Analysis of clinical benefit, harms, and cost-effectiveness of screening women for abdominal aortic aneurysm.

BACKGROUND: A third of deaths in the UK from ruptured abdominal aortic aneurysm (AAA) are in women. In men, national screening programmes reduce deaths from AAA and are cost-effective. The benefits, harms, and cost-effectiveness in offering a similar programme to women have not been formally assessed, and this was the aim of this study. METHODS: We developed a decision model to assess predefined outcomes of death caused by AAA, life years, quality-adjusted life years, costs, and the incremental cost-effectiveness ratio for a population of women invited to AAA screening versus a population who were not invited to screening. A discrete event simulation model was set up for AAA screening, surveillance, and intervention. Relevant women-specific parameters were obtained from sources including systematic literature reviews, national registry or administrative databases, major AAA surgery trials, and UK National Health Service reference costs. FINDINGS: AAA screening for women, as currently offered to UK men (at age 65 years, with an AAA diagnosis at an aortic diameter of ≥3·0 cm, and elective repair considered at ≥5·5cm) gave, over 30 years, an estimated incremental cost-effectiveness ratio of £30 000 (95% CI 12 000-87 000) per quality-adjusted life year gained, with 3900 invitations to screening required to prevent one AAA-related death and an overdiagnosis rate of 33%. A modified option for women (screening at age 70 years, diagnosis at 2·5 cm and repair at 5·0 cm) was estimated to have an incremental cost-effectiveness ratio of £23 000 (9500-71 000) per quality-adjusted life year and 1800 invitations to screening required to prevent one AAA-death, but an overdiagnosis rate of 55%. There was considerable uncertainty in the cost-effectiveness ratio, largely driven by uncertainty about AAA prevalence, the distribution of aortic sizes for women at different ages, and the effect of screening on quality of life. INTERPRETATION: By UK standards, an AAA screening programme for women, designed to be similar to that used to screen men, is unlikely to be cost-effective. Further research on the aortic diameter distribution in women and potential quality of life decrements associated with screening are needed to assess the full benefits and harms of modified options. FUNDING: UK National Institute for Health Research Health Technology Assessment programme.

Biomarkers and clinical outcomes in COPD: a systematic review and meta-analysis.

BACKGROUND: Conventional measures to evaluate COPD may fail to capture systemic problems, particularly musculoskeletal weakness and cardiovascular disease. Identifying these manifestations and assessing their association with clinical outcomes (ie, mortality, exacerbation and COPD hospital admission) is of increasing clinical importance. OBJECTIVE: To assess associations between 6 min walk distance (6MWD), heart rate, fibrinogen, C reactive protein (CRP), white cell count (WCC), interleukins 6 and 8 (IL-6 and IL-8), tumour necrosis factor-alpha, quadriceps maximum voluntary contraction, sniff nasal inspiratory pressure, short physical performance battery, pulse wave velocity, carotid intima-media thickness and augmentation index and clinical outcomes in patients with stable COPD. METHODS: We systematically searched electronic databases (August 2018) and identified 61 studies, which were synthesised, including meta-analyses to estimate pooled HRs, following Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: Shorter 6MWD and elevated heart rate, fibrinogen, CRP and WCC were associated with higher risk of mortality. Pooled HRs were 0.80 (95% CI 0.73 to 0.89) per 50 m longer 6MWD, 1.10 (95% CI 1.02 to 1.18) per 10 bpm higher heart rate, 3.13 (95% CI 2.14 to 4.57) per twofold increase in fibrinogen, 1.17 (95% CI 1.06 to 1.28) per twofold increase in CRP and 2.07 (95% CI 1.29 to 3.31) per twofold increase in WCC. Shorter 6MWD and elevated fibrinogen and CRP were associated with exacerbation, and shorter 6MWD, higher heart rate, CRP and IL-6 were associated with hospitalisation. Few studies examined associations with musculoskeletal measures. CONCLUSION: Findings suggest 6MWD, heart rate, CRP, fibrinogen and WCC are associated with clinical outcomes in patients with stable COPD. Use of musculoskeletal measures to assess outcomes in patients with COPD requires further investigation. TRIAL REGISTRATION NUMBER: CRD42016052075.

Effect of interpregnancy weight change on perinatal outcomes: systematic review and meta-analysis.

BACKGROUND: Although obesity is a well-known risk factor for adverse pregnancy outcomes, evidence is sparse about the effects of interpregnancy weight change on the risk of adverse perinatal complications in a subsequent pregnancy. The current study aims to assess the effect of interpregnancy weight change on the risk of developing gestational diabetes, pre-eclampsia, pregnancy induced hypertension, preterm birth, or delivering a large- or small-for-gestational age neonate. METHODS: Pubmed, Ovid Embase, ClinicalTrial.gov and the Cochrane library were systematically searched up until July 24th, 2019. Interpregnancy weight change was defined as the difference between pre-pregnancy weight of an index pregnancy and a consecutive pregnancy. Inclusion criteria included full text original articles reporting quantitative data about interpregnancy weight change in multiparous women with any time interval between consecutive births and the risk of any perinatal complication of interest. Studies reporting adjusted odds ratios and a reference group of - 1 to + 1 BMI unit change between pregnancies were harmonised by meta-analysis. RESULTS: Twenty-three cohort studies identified a total of 671,906 women with two or more consecutive pregnancies. Seven of these studies were included in the meta-analysis (280,672 women). Interpregnancy weight gain was consistently associated with a higher risk of gestational diabetes, pre-eclampsia, pregnancy induced hypertension and large-for-gestational age births. In contrast, interpregnancy weight loss was associated with a lower risk of delivering a large-for-gestational age neonate. The effect magnitude (relative risk) of interpregnancy weight gain on pregnancy induced hypertension or delivering a large-for-gestational age neonate was greater among women with a normal BMI in the index pregnancy compared to women with a starting BMI ≥25 kg/m2. CONCLUSION: These findings confirm that interpregnancy weight change impacts the risk of developing perinatal complications in a subsequent pregnancy. This provides evidence in support of guidelines encouraging women to achieve post-partum weight loss, as their risk of perinatal complications might be minimised if they return to their pre-pregnancy weight before conceiving again. Prospectively registered with PROSPERO (CRD42017067326).

Lipoprotein(a) and incident type-2 diabetes: results from the prospective Bruneck study and a meta-analysis of published literature.

AIMS: We aimed to (1) assess the association between lipoprotein(a) [Lp(a)] concentration and incident type-2 diabetes in the Bruneck study, a prospective population-based study, and (2) combine findings with evidence from published studies in a literature-based meta-analysis. METHODS: We used Cox proportional hazards models to calculate hazard ratios (HR) for incident type-2 diabetes over 20 years of follow-up in 815 participants of the Bruneck study according to their long-term average Lp(a) concentration. For the meta-analysis, we searched Medline, Embase and Web of Science for relevant prospective cohort studies published up to October 2016. RESULTS: In the Bruneck study, there was a 12% higher risk of type-2 diabetes for a one standard deviation lower concentration of log Lp(a) (HR = 1.12 [95% CI 0.95-1.32]; P = 0.171), after adjustment for age, sex, alcohol consumption, body mass index, smoking status, socioeconomic status, physical activity, systolic blood pressure, HDL cholesterol, log high-sensitivity C-reactive protein and waist-hip ratio. In a meta-analysis involving four prospective cohorts with a total of 74,575 participants and 4514 incident events, the risk of type-2 diabetes was higher in the lowest two quintiles of Lp(a) concentrations (weighted mean Lp(a) = 3.3 and 7.0 mg/dL, respectively) compared to the highest quintile (62.9 mg/dL), with the highest risk of type-2 diabetes seen in quintile 1 (HR = 1.28 [1.14-1.43]; P < 0.001). CONCLUSIONS: The current available evidence from prospective studies suggests that there is an inverse association between Lp(a) concentration and risk of type-2 diabetes, with a higher risk of type-2 diabetes at low Lp(a) concentrations (approximately <7 mg/dL).

APOL1 genetic variants, chronic kidney diseases and hypertension in mixed ancestry South Africans.

BACKGROUND: The frequencies of apolipoprotein L1 (APOL1) variants and their associations with chronic kidney disease (CKD) vary substantially in populations from Africa. Moreover, available studies have used very small sample sizes to provide reliable estimates of the frequencies of these variants in the general population. We determined the frequency of the two APOL1 risk alleles (G1 and G2) and investigated their association with renal traits in a relatively large sample of mixed-ancestry South Africans. APOL1 risk variants (G1: rs60910145 and rs73885319; G2: rs71785313) were genotyped in 859 African mixed ancestry individuals using allele-specific TaqMan technology. Glomerular filtration rate (eGFR) was estimated using the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations. RESULTS: The frequencies of rs73885319, rs60910145 and rs71785313 risk alleles were respectively, 3.6%, 3.4%, and 5.8%, resulting in a 1.01% frequency of the APOL1 two-risk allele (G1:G1 or G1:G2 or G2:G2). The presence of the two-risk allele increased serum creatinine with a corresponding reduction in eGFR (either MDRD or CKD-EPI based). In dominant and log-additive genetic models, significant associations were found between rs71785313 and systolic blood pressure (both p ≤ 0.025), with a significant statistical interaction by diabetes status, p = 0.022, reflecting a negative non-significant effect in nondiabetics and a positive effect in diabetics. CONCLUSIONS: Although the APOL1 variants are not common in the mixed ancestry population of South Africa, the study does provide an indication that APOL1 variants may play a role in conferring an increased risk for renal and cardiovascular risk in this population.

4-Hydroxyglutamate is a novel predictor of pre-eclampsia.

BACKGROUND: Pre-term pre-eclampsia is a major cause of maternal and perinatal morbidity and mortality worldwide. A multi-centre randomized-controlled trial has shown that first-trimester screening followed by treatment of high-risk women with aspirin reduces the risk of pre-term pre-eclampsia. However, the biomarkers currently employed in risk prediction are only weakly associated with the outcome. METHODS: We conducted a case-cohort study within the Pregnancy Outcome Prediction study to analyse untargeted maternal serum metabolomics in samples from 12, 20, 28 and 36 weeks of gestational age (wkGA) in women with pre-eclampsia delivering at term (n = 165) and pre-term (n = 29), plus a random sample of the cohort (n = 325). We used longitudinal linear mixed models to identify candidate metabolites at 20/28 wkGA that differed by term pre-eclampsia status. Candidates were validated using measurements at 36 wkGA in the same women. We then tested the association between the 12-, 20- and 28-wkGA measurements and pre-term pre-eclampsia. We externally validated the association using 24- to 28-wkGA samples from the Born in Bradford study (25 cases and 953 controls). RESULTS: We identified 100 metabolites that differed most at 20/28 wkGA in term pre-eclampsia. Thirty-three of these were validated (P < 0.0005) at 36 wkGA. 4-Hydroxyglutamate and C-glycosyltryptophan were independently predictive at 36 wkGA of term pre-eclampsia. 4-Hydroxyglutamate was also predictive (area under the receiver operating characteristic curve, 95% confidence interval) of pre-term pre-eclampsia at 12 (0.673, 0.558-0.787), 20 (0.731, 0.657-0.806) and 28 wkGA (0.733, 0.627-0.839). The predictive ability of 4-hydroxyglutamate at 12 wkGA was stronger than two existing protein biomarkers, namely PAPP-A (0.567, 0.439-0.695) and placenta growth factor (0.589, 0.463-0.714). Finally, 4-hydroxyglutamate at 24-28 wkGA was positively associated with pre-eclampsia (term or pre-term) among women from the Born in Bradford study. CONCLUSIONS: 4-hydroxyglutamate is a novel biochemical predictor of pre-eclampsia that provides better first-trimester prediction of pre-term disease than currently employed protein biomarkers.

Effect of model updating strategies on the performance of prevalent diabetes risk prediction models in a mixed-ancestry population of South Africa.

BACKGROUND: Prediction model updating methods are aimed at improving the prediction performance of a model in a new setting. This study sought to critically assess the impact of updating techniques when applying existent prevalent diabetes prediction models to a population different to the one in which they were developed, evaluating the performance in the mixed-ancestry population of South Africa. METHODS: The study sample consisted of 1256 mixed-ancestry individuals from the Cape Town Bellville-South cohort, of which 173 were excluded due to previously diagnosed diabetes and 162 individuals had undiagnosed diabetes. The primary outcome, undiagnosed diabetes, was based on an oral glucose tolerance test. Model updating techniques and prediction models were identified via recent systematic reviews. Model performance was assessed using the C-statistic and expected/observed (E/O) events rates ratio. RESULTS: Intercept adjustment and logistic calibration improved calibration across all five models (Cambridge, Kuwaiti, Omani, Rotterdam and Simplified Finnish diabetes risk models). This was improved further by model revision, where likelihood ratio tests showed that the effect of body mass index, waist circumference and family history of diabetes required additional adjustment (Omani, Rotterdam and Finnish models). However, discrimination was poor following internal validation of these models. Re-estimation of the regression coefficients did not increase performance, while the addition of new variables resulted in the highest discriminatory and calibration performance combination for the models it was undertaken in. CONCLUSIONS: While the discriminatory performance of the original existent models during external validation were higher, calibration was poor. The highest performing models, based on discrimination and calibration, were the Omani diabetes model following model revision, and the Cambridge diabetes risk model following the addition of waist circumference as a predictor. However, while more extensive methods incorporating development population information were superior over simpler methods, the increase in model performance was not great enough for recommendation.

Discrete Event Simulation for Decision Modeling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening.

Markov models are often used to evaluate the cost-effectiveness of new healthcare interventions but they are sometimes not flexible enough to allow accurate modeling or investigation of alternative scenarios and policies. A Markov model previously demonstrated that a one-off invitation to screening for abdominal aortic aneurysm (AAA) for men aged 65 y in the UK and subsequent follow-up of identified AAAs was likely to be highly cost-effective at thresholds commonly adopted in the UK (£20,000 to £30,000 per quality adjusted life-year). However, new evidence has emerged and the decision problem has evolved to include exploration of the circumstances under which AAA screening may be cost-effective, which the Markov model is not easily able to address. A new model to handle this more complex decision problem was needed, and the case of AAA screening thus provides an illustration of the relative merits of Markov models and discrete event simulation (DES) models. An individual-level DES model was built using the R programming language to reflect possible events and pathways of individuals invited to screening v. those not invited. The model was validated against key events and cost-effectiveness, as observed in a large, randomized trial. Different screening protocol scenarios were investigated to demonstrate the flexibility of the DES. The case of AAA screening highlights the benefits of DES, particularly in the context of screening studies.

Screening women aged 65 years or over for abdominal aortic aneurysm: a modelling study and health economic evaluation.

BACKGROUND: Abdominal aortic aneurysm (AAA) screening programmes have been established for men in the UK to reduce deaths from AAA rupture. Whether or not screening should be extended to women is uncertain. OBJECTIVE: To evaluate the cost-effectiveness of population screening for AAAs in women and compare a range of screening options. DESIGN: A discrete event simulation (DES) model was developed to provide a clinically realistic model of screening, surveillance, and elective and emergency AAA repair operations. Input parameters specifically for women were employed. The model was run for 10 million women, with parameter uncertainty addressed by probabilistic and deterministic sensitivity analyses. SETTING: Population screening in the UK. PARTICIPANTS: Women aged ≥ 65 years, followed up to the age of 95 years. INTERVENTIONS: Invitation to ultrasound screening, followed by surveillance for small AAAs and elective surgical repair for large AAAs. MAIN OUTCOME MEASURES: Number of operations undertaken, AAA-related mortality, quality-adjusted life-years (QALYs), NHS costs and cost-effectiveness with annual discounting. DATA SOURCES: AAA surveillance data, National Vascular Registry, Hospital Episode Statistics, trials of elective and emergency AAA surgery, and the NHS Abdominal Aortic Aneurysm Screening Programme (NAAASP). REVIEW METHODS: Systematic reviews of AAA prevalence and, for elective operations, suitability for endovascular aneurysm repair, non-intervention rates, operative mortality and literature reviews for other parameters. RESULTS: The prevalence of AAAs (aortic diameter of ≥ 3.0 cm) was estimated as 0.43% in women aged 65 years and 1.15% at age 75 years. The corresponding attendance rates following invitation to screening were estimated as 73% and 62%, respectively. The base-case model adopted the same age at screening (65 years), definition of an AAA (diameter of ≥ 3.0 cm), surveillance intervals (1 year for AAAs with diameter of 3.0-4.4 cm, 3 months for AAAs with diameter of 4.5-5.4 cm) and AAA diameter for consideration of surgery (5.5 cm) as in NAAASP for men. Per woman invited to screening, the estimated gain in QALYs was 0.00110, and the incremental cost was £33.99. This gave an incremental cost-effectiveness ratio (ICER) of £31,000 per QALY gained. The corresponding incremental net monetary benefit at a threshold of £20,000 per QALY gained was -£12.03 (95% uncertainty interval -£27.88 to £22.12). Almost no sensitivity analyses brought the ICER below £20,000 per QALY gained; an exception was doubling the AAA prevalence to 0.86%, which resulted in an ICER of £13,000. Alternative screening options (increasing the screening age to 70 years, lowering the threshold for considering surgery to diameters of 5.0 cm or 4.5 cm, lowering the diameter defining an AAA in women to 2.5 cm and lengthening the surveillance intervals for the smallest AAAs) did not bring the ICER below £20,000 per QALY gained when considered either singly or in combination. LIMITATIONS: The model for women was not directly validated against empirical data. Some parameters were poorly estimated, potentially lacking relevance or unavailable for women. CONCLUSION: The accepted criteria for a population-based AAA screening programme in women are not currently met. FUTURE WORK: A large-scale study is needed of the exact aortic size distribution for women screened at relevant ages. The DES model can be adapted to evaluate screening options in men. STUDY REGISTRATION: This study is registered as PROSPERO CRD42015020444 and CRD42016043227. FUNDING: The National Institute for Health Research Health Technology Assessment programme.

Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa.

BACKGROUND: Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing data can be just as accurate as complex methods. The objective of this study was to implement a number of simple and more complex imputation methods, and assess the effect of these techniques on the performance of undiagnosed diabetes risk prediction models during external validation. METHODS: Data from the Cape Town Bellville-South cohort served as the basis for this study. Imputation methods and models were identified via recent systematic reviews. Models' discrimination was assessed and compared using C-statistic and non-parametric methods, before and after recalibration through simple intercept adjustment. RESULTS: The study sample consisted of 1256 individuals, of whom 173 were excluded due to previously diagnosed diabetes. Of the final 1083 individuals, 329 (30.4%) had missing data. Family history had the highest proportion of missing data (25%). Imputation of the outcome, undiagnosed diabetes, was highest in stochastic regression imputation (163 individuals). Overall, deletion resulted in the lowest model performances while simple imputation yielded the highest C-statistic for the Cambridge Diabetes Risk model, Kuwaiti Risk model, Omani Diabetes Risk model and Rotterdam Predictive model. Multiple imputation only yielded the highest C-statistic for the Rotterdam Predictive model, which were matched by simpler imputation methods. CONCLUSIONS: Deletion was confirmed as a poor technique for handling missing data. However, despite the emphasized disadvantages of simpler imputation methods, this study showed that implementing these methods results in similar predictive utility for undiagnosed diabetes when compared to multiple imputation.

Predictive modeling for incident and prevalent diabetes risk evaluation.

With half of individuals with diabetes undiagnosed worldwide and a projected 55% increase of the population with diabetes by 2035, the identification of undiagnosed and high-risk individuals is imperative. Multivariable diabetes risk prediction models have gained popularity during the past two decades. These have been shown to predict incident or prevalent diabetes through a simple and affordable risk scoring system accurately. Their development requires cohort or cross-sectional type studies with a variable combination, number and definition of included risk factors, with their performance chiefly measured by discrimination and calibration. Models can be used in clinical and public health settings. However, the impact of their use on outcomes in real-world settings needs to be evaluated before widespread implementation.

Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review.

Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this more efficiently, these issues were analyzed and illustrated through a systematic review on the reporting of missing data and imputation methods (prediction of missing values through relationships within and between variables) undertaken in risk prediction studies of undiagnosed diabetes. Prevalent diabetes risk models were selected based on a recent comprehensive systematic review, supplemented by an updated search of English-language studies published between 1997 and 2014. Reporting of missing data has been limited in studies of prevalent diabetes prediction. Of the 48 articles identified, 62.5% (n = 30) did not report any information on missing data or handling techniques. In 21 (43.8%) studies, researchers opted out of imputation, completing case-wise deletion of participants missing any predictor values. Although imputation methods are encouraged to handle missing data and ensure the accuracy of inferences, this has seldom been the case in studies of diabetes risk prediction. Hence, we elaborated on the various types and patterns of missing data, the limitations of case-wise deletion and state-of the-art methods of imputations and their challenges. This review highlights the inexperience or disregard of investigators of the effect of missing data in risk prediction research. Formal guidelines may enhance the reporting and appropriate handling of missing data in scientific journals.