Post-Acute Sequelae of SARS-CoV-2 Infection in Pregnant Females: 
An Electronic Health Records Analysis from the RECOVER Initiative (PCORnet and N3C)

doi:10.21203/rs.3.rs-5026783/v1

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Post-Acute Sequelae of SARS-CoV-2 Infection in Pregnant Females: An Electronic Health Records Analysis from the RECOVER Initiative (PCORnet and N3C)

https://doi.org/10.21203/rs.3.rs-5026783/v1

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While pregnancy has been associated with an altered immune response and distinct clinical manifestations of COVID-19, the influence of pregnancy on the persistence and severity of post-acute sequelae of SARS-CoV-2 infection (PASC), or Long COVID, remains uncertain. This study investigated PASC risk in individuals with SARS-CoV-2 infection during pregnancy and compared it with that in reproductive-age females with SARS-CoV-2 infection outside of pregnancy. This retrospective analysis identified 72,151 individuals who contracted SARS-CoV-2 during pregnancy and 1,439,354 females who contracted SARS-CoV-2 outside of pregnancy, aged 18 to 50 years old, from March 2020 to June 2023 in the National Patient-Centered Clinical Research Network (PCORnet) and the National COVID Cohort Collaborative (N3C). A comprehensive list of PASC outcomes was investigated, including a PCORnet rule-based PASC definition, an N3C PASC machine learning (ML) Phenotype, unspecified PASC ICD-10 diagnoses (ICD10 codes U09.9 or B94.8), and a cluster of cognitive, fatigue, and respiratory conditions. Overall, the estimated risk of PASC at 180 days of follow-up for those infected during pregnancy was 16.47 events per 100 persons (95% CI, 16.00 to 16.95) in the PCORnet cohort, based on the PCORnet rule-based PASC definition, and 4.37 events per 100 persons (95% CI, 4.18 to 4.57) in the N3C cohort based on the ML model. The risks of unspecified PASC diagnoses were 0.19 events per 100 persons (95% CI, 0.14 to 0.25) in PCORnet, and 0.23 events per 100 persons (95% CI, 0.19 to 0.28) in N3C; and the risks of any post-acute cognitive, fatigue, and respiratory condition were 4.86 events per 100 persons (95% CI, 4.59 to 5.14) in PCORnet, and 6.83 events per 100 persons (95% CI, 6.59 to 7.08) in N3C. The PASC risk varied across different subpopulations within pregnant females. The observed risk factors for PASC included self-reported Black race, advanced maternal age, infection during the first two trimesters, obesity, and the presence of baseline comorbid conditions. While the findings suggest a high incidence of PASC in individuals following SARS-CoV-2 infection during pregnancy, the risk of PASC in pregnant females was lower than in matched non-pregnant females.

Health sciences/Health care/Public health/Epidemiology

Health sciences/Medical research/Epidemiology

Many individuals who contract SARS-CoV-2 infection¹ experience new, persistent, or exacerbated symptoms for months, or even years, afterward, often referred to as post-acute sequelae of SARS-CoV-2 infection (PASC), or Long COVID.² Existing knowledge on PASC, including its incidence, risk factors, subtypes, treatment, and pathophysiology were mostly developed from non-pregnant, adult populations.^2–5 Little is known about PASC after SARS-CoV-2 infection during pregnancy.

The SARS-CoV-2 infection in pregnancy presents a unique set of challenges, intertwining aspects of virology, obstetrics, pediatrics, and public health.^6,7 Acquiring SARS-CoV-2 infection during pregnancy is associated with an increased risk of mortality and obstetric complications.^6,8–10 These adverse pregnancy outcomes can extend beyond maternal health to affect the short- and long-term quality of life of the offspring.^11–13 The immune response and proteomic changes during pregnancy in the context of COVID-19 exhibit distinct characteristics compared to non-pregnant individuals, indicating a nuanced relationship between maternal protection of the fetus and susceptibility to severe disease manifestations.⁷ While SARS-CoV-2 infection acquired in pregnancy is associated with worse perinatal outcomes, infection during pregnancy has been described as protective against PASC.¹² However, prior studies have been conducted on relatively small pregnancy cohorts,¹² limiting the generalizability of the results. Further, knowledge gaps still exist for patient counseling including further consideration of gestational age at the time of SARS-CoV-2 infection in pregnancy and interval PASC risk, as well as the influence of pre-existing co-morbid health conditions.

In this study, within the National Institutes of Health (NIH) Researching COVID to Enhance Recovery (RECOVER) initiative,¹⁴ electronic health records (EHR) data from 29 sites from the National Patient-Centered Clinical Research Networks (PCORnet) and 65 sites from the National COVID Cohort Collaborative (N3C) were analyzed to build one of the largest retrospective cohorts of females with SARS-CoV-2 infection during pregnancy. The objective of this study was to estimate PASC risk in individuals acquiring SARS-CoV-2 infection during pregnancy compared with a similar cohort of reproductive-age females who acquired SARS-CoV-2 outside of pregnancy. The secondary aim was to evaluate the influence of other variables such as race, infection by pregnancy trimester, SARS-CoV-2 variants, body mass index, baseline co-morbid health conditions, and vaccination status on the risk of developing PASC.

A total of 1,511,505 eligible reproductive-age females, with documented SARS-CoV-2 infection between March 1, 2020, and October 31, 2022, and follow-up to June 1, 2023, who were connected to the healthcare network before infection, were identified. Of those, 72,151 were pregnant when they acquired a SARS-CoV-2 infection (29,975 in the PCORnet cohort and 42,176 in the N3C cohort). For each pregnant individual, non-pregnant females were selected for comparison by exactly matching on region, age, infection time, acute severity, and baseline comorbidities (Method) with a ratio of 1:3, resulting in a total of 207,859 individuals in the comparison group (87,127 in the PCORnet cohort and 120,732 in the N3C cohort). The patient selection flow and the population characteristics are presented in Fig. 1 and Table 1, respectively. See the population characteristics before matching in Extended Table 1.

Table 1

Baseline characteristics of SARS-CoV-2 positive pregnant females and matched SARS-CoV-2 positive non-pregnant females from PCORnet and N3C, March 2020 to October 2022.^a
	PCORnet			N3C
	COVID Positive Pregnant females	COVID Positive Non-Pregnant females	SMD^b	COVID Positive Pregnant Females	COVID Positive Non-Pregnant females	SMD
N	29,975	87,127		42,176	120,732
Age (years) — Median (IQR)	30 (26—34)	30 (26—35)	0.00	30 (26–34)	30(26–34)	-0.04
Age group — N (%)
18-<25 years	5,858 (19.5)	17,065 (19.6)	0.00	7,932 (18.8)	23,057 (19.1)	-0.01
25-<30 years	8,212 (27.4)	23,784 (27.3)	0.00	11,622 (27.6)	32,855 (27.2)	0.01
30-<35 years	9,303 (31.0)	26,956 (30.9)	0.00	13,668 (32.4)	38,855 (32.2)	0.00
35-<40 years	5,305 (17.7)	15,553 (17.9)	0.00	7,293 (17.3)	21,059 (17.4)	0.00
40-<45 years	1,188 (4.0)	3,460 (4.0)	0.00	1,572 (3.7)	4,648 (3.8)	-0.01
45–50 years	109 (0.4)	309 (0.4)	0.00	86 (0.2)	258 (0.2)	0.00
Acute severity — N (%)
ICU/Ventilation	264 (0.9)	147 (0.2)	0.10	25 (0.1)	154 (0.1)	-0.02
Race — N (%)
Asian	1,301 (4.3)	3,756 (4.3)	0.00	1,533 (3.6)	3,926 (3.3)	0.02
Black or African American	5,250 (17.5)	14,888 (17.1)	0.01	7,049 (16.7)	21,524 (17.8)	-0.03
White	16,874 (56.3)	47,946 (55.0)	0.03	27,250 (64.6)	77,663 (64.3)	0.01
Other^c	3,221 (10.7)	6,667 (7.7)	0.11	818 (1.9)	4,134 (3.4)	-0.09
Missing	3,329 (11.1)	13,870 (15.9)	-0.14	5,526 (13.1)	13,484 (11.2)	0.06
Hispanic Ethnicity — N (%)
Yes	6,970 (23.3)	12,580 (14.4)	0.23	6,476 (15.4)	14,821 (12.3)	0.09
No	21,837 (72.9)	64,370 (73.9)	-0.02	32,150 (76.2)	94,772 (78.5)	-0.05
Missing	1,168 (3.9)	10,177 (11.7)	-0.29	3,539 (8.4)	11,129 (9.2)	-0.03
Area Deprivation Index — Median (IQR)	45 (24—67)	42 (19—63)	0.13	45 (15–75)	45 (25–75)	-0.08
BMI—Median(IQR)	30 (26—36)	28 (23—35)	-0.02	31 (27–36)	29 (24–37)	0.07
BMI: <18.5 underweight	173 (0.6)	1,456 (1.7)	-0.10	102 (0.2)	999 (0.8)	-0.08
BMI: 18.5-<25 normal weight	4,801 (16.0)	19,631 (22.5)	-0.17	4,402 (10.4)	18,072 (15.0)	-0.14
BMI: 25-<30 overweight	8,076 (26.9)	13,714 (15.7)	0.28	9,146 (21.7)	17,959 (14.9)	0.18
BMI: >=30 obese	13,758 (45.9)	24,987 (28.7)	0.36	17,962 (42.6)	36,641 (30.3)	0.26
BMI: missing	3,167 (10.6)	27,339 (31.4)	-0.53	10,564 (25.0)	47,061 (39.0)	-0.3
Smoking Status
Never	11,540 (38.5)	31,913 (36.6)	0.04
Current	1,526 (5.1)	5,287 (6.1)	-0.04
Former	2,094 (7.0)	4,201 (4.8)	0.09	3,402 (8.1)	9,719 (8.1)	0.00
Missing	14,815 (49.4)	45,726 (52.5)	-0.06
Pre-Infection Vaccination Status— N(%)
Fully vaccinated	2,882 (9.6)	13,091 (15.0)	-0.17	6,104 (14.5)	22,067 (18.3)	-0.1
Partially vaccinated	1,497 (5.0)	5,795 (6.7)	-0.07	1,328 (3.1)	3,935 (3.3)	-0.01
No evidence	25,631 (85.5)	68,377 (78.5)	0.18	34,744 (82.4)	94,730 (78.5)	0.1
Index Time of Infection — N(%)
03/01/20 − 06/01/20	1,849 (6.2)	5,245 (6.0)	0.01	1,738 (4.1)	4,375 (3.6)	0.03
07/01/20 − 10/01/20	2,768 (9.2)	8,035 (9.2)	0.00	2,715 (6.4)	7,678 (6.4)	0.00
11/01/20 − 02/01/21	4,531 (15.1)	13,296 (15.3)	0.00	5,669 (13.4)	16,644 (13.8)	-0.01
03/01/21 − 06/01/21	2,090 (7.0)	5,589 (6.4)	0.02	2,530 (6.0)	7,080 (5.9)	0.01
07/01/21 − 10/01/21	3,401 (11.3)	9,859 (11.3)	0.00	4,717 (11.2)	13,655 (11.3)	0.00
11/01/21 − 02/01/22	8,368 (27.9)	24,832 (28.5)	-0.01	14,047 (33.3)	41,253 (34.2)	-0.02
03/01/22 − 06/01/22	3,332 (11.1)	9,691 (11.1)	0.00	5,041 (12.0)	14,191 (11.8)	0.01
07/01/22 − 10/01/22	3,636 (12.1)	10,580 (12.1)	0.00	5,719 (13.6)	15,856 (13.1)	0.01
Coexisting Conditions — N(%)
Anemia	3,871 (12.9)	6,146 (7.1)	0.20	7,931 (18.8)	9,456 (7.8)	0.33
Asthma	3,237 (10.8)	8,709 (10.0)	0.03	4,675 (11.1)	12,573 (10.4)	0.02
Cancer	396 (1.3)	1,862 (2.1)	-0.06	524 (1.2)	2,299 (1.9)	-0.05
Chronic Kidney Disease	156 (0.5)	579 (0.7)	-0.02	406 (1.0)	873 (0.7)	0.03
Chronic Pulmonary Disorders	3,531 (11.8)	10,362 (11.9)	0.00	5,260 (12.5)	15,014 (12.4)	0.00
Coagulopathy	1,244 (4.2)	1,481 (1.7)	0.15	1,671 (4.0)	1,997 (1.7)	0.14
Diabetes (Type 1 or 2)	794 (2.6)	1,636 (1.9)	0.05	1,374 (3.3)	2,971 (2.5)	0.05
Hypertension	1,548 (5.2)	3,765 (4.3)	0.04	2,440 (5.8)	6,260 (5.2)	0.03
Mental Health Disorders	4,223 (14.1)	11,768 (13.5)	0.02	7,815 (18.5)	21,891 (18.1)	0.01
Substance Abuse	2,316 (7.7)	6,521 (7.5)	0.01	1,468 (3.5)	3,763 (3.1)	0.02
Obstructive sleep apnea	407 (1.4)	1,830 (2.1)	-0.06	607 (1.4)	3,348 (2.8)	-0.09
Prescription of Corticosteroids	2,957 (9.9)	10,823 (12.4)	-0.08	3,269 (7.8)	9,255 (7.7)	0.00
Prescription of Immunosuppressant drug	1,748 (5.8)	3,326 (3.8)	0.09	370 (0.9)	1,189 (1.0)	-0.01
Autoimmune/ Immune Suppression^d	4,581 (15.3)	13,114 (15.1)	0.01	3,869 (9.2)	10,686 (8.9)	0.01
Severe Obesity^e	4,255 (14.2)	11,578 (13.3)	0.03	4,903 (11.6)	13,116 (10.9)	0.02
IQR, interquartile range. BMI, Body Mass Index. CCI, Charlson Comorbidity Index. a. The SARS-CoV-2 positive were identified by polymerase chain reaction (PCR) test or antigen test or diagnosis U07.1 or prescription of Paxlovid or Remdesivir. The percentage may not sum up to 100 because of rounding. Each pregnant individual was matched with non-pregnant females on site region, age, infection time, acute severity, and selected baseline comorbidities including diabetes, hypertension, autoimmune or immune suppression, mental health disorders, severe obesity, and asthma with a ratio of 1 to 3. b. A standardized mean difference (SMD) of > 0.10 or <-0.10 indicates an important effect size difference between the two populations, otherwise, no significant difference is assumed. c. The other category encompasses American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, Multiple race, and other categories in the PCORnet Common Data Model d. Autoimmune/immune suppression denotes any prescription of corticosteroids or immunosuppressant drugs, or any diagnosis of Lupus/Systemic Lupus Erythematosus, rheumatoid arthritis, or inflammatory bowel disorder. e. Severe obesity derived from either BMI > = 40 kg/m2 or any severe obesity diagnosis.

Before matching, as shown in Extended Table 1, the median age in the pregnant female group was younger than the non-pregnant female group (30 [interquartile range (IQR), 26–34] vs 35 [IQR 27–43]) in PCORnet and 30 [IQR, 26–34 vs 36 [IQR 27–44] in N3C). Compared to non-pregnant females, pregnant females were less likely to have cancer, chronic kidney disease, chronic pulmonary disorders, hypertension, mental health disorders, severe obesity, or to be fully vaccinated at baseline. By contrast, pregnant females were more likely to have anemia, coagulopathy, and to be overweight compared with the non-pregnant females in both cohorts. After matching, as shown in Table 1, the two comparison groups became more comparable in terms of these baseline covariates. To further adjust for any residual differences, inverse probability of treatment weighting (IPTW) was applied to the matched cohorts (see Methods) for estimating relative risks. All the measured variables were well-balanced between the two comparison groups in PCORI and N3C as summarized in the Extended Table 2.

Four PASC definitions were comprehensively examined: a PCORnet rule-based PASC definition which includes 15 incident conditions across multi-organ systems on the PCORnet cohort,^5,15 an N3C Long COVID ML Phenotype trying to predict miss- or under-diagnosed PASC diagnosis U09.9 on the N3C cohort,^16,17 unspecified PASC ICD-10 diagnosis U09.9/B94.8, and a sub-cluster of cognitive, fatigue, and respiratory diagnoses.¹⁸ The latter two were cross-checked among two cohorts as sensitivity analysis.

PASC risk in the PCORnet cohort

At 180 days of follow-up, the estimated risk of PASC was 16.47 events per 100 persons (95% confidence interval (CI), 16.00 to 16.95) in the pregnant group, and 18.88 (95% CI, 18.59 to 19.17) in the non-pregnant group (Fig. 2). Compared to non-pregnant females, pregnant females had a lower risk of PASC, with a Hazard Ratio (HR) of 0.86 (95% CI, 0.83 to 0.90) and risk reduction of 2.41 events per 100 persons (95% CI, 1.85 to 2.96).

Lower risk of incident PASC in the pregnant group was observed across systems as shown in Fig. 2, including post-acute neurological conditions (sleep disorders, cognitive problems, encephalopathy), post-acute pulmonary conditions (pulmonary fibrosis, acute pharyngitis, shortness of breath), post-acute circulatory condition (chest pain), and some general conditions in the post-acute phase (e.g., malaise and fatigue, unspecified Post-COVID-19 diagnostic codes U099/B948, smell, and taste). A few exceptions are post-acute metabolic conditions (edema, diabetes, malnutrition), post-acute musculoskeletal conditions (joint pain), pulmonary fibrosis, and fever, which showed no significant difference between the two groups.

Comparison with the N3C cohort

Due to a different primary definition of PASC applied in the N3C cohort (ML phenotype), the estimated risk of PASC at 180 days in the N3C cohort was 4.37 events per 100 persons (95% CI, 4.18 to 4.57) in the pregnant group and 6.21 (95% CI, 6.07 to 6.35) in the non-pregnant group. However, the same relatively lower risk of PASC in the pregnant group compared to the non-pregnant group was observed in the N3C cohort (Fig. 2) with HR of 0.70 (95% CI, 0.66 to 0.74) and risk reduction of 1.84 events per 100 persons (95% CI, 1.60 to 2.08).

PASC Risk in Sub-populations

Regarding absolute risks in the pregnant female group, as shown in Fig. 3, we observed higher PASC risk in several subgroups: self-reported Black individuals compared to White individuals, individuals with advanced maternal age (\(\:\ge\:\)35 years compared to those aged < 35 years), those infected during the first two trimesters compared to the third trimester, those infected during the Delta and Omicron periods (compared to earlier variants), individuals with obesity compared to those who were overweight or of normal weight, and those with baseline chronic medical conditions compared to those without. Similar absolute risks were observed in subgroups regardless of vaccination status.

When compared to the non-pregnant group, the same relatively lower risk of PASC in the pregnant group was obtained across different subpopulations stratified by self-reported race (White, Black), age (< 35 years, \(\:\ge\:\)35 years), SARS-CoV-2 variants of concern (ancestral, Alpha, Delta, and Omicron), body mass index (normal, overweight, and obese), having baseline chronic medical conditions (yes or no), vaccination status (fully vaccinated, any vaccine records, or no vaccine records), and acquiring SARS-CoV-2 during the 3rd trimester, across two cohorts (Fig. 3). A few exceptions are no significant or moderate higher risk in patients infected during the 1st trimester (HR 1.07 (0.97 to 1.19) in PCORnet, HR 1.17 (1.03, 1.34) in N3C) or 2nd trimester (HR 1.15 (1.08 to 1.23) in PCORnet, HR 0.89 (0.81, 0.97) in N3C.

Sensitivity analyses

We further cross-checked the results in both cohorts in terms of unspecified PASC ICD-10 diagnostic codes U099 or B948, and a subcluster of post-acute cognitive, fatigue, and respiratory conditions as shown in Fig. 4.

Regarding the PASC diagnostic codes, the estimated risk at 180 days was 0.19 (95% CI, 0.14 to 0.25) events per 100 persons in the pregnant group and 0.60 (0.55 to 0.66) in the non-pregnant group within the PCORnet cohort. In the N3C cohort, the estimated risk was 0.23 (0.19 to 0.28) events per 100 persons in the pregnant group and 0.44 (0.40 to 0.48) in the non-pregnant group. This indicates that the pregnant group consistently exhibited a relatively lower risk—approximately two to three times lower—compared to the matched non-pregnant group across both cohorts.

For having any post-acute cognitive, fatigue, and respiratory conditions, the estimated risk was 4.86 (4.59 to 5.14) events per 100 persons in the pregnant group and 6.79 (6.60 to 6.97) events per 100 persons in the non-pregnant group within the PCORnet cohort. In the N3C cohort, the estimated risk was 6.83 (6.59 to 7.08) events per 100 persons in the pregnant group and 9.54 (95% CI, 9.37 to 9.71) events per 100 persons in the non-pregnant group.

Consistency was observed in both absolute and relative risks when applying these two PASC definitions across the two cohorts. Regarding different PASC outcomes in various subpopulations (Figs. 5 and 6), we observed a consistent pattern of lower relative risk in pregnant females compared with non-pregnant females, along with similar gradients of absolute risks across subgroups within the pregnant group. One exception was a higher incidence of unspecified PASC diagnoses in the Delta era among pregnant groups compared to other periods.

In this retrospective cohort study involving 29 PCORnet sites and 65 N3C sites as part of the RECOVER initiative, we estimated the risk of PASC in pregnant females with SARS-CoV-2 infection during pregnancy. The long-term implications of COVID-19 in pregnancy are significant, as reflected in the different PASC outcomes captured across the two cohorts. In the PCORnet cohort, the estimated risk of PASC at 180 days of follow-up was 16.47 events per 100 persons (95% CI, 16.00 to 16.95) based on a rule-based PASC capture. In the N3C cohort, the estimated risk of PASC was events per 100 persons 4.37 (4.18 to 4.57) using a machine learning-based approach. The risks of unspecified PASC diagnostic codes U099 or B948 were 0.19 events per 100 persons (95% CI, 0.14 to 0.25) in PCORnet and 0.23 events per 100 persons (95% CI, 0.19 to 0.28) in N3C. The risks of post-acute cognitive, fatigue, and respiratory condition were 4.86 events per 100 persons (95% CI, 4.59 to 5.14) in PCORnet and 6.83 events per 100 persons (95% CI, 6.59 to 7.08) in N3C. A higher incidence of PASC was observed in self-reported Black patients, patients with advanced maternal age, those infected during the first two trimesters, individuals with obesity, and those with baseline conditions.

After adjustments, we observed a relatively lower risk of PASC in pregnant females compared to SARS-CoV-2-infected non-pregnant females who were exactly matched on region, age, infection time, acute severity, and baseline comorbidities. When comparing relative risks between corresponding subgroups among pregnant and non-pregnant females, the pattern of relatively lower risk of PASC was largely consistent across different subpopulations, various PASC definitions, and both the PCORnet and N3C cohorts.

Pregnancy reflects a period of physiologic immune tolerance to accommodate fetal development. Differences in regulatory T cells, cytokines, and other immune cells have been described during pregnancy and are thought to prevent maternal immune system rejection of the fetus.¹⁹ More severe disease courses from other viruses, such as influenza, have been described during pregnancy and attributed to these immune alterations.²⁰ The observed risk differences in pregnant females compared to non-pregnant females in this analysis also suggest future dedicated pathophysiology studies of PASC in pregnant individuals are warranted. A higher risk of PASC in self-reported Black females draws attention to racial and ethnic disparities both in the acquisition of SARS-CoV-2 infection and the development of PASC, which may be related to factors such as inequitable healthcare access, socioeconomic factors, and structural racism.

This study has several strengths. First, the utilization of two large-scale clinical data networks, consisting of 73 unique hospital systems, allowed for more comprehensive analyses with substantial statistical power, particularly for the pregnant groups. In a prior publication,¹² a subset of 5,397 eligible pregnant females acquiring COVID-19 during pregnancy from 19 PCORnet sites was reported. The sample size precluded subgroup analyses with adequate power. Through collaborative efforts from PCORnet, N3C, and the RECOVER-Pregnancy Cohort within RECOVER, for this analysis, 72,151 eligible pregnant females with infection during pregnancy, and 207,859 exactly matched infected non-pregnant females with a ratio of 1:3, were identified. Second, Detailed subgroup analyses were performed, stratified by self-reported race, maternal age, variants of concern, BMI, baseline co-morbid health conditions, and infection by trimester. Third, we characterized and cross-checked the PASC risk in terms of four different definitions including a rule-based definition organized by multi-organ systems in PCORnet,^5,15 a machine-learning Long COVID phenotype in N3C,¹⁶ unspecified PASC diagnosis U099/B948, and a sub-cluster of cognitive, fatigue, and respiratory diagnoses.¹⁸ The similar patterns and triangulation from different PASC definitions across two different cohorts further strengthen the confidence in these findings.

There are also several limitations. First, this is a retrospective observational study based on electronic health records, which might suffer from potential residual confounding, misclassification of pregnancy, and study variables. Second, due to separate data systems, we did not implement the PCORnet PASC definition for the N3C cohort or the N3C PASC predictive model for the PCORnet cohort. However, un-specific PASC diagnoses and the cognitive, fatigue, and respiratory conditions were cross-checked in both cohorts. Third, the associations between vaccine status and PASC require further dedicated investigation. More than 82% of patients in the pregnant female group showed no vaccine data (Table 1), higher than the nearly 77% no data portion in the infected non-pregnant group. The no-vaccine data could have derived from both poor capture of vaccine data in EHR and the initial low public confidence about COVID-19 vaccination in pregnancy (due to lack of enrollment of pregnant people in the early vaccine trials), and thus low vaccination rates in pregnant individuals. Forth, we did not investigate long-term implications on the child's development, which also requires future investigation. Finally, though adjusting for healthcare utilizations at baseline, pregnant individuals usually have frequent prenatal care visits (particularly for first and second-trimester infections), which may result in higher rates of detection of the PASC outcome variables in those populations.

Data

This study utilized electronic healthcare records (EHR) data from two clinical research networks (CRN), namely the National Patient-Centered CRN (PCORnet) and the National COVID Cohort Collaborative (N3C), within the RECOVER initiative. Analyses were conducted separately for each cohort by following a common protocol and the same statistical analytics.

The PCORnet RECOVER infrastructure leveraged PCORnet to develop a single, unified EHR/RWD repository to study PASC across ~ 28.25 million (18.75 million adult − 9.5 million pediatric) patients from 40 adult and pediatric health systems nationwide who continue to refresh their data at least quarterly. The source data includes patients tested for COVID-19 (regardless of result), those diagnosed with COVID-19, those who received COVID-19 vaccine and therapeutics (e.g., Remdesivir and Paxlovid), and/or those who have received a respiratory diagnosis since 2019. The enclave contains structured EHR data consisting of inpatient and ambulatory encounters, laboratory results, vital signs, medications, diagnoses, procedures, birthdates, gender, and race information. The EHR data is linked to geocoded data to the level of the census tract, block group, and/or 9-digit zip code to allow linkage to exposome information to assess the influence of SDoH and environmental exposures on COVID-19 outcomes. In addition, the data enclave includes clinical notes for NLP, vaccine registries, and death registries.

Individual EHR data is stored in the N3C Data Enclave, which provides access to harmonized EHRs from 84 health sites with data from over 22.8 million patients (as of August 1st, 2024). For the current investigation, we used N3C data from version 152 (2023-12-07), and our final cohort encompasses contributions from 65 sites that had individuals who met our inclusion criteria. The N3C Data Enclave uses the Palantir Foundry platform (2021, Denver, CO), a secure analytics platform, for data access and analysis. N3C’s methods for patient identification, data acquisition, ingestion, data quality assessment, and harmonization have been described previously.^21,22 The N3C EHR data is structured in a similar way to PCORnet, consisting of inpatient and ambulatory encounters, laboratory results, vital signs, medications, diagnoses, procedures, birthdates, gender, and race information. Data for individuals is geocoded at the 9-digit zip code level, and sites are linked to vaccine registries, as well as a privacy-preserving record linkage to mortality and CMS (Medicare and Medicaid) claims data.

Study Cohort

For our base cohort in PCORnet, we included SARS-CoV-2 patients with at least one positive SARS-CoV-2 polymerase-chain-reaction (PCR) or antigen laboratory test, COVID-19 diagnosis code U07.1, or prescription of Paxlovid or Remdesivir, between March 01, 2020, and June 30, 2023. The COVID-19 index date was defined as the date of the first documented positive COVID-19 record if they had (a) positive SARS-CoV-2 polymerase-chain-reaction (PCR) or antigen laboratory tests; (b) the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code U07.1 representing COVIID-19 diagnosis; or (c) Paxlovid (nirmatrelvir/ritonavir) or Remdesivir prescriptions, whichever occurred earlier. We required female patients, aged between 18 to 50 years old, and at least one diagnosis code within three years to seven days before the index date to be included in the cohort. The baseline period was defined as three years before the index date, and the post-acute phase, or the follow-up period, was set as 31 days to 180 days after the index date. We further require the index date before October 31, 2022, to guarantee at least a 180-day follow-up period.

For our base cohort in N3C, we included SARS-CoV-2 patients with at least one positive SARS-CoV-2 polymerase-chain-reaction (PCR) or antigen laboratory test, or COVID-19 diagnosis code U07.1 before October 31, 2022. The COVID-19 index date was defined as the date of the first documented positive COVID-19 lab test or diagnosis. The baseline period included all individual records going back to 2018, and we required at least two visits within one year before the index date. We further required at least one visit more than 100 days after the index date to ensure individuals didn’t leave our data sample.

The primary exposure group included SARS-CoV-2 infection during pregnancy compared with outside of pregnancy. Thus, we identified two comparison groups: females acquiring SARS-CoV-2 during pregnancy versus outside pregnancy, applying additional eligibility criteria requiring infection in the gestational period for the pregnant females. The infection during pregnancy was defined as the first documented SARS-CoV-2 infection occurring between the start of pregnancy and the date of delivery. The delivery event was ascertained by identifying diagnosis codes related to delivery outcomes or delivery-related procedures²³ after March 01, 2020. The start of the pregnancy and gestational age were approximated using the Z3A codes associated with the date of the delivery in PCORnet.²⁴ Pregnancies in N3C were identified using a hierarchical rules-based algorithm described in a previous paper, which also uses Z3A codes to define gestational age.²⁵ The gestational period was defined as the start of the pregnancy to the delivery event. In both PCORnet and N3C, we identified the SARS-CoV-2-infected pregnant group as those females with identified delivery events and SARS-CoV-2-infection occurring within the gestational period. The SARS-CoV-2-infected non-pregnant group consisted of individuals without any identified delivery events within the study windows.

The pregnant individuals were compared with exactly matched non-pregnant females on site region, age, infection time, acute severity, and selected baseline comorbidities including diabetes, hypertension, autoimmune or immune suppression, mental health disorders, severe obesity, and asthma with a ratio of 1 to 3. The cohort selection flow is illustrated in Fig. 1a.

Outcomes

The definition of Post-acute Sequelae of SARS-CoV-2 (PASC, or Long COVID) used for this study varies between PCORnet and N3C. In PCORnet, the PASC definition for pregnant females is a rules-based computable phenotyping algorithm leveraging International Classification of Diseases (ICD) 10th Version codes for 15 incident conditions, including cognitive problems, encephalopathy, sleep disorders, acute pharyngitis, shortness of breath (dyspnea), pulmonary fibrosis, chest pain, diabetes, edema, malnutrition, joint pain, fever, malaise and fatigue, ICD-10-CM diagnosis codes U099/B948 for unspecified PASC, and smell and taste. These conditions were identified based on previous studies,^5,15 evidence from the literature, ^15,26,27, and tailored for pregnant females.¹² An incident condition was defined as occurring in SARS-CoV-2 infected patients who developed the condition between 31 days and 180 days after the acute infection, provided they did not have the condition three years to seven days before their acute infection. PASC was defined as having any incident condition from the abovementioned list.

In contrast, in the N3C cohort, PASC was defined primarily through a machine learning algorithm, specifically, the PASC Machine Learning 2.0 (LCM 2.0).^17,28 This machine-learning pipeline predicts the presence of PASC using information extracted from the EHR data, creating a computable phenotype for PASC. The model was designed to address challenges such as missing data and idiosyncratic coding practices inherent in EHRs. Unlike its predecessor, LCM 1.0, which relied on the acute COVID-19 date as an anchor point for analysis, LCM 2.0 employs set time windows applicable to all patients, regardless of their COVID-19 index dates. These time windows, progressing through overlapping 100-day periods, enable the model to assess the probability of PASC across diverse patient populations, including those with suspected or untested COVID-19 cases and individuals experiencing multiple SARS-CoV-2 reinfections.

Two alternative definitions for PASC were further cross-checked in both PCORnet and N3C including a) un-specific PASC ICD-10-CM diagnostic codes U099 (Post COVID-19 condition, unspecified) or B948 (Sequelae of other specified infectious and parasitic diseases) and b) cognitive, fatigue, and respiratory diagnoses cluster.¹⁸

Baseline covariates

A broad range of potential confounders collected at the time of infection were considered for the adjusted analyses. These covariates included age at infection, self-reported Race and Ethnicity, national-level Area Deprivation Index (ADI),²⁹ healthcare utilization, time of infection, the most recent body mass index (BMI), smoking status, ICU or ventilation in acute infection, COVID-19 vaccine status, and a range of baseline health comorbidities. Age was categorized into 18–24 years, 25–29 years, 30–34 years, 35–39 years, 40–44 years, and 45–50 years). The self-reported Race was categorized as Asian, Black or African American, White, other (by grouping American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, Multiple race, and other categories in the PCORnet Common Data Model³⁰), missing, and self-reported ethnicity as Hispanic, not Hispanic, and other/missing. The ADI was used to capture the socioeconomic disadvantage of patients’ residential neighborhoods.²⁹ We used geocodes or 9-digit zip codes to link to the national ADI percentiles (ranging from 1 to 100, with higher numbers indicating higher levels of disadvantage. Healthcare utilization was measured as the number of inpatients and emergency encounters (0 visits, 1 or 2 visits, 3 or 4 visits, and 5 or more visits for each encounter type). The infection time was categorized into bins spanning every four months since March 2020 to account for different periods of the pandemic. BMI was categorized into underweight (< 18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese ( > = 30.0 kg/m²), and missing according to the Centers for Disease Control and Prevention guideline for adults.³¹ The severe acute infection was approximated by the ventilation status and critical care during the infection.

We collected a wide range of baseline co-morbid health conditions based on a tailored list of the Elixhauser comorbidities³² and related drug categories, including alcohol abuse, anemia, arrhythmia, asthma, cancer, chronic kidney disease, chronic pulmonary disorders, cirrhosis, coagulopathy, congestive heart failure, chronic obstructive pulmonary disease, coronary artery disease, dementia, diabetes type 1 or 2, end-stage renal disease on dialysis, hemiplegia, HIV, hypertension, inflammatory bowel disorder, lupus or systemic lupus erythematosus, mental health disorders, multiple sclerosis, Parkinson's disease, peripheral vascular disorders, pulmonary circulation disorder, rheumatoid arthritis, seizure/epilepsy, severe obesity (BMI > = 40 kg/m²), weight loss, Down syndrome, other substance abuse, cystic fibrosis, autism, sickle cell, obstructive sleep apnea, Epstein-Barr and Infectious Mononuclesosi, Herpes Zoster, corticosteroid drug prescriptions, and immunosuppressant drug prescriptions. Patients in PCORnet were considered to have a condition if they had at least one corresponding diagnosis or medication documented in the three years before the COVID-19 index date, and in N3C conditions were defined as any corresponding diagnosis or medication in the data (starting in 2018) prior to COVID-19 index date. The N3C used OMOP concept sets to match corresponding variables in PCORnet, but did not include cirrhosis, multiple sclerosis, lupus, Parkinson’s disease, seizure/epilepsy, cystic fibrosis, autism, Epstein-Barr and Infectious Mononucleosis, or Herpes Zoster as health conditions. Corticosteroid and immunosuppressant prescription variables were created using the same drug codes as PCORnet.

Follow-up Period

We followed each patient from 30 days after their index date until the occurrence of the first target outcome, documented death, loss of follow-up in the database, 180 days after the baseline, or the end of our observational window (June 30, 2023), whichever came first.

Statistical Analyses

For each individual in the pregnant group, the SARS-CoV-2 infected non-pregnant comparators were exactly matched on the site region, age, infection time, acute severity, and selected baseline comorbidities including diabetes, hypertension, autoimmune or immune suppression, mental health disorders, severe obesity, and asthma with a ratio of 1:3. Based on pregnant and matched non-pregnant cohorts, the relative risks were further adjusted via inverse probability of treatment weighting (IPTW) by considering a broader range baseline covariates. The propensity scores for the two groups were calculated with the regularized logistic regression with L2 norm with all the baseline covariates as independent variables.^15,33 The stabilized IPTW was used and extreme weights beyond their 1st or 99th percentiles were further trimmed to reduce variability³⁴. The balance of covariates was evaluated by comparing standardized mean differences (SMD), with a difference of less than 0.1 considered to be balanced. The cumulative incidence for the two groups was estimated with the Aalen-Johansen model in the matched and reweighted population by considering death as a competing risk.³⁵ The hazard ratios were estimated by the Cox survival model in the matched and reweighted population and two-sided 95% confidence intervals were calculated with the use of a robust variance estimator to account for stabilized IPTW weights. The absolute risk reduction was the difference in cumulative incidences at 180 days of follow-up between pregnant and non-pregnant groups.

The subgroup analysis was conducted by stratifying patients in both pregnant and non-pregnant groups by self-reported race, maternal age, trimesters when acquiring infection, variants by infection time, body mass index, baseline comorbidities (diabetes, hypertension, asthma, class III obesity), and vaccination status. To check the robustness of results in two cohorts, the unspecific PASC diagnostic codes U099 or B948 and the post-acute cognitive, fatigue, and respiratory conditions were cross-checked in both PCORnet and N3C cohorts, in terms of overall population and different sub-populations.

Data Availability

Data utilized for this study was obtained from the PCORnet-RECOVER Amazon Warehouse Services (AWS) enclave which is comprised of 40 participating sites from PCORnet. Please send all data questions or access requests to the corresponding author, who will direct them accordingly. All data from N3C used in this study is available through the N3C Enclave to approved users. See https://covid.cd2h.org/for-researchers for instructions on how to access the data. We used N3C data from version 152 (2023-12-07).

Code availability

For reproducibility, our codes are available at https://github.com/calvin-zcx/pasc_phenotype. We used Python 3.9, python package lifelines-0.2666 for survival analysis, and scikit-learn-0.2318 for machine learning models.

Ethics Oversight

Institute Review Board (IRB) approval was obtained under Biomedical Research Alliance of New York (BRANY) protocol #21-08-508. As part of the Biomedical Research Alliance of New York (BRANY IRB) process, the protocol has been reviewed in accordance with the institutional guidelines. The Biomedical Research Alliance of New York (BRANY) waived the need for consent and HIPAA authorization. Institutional Review Board oversight was provided by the Biomedical Research Alliance of New York, protocol # 21-08-508-380.

The N3C data transfer is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources. This work was conducted under DUR RP-5677B5. The N3C received a waiver of consent from NIH Institutional Review Board under the 1996 Health Insurance Portability andmetho Accountability Act privacy regulations for a Limited Data Set.

Acknowledgment

This research was funded by the National Institutes of Health (NIH) Agreement OTA

OT2HL161847 (contract number EHR-01-21) as part of the Researching COVID to Enhance

Recovery (RECOVER) research program. The PCORnet® Study reported in this work was conducted using PCORnet®, the National Patient-Centered Clinical Research Network. PCORnet® has been developed with funding from the Patient-Centered Outcomes Research Institute® (PCORI®). This work was conducted through the use of data from the INSIGHT Clinical Research Network and supported in part by the Patient-Centered Outcomes Research Institute (PCORI) PCORnet grant to the INSIGHT Clinical Research Network (Grant # RI-CORNELL-01-MC). The statements presented in this work are solely the responsibility of the author(s) and do not necessarily represent the views of other organizations participating in, collaborating with, or funding PCORnet® or of the Patient-Centered Outcomes Research Institute® (PCORI®).

The analyses described in this publication were conducted with data and tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution & Publication Policy v 1.2-2020-08-25b supported by NCATS Contract No. 75N95023D00001, Axle Informatics Subcontract: NCATS-P00438-B. This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the on-going development of this community resource [https://doi.org/10.1093/jamia/ocaa196].

RECOVER-EHR Consortium Members

Chengxi Zang, Daniel Guth, Nariman Ammar, Robert Chew, Emily Hadley, Tanzy Love, Brenda McGrath, Sharad Kumar Singh, Kenneth Wilkins, Elaine Hill, Thomas Carton, Miles Crosskey, Tomas McIntree

PCORnet Core Contributors

Louisiana Public Health Institute: Tom Carton, mPI, Anna Legrand, Elizabeth Nauman

Weill Cornell Medicine: Rainu Kaushal, mPI, Mark Weiner, mPI, Sajjad Abedian, Dominique Brown, Christopher Cameron, Thomas Campion, Andrea Cohen, Marietou Dione, Rosie Ferris, Wilson Jacobs, Michael Koropsak, Alex LaMar, Colby V. Lewis, Dmitry Morozyuk, Peter Morrisey, Duncan Orlander, Jyotishman Pathak, Mahfuza Sabiha, Edward J. Schenck, Stephenson Strobel, Zoe Verzani, Fei Wang, Zhenxing Xu, Chengxi Zang, Yongkang Zhang

PCORnet Data Contributors

Albert Einstein College of Medicine Selvin Soby | Columbia University Soumitra Sengupta, PI | Duke University Health System Curtis Kieler, Janis Curtis | Emory University Nita N. Deshpande | Icahn School of Medicine at Mount Sinai Carol R. Horowitz, PI | Intermountain Health Benjamin D. Horne, PI, Heidi T. May | Medical College of Wisconsin Reza Shaker, PI, Bradley W. Taylor, PI, Alex Stoddard | Medical University of South Carolina | Nicklaus Children's Hospital Sandy L. Gonzalez, PI, Maurice Duque | New York University Langone Health Saul Blecker, PI, Nathalia Ladino | OCHIN, Inc. Marion R. Sills, PI| Ochsner Health System Daniel Fort, PI | Penn State University College of Medicine Cynthia H. Chuang, PI,Wenke Huang| Temple University Sharon J. Herring, PI | The Ohio State University Soledad A. Fernandez, PI, NeenaThomas | University Medical Center New Orleans Yuriy Bisyuk, PI | University of California San Francisco Susan Kim, PI, Mark Pletcher | University of Florida Mei Liu, PI, Jiang Bian | University of Iowa Elizabeth A. Chrischilles, PI, | University of Miami | University of Michigan David A. Williams, PI | University of Missouri Abu Saleh Mohammad Mosa, PI, Xing Song | University of Nebraska Medical Center Carol Reynolds Geary, PI | University of Pittsburgh Jonathan Arnold, PI, Michael J. Becich, PI,Yalini Senathirajah, Nickie Cappella | University of South Florida | University of Texas Southwestern Medical Center Lindsay G. Cowell, PI | University of Utah Mollie R. Cummins, PI, Ramkiran Gouripeddi | Vanderbilt University Medical Center Yacob Tedla, PI | Weill Cornell Medicine Rainu Kaushal, PI, Thomas Campion

N3C Data Contributors

The N3C Publication committee confirmed that this manuscript is in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program.

We gratefully acknowledge the following core contributors to N3C:

Adam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew Southerland, Andrew T. Girvin, Anita Walden, Anjali A. Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, Caleb Alexander, Carolyn Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego Mazzotti, Don Brown, Eilis Boudreau, Elaine Hill, Emily Carlson Marti, Emily R. Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold Lehmann, Heidi Spratt, Hemalkumar Mehta, J.W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Islam, Jin Ge, Joel Gagnier, Johanna Loomba, John Buse, Jomol Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M. Walters, Ken Wilkins, Kenneth R. Gersing, Kenrick Dwain Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Allan Pyles,, Lesley Cottrell, Lili Portilla, Mariam Deacy, Mark M. Bissell, Marshall Clark, Mary Emmett, Matvey B. Palchuk, Melissa A. Haendel, Meredith Adams, Meredith Temple-O'Connor, Michael G. Kurilla, Michele Morris, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Philip R.O. Payne, Randeep Jawa, Rebecca Erwin-Cohen, Rena Patel, Richard A. Moffitt, Richard L. Zhu, Rishi Kamaleswaran, Robert Hurley, Robert T. Miller, Saiju Pyarajan, Sam G. Michael, Samuel Bozzette, Sandeep Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O'Neil, Soko Setoguchi, Stephanie S. Hong, Steve Johnson, Tellen D. Bennett, Tiffany Callahan, Umit Topaloglu, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang. Details of contributions available at covid.cd2h.org/core-contributors

The following institutions whose data is released or pending:

Available: Advocate Health Care Network — UL1TR002389: The Institute for Translational Medicine (ITM) • Aurora Health Care Inc — UL1TR002373: Wisconsin Network For Health Research • Boston University Medical Campus — UL1TR001430: Boston University Clinical and Translational Science Institute • Brown University — U54GM115677: Advance Clinical Translational Research (Advance-CTR) • Carilion Clinic — UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • Case Western Reserve University — UL1TR002548: The Clinical & Translational Science Collaborative of Cleveland (CTSC) • Charleston Area Medical Center — U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) • Children’s Hospital Colorado — UL1TR002535: Colorado Clinical and Translational Sciences Institute • Columbia University Irving Medical Center — UL1TR001873: Irving Institute for Clinical and Translational Research • Dartmouth College — None (Voluntary) Duke University — UL1TR002553: Duke Clinical and Translational Science Institute • George Washington Children’s Research Institute — UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • George Washington University — UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • Harvard Medical School — UL1TR002541: Harvard Catalyst • Indiana University School of Medicine — UL1TR002529: Indiana Clinical and Translational Science Institute • Johns Hopkins University — UL1TR003098: Johns Hopkins Institute for Clinical and Translational Research • Louisiana Public Health Institute — None (Voluntary) • Loyola Medicine — Loyola University Medical Center • Loyola University Medical Center — UL1TR002389: The Institute for Translational Medicine (ITM) • Maine Medical Center — U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • Mary Hitchcock Memorial Hospital & Dartmouth Hitchcock Clinic — None (Voluntary) • Massachusetts General Brigham — UL1TR002541: Harvard Catalyst • Mayo Clinic Rochester — UL1TR002377: Mayo Clinic Center for Clinical and Translational Science (CCaTS) • Medical University of South Carolina — UL1TR001450: South Carolina Clinical & Translational Research Institute (SCTR) • MITRE Corporation — None (Voluntary) • Montefiore Medical Center — UL1TR002556: Institute for Clinical and Translational Research at Einstein and Montefiore • Nemours — U54GM104941: Delaware CTR ACCEL Program • NorthShore University HealthSystem — UL1TR002389: The Institute for Translational Medicine (ITM) • Northwestern University at Chicago — UL1TR001422: Northwestern University Clinical and Translational Science Institute (NUCATS) • OCHIN — INV-018455: Bill and Melinda Gates Foundation grant to Sage Bionetworks • Oregon Health & Science University — UL1TR002369: Oregon Clinical and Translational Research Institute • Penn State Health Milton S. Hershey Medical Center — UL1TR002014: Penn State Clinical and Translational Science Institute • Rush University Medical Center — UL1TR002389: The Institute for Translational Medicine (ITM) • Rutgers, The State University of New Jersey — UL1TR003017: New Jersey Alliance for Clinical and Translational Science • Stony Brook University — U24TR002306 • The Alliance at the University of Puerto Rico, Medical Sciences Campus — U54GM133807: Hispanic Alliance for Clinical and Translational Research (The Alliance) • The Ohio State University — UL1TR002733: Center for Clinical and Translational Science • The State University of New York at Buffalo — UL1TR001412: Clinical and Translational Science Institute • The University of Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • The University of Iowa — UL1TR002537: Institute for Clinical and Translational Science • The University of Miami Leonard M. Miller School of Medicine — UL1TR002736: University of Miami Clinical and Translational Science Institute • The University of Michigan at Ann Arbor — UL1TR002240: Michigan Institute for Clinical and Health Research • The University of Texas Health Science Center at Houston — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • The University of Texas Medical Branch at Galveston — UL1TR001439: The Institute for Translational Sciences • The University of Utah — UL1TR002538: Uhealth Center for Clinical and Translational Science • Tufts Medical Center — UL1TR002544: Tufts Clinical and Translational Science Institute • Tulane University — UL1TR003096: Center for Clinical and Translational Science • The Queens Medical Center — None (Voluntary) • University Medical Center New Orleans — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • University of Alabama at Birmingham — UL1TR003096: Center for Clinical and Translational Science • University of Arkansas for Medical Sciences — UL1TR003107: UAMS Translational Research Institute • University of Cincinnati — UL1TR001425: Center for Clinical and Translational Science and Training • University of Colorado Denver, Anschutz Medical Campus — UL1TR002535: Colorado Clinical and Translational Sciences Institute • University of Illinois at Chicago — UL1TR002003: UIC Center for Clinical and Translational Science • University of Kansas Medical Center — UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute • University of Kentucky — UL1TR001998: UK Center for Clinical and Translational Science • University of Massachusetts Medical School Worcester — UL1TR001453: The UMass Center for Clinical and Translational Science (UMCCTS) • University Medical Center of Southern Nevada — None (voluntary) • University of Minnesota — UL1TR002494: Clinical and Translational Science Institute • University of Mississippi Medical Center — U54GM115428: Mississippi Center for Clinical and Translational Research (CCTR) • University of Nebraska Medical Center — U54GM115458: Great Plains IDeA-Clinical & Translational Research • University of North Carolina at Chapel Hill — UL1TR002489: North Carolina Translational and Clinical Science Institute • University of Oklahoma Health Sciences Center — U54GM104938: Oklahoma Clinical and Translational Science Institute (OCTSI) • University of Pittsburgh — UL1TR001857: The Clinical and Translational Science Institute (CTSI) • University of Pennsylvania — UL1TR001878: Institute for Translational Medicine and Therapeutics • University of Rochester — UL1TR002001: UR Clinical & Translational Science Institute • University of Southern California — UL1TR001855: The Southern California Clinical and Translational Science Institute (SC CTSI) • University of Vermont — U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • University of Virginia — UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • University of Washington — UL1TR002319: Institute of Translational Health Sciences • University of Wisconsin-Madison — UL1TR002373: UW Institute for Clinical and Translational Research • Vanderbilt University Medical Center — UL1TR002243: Vanderbilt Institute for Clinical and Translational Research • Virginia Commonwealth University — UL1TR002649: C. Kenneth and Dianne Wright Center for Clinical and Translational Research • Wake Forest University Health Sciences — UL1TR001420: Wake Forest Clinical and Translational Science Institute • Washington University in St. Louis — UL1TR002345: Institute of Clinical and Translational Sciences • Weill Medical College of Cornell University — UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center • West Virginia University — U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) Submitted: Icahn School of Medicine at Mount Sinai — UL1TR001433: ConduITS Institute for Translational Sciences • The University of Texas Health Science Center at Tyler — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • University of California, Davis — UL1TR001860: UCDavis Health Clinical and Translational Science Center • University of California, Irvine — UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS) • University of California, Los Angeles — UL1TR001881: UCLA Clinical Translational Science Institute • University of California, San Diego — UL1TR001442: Altman Clinical and Translational Research Institute • University of California, San Francisco — UL1TR001872: UCSF Clinical and Translational Science Institute NYU Langone Health Clinical Science Core, Data Resource Core, and PASC Biorepository Core — OTA-21-015A: Post-Acute Sequelae of SARS-CoV-2 Infection Initiative (RECOVER) Pending: Arkansas Children’s Hospital — UL1TR003107: UAMS Translational Research Institute • Baylor College of Medicine — None (Voluntary) • Children’s Hospital of Philadelphia — UL1TR001878: Institute for Translational Medicine and Therapeutics • Cincinnati Children’s Hospital Medical Center — UL1TR001425: Center for Clinical and Translational Science and Training • Emory University — UL1TR002378: Georgia Clinical and Translational Science Alliance • HonorHealth — None (Voluntary) • Loyola University Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • Medical College of Wisconsin — UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin • MedStar Health Research Institute — None (Voluntary) • Georgetown University — UL1TR001409: The Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) • MetroHealth — None (Voluntary) • Montana State University — U54GM115371: American Indian/Alaska Native CTR • NYU Langone Medical Center — UL1TR001445: Langone Health’s Clinical and Translational Science Institute • Ochsner Medical Center — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • Regenstrief Institute — UL1TR002529: Indiana Clinical and Translational Science Institute • Sanford Research — None (Voluntary) • Stanford University — UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education • The Rockefeller University — UL1TR001866: Center for Clinical and Translational Science • The Scripps Research Institute — UL1TR002550: Scripps Research Translational Institute • University of Florida — UL1TR001427: UF Clinical and Translational Science Institute • University of New Mexico Health Sciences Center — UL1TR001449: University of New Mexico Clinical and Translational Science Center • University of Texas Health Science Center at San Antonio — UL1TR002645: Institute for Integration of Medicine and Science • Yale New Haven Hospital — UL1TR001863: Yale Center for Clinical Investigation

We would like to thank the National Community Engagement Group (NCEG), all patient, caregiver and community Representatives, and all the participants enrolled in the RECOVER Initiative.

Author contributions

Authorship was determined using ICMJE recommendations.

Competing interests

The authors declare no competing interests.

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Post-Acute Sequelae of SARS-CoV-2 Infection in Pregnant Females: An Electronic Health Records Analysis from the RECOVER Initiative (PCORnet and N3C)

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Abstract

Figures

Introduction

Results

PASC risk in the PCORnet cohort

Comparison with the N3C cohort

PASC Risk in Sub-populations

Sensitivity analyses

Discussion

Methods

Data

Study Cohort

Outcomes

Baseline covariates

Follow-up Period

Statistical Analyses

Declarations

Data Availability

Code availability

Ethics Oversight

Acknowledgment

Author contributions

Competing interests

References

Additional Declarations

Supplementary Files

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