A clinical predictive score for mood disorder risk in low-income primary care settings.

BACKGROUND: Despite availability of validated screening tests for mood disorders, busy general practitioners (GPs) often lack the time to use them routinely. This study aimed to develop a simplified clinical predictive score to help screen for presence of current mood disorder in low-income primary care settings. METHODS: In a cross-sectional study, 197 patients seen at 10 primary care centers in Santiago, Chile completed self-administered screening tools for mood disorders: the Patient Health questionnaire (PHQ-9) and the Mood Disorder Questionnaire (MDQ). To determine participants' current-point mood disorder status, trained clinicians applied a gold-standard diagnostic interview (SCID-I). A simplified clinical predictive model (CM) was developed based on clinical features and selected questions from the screening tools. Using CM, a clinical predictive score (PS) was developed. Full PHQ-9 and GP assessment were compared with PS. RESULTS: Using multivariate logistic regression, clinical and demographic variables predictive of current mood disorder were identified for a simplified 8-point predictive score (PS). PS had better discrimination than GP assessment (auROC-statistic=0.80 [95% CI 0.72, 0.85] vs. 0.58 [95% CI 0.52, 0.62] p-value <0.0001), but not as good as the full PHQ-9 (0.89 [95% CI 0.85, 0.93], p-value=0.03). Compared with GP assessment, PS increased sensitivity by 50% at a fixed specificity of 90%. Administered in a typical primary care clinical population, it correctly predicted almost 80% of cases. LIMITATIONS: Further research must verify external validity of the PS. CONCLUSION: An easily administered clinical predictive score determined, with reasonable accuracy, the current risk of mood disorders in low-income primary care settings.

The potential use of ECG-based prognostic instruments in clinical trials and cost-effectiveness analyses of new therapies in acute cardiac ischemia.

The dramatic improvements in outcomes in acute cardiac ischemia because of therapeutic advances has led to "diminishing returns" with increasingly intensive therapies. This article explores the potential of electrocardiograph (ECG)-based prognostic instruments to identify patients likely to benefit from intense regimens, even in the absence of overall average benefit in the population, with 2 clinical examples: 1) Reperfusion therapy in acute myocardial infarction (AMI); and 2) anticoagulation/antiplatelet therapy in unstable angina. Based on previously developed, ECG-based prognostic instruments we explored the distribution of potential benefits in individual patients from increasingly intense therapy in both AMI and unstable angina. Predictions were obtained on community-based patient samples with both AMI and unstable angina to examine the distribution of effectiveness and cost-effectiveness. For both AMI and unstable angina, much of the benefit of intensifying therapy can be obtained by targeting a subgroup of patients that can be identified in multivariable dimensions by clinical and ECG characteristics. Treatment of these patients with more potent agents (such as hirudin or the glycoprotein inhibitors in unstable angina) is likely to be both effective and cost-effective. However, treatment of "low benefit" patients is unlikely to be effective or cost-effective, and some candidates for therapy are more likely to be harmed, than to benefit, by the more intensive regimens. Multivariable stratification can improve clinical and economic outcomes in acute cardiac ischemia, particularly when such models help identify "high benefit" patients early in their clinical course. Additionally, using validated models in the planning and execution of clinical trials of new therapies can improve the power of the trial and help target the therapies to patients most likely to benefit.