Intrapartum and 30-Day Postpartum Complications in Patients With Antenatal COVID-19 Infection: A Retrospective Cohort Study

(125) INTRAPARTUM AND 30-DAY POSTPARTUM COMPLICATIONS IN PREGNANCIES WITH ANTENATAL SARS-COV-2 INFECTION

Pre-Existing Pancreatitis and Elevated Risks of COVID-19 Severity and Mortality

Objective:Real-world data have been critical for rapid-knowledge generation throughout the COVID-19 pandemic. To ensure high-quality results are delivered to guide clinical decision making and the public health response, as well as characterize the response to interventions, it is essential to establish the accuracy of COVID-19 case definitions derived from administrative data to identify infections and hospitalizations.Methods:Electronic Health Record (EHR) data were obtained from the clinical data warehouse of the Yale New Haven Health System (Yale, primary site) and 3 hospital systems of the Mayo Clinic (validation site). Detailed characteristics on demographics, diagnoses, and laboratory results were obtained for all patients with either a positive SARS-CoV-2 PCR or antigen test or ICD-10 diagnosis of COVID-19 (U07.1) between April 1, 2020 and March 1, 2021. Various computable phenotype definitions were evaluated for their accuracy to identify SARS-CoV-2 infection and COVID-19 hospitalizations.Results:Of the 69,423 individuals with either a diagnosis code or a laboratory diagnosis of a SARS-CoV-2 infection at Yale, 61,023 had a principal or a secondary diagnosis code for COVID-19 and 50,355 had a positive SARS-CoV-2 test. Among those with a positive laboratory test, 38,506 (76.5%) and 3449 (6.8%) had a principal and secondary diagnosis code of COVID-19, respectively, while 8400 (16.7%) had no COVID-19 diagnosis. Moreover, of the 61,023 patients with a COVID-19 diagnosis code, 19,068 (31.2%) did not have a positive laboratory test for SARS-CoV-2 in the EHR. Of the 20 cases randomly sampled from this latter group for manual review, all had a COVID-19 diagnosis code related to asymptomatic testing with negative subsequent test results. The positive predictive value (precision) and sensitivity (recall) of a COVID-19 diagnosis in the medical record for a documented positive SARS-CoV-2 test were 68.8% and 83.3%, respectively. Among 5,109 patients who were hospitalized with a principal diagnosis of COVID-19, 4843 (94.8%) had a positive SARS-CoV-2 test within the 2 weeks preceding hospital admission or during hospitalization. In addition, 789 hospitalizations had a secondary diagnosis of COVID-19, of which 446 (56.5%) had a principal diagnosis consistent with severe clinical manifestation of COVID-19 (e.g., sepsis or respiratory failure). Compared with the cohort that had a principal diagnosis of COVID-19, those with a secondary diagnosis had a more than 2-fold higher in-hospital mortality rate (13.2% vs 28.0%, P<0.001). In the validation sample at Mayo Clinic, diagnosis codes more consistently identified SARS-CoV-2 infection (precision of 95%) but had lower recall (63.5%) with substantial variation across the 3 Mayo Clinic sites. Similar to Yale, diagnosis codes consistently identified COVID-19 hospitalizations at Mayo, with hospitalizations defined by secondary diagnosis code with 2-fold higher in-hospital mortality compared to those with a primary diagnosis of COVID-19.Conclusions:COVID-19 diagnosis codes misclassified the SARS-CoV-2 infection status of many people, with implications for clinical research and epidemiological surveillance. Moreover, the codes had different performance across two academic health systems and identified groups with different risks of mortality. Real-world data from the EHR can be used to in conjunction with diagnosis codes to improve the identification of people infected with SARS-CoV-2.

Objectives:This study aimed to evaluate the utility of electronic health record (EHR) diagnosis codes for monitoring SARS-CoV-2 infections among nursing home residents.Design:A retrospective cohort study design was used to analyze data collected from nursing homes operating under the tradename Signature Healthcare between January 2022 and June 2023.Setting and Participants:Data from 31,136 nursing home residents across 76 facilities in Kentucky, Tennessee, Indiana, Ohio, North Carolina, Georgia, Alabama, and Virginia were included.Methods:Resident demographics, diagnosis codes associated with clinical diagnoses (including COVID-19), and SARS-CoV-2 testing information were collected from the EHR and supplemental testing data sources. We described the rates of infection and the clinical characteristics of residents with incident-positive SARS-CoV-2 tests and new-onset COVID-19 diagnoses. Positive predictive values (PPVs) of COVID-19 diagnosis codes were calculated for residents stratified by whether a resident was continuously present in a facility for ±3 days from the diagnosis onset date listed in EHRs, using positive SARS-CoV-2 tests to confirm infection.Results:A total of 4876 incident-positive SARS-CoV-2 tests and 6346 new-onset COVID-19 diagnoses were recorded during the study period. Weekly rates of new-onset diagnoses were significantly higher than positive test rates, although trends followed similar trajectories. Among residents continuously present in the nursing home ±3 days from the diagnosis onset date, the PPV of COVID-19 diagnosis codes was high (3395 of 3685 = 92%; 95% CI, 91%–93%). The PPV among this group significantly varied by study quarter (P <.001). The PPV was substantially lower for 2661 diagnoses among residents not continuously present in the nursing home (24%; 95% CI, 22%–26%).Conclusions and Implications:This study demonstrates the utility of diagnosis codes for assessment of COVID-19 epidemiology and trends when testing data are unavailable for residents during their stay in a nursing home. Future research should explore strategies to evaluate the utility of diagnosis codes at admission and discharge to nursing homes to enhance surveillance efforts.

COVID-19 Vaccination Is Safe and Effective in Patients With Inflammatory Bowel Disease: Analysis of a Large Multi-institutional Research Network in the United States

72 Background: Identifying cancer cases within the electronic health record (EHR) or claims data can be challenging because diagnosis codes are often entered into patient records during routine screenings or as “rule out” diagnosis codes when the patient is referred to a procedure. To improve accuracy of prostate cancer (PCa) case ascertainment, we compared algorithms that used diagnoses codes to natural language processing (NLP) tools applied to clinical notes and pathology reports to identify Veterans with prostate cancer (PCa). Methods: This is a retrospective observational cohort study using VA EHR data to identify veterans diagnosed with PCa between 2000 and 2020. Using International Classification of Diseases (ICD-10 CM or ICD-9 CM) diagnosis and procedure codes, we identified veterans who may have PCa. We deployed validated NLP tools to identify the presence of Gleason score, metastatic PCa, and castration sensitivity to identify evidence of PCa within the notes. We conducted a descriptive analysis to compare the results of algorithms that relied exclusively on diagnosis codes compared to use of NLP tools. Results: From 2000 through 2020,1,031,296 veterans had one or more PCa diagnosis code. This number decreased by 11% for each additional PCa diagnosis code required. When we required 4 or more PCa diagnosis codes to be present, only 746,350 veterans had PCa. When we deployed NLP tools to identify mention of a Gleason score or an indicator of mPCa, only 685,847 Veterans had these indicators of PCa, a 35% decrease in the number of PCa cases with a single diagnosis code. Chart review of patients with their first PCa diagnosis codes in 2019 and 4 or more codes in their records illustrated no evidence of Gleason score or mPCa disease in their EHR. Analysis of their pathology reports revealed that these patients had prostatic intraepithelial neoplasia or atypical small acinar proliferation and had not yet developed prostate cancer. Conclusions: Accurate ascertainment of PCa using EHR and claims data requires using NLP tools and clinical notes combined with structured data sources such as diagnosis codes. Relying on ICD diagnosis codes alone will overestimate the burden of PCa up to 30%.

492: Non-black infants are at increased risk for respiratory complications in the late preterm period

The electronic medical record and COVID-19: Is it up to the challenge?

There is increasing recognition of the long-term health effects of SARS-CoV-2 infection (sometimes called long COVID). However, little is yet known about the clinical diagnosis and management of long COVID within health systems. To describe dominant themes pertaining to the clinical diagnosis and management of long COVID in the electronic health records (EHRs) of patients with a diagnostic code for this condition (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10] code U09.9). This qualitative analysis used data from EHRs of a national random sample of 200 patients receiving care in the Department of Veterans Affairs (VA) with documentation of a positive result on a polymerase chain reaction (PCR) test for SARS-CoV-2 between February 27, 2020, and December 31, 2021, and an ICD-10 diagnostic code for long COVID between October 1, 2021, when the code was implemented, and March 1, 2022. Data were analyzed from February 5 to May 31, 2022. A text word search and qualitative analysis of patients' VA-wide EHRs was performed to identify dominant themes pertaining to the clinical diagnosis and management of long COVID. In this qualitative analysis of documentation in the VA-wide EHR, the mean (SD) age of the 200 sampled patients at the time of their first positive PCR test result for SARS-CoV-2 in VA records was 60 (14.5) years. The sample included 173 (86.5%) men; 45 individuals (22.5%) were identified as Black and 136 individuals (68.0%) were identified as White. In qualitative analysis of documentation pertaining to long COVID in patients' EHRs 2 dominant themes were identified: (1) clinical uncertainty, in that it was often unclear whether particular symptoms could be attributed to long COVID, given the medical complexity and functional limitations of many patients and absence of specific markers for this condition, which could lead to ongoing monitoring, diagnostic testing, and specialist referral; and (2) care fragmentation, describing how post-COVID-19 care processes were often siloed from and poorly coordinated with other aspects of care and could be burdensome to patients. This qualitative study of documentation in the VA EHR highlights the complexity of diagnosing long COVID in clinical settings and the challenges of caring for patients who have or are suspected of having this condition.

Electronic health records are a potentially valuable source of information for identifying patients with opioid use disorder (OUD). To evaluate whether proxy measures from electronic health record data can be used reliably to identify patients with probable OUD based on Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria. This retrospective cross-sectional study analyzed individuals within the Geisinger health system who were prescribed opioids between December 31, 2000, and May 31, 2017, using a mixed-methods approach. The cohort was identified from 16 253 patients enrolled in a contract-based, Geisinger-specific medication monitoring program (GMMP) for opioid use, including patients who maintained or violated contract terms, as well as a demographically matched control group of 16 253 patients who were prescribed opioids but not enrolled in the GMMP. Substance use diagnoses and psychiatric comorbidities were assessed using automated electronic health record summaries. A manual medical record review procedure using DSM-5 criteria for OUD was completed for a subset of patients. The analysis was conducted beginning from June 5, 2017, until May 29, 2020. The primary outcome was the prevalence of OUD as defined by proxy measures for DSM-5 criteria for OUD as well as the prevalence of comorbidities among patients prescribed opioids within an integrated health system. Among the 16 253 patients enrolled in the GMMP (9309 women [57%]; mean [SD] age, 52 [14] years), OUD diagnoses as defined by diagnostic codes were present at a much lower rate than expected (291 [2%]), indicating the necessity for alternative diagnostic strategies. The DSM-5 criteria for OUD can be assessed using manual medical record review; a manual review of 200 patients in the GMMP and 200 control patients identifed a larger percentage of patients with probable moderate to severe OUD (GMMP, 145 of 200 [73%]; and control, 27 of 200 [14%]) compared with the prevalence of OUD assessed using diagnostic codes. These results suggest that patients with OUD may be identified using information available in the electronic health record, even when diagnostic codes do not reflect this diagnosis. Furthermore, the study demonstrates the utility of coding for DSM-5 criteria from medical records to generate a quantitative DSM-5 score that is associated with OUD severity.

320: Pitfalls of an Electronic Medical Record In Emergency Department Patients With Elevated Blood Pressure

Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an emerging medical condition that has been observed in several patients with a positive diagnosis for COVID-19. Historical Electronic Health Records (EHR) like diagnosis codes, lab results and clinical notes have been analyzed using deep learning and have been used to predict future clinical events. In this paper, we propose an interpretable deep learning approach to analyze historical diagnosis code data from the National COVID Cohort Collective (N3C)¹ to find the risk factors contributing to developing Long COVID. Using our deep learning approach, we are able to predict if a patient is suffering from Long COVID from a temporally ordered list of diagnosis codes up to 45 days post the first COVID positive test or diagnosis for each patient, with an accuracy of 70.48%. We are then able to examine the trained model using Gradient-weighted Class Activation Mapping (GradCAM) to give each input diagnoses a score. The highest scored diagnosis were deemed to be the most important for making the correct prediction for a patient. We also propose a way to summarize these top diagnoses for each patient in our cohort and look at their temporal trends to determine which codes contribute towards a positive Long COVID diagnosis.

To characterise maternal demographics and ascertain whether clinically important differences exist in the intrapartum and neonatal outcomes associated with assisted reproductive technology (ART). A retrospective study was undertaken between January 2007 and December 2013 of all singleton pregnancies conceived via ART at a major tertiary unit in Brisbane, Australia. Intrapartum outcomes were mode of delivery and indication for emergency caesarean. Neonatal outcomes investigated were gestation at delivery, birth weight, Apgar scores, acidosis at birth, respiratory distress, need for resuscitation, admission to neonatal intensive care and stillbirth. There were 4733 (7.4%) ART and 59277 (92.6%) spontaneous conception pregnancies. Women who conceived using ART were less likely to have a spontaneous vaginal delivery (odds ratio (OR) 0.60, 95% CI 0.57-0.64) and were more likely to require operative or assisted birth: elective caesarean (adjusted OR (aOR) 1.31, 95% CI 1.22-1.40), emergency caesarean (aOR 1.19, 95% CI 1.09-1.28), or instrumental delivery (aOR 1.45, 95% CI 1.32-1.58). Neonates who were conceived using ART were less likely to be born at term (aOR 0.64, 95% CI 0.58-0.71) and have lower birth weights. No differences were observed in rates of respiratory distress, admission to the neonatal intensive care unit, or stillbirth between the ART and spontaneous conception cohorts. The odds of neonatal acidosis (OR 0.71, 95% CI0.63-0.81) were lower in the ART cohort. Although higher rates of operative deliveries were seen for women who conceive using ART, neonatal outcomes were generally no different between the two cohorts.

POS1207 REAL WORLD POPULATION-BASED ASSESSMENT OF COVID-19 OUTCOMES AMONG RHEUMATOID ARTHRITIS PATIENTS USING BIOLOGIC OR SYNTHETIC DMARDs

Objective - To investigate textual content, health problems and diagnostic codes in everyday electronic patient records. Design - Retrospective and observational database study. Setting - Primary health care in Stockholm. Subjects - Twenty randomly selected general practitioners with 20 records each. Main outcome measures - The frequency of use of problem-oriented medical records. The number of words, problems and diagnostic codes. The completeness and correctness of the diagnostic codes. Results - About 14.5% of 400 studied records were problem-oriented. The mean number of words per record was 99.4, and the mean number of problems managed per record was 1.2. On average, there were 1.1 diagnostic codes per record and this differed widely among GPs and also among the electronic patient record systems. The mean number of codes per problem was 0.9, and the proportion of correct codes was 97.4%. Conclusions - The electronic patient records in general practice in Stockholm have an extensive textual content. A vast majority of the problems are coded and the completeness and correctness of diagnostic codes are high. It seems that problem-oriented electronic patient record systems enforce coding activities. It is feasible to establish a database of diagnostic data for research and health care planning based on electronic patient records.