The value of risk scores

Risk score to stratify miscarriage risk levels in preconception women

Spontaneous miscarriage is one of the most common complications of pregnancy. Even though some risk factors are well documented, there is a paucity of risk scoring tools during preconception. In the S-PRESTO cohort study, Asian women attempting to conceive, aged 18-45 years, were recruited. Multivariable logistic regression model coefficients were used to determine risk estimates for age, ethnicity, history of pregnancy loss, body mass index, smoking status, alcohol intake and dietary supplement intake; from these we derived a risk score ranging from 0 to 17. Miscarriage before 16 weeks of gestation, determined clinically or via ultrasound. Among 465 included women, 59 had miscarriages and 406 had pregnancy ≥ 16 weeks of gestation. Higher rates of miscarriage were observed at higher risk scores (5.3% at score ≤ 3, 17.0% at score 4–6, 40.0% at score 7–8 and 46.2% at score ≥ 9). Women with scores ≤ 3 were defined as low-risk level (< 10% miscarriage); scores 4–6 as intermediate-risk level (10% to < 40% miscarriage); scores ≥ 7 as high-risk level (≥ 40% miscarriage). The risk score yielded an area under the receiver-operating-characteristic curve of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). This novel scoring tool allows women to self-evaluate their miscarriage risk level, which facilitates lifestyle changes to optimize modifiable risk factors in the preconception period and reduces risk of spontaneous miscarriage.

Symptom- and Laboratory-Based Ebola Risk Scores to Differentiate Likely Ebola Infections

Rapidly identifying likely Ebola patients is difficult because of a broad case definition, overlap of symptoms with common illnesses, and lack of rapid diagnostics. However, rapid identification is critical for care and containment of contagion. We analyzed retrospective data from 252 Ebola-positive and 172 Ebola-negative patients at a Sierra Leone Ebola treatment center to develop easy-to-use risk scores, based on symptoms and laboratory tests (if available), to stratify triaged patients by their likelihood of having Ebola infection. Headache, diarrhea, difficulty breathing, nausea/vomiting, loss of appetite, and conjunctivitis comprised the symptom-based score. The laboratory-based score also included creatinine, creatine kinase, alanine aminotransferase, and total bilirubin. This risk score correctly identified 92% of Ebola-positive patients as high risk for infection; both scores correctly classified >70% of Ebola-negative patients as low or medium risk. Clinicians can use these risk scores to gauge the likelihood of triaged patients having Ebola while awaiting laboratory confirmation.

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.

Kidney function and estimated vascular risk in patients with primary dyslipidemia

Background:

Chronic kidney disease (CKD) is associated with increased vascular risk. Some studies suggested that considering markers of CKD might improve the predictive accuracy of the Framingham risk equation.

Aim:

To evaluate the links between kidney function and risk stratification in patients with primary dyslipidemia.

Methods:

Dyslipidemic patients (n = 156; 83 men) who were non-smokers, did not have diabetes mellitus or evident vascular disease and were not on lipid-lowering or antihypertensive agents were recruited. Creatinine clearance (CrCl) was estimated using the Cockcroft-Gault equation. Estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease (MDRD) equation. We estimated vascular risk using the Framingham equation.

Results:

In both men and women, there was a significant negative correlation between estimated Framingham risk and both eGFR and CrCl (p < 0.001 for all correlations). When men were divided according to creatinine tertiles, there were no significant differences in any parameter between groups. When men were divided according to either eGFR or CrCl tertiles, all estimated Framingham risks significantly increased as renal function declined (p<0.001 for all trends). When women were divided according to creatinine tertiles, all estimated Framingham risks except for stroke significantly increased as creatinine levels increased. When women were divided according to either eGFR or CrCl tertiles, all estimated Framingham risks significantly increased as renal function declined.

Conclusions:

Estimated vascular risk increases as renal function declines. The possibility that incorporating kidney function in the Framingham equation will improve risk stratification requires further evaluation.

Increased Framingham Coronary Heart Disease Risk Score in dementia caregivers relative to non-caregiving controls

BACKGROUND

Elderly individuals who provide care to a spouse suffering from dementia bear an increased risk of coronary heart disease (CHD).

OBJECTIVE

To test the hypothesis that the Framingham CHD Risk Score would be higher in dementia caregivers relative to non-caregiving controls.

METHODS

We investigated 64 caregivers providing in-home care for their spouse with Alzheimer's disease and 41 gender-matched non-caregiving controls. All subjects (mean age 70 +/- 8 years, 75% women, 93% Caucasian) had a negative history of CHD and cerebrovascular disease. The original Framingham CHD Risk Score was computed adding up categorical scores for age, blood lipids, blood pressure, diabetes, and smoking with adjustment made for sex.

RESULTS

The average CHD risk score was higher in caregivers than in controls even when co-varying for socioeconomic status, health habits, medication, and psychological distress (8.0 +/- 2.9 vs. 6.3 +/- 3.0 points, p = 0.013). The difference showed a medium effect size (Cohen's d = 0.57). A relatively higher blood pressure in caregivers than in controls made the greatest contribution to this difference. The probability (area under the receiver operator curve) that a randomly selected caregiver had a greater CHD risk score than a randomly selected non-caregiver was 65.5%.

CONCLUSIONS

Based on the Framingham CHD Risk Score, the potential to develop overt CHD in the following 10 years was predicted to be greater in dementia caregivers than in non-caregiving controls. The magnitude of the difference in the CHD risk between caregivers and controls appears to be clinically relevant. Clinicians may want to monitor caregiving status as a routine part of standard evaluation of their elderly patients' cardiovascular risk.