Performance of the first-trimester Fetal Medicine Foundation competing risks model for preeclampsia prediction: an external validation study in Brazil.

BACKGROUND: The current version of the Fetal Medicine Foundation competing risks model for preeclampsia prediction has not been previously validated in Brazil. OBJECTIVE: This study aimed (1) to validate the Fetal Medicine Foundation combined algorithm for the prediction of preterm preeclampsia in the Brazilian population and (2) to describe the accuracy and calibration of the Fetal Medicine Foundation algorithm when considering the prophylactic use of aspirin by clinical criteria. STUDY DESIGN: This was a cohort study, including consecutive singleton pregnancies undergoing preeclampsia screening at 11 to 14 weeks of gestation, examining maternal characteristics, medical history, and biophysical markers between October 2010 and December 2018 in a university hospital in Brazil. Risks were calculated using the 2018 version of the algorithm available on the Fetal Medicine Foundation website, and cases were classified as low or high risk using a cutoff of 1/100 to evaluate predictive performance. Expected and observed cases with preeclampsia according to the Fetal Medicine Foundation-estimated risk range (≥1 in 10; 1 in 11 to 1 in 50; 1 in 51 to 1 in 100; 1 in 101 to 1 in 150; and <1 in 150) were compared. After identifying high-risk pregnant women who used aspirin, the treatment effect of 62% reduction in preterm preeclampsia identified in the Combined Multimarker Screening and Randomized Patient Treatment with Aspirin for Evidence-Based Preeclampsia Prevention trial was used to evaluate the predictive performance adjusted for the effect of aspirin. The number of potentially unpreventable cases in the group without aspirin use was estimated. RESULTS: Among 2749 pregnancies, preterm preeclampsia occurred in 84 (3.1%). With a risk cutoff of 1/100, the screen-positive rate was 25.8%. The detection rate was 71.4%, with a false positive rate of 24.4%. The area under the curve was 0.818 (95% confidence interval, 0.773-0.863). In the risk range ≥1/10, there is an agreement between the number of expected cases and the number of observed cases, and in the other ranges, the predicted risk was lower than the observed rates. Accounting for the effect of aspirin resulted in an increase in detection rate and positive predictive values and a slight decrease in the false positive rate. With 27 cases of preterm preeclampsia in the high-risk group without aspirin use, we estimated that 16 of these cases of preterm preeclampsia would have been avoided if this group had received prophylaxis. CONCLUSION: In a high-prevalence setting, the Fetal Medicine Foundation algorithm can identify women who are more likely to develop preterm preeclampsia. Not accounting for the effect of aspirin underestimates the screening performance.

External validation of a minimal-resource model to predict reduced estimated glomerular filtration rate in people with type 2 diabetes without diagnosis of chronic kidney disease in Mexico: a comparison between country-level and regional performance.

Background: Patients with type 2 diabetes are at an increased risk of chronic kidney disease (CKD) hence it is recommended that they receive annual CKD screening. The huge burden of diabetes in Mexico and limited screening resource mean that CKD screening is underperformed. Consequently, patients often have a late diagnosis of CKD. A regional minimal-resource model to support risk-tailored CKD screening in patients with type 2 diabetes has been developed and globally validated. However, population heath and care services between countries within a region are expected to differ. The aim of this study was to evaluate the performance of the model within Mexico and compare this with the performance demonstrated within the Americas in the global validation. Methods: We performed a retrospective observational study with data from primary care (Clinic Specialized in Diabetes Management in Mexico City), tertiary care (Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán) and the Mexican national survey of health and nutrition (ENSANUT-MC 2016). We applied the minimal-resource model across the datasets and evaluated model performance metrics, with the primary interest in the sensitivity and increase in the positive predictive value (PPV) compared to a screen-everyone approach. Results: The model was evaluated on 2510 patients from Mexico (primary care: 1358, tertiary care: 735, ENSANUT-MC: 417). Across the Mexico data, the sensitivity was 0.730 (95% CI: 0.689 - 0.779) and the relative increase in PPV was 61.0% (95% CI: 52.1% - 70.8%). These were not statistically different to the regional performance metrics for the Americas (sensitivity: p=0.964; relative improvement: p=0.132), however considerable variability was observed across the data sources. Conclusion: The minimal-resource model performs consistently in a representative Mexican population sample compared with the Americas regional performance. In primary care settings where screening is underperformed and access to laboratory testing is limited, the model can act as a risk-tailored CKD screening solution, directing screening resources to patients who are at highest risk.

Comparison of different prediction tools for the risk of intravesical recurrence after radical nephroureterectomy for upper tract urothelial carcinoma: a propensity-matched analysis.

OBJECTIVE: To compare the predictive performance of the current clinical prediction models for predicting intravesical recurrence (IVR) after radical nephroureterectomy (RNU) in patients with upper tract urothelial carcinoma (UTUC). METHODS: We retrospectively analysed upper tract urothelial carcinoma patients who underwent radical nephroureterectomy in our centre from January 2009 to December 2019. We used the propensity score matching (PSM) method to adjust the confounders between the IVR and non-IVR groups. Additionally, Xylinas' reduce model and full model, Zhang's model, and Ishioka's risk stratification model were used to retrospectively calculate predictions for each patient. Receiver operating characteristic (ROC) curves were generated, and the areas under the curves (AUCs) were compared to identify the method with the highest predictive value. RESULTS: We included 217 patients with a median follow-up of 41 months, of which 57 had IVR. After PSM analysis, 52 pairs of well-matched patients were included in the comparative study. No significant difference was found in clinical indicators besides hydronephrosis. The model comparison showed that the AUCs of the reduced Xylinas' model for 12 months, 24 months, and 36 months were 0.69, 0.73, and 0.74, respectively, and those of the full Xylinas' model were 0.72, 0.75, and 0.74, respectively. The AUC of Zhang's model for 12 months, 24 months, and 36 months was 0.63, 0.71, and 0.71, respectively, the performance of Ishioka's model is that the AUC of 12 months, 24 months and 36 months was 0.66, 0.71, and 0.74, respectively. CONCLUSION: The external verification results of the four models show that more comprehensive data and a larger sample size of patients are needed to strengthen the models' derivation and updating procedure, to better apply them to different populations.

Comparison of the First and Second Waves of the Coronavirus Disease 2019 Pandemic in Children and Adolescents in a Middle-Income Country: Clinical Impact Associated with Severe Acute Respiratory Syndrome Coronavirus 2 Gamma Lineage.

OBJECTIVE: To evaluate the severity and clinical outcomes of the SARS-CoV-2 gamma variant in children and adolescents hospitalized with COVID-19 in Brazil. STUDY DESIGN: In this observational retrospective cohort study, we performed an analysis of all 21 591 hospitalized patients aged <20 years with confirmed SARS-CoV-2 infection registered in a national database in Brazil. The cohort was divided into 2 groups according to the predominance of SARS-CoV-2 lineages (WAVE1, n = 11 574; WAVE2, n = 10 017). The characteristics of interest were age, sex, geographic region, ethnicity, clinical presentation, and comorbidities. The primary outcome was time to death, which was evaluated by competing-risks analysis, using cumulative incidence functions. A predictive Fine and Gray competing-risks model was developed based on the WAVE1 cohort with temporal validation in the WAVE2 cohort. RESULTS: Compared with children and adolescents admitted during the first wave, those admitted during the second wave had significantly more hypoxemia (52.5% vs 41.1%; P < .0001) and intensive care unit admissions (28.3% vs 24.9%; P < .0001) and needed more noninvasive ventilatory support (37.3% vs 31.6%; P < .0001). In-hospital deaths and death rates were 896 (7.7%) in the first wave and 765 (7.6%) in the second wave (P = .07). The prediction model of death included age, ethnicity, region, respiratory symptoms, and comorbidities. In the validation set (WAVE2), the C statistic was 0.750 (95% CI, 0.741-0.758; P < .0001). CONCLUSIONS: This large national study found a more severe spectrum of risk for pediatric patients with COVID-19 caused by the gamma variant. However, there was no difference regarding the probability of death between the waves.

Evaluation of fetal medicine foundation algorithm in predicting small-for-gestational-age neonates.

OBJECTIVES: To examine the performance of the Fetal Medicine Foundation (FMF) 2012 predictive model and of isolated biophysical markers (uterine artery pulsatility index and mean arterial pressure) for small-for-gestational-age (SGA), in patients from Rio de Janeiro, Brazil. METHODS: For this cross-sectional study, SGA was diagnosed when a newborn presented birth weight below the fifth percentile for gestational age. FMF2012 algorithm sensitivity and specificity, positive (PPV) and negative (NPV) predictive value, positive likelihood ratio (LR +) and area under the ROC curve (AUC) were calculated to predict total and preterm SGA (SGA < 37). The performance of isolated biophysical markers - mean arterial pressure (MAP) and mean uterine artery pulsatility index (UtAPI) were studied. RESULTS: The final sample consisted of 1480 cases: 69 (4.6%) developed SGA, including 12 patients (0.8%) who were SGA < 37. The AUC showed that the performances of the FMF2012 combined model for SGA prediction was 0.687 and for preterm SGA was 0.824. With risk cutoff of 1:150, SGA screening yielded the following: sensitivity, 47%; specificity, 75%; LR +, 1.88; PPV, 8.66%; NPV, 96.72%. When screening for preterm SGA, we found sensitivity 66.6%, specificity 74.59%, LR +: 2.58, PPV 2%, and NPV 99.63%. CONCLUSIONS: Performance of the FMF2012 algorithm in predicting SGA in our population was similar to that obtained in the reference population, according to sensitivity, but our false positive rate is significantly higher than the reference population.

A clinical predictive model of renal injury in children with isolated antenatal hydronephrosis.

BACKGROUND: Antenatal hydronephrosis (ANH) affects ∼1-5% of pregnancies. The aim of this study was to develop a clinical prediction model of renal injury in a large cohort of infants with isolated ANH. METHODS: This is a longitudinal cohort study of 447 infants with ANH admitted since birth between 1989 and 2015 at a tertiary care center. The primary endpoint was time until the occurrence of a composite event of renal injury, which includes proteinuria, hypertension and chronic kidney disease (CKD). A predictive model was developed using a Cox proportional hazards model and evaluated by C-statistics. RESULTS: Renal pelvic dilatation (RPD) was classified into two groups [Grades 1-2 (n = 255) versus Grades 3-4 (n = 192)]. The median follow-up time was 6.4 years (interquartile range 2.8-12.5). Thirteen patients (2.9%) developed proteinuria, 6 (1.3%) hypertension and 14 (3.1%) CKD Stage 2. All events occurred in patients with RPD Grades 3-4. After adjustment, three covariables remained as predictors of the composite event: creatinine {hazard ratio [HR] 1.27, [95% confidence interval (CI) 1.05-1.56]}, renal parenchyma thickness at birth [HR 0.78(95% CI 0.625-0.991)] and recurrent urinary tract infections [HR 4.52 (95% CI 1.49-13.6)]. The probability of renal injury at 15 years of age was estimated as 0, 15 and 24% for patients assigned to the low-risk, medium-risk and high-risk groups, respectively (P < 0.001). CONCLUSION: Our findings indicate an uneventful clinical course for patients with Society for Fetal Urology (SFU) Grades 1-2 ANH. Conversely, for infants with SFU Grades 3-4 ANH, our prediction model enabled the identification of a subgroup of patients with increased risk of renal injury over time.