Routinely collected antenatal data for longitudinal prediction of preeclampsia in nulliparous women: a population-based study.
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ABSTRACT:
SUBMITTER: Sandstrom A
PROVIDER: S-EPMC8429420 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGuidelines recommend identifying in early pregnancy women at elevated risk of pre-eclampsia. The aim of this study was to develop and validate a pre-eclampsia risk prediction model for nulliparous women attending routine antenatal care "the Western Sydney (WS) model"; and to compare its performance with the National Institute of Health and Care Excellence (NICE) risk factor-list approach for classifying women as high-risk.MethodsThis retrospective cohort study included all nulliparous women who gave birth in three public hospitals in the Western-Sydney-Local-Health-District, Australia 2011-2014. Using births from 2011 to 2012, multivariable logistic regression incorporated established maternal risk factors to develop and internally validate the WS model. The WS model was then externally validated using births from 2013 to 2014, assessing its discrimination and calibration. We fitted the final WS model for all births from 2011 to 2014, and compared its accuracy in predicting pre-eclampsia with the NICE approach.ResultsAmong 12,395 births to nulliparous women in 2011-2014, there were 293 (2.4%) pre-eclampsia events. The WS model included: maternal age, body mass index, ethnicity, multiple pregnancy, family history of pre-eclampsia, autoimmune disease, chronic hypertension and chronic renal disease. In the validation sample (6201 births), the model c-statistic was 0.70 (95% confidence interval 0.65-0.75). The observed:expected ratio for pre-eclampsia was 0.91, with a Hosmer-Lemeshow goodness-of-fit test p-value of 0.20. In the entire study sample of 12,395 births, 374 (3.0%) women had a WS model-estimated pre-eclampsia risk ≥8%, the pre-specified risk-threshold for considering aspirin prophylaxis. Of these, 54 (14.4%) developed pre-eclampsia (sensitivity 18% (14-23), specificity 97% (97-98)). Using the NICE approach, 1173 (9.5%) women were classified as high-risk, of which 107 (9.1%) developed pre-eclampsia (sensitivity 37% (31-42), specificity 91% (91-92)). The final model showed similar accuracy to the NICE approach when using lower risk-threshold of ≥4% to classify women as high-risk for pre-eclampsia.ConclusionThe WS risk model that combines readily-available maternal characteristics achieved modest performance for prediction of pre-eclampsia in nulliparous women. The model did not outperform the NICE approach, but has the advantage of providing individualised absolute risk estimates, to assist with counselling, inform decisions for further testing, and consideration of aspirin prophylaxis.
| S-EPMC6945640 | biostudies-literature
Project description:ObjectivesTo determine the rate of complicated birth at term in women classified at low risk according to the National Institute for Health and Care Excellence guideline for intrapartum care (no pre-existing medical conditions, important obstetric history, or complications during pregnancy) and to assess if the risk classification can be improved by considering parity and the number of risk factors.DesignCohort study using linked electronic maternity records.Participants276 766 women with a singleton birth at term after a trial of labour in 87 NHS hospital trusts in England between April 2015 and March 2016.Main outcome measureA composite outcome of complicated birth, defined as a birth with use of an instrument, caesarean delivery, anal sphincter injury, postpartum haemorrhage, or Apgar score of 7 or less at five minutes.ResultsMultiparous women without a history of caesarean section had the lowest rates of complicated birth, varying from 8.8% (4879 of 55 426 women, 95% confidence interval 8.6% to 9.0%) in those without specific risk factors to 21.8% (613 of 2811 women, 20.2% to 23.4%) in those with three or more. The rate of complicated birth was higher in nulliparous women, with corresponding rates varying from 43.4% (25 805 of 59 413 women, 43.0% to 43.8%) to 64.3% (364 of 566 women, 60.3% to 68.3%); and highest in multiparous women with previous caesarean section, with corresponding rates varying from 42.9% (3426 of 7993 women, 41.8% to 44.0%) to 66.3% (554 of 836 women, 63.0% to 69.5%).ConclusionsNulliparous women without risk factors have substantially higher rates of complicated birth than multiparous women without a previous caesarean section even if the latter have multiple risk factors. Grouping women first according to parity and previous mode of birth, and then within these groups according to presence of specific risk factors would provide greater and more informed choice to women, better targeting of interventions, and fewer transfers during labour than according to the presence of risk factors alone.
| S-EPMC7527835 | biostudies-literature
Project description:The objective of this study was to assess the reliability of individual risk predictions based on routinely collected data considering the heterogeneity between clinical sites in data and populations. Cardiovascular disease (CVD) risk prediction with QRISK3 was used as exemplar. The study included 3.6 million patients in 392 sites from the Clinical Practice Research Datalink. Cox models with QRISK3 predictors and a frailty (random effect) term for each site were used to incorporate unmeasured site variability. There was considerable variation in data recording between general practices (missingness of body mass index ranged from 18.7% to 60.1%). Incidence rates varied considerably between practices (from 0.4 to 1.3 CVD events per 100 patient-years). Individual CVD risk predictions with the random effect model were inconsistent with the QRISK3 predictions. For patients with QRISK3 predicted risk of 10%, the 95% range of predicted risks were between 7.2% and 13.7% with the random effects model. Random variability only explained a small part of this. The random effects model was equivalent to QRISK3 for discrimination and calibration. Risk prediction models based on routinely collected health data perform well for populations but with great uncertainty for individuals. Clinicians and patients need to understand this uncertainty.
| S-EPMC6677736 | biostudies-literature
Project description:BackgroundRecent decreases in neonatal mortality have been slower than expected for most countries. This study aims to predict the risk of neonatal mortality using only data routinely available from birth records in the largest city of the Americas.MethodsA probabilistic linkage of every birth record occurring in the municipality of São Paulo, Brazil, between 2012 e 2017 was performed with the death records from 2012 to 2018 (1,202,843 births and 447,687 deaths), and a total of 7282 neonatal deaths were identified (a neonatal mortality rate of 6.46 per 1000 live births). Births from 2012 and 2016 (N = 941,308; or 83.44% of the total) were used to train five different machine learning algorithms, while births occurring in 2017 (N = 186,854; or 16.56% of the total) were used to test their predictive performance on new unseen data.ResultsThe best performance was obtained by the extreme gradient boosting trees (XGBoost) algorithm, with a very high AUC of 0.97 and F1-score of 0.55. The 5% births with the highest predicted risk of neonatal death included more than 90% of the actual neonatal deaths. On the other hand, there were no deaths among the 5% births with the lowest predicted risk. There were no significant differences in predictive performance for vulnerable subgroups. The use of a smaller number of variables (WHO's five minimum perinatal indicators) decreased overall performance but the results still remained high (AUC of 0.91). With the addition of only three more variables, we achieved the same predictive performance (AUC of 0.97) as using all the 23 variables originally available from the Brazilian birth records.ConclusionMachine learning algorithms were able to identify with very high predictive performance the neonatal mortality risk of newborns using only routinely collected data.
| S-EPMC8293479 | biostudies-literature
Project description:CA125 is widely used as an initial investigation in women presenting with symptoms of possible ovarian cancer. We sought to develop CA125-based diagnostic prediction models and to explore potential implications of implementing model-based thresholds for further investigation in primary care. This retrospective cohort study used routinely collected primary care and cancer registry data from symptomatic, CA125-tested women in England (2011-2014). A total of 29,962 women were included, of whom 279 were diagnosed with ovarian cancer. Logistic regression was used to develop two models to estimate ovarian cancer probability: Model 1 consisted of age and CA125 level; Model 2 incorporated further risk factors. Model discrimination (AUC) was evaluated using 10-fold cross-validation. The sensitivity and specificity of various model risk thresholds (≥1% to ≥3%) were compared with that of the current CA125 cut-off (≥35 U/mL). Model 1 exhibited excellent discrimination (AUC: 0.94) on cross-validation. The inclusion of additional variables (Model 2) did not improve performance. At a risk threshold of ≥1%, Model 1 exhibited greater sensitivity (86.4% vs. 78.5%) but lower specificity (89.1% vs. 94.5%) than CA125 (≥35 U/mL). Applying the ≥1% model threshold to the cohort in place of the current CA125 cut-off, 1 in every 74 additional women identified had ovarian cancer. Following external validation, Model 1 could be used as part of a 'risk-based triage' system in which women at high risk of undiagnosed ovarian cancer are selected for urgent specialist investigation, while women at 'low risk but not no risk' are offered non-urgent investigation or interval CA125 re-testing. Such an approach has the potential to expedite ovarian cancer diagnosis, but further research is needed to evaluate the clinical impact and health-economic implications.
| S-EPMC8228892 | biostudies-literature
Project description:OBJECTIVE:To evaluate the capacity of multivariable prediction of preeclampsia during pregnancy, based on detailed routinely collected early pregnancy data in nulliparous women. DESIGN AND SETTING:A population-based cohort study of 62 562 pregnancies of nulliparous women with deliveries 2008-13 in the Stockholm-Gotland Counties in Sweden. METHODS:Maternal social, reproductive and medical history and medical examinations (including mean arterial pressure, proteinuria, hemoglobin and capillary glucose levels) routinely collected at the first visit in antenatal care, constitute the predictive variables. Predictive models for preeclampsia were created by three methods; logistic regression models using 1) pre-specified variables (similar to the Fetal Medicine Foundation model including maternal factors and mean arterial pressure), 2) backward selection starting from the full suite of variables, and 3) a Random forest model using the same candidate variables. The performance of the British National Institute for Health and Care Excellence (NICE) binary risk classification guidelines for preeclampsia was also evaluated. The outcome measures were diagnosis of preeclampsia with delivery <34, <37, and ?37 weeks' gestation. RESULTS:A total of 2 773 (4.4%) nulliparous women subsequently developed preeclampsia. The pre-specified variables model was superior the other two models, regarding prediction of preeclampsia with delivery <34 and <37 weeks, both with areas under the curve of 0.68, and sensitivity of 30.6% (95% CI 24.5-37.2) and 29.2% (95% CI 25.2-33.4) at a 10% false positive rate, respectively. The performance of these customizable multivariable models at the chosen false positive rate, was significantly better than the binary NICE-guidelines for preeclampsia with delivery <37 and ?37 weeks' gestation. CONCLUSION:Multivariable models in early pregnancy had a modest performance, although providing advantages over the NICE-guidelines, in predicting preeclampsia in nulliparous women. Use of a machine learning algorithm (Random forest) did not result in superior prediction.
| S-EPMC6881002 | biostudies-literature
Project description:ObjectiveTo examine whether gender differences in primary care consultation rates (1) vary by age and deprivation status and (2) diminish when consultation for reproductive reasons or common underlying morbidities are accounted for.DesignCross-sectional study of a cohort of patients registered with general practice.SettingUK primary care.SubjectsPatients (1 869 149 men and 1 916 898 women) registered with 446 eligible practices in 2010.Primary outcome measuresPrimary care consultation rate.ResultsThis study analyses routinely collected primary care consultation data. The crude consultation rate was 32% lower in men than women. The magnitude of gender difference varied across the life course, and there was no 'excess' female consulting in early and later life. The greatest gender gap in primary care consultations was seen among those aged between 16 and 60 years. Gender differences in consulting were higher in people from more deprived areas than among those from more affluent areas. Accounting for reproductive-related consultations diminished but did not eradicate the gender gap. However, consultation rates in men and women who had comparable underlying morbidities (as assessed by receipt of medication) were similar; men in receipt of antidepressant medication were only 8% less likely to consult than women in receipt of antidepressant medication (relative risk (RR) 0.916, 95% CI 0.913 to 0.918), and men in receipt of medication to treat cardiovascular disease were just 5% less likely to consult (RR=0.950, 95% CI 0.948 to 0.952) than women receiving similar medication. These small gender differences diminished further, particularly for depression (RR=0.950, 95% CI 0.947 to 0.953), after also taking account of reproductive consultations.ConclusionsOverall gender differences in consulting are most marked between the ages of 16 and 60 years; these differences are only partially accounted for by consultations for reproductive reasons. Differences in consultation rates between men and women were largely eradicated when comparing men and women in receipt of medication for similar underlying morbidities.
| S-EPMC3753483 | biostudies-literature
Project description:Weight gain in early pregnancy may influence a woman's risk of developing preeclampsia. However, the consequences of weight gain throughout pregnancy up to the diagnosis of preeclampsia are unknown. The aim of this study was to determine whether pregnancy weight gain before the diagnosis of preeclampsia is associated with increased risks of preeclampsia (overall and by preeclampsia subtype). The study population included nulliparous pregnant women in the Swedish counties of Gotland and Stockholm, 2008 to 2013, stratified by early pregnancy body mass index category. Electronic medical records were linked with population inpatient and outpatient records to establish date of preeclampsia diagnosis (classified as any, early preterm <34 weeks, late preterm 34-36 weeks, or term ?37 weeks). Antenatal weight gain measurements were standardized into gestational age-specific z scores. Among 62?705 nulliparous women, 2770 (4.4%) developed preeclampsia. Odds of preeclampsia increased by ?60% with every 1 z score increase in pregnancy weight gain among normal weight and overweight women and by 20% among obese women. High pregnancy weight gain was more strongly associated with term preeclampsia than early preterm preeclampsia (eg, 64% versus 43% increased odds per 1 z score difference in weight gain in normal weight women, and 30% versus 0% in obese women, respectively). By 25 weeks, the weight gain of women who subsequently developed preeclampsia was significantly higher than women who did not (eg, 0.43 kg in normal weight women). In conclusion, high pregnancy weight gain before diagnosis increases the risk of preeclampsia in nulliparous women and is more strongly associated with later-onset preeclampsia than early-onset preeclampsia.
| S-EPMC6043369 | biostudies-literature
Project description:Accuracy of risk assessments for clinical outcomes in patients with chronic liver disease has been limited given the nonlinear nature of disease progression. Longitudinal prediction models may more accurately capture this dynamic risk. The aim of this study was to construct accurate models of short- and long-term risk of disease progression in patients with chronic hepatitis C by incorporating longitudinal clinical data. Data from the Hepatitis C Antiviral Long-term Treatment Against Cirrhosis trial were analysed (n = 533 training cohort; n = 517 validation cohort). Outcomes included a composite liver outcome (liver-related death, decompensation, hepatocellular carcinoma (HCC) or liver transplant), decompensation, HCC and overall mortality. Longitudinal models were constructed for risk of outcomes at 1, 3 and 5 years and compared with models using data at baseline only or baseline and a single follow-up time point. A total of 25.1% of patients in the training and 20.8% in the validation cohort had an outcome during a median follow-up of 6.5 years (range 0.5-9.2). The most important predictors were as follows: albumin, aspartate aminotransferase/alanine aminotransferase ratio, bilirubin, alpha-fetoprotein and platelets. Longitudinal models outperformed baseline models with higher true-positive rates and negative predictive values. The areas under the receiver-operating characteristic curve for the composite longitudinal model were 0.89 (0.80-0.96), 0.83 (0.76-0.88) and 0.81 (0.75-0.87) for 1-, 3-, and 5-year risk prediction, respectively. Model performance was retained for decompensation and overall mortality but not HCC. Longitudinal prediction models provide accurate risk assessments and identify patients in need of intensive monitoring and care.
| S-EPMC5809174 | biostudies-other
Project description:In most countries with large drug resistant tuberculosis epidemics, only those cases that are at highest risk of having MDRTB receive a drug sensitivity test (DST) at the time of diagnosis. Because of this prioritized testing, identification of MDRTB transmission hotspots in communities where TB cases do not receive DST is challenging, as any observed aggregation of MDRTB may reflect systematic differences in how testing is distributed in communities. We introduce a new disease mapping method, which estimates this missing information through probability-weighted locations, to identify geographic areas of increased risk of MDRTB transmission. We apply this method to routinely collected data from two districts in Lima, Peru over three consecutive years. This method identifies an area in the eastern part of Lima where previously untreated cases have increased risk of MDRTB. This may indicate an area of increased transmission of drug resistant disease, a finding that may otherwise have been missed by routine analysis of programmatic data. The risk of MDR among retreatment cases is also highest in these probable transmission hotspots, though a high level of MDR among retreatment cases is present throughout the study area. Identifying potential multidrug resistant tuberculosis (MDRTB) transmission hotspots may allow for targeted investigation and deployment of resources.
| S-EPMC3323731 | biostudies-literature