Estimating the Incidence and Key Risk Factors of Cardiovascular Disease in Patients at High Risk of Imminent Fracture Using Routinely Collected Real-World Data From the UK

The objective of this work was to estimate the incidence rate of cardiovascular disease (CVD) events (myocardial infarction, stroke, or CVD death) at 1 year among three cohorts of patients at high risk of fracture (osteoporosis, previous fracture, and anti-osteoporosis medication) and to identify the key risk factors of CVD events in these three cohorts. To do so, this prospective cohort study used data from the Clinical Practice Research Datalink, a primary care database from United Kingdom. Major adverse cardiovascular events (MACE, a composite outcome for the occurrence of either myocardial infarction [MI], stroke, or CVD death) were identified in patients aged 50 years or older at high or imminent fracture risk identified in three different cohorts (not mutually exclusive): recently diagnosed with osteoporosis (OST, n = 65,295), incident fragility fracture (IFX, n = 67,065), and starting oral bisphosphonates (OBP, n = 145,959). About 1.90%, 4.39%, and 2.38% of the participants in OST, IFX, and OBP cohorts, respectively, experienced MACE events. IFX was the cohort with the higher risk: MACE incidence rates (cases/1000 person-years) were 19.63 (18.54-20.73) in OST, 52.64 (50.7-54.5) in IFX, and 26.26 (25.41-27.12) in OBP cohorts. Risk of MACE events at 1 year was predicted in the three cohorts. Models using a set of general, CVD, and fracture candidates selected by lasso regression had a good discrimination (≥70%) and internal validity and generally outperformed the models using only the CVD risk factors of general population listed in QRISK tool. Main risk factors common in all MACE models were sex, age, smoking, alcohol, atrial fibrillation, antihypertensive medication, prior MI/stroke, established CVD, glomerular filtration rate, systolic blood pressure, cholesterol levels, and number of concomitant medicines. Identified key risk factors highlight the differences of patients at high risk of fracture versus general population. Proposed models could improve prediction of CVD events in patients with osteoporosis in primary care settings. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Predicting Imminent Fractures in Patients With a Recent Fracture or Starting Oral Bisphosphonate Therapy: Development and International Validation of Prognostic Models

The availability of anti-osteoporosis medications with rapid onset and high potency requires tools to identify patients at high imminent fracture risk (IFR). There are few tools that predict a patient's IFR. We aimed to develop and validate tools for patients with a recent fracture and for patients initiating oral bisphosphonate therapy. Models for two separate cohorts, those with incident fragility fracture (IFx) and with incident oral bisphosphonate prescription (OBP), were developed in primary care records from Spain (SIDIAP database), UK (Clinical Practice Research Datalink GOLD), and Denmark (Danish Health Registries). Separate models were developed for hip, major, and any fracture outcomes. Only variables present in all databases were included in Lasso regression models for the development and logistic regression models for external validation. Discrimination was tested using area under curve (AUC) and calibration was assessed using observed versus predicted risk plots stratified by age, sex, and previous fracture history. The development analyses included 35,526 individuals in the IFx and 41,401 in the OBP cohorts, with 671,094 in IFx and 330,256 in OBP for the validation analyses. Both the IFx and OBP models demonstrated similarly good performance for hip fracture at 1 year (with AUCs of 0.79 [95% CI 0.75 to 0.82] and 0.87 [0.83 to 0.91] in Spain, 0.71 [0.71 to 0.72] and 0.73 [0.72 to 0.74] in the UK, and 0.70 [0.70 to 0.70] and 0.69 [0.68 to 0.70] in Denmark), and lower discrimination for major osteoporotic and any fracture sites. Calibration was good across all three countries. Discrimination and calibration for the 2-year models was similar. The proposed IFR prediction models could be used to identify more precisely patients at high imminent risk of fracture and inform anti-osteoporosis treatment selection. The freely available model parameters permit local validation and implementation. © 2021 American Society for Bone and Mineral Research (ASBMR).

Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: an individual participant data meta-analysis

BACKGROUND

Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management.

OBJECTIVES

To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers.

DESIGN

This was an individual participant data meta-analysis of cohort studies.

SETTING

Source data from secondary and tertiary care.

PREDICTORS

We identified predictors from systematic reviews, and prioritised for importance in an international survey.

PRIMARY OUTCOMES

Early-onset (delivery at < 34 weeks' gestation), late-onset (delivery at ≥ 34 weeks' gestation) and any-onset pre-eclampsia.

ANALYSIS

We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration. We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I ² and τ². A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals.

RESULTS

The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia.

LIMITATIONS

Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data.

CONCLUSION

For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings.

FUTURE WORK

Recalibration of model parameters within populations may improve calibration performance. Additional strong predictors need to be identified to improve model performance and consistency. Validation, including examination of calibration heterogeneity, is required for the models we could not validate.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42015029349.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 72. See the NIHR Journals Library website for further project information.

A prognostic model, including quantitative fetal fibronectin, to predict preterm labour: the QUIDS meta-analysis and prospective cohort study

BACKGROUND

The diagnosis of preterm labour is challenging. False-positive diagnoses are common and result in unnecessary, potentially harmful treatments (e.g. tocolytics, antenatal corticosteroids and magnesium sulphate) and costly hospital admissions. Measurement of fetal fibronectin in vaginal fluid is a biochemical test that can indicate impending preterm birth.

OBJECTIVES

To develop an externally validated prognostic model using quantitative fetal fibronectin concentration, in combination with clinical risk factors, for the prediction of spontaneous preterm birth and to assess its cost-effectiveness.

DESIGN

The study comprised (1) a qualitative study to establish the decisional needs of pregnant women and their caregivers, (2) an individual participant data meta-analysis of existing studies to develop a prognostic model for spontaneous preterm birth within 7 days in women with symptoms of preterm labour based on quantitative fetal fibronectin and clinical risk factors, (3) external validation of the prognostic model in a prospective cohort study across 26 UK centres, (4) a model-based economic evaluation comparing the prognostic model with qualitative fetal fibronectin, and quantitative fetal fibronectin with cervical length measurement, in terms of cost per QALY gained and (5) a qualitative assessment of the acceptability of quantitative fetal fibronectin.

DATA SOURCES/SETTING

The model was developed using data from five European prospective cohort studies of quantitative fetal fibronectin. The UK prospective cohort study was carried out across 26 UK centres.

PARTICIPANTS

Pregnant women at 22⁺⁰-34⁺⁶ weeks' gestation with signs and symptoms of preterm labour.

HEALTH TECHNOLOGY BEING ASSESSED

Quantitative fetal fibronectin.

MAIN OUTCOME MEASURES

Spontaneous preterm birth within 7 days.

RESULTS

The individual participant data meta-analysis included 1783 women and 139 events of spontaneous preterm birth within 7 days (event rate 7.8%). The prognostic model that was developed included quantitative fetal fibronectin, smoking, ethnicity, nulliparity and multiple pregnancy. The model was externally validated in a cohort of 2837 women, with 83 events of spontaneous preterm birth within 7 days (event rate 2.93%), an area under the curve of 0.89 (95% confidence interval 0.84 to 0.93), a calibration slope of 1.22 and a Nagelkerke R ² of 0.34. The economic analysis found that the prognostic model was cost-effective compared with using qualitative fetal fibronectin at a threshold for hospital admission and treatment of ≥ 2% risk of preterm birth within 7 days.

LIMITATIONS

The outcome proportion (spontaneous preterm birth within 7 days of test) was 2.9% in the validation study. This is in line with other studies, but having slightly fewer than 100 events is a limitation in model validation.

CONCLUSIONS

A prognostic model that included quantitative fetal fibronectin and clinical risk factors showed excellent performance in the prediction of spontaneous preterm birth within 7 days of test, was cost-effective and can be used to inform a decision support tool to help guide management decisions for women with threatened preterm labour.

FUTURE WORK

The prognostic model will be embedded in electronic maternity records and a mobile telephone application, enabling ongoing data collection for further refinement and validation of the model.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42015027590 and Current Controlled Trials ISRCTN41598423.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 52. See the NIHR Journals Library website for further project information.

A prognostic model to guide decision-making on timing of delivery in late preterm pre-eclampsia: the PEACOCK prospective cohort study

BACKGROUND

Pre-eclampsia affects around 2-3% of all pregnancies, and is associated with potential serious complications for the woman and the baby. Once diagnosed, progression of the syndrome can be unpredictable, and decisions around timing of delivery need to take into account evolving maternal complications and perinatal morbidity. Novel prognostic models and blood biomarkers for determination of need for delivery in pregnancies with pre-eclampsia are now emerging.

OBJECTIVE

The objective of the study was to establish a prognostic model to inform optimal timing of delivery in women with late preterm pre-eclampsia (34^+ 0 to 36^+ 6 weeks' gestation), comparing novel candidate biomarkers (e.g. placental growth factor) with clinical and routinely collected blood/urinary parameters [incorporated into the PREP-S (Prediction models for Risk of Early-onset Pre-eclampsia - Survival) model] to determine clinically indicated need for delivery for pre-eclampsia (or related complications) within 7 days of assessment.

METHODS

Prospective recruitment of women in whom blood samples for placental growth factor and soluble fms-like tyrosine kinase-1 testing was obtained, alongside clinical data, for use within the PREP-S model. Candidate variables were compared using standard methods (sensitivity, specificity, receiver operator curve areas). Estimated probability of early delivery from PREP-S was compared with actual event rates by calibration.

SETTING

The PEACOCK (Prognostic indicators of severe disEAse in women with late preterm pre-eClampsia tO guide deCision maKing on timing of delivery) study was a prospective cohort study, nested within the PHOENIX (Pre-eclampsia in HOspital: Early iNductIon or eXpectant management) trial.

PARTICIPANTS

Women between 34^+ 0 and 36^+ 6 weeks' gestation, with a diagnosis of pre-eclampsia, in whom a plasma (ethylenediaminetetraacetic acid) blood sample for placental growth factor testing was obtained, alongside clinical data for the assessment of variables in a prognostic model.

MAIN OUTCOME MEASURES

Clinically indicated need for delivery for pre-eclampsia within 7 days of assessment. Statistical analysis: both PREP-S and placental growth factor were assessed and compared using standard methods (sensitivity and specificity for placental growth factor thresholds of 100 pg/ml and < 12 pg/ml, and receiver operating characteristic areas for continuous measurements). The estimated probability of early delivery from PREP-S was compared with actual event rates for women with similar probabilities by calibration. Calibration using logistic regression was also used.

RESULTS

Between 27 April 2016 and 24 December 2018, 501 women were recruited to the study. Although placental growth factor testing had high sensitivity (97.9%) for delivery within 7 days, the negative predictive value was only 71.4% and the specificity was low (8.4%). The area under the curve for the clinical prediction model (PREP-S) and placental growth factor in this cohort in determining need for delivery within 7 days was 0.64 (standard error 0.03) and 0.60 (standard error 0.03), respectively, and 0.65 (standard error 0.03) in combination.

LIMITATIONS

A high proportion of women in this cohort already had low placental growth factor concentrations at the time of confirmed diagnosis, which reduced the ability of the biomarker to further predict adverse outcomes.

CONCLUSIONS

In this group of women with late preterm pre-eclampsia, placental growth factor measurement is not likely to add to the current clinical assessment to help plan care for late preterm pre-eclampsia regarding timing of delivery. Existing models developed in women with early-onset pre-eclampsia to predict complications cannot be used to predict clinically indicated need for delivery in women with late preterm pre-eclampsia.

FUTURE WORK

Further statistical modelling and subgroup analysis is being considered to assess if improved model performance in the whole cohort or a subgroup can be achieved. Addition of other biomarkers to the model may also be of use and will be explored.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN01879376.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 30. See the NIHR Journals Library website for further project information.

The Generation and Validation of a 20-Genes Model Influencing the Prognosis of Colorectal Cancer

Colorectal cancer is a common malignant tumor with high incidence affecting the digestive system. This study aimed to identify the key genes relating to prognosis of colorectal cancer and to construct a prognostic model for its risk evaluation. Gene expression profiling of colorectal cancer patients, GSE17537, was downloaded from Gene Expression Omnibus database (GEO). A total of 55 samples from patients ranging from stages 1 to 4 were available. Differentially expressed genes were screened, with which single factor survival analysis was performed to identify the response genes. Interacting network and KEGG enrichment analysis of responsive genes were performed to identify key genes. In return, Fisher enrichment analysis, literature mining, and Kaplan-Meier analysis were used to verify the effectiveness of the prognostic model. The 20-gene model generated in this study posed significant influences on the prognoses (P = 9.691065e-09). Significance was verified via independent dataset GSE38832 (P = 9.86581e-07) and GSE17536 (P = 2.741e-08). The verified effective 20-gene model could be utilized to predict prognosis of patients with colorectal cancer and would contribute to post-operational treatment and follow-up strategies. J. Cell. Biochem. 118: 3675-3685, 2017. © 2017 Wiley Periodicals, Inc.