use electronic health record data from more than 12 million individuals from over seventy healthcare systems across the U.S. to study the impact of COVID-19 on pregnancy and women’s health. However, doing research with electronic health record data from different sources can be challenging as data can often be reported in many ways and formats. To address this challenge, we developed an approach known as Hierarchy and rule-based pregnancy episode Inference integrated with Pregnancy Progression Signatures (HIPPS) that can 1) find the start and end of a pregnancy, 2) infer whether the pregnancy resulted in a live birth or pregnancy loss, and 3) determine the gestational age at the end of pregnancy. We observed from a subset of data that our approach had high agreement with how clinicians would collect this information from electronic health records. When applying our approach on all the data in N3C, we identified 816K pregnancies from 628K individuals. Of these individuals, 62K had COVID-19 during pregnancy. Our research demonstrates that our HIPPS approach can enable COVID-19-related research in pregnancy with electronic health record data.

Background Identifying individuals with a higher risk of developing severe COVID-19 outcomes will inform targeted or more intensive clinical monitoring and management. To date, there is mixed evidence regarding the impact of pre-existing autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure on developing severe COVID-19 outcomes. Methods A retrospective cohort of adults diagnosed with COVID-19 was created in the National COVID Cohort Collaborative enclave. Two outcomes, life-threatening disease, and hospitalization were evaluated by using logistic regression models with and without adjustment for demographics and comorbidities. Results Of the 2,453,799 adults diagnosed with COVID-19, 191,520 (7.81%) had a pre-existing AID diagnosis and 278,095 (11.33%) had a pre-existing IS exposure. Logistic regression models adjusted for demographics and comorbidities demonstrated that individuals with a pre-existing AID (OR = 1.13, 95% CI 1.09 - 1.17; P< 0.001), IS (OR= 1.27, 95% CI 1.24 - 1.30; P< 0.001), or both (OR = 1.35, 95% CI 1.29 - 1.40; P< 0.001) were more likely to have a life-threatening COVID-19 disease. These results were consistent when evaluating hospitalization. A sensitivity analysis evaluating specific IS revealed that TNF inhibitors were protective against life-threatening disease (OR = 0.80, 95% CI 0.66- 0.96; P=0.017) and hospitalization (OR = 0.80, 95% CI 0.73 - 0.89; P< 0.001). Conclusions Patients with pre-existing AID, exposure to IS, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19.

Patient: Male, 75-year-old Final Diagnosis: Dementia with Lewy bodies Symptoms: Parkinsonism Medication:— Clinical Procedure: — Specialty: Neurology Objective: Unusual clinical course Background: Antidopaminergic medications, including antipsychotics, are known to worsen motor and neuropsychiatric symptoms, including cognition and psychosis, in patients with dementia with Lewy body (DLB). The intensity of worsened clinical symptoms may vary and can result in mortality in certain situations. There have been some reports supporting clozapine, quetiapine and pimavanserin use in psychosis control in this population. Case Report: We describe the case of 75-year-old man with diagnosis of DLB and the post-treatment outcome with olanzapine for psychosis during hospitalization. He experienced worsened cognitive and motor functions. Discontinuation of olanzapine resulted in resolution of the clinical worsening. Further, re-initiation of Pimavanserin helped treat his hallucinations. He returned back to his baseline during a follow-up visit in the clinic at 1 month after discharge. Further, we incorporated the use of Best Practice Alert (BPA) as a part of the electronic health record (EHR) system to help providers identify patients prone to neuroleptic sensitivity and help select appropriate medications to treat psychosis in this patient population. Conclusions: Administration of antipsychotics in patients with parkinsonism, especially DLB, requires close clinical monitoring and judicious use. Awareness of morbidity and mortality associated with such use is of importance, especially during hospitalization. From our experience, we incorporated use of BPA, which can help providers make judicious choices while treating this patient population. Pimavanserin, which is FDA-approved for psychosis in Parkinson’s disease, could be a potential safe and effective treatment option in this patient population.

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Background: Acute Kidney Injury (AKI) is a common complication after cardiac surgery and is associated with worse outcomes. Its management relies on early diagnosis, and therefore, electronic alerts have been used to alert clinicians for development of AKI. Electronic alerts are, however, associated with high rates of alert fatigue. Objectives: We designed this study to assess the acceptance of user-centered electronic AKI alert by clinicians. Methods: We developed a user-centered electronic AKI alert, that provided alerted clinicians of development of AKI in a persistent yet non-interruptive fashion. As the goal of the alert was to alert towards new or worsening AKI, it disappeared 48 hours after being activated. We assessed the acceptance of the alert using surveys at 6 months and 12 months after the alert went live. Results: At 6 months after their implementation 38.9% providers reported that they would not have recognized AKI as early as they did without this alert. This number increased to 66.7% by 12 month survey. Most providers also shared that they re-dosed or discontinued medications earlier, provided earlier management of volume status, avoided IV contrast use and evaluated patients by using point of care ultrasounds more due to the alert. Overall, 83.3% respondents reported satisfaction with the electronic AKI alerts at 6 months and 94.4% at 12 months. Conclusions: This study showed high rates of acceptance of a user-centered electronic AKI alert over time by clinicians taking of patients with AKI.