A Comparative Assessment of Non-Laboratory-Based versus Commonly Used Laboratory-Based Cardiovascular Disease Risk Scores in the NHANES III Population

National and international primary CVD risk screening guidelines focus on using total CVD risk scores. Recently, we developed a non-laboratory-based CVD risk score (inputs: age, sex, smoking, diabetes, systolic blood pressure, treatment of hypertension, body-mass index), which can assess risk faster and at lower costs compared to laboratory-based scores (inputs include cholesterol values). We aimed to assess the exchangeability of the non-laboratory-based risk score to four commonly used laboratory-based scores (Framingham CVD [2008, 1991 versions], and Systematic COronary Risk Evaluation [SCORE] for low and high risk settings) in an external validation population.Analyses were based on individual-level, score-specific rankings of risk for adults in the Third National Health and Nutrition Examination Survey (NHANES III) aged 25–74 years, without history of CVD or cancer (n = 5,999). Risk characterization agreement was based on overlap in dichotomous risk characterization (thresholds of 10-year risk >10–20%) and Spearman rank correlation. Risk discrimination was assessed using receiver operator characteristic curve analysis (10-year CVD death outcome). Risk characterization agreement ranged from 91.9–95.7% and 94.2–95.1% with Spearman correlation ranges of 0.957–0.980 and 0.946–0.970 for men and women, respectively. In men, c-statistics for the non-laboratory-based, Framingham (2008, 1991), and SCORE (high, low) functions were 0.782, 0.776, 0.781, 0.785, and 0.785, with p-values for differences relative to the non-laboratory-based score of 0.44, 0.89, 0.68 and 0.65, respectively. In women, the corresponding c-statistics were 0.809, 0.834, 0.821, 0.792, and 0.792, with corresponding p-values of 0.04, 0.34, 0.11 and 0.09, respectively.Every score discriminated risk of CVD death well, and there was high agreement in risk characterization between non-laboratory-based and laboratory-based risk scores, which suggests that the non-laboratory-based score can be a useful proxy for Framingham or SCORE functions in resource-limited settings. Future external validation studies can assess whether the sex-specific risk discrimination results hold in other populations

ASCORE: an up-to-date cardiovascular risk score for hypertensive patients reflecting contemporary clinical practice developed using the (ASCOT-BPLA) trial data.

A number of risk scores already exist to predict cardiovascular (CV) events. However, scores developed with data collected some time ago might not accurately predict the CV risk of contemporary hypertensive patients that benefit from more modern treatments and management. Using data from the randomised clinical trial Anglo-Scandinavian Cardiac Outcomes Trial-BPLA, with 15 955 hypertensive patients without previous CV disease receiving contemporary preventive CV management, we developed a new risk score predicting the 5-year risk of a first CV event (CV death, myocardial infarction or stroke). Cox proportional hazard models were used to develop a risk equation from baseline predictors. The final risk model (ASCORE) included age, sex, smoking, diabetes, previous blood pressure (BP) treatment, systolic BP, total cholesterol, high-density lipoprotein-cholesterol, fasting glucose and creatinine baseline variables. A simplified model (ASCORE-S) excluding laboratory variables was also derived. Both models showed very good internal validity. User-friendly integer score tables are reported for both models. Applying the latest Framingham risk score to our data significantly overpredicted the observed 5-year risk of the composite CV outcome. We conclude that risk scores derived using older databases (such as Framingham) may overestimate the CV risk of patients receiving current BP treatments; therefore, 'updated' risk scores are needed for current patients

Age at quitting smoking as a predictor of risk of cardiovascular disease incidence independent of smoking status, time since quitting and pack-years

Background: Risk prediction for CVD events has been shown to vary according to current smoking status, pack-years smoked over a lifetime, time since quitting and age at quitting. The latter two are closely and inversely related. It is not known whether the age at which one quits smoking is an additional important predictor of CVD events. The aim of this study was to determine whether the risk of CVD events varied according to age at quitting after taking into account current smoking status, lifetime pack-years smoked and time since quitting. Findings. We used the Cox proportional hazards model to evaluate the risk of developing a first CVD event for a cohort of participants in the Framingham Offspring Heart Study who attended the fourth examination between ages 30 and 74 years and were free of CVD. Those who quit before the median age of 37 years had a risk of CVD incidence similar to those who were never smokers. The incorporation of age at quitting in the smoking variable resulted in better prediction than the model which had a simple current smoker/non-smoker measure and the one that incorporated both time since quitting and pack-years. These models demonstrated good discrimination, calibration and global fit. The risk among those quitting more than 5 years prior to the baseline exam and those whose age at quitting was prior to 44 years was similar to the risk among never smokers. However, the risk among those quitting less than 5 years prior to the baseline exam and those who continued to smoke until 44 years of age (or beyond) was two and a half times higher than that of never smokers. Conclusions: Age at quitting improves the prediction of risk of CVD incidence even after other smoking measures are taken into account. The clinical benefit of adding age at quitting to the model with other smoking measures may be greater than the associated costs. Thus, age at quitting should be considered in addition to smoking status, time since quitting and pack-years when counselling individuals about their cardiovascular risk

The value of the pragmatic-explanatory continuum indicator summary wheel in an ongoing study: the bullous pemphigoid steroids and tetracyclines study

BACKGROUND: The Pragmatic-Explanatory Continuum Indicator Summary (PRECIS) tool is intended to be used in the design phase of trials to help investigative teams design trials in-line with their purpose. Our team applied this tool to an ongoing trial (BLISTER) to determine whether the initial suggestion among some team members that the trial could be described as largely pragmatic was the consensus. METHODS: Each of the six members of the BLISTER trial team was sent a blank PRECIS wheel to independently complete. The results obtained were averaged and plotted on a single PRECIS wheel to illustrate the degree of pragmatism of the trial. RESULTS: The trial team found that the design of the trial was closest to the pragmatic end of the pragmatic-explanatory continuum. The strongest consensus was found on the 'flexibility of the comparison intervention' and 'practitioner adherence' domains (SD = 13). The trial team appeared to disagree most on the 'eligibility criteria' (SD = 35) and 'participant compliance' (SD = 31) domains, although the large standard deviations were a result of a single outlier in the two domains. CONCLUSION: The PRECIS tool can be used to retrospectively determine the pragmatism of a trial provided enough expertise and information on the trial is available. Illustrating the design of a trial on the PRECIS wheel can help research users more easily identify studies of interest. We hope our recommendations for applying this useful tool will encourage others to consider using it when designing, conducting and reporting studies. TRIAL REGISTRATION: Current Controlled Trials http://www.controlled-trials.com/ISRCTN13704604

Construction of an odds model of coronary heart disease using published information: the Cardiovascular Health Improvement Model (CHIME)

Background: There is a need for a new cardiovascular disease model that includes a wider range of relevant risk factors, in particular lifestyle factors, to aid targeting of interventions and improve population models of the impact of cardiovascular disease and preventive strategies. The model needs to be applicable to a wider population including different ethnic groups, different countries and to those with and without cardiovascular disease. This paper describes the construction of the Cardiovascular Health Improvement Model that aims to meet these requirements. Method: An odds model is used. Information was taken from 2003 mortality statistics for England and Wales, the Health Survey for England 2003 and published data on relative risk in those with and without CVD and mean blood pressure values in hypertensives. The odds ratios used were taken from the INTERHEART study. Results: A worked example is given calculating the 10-year coronary heart disease risk for a 57 year-old non-diabetic male with no personal or family history of cardiovascular disease, who smokes 30 cigarettes a day and has a systolic blood pressure of 137 mmHg, a total cholesterol (TC) of 6.2 mmol/l, a high density lipoprotein (HDL) of 1.3 mol/l, and a body mass index of 21. He neither drinks regularly nor exercises. He can give no reliable information about his mental health or fruit and vegetable intake. His 10-year risk of CHD death is 2.47%. Conclusion: This paper demonstrates a method for developing a CHD risk model. Further improvements could be made to the model with additional information. The method is applicable to other causes of death

"Summary Page": a novel tool that reduces omitted data in research databases

<p>Abstract</p> <p>Background</p> <p>Data entry errors are common in clinical research databases. Omitted data are of particular concern because they are more common than erroneously inserted data and therefore could potentially affect research findings. However, few affordable strategies for their prevention are available.</p> <p>Methods</p> <p>We have conducted a prospective observational study of the effect of a novel tool called "<it>Summary Page</it>" on the frequency of correction of omitted data errors in a radiation oncology research database between July 2008 and March 2009. "<it>Summary Page</it>" was implemented as an optionally accessed screen in the database that visually integrates key fields in the record. We assessed the frequency of omitted data on the example of the <it>Date of Relapse </it>field. We considered the data in this field to be omitted for all records that had empty <it>Date of Relapse </it>field and evidence of relapse elsewhere in the record.</p> <p>Results</p> <p>A total of 1,156 records were updated and 200 new records were entered in the database over the study period. "<it>Summary Page</it>" was accessed for 44% of all updated records and for 69% of newly entered records. Frequency of correction of the omitted date of cancer relapse was six-fold higher in records for which "<it>Summary Page</it>" was accessed (p = 0.0003).</p> <p>Conclusions</p> <p>"<it>Summary Page</it>" was strongly associated with an increased frequency of correction of omitted data errors. Further, controlled, studies are needed to confirm this finding and elucidate its mechanism of action.</p

A risk prediction model for the assessment and triage of women with hypertensive disorders of pregnancy in low-resourced settings: the miniPIERS (Pre-eclampsia Integrated Estimate of RiSk) multi-country prospective cohort study.

BACKGROUND: Pre-eclampsia/eclampsia are leading causes of maternal mortality and morbidity, particularly in low- and middle- income countries (LMICs). We developed the miniPIERS risk prediction model to provide a simple, evidence-based tool to identify pregnant women in LMICs at increased risk of death or major hypertensive-related complications. METHODS AND FINDINGS: From 1 July 2008 to 31 March 2012, in five LMICs, data were collected prospectively on 2,081 women with any hypertensive disorder of pregnancy admitted to a participating centre. Candidate predictors collected within 24 hours of admission were entered into a step-wise backward elimination logistic regression model to predict a composite adverse maternal outcome within 48 hours of admission. Model internal validation was accomplished by bootstrapping and external validation was completed using data from 1,300 women in the Pre-eclampsia Integrated Estimate of RiSk (fullPIERS) dataset. Predictive performance was assessed for calibration, discrimination, and stratification capacity. The final miniPIERS model included: parity (nulliparous versus multiparous); gestational age on admission; headache/visual disturbances; chest pain/dyspnoea; vaginal bleeding with abdominal pain; systolic blood pressure; and dipstick proteinuria. The miniPIERS model was well-calibrated and had an area under the receiver operating characteristic curve (AUC ROC) of 0.768 (95% CI 0.735-0.801) with an average optimism of 0.037. External validation AUC ROC was 0.713 (95% CI 0.658-0.768). A predicted probability ≥25% to define a positive test classified women with 85.5% accuracy. Limitations of this study include the composite outcome and the broad inclusion criteria of any hypertensive disorder of pregnancy. This broad approach was used to optimize model generalizability. CONCLUSIONS: The miniPIERS model shows reasonable ability to identify women at increased risk of adverse maternal outcomes associated with the hypertensive disorders of pregnancy. It could be used in LMICs to identify women who would benefit most from interventions such as magnesium sulphate, antihypertensives, or transportation to a higher level of care

Surgical revascularization versus amputation for peripheral vascular disease in dialysis patients: a cohort study

BACKGROUND: Surgical treatment of peripheral vascular disease (PVD) in dialysis patients is controversial. METHODS: We examined the post-operative morbidity and mortality of surgical revascularization or amputation for PVD in a retrospective analysis of United States Renal Data System. Propensity scores for undergoing amputation were derived from a multivariable logistic regression model of amputation. RESULTS: Of the Medicare patients initiated on dialysis from Jan 1, 1995 to Dec 31, 1999, patients underwent surgical revascularization (n = 1,896) or amputation (n = 2,046) in the first 6 months following initiation of dialysis were studied. In the logistic regression model, compared to claudication, presence of gangrene had a strong association with amputation [odds ratio (OR) 19.0, 95% CI (confidence interval) 13.86–25.95]. The odds of dying within 30 days and within1 year were higher (30 day OR: 1.85, 95% CI: 1.45–2.36; 1 yr OR: 1.46, 95% CI: 1.25–1.71) in the amputation group in logistic regression model adjusted for propensity scores and other baseline factors. Amputation was associated with increased odds of death in patients with low likelihood of amputation (< 33(rd )percentile of propensity score) and moderate likelihood of amputation (33(rd )to 66(th )percentile) but not in high likelihood group (>66(th )percentile). The number of hospital days in the amputation and revascularization groups was not different. CONCLUSION: Amputation might be associated with higher mortality in dialysis patients. Where feasible, revascularization might be preferable over amputation in dialysis patients

Psoriasis prediction from genome-wide SNP profiles

<p>Abstract</p> <p>Background</p> <p>With the availability of large-scale genome-wide association study (GWAS) data, choosing an optimal set of SNPs for disease susceptibility prediction is a challenging task. This study aimed to use single nucleotide polymorphisms (SNPs) to predict psoriasis from searching GWAS data.</p> <p>Methods</p> <p>Totally we had 2,798 samples and 451,724 SNPs. Process for searching a set of SNPs to predict susceptibility for psoriasis consisted of two steps. The first one was to search top 1,000 SNPs with high accuracy for prediction of psoriasis from GWAS dataset. The second one was to search for an optimal SNP subset for predicting psoriasis. The sequential information bottleneck (sIB) method was compared with classical linear discriminant analysis(LDA) for classification performance.</p> <p>Results</p> <p>The best test harmonic mean of sensitivity and specificity for predicting psoriasis by sIB was 0.674(95% CI: 0.650-0.698), while only 0.520(95% CI: 0.472-0.524) was reported for predicting disease by LDA. Our results indicate that the new classifier sIB performs better than LDA in the study.</p> <p>Conclusions</p> <p>The fact that a small set of SNPs can predict disease status with average accuracy of 68% makes it possible to use SNP data for psoriasis prediction.</p