A Population‐based Study of Perinatal Infection Risk in Women with and without Systemic Lupus Erythematosus and their Infants

Abstract

Background: Increased risk of adverse birth outcomes is well described in women with systemic lupus erythematosus (SLE), but risk of maternal or infant infection in the peripartum period has not been well studied. We conducted a population-based cohort study of infection risk in women with and without SLE and their infants. Methods: Linked birth-hospital discharge data identified 1297 deliveries to women with SLE and a 4:1 comparison cohort of deliveries to women without SLE in Washington State, 1987State, -2013. Maternal and infant infections during the first 30 days after delivery were identified. Relative risks (RR) and 95% confidence intervals (CI) were estimated. Results: Women with SLE were 1.7 times more likely (95% CI 1.4, 2.0) to have an infection during the birth hospitalisation and more likely to receive antibiotics during labour (RR 1.3, 95% CI 1.1, 1.5), though there was no increased risk of chorioamnionitis in women with SLE. Infants of women with SLE had an increased risk for an infection during the birth hospitalisation (RR 2.2, 95% CI 1.3, 3.5), although the size of the difference was smaller when adjusted for gestational age (RR 1.4, 95% CI 0.9, 2.1). Risks of neonatal infection, sepsis, receipt of antibiotics, and admission to neonatal intensive care were also increased, and were also attenuated after adjustment for gestational age. Conclusions: Women with SLE have an increased risk of peripartum infections and antibiotic exposure. Their neonates have a greater likelihood of infection, much of which is attributable to preterm birth.

Rethinking the pros and cons of randomized controlled trials and observational studies in the era of big data and advanced methods: a panel discussion

“…[ 19 ] EHRs may facilitate pre-screening of patients by age, sex, and diagnosis, helping to exclude ineligible patients, and reduce the overall screening duration in clinical trials [ 32 ] Price et al 2017 [ 33 ] Bereznicki et al 2008 [ 34 ] Observational studies Innovation Definition Strengths Application of the method Causal inference methods Causal inference in observational studies refers to an intellectual discipline which allows researchers to draw causal conclusions based on data by considering the assumptions, study design, and estimation strategies [ 20 ] Causal inference methods, through their well-defined frameworks and assumptions have helped to overcome concerns about bias in the analysis of observational studies [ 10 ] Ekline et al 2011[ 35 ] Skerritt et al 2021 [ 36 ] DAG (Directed acyclic graph) When considering the effect of one variable on another, DAGs serve as a visual representation of causal assumptions. This structured approach moves the conversation forward by serving as a visual aid that makes underlying relations explicit [ 37 ] DAGs can help identify possible confounding for the causal question being considered [ 37 ] Pakzad et al 2023 [ 38 ] Byrne et al 2019 [ 39 ] E-value The E-value is “the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates” [ 24 ] The E-value is an intuitive metric to help determine how robust the results of a study are to unmeasured confounding [ 24 ] Bender Ignacio et al 2018 [ 40 ] Eastwood et al 2018 [ 41 ] Use of “big data” Large observational studies have become more popular in the era of big data because of their ability t...…”

“…Application of the method Causal inference methods Causal inference in observational studies refers to an intellectual discipline which allows researchers to draw causal conclusions based on data by considering the assumptions, study design, and estimation strategies [20] Causal inference methods, through their well-defined frameworks and assumptions have helped to overcome concerns about bias in the analysis of observational studies [10] Ekline et al 2011 [35] Skerritt et al 2021 [36] DAG (Directed acyclic graph) When considering the effect of one variable on another, DAGs serve as a visual representation of causal assumptions. This structured approach moves the conversation forward by serving as a visual aid that makes underlying relations explicit [37] DAGs can help identify possible confounding for the causal question being considered [37] Pakzad et al 2023 [38] Byrne et al 2019 [39] E-value The E-value is "the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment-outcome association, conditional on the measured covariates" [24] The E-value is an intuitive metric to help determine how robust the results of a study are to unmeasured confounding [24] Bender Ignacio et al 2018 [40] Eastwood et al 2018 [41] Use of "big data" Large observational studies have become more popular in the era of big data because of their ability to leverage and analyze multiple sources of observational data [22] such as from population databases, social media, and digital health tools [23] Use of big data in research can help with hypothesis generating, and focuses on the temporal stability of the association [23] Khera et al 2018 [42] Ahmed et al 2023 [43] experimental research methodologies (see Appendix A). One concern is how to apply innovations to new contexts, different topics, and novel areas of research.…”

Birth Outcomes and Rehospitalizations Among Pregnant Women With Rheumatoid Arthritis and Systemic Lupus Erythematosus and Their Offspring

“…Preterm infants experience increased rehospitalization rates due to respiratory‐ and infection‐related causes (34). Infants of women with SLE may have a greater likelihood of infection (35), suggesting that this increased risk may be due to the women's impaired ability to provide antibodies to their offspring in the setting of immunosuppression. Maternal immunosuppressive medication has not been shown to cause increased infant infection risk or significant immune system dysfunction beyond slight alteration to cell counts within the first year of life (36).…”

¿Se puede prevenir el parto prematuro por infección bacteriana ascendente y sus resultados adversos en los hospitales públicos de Chile?

“…Las mujeres embarazadas con LES tienen respectivamente 1,7 y 1,3 veces más probabilidades de tener infección (sin aumento de corioamnionitis) y de recibir antimicrobianos durante el trabajo de parto, que las gestantes sin LES. Los niños de mujeres con LES tuvieron 1,4 mayor riesgo de padecer infección durante la hospitalización neonatal, gran parte de la cual es atribuible al nacimiento prematuro 69 .…”

“…Los siguientes son factores de riesgo de infección ascendente: a) antecedente de aborto espontáneo de segundo trimestre asociado a IGU y/o con hallazgos en la biopsia placentaria de corioamnionitis, funisitis; b) antecedente de PP asociado a IGU y/o con hallazgos en la biopsia placentaria de corioamnionitis, funisitis; en el embarazo actual presencia de: c) ICV recurrente (tres episodios o más); d) ITU recurrente; e) diabetes pre y gestacional, f) depresión, g) obesidad y h) LES 9,27,30,53,[64][65][66]69 . Recomendación B (CTFPHC,SIGN) 70 .…”