Cardiovascular risk prediction model development and assessment

The aim of this article is to review the development and assessment of cardiovascular risk prediction models and to discuss the predictive value of a risk factor as well as to introduce new assessment methods to evaluate a risk prediction model. Many cardiovascular risk prediction models have been developed during the past three decades. However, there has not been consistent agreement regarding how to appropriately assess a risk prediction model, especially when new markers are added to an existing model. The area under the receiver operating characteristic (ROC) curve has traditionally been used to assess the discriminatory ability of a risk prediction model. However, recent studies suggest that this method has its limitations and cannot be the sole approach to evaluate the usefulness of a new marker. New assessment methods are being developed to appropriately assess a risk prediction model and they will be gradually used in clinical and epidemiological studies.

An integrated framework for suicide risk prediction

Suicide is a major concern in society. Despite of great attention paid by the community with very substantive medico-legal implications, there has been no satisfying method that can reliably predict the future attempted or completed suicide. We present an integrated machine learning framework to tackle this challenge. Our proposed framework consists of a novel feature extraction scheme, an embedded feature selection process, a set of risk classifiers and finally, a risk calibration procedure. For temporal feature extraction, we cast the patient’s clinical history into a temporal image to which a bank of one-side filters are applied. The responses are then partly transformed into mid-level features and then selected in 1-norm framework under the extreme value theory. A set of probabilistic ordinal risk classifiers are then applied to compute the risk probabilities and further re-rank the features. Finally, the predicted risks are calibrated. Together with our Australian partner, we perform comprehensive study on data collected for the mental health cohort, and the experiments validate that our proposed framework outperforms risk assessment instruments by medical practitioners.

Predicting risk of suicide attempt using history of physical illnesses from electronic medical records

BACKGROUND: Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk.

OBJECTIVE: The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data.

METHODS: We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC).

RESULTS: The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians.

CONCLUSIONS: This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk.

Overview of risk prediction models in cardiovascular disease research

Many risk prediction models have been developed for cardiovascular diseases in different countries during the past three decades. However, there has not been consistent agreement regarding how to appropriately assess a risk prediction model, especially when new markers are added to an established risk prediction model. Researchers often use the area under the receiver operating characteristic curve (ROC) to assess the discriminatory ability of a risk prediction model. However, recent studies suggest that this method has serious limitations and cannot be the sole approach to evaluate the usefulness of a new marker in clinical and epidemiological studies. To overcome the shortcomings of this traditional method, new assessment methods have been proposed. The aim of this article is to overview various risk prediction models for cardiovascular diseases, to describe the receiver operating characteristic curve method and discuss some new assessment methods proposed recently. Some of the methods were illustrated with figures from a cardiovascular disease study in Australia.

Longitudinal evaluation of a risk-factor model for adolescent suicidality

This research developed two best-fitting structural equation models of risk factors for adolescent depression and suicidality: a core model, which included parenting factors, gender, depression, and suicidality, and an extended model, which also encompassed personality traits (Introversion and Impulsivity) and mood factors (Anxiety and Anger). Further, this research investigated the consistency of model fit across time (Le., 1 month & 12 months) and samples, and explored the effectiveness of the ReachOut! Internet site as a psychoeducational prevention strategy for adolescent depression and suicidality. Gender, age, and location differences were also explored. Participants were 185 Year-9 students and 93 Year-10 students aged 14 - 16 years, from seven secondary schools in regional and rural Victoria. Students were given a survey which included the Parental Bonding Instrument (Parker, Tupling, & Brown, 1979), the Millon Adolescent Personality Inventory (Millon, Green, & Meagher, 1982), the Profile of Mood States Inventory (McNair & Lorr, 1964), items on suicidal behaviour including some questions from the Revised Adolescent Suicide Questionnaire (Pearce & Martin, 1994), and questions on loss and general demographics. Results supported an indirect model of risk factors, with family factors directly influencing personality factors, which in turn influenced mood factors, including depression, which then influenced suicidality. At the theoretical level, results supported Attachment Theory (Bowlby, 1969), demonstrating that perceived parenting styles that are warm and not overly controlling are more conducive to an adolescent's emotional well-being than are parenting styles that are cold and controlling. Further, results supported Millon's theory of personality (1981), demonstrating that parenting style influences a child's personality. Short-term intervention effects from the internet site were a decrease in Introversion for the full sample, and decreased Inhibition and Suicidality for a high-risk subgroup. Long-term age effects were decreased Inhibition and increased Anxiety for the fall sample. There was also a probable intervention effect for Depression for the high-risk subgroup. No location differences for the risk factors were found between regional and rural areas.

Framingham risk prediction equations for incidence of cardiovascular disease using detailed measures for smoking

Current prediction models for risk of cardiovascular disease (CVD) incidence incorporate smoking as a dichotomous yes/no measure. However, the risk of CVD associated with smoking also varies with the intensity and duration of smoking and there is a strong association between time since quitting and the risk of disease onset. This study aims to develop improved risk prediction equations for CVD incidence incorporating intensity and duration of smoking and time since quitting. The risk of developing a first CVD event was evaluated using a Cox’s model for participants in the Framingham offspring cohort who attended the fourth examination (1988–92) between the ages of 30 and 74 years and were free of CVD (n=3751). The full models based on the smoking variables and other risk factors, and reduced models based on the smoking variables and non-laboratory risk factors demonstrated good discrimination, calibration and global fit. The incorporation of both time since quitting among past smokers and pack-years among current smokers resulted in better predictive performance as compared to a dichotomous current/non-smoker measure and a current/quitter/never smoker measure. Compared to never smokers, the risk of CVD incidence increased with pack-years. Risk among those quitting more than five years prior to the baseline exam and within five years prior to the baseline exam were similar and twice as high as that of never smokers. A CVD risk equation incorporating the effects of pack-years and time since quitting provides an improved tool to quantify risk and guide preventive care.

Stabilized sparse ordinal regression for medical risk stratification

The recent wide adoption of electronic medical records (EMRs) presents great opportunities and challenges for data mining. The EMR data are largely temporal, often noisy, irregular and high dimensional. This paper constructs a novel ordinal regression framework for predicting medical risk stratification from EMR. First, a conceptual view of EMR as a temporal image is constructed to extract a diverse set of features. Second, ordinal modeling is applied for predicting cumulative or progressive risk. The challenges are building a transparent predictive model that works with a large number of weakly predictive features, and at the same time, is stable against resampling variations. Our solution employs sparsity methods that are stabilized through domain-specific feature interaction networks. We introduces two indices that measure the model stability against data resampling. Feature networks are used to generate two multivariate Gaussian priors with sparse precision matrices (the Laplacian and Random Walk). We apply the framework on a large short-term suicide risk prediction problem and demonstrate that our methods outperform clinicians to a large margin, discover suicide risk factors that conform with mental health knowledge, and produce models with enhanced stability. © 2014 Springer-Verlag London.

A meta-analysis of trabecular bone score in fracture risk prediction and its relationship to FRAX

Trabecular bone score (TBS) is a grey-level textural index of bone microarchitecture derived from lumbar spine dual-energy X-ray absorptiometry (DXA) images. TBS is a BMD-independent predictor of fracture risk. The objective of this meta-analysis was to determine whether TBS predicted fracture risk independently of FRAX probability and to examine their combined performance by adjusting the FRAX probability for TBS. We utilized individual level data from 17,809 men and women in 14 prospective population-based cohorts. Baseline evaluation included TBS and the FRAX risk variables and outcomes during follow up (mean 6.7 years) comprised major osteoporotic fractures. The association between TBS, FRAX probabilities and the risk of fracture was examined using an extension of the Poisson regression model in each cohort and for each sex and expressed as the gradient of risk (GR; hazard ratio per 1SD change in risk variable in direction of increased risk). FRAX probabilities were adjusted for TBS using an adjustment factor derived from an independent cohort (the Manitoba Bone Density Cohort). Overall, the GR of TBS for major osteoporotic fracture was 1.44 (95% CI: 1.35-1.53) when adjusted for age and time since baseline and was similar in men and women (p > 0.10). When additionally adjusted for FRAX 10-year probability of major osteoporotic fracture, TBS remained a significant, independent predictor for fracture (GR 1.32, 95%CI: 1.24-1.41). The adjustment of FRAX probability for TBS resulted in a small increase in the GR (1.76, 95%CI: 1.65, 1.87 vs. 1.70, 95%CI: 1.60-1.81). A smaller change in GR for hip fracture was observed (FRAX hip fracture probability GR 2.25 vs. 2.22). TBS is a significant predictor of fracture risk independently of FRAX. The findings support the use of TBS as a potential adjustment for FRAX probability, though the impact of the adjustment remains to be determined in the context of clinical assessment guidelines.

A risk analysis model to reduce computer security risks among healthcare organizations

The paper describes the on-going development of a new computer-based security risk analysis methodology that may be used to determine the computer security requirements of medical computer systems. The methodology has been developed for use within healthcare, with particular emphasis placed upon protecting medical information systems. The paper goes on to describe some of the problems with existing automated risk analysis systems, and how the ODESSA system may overcome the majority of these problems. Examples of security scenarios are also presented.

Impact of an empowerment-based parent education program on the reduction of youth suicide risk factors

Purpose: To evaluate the impact of parent education groups on youth suicide risk factors. The potential for informal transmission of intervention impacts within school communities was assessed.

Methods: Parent education groups were offered to volunteers from 14 high schools that were closely matched to 14 comparison schools. The professionally led groups aimed to empower parents to assist one another to improve communication skills and relationships with adolescents. Australian 8th-grade students (aged 14 years) responded to classroom surveys repeated at baseline and after 3 months. Logistic regression was used to test for intervention impacts on adolescent substance use, deliquency, self-harm behavior, and depression. There were no differences between the intervention (n = 305) and comparison (n = 272) samples at baseline on the measures of depression, health behavior, or family relationships.

Results: Students in the intervention schools demonstrated increased maternal care (adjusted odds ratio [AOR] 1.9), reductions in conflict with parents (AOR .5), reduced substance use (AOR .5 to .6), and less delinquency (AOR .2). Parent education group participants were more likely to be sole parents and their children reported higher rates of substance use at baseline. Intervention impacts revealed a dose-response with the largest impacts associated with directly participating parents, but significant impacts were also evident for others in the intervention schools. Where best friend dyads were identified, the best friend’s positive family relationships reduced subsequent substance use among respondents. This and other social contagion processes were posited to explain the transfer of positive impacts beyond the minority of directly participating families.

Conclusions: A whole-school parent education intervention demonstrated promising impacts on a range of risk behaviors and protective factors relevant to youth self-harm and suicide.

An artificial neural network-based hairiness prediction model for worsted wool yarns

This study evaluated the performance of multilayer perceptron (MLP) and multivariate linear regression (MLR) models for predicting the hairiness of worsted-spun wool yarns from various top, yarn and processing parameters. The results indicated that the MLP model predicted yarn hairiness more accurately than the MLR model, and should have wide mill specific applications. On the basis of sensitivity analysis, the factors that affected yarn hairiness significantly included yarn twist, ring size, average fiber length (hauteur), fiber diameter and yarn count, with twist having the greatest impact on yarn hairiness.

Laboratory and non-laboratory-based risk prevention models for secondary prevention of cardiovascular disease : the LIPID study

Aims : The aims of this study were to examine whether risk prediction models for recurrent cardiovascular disease (CVD) events have prognostic value, and to particularly examine the performance of those models based on non-laboratory data. We also aimed to construct a risk chart based on the risk factors that showed the strongest relationship with CVD.

Methods and results : Cox proportional hazards models were used to calculate a risk score for each recurrent event in a CVD patient who was enrolled in a very large randomized controlled clinical trial. Patients were then classified into groups according to quintiles of their risk score. These risk models were validated by calibration and discrimination analyses based on data from patients recruited in New Zealand for the same study. Non-laboratory-based risk factors, such as age, sex, body mass index, smoking status, angina grade, history of myocardial infarction, revascularization, stroke, diabetes or hypertension and treatment with pravastatin, were found to be significantly associated with the risk of developing a recurrent CVD event. Patients who were classified into the medium and high-risk groups had two-fold and four-fold the risk of developing a CVD event compared with those in the low-risk group, respectively. The risk prediction models also fitted New Zealand data well after recalibration.

Conclusion : A simpler non-laboratory-based risk prediction model performed equally as well as the more comprehensive laboratory-based risk prediction models. The risk chart based on the further simplified Score Model may provide a useful tool for clinical cardiologists to assess an individual patient's risk for recurrent CVD events.