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Language: search.filters.language.EnglishSubject: search.filters.subject.Logistic regression

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    Prediction of preeclampsia before 11th week of gestation: a secondary analysis of the ASPIRIN trial
    (Elsevier BV, 2025)
    Capdeville, Gabriela
    ;
    Godinez-Medina, Andrea
    ;
    Copado-Mendoza, Diana Y.
    ;
    Acevedo-Gallegos, Sandra
    ;
    Rodriguez-Bosch, Mario R.
    Background: Early screening for preeclampsia is crucial for preventing adverse maternal and fetal events. Current first-trimester algorithms for predicting preeclampsia are designed to evaluate individual risk between 11.0 and 13.6 weeks of gestation based on various maternal characteristics while integrating biophysical and biochemical features. However, there is limited information regarding risk assessment during earlier stages of pregnancy (i.e., <11.0 weeks gestation). Objective: To develop a prediction model for preeclampsia/eclampsia before 11.0 weeks of gestation as a proof-of-concept in a secondary analysis of the ASPIRIN trial. Study design: This study is a secondary analysis of the ASPIRIN trial, a multinational, randomized, double-blind, placebo-controlled trial. The ASPIRIN trial database, obtained from NICHD DASH, included 11,976 nulliparous pregnant women aged 18–40 with gestational ages of 6.0–13.6 weeks at randomization. Participants were assigned to receive either aspirin (81 mg/day) or placebo until 36.0 weeks or delivery. This secondary analysis included pregnancies delivered at ≥20.0 weeks, excluding those in the aspirin group or with gestational ages ≥11.0 weeks at enrollment. The composite outcome was preeclampsia/eclampsia, as reported in the ASPIRIN trial. Predictor variables available in the dataset included maternal age, education (4 levels), body mass index (BMI kg/m2), gravidity, baseline hemoglobin, baseline systolic blood pressure, and baseline diastolic blood pressure. Logistic regression, with logarithmic transformation for continuous variables, was used to develop the model. The area under the ROC curve with a 95% confidence interval (CI) estimated via bootstrap resampling (1,000 iterations) and the P-value of the Hosmer-Lemeshow statistical test are reported as discrimination and calibration measures. This study used the entire available sample using a complete case approach. Results: A total of 3421 participants met the inclusion criteria, with a cumulative incidence of preeclampsia/eclampsia of 2.9% (99/3,421). Maternal age (21.96 ± 4.13 vs 20.86 ± 3.21, P<.001) and BMI (22.49 ± 4.77 vs 20.79 ± 3.55, P<.001) were significantly higher in the preeclampsia/eclampsia group. Gravidity was lower (P=.023), and hemoglobin levels were slightly elevated (11.88 ± 1.52 g/dL vs 11.50 ± 1.61 g/dL, P=.019) in the preeclampsia/eclampsia group. Educational level (P=.070), systolic blood pressure (P=.720), and diastolic blood pressure (P=.390) showed no significant differences between groups. The logistic regression model yielded an AUC of 0.69 (95% CI 0.63–0.74), and the Hosmer-Lemeshow test P-value was 0.094, indicating acceptable discrimination and calibration. Conclusions: This proof-of-concept logistic regression model using first-trimester maternal characteristics demonstrated acceptable predictive performance for preeclampsia/eclampsia before 11.0 weeks of gestation. During this critical period, several interventions could be proposed to reduce preeclampsia risk, including medication adjustments, lifestyle changes, and appropriate referral if needed. Further studies are required to validate these findings and assess their clinical utility in different settings. © The authors © Elsevier.
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    Microdata analytics of out-of-pocket and catastrophic health spending in Mexico: an analysis by quantiles
    (2022)
    Rodríguez Aguilar, Román  
    Out-of-pocket and catastrophic health spending are key indicators for assessing the financial coverage of a health system. Out-of-pocket spending represents expenditures related to the health care of a household member, while catastrophic spending represents expenditures that constitute more than 30 % of the household’s ability to pay. Measurements in Mexico of out-of-pocket household spending show that it is an item that has not decreased from 2016 to 2018, the out-of-pocket household spending increased by 4 % real representing in 2018, 109 billion of Mexican pesos. Analysis of out-of-pocket spending by quintile shows that average monthly household spending on health is in the range of Q1-$17 to Q5-$1,900 pesos with high dispersion in the data (SD=1,446). The quantile regression shows that there are significant differences between the factors associated to out-of-pocket spending among the quintiles, especially due to the presence of chronic diseases in the household, belonging to the rural environment, the age of the head of the household and the total number of household members. The incidence of catastrophic spending represented 2.19 % [2.18-2.19, N=760,3030] of total households. According to the results of the logistic model, the incidence of catastrophic spending is mainly influenced by households that had hospital spending (OR=20.13) and maternity spending (OR=20.77). Affiliation with a health institution decreases the probability of incurring catastrophic spending (OR=0.93), and when households are segmented by income quintile, the incidence is higher in Q2 and Q4. Mainly affected by spending on hospitalization and maternal care. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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