Risk for stillbirth among pregnant individuals with SARS-CoV-2 infection varied by gestational age

Abstract

Background

Despite previous research findings on higher risks of stillbirth among pregnant individuals with SARS-CoV-2 infection, it is unclear whether the gestational timing of viral infection modulates this risk.

Objective

This study aimed to examine the association between timing of SARS-CoV-2 infection during pregnancy and risk of stillbirth.

Study Design

This retrospective cohort study used multilevel logistic regression analyses of nationwide electronic health records in the United States. Data were from 75 healthcare systems and institutes across 50 states. A total of 191,403 pregnancies of 190,738 individuals of reproductive age (15–49 years) who had childbirth between March 1, 2020 and May 31, 2021 were identified and included. The main outcome was stillbirth at ≥20 weeks of gestation. Exposures were the timing of SARS-CoV-2 infection: early pregnancy (<20 weeks), midpregnancy (21–27 weeks), the third trimester (28–43 weeks), any time before delivery, and never infected (reference).

Results

We identified 2342 (1.3%) pregnancies with COVID-19 in early pregnancy, 2075 (1.2%) in midpregnancy, and 12,697 (6.9%) in the third trimester. After adjusting for maternal and clinical characteristics, increased odds of stillbirth were observed among pregnant individuals with SARS-CoV-2 infection only in early pregnancy (odds ratio, 1.75, 95% confidence interval, 1.25–2.46) and midpregnancy (odds ratio, 2.09; 95% confidence interval, 1.49–2.93), as opposed to pregnant individuals who were never infected. Older age, Black race, hypertension, acute respiratory distress syndrome or acute respiratory failure, and placental abruption were found to be consistently associated with stillbirth across different trimesters.

Conclusion

Increased risk of stillbirth was associated with COVID-19 only when pregnant individuals were infected during early and midpregnancy, and not at any time before the delivery or during the third trimester, suggesting the potential vulnerability of the fetus to SARS-CoV-2 infection in early pregnancy. Our findings underscore the importance of proactive COVID-19 prevention and timely medical intervention for individuals infected with SARS-CoV-2 during early and midpregnancy.

Introduction

SARS-CoV-2, the virus that causes COVID-19, has claimed >82.4 million infections and 998,000 deaths in the United States as of May 2022.¹ Pregnant individuals with COVID-19 (PIWC) are at increased risk for COVID-19–related illness.2, 3, 4 Despite mixed findings, existing studies suggest that pregnancy complications (eg, stillbirth, preeclampsia, preterm birth) are associated with SARS-CoV-2 infection.^{5 , 6}

AJOG at a Glance.

Why was this study conducted?

The Centers for Disease Control and Prevention (CDC) Morbidity and Mortality Weekly Report (MMWR) reported an association between increased risk of stillbirth and COVID-19. Yet, the timing of viral infection during pregnancy and how it affects stillbirth have not been reported. This research question is particularly challenging, in part because time (eg, gestational week) relevant to clinical events is not documented, or is often missing, inconsistent, or not specific in electronic health records (EHRs).

Key findings

This nationwide EHR cohort study (data from 50 states, >75 hospital systems) found that stillbirth is associated with viral infection only when infection occurs at early and midpregnancy but not the third trimester or before delivery.

What does this add to what is known?

Using a large US EHR cohort, this study confirmed CDC MMWR’s findings and added further evidence on how the effect of viral infection on stillbirth risk varies according to the gestational week at which infection occurs. These findings highlight the urgent need for improving clinical guidelines and public health policy and are informative for future studies investigating EHR-based pregnancy outcomes other than stillbirth.

Studies assessing stillbirth incidence before and during the COVID-19 pandemic have resulted in mixed conclusions.7, 8, 9, 10, 11 The Morbidity and Mortality Weekly Report (MMWR) from the US Centers for Disease Control and Prevention (CDC) analyzed 1,249,634 delivery hospitalizations from March 2020 to September 2021 from 736 hospitals, and reported that PIWC had doubled risk for stillbirth compared with those without COVID-19 diagnoses.¹² Another study of 489,471 delivery hospitalizations from a Swedish hospital and other clinics from multiple states in the United States (including AK, CA, MT, OR, and WA) between March 5, 2020 and July 4, 2021 found a marginal effect on stillbirth among PIWC compared with those who had never tested positive for COVID-19.³

These preliminary studies suggest a positive association between SARS-CoV-2 infection and stillbirth. Yet, it remains unclear how the viral infection at different time points during the pregnancy would modulate the risk of stillbirth. The timing of exposure to viral infection is a crucial risk factor for pregnancy complications. First, the risk of adverse pregnancy outcomes is generally higher following exposure to viral infection and other severe life events.^{13 , 14} However, there are no empirical pathologic studies examining the association between the timing of SARS-CoV-2 infection and adverse pregnancy outcomes. Second, existing clinical evidence of vertical transmission of SARS-CoV-2 needs to be further validated.^{15 , 16} Studies have suggested that early placental development and the fetus could be harmed by viral infection, but the consequences vary by viral entry receptor expression, maternal immune response, and gestational age.¹⁴ Viral infection during early-to-mid pregnancy has been associated with worse neonatal outcomes.^{17 , 18} Some preliminary studies have suggested that transplacental transmission of SARS-CoV-2 is possible.^{19 , 20} Comprehensive analyses with data from multiple centers and large populations are urgently needed.

To understand the role of maternal SARS-CoV-2 infection and the timing of infection with respect to risk of stillbirth, we report a retrospective analysis using multicenter and nationwide electronic health records (EHR) as part of the National COVID Cohort Collaborative (N3C) consortium in the United States.²¹

Methods

Study design and data sources

This was an EHR-based retrospective cohort study. We used EHRs from N3C, a multicenter clinical data repository that contains deidentified EHR data of individuals with COVID-19 blended with controls (ie, non–COVID-19).²¹ N3C currently has EHR and medical claims data from >75 healthcare systems and institutes across 50 states. These EHR data are normalized using the Observational Medical Outcomes Partnership (OMOP) Clinical Data Model (CDM).^{21 , 22} All data elements are deidentified. The EHR data used in this study contain exact dates of clinical events.

Study cohort

To retain complete clinical events that occurred in each pregnancy, the study cohort included pregnant individuals who: (1) had at least 1 childbirth between March 1, 2020 and May 31, 2021, (2) were aged between 15 and 49 years, and (3) had at least 1 gestational age–related record during the pregnancy (Appendix 1 contains the definitions of gestational age–related records). COVID-19–positive patients were those who had diagnosed or laboratory-confirmed infection, including positive results on reverse transcription polymerase chain reaction and antibodies during the pregnancy.²³ Figure 1 shows the flowchart of cohort development. EHR data extraction and cohort development were completed jointly using SQL (Structured Query Language) and Python.

Figure 1.

Flowchart of cohort development

EHR, electronic health record; N3C, National COVID Cohort Collaborative.

Lyu. Stillbirth and different maternal SARS-CoV-2 infection time. Am J Obstet Gynecol 2023.

Electronic health record phenotyping and clinical information extraction

To establish the cohort and extract variables from EHRs, we used EHR phenotyping specialized for OMOP CDM normalized EHRs. EHR phenotyping refers to computerized clinical information extraction methods for identifying patients with particular phenotypes (eg, clinical conditions and characteristics that typically cannot be identified simply by medical codes).²⁴ For OMOP CDM normalized EHRs, phenotypes are represented by OMOP CDM concepts.

Because there could be multiple occurrences of a clinical event (eg, presence of gestational diabetes mellitus) in the EHR, we used the first occurrence of a medical condition and the associated gestational week as the time point of condition onset (constrained to the period before childbirth). Every SARS-CoV-2 infection and onset of a medical condition were annotated with a specific gestational week using a validated temporal clinical information extraction algorithm (ie, TED-PC [Temporal Events Detector for Pregnancy Care]) described elsewhere.²⁵

Outcomes

The outcome of interest was stillbirth. We identified stillbirth records for each pregnancy by unique patient identification and childbirth delivery date. One researcher (T.L.) performed phenotyping for stillbirth using Athena, the controlled vocabulary that underpins OMOP CDM.^{22 , 26} Our phenotyping strategy integrates regular expressions and semantic relation validation methods. Specifically, we identified OMOP CDM concepts corresponding to existing ICD (International Classification of Diseases) and CPT (Current Procedural Terminology) codes suggestive of stillbirth^{12 , 27} (Appendix 2). To increase the accuracy of the stillbirth indicator, a stillbirth record within ±5 days of childbirth delivery was coded as stillbirth for a given pregnancy. We used a sliding time window because a small degree of deviation is common for documented dates of clinical events. For example, an estimated delivery date may be slightly earlier or later than the actual delivery date because of EHR documentation deviation or EHR data estimation bias, which we have reported elsewhere.²⁵ This procedure is appropriate because a patient often has repeated childbirth delivery records for the same childbirth delivery, and the time frame of a childbirth-related hospitalization ranges from 1 day to 1 week.

Exposure

The exposure variable was SARS-CoV-2 infection. An infection is identified by the acute phase of COVID-19, defined as 21 days since the first occurrence of the COVID-19–related record.²⁸ Each pregnancy may have infections across none, any, or all of the 3 gestational periods: early pregnancy (0–20 gestational weeks), midpregnancy (21–27 gestational weeks), and third trimester (28–43 gestational weeks). We defined these 3 gestational periods because stillbirth occurs at ≥20 weeks. Control cases were pregnant individuals with negative laboratory testing results for SARS-CoV-2 or those excluded from cases (nonpositive cases by diagnoses and/or laboratory results).

Covariates

To control for potential confounders, covariates such as demographics (ie, age, race and ethnicity) and medical conditions (eg, obesity/overweight, hypertensive disorders) were chosen per previous research.¹² Age was categorized into 7 groups (ie, 15–19, 20–24, 25–29, 30–34, 35–39, 40–44, and 45–49 years). Maternal race and ethnicity were divided into non-Hispanic White (hereafter, White), non-Hispanic Black (hereafter, Black), Hispanic or Latino, non-Hispanic Asian (hereafter, Asian), Native Hawaiian and Other Pacific Islander (NHOPI), non-Hispanic other/unknown race (hereafter, other/unknown), and multiple races.²⁹ The medical conditions included obesity/overweight, hypertensive disorders, diabetes mellitus, chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome (ARDS) or acute respiratory failure (ARF), congestive heart failure (CHF), myocardial infarction (MI), placental abruption, and HIV/AIDS.

Phenotyping for obesity or overweight, preexisting/gestational hypertensive disorders, preexisting/gestational diabetes mellitus, COPD, ARDS or ARF, CHF, MI, and HIV/AIDS was completed by N3C Domain Team researchers and made available on the N3C platform. Therefore, we used these existing OMOP CDM concept sets to identify the variables (Appendix 3). To phenotype placental abruption, one researcher (T.L.) used regular expressions combined with relevant medical terminologies on Athena vocabulary and built the concept set (Appendix 2). The onset of each of the aforementioned medical conditions was defined as true if a patient had 1 EHR entry in their medical history before the start date of pregnancy (as identified by TED-PC²⁵).

The phenotyping results were evaluated using a 2-step validation process. In step 1, content validity was assessed by 2 researchers (C.L. and N.G.), who independently reviewed the OMOP CDM concepts and their semantic relationships in Athena vocabulary, and then rated dichotomously whether a concept was relevant. Disagreements were resolved with a senior obstetrics and gynecology physician (B.C.). In step 2, clinical validity was assessed using the EHR chart review method. For every OMOP CDM concept set, 20 patients were randomly selected by concepts validated from step 1. All charts within ±14 days of the date of the concept were extracted and reviewed by 2 researchers (C.L. and N.G.) independently to determine whether a patient was appropriately identified given all documented clinical events of this patient. We used Cohen’s kappa to measure the interrater reliability for each of the 2 steps. Appendix 4 contains the complete phenotyping evaluation results.

Statistical analysis

The cohort’s demographic and medical characteristics were summarized with percentages. Multiple logistic regressions were used to explore the association between the outcome and exposure. We first performed bivariate analyses and then built 4 regression models to examine the 4 exposures defined by the timing of viral infection: early pregnancy (0–20 weeks), midpregnancy (21–27 weeks), the third trimester (28–43 weeks), and any time before the date of delivery (DOD). The associations were assessed through odds ratios (ORs) with 95% confidence intervals (CIs). All statistical analyses were undertaken using R (R Core Team, Vienna, Austria).

Role of the funding source

The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript.

Results

Study population

A total of 191,403 pregnancies among 190,738 pregnant individuals were identified between March 1, 2020 and May 31, 2021. The mean age was 30.5 years. The mean length of gestation was 273.8 days (39.1 weeks). With regard to race/ethnicity, 49.9% of patients were White, 17.3% were Black, 19.4% were Hispanic/Latino, 5.2% were Asian, and 8.2% were others. In addition, 30.0% had obesity or overweight, 21.5% had hypertensive disorders, and 12.6% had diabetes mellitus.

There were 37,879 pregnancies (19.8%) with COVID-19 diagnosis at any time before childbirth. Overall, the COVID-19 infection rate before the DOD was 8.7%. We identified 2342 (1.3%) pregnancies with COVID-19 in early pregnancy, 2075 (1.2%) in midpregnancy, and 12,697 (6.9%) in the third trimester. A total of 1598 cases of stillbirth were identified, affecting 0.8% of the pregnancies with and without COVID-19. There were 1500 (0.8%) pregnancies with stillbirth in early pregnancy and onward, 1372 (0.8%) in midpregnancy and onward, and 784 (0.4%) in the third trimester. It should be noted thats the denominators were not the same for this calculation. Individuals who had a stillbirth in an earlier stage were not counted in the denominators in later stages. The mean interval between the first maternal infection and stillbirth was 22.8 days. The mean gestational age at stillbirth was 206.3 days (Table 1 ).

Table 1.

Demographics and health characteristics of pregnant women with or without SARS-CoV-2 infection

Characteristics	Total		COVID-19 infection:				COVID-19 infection:					COVID-19 infection:
			0–20 wk				21–27 wk					28–43 wk
			of gestation				of gestationa					of gestationb
			No		Yes		No			Yes		No		Yes
	n=191,403		n=174,743		n=2342		n=174,237			n=2075		n=171,442		n=12,697
			N	Col. %	N	Col. %	N	Col. %	N		Col. %	N	Col. %	N	Col. %
Age group
15–19	5366	2.80%	4778	2.70%	—	—	4773	2.70%	—		—	4695	2.70%	499	3.90%
20–24	28,000	14.60%	25,050	14.30%	367	17.80%	24,978	14.30%	333		16.00%	24,561	14.30%	2305	18.20%
25–29	46,867	24.50%	42,406	24.30%	632	30.70%	42,285	24.30%	555		26.70%	41,616	24.30%	3400	26.80%
30–34	60,815	31.80%	56,057	32.10%	699	33.90%	55,916	32.10%	628		30.30%	55,120	32.20%	3571	28.10%
35–39	39,162	20.50%	36,173	20.70%	440	21.30%	36,044	20.70%	393		18.90%	35,412	20.70%	2236	17.60%
40–44	10,402	5.40%	9553	5.50%	140	6.80%	9519	5.50%	107		5.20%	9332	5.40%	634	5.00%
45–49	791	0.40%	726	0.40%	—	—	722	0.40%	—		—	706	0.40%	52	0.40%
Race
White	95,517	49.90%	88,898	50.90%	1033	44.10%	88,680	50.90%	927		44.70%	87,550	51.10%	4784	37.70%
Black	33,144	17.30%	30,566	17.50%	364	15.50%	30,437	17.50%	322		15.50%	29,631	17.30%	1937	15.30%
Hispanic/Latino	37,095	19.40%	31,841	18.20%	678	28.90%	31,753	18.20%	538		25.90%	31,247	18.20%	4261	33.60%
Asian	9952	5.20%	9289	5.30%	99	4.20%	9262	5.30%	92		4.40%	9151	5.30%	496	3.90%
NHOPI	375	0.20%	325	0.20%	—	—	322	0.20%	—		—	312	0.20%	35	0.30%
Other/unknown	13,996	7.30%	12,643	7.20%	145	6.20%	12,611	7.20%	166		8.00%	12,397	7.20%	1071	8.40%
Multiracial	1324	0.70%	1181	0.70%	—	—	1172	0.70%	—		—	1154	0.70%	113	0.90%
Obesity/overweight
No	133,988	70.00%	123,180	70.50%	1474	62.90%	122,822	70.50%	1328		64.00%	120,957	70.60%	8179	64.40%
Yes	57,415	30.00%	51,563	29.50%	868	37.10%	51,415	29.50%	747		36.00%	50,485	29.40%	4518	35.60%
Hypertensive disorders (any)c
No	150,308	78.50%	137,225	78.50%	1845	78.80%	136,810	78.50%	1577		76.00%	134,710	78.60%	9992	78.70%
Yes	41,095	21.50%	37,518	21.50%	497	21.20%	37,427	21.50%	498		24.00%	36,732	21.40%	2705	21.30%
Diabetes mellitus (any)d
No	167,225	87.40%	152,859	87.50%	2026	86.50%	152,396	87.50%	1753		84.50%	149,893	87.40%	10,936	86.10%
Yes	24,178	12.60%	21,884	12.50%	316	13.50%	21,841	12.50%	322		15.50%	21,549	12.60%	1761	13.90%
COPD
No	191,120	99.90%	188,782	99.90%	—	—	173,995	99.90%	—		—	171,204	99.90%	12,666	99.80%
Yes	283	0.10%	279	0.10%	—	—	242	0.10%	—		—	238	0.10%	31	0.20%
ARDS/ARF
No	190,669	99.60%	174,315	99.80%	2313	98.80%	173,812	99.80%	2020		97.30%	171,043	99.80%	12,448	98.00%
Yes	734	0.40%	428	0.20%	29	1.20%	425	0.20%	55		2.70%	399	0.20%	249	2.00%
Congestive heart failure
No	190,673	99.60%	174,080	99.60%	—	—	173,579	99.60%	—		—	170,803	99.60%	12,646	99.60%
Yes	730	0.40%	663	0.40%	—	—	658	0.40%	—		—	639	0.40%	51	0.40%
Myocardial infarction
No	191,132	99.90%	174,493	99.90%	—	—	173,989	99.90%	—		—	171,204	99.90%	—	—
Yes	271	0.10%	250	0.10%	—	—	248	0.10%	—		—	238	0.10%	—	—
HIV/AIDS
No	190,583	99.60%	174,020	99.60%	—	—	173,519	99.60%	—		—	170,737	99.60%	12,620	99.40%
Yes	820	0.40%	723	0.40%	—	—	718	0.40%	—		—	705	0.40%	77	0.60%
Placental abruption
No	189,319	98.90%	172,831	98.90%	2319	99.00%	172,338	98.90%	2050		98.80%	169,707	99.00%	12,572	99.00%
Yes	2084	1.10%	1912	1.10%	23	1.00%	1899	1.10%	25		1.20%	1735	1.00%	125	1.00%
Stillbirth
No	189,805	99.20%	173,278	99.20%	2307	98.50%	172,901	99.20%	2039		98.30%	170,707	99.60%	12,648	99.60%
Yes	1598	0.80%	1465	0.80%	35	1.50%	1336	0.80%	36		1.70%	735	0.40%	49	0.40%

ARDS/ARF, acute respiratory distress syndrome or acute respiratory failure; COPD, chronic obstructive pulmonary disease; NHOPI, Native Hawaiian and Other Pacific Islander; HELLP, haemolysis, elevated liver enzymes, low platelet count.

Lyu. Stillbirth and different maternal SARS-CoV-2 infection time. Am J Obstet Gynecol 2023.

^aAmong pregnancies with gestational length >21 weeks

^bAmong pregnancies with gestational length >28 weeks

^cIncludes chronic hypertension, gestational hypertension, preeclampsia, eclampsia, and HELLP syndrome

^dIncludes prepregnancy diabetes mellitus and gestational diabetes mellitus.

Associations between stillbirth and timing of viral infection

The 4 regression models were built to test the impact of COVID-19 infection on stillbirth in different time frames (ie, infection at any time before the DOD, during early pregnancy, midpregnancy, and the third trimester). After adjusting for all covariates, COVID-19 infection during early or midpregnancy was significantly associated with stillbirth, with a 1.75-fold (95% CI, 1.25–2.46) and 2.09-fold (95% CI, 1.49–2.93) increased risk among infected patients compared with pregnant individuals who had never been infected by childbirth delivery (Table 2 ). In contrast, whether a pregnant individual had COVID-19 before the DOD or during the third trimester was not associated with stillbirth (OR, 0.90; 95% CI, 0.75–1.08 and OR, 0.86; 95% CI, 0.64–1.15; respectively) (Appendix 5).

Table 2.

Risk for stillbirth by demographics and maternal characteristics

Characteristic	Crude		COVID-19 infection		COVID-19 infection
			0–20 gestational wk, adjusted		21–27 gestational wk, adjusted
	OR	95% CI	OR	95% CI	OR	95% CI
Age group
15–19	1.21	(0.92–1.60)	1.07	(0.79–1.44)	1.15	(0.85–1.55)
20–24	1.19a	(1.04–1.36)a	1.03	(0.88–1.21)	1.02	(0.86–1.21)
25–29	1.06	(0.95–1.19)	ref.	ref.	ref.	ref.
30–34	0.82	(0.73–0.92)	0.87	(0.76–1.00)	0.87	(0.75–1.00)
35–39	0.95	(0.84–1.08)	0.96	(0.83–1.12)	0.92	(0.79–1.09)
40–44	1.10	(0.89–1.36)	1.04	(0.82–1.32)	0.99	(0.77–1.27)
45–49	1.84a	(1.04–3.25)a	1.88a	(1.05–3.37)a	1.87a	(1.02–3.42)a
Race and ethnicity
White	0.70a	(0.63–0.77)a	ref.	ref.	ref.	ref.
Black	1.97a	(1.77–2.20)a	1.98a	(1.74–2.25)a	1.99a	(1.74–2.27)a
Hispanic/Latino	0.89	(0.78–1.01)	1.14	(0.98–1.32)	1.14	(0.97–1.33)
Asian	0.59a	(0.44–0.78)a	0.76	(0.56–1.02)	0.73	(0.53–1.00)
NHOPI	2.60a	(1.29–5.24)a	2.18	(0.90–5.30)	2.80a	(1.24–6.33)a
Multiracial	1.27	(0.75–2.16)	1.44	(0.81–2.56)	1.17	(0.60–2.26)
Other/unknown	1.06	(0.88–1.27)	1.31a	(1.07–1.60)a	1.36a	(1.11––1.68)a
COVID-19 infection: 0–20 gestational wk	1.79a	(1.28–2.52)a	1.75a	(1.25–2.46)a	—	—
COVID-19 infection: 21–27 gestational wk	2.28a	(1.67–3.25)a	—	—	2.09a	(1.49–2.93)a
Obesity/overweight	1.20a	(1.08–1.33)a	1.07	(0.96–1.20)	1.07	(0.95–1.20)
Hypertension	1.31a	(1.17–1.46)a	1.16a	(1.03–1.31)a	1.18a	(1.04–1.34)a
Diabetes mellitus	1.04	(0.89–1.20)	0.95	(0.81–1.12)	1.00	(0.85–1.18)
COPD	1.70	(0.63–4.58)	1.15	(0.42–3.19)	0.87	(0.27–2.80)
ARDS/ARF	4.42a	(2.98–6.55)a	3.50a	(2.15–5.71)a	4.26a	(2.69–6.73)a
Congestive heart failure	2.33a	(1.37–3.97)a	1.55	(0.88–2.72)	1.33	(0.72–2.45)
Myocardial infarction	2.24	(0.92–5.42)	1.60	(0.64–3.97)	1.76	(0.71–4.37)
HIV/AIDS	1.32	(0.68–2.55)	1.02	(0.51–2.05)	1.22	(0.63–2.37)
Placental abruption	4.94a	(3.93–6.21)a	4.46a	(3.50–5.67)a	4.45a	(3.46–5.72)a

ARDS/ARF, acute respiratory distress syndrome or acute respiratory failure; CI, confidence interval; COPD, chronic obstructive pulmonary disease; NHOPI, Native Hawaiian and Other Pacific Islander; OR, odds ratio; ref., reference interval.

Lyu. Stillbirth and different maternal SARS-CoV-2 infection time. Am J Obstet Gynecol 2023.

^aSignificant difference compared with all levels or reference levels.

Associations between stillbirth and maternal characteristics

For the adjusted models of COVID-19 during early pregnancy and midpregnancy, compared with the 25- to 29-year age group, the 30- to 34-year age group was at marginally lower risk of stillbirth (OR_{early-pregnancy}, 0.87; 95% CI, 0.76–1.00; and OR_{mid-pregnancy}, 0.87; 95% CI, 0.75–1.00), whereas the 45- to 49-year age group was at higher risk of stillbirth (OR_{early-pregnancy}, 1.88; 95% CI, 1.05–3.37; and OR_{mid-pregnancy}, 1.87; 95% CI, 1.02–3.42; respectively) in both models. Compared with White race, Black race (OR_{early-pregnancy}, 1.98; 95% CI, 1.74–2.25; and OR_{mid-pregnancy}, 1.99; 95% CI, 1.74–2.27; respectively) and other/unknown race (OR_{early-pregnancy}, 1.31; 95% CI, 1.07–1.60; and OR_{mid-pregnancy}, 1.36; 95% CI, 1.11–1.68; respectively) were significantly associated with stillbirth. Hypertension (OR_{early-pregnancy}, 1.16; 95% CI, 1.03–1.31; and OR_{mid-pregnancy}, 1.18; 95% CI, 1.04–1.34; respectively), ARDS/ARF (OR_{early-pregnancy}, 3.50; 95% CI, 2.15–5.71; and OR_{mid-pregnancy}, 4.26; 95% CI, 2.69–6.73; respectively), and placental abruption (OR_{early-pregnancy}, 4.46; 95% CI, 3.50–5.67; and OR_{mid-pregnancy}, 4.45; 95% CI, 3.46–5.72; respectively) were positively associated with stillbirth in the adjusted models (Table 2). Figure 2 and Table 2 show the ORs for stillbirth among pregnant individuals with viral infection during early and midpregnancy. Appendices 5 and 6 display charts of ORs for stillbirth before DOD and in the third trimester.

Figure 2.

The ORs for stillbirth among pregnant women with COVID-19 in early and midpregnancy

ARDS, acute respiratory distress syndrome; COPD, chronic obstructive pulmonary disease; NHOPI, Native Hawaiian and Other Pacific Islander; OR, odds ratio.

Lyu. Stillbirth and different maternal SARS-CoV-2 infection time. Am J Obstet Gynecol 2023.

For individuals with viral infection during early pregnancy, we observed increased risk of stillbirth for the 45- to 49-year age group (OR, 1.88; 95% CI, 1.05–3.37), Black race (OR, 1.98; 95% CI, 1.74–2.25), other/unknown race (OR, 1.31; 95% CI, 1.07–1.60), hypertension (OR, 1.16; 95% CI, 1.03–1.31), ARDS/ARF (OR, 3.50; 95% CI, 2.15–5.71), and placental abruption (OR, 4.46; 95% CI, 3.50–5.67). For individuals with viral infection during midpregnancy, we observed increased risk of stillbirth for the 45- to 49-year age group (OR, 1.87; 95% CI, 1.02–3.42), Black race (OR, 1.99; 95% CI, 1.74–2.27), NHOPI race (OR, 2.80; 95% CI, 1.24–6.33), other/unknown race (OR, 1.36; 95% CI, 1.11–1.68), hypertension (OR, 1.18; 95% CI, 1.04–1.34), ARDS/ARF (OR, 4.26; 95% CI, 2.69–6.73), and placental abruption (OR, 4.45; 95% CI, 3.46–5.72) (Table 2). Figure 2 shows the forest plot for early and mid pregnancy. Figure 3 shows the forest plots of crude model.

Figure 3.

ORs for stillbirth among pregnant women with COVID-19 (crude model)

ARDS, acute respiratory distress syndrome; COPD, chronic obstructive pulmonary disease; DOD, date of delivery; NHOPI, Native Hawaiian and Other Pacific Islander; OR, odds ratio.

Lyu. Stillbirth and different maternal SARS-CoV-2 infection time. Am J Obstet Gynecol 2023.

Discussion

Principal findings

This was a nationwide retrospective EHR study examining the association between risk of stillbirth and specific time points of SARS-CoV-2 infection. We found that pregnant individuals who had COVID-19 in early or midpregnancy had a nearly 2-fold increased risk of stillbirth compared with pregnant individuals with no infection. SARS-CoV-2 infection during the third trimester was not associated with increased risk of stillbirth. This timely finding adds to the evidence from the CDC’s MMWR on the association between SARS-CoV-2 infection and increased risk of stillbirth. To the best of our knowledge, no studies have investigated whether the risk differs when SARS-CoV-2 infection occurs at different stages of pregnancy, in part because identifying the gestational week of a clinical event (eg, viral infection) in medical records has been extremely challenging.¹² Understanding the association between viral infection time and stillbirth provides critical data for developing evidence-based antenatal maternal and fetal screening.

Results in the context of what is known

Although it is possible that viral infections in the early stage of pregnancy increase the risk of complications such as fetal growth retardation, loss of pregnancy, or infants born with adverse outcomes, clinical evidence with respect to SARS-CoV-2 remains missing. Our study provided timely evidence for the role of SARS-CoV-2 infection time in stillbirth using nationwide real-world data from the United States. Further clinical studies are warranted to delineate the interactions between viral infection and maternal/fetal complications over different timing of pregnancy. The study also highlighted the importance of finding evidence for the roles of COVID-19–induced maternal symptoms and vaccination in stillbirth, and possible pathophysiological mechanisms such as coagulopathy in COVID-19–positive pregnant women.

Research implications

Our study identified subgroups who are at higher risk of stillbirth. Future programs should target these groups in prevention efforts for reducing stillbirth during the pandemic. These subgroups were Black and NHOPI pregnant individuals, who had a nearly 2-fold increased risk of stillbirth, and those aged 45 to 49 years. This finding is consistent with existing evidence that Black women have more than twice the rate of stillbirth compared with White or Hispanic women.³⁰ Racial disparities identified in our study exist regardless of pregnant individuals’ medical conditions and age groups. Having obesity/overweight, hypertensive disorders, ARDS/ARF, CHF, and placental abruption was associated with increased risk of stillbirth. These findings are consistent with existing evidence that obesity, hypertension, and placental abruption are associated with higher risk of stillbirth.31, 32, 33, 34, 35 Our finding that older age is associated with higher risk of stillbirth is also consistent with existing studies.³⁶ In sum, the identified high-risk populations should be targeted in the prevention efforts for reducing stillbirths during the pandemic.

In addition, this study contributes to the existing literature by using specific gestational weeks of viral infection to examine how the timing of infection is associated with adverse clinical outcomes using EHR data. Although EHRs provide rich clinical data to explore the course of pregnancy, they pose the challenge of estimating gestational age.³⁷ The approach used in this EHR cohort study is generalizable for investigating other obstetrical and gynecologic research questions in terms of precisely measuring gestational weeks at which clinical events are observed for pregnant individuals.

Clinical implications

Our findings underscore the need to promote infection prevention in patients of reproductive age to avoid potentially severe pregnancy outcomes. For historically vaccine-hesitant patients, this information should be propagated to encourage pregnant patients, those considering pregnancy, and their families to practice preventive care through vaccination and other proven deterrents to viral spread (eg, masking, social distancing). In addition, patients with a history of COVID-19 infection earlier in pregnancy (<27 weeks) should be identified and encouraged to consider antenatal fetal surveillance initiated in the third trimester to decrease the risk of stillbirth. Because such antenatal surveillance could be costly, we suggest considering stratifying and prioritizing the surveillance per maternal risk factors (eg, underlying conditions, maternal symptoms) and vulnerable subgroups (eg, racial/ethnic groups, age groups) using the findings of the study. This study further supports the importance of implementing evidence-based preventive strategies including vaccination before and during pregnancy, as suggested by the CDC.¹²

Strengths and limitations

This study has a few limitations. First, because case numbers of some medical conditions were small and the EHRs were not linked with external data sources (eg, vaccine administration data, birth certificates), our data do not support robust statistical analysis with additional variables, including vaccination status and analysis of interaction. Second, there could be sampling biases because the EHRs that we used are voluntarily provided by healthcare systems in participation with N3C across the states. Clinics with limited resources, a low level of Meaningful Use (US government minimum standards for EHR), and in remote areas may not be able to contribute the data.³⁸ However, because the size of our EHR is substantially large, this limitation is diminished. Third, our findings should be interpreted with caution. We did not account for some pregnant individuals not having COVID-19 tests during specific trimesters. The finding that SARS-CoV-2 infection during the third trimester is not associated with increased risk of stillbirth could be attributed to the active management of COVID-19 among individuals at the late stage of pregnancy or the possibly stronger fetus during the third trimester. Fourth, the timing of viral infection could also be associated with worse maternal outcomes. This research question cannot be answered in this study but is worthy of further exploration.

Conclusion

Increased risk of stillbirth is associated with COVID-19 only when pregnant individuals are infected during early and midpregnancy, and not during any time before the delivery or the third trimester. Our findings underscore the importance of proactive COVID-19 prevention and timely medical intervention for individuals in the early and midpregnancy who have exposure to COVID-19. Further investigation through clinical trials is warranted to examine pathophysiological pathways between viral infection and increased risk of stillbirth.

Individual Acknowledgments for Core Contributors

We gratefully acknowledge the following core contributors to the National COVID Cohort Collaborative:

Adam B. Wilcox; Adam M. Lee; Alexis Graves; Alfred (Jerrod) Anzalone; Amin Manna; Amit Q11 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; ChunleiWu; Clare Schmitt; Cliff Takemoto; Dan Housman; Davera Gabriel; David A. Eichmann; Diego Mazzotti; Don Brown; Eilis Boudreau; Elaine Hill; Elizabeth Zampino; Emily Carlson Marti; Emily R. Pfaff; Evan French; Farrukh M. Koraishy; Federico Mariona; Fred Prior; George okos; Greg Martin; Harold Lehmann; Heidi Spratt; HemalkumarMehta; Hongfang Liu; Hythem Sidky; J.W. Awori Hayanga; Jami Pincavitch; Jaylyn Clark; Jeremy Richard Harper; Jessica Islam; Jin Ge; Joel Gagnier; Joel H. Saltz; Joel Saltz; 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; Leonie Misquitta; Lesley Cottrell; Lili Portilla; Mariam Deacy; Mark M. Bissell; Marshall Clark; Mary Emmett; Mary Morrison Saltz; Matvey B. Palchuk; Melissa A. Haendel; Meredith Adams; Meredith Temple-O'Connor; Michael G. Kurilla; Michele Morris; Nabeel Qureshi; Nasia Safdar; Nicole Garbarini; Noha Sharafeldin; Ofer Sadan; Patricia A. Francis; Penny Wung Burgoon; Peter Robinson; Philip R.O. Payne; Rafael Fuentes; Randeep Jawa; Rebecca Erwin-Cohen; Rena Patel; Richard A. Moffitt; Richard L. Zhu; Rishi Kamaleswaran; RobertHurley; 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;Usman Sheikh; Valery Gordon; Vignesh Subbian;Warren A. Kibbe;Wenndy Hernandez; Will Beasley; Will Cooper; William Hillegass; Xiaohan Tanner Zhang.

Data Partners with Released Data

The following institutions whose data are released or pending:

Available: Advocate Health Care Network — UL1TR002389: The Institute for Translational Medicine (ITM) • 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 • 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 • 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) • Indiana University School of Medicine — UL1TR002529: Indiana Clinical and Translational Science Institute • Johns Hopkins University — UL1TR003098: Johns Hopkins Institute for Clinical and Translational Research • 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 • 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) • 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 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 • 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 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 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

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 — 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

International Committee of Medical Journal Editors (ICMJE) Statement

Authorship was determined using ICMJE recommendations.

Supplementary Data

Video 1

Risk for stillbirth among pregnant individuals with SARS-CoV-2 infection varied by gestational age.

Lyu. Stillbirth and different maternal SARS-CoV-2 infection time. Am J Obstet Gynecol 2023.

Video 2

Risk for stillbirth among pregnant individuals with SARS-CoV-2 infection varied by gestational age.

Lyu. Stillbirth and different maternal SARS-CoV-2 infection time. Am J Obstet Gynecol 2023.