Prediction of first coronary events with the Framingham score: a systematic review

BACKGROUND: Uncertainty exists about the performance of the Framingham risk score when applied in different populations. OBJECTIVE: We assessed calibration of the Framingham risk score (ie, relationship between predicted and observed coronary event rates) in US and non-US populations free of cardiovascular disease. METHODS: We reviewed studies that evaluated the performance of the Framingham risk score to predict first coronary events in a validation cohort, as identified by Medline, EMBASE, BIOSIS, and Cochrane library searches (through August 2005). Two reviewers independently assessed 1496 studies for eligibility, extracted data, and performed quality assessment using predefined forms. RESULTS: We included 25 validation cohorts of different population groups (n = 128,000) in our main analysis. Calibration varied over a wide range from under- to overprediction of absolute risk by factors of 0.57 to 2.7. Risk prediction for 7 cohorts (n = 18658) from the United States, Australia, and New Zealand was well calibrated (corresponding figures: 0.87-1.08; for the 5 biggest cohorts). The estimated population risks for first coronary events were strongly associated (goodness of fit: R2 = 0.84) and in good agreement with observed risks (coefficient for predicted risk: beta = 0.84; 95% CI 0.41-1.26). In 18 European cohorts (n = 109499), the corresponding figures indicated close association (R2 = 0.72) but substantial overprediction (beta = 0.58, 95% CI 0.39-0.77). The risk score was well calibrated on the intercept for both population clusters. CONCLUSION: The Framingham score is well calibrated to predict first coronary events in populations from the United States, Australia, and New Zealand. Overestimation of absolute risk in European cohorts requires recalibration procedures.

Risk of severe asthma episodes predicted from fluctuation analysis of airway function

Asthma is an increasing health problem worldwide, but the long-term temporal pattern of clinical symptoms is not understood and predicting asthma episodes is not generally possible. We analyse the time series of peak expiratory flows, a standard measurement of airway function that has been assessed twice daily in a large asthmatic population during a long-term crossover clinical trial. Here we introduce an approach to predict the risk of worsening airflow obstruction by calculating the conditional probability that, given the current airway condition, a severe obstruction will occur within 30 days. We find that, compared with a placebo, a regular long-acting bronchodilator (salmeterol) that is widely used to improve asthma control decreases the risk of airway obstruction. Unexpectedly, however, a regular short-acting beta2-agonist bronchodilator (albuterol) increases this risk. Furthermore, we find that the time series of peak expiratory flows show long-range correlations that change significantly with disease severity, approaching a random process with increased variability in the most severe cases. Using a nonlinear stochastic model, we show that both the increased variability and the loss of correlations augment the risk of unstable airway function. The characterization of fluctuations in airway function provides a quantitative basis for objective risk prediction of asthma episodes and for evaluating the effectiveness of therapy.

Evolving biomarkers improve prediction of long-term mortality in patients with stable coronary artery disease: the BIO-VILCAD score.

OBJECTIVE Algorithms to predict the future long-term risk of patients with stable coronary artery disease (CAD) are rare. The VIenna and Ludwigshafen CAD (VILCAD) risk score was one of the first scores specifically tailored for this clinically important patient population. The aim of this study was to refine risk prediction in stable CAD creating a new prediction model encompassing various pathophysiological pathways. Therefore, we assessed the predictive power of 135 novel biomarkers for long-term mortality in patients with stable CAD. DESIGN, SETTING AND SUBJECTS We included 1275 patients with stable CAD from the LUdwigshafen RIsk and Cardiovascular health study with a median follow-up of 9.8 years to investigate whether the predictive power of the VILCAD score could be improved by the addition of novel biomarkers. Additional biomarkers were selected in a bootstrapping procedure based on Cox regression to determine the most informative predictors of mortality. RESULTS The final multivariable model encompassed nine clinical and biochemical markers: age, sex, left ventricular ejection fraction (LVEF), heart rate, N-terminal pro-brain natriuretic peptide, cystatin C, renin, 25OH-vitamin D3 and haemoglobin A1c. The extended VILCAD biomarker score achieved a significantly improved C-statistic (0.78 vs. 0.73; P = 0.035) and net reclassification index (14.9%; P < 0.001) compared to the original VILCAD score. Omitting LVEF, which might not be readily measureable in clinical practice, slightly reduced the accuracy of the new BIO-VILCAD score but still significantly improved risk classification (net reclassification improvement 12.5%; P < 0.001). CONCLUSION The VILCAD biomarker score based on routine parameters complemented by novel biomarkers outperforms previous risk algorithms and allows more accurate classification of patients with stable CAD, enabling physicians to choose more personalized treatment regimens for their patients.

Neural correlates of anticipation risk reflect risk preferences

Individual risk preferences have a large influence on decisions, such as financial investments, career and health choices, or gambling. Decision making under risk has been studied both behaviorally and on a neural level. It remains unclear, however, how risk attitudes are encoded and integrated with choice. Here, we investigate how risk preferences are reflected in neural regions known to process risk. We collected functional magnetic resonance images of 56 human subjects during a gambling task (Preuschoff et al., 2006). Subjects were grouped into risk averters and risk seekers according to the risk preferences they revealed in a separate lottery task. We found that during the anticipation of high-risk gambles, risk averters show stronger responses in ventral striatum and anterior insula compared to risk seekers. In addition, risk prediction error signals in anterior insula, inferior frontal gyrus, and anterior cingulate indicate that risk averters do not dissociate properly between gambles that are more or less risky than expected. We suggest this may result in a general overestimation of prospective risk and lead to risk avoidance behavior. This is the first study to show that behavioral risk preferences are reflected in the passive evaluation of risky situations. The results have implications on public policies in the financial and health domain.

Prediction of arrhythmic events by Wedensky modulation in patients with coronary artery disease.

PRINCIPLES Prediction of arrhythmic events (AEs) has gained importance with the availability of implantable cardioverter-defibrillators (ICDs), but is still imprecise. This study evaluated the innovative Wedensky modulation index (WMI) as predictor of AEs. METHODS In this prospective cohort, 179 patients with coronary artery disease (CAD) referred for AE risk assessment underwent baseline evaluation including measurement of R-/T-wave WMI (WMI(RT)) and left ventricular ejection fraction (LVEF). Two endpoints were assessed 3 years after the baseline evaluation: sudden cardiac death or appropriate ICD event (EP1) and any cardiac death or appropriate ICD event (EP2). Associations between baseline predictors (WMI(RT) and LVEF) and endpoints were evaluated in regression models. RESULTS Only three patients were lost to follow-up. EP1 and EP2 occurred in 24 and 27 patients, respectively. WMI(RT) (odds ratio [OR] per 1 point increase for EP1 20.1, 95% confidence interval [CI] 1.8-221.4, p = 0.014, and for EP2 73.3, 95% CI 6.6-817.7, p <0.001) and LVEF (OR per 1% increase for EP1 0.94, 95% CI 0.90-0.99, p = 0.013, and for EP2 0.93, 95% CI 0.89-0.97, p = 0.002) were significantly associated with both endpoints. In bivariable regression controlled for LVEF, WMI(RT) was independently associated with EP1 (p = 0.047) and EP2 (p = 0.007). The combination of WMI(RT) ≥0.60 and LVEF ≤30% resulted in a positive predictive value of 36% for EP1 and 50% for EP2. CONCLUSIONS WMI(RT) is a significant predictor of AEs independent of LVEF and has potential to improve AE risk prediction in CAD patients. However, WMI(RT) should be evaluated in larger and independent samples before recommendations for clinical routine can be made.

Normal Thyroid Function and the Risk of Atrial Fibrillation: the Rotterdam Study

CONTEXT Hyperthyroidism is an established risk factor for atrial fibrillation (AF), but information concerning the association with variations within the normal range of thyroid function and subgroups at risk is lacking. OBJECTIVE This study aimed to investigate the association between normal thyroid function and AF prospectively and explore potential differential risk patterns. DESIGN, SETTING, AND PARTICIPANTS From the Rotterdam Study we included 9166 participants ≥ 45 y with TSH and/or free T4 (FT4) measurements and AF assessment (1997-2012 median followup, 6.8 y), with 399 prevalent and 403 incident AF cases. MAIN OUTCOME MEASURES Outcome measures were 3-fold: 1) hazard ratios (HRs) for the risk of incident AF by Cox proportional-hazards models, 2) 10-year absolute risks taking competing risk of death into account, and 3) discrimination ability of adding FT4 to the CHARGE-AF simple model, an established prediction model for AF. RESULTS Higher FT4 levels were associated with higher risks of AF (HR 1.63, 95% confidence interval, 1.19-2.22), when comparing those in the highest quartile to those in lowest quartile. Absolute 10-year risks increased with higher FT4 in participants ≤ 65 y from 1-9% and from 6-12% in subjects ≥ 65 y. Discrimination of the prediction model improved when adding FT4 to the simple model (c-statistic, 0.722 vs 0.729; P = .039). TSH levels were not associated with AF. CONCLUSIONS There is an increased risk of AF with higher FT4 levels within the normal range, especially in younger subjects. Adding FT4 to the simple model slightly improved discrimination of risk prediction.

Contribution of activity and skin temperature to the risk of developing pressure ulcers in nursing facility residents

Purpose. The central concepts in pressure ulcer risk are exposure to external pressure caused by inactivity and tissue tolerance to pressure, a factor closely related to blood flow. Inactivity measures are effective in predicting pressure ulcer risk. The purpose of the study is to evaluate whether a physiological measure of skin blood flow improves pressure ulcer risk prediction. Skin temperature regularity and self-similarity, as proxy measures of blood flow, and not previously described, may be undefined pressure ulcer risk factors. The specific aims were to determine whether a sample of nursing facility residents at high risk of pressure ulcers classified using the Braden Scale for Pressure Sore Risk© differ from a sample of low risk residents according to (1) exposure to external pressure as measured by resident activity, (2) tissue tolerance to external pressure as measured by skin temperature, and (3) skin temperature fluctuations and recovery in response to a commonly occurring stressor, bathing and additionally whether (4) scores on the Braden Scale mobility subscale score are related to entropy and the spectral exponent. ^ Methods. A two group observational time series design was used to describe activity and skin temperature regularity and self-similarity, calculating entropy and the spectral exponent using detrended fluctuation analysis respectively. Twenty nursing facility residents wore activity and skin temperature monitors for one week. One bathing episode was observed as a commonly occurring stressor for skin temperature.^ Results. Skin temperature multiscale entropy (MSE), F(1, 17) = 5.55, p = .031, the skin temperature spectral exponent, F(1, 17) = 6.19, p = .023, and the activity mean MSE, F(1, 18) = 4.52, p = .048 differentiated the risk groups. The change in skin temperature entropy during bathing was significant, t(16) = 2.55, p = .021, (95% CI, .04-.40). Multiscale entropy for skin temperature was lowest in those who developed pressure ulcers, F(1, 18) = 35.14, p < .001.^ Conclusions. This study supports the tissue tolerance component of the Braden and Bergstrom conceptual framework and shows differences in skin temperature multiscale entropy between pressure ulcer risk categories, pressure ulcer outcome, and during a commonly occurring stressor. ^

Improving risk stratification in non-ST-segment elevation myocardial infarction with combined assessment of GRACE and CRUSADE risk scores

BACKGROUND: Risk assessment is fundamental in the management of acute coronary syndromes (ACS), enabling estimation of prognosis. AIMS: To evaluate whether the combined use of GRACE and CRUSADE risk stratification schemes in patients with myocardial infarction outperforms each of the scores individually in terms of mortality and haemorrhagic risk prediction. METHODS: Observational retrospective single-centre cohort study including 566 consecutive patients admitted for non-ST-segment elevation myocardial infarction. The CRUSADE model increased GRACE discriminatory performance in predicting all-cause mortality, ascertained by Cox regression, demonstrating CRUSADE independent and additive predictive value, which was sustained throughout follow-up. The cohort was divided into four different subgroups: G1 (GRACE<141; CRUSADE<41); G2 (GRACE<141; CRUSADE≥41); G3 (GRACE≥141; CRUSADE<41); G4 (GRACE≥141; CRUSADE≥41). RESULTS: Outcomes and variables estimating clinical severity, such as admission Killip-Kimbal class and left ventricular systolic dysfunction, deteriorated progressively throughout the subgroups (G1 to G4). Survival analysis differentiated three risk strata (G1, lowest risk; G2 and G3, intermediate risk; G4, highest risk). The GRACE+CRUSADE model revealed higher prognostic performance (area under the curve [AUC] 0.76) than GRACE alone (AUC 0.70) for mortality prediction, further confirmed by the integrated discrimination improvement index. Moreover, GRACE+CRUSADE combined risk assessment seemed to be valuable in delineating bleeding risk in this setting, identifying G4 as a very high-risk subgroup (hazard ratio 3.5; P<0.001). CONCLUSIONS: Combined risk stratification with GRACE and CRUSADE scores can improve the individual discriminatory power of GRACE and CRUSADE models in the prediction of all-cause mortality and bleeding. This combined assessment is a practical approach that is potentially advantageous in treatment decision-making.

Exploring the potential role of allostatic load biomarkers in risk assessment of patients presenting with depressive symptoms

Background: Depression is a major health problem worldwide and the majority of patients presenting with depressive symptoms are managed in primary care. Current approaches for assessing depressive symptoms in primary care are not accurate in predicting future clinical outcomes, which may potentially lead to over or under treatment. The Allostatic Load (AL) theory suggests that by measuring multi-system biomarker levels as a proxy of measuring multi-system physiological dysregulation, it is possible to identify individuals at risk of having adverse health outcomes at a prodromal stage. Allostatic Index (AI) score, calculated by applying statistical formulations to different multi-system biomarkers, have been associated with depressive symptoms. Aims and Objectives: To test the hypothesis, that a combination of allostatic load (AL) biomarkers will form a predictive algorithm in defining clinically meaningful outcomes in a population of patients presenting with depressive symptoms. The key objectives were: 1. To explore the relationship between various allostatic load biomarkers and prevalence of depressive symptoms in patients, especially in patients diagnosed with three common cardiometabolic diseases (Coronary Heart Disease (CHD), Diabetes and Stroke). 2 To explore whether allostatic load biomarkers predict clinical outcomes in patients with depressive symptoms, especially in patients with three common cardiometabolic diseases (CHD, Diabetes and Stroke). 3 To develop a predictive tool to identify individuals with depressive symptoms at highest risk of adverse clinical outcomes. Methods: Datasets used: ‘DepChron’ was a dataset of 35,537 patients with existing cardiometabolic disease collected as a part of routine clinical practice. ‘Psobid’ was a research data source containing health related information from 666 participants recruited from the general population. The clinical outcomes for 3 both datasets were studied using electronic data linkage to hospital and mortality health records, undertaken by Information Services Division, Scotland. Cross-sectional associations between allostatic load biomarkers calculated at baseline, with clinical severity of depression assessed by a symptom score, were assessed using logistic and linear regression models in both datasets. Cox’s proportional hazards survival analysis models were used to assess the relationship of allostatic load biomarkers at baseline and the risk of adverse physical health outcomes at follow-up, in patients with depressive symptoms. The possibility of interaction between depressive symptoms and allostatic load biomarkers in risk prediction of adverse clinical outcomes was studied using the analysis of variance (ANOVA) test. Finally, the value of constructing a risk scoring scale using patient demographics and allostatic load biomarkers for predicting adverse outcomes in depressed patients was investigated using clinical risk prediction modelling and Area Under Curve (AUC) statistics. Key Results: Literature Review Findings. The literature review showed that twelve blood based peripheral biomarkers were statistically significant in predicting six different clinical outcomes in participants with depressive symptoms. Outcomes related to both mental health (depressive symptoms) and physical health were statistically associated with pre-treatment levels of peripheral biomarkers; however only two studies investigated outcomes related to physical health. Cross-sectional Analysis Findings: In DepChron, dysregulation of individual allostatic biomarkers (mainly cardiometabolic) were found to have a non-linear association with increased probability of co-morbid depressive symptoms (as assessed by Hospital Anxiety and Depression Score HADS-D≥8). A composite AI score constructed using five biomarkers did not lead to any improvement in the observed strength of the association. In Psobid, BMI was found to have a significant cross-sectional association with the probability of depressive symptoms (assessed by General Health Questionnaire GHQ-28≥5). BMI, triglycerides, highly sensitive C - reactive 4 protein (CRP) and High Density Lipoprotein-HDL cholesterol were found to have a significant cross-sectional relationship with the continuous measure of GHQ-28. A composite AI score constructed using 12 biomarkers did not show a significant association with depressive symptoms among Psobid participants. Longitudinal Analysis Findings: In DepChron, three clinical outcomes were studied over four years: all-cause death, all-cause hospital admissions and composite major adverse cardiovascular outcome-MACE (cardiovascular death or admission due to MI/stroke/HF). Presence of depressive symptoms and composite AI score calculated using mainly peripheral cardiometabolic biomarkers was found to have a significant association with all three clinical outcomes over the following four years in DepChron patients. There was no evidence of an interaction between AI score and presence of depressive symptoms in risk prediction of any of the three clinical outcomes. There was a statistically significant interaction noted between SBP and depressive symptoms in risk prediction of major adverse cardiovascular outcome, and also between HbA1c and depressive symptoms in risk prediction of all-cause mortality for patients with diabetes. In Psobid, depressive symptoms (assessed by GHQ-28≥5) did not have a statistically significant association with any of the four outcomes under study at seven years: all cause death, all cause hospital admission, MACE and incidence of new cancer. A composite AI score at baseline had a significant association with the risk of MACE at seven years, after adjusting for confounders. A continuous measure of IL-6 observed at baseline had a significant association with the risk of three clinical outcomes- all-cause mortality, all-cause hospital admissions and major adverse cardiovascular event. Raised total cholesterol at baseline was associated with lower risk of all-cause death at seven years while raised waist hip ratio- WHR at baseline was associated with higher risk of MACE at seven years among Psobid participants. There was no significant interaction between depressive symptoms and peripheral biomarkers (individual or combined) in risk prediction of any of the four clinical outcomes under consideration. Risk Scoring System Development: In the DepChron cohort, a scoring system was constructed based on eight baseline demographic and clinical variables to predict the risk of MACE over four years. The AUC value for the risk scoring system was modest at 56.7% (95% CI 55.6 to 57.5%). In Psobid, it was not possible to perform this analysis due to the low event rate observed for the clinical outcomes. Conclusion: Individual peripheral biomarkers were found to have a cross-sectional association with depressive symptoms both in patients with cardiometabolic disease and middle-aged participants recruited from the general population. AI score calculated with different statistical formulations was of no greater benefit in predicting concurrent depressive symptoms or clinical outcomes at follow-up, over and above its individual constituent biomarkers, in either patient cohort. SBP had a significant interaction with depressive symptoms in predicting cardiovascular events in patients with cardiometabolic disease; HbA1c had a significant interaction with depressive symptoms in predicting all-cause mortality in patients with diabetes. Peripheral biomarkers may have a role in predicting clinical outcomes in patients with depressive symptoms, especially for those with existing cardiometabolic disease, and this merits further investigation.

DEVELOPMENT AND VALIDATION OF THE ANTERIOR CRUCIATE LIGAMENT INJURY-RISK-ESTIMATION QUIZ (ACL-IQ)

Over 2 million Anterior Cruciate Ligament (ACL) injuries occur annually worldwide resulting in considerable economic and health burdens (e.g., suffering, surgery, loss of function, risk for re-injury, and osteoarthritis). Current screening methods are effective but they generally rely on expensive and time-consuming biomechanical movement analysis, and thus are impractical solutions. In this dissertation, I report on a series of studies that begins to investigate one potentially efficient alternative to biomechanical screening, namely skilled observational risk assessment (e.g., having experts estimate risk based on observations of athletes movements). Specifically, in Study 1 I discovered that ACL injury risk can be accurately and reliably estimated with nearly instantaneous visual inspection when observed by skilled and knowledgeable professionals. Modern psychometric optimization techniques were then used to develop a robust and efficient 5-item test of ACL injury risk prediction skill—i.e., the ACL Injury-Risk-Estimation Quiz or ACL-IQ. Study 2 cross-validated the results from Study 1 in a larger representative sample of both skilled (Exercise Science/Sports Medicine) and un-skilled (General Population) groups. In accord with research on human expertise, quantitative structural and process modeling of risk estimation indicated that superior performance was largely mediated by specific strategies and skills (e.g., ignoring irrelevant information), independent of domain general cognitive abilities (e.g., metal rotation, general decision skill). These cognitive models suggest that ACL-IQ is a trainable skill, providing a foundation for future research and applications in training, decision support, and ultimately clinical screening investigations. Overall, I present the first evidence that observational ACL injury risk prediction is possible including a robust technology for fast, accurate and reliable measurement—i.e., the ACL-IQ. Discussion focuses on applications and outreach including a web platform that was developed to house the test, provide a repository for further data collection, and increase public and professional awareness and outreach (www.ACL-IQ.org). Future directions and general applications of the skilled movement analysis approach are also discussed.

Possible genetic predisposition to lymphedema after breast cancer

Background: Known risk factors for secondary lymphedema only partially explain who develops lymphedema following cancer, suggesting that inherited genetic susceptibility may influence risk. Moreover, identification of molecular signatures could facilitate lymphedema risk prediction prior to surgery or lead to effective drug therapies for prevention or treatment. Recent advances in the molecular biology underlying development of the lymphatic system and related congenital disorders implicate a number of potential candidate genes to explore in relation to secondary lymphedema. Methods and Results: We undertook a nested case-control study, with participants who had developed lymphedema after surgical intervention within the first 18 months of their breast cancer diagnosis serving as cases (n=22) and those without lymphedema serving as controls (n=98), identified from a prospective, population-based, cohort study in Queensland, Australia. TagSNPs that covered all known genetic variation in the genes SOX18, VEGFC, VEGFD, VEGFR2, VEGFR3, RORC, FOXC2, LYVE1, ADM and PROX1 were selected for genotyping. Multiple SNPs within three receptor genes, VEGFR2, VEGFR3 and RORC, were associated with lymphedema defined by statistical significance (p<0.05) or extreme risk estimates (OR<0.5 or >2.0). Conclusions: These provocative, albeit preliminary, findings regarding possible genetic predisposition to secondary lymphedema following breast cancer treatment warrant further attention for potential replication using larger datasets.

What patient information allows us to make accurate predictions of outcome?

Only some of the information contained in a medical record will be useful to the prediction of patient outcome. We describe a novel method for selecting those outcome predictors which allow us to reliably discriminate between adverse and benign end results. Using the area under the receiver operating characteristic as a nonparametric measure of discrimination, we show how to calculate the maximum discrimination attainable with a given set of discrete valued features. This upper limit forms the basis of our feature selection algorithm. We use the algorithm to select features (from maternity records) relevant to the prediction of failure to progress in labour. The results of this analysis motivate investigation of those predictors of failure to progress relevant to parous and nulliparous sub-populations.

Using upper bounds on attainable discrimination to select discrete valued features

Selection of features that will permit accurate pattern classification is a difficult task. However, if a particular data set is represented by discrete valued features, it becomes possible to determine empirically the contribution that each feature makes to the discrimination between classes. This paper extends the discrimination bound method so that both the maximum and average discrimination expected on unseen test data can be estimated. These estimation techniques are the basis of a backwards elimination algorithm that can be use to rank features in order of their discriminative power. Two problems are used to demonstrate this feature selection process: classification of the Mushroom Database, and a real-world, pregnancy related medical risk prediction task - assessment of risk of perinatal death.

Feature selection using expected attainable discrimination

We propose expected attainable discrimination (EAD) as a measure to select discrete valued features for reliable discrimination between two classes of data. EAD is an average of the area under the ROC curves obtained when a simple histogram probability density model is trained and tested on many random partitions of a data set. EAD can be incorporated into various stepwise search methods to determine promising subsets of features, particularly when misclassification costs are difficult or impossible to specify. Experimental application to the problem of risk prediction in pregnancy is described.