Performance of machine-learning approach for prediction of pre-eclampsia in a middle-income country.

OBJECTIVE: Pre-eclampsia (PE) is a serious complication of pregnancy associated with maternal and fetal morbidity and mortality. As current prediction models have limitations and may not be applicable in resource-limited settings, we aimed to develop a machine-learning (ML) algorithm that offers a potential solution for developing accurate and efficient first-trimester prediction of PE. METHODS: We conducted a prospective cohort study in Mexico City, Mexico to develop a first-trimester prediction model for preterm PE (pPE) using ML. Maternal characteristics and locally derived multiples of the median (MoM) values for mean arterial pressure, uterine artery pulsatility index and serum placental growth factor were used for variable selection. The dataset was split into training, validation and test sets. An elastic-net method was employed for predictor selection, and model performance was evaluated using area under the receiver-operating-characteristics curve (AUC) and detection rates (DR) at 10% false-positive rates (FPR). RESULTS: The final analysis included 3050 pregnant women, of whom 124 (4.07%) developed PE. The ML model showed good performance, with AUCs of 0.897, 0.963 and 0.778 for pPE, early-onset PE (ePE) and any type of PE (all-PE), respectively. The DRs at 10% FPR were 76.5%, 88.2% and 50.1% for pPE, ePE and all-PE, respectively. CONCLUSIONS: Our ML model demonstrated high accuracy in predicting pPE and ePE using first-trimester maternal characteristics and locally derived MoM. The model may provide an efficient and accessible tool for early prediction of PE, facilitating timely intervention and improved maternal and fetal outcome. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Eficiencia de un enfoque de aprendizaje automático para la predicción de la preeclampsia en un país de ingresos medios OBJETIVO: La preeclampsia (PE) es una complicación grave del embarazo asociada a morbilidad y mortalidad materna y del feto. Dado que los modelos de predicción actuales tienen limitaciones y pueden no ser aplicables en situaciones con recursos limitados, se propuso desarrollar un algoritmo de aprendizaje automático (AA) que ofrezca una solución con potencial para desarrollar una predicción precisa y eficiente de la PE en el primer trimestre. MÉTODOS: Se realizó un estudio de cohorte prospectivo en Ciudad de México para desarrollar un modelo de predicción de la PE pretérmino (PEp) en el primer trimestre utilizando AA. Para la selección de variables se utilizaron las características maternas y los múltiplos de la mediana (MdM) obtenidos localmente para la presión arterial media, el índice de pulsatilidad de la arteria uterina y el factor de crecimiento placentario sérico. El conjunto de datos se dividió en subconjuntos de datos de entrenamiento, de validación y de test estadístico. Se empleó un método de red elástica para la selección de predictores, y el rendimiento del modelo se evaluó mediante el área bajo la curva de características operativas del receptor (ABC) y las tasas de detección (TD) con tasas de falsos positivos (TFP) del 10%. RESULTADOS: El análisis final incluyó a 3050 mujeres embarazadas, de las cuales 124 (4,07%) desarrollaron PE. El modelo de AA mostró una buena eficiencia, con un ABC de 0,897, 0,963 y 0,778 para la PEp, la PE de aparición temprana (PEat) y cualquier tipo de PE (todas las PE), respectivamente. Las TD con TFP del 10% fueron del 76,5%, 88,2% y 50,1% para la PEp, PEat y todas las PE, respectivamente. CONCLUSIONES: Nuestro modelo de AA demostró una alta precisión en la predicción de la PEp y la PEat utilizando características maternas del primer trimestre y MdM calculados localmente. El modelo puede proporcionar una herramienta eficiente y accesible para la predicción temprana de la PE, facilitando la intervención oportuna y la mejora de los resultados maternos y del feto.

Comorbidity, poverty and social vulnerability as risk factors for mortality in pregnant women with confirmed SARS-CoV-2 infection: analysis of 13 062 positive pregnancies including 176 maternal deaths in Mexico.

OBJECTIVE: Mortality in pregnancy due to coronavirus disease 2019 (COVID-19) is a current health priority in developing countries. Identification of clinical and sociodemographic risk factors related to mortality in pregnant women with COVID-19 could guide public policy and encourage such women to accept vaccination. We aimed to evaluate the association of comorbidities and socioeconomic determinants with COVID-19-related mortality and severe disease in pregnant women in Mexico. METHODS: This is an ongoing nationwide prospective cohort study that includes all pregnant women with a positive reverse-transcription quantitative polymerase chain reaction result for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from the Mexican National Registry of Coronavirus. The primary outcome was maternal death due to COVID-19. The association of comorbidities and socioeconomic characteristics with maternal death was explored using a log-binomial regression model adjusted for possible confounders. RESULTS: There were 176 (1.35%) maternal deaths due to COVID-19 among 13 062 consecutive SARS-CoV-2-positive pregnant women. Maternal age, as a continuous (adjusted relative risk (aRR), 1.08 (95% CI, 1.05-1.10)) or categorical variable, was associated with maternal death due to COVID-19; women aged 35-39 years (aRR, 3.16 (95% CI, 2.34-4.26)) or 40 years or older (aRR, 4.07 (95% CI, 2.65-6.25)) had a higher risk for mortality, as compared with those aged < 35 years. Other clinical risk factors associated with maternal mortality were pre-existing diabetes (aRR, 2.66 (95% CI, 1.65-4.27)), chronic hypertension (aRR, 1.75 (95% CI, 1.02-3.00)) and obesity (aRR, 2.15 (95% CI, 1.46-3.17)). Very high social vulnerability (aRR, 1.88 (95% CI, 1.26-2.80)) and high social vulnerability (aRR, 1.49 (95% CI, 1.04-2.13)) were associated with an increased risk of maternal mortality, while very low social vulnerability was associated with a reduced risk (aRR, 0.47 (95% CI, 0.30-0.73)). Being poor or extremely poor were also risk factors for maternal mortality (aRR, 1.53 (95% CI, 1.09-2.15) and aRR, 1.83 (95% CI, 1.32-2.53), respectively). CONCLUSION: This study, which comprises the largest prospective consecutive cohort of pregnant women with COVID-19 to date, has confirmed that advanced maternal age, pre-existing diabetes, chronic hypertension, obesity, high social vulnerability and low socioeconomic status are risk factors for COVID-19-related maternal mortality. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.