Randomization, Intervention, and Outcome Measures. Part 2
Formal interim analyses for efficacy were conducted as requested by the data and safety monitoring board using the method of Lan and DeMets,47 with an O’Brien-Fleming–type spending function48 that adjusted for multiple looks at the data while preserving a near-nominal overall significance level.
Data analysis was on an intent-to-treat basis. Since we were able to either assess patients to the end of the study or track end point status through the Department of Veterans Affairs national database located in Austin, Tex,49 data from all randomized patients were included in the primary and secondary end point analyses, even though some patients were withdrawn from the study early.
Baseline patient characteristics were compared using the χ2 test, t test, or analysis of variance. Survival curves were used to characterize the timing of the primary and secondary end points during follow-up according to the method of Kaplan and Meier.50 Since accrual rate and duration, as well as control event rates, differed from prior assumptions, the achieved study precision was best revealed by the width of confidence intervals (CIs) for effect. The Cox proportional hazards regression model51 was used to compute hazard ratios (HRs) and 95% CIs, with adjustment for covariates.
The 5 prespecified biological covariates identified at entry (age, smoking status, diagnosis of DM, HDL-C/LDL-C ratio, and ferritin level) were analyzed using corresponding product terms in the proportional hazards regression models for possible interaction with treatment assignment. The interaction analysis was an exploratory, post hoc analysis; adjustments for multiple comparisons for this interaction analysis were not performed.
To explore and describe the nonlinear effect of the age interaction with treatment on the outcomes, age was fitted in the linear tail-restricted cubic spline function with 3 knots in the Cox proportional hazards model, and the log relative hazards were plotted. Interaction analyses of the 5 stratifiers were plotted with age, HDL-C/LDL-C ratio, and ferritin level presented as quartiles.
Randomization, Intervention, and Outcome Measures
Patients were assigned to control or iron-reduction groups through computer randomization stratified according to participating hospital, age (≤60 and >60 years), ferritin level at entry (calculated based on the rolling mean of prior entrants), diagnosis of DM, smoking status, and ratio of high-density lipoprotein cholesterol (HDL-C) level to low-density lipoprotein cholesterol (LDL-C) level (also calculated based on the rolling mean of prior entrants). Randomization was performed using the adaptive allocation method balanced on the marginal total of each factor.
For patients in the iron-reduction group, phlebotomy was scheduled at 6-month intervals so that appropriate volumes of blood were removed repeatedly throughout follow-up to achieve trough ferritin levels of approximately 25 ng/mL and peak ferritin levels prior to the next phlebotomy episode of approximately 60 ng/mL, a range presumed to be optimal. Compliance with intervention was assessed by 2 methods. First, the cumulative percentage of the amount of blood calculated for removal that was actually removed across all phlebotomy episodes was determined. Second, analysis of the effect of phlebotomy on the separation of ferritin levels over time between the 2 strategies was calculated. Follow-up data were obtained at 6-month intervals, at which time patients were interviewed and medical records reviewed for interim data-sheet entries by an observer blinded to intervention status. Follow-up began at the time of randomization.
The primary end point was all-cause mortality; the secondary end point was death plus nonfatal MI and stroke. Briefly, the diagnosis of nonfatal MI required the presence of definite biomarkers of MI in addition to symptoms consistent with acute MI or electrocardiographic changes consistent with MI or ischemia. The diagnosis of nonfatal stroke required evidence of ischemic or hemorrhagic brain injury manifested by either persistent impairment of motor ability, loss of vision in 1 or both eyes, or impairment of language use or speech production, each lasting 24 hours or longer; or severe headache associated with loss or alteration of consciousness, persistent neurologic signs, and/or neck stiffness (meningismus).
An external data and safety monitoring board reviewed all data during the course of the study. An external end points adjudication committee blinded to intervention status adjudicated primary and secondary study end points.
Statistical Methods
The target sample size was calculated using the method of Lakatos for a comparative time-to-event study based on the log-rank statistic. Assumptions included an annual mortality rate of 6.8% in the control group, a 30% decreased mortality in the iron-reduction group, a 5% 2-sided significance level, and 85% power. After adjusting for staggered accrual, lag in 3-month treatment effect, annual rate of losses to follow-up of 1%, and a 2.5% rate of noncompliance in year 1 and a rate of 1% thereafter, the sample size was calculated to be 1600 for a planned minimum follow-up of 2.5 years. Although randomization was stratified by hospital, age, and baseline smoking status, DM status, HDL-C/LDL-C ratio, and ferritin level, we did not incorporate stratification in the sample-size calculations and instead assumed average rates across strata. The lower than expected sample size of 1277 achieved, extension of the study from 5 to 6 years, and observed noncompliance rates of 16% in the first year and 3.2% thereafter (which were higher than expected) resulted in 68% power to detect a 30% reduction in mortality.
Reduction of Iron Stores and Cardiovascular Outcomes in Patients With Peripheral Arterial Disease. Patients
This study was a multicenter, randomized, controlled, single-blinded trial conducted within the Department of Veterans Affairs Cooperative Studies Program and designed to test the hypothesis that reduction in body iron stores by phlebotomy would influence clinical outcomes in patients with symptomatic but stable PAD. Experimental intervention was based on the Iron (Fe) and Atherosclerosis Study (FeAST) (VA Cooperative Study #410), a pilot study that demonstrated the accuracy of a formula for calculating the amount of blood required to be removed to achieve the desired ferritin reduction safely and without causing iron deficiency.
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Details of methods including participating site selection; patient entry characteristics; reasons for patient exclusion; informed consent procedures; and methods of randomization, reduction of iron stores by phlebotomy with removal of defined volumes of blood at 6-month intervals, single-blinded outcome assessment, intent-to-treat follow-up procedures, and study administration have been reported. Men and postmenopausal women with symptomatic but stable PAD and an ankle-brachial blood pressure ratio (ankle/brachial index) of 0.85 or less on 2 separate occasions were included provided they were not part of another experimental protocol and were judged able to meet protocol requirements. Included patients were required to have no bleeding within the past 6 months, no abnormality of iron metabolism, and to avoid taking iron supplements and donating blood during the study. The protocol was approved by the institutional review boards at each participating institution and by a national board; all included patients provided written informed consent.41
Entry criteria minimized accrual of patients with acute-phase elevation of ferritin level; patients with visceral malignancy within the preceding 5 years were excluded. Patients older than 21 years with advanced but stable PAD meeting defined entry criteria were entered over 3.5 years. Participants were not excluded based on severity or site of vascular disease in addition to PAD; medication use; or comorbid conditions including diabetes mellitus (DM), hypertension, chronic obstructive pulmonary disease, or degenerative joint disease (scored on data forms when patients required treatment). Patients were required to have a hematocrit greater than 35% (in the absence of iron deficiency) and a ferritin level less than 400 ng/mL, but there was no predefined minimum ferritin level.
Demographic, medical, and lifestyle information was collected at study entry by interview and review of the medical records. Race was self-reported using standard federal categories. Body mass index was calculated as weight in kilograms divided by height in meters squared, based on direct measurement. Smoking was recorded as ever vs never used inhaled tobacco products regularly. Alcohol use was recorded as the number of drinks usually consumed per week. For this report, alcohol was assessed as either used or not used currently. Angina class was based on the Goldman Scale. Patient recruitment began on May 1, 1999, and ended on October 31, 2002; follow-up ended on April 30, 2005 (6-year study duration).
Reduction of Iron Stores and Cardiovascular Outcomes in Patients With Peripheral Arterial Disease
Context Accumulation of iron in excess of physiologic requirements has been implicated in risk of cardiovascular disease because of increased iron-catalyzed free radical–mediated oxidative stress.
Objective To test the hypothesis that reducing body iron stores through phlebotomy will influence clinical outcomes in a cohort of patients with symptomatic peripheral arterial disease (PAD).
Design, Setting, and Patients Multicenter, randomized, controlled, single-blinded clinical trial based on the Iron (Fe) and Atherosclerosis Study (FeAST) (VA Cooperative Study #410) and conducted between May 1, 1999, and April 30, 2005, within the Department of Veterans Affairs Cooperative Studies Program and enrolling 1277 patients with symptomatic but stable PAD. Those with conditions likely to cause acute-phase increase of the ferritin level or with a diagnosis of visceral malignancy within the preceding 5 years were excluded. Analysis was by intent-to-treat.
Intervention Patients were assigned to a control group (n = 641) or to a group undergoing reduction of iron stores by phlebotomy with removal of defined volumes of blood at 6-month intervals (avoiding iron deficiency) (n = 636), stratified by hospital, age, and baseline smoking status, diagnosis of diabetes mellitus, ratio of high-density to low-density lipoprotein cholesterol level, and ferritin level.
Main Outcome Measures The primary end point was all-cause mortality; the secondary end point was death plus nonfatal myocardial infarction and stroke.
Results There were no significant differences between treatment groups for the primary or secondary study end points. All-cause deaths occurred in 148 patients (23%) in the control group and in 125 (20%) in the iron-reduction group (hazard ratio (HR), 0.85; 95% confidence interval (CI), 0.67-1.08; P = .17). Death plus nonfatal myocardial infarction and stroke occurred in 205 patients (32%) in the control group and in 180 (28%) in the iron-reduction group (HR, 0.88; 95% CI, 0.72-1.07; P = .20).
Conclusion Reduction of body iron stores in patients with symptomatic PAD did not significantly decrease all-cause mortality or death plus nonfatal myocardial infarction and stroke.
Accumulation of iron in excess of physiologic requirements has been implicated in the risk of several chronic diseases through increased iron-catalyzed free radical–mediated oxidative stress. Common diseases of aging that have been attributed to this mechanism include cardiovascular disease and cancer.
Sullivan formulated the iron-heart hypothesis of atherosclerotic cardiovascular disease to explain the age-related increase in risk of myocardial infarction (MI) in women following menopause. Serum ferritin levels average about 25 ng/mL in children and in women prior to menopause but increase in concert with increasing MI risk in women with cessation of menstrual blood loss. Rates of MI increase earlier in men, in whom ferritin levels begin to increase from childhood levels in the late teens. Increasing levels of body iron might be causative, a hypothesis that can be tested by reducing iron stores.
Substantial preclinical and clinical literature supports the contribution of iron-related oxidative stress to the pathogenesis of atherosclerotic cardiovascular disease. However, this concept remains controversial because of differences in findings between clinical studies having variable experimental design. Nonetheless, this hypothesis has continued to gain support from mechanistic and clinical studies. Several studies have suggested that iron may contribute to the pathogenesis of atherosclerosis relatively early in its course.
We conducted a randomized controlled study of reduction of body iron stores in patients with peripheral arterial disease (PAD). Phlebotomy was the intervention chosen because reducing iron levels through phlebotomy ameliorates iron-induced lipid peroxidation and because routine blood donation, an “over-the-counter” intervention, has been associated with improved health status and reduced risk of MI.
Specialist referral framework for nulliparous women. Conclusion
Conclusions
Wehave identified the most important clinical risk factors for pre-eclampsia in healthy nulliparous women and provided new information on the level of risk associated with specific combinations of risk factors. The predictive performance of the algorithm is modest, but offers a considerable improvement on current practice in healthy nulliparous women. As all known risk factors were included in this large prospective cohort, it shows the expected performance and limitations of using clinical phenotype to predict pre-eclampsia. The algorithm serves as a prototype that requires alidation in other nulliparous populations. If validated, it might provide a personalised clinical risk profile for nulliparous women to which biomarkers could be added.
We thank the pregnant women who participated in the SCOPE study, Claire Roberts for her contributions in establishing the SCOPE study in Adelaide, Denise Healy for coordinating the Australian SCOPE study, Annette Briley for coordinating the UK MAPS (SCOPE) study, Nicolai Murphy for coordinating the Cork SCOPE study, the SCOPE research midwives, and Steven Wu for his assistance with data imputation. Contributors: RAN was responsible for conception and design, analysis and interpretation of data, and drafting the article and revising it critically for important intellectual content. LMEMcC, GAD, LP, JJW, and PNB were responsible for conception and design and interpretation of data, and critical revision of paper for important intellectual content. EHYC, AWS, and MAB were responsible for statistical analyses and interpretation of data, and revising the article critically for important statistical content. RST was responsible for study design, coordination of clinical study, and revising the article critically for important intellectual content. LCK was responsible for conception and design, interpretation of data, drafting the article, and critical revision of paper for important intellectual content. All authors had full access to all of the data (including statistical reports and tables) in the study, can take responsibility for the integrity of the data and the accuracy of the data analysis, and approved the final version to be published. RAN is guarantor.
Funding: This study was funded by New Enterprise Research Fund, Foundation for Research Science and Technology; Health Research Council (04/198); Evelyn Bond Fund, Auckland District Health Board Charitable Trust; Premier’s Science and Research Fund, South Australian Government; Guy’s and St Thomas’ Charity, Tommy’s the baby Charity; Biotechnology and Biological Sciences Research Council (GT084), UK National Health Services (NEAT grant FSD025), University of Manchester Proof of Concept Funding, NIHR; Health Research Board, Ireland (CSA/ 2007/2). The study sponsors had no role in study design, data analysis or writing this report. MAB received consultancy fees from the SCO PE Study, University of Auckland, which were funded by the New Zealand Health Research Council and the New Enterprise Research Fund, foundation for Research Science and Technology.
Competing interests: All authors have completed the Unified Competing Interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; RAN and PNB have had consultancy relationships with Pronota in the previous three years; RAN has a consultancy relationship with Alere; LCK and PNB declare a US Provisional Patent Application in the name of University College Cork, Ireland (Louise Kenny and Philip Baker) “Detection of risk of preeclampsia” Application No USSN 61/288,465; RAN and MAB declares the following patent, which to date has not been licensed to a company: Blumenstein M, North RA, McMaster MT, Black MA, Kasabov NK, Cooper GJS. Biomarkers for prediction of pre-eclampsia and/or cardiovascular disease, PCT number WO/2009/108073. LP has a consultancy relationship with Tate and Lyle Research Advisory Group and is chairing a working party with ILSI Europe; both are outside the area of the submitted work.
Ethical approval: This study was approved by local ethics committees (New Zealand AKX/02/00/364, Australia REC 1712/5/2008, London and Manchester 06/MRE01/98, and Cork ECM5 (10) 05/02/08), and all women provided written informed consent.
Specialist referral framework for nulliparous women. Part 4
Confirming the results of a case-control study, a lower maternal birth weight was associated with an increased risk of pre-eclampsia, with an even greater risk when low maternal birth weight coexisted with other key risk factors. Prolonged vaginal bleeding in early pregnancy was associated with a twofold increase in risk of pre-eclampsia. As reported by others, most of these bleeds were mild in severity, suggesting that a discrete bleeding pattern could be associated with later pre-eclampsia. Several factors were associated with a reduced risk of pre-eclampsia. A single early miscarriage with the same partner, eating a lot of fruit, and smoking were protective, again reassuringly consistent with previous reports. The protective influence of cigarette smoking in our cohort was less than previously reported, and cigarettes did not remain in the model when we added uterine artery Doppler indices. Alcohol use in the first trimester was protective but requires confirmation in other cohorts. Obese women are reported to drink less alcohol, possibly because food fulfils their addictive behaviour. Obesity is unlikely to be the only explanation, however, as the protective effect of alcohol is retained with BMI in the model, and there was no interaction between BMI and alcohol. A recent series of publications reported algorithms to predict pre-eclampsia based on clinical risk factors in a general population comprising high risk women (previous pre-eclampsia and medical conditions), nulliparous women, and low risk women (multiparas with previous uncomplicated pregnancies). A model is fitted to the population in which it was developed, using the available candidate predictors. A general antenatal population constructed of subgroups with different risk profiles is difficult to replicate and future “general populations” are likely to comprise a different case-mix. The importance of population differences is evident in the failure of one proposed algorithm to validate in a high risk population, raising questions as to more general applicability to other populations such as “healthy” nulliparous women. Poor performance on validation might also occur because key predictors are missing from the model. When the list of candidate predictors includes strongly predictive factors, such as previous pre-eclampsia, renal disease, and chronic hypertension, these will take precedence, replacing other factors that might be more relevant to healthy nulliparous women. In contrast, in SCOPE, we investigated candidate predictors applicable to healthy nulliparous women.
Clinical relevance
The new information on the rate of pre-eclampsia in the presence of combinations of specific risk factors could be used by clinicians to improve current guidelines for specialist referral in nulliparous women. When we applied the criteria proposed in the NICE guidelines to the SCOPE cohort, 16.5% of nulliparous women would be referred for a specialist opinion of whom 10% would develop pre-eclampsia. This included 31% of the 186 women who developed preeclampsia and 38% of the 47 of those who developed preterm pre-eclampsia. If we included only first pregnancy, as in the NICE guidelines, 52 12% would be referred and 23% and 28% cases of pre-eclampsia and preterm pre-eclampsia, respectively, would be detected. Our proposed framework for specialist referral based on the algorithm, along with uterine artery Doppler screening of a subpopulation, performed better than the NICE guidelines but requires validation. Among the referred women (9% of nulliparas), the rate of pre-eclampsia was 21%. Thirty four per cent of cases of pre-eclampsia and 53% of cases of preterm preeclampsia were identified. This framework has the potential to identify a subgroup of nulliparous women at high risk of pre-eclampsia who could benefit from low dose aspirin and more intensive antenatal surveillance. It does not, however, provide additional information for the rest, whose risk is similar to an unscreened nulliparous population. Hence a negative “test” result would not modify current clinical care. The algorithm requires external validation, followed by assessment of the impact of increased surveillance, the false positive and false negative results, and a health economics analysis. If externally validated, this algorithm could help to inform future NICE guidelines for specialist referral. It could be made accessible, including via the web, as a support for risk stratification of healthy nulliparous women in low resource settings. To improve overall accuracy and detection of cases, the clinical algorithm will require the addition of biomarkers.
Specialist referral framework for nulliparous women. Part 3
Comparisons with other studies
Previous studies investigating risk factors for preeclampsia have used birth registries or hospital databases,634 randomised trials with negative results (that is, no treatment effect shown), and, in a few studies, prospective cohorts (usually general obstetric populations) designed to investigate outcomes of pregnancy. Consistent with other contemporary studies, the women who developed pre-eclampsia were younger, more obese, and more likely to have lower socioeconomic status. Many of the risk factors included in the algorithm presented here are associated with a similar degree of risk to that previously reported, giving confidence regarding the potential applicability of the algorithm to other populations. Higher blood pressure within the normal range, a higher BMI, and a family history of pre-eclampsia had similar predictive characteristics to those observed in other studies. In our algorithm, blood pressure was the most important risk factor driving the estimated probability of pre-eclampsia. Mean arterial pressure, rather than systolic or diastolic blood pressure blood, was selected by stepwise logistic regression and included in the model.8 If it was implemented into clinical practice, the clinician would derive the mean arterial pressure from systolic and diastolic blood pressure entered into the algorithm. A history of coronary artery disease in the woman’s father was associated with a 1.9-fold increase in the risk of preeclampsia, consistent with a previous report and the association between pre-eclampsia and subsequent.
Comparisons with other studies
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Previous studies investigating risk factors for preeclampsia have used birth registries or hospital databases,634 randomised trials with negative results (that is, no treatment effect shown), and, in a few studies, prospective cohorts (usually general obstetric populations) designed to investigate outcomes of pregnancy. Consistent with other contemporary studies, the women who developed pre-eclampsia were younger, more obese, and more likely to have lower socioeconomic status. Many of the risk factors included in the algorithm presented here are associated with a similar degree of risk to that previously reported, giving confidence regarding the potential applicability of the algorithm to other populations. Higher blood pressure within the normal range, a higher BMI, and a family history of pre-eclampsia had similar predictive characteristics to those observed in other studies. In our algorithm, blood pressure was the most important risk factor driving the estimated probability of pre-eclampsia. Mean arterial pressure, rather than systolic or diastolic blood pressure blood, was selected by stepwise logistic regression and included in the model. If it was implemented into clinical practice, the clinician would derive the mean arterial pressure from systolic and diastolic blood pressure entered into the algorithm. A history of coronary artery disease in the woman’s father was associated with a 1.9-fold increase in the risk of preeclampsia, consistent with a previous report and the association between pre-eclampsia and subsequent.
Specialist referral framework for nulliparous women. Part 2
The concept of a personalised clinical risk estimate for disease, to which biomarkers can be added, is established in several areas of medicine. The algorithm to predict pre-eclampsia reported here provides a first step towards a personalised risk score for pre-eclampsia among nulliparous women. It is inevitable the model will be overfitted to our population and external validation of the algorithm in other nulliparous populations is essential.Weplan to evaluate its performance in the next 3000 women recruited into SCOPE, nearly all of whom will be recruited in different centres than the initial 3500 women. Validation should also be performed in other study populations of nulliparous women.
Strengths and weaknesses
A major strength of this study is its large multicenter prospective design with excellent follow-up. As the focus of the SCOPE study is development of tests to predict pregnancy outcome with potential to translate into clinical care, we recruited a clearly defined population of nulliparous women, enabling identification of similar populations for external validation. This is critical for generalisability of a risk assessment algorithm; the population in which the algorithm is developed needs to be identifiable if a screening test is to be used in clinical care.
We obtained high quality data for all known risk factors for pre-eclampsia from questionnaires administered at interviews, along with detailed standard operating procedures. Use of a real time database, with automated checking procedures, reduced data entry errors and transcription errors. For a dataset of this size, the rate of missing data was minimal. The intensive two stage data monitoring adds confidence in data integrity. Potential measurement errors, such as in self reported family history, could have occurred, but as the goal was to develop a prototype algorithm ultimately for clinical use, this limitation was accepted. Principal investigators reviewed outcome data for cases, ensuring accurate diagnosis. One of the challenges when predicting rare events in prospective cohorts, such as SCOPE, is the relatively low numbers of cases compared with studies based on huge epidemiological databases. While the latter might have a substantially greater number of events, their interpretation is restrained by less accurate diagnosis. There is no consensus as to the best method for selection of variables. Given the rich dataset of potential predictors for pre-eclampsia, we used a pruning step based on significance testing and then selected a subset of candidate variables on a priori knowledge. This could have introduced variable selection bias, but it is reassuring that the clinical risk factors and their strength of association with pre-eclampsia are consistent with the literature. While we could undertake only internal validation at this stage, external validation is planned.
Clinical risk prediction for pre-eclampsia in nulliparous women. Part 6
The predicted probability of pre-eclampsia can then be calculated from 1/(1+e−riskscore). For example, for a 28 year old nulliparous woman whose birth weight was 2400 g, with a mean arterial pressure of 96 mm Hg, BMI 30, a family history of pre-eclampsia, and no protective factors, her probability of pre-eclampsia is 39%. Her risk of pre-eclampsia decreases as each risk factor is removed in stepwise fashion; if her mean arterial pressure is 80 mm Hg her probability of pre-eclampsia would be 18%, if her BMI was 24 her probability would be 14%, if she had no family history of preeclampsia her probability would be 8%, and if her birth weight had been 3500 g her probability would be 5%. If she had protective factors, such as a previous early miscarriage with her partner, her risk would be reduced to 2%.
Impact of definition of pre-eclampsia
To evaluate the impact of 24 women receiving a diagnosis of pre-eclampsia based on the presence of gestational hypertension combined with multisystem complications, the model was reconstructed defining the cases as pre-eclamptic women with proteinuria (n=162). Most risk factors and protective factors remained with similar odds ratios, except that age, high intake of fruit, and cigarettes were excluded and a sexual relationship of six months or less (odds ratio 1.7, 95% confidence interval 1.05 to 2.7), hyperemesis at 15 weeks (2.0, 1.1 to 3.7), and maternal height (0.87, 0.76 to 1.0) per 5 cm increase) were included.
Reproducibility of prediction model
To investigate the stability and potential reproducibility of the model (using all candidate predictors) we constructed 10 “best models” that included 10 variables. The risk factors (mean arterial blood pressure, BMI, family history of pre-eclampsia, family history of coronary heart disease (woman’s father), participant’s birth weight) and protective factors (≥12 months to conceive, alcohol used in the first trimester) occurred in all “10 best models.” Of the other variables in our model, six occurred in three to seven of the best models, while cigarettes a day was not selected by the stepwise model fitting procedure.
Risk estimates with specific combinations of clinical risk factors
Table 5 shows the proportion of women with specific combinations of key clinical risk factors and abnormal result on uterine artery Doppler who developed preeclampsia. We have shown systolic blood pressure rather thanmean arterial blood pressure as that requires calculation and, unless incorporated into an algorithm, is not easily applied in a routine clinic setting.
Clinical risk prediction for pre-eclampsia in nulliparous women. Statistical methods
WeusedSAS (version 9.1) for univariable data analysis and to generate a multivariable logistic regression model. We used Student’s t test, Wilcoxon rank sum test, or χ2 test for comparing characteristics in the study population and pregnancy outcomes between women who did and did not develop pre-eclampsia. Stepwise logistic regression was used to determine independent risk factors for pre-eclampsia in both datasets. The order of variable selection was determined by the χ2 statistic for each potential variable and the forward selection step could be followed by removal of variables in one or more backward elimination steps. We calculated receiver operating characteristics curves and determined screening test characteristics at a 25%, 10%, and 5% false positive rate. For internal from the 20±1 week dataset, in most cases because of missing data from the Doppler ultrasound (n=157).
In total 186 (5%) women developed pre-eclampsia; in eight the diagnosis was postpartum and 47 (1%) delivered preterm. Women who developed pre-eclampsia were younger, had a lower socioeconomic index, and at 15 weeks’ gestation were more likely to be obese and have higher blood pressure. Pre-eclampsia developed at a mean of 36.9 (SD 3.3) weeks’ gestation, with a median protein: creatinine ratio of 88 mg/mmol (range 30-2445 mg/ mmol) and 24 hour urinary protein excretion of 0.78 g (range 0.30-9.9 g). The diagnosis of pre-eclampsia was based on hypertension in combination with multisystem complications in 24 of the 186 women (13%), four of whom had “+” proteinuria. Forty two per cent of the women had at least one multisystem complication: 8% (n=14) had a diagnosis of HELLP (haemolysis, elevated liver enzymes, and low platelets) or ELLP (elevated liver enzymes and low platelets), 5% (n=9) developed impaired renal function, and one woman had eclampsia. A quarter of the babies were born preterm and 24% were small for gestational age.
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Prediction of pre-eclampsia with clinical risk factors and uterine artery Doppler
Addition of ultrasound scan variables to the 15 week clinical data resulted in age and the number of cigarettes a day being removed from the model and inclusion of duration of sexual relationship of six months or less and uterine artery Doppler waveform indices. Based on clinical risk factors, the mean AUC from the ten 10-fold cross validations was 0.71 (SE 0.002). The AUC for the proposed model based on the observations used to create the model was 0.76, indicating a bias in the C statistic of about 5%. The addition of 20 week uterine artery Doppler indices did not improve performance based on the study population (internally validated AUC 0.71 (SE 0.003)). Figure 3 shows that the model has a reasonable level of calibration, but there is an indication that, at the higher probabilities for preeclampsia, it might underestimate cases. Table 4 summarises the screening characteristics of the models at a false positive rate of 5%, 10%, and 25% based on the women from whom the model was created and from the internal validation where the values reported are the means of those derived from each of the cross validation analyses.