Common Risk Scores Research Articles

Left atrial function for predicting atrial fibrillation: a machine learning approach

Abstract Background Common risk scores for atrial fibrillation (AF) are based on clinical risk factors, and little is known about the contribution of left atrial (LA) function parameters to risk assessment. Purpose To determine whether a machine learning-based model that includes LA function parameters outperforms a commonly used risk scores, the CHARGE-AF, in predicting AF. Methods Participants in the Atherosclerosis Risk in Communities (ARIC) Study (a community-based cohort study in the USA) who attended visit 5 (2011-2013) and without a history of AF were included in this analysis. Incident AF was ascertained from ECG during study visits, hospitalization discharge codes, and death certificates. Random survival forest methodology was used to build a prediction model for AF; candidate variables included traditional clinical risk factors and measures of LA structure and function. Dataset was randomly split into a training (75%) and testing (25%) cohort. For each variable, importance was measured based on the reduction in Harrell’s c-statistic if the variable was randomly permuted prior to obtaining predicted values. Variables significantly associated with AF were included in the final model. Model calibration was tested using the Greenwood-Nam-D'Agostino (GND) test. Discrimination ability of our model was assessed in the testing cohort using Harrell’s c-statistic and compared with that of the CHARGE-AF model. Results 4,909 participants (59% female, 22% Black, mean age 75 years) were included. During a median follow-up of 7.0 years (1st-3rd quartile: 5.4-7.8), 650 participants developed AF. Variables significantly associated with incident AF were race, triglycerides, systolic blood pressure, coronary heart disease, age, LA maximal volume index, NT-proBNP, and LA reservoir strain (Figure 1). LA reservoir strain was the variable most strongly associated with AF. The final model showed good discrimination (c-statistic (95% CI): 0.734 (0.698-0.768)). The GND test did not detect statistically significant miscalibration (GND p=0.08). Compared with our model, CHARGE-AF had lower discrimination ability (c-statistic (95% CI): 0.635 (0.594-0.677)) and the difference was statistically significant (difference in c-statistic (95% CI): 0.099 (0.043-0.152)). Conclusion A machine learning-based model with few variables including LA function outperforms a commonly used risk model in predicting AF. Incorporation of LA functional assessment may help inform clinicians regarding individual risk of AF.

The importance of liver function assessment before cardiac surgery: A narrative review.

The demand for cardiac surgery procedures is increasing globally. Thanks to an improvement in survival driven by medical advances, patients with liver disease undergo cardiac surgery more often. Liver disease is associated with the development of heart failure, especially in patients with advanced cirrhosis. Cardiovascular risk factors can also contribute to the development of both cardiomyopathy and liver disease and heart failure itself can worsen liver function. Despite the risk that liver disease and cirrhosis represent for the perioperative management of patients who undergo cardiac surgery, liver function is often not included in common risk scores for preoperative evaluation. These patients have worse short and long-term survival when compared with other cardiac surgery populations. Preoperative evaluation of liver function, postoperative management and close postoperative follow-up are crucial for avoiding complications and improving results. In the present narrative review, we discuss the pathophysiological components related with postoperative complications and mortality in patients with liver disease who undergo cardiac surgery and provide recommendations for the perioperative management.

Socioeconomic background affects mortality in Danish children with severe chronic disease.

To assess the association between socioeconomic factors and mortality in Danish children diagnosed with different types of severe chronic disease, including cancer. National cohort study 1994-2020 including Danish children with chronic disease. Inclusion was based on diagnoses in The National Patient Register, socioeconomic information was obtained from Statistics Denmark and mortality was ascertained from the Cause of Death Register. Hazard ratios (HR) with 95% confidence intervals (CIs) were based on Cox regression. The factors were combined in one common risk score and the association with disease-specific mortality was analysed overall and by ethnicity status. Overall, non-Danish ethnicity (HR=1.96 (95% CI 1.69-2.28)) was associated with all-cause mortality in 128 129 children (69 435 male and 58 694 female) with chronic disease. Median age at first diagnosis was 1.42 years (range 0-18 years). Low family income was associated with mortality regardless of ethnicity status, and young maternal age was also a notable risk factor across ethnicities. The socioeconomic association was more pronounced in children with cancer. In the high-income setting of Denmark, ethnicity and differences in socioeconomic background were associated with child mortality even among children with severe chronic disease. The pattern was more pronounced in paediatric cancer patients.

A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure

BackgroundPredicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units.MethodsUsing a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients’ clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. The logistic regression model and a common risk score for mortality were used for comparison. The eICU Collaborative Research Database dataset was used for external validation.ResultsThe performance of the machine learning model was superior to that of conventional risk predictive methods, with the area under curve 0.831 (95% CI 0.820–0.843) and acceptable calibration. In external validation, the model had an area under the curve of 0.809 (95% CI 0.805–0.814). Risk stratification through the model was specific when the hospital mortality was very low, low, moderate, high, and very high (2.0%, 10.2%, 11.5%, 21.2% and 56.2%, respectively). The decision curve analysis verified that the machine learning model is the best clinically valuable in predicting mortality risk.ConclusionUsing readily available clinical data in the intensive care unit, we built a machine learning-based mortality risk tool with prediction accuracy superior to that of linear regression model and common risk scores. The risk tool may support clinicians in assessing individual patients and making individualized treatment.

Impact of diabetes mellitus on long-term survival after transcatheter mitral valve edge-to-edge repair

Abstract Introduction Diabetes mellitus (DM) represents a notable risk factor after surgical and interventional procedures but data on the influence of DM on long-term survival after Transcatheter Edge-to-edge Repair (TEER) for Mitral valve Regurgitation (MR) are sparse. Purpose To compare the outcome of patients with and without DM after TEER. Methods Retrospective monocentric assessment of patients after successful treatment of MR by TEER (exclusion of combined forms of transcatheter repair) between 06/2010 and 03/2018. Patients were stratified for DM at baseline and observed regarding mortality during follow-up. Cox regression analyses were performed for survival analyses. Results 627 patients (47.0% females, 88.2% aged ≥70 years) and among these 174 subjects with DM (27.3%) were included with a median follow-up period of 486 days [IQR 157–916 days]). Within the investigation period, 20 patients (3.2%) were lost to follow-up. Patients with DM more often presented severe comorbidities like obesity (27.3% vs. 9.2%, p<0.001), arterial hypertension (91.4% vs. 83.7%, p=0.013), renal insufficiency (63.8% vs. 43.9%, p<0.001), coronary artery disease (77.0% vs. 59.8%, p<0.001) or peripheral artery disease (14.4% vs. 8.4%, p=0.026) and had a higher median logistic Euroscore I (29.4% [20.0/43.0] vs. 25.0% [16.7/36.6], p=0.001) as well as reduced systolic function (LVEF 35% [30/50] vs. 45% [30/55], p<0.001). No statistical differences in short- and long-term survival were detected between patients with and without DM (in-hospital mortality 1.7 vs. 2.6%, p=0.771; at 30-days 5.0 vs. 6.0%, p=0.842, 1-year 28.7 vs. 25.0%, p=0.419, 3-years 49.2 vs. 44.1%, p=0.554, 5-years 69.0 vs. 68.3%, p=0.497). By calculating cox regression analyses, DM was not predictive for a higher mortality, even after adjustment for other risk factors (HR 1-year 1.17 [95% CI 0.80–1.71], p=0.419; HR long-term 1.13 [95% CI 0.86–1.49], p=0.373) in the total cohort, as well as after stratification for the underlying mitral valve pathology (functional MR: 1-year HR 0.99 [95% CI 0.01–1.62], p=0.969, long-term HR 0.903 [95% CI 0.63–1.29, p=0.571; primary MR: 1-year HR 1.48 [95% CI 0.66–3.35, p=0.344, long-term HR1.66 [95% CI 0.89–3.09], p=0.110). Conclusions Even though DM-patients presented with a more vulnerable clinical profile, no relevant differences in short- and long-term mortality after TEER for MR were found. Although being factored in most common risk scores, DM could not be associated with an adverse prognosis after transcatheter therapy of MR. Funding Acknowledgement Type of funding sources: None.

Global longitudinal strain predicts atrial fibrillation in individuals without hypertension: A Community-based cohort study

Global longitudinal strain (GLS) is a sensitive marker of myocardial dysfunction and atrial reservoir function. We sought to evaluate its value for predicting atrial fibrillation (AF) in the general population. Participants from the Copenhagen City Heart Study examined with echocardiography, including speckle tracking analyses, were included. The endpoint was AF obtained through national registries. Proportional hazards Cox regression was applied, including multivariable adjustments made for CHADS2 and CHARGE-AF risk factors. Abnormal GLS was defined as >-18%. The data from 1,309 participants were analyzed. Of those, 153 (12%) developed AF during a median follow-up time of 15.9years. The follow-up was 100%. The mean age was 57years, 38% had hypertension, and GLS was -18%. In unadjusted analysis, GLS was a univariable predictor of outcome (1.08 (1.04-1.13), p < 0.001, per 1% absolute decrease), but did not remain an independent predictor after adjusting for neitherCHADS2 nor CHARGE-AF risk factors. However, hypertension modified the relationship between GLS and AF (p for interaction = 0.010), such that GLS only predicted AF in subjects without hypertension. In participants without hypertension, GLS remained an independent predictor of AF after adjusting for CHADS2 and CHARGE-AF (HR = 1.11 (1.03-1.20) and HR = 1.09 (1.01-1.19), respectively). In these participants, an abnormal GLS was associated with a more than twofold increased risk of AF (HR = 2.16 (1.26-3.72). The incidence rate was 3.17 and 6.81 per 1000 person-years for normal vs. abnormal GLS, respectively. Global longitudinal strain predicts AF in individuals without hypertension from the general population, independently of common risk scores.

Primary care practices\u2019 ability to predict future risk of expenditures and hospitalization using risk stratification and segmentation

BackgroundPatients with complex health care needs may suffer adverse outcomes from fragmented and delayed care, reducing well-being and increasing health care costs. Health reform efforts, especially those in primary care, attempt to mitigate risk of adverse outcomes by better targeting resources to those most in need. However, predicting who is susceptible to adverse outcomes, such as unplanned hospitalizations, ED visits, or other potentially avoidable expenditures, can be difficult, and providing intensive levels of resources to all patients is neither wanted nor efficient. Our objective was to understand if primary care teams can predict patient risk better than standard risk scores.MethodsSix primary care practices risk stratified their entire patient population over a 2-year period, and worked to mitigate risk for those at high risk through care management and coordination. Individual patient risk scores created by the practices were collected and compared to a common risk score (Hierarchical Condition Categories) in their ability to predict future expenditures, ED visits, and hospitalizations. Accuracy of predictions, sensitivity, positive predictive values (PPV), and c-statistics were calculated for each risk scoring type. Analyses were stratified by whether the practice used intuition alone, an algorithm alone, or adjudicated an algorithmic risk score.ResultsIn all, 40,342 patients were risk stratified. Practice scores had 38.6% agreement with HCC scores on identification of high-risk patients. For the 3,381 patients with reliable outcomes data, accuracy was high (0.71–0.88) but sensitivity and PPV were low (0.16–0.40). Practice-created scores had 0.02–0.14 lower sensitivity, specificity and PPV compared to HCC in prediction of outcomes. Practices using adjudication had, on average, .16 higher sensitivity.ConclusionsPractices using simple risk stratification techniques had slightly worse accuracy in predicting common outcomes than HCC, but adjudication improved prediction.

Risk stratification in unprotected left main coronary disease: do we have the tools?

Abstract Introduction The evolution of percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) techniques made the choice of the optimal revascularization strategy of unprotected left main coronary disease (ULMD) challenging. Scoring systems are useful tools for the decision-making process and for risk stratification. Purpose To evaluate 1) the performance of the SYNTAX score I (SSI) and II (SSII) and Euroscore II (EII) in risk stratification and 2) the outcome predictors of patients (pts) with ULMD, according to the treatment strategy chosen (PCI or CABG). Methods Retrospective single centre cohort study of 440 consecutive pts (age 68±11 years; 76.6% male) with significant ULMD (defined as left main coronary artery stenosis &amp;gt;50%, with no patent arterial or venous bypass graft to left anterior descending artery), who were submitted to PCI (n=135) or CABG (n=307), between January 2006 and December 2018. Median follow-up (FU) was 4.0±1.8 years. The primary outcome was a composite of cardiovascular (CV) death, non-fatal myocardial infarction (MI) and target lesion revascularization (TLR). Results During the FU period, there were 112 (25.5%) CV deaths, 26 (5,9%) non-fatal MI and 53 (12.0%) TLR. Multivariate analysis of pts submitted to PCI showed that SSII and anatomical complete revascularization were independent predictors of the primary outcome (HR 1.045, CI 1.015–1.075, p=0.003 and HR 3,014, CI 1.655–5.489, p&amp;lt;0.0001, respectively). The 63 pts submitted to PCI, who had a SSII favoring CABG, had slightly more adverse events (42.9% vs 41.7%, p=0.889). In the CABG cohort, only SSII was an independent predictor of the outcome by multivariate analysis (HR 1.061, CI 1.035–1.086, p&amp;lt;0.0001). The ROC curve analysis for all cohort presented no discriminative capacity for SSI (AUC 0.538, CI 0.482–0.593, p=0.186) and a weak discrimination for SSII (AUC 0.659, CI 0.605–0.713, p&amp;lt;0.0001) and EII (AUC 0.653, CI 0.599–0.707, p&amp;lt;0.0001; Figure 1). The difference between SSII and EII was not statistically significant (DeLong test p=0.828). Similar results were found when analysing the CAGB group, however, in PCI cohort, SII and EII showed an acceptable discriminative capacity (AUC 0.722, CI 0.636–0.809, p&amp;lt;0.0001 and AUC 0.700, CI 0.610–0.791, p&amp;lt;0.0001, respectively). Conclusion In a real-world ULMD population, the most common risk scores, mainly those integrating anatomical and clinical features, presented a very modest role in the risk stratification, both in chronic and acute coronary syndromes. However, in pts with ULMD submitted to PCI, those risk scores had a more significant role in the risk stratification of these pts. Figure 1. SSI, SSII, EII ROC curves for all cohort Funding Acknowledgement Type of funding source: None

Assessing Baseline and Temporal Changes in Cardiometabolic Risk Using Metabolic Syndrome Severity and Common Risk Scores.

BackgroundType 2 diabetes mellitus (T2DM) is considered a cardiovascular disease (CVD) risk equivalent, thereby linking assessment of cardiometabolic risk with that of CVD risk over time. Our goal was to determine how commonly used CVD risk scores and metabolic syndrome (MetS) severity performed in predicting T2DM with and without ultimate CVD.Methods and ResultsWe assessed data from 8273 participants of the ARIC (Atherosclerosis Risk in Communities) Study, using the pooled cohort atherosclerotic CVD risk score, the Framingham Risk Score, and a MetS severity Z score to assess their association with future risk for CVD alone, T2DM alone, or both over 20 years of follow‐up. Baseline levels of all scores were significantly associated with isolated incident T2DM (odds ratios [ORs] for each 1‐SD increase: atherosclerotic CVD=1.7, Framingham risk score=1.7, MetS Z score=5.1). All 3 baseline scores were also significantly associated with isolated incident CVD (atherosclerotic CVD OR=2.4, Framingham risk score OR=2.3, MetS Z‐score OR=1.8), with the 2 CVD scores remaining significant independent of MetS severity. MetS severity was strongly associated with future T2DM leading to CVD (MetS Z‐score OR=7.0, atherosclerotic CVD OR=3.9, Framingham risk score OR=3.5). Furthermore, changes in MetS severity were independently associated with future T2DM‐CVD progression.Conclusions CVD risk scores are associated with risk for future isolated T2DM in addition to isolated CVD. However, MetS severity (both baseline and changes over time) was more strongly associated with T2DM, including T2DM ultimately leading to CVD. Following MetS severity within patients over time may identify those at greatest risk of combined cardiometabolic disease.

The Role of Frailty in Acute Coronary Syndromes in the Elderly

Background: Myocardial infarction (MI) patients are increasingly older, and common risk scores include chronological age, but do not consider chronic comorbidity or biological age. Frailty status reflects these variables and may be independently correlated with prognosis in this setting. Objective: This study investigated the impact of frailty on the prognosis of elderly patients admitted due to MI. Methods: This prospective and observational study included patients ≥75 years admitted to three tertiary hospitals in Spain due to MI. Frailty assessment was performed at admission using the Survey of Health, Ageing and Retirement in Europe Frailty Index (SHARE-FI) tool. The primary endpoint was the composite of death or non-fatal reinfarction during a follow-up of 1 year. Overall mortality, reinfarction, the composite of death, reinfarction and stroke, major bleeding, and readmission rates were also explored. Results: A total of 285 patients were enrolled. Frail patients (109, 38.2%) were older, with a higher score in the Charlson Comorbidity Index and with a higher risk score addressed in the GRACE and CRUSADE indexes. On multivariate analysis including GRACE, CRUSADE, maximum creatinine level, culprit lesion revascularization, complete revascularization, and dual antiplatelet therapy at discharge, frailty was an independent predictor of the composite of death and reinfarction (2.81, 95% CI 1.16–6.78) and overall mortality (3.07, 95% CI 1.35–6.98). Conclusion: Frailty is an independent prognostic marker of the composite of mortality and reinfarction and of overall mortality in patients aged ≥75 years admitted due to MI.

Impact of Neoadjuvant Chemotherapy on Clinical Risk Scores and Survival in Patients with Colorectal Liver Metastases

BackgroundSeveral clinical risk scores for patients with colorectal liver metastases (CLM) were established in cohorts of patients undergoing liver resection (LR) without neoadjuvant chemotherapy (NAC). The purpose of the study was to evaluate the predictive values of four common risk scores in the setting of NAC and the impact of score changes during NAC.MethodsRisk scores (Fong, Nordlinger, Nagashima, and Konopke) were retrospectively calculated for 336 patients undergoing LR for CLM, including 109 patients without and 227 patients with NAC. In patients with NAC, the scores were calculated before and after NAC.ResultsIn patients without NAC (n = 109), all risk scores except the Konopke score showed a significant correlation with disease-free survival (DFS). Only the Nagashima score also was predictive for overall survival (OS). In patients with NAC (n = 227), all scores except the Konopke score were predictive for DFS and OS before and after NAC. Score changes in the Fong and the Nagashima score showed a significant correlation with DFS and OS.ConclusionsNagashima score was the most universally applicable score and predicted prognosis in all tested scenarios.

Oxidative stress, inflammation, endothelial dysfunction and incidence of type 2 diabetes.

BackgroundOxidative stress, inflammation and endothelial dysfunction are interrelated factors in the etiology of cardiovascular disease, but their linkage to type 2 diabetes is less clear. We examined the association of these biomarkers with incident type 2 diabetes (T2D).MethodsAnalysis of 2339 participants in the community-based coronary artery risk development in young adults (CARDIA) study. Participants (age 40.1 ± 3.6 years, 44 % Black, 58 % women) were free of diabetes, and were followed 10 years. Cox regression was used to estimate hazard ratios (HRs) for incident T2D adjusting for the other biomarkers under study, demographic and lifestyle measures, dietary biomarkers, BMI (kg/m2) and metabolic syndrome components.ResultsF2-isoprostanes and oxidized LDL (oxidative stress) were positively associated with incident T2D, but the associations were attenuated by adjustment for BMI. C-reactive protein was positively associated with T2D even with full adjustment: HR (95 % CI) = 2.21 (1.26–3.88) for quartile 4 (Q4) v. quartile 1 (Q1). The HR (95 % CI) for T2D for biomarkers of endothelial dysfunction ICAM-1 and E-selectin for Q4 v. Q1 were 1.64 (0.96–2.81) and 1.68 (1.04–2.71) respectively, with full adjustment. Including these two markers in a common risk score incorporating BMI and clinical measures improved the prediction probability of T2D: relative risk for the average person classified up compared to the average person classified down: 1.09, (1.06–1.13), P < 0.0001.ConclusionsBiomarkers of inflammation and endothelial dysfunction were positively associated with incident T2D. ICAM-1 and E-selectin add to the prediction of T2D beyond a common risk score.

Coronary Artery Calcium as an Independent Surrogate Marker in the Risk Assessment of Patients With Atrial Fibrillation and an Intermediate Pretest Likelihood for Coronary Artery Disease Admitted to a German Chest Pain Unit.

About 10% of patients admitted to a chest pain unit (CPU) exhibit atrial fibrillation (AF). To determine whether calcium scores (CS) are superior over common risk scores for coronary artery disease (CAD) in patients presenting with atypical chest pain, newly diagnosed AF, and intermediate pretest probability for CAD within the CPU. In 73 subjects, CS was related to the following risk scores: Global Registry of Acute Coronary Events (GRACE) score, including a new model of a frequency-normalized approach; Thrombolysis In Myocardial Infarction score; European Society of Cardiology Systematic Coronary Risk Evaluation (SCORE); Framingham risk score; and Prospective Cardiovascular Münster Study score. Revascularization rates during index stay were assessed. Median CS was 77 (interquartile range, 1-270), with higher values in men and the left anterior descending artery. Only the modified GRACE (ρ = 0.27; P = 0.02) and the SCORE (ρ = 0.39; P < 0.005) were significantly correlated with CS, whereas the GRACE (τ = 0.21; P = 0.04) and modified GRACE (τ = 0.23; P = 0.02) scores were significantly correlated with percentile groups. Only the CS significantly discriminated between those with and without stenosis (P < 0.01). Apart from modified GRACE score, overall correlations between risk scores and calcium burden, as well as revascularization rates during index stay, were low. By contrast, the determination of CS may be used as an additional surrogate marker in risk stratification in AF patients with intermediate pretest likelihood for CAD admitted to a CPU.

Driver behaviour profiles for road safety analysis

Driver behaviour is a contributing factor in over 90 percent of road crashes. As a consequence, there is significant benefit in identifying drivers who engage in unsafe driving practices. Driver behaviour profiles (DBPs) are introduced here as an approach for evaluating driver behaviour as a function of the risk of a casualty crash. They employ data collected using global positioning system (GPS) devices, supplemented with spatiotemporal information. These profiles are comprised of common risk scores that can be used to compare drivers between each other and across time and space. The paper details the development of these DBPs and demonstrates their use as an input into modelling the factors that influence driver behaviour. The results show that even having controlled for the influence of the road environment, these factors remain the strongest predictors of driver behaviour suggesting different spatiotemporal environments elicit a variety of psychological responses in drivers. The approach and outcomes will be of interest to insurance companies in enhancing the risk-profiling of drivers with on-road driving and government through assessing the impacts of behaviour-change interventions.

The German CPU Registry: Dyspnea independently predicts negative short-term outcome in patients admitted to German Chest Pain Units

BackgroundWhile dyspnea is a common symptom in patients admitted to Chest Pain Units (CPUs) little is known about the impact of dyspnea on their outcome. The purpose of this study was to evaluate the impact of dyspnea on the short-term outcome of CPU patients. MethodsWe analyzed data from a total of 9169 patients admitted to one of the 38 participating CPUs in this registry between December 2008 and January 2013. Only patients who underwent coronary angiography for suspected ACS were included. 2601 patients (28.4%) presented with dyspnea. ResultsPatients with dyspnea at admission were older and frequently had a wide range of comorbidities compared to patients without dyspnea. Heart failure symptoms in particular were more common in patients with dyspnea (21.0% vs. 5.3%, p<0.05) at admission. Importantly, in patients presenting with dyspnea the 3month mortality was fourfold higher compared to patients without dyspnea (8.6% vs. 2.1%, p<0.05, OR death: 4.40 95% CI 3.14–6.03). Interestingly, the mortality estimated from the GRACE risk score was below the actual mortality assessed after the 3month follow-up. After adjustment for the GRACE risk score or for heart failure, dyspnea remained highly predictive of death and myocardial infarction within 3months (OR death adjusted for heart failure: 2.99 95% CI 1.99–4.47 and OR death adjusted for GRACE risk score: 3.37 95% CI 2.27–4.99). ConclusionDyspnea is a common symptom in CPU patients. Our data show that dyspnea is associated with a fourfold higher 3month mortality which is underestimated by the established ACS risk scores. To improve their predictive value we therefore propose to add dyspnea as an item to common risk scores.

Subclinical hypothyroidism in addition to common risk scores for prediction of cardiovascular disease: a 10-year community-based cohort study.

This study was carried out to determine whether serum TSH levels improve the prediction of cardiovascular risk in addition to common clinical risk scores, given the association between subclinical hypothyroidism (SCH) and cardiovascular disease (CVD). We carried out an observational study in a prospective cohort. The study included a total of 344 SCH and 2624 euthyroid participants aged over 40 years and who were without previously recorded CVDs were included in this study analysis. We measured thyroid function and traditional risk factors at baseline and estimated the 10-year cumulative incidence of CVD in a gender-stratified analysis. During 10 years of follow-up, 251 incident cardiovascular events were recorded. The elevation of serum TSH levels significantly increased the CV risk independent of conventional risk factors in men. In the atherosclerotic CVD (ASCVD) risk score or the Reynolds risk score (RRS) model, the addition of serum TSH levels had no effect on model discrimination as measured by the area under the curve in either women or men. Adding serum TSH did not improve the net reclassification improvement in either women (3.48% (P=0.29) in the ASCVD, -0.89% (P=0.75) in the RRS, respectively) or men (-1.12% (P=0.69), 3.45% (P=0.20), respectively) and only mildly affected the integrated discrimination Improvement in the ASCVD-adjusted model (0.30% in women and 0.42% in men, both P=0.05). In the context of common risk scoring models, the additional assessment of serum TSH levels provided little incremental benefit for the prediction of CV risk.

Frailty is a predictor of short- and mid-term mortality after elective cardiac surgery independently of age

Assessment of perioperative risk of elderly patients in cardiac surgery is difficult, and most of the common risk scores show over- or underestimation. Two frailty scores, the comprehensive assessment of frailty (CAF) score and the Frailty predicts death One yeaR after CArdiac Surgery Test (FORECAST), were developed as additional tools to estimate the preoperative mortality risk, taking into consideration the frailty status of elderly patients. Four hundred and fifty patients who were referred for elective cardiac surgery were included. All the patients were assessed with the CAF test and the FORECAST. Thirty-day and 1-year mortality were evaluated by telephone interview. Univariate and bivariate logistic regression were performed to test the predictive power of the tests on mortality. Correlation of the scores with age was calculated with Spearman ranks. Three commensurate groups were built for each of the frailty scores and the outcome was compared between the groups. All analyses were performed for Society of Thoracic Surgeons (STS) and European System for Cardiac Operative Risk Evaluation (EuroSCORE) accordingly. A total of 227 male and 223 female patients were included. Thirty-day mortality was 6.1%, and 1-year mortality was 13.3%. Logistic regression showed that both scores are able to predict 30-day as well as 1-year mortality. Bivariate logistic regression showed that both frailty scores give relevant additional information to the STS and EuroSCORE for the prediction of 1-year mortality. The frailty scores were only weakly correlated with age in contrast to STS and EuroSCORE and therefore can be used as indicator of the biological age of patients besides the numerical age. Survival up to 1 year was relevantly reduced in the group of patients with the higher frailty scores. CAF and FORECAST are additional tools to evaluate elderly patients adequately before elective cardiac surgery, and showed an association with short- and mid-term mortality independently of age.

A Worldwide Observational Registry Collecting Longitudinal Data On the Management of CML Patients (The WORLD CML Registry) - Summary of the First 1001 Patients.

Abstract 4267 IntroductionThis multicenter, international, prospective registry is a longitudinal (5 year) assessment of diagnosis methods, management strategies, and outcomes for CML patients (pts) under ‘real world' conditions. Pts were enrolled from Latin America (LA), United States (US), Asia Pacific (AP), Middle East and Africa (MEA), and Russia and Turkey (RT). This first, interim data summary reports baseline characteristics on the first 1001 enrolled pts. MethodsEligible pts were ≥ 16 yrs of age and entered the Registry within 6 months of CML diagnosis. Demographics, medical history, current disease status and management information are collected at baseline. Follow up data are collected at approximately 6-month intervals. Center practices in disease detection and monitoring are collected annually. Results1001 patients from 148 sites worldwide were enrolled with a data cut-off of May 2009. Represented in this dataset: 165 (16.5%) from LA, 179 (17.9%) from US, 372 (37.2%) from AP, 84 (8.4%) from MEA, and 201 (20.1%) from RT. The majority of patients in LA (50%) were from government hospitals, US from private hospitals/practices (61.1%), AP and MEA from academic institutions (54.8% and 64.7% respectively), and RT from regional/community hospitals (55%). Overall, 953 (95.2%) pts were in chronic phase CML, with little variation across regions. Overall, 58.4% of pts were male (LA, 51.5%; US, 57.0%; AP, 67.7%; MEA, 53.6%; RT, 50.2%) and median age was 46 yrs (range, 15–91). Notably, median age in the AP region was just 40 yrs (LA, 48; US, 53; MEA, 41.5; RT, 52). Hematologic, bone marrow, and cytogenetic (Cy) assessments were used for CML diagnosis in most pts (88.7%, 84.4%, 82.6%, respectively). For Cy assessment, the method used varied widely by region. Most centers appeared to use both karyotype and fluorescence in situ hybridization (FISH), with FISH being used in a greater proportion of pts in the US and MEA (Table 1).Table 1Type of cytogenetic assessment used at diagnosis by regionCytogenetic Assessment, n (%)LA (n = 165)US (n = 179)AP (n = 372)MEA (n = 84)RT (n = 201)Overall (n = 1001)Overall148 (89.7)146 (81.6)320 (86.0)64 (76.2)149 (74.1)827 (82.6)Karyotype140 (94.6)91 (62.3)281 (87.8)38 (59.4)118 (79.2)668 (80.8)FISH14 (9.5)113 (77.4)98 (30.6)45 (70.3)37 (24.8)307 (37.1)Only 51.6% of pts had molecular assessments at diagnosis, ranging from 27.4% in RT to 70.2% in MEA. The majority (54.3%) of pts had no risk score assigned at diagnosis. Sokal score, assessed in 39.1% of pts, was the most common risk score used in all regions (LA, 46.7%; US, 5.6%; AP, 36.0%; MEA, 50.0%; RT, 63.7%). The most common comorbidity was cardiac disorders, in 211 (21.1%) pts, followed by diabetes in 74 (7.4%) pts. Pulmonary disorders were present in 33 (3.3%) patients. At the time of Registry enrollment, imatinib was the most commonly prescribed therapy. Most centers (83.1%) were not performing plasma drug monitoring of anti-leukemic therapy, primarily due to lack of availability (58.5%); 89.2% of those centers would consider performing plasma drug level testing if it were readily available. ConclusionsThe first interim results of this Registry reveal some regional differences in CML diagnosis and management. Notably, not all pts appear to be treated according to published disease management guidelines. For example, although only karyotyping can detect additional chromosomal abnormalities in Philadelphia chromosome-positive CML, approximately 20% of patients overall and 40% of patients in the US and MEA did not have karyotyping performed at diagnosis. Furthermore, molecular assessments were not universally performed at diagnosis. As these data mature, ongoing interim summaries from the WORLD CML Registry will continue to assess global trends in CML diagnostic methods, treatment approaches, adherence to disease management guidelines and response to treatment. Educational initiatives can thus be regionally tailored to optimize patient care in CML. Additional data summaries by center setting (private hospitals/practices, academic, etc) will also be available. Disclosures:Cortes-Franco:Novartis: Honoraria, Research Funding, Speakers Bureau; Wyeth: Honoraria, Research Funding, Speakers Bureau; BMS: Honoraria, Research Funding, Speakers Bureau. Joske:Novartis Australia: Membership on an entity's Board of Directors or advisory committees; Novartis Pharma AG: Membership on an entity's Board of Directors or advisory committees. Mongay:Novartis: Employment. Eng:Novartis: Employment. Wang:Novartis: Employment. Hughes:Bristol-Myers Squibb: Advisor, Honoraria, Research Funding; Novartis: Advisor, Honoraria, Research Funding. Kim:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Wyeth: Research Funding.

Comparison of Two Different Methods for Cardiovascular Risk Assessment: Framingham Risk Score and Score System

Comparison of Two Different Methods for Cardiovascular Risk Assessment:Framingham Risk ScoreandScoreSystemNumerous studies have shown that the major risk factors for coronary heart disease (cigarette smoking, hypertension, elevated serum total cholesterol and low-density lipoprotein cholesterol - LDL, low serum high-density lipoprotein cholesterol - HDL, diabetes mellitus and advancing age), are additive in predictive power. Accordingly, the total risk of a person can be estimated by summing up the risk imparted by each of the major risk factors. Using data obtained from population studies, various risk assessment algorithms have been developed. The aim of this study was to compare the two most common risk scores. Risk assessment for determining 10-year risk in 185 healthy, asymptomatic individuals of both sexes, 30-85 years old, was carried out according to both Framingham (FRS) and SCORE risk scoring. The risk factors included in the calculation of 10-year risk are gender, age, total cholesterol, HDL-cholesterol, systolic blood pressure, treatment for hypertension and cigarette smoking. The determinations of total cholesterol and HDL-cholesterol were made in sera collected after a 12h fasting period using an Olympus AU2700 automated analyzer. The Framingham risk score was determined using an electronic calculator - ATP III Risk Estimator, and the risk status according to SCORE was obtained using charts for the 10-year risk in populations at high risk. Among 185 participants, in 152 (82%) 10-year risk for Coronary Heart Disease (CHD) death was &lt;10%, 24 (13%) had intermediate and 9 (5%) had high risk (⩾20%) according to FRS. According to SCORE, 110 (60%) participants had &lt;1%, 56 (30%) had 1-5% and 19 (10%) had ⩾5% of 10-year risk for cardiovascular death. Different categories of risk were assigned to ~30% of individuals according to different risk assessment models. Differences in risk classification when using two different risk assessment algorithms can be explained with several important issues, including different endpoints, consideration of interactions and incorporation of antihypertensive use. It is important to note that neither FRS nor SCORE have been appropriately adjusted for our population, according to the national cardiovascular mortality rate.

Assessing the risk of mortality in paediatric cancer patients admitted to the paediatric intensive care unit: a novel risk score?

Intensive front-line protocols have improved survival in children with malignancies; however, intensive multimodal therapy of paediatric malignancies can be associated with a significant risk of serious adverse events. Common risk scores (PRISM, PRISM III, APACHE-II) fail to predict mortality in these patients. A retrospective chart analysis of 32 paediatric cancer patients admitted to the Paediatric Intensive Care Unit (PICU) at the University Hospital of Saarland between January 2001 and December 2003 for life-threatening complications was performed. The aim of this study was to assess risk factors for short-term outcome (survival vs. non-survival when leaving the PICU) and to develop a risk score to estimate outcome in these patients. Overall survival was good (25 of 32 patients). Mortality rate was significantly related to leukaemia/lymphoma ( P = 0.029), to the number of organ failures ( P < 0.0001), neutropenia ( P = 0.001), septic shock ( P = 0.025), mechanical ventilation ( P = 0.01) and inotropic support ( P = 0.01). Employing multiple logistic regression, the strongest predictor for poor outcome was the number of organ failures ( P < 0.05). A risk score (cut-off value: >3 points for non-survival) which included the following risk factors (non-solid tumour, number of organ failures ( n > 2), neutropenia, septic shock, mechanical ventilation, and inotropic medication) yielded a sensitivity of 7/7 (95% CI: 4.56-7.00), a specificity of 23/25 (95% CI: 18.49-24.75), a positive predictive value of 23/23 (95% CI: 19.80-23.00), and a negative predictive value of 7/9 (95% CI: 3.60-8.74) for the time of admission to the PICU. Although our risk of mortality score is of prognostic value in assessing short-term outcome in these patients, prospective validation in a larger study cohort is mandatory. Furthermore, it must be emphasised that this risk score must not be used for decision-making in an individual patient.