Porcentaje de cesáreas en mujeres de bajo riesgo: un indicador útil para comparar hospitales que atienden partos con riesgos diferentes

Librero, Julián; Peiró, Salvador; Belda, Ana Martí; Calabuig, Julia

doi:10.4321/s1135-57272014000300002

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…In this pro le, only 6% of women will have an EmCS. Similar ndings were found in Spanish hospitals, which de ned low-risk of CS to under-35, with no history of CS, between 37 and 41 gestational weeks, single fetus, cephalic presentation, and normal birthweight (27).…”

Section: Discussionsupporting

confidence: 85%

Prediction of Emergency Cesarean Delivery in Chile using Machine Learning

Mondschein,

Garmendia,

Montiel

et al. 2024

Preprint

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Background: Emergency cesarean section (EmCS) is associated with a higher risk of intraoperative and postoperative maternal complications for both the mother and the offspring. Identifying which women who deliver by EmCS without indication for elective C-section should be a concern for health systems. Objective: To examine predictors related to EmCS in women with a medium-low socioeconomic status from the southeast area of Santiago de Chile. Methods: This study involves a secondary analysis of all single birth records at Dr. Sótero del Río Hospital in the southeast public health district of Santiago, Chile, from 2002 to 2018 (n = 83,936). In total, fifty-nine potential predictors of EmCS were studied, 28 variables related to the pregnancy period, and the other 31 variables were related to the delivery period. Fivemachine learning (ML) algorithms were applied: Logistic regression, Random forest, AdaBoost, XGBoost, and Optimal classification tree. Results: The prevalence of EmCS was 18.6%, with an increase of 48.8% in the study period. Women's profiles were identified using eight factors that predicted EmCS (parity, previous cesarean section, labor already initiated, maternal age, gestational age, maternal height, pregestational body mass index, and the appearance of amniotic fluid). The Optimal classification tree was the algorithm with the highest sensitivity (0.74). The highest probability of EmCS (46%) occurred in multiparous women with one previous cesarean section. Conclusions: Most of the EmCS predictors are easily identifiable before delivery (age, parity, previous cesarean section, and maternal anthropometry). ML techniques are useful tools for predicting the risk of EmCS, potentially guiding the clinical decisions of health professionals.

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Section: Discussionsupporting

confidence: 85%

Prediction of Emergency Cesarean Delivery in Chile using Machine Learning

Mondschein,

Garmendia,

Montiel

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The rate of C-sections is thus 84.5% of all deliveries, significantly higher than the expected rate of approximately 15% for pregnancies in the general population. 17,18 This rate of C-section is also higher than that in pregnancies complicated by viral Influenza infection, where the likelihood of C-section did not increase relative to the general population. 19…”

Section: Resultsmentioning

confidence: 81%

Pregnancy and Neonatal Outcomes in SARS-CoV-2 Infection: a systematic review

Chamseddine

Wahbeh

Chervenak

et al. 2020

Preprint

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With the emergence of SARS-CoV-2 and its rapid spread, concerns regarding its effects on pregnancy outcomes have been growing. We reviewed 164 pregnancies complicated by maternal SARS-CoV-2 infection across 20 studies. The most common clinical presentations were fever (57.9%), cough (35.4%), fatigue (15.2%), and dyspnea (12.2%). Only 2.4% of patients developed respiratory distress. Of all patients, 84.5% delivered via Cesarean section, with a 23.9% rate of maternal gestational complications, 20.3% rate of preterm delivery, and a concerning 2.3% rate of stillbirth delivery. Relative to known viral infections, the prognosis for pregnant women with SARS-CoV-2 is good, even in the absence of specific antiviral treatment. However, neonates and acute patients, especially those with gestational or pre-existing co-morbidities, must be actively managed to prevent severe outcomes.

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“…Of 201 deliveries reported, 182 were performed as C-sections. The rate of C-sections is thus 89% of all deliveries, significantly higher than the expected rate of approximately 15% for pregnancies in the general population [ 21 , 22 ]. This rate of C-section is also higher than that in pregnancies complicated by viral Influenza infection, where the likelihood of C-section did not increase relative to the general population [ 23 ].…”

Section: Resultsmentioning

confidence: 99%

Pregnancy and Neonatal Outcomes in SARS-CoV-2 Infection: A Systematic Review

Chamseddine

Wahbeh

Chervenak

et al. 2020

Journal of Pregnancy

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With the emergence of SARS-CoV-2 and its rapid spread, concerns regarding its effects on pregnancy outcomes have been growing. We reviewed 245 pregnancies complicated by maternal SARS-CoV-2 infection across 48 studies listed on PubMed and MedRxiv. The most common clinical presentations were fever (55.9%), cough (36.3%), fatigue (11.4%), and dyspnea (12.7%). Only 4.1% of patients developed respiratory distress. Of all patients, 89.0% delivered via cesarean section ( n = 201 ), with a 33.3% rate of gestational complications, a 35.3% rate of preterm delivery, and a concerning 2.5% rate of stillbirth delivery or neonatal death. Among those tested, 6.45% of newborns were reported positive for SARS-CoV-2 infection. Relative to known viral infections, the prognosis for pregnant women with SARS-CoV-2 is good, even in the absence of specific antiviral treatment. However, neonates and acute patients, especially those with gestational or preexisting comorbidities, must be actively managed to prevent the severe outcomes being increasingly reported in the literature.

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Porcentaje de cesáreas en mujeres de bajo riesgo: un indicador útil para comparar hospitales que atienden partos con riesgos diferentes

Cited by 5 publications

References 15 publications

Prediction of Emergency Cesarean Delivery in Chile using Machine Learning

Prediction of Emergency Cesarean Delivery in Chile using Machine Learning

Pregnancy and Neonatal Outcomes in SARS-CoV-2 Infection: a systematic review

Pregnancy and Neonatal Outcomes in SARS-CoV-2 Infection: A Systematic Review

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