The Patient Health Questionnaire-9 for detection of major depressive disorder in primary care: consequences of current thresholds in a crosssectional study

<p>Abstract</p> <p>Background</p> <p>There is a need for brief instruments to ascertain the diagnosis of major depressive disorder. In this study, we present the reliability, construct validity and accuracy of the PHQ-9 and PHQ-2 to detect major depressive disorder in primary care.</p> <p>Methods</p> <p>Cross-sectional analyses within a large prospective cohort study (PREDICT-NL). Data was collected in seven large general practices in the centre of the Netherlands. 1338 subjects were recruited in the general practice waiting room, irrespective of their presenting complaint. The diagnostic accuracy (the area under the ROC curve and sensitivities and specificities for various thresholds) was calculated against a diagnosis of major depressive disorder determined with the Composite International Diagnostic Interview (CIDI).</p> <p>Results</p> <p>The PHQ-9 showed a high degree of internal consistency (ICC = 0.88) and test-retest reliability (correlation = 0.94). With respect to construct validity, it showed a clear association with functional status measurements, sick days and number of consultations. The discriminative ability was good for the PHQ-9 (area under the ROC curve = 0.87, 95% CI: 0.84-0.90) and the PHQ-2 (ROC area = 0.83, 95% CI 0.80-0.87). Sensitivities at the recommended thresholds were 0.49 for the PHQ-9 at a score of 10 and 0.28 for a categorical algorithm. Adjustment of the threshold and the algorithm improved sensitivities to 0.82 and 0.84 respectively but the specificity decreased from 0.95 to 0.82 (threshold) and from 0.98 to 0.81 (algorithm). Similar results were found for the PHQ-2: the recommended threshold of 3 had a sensitivity of 0.42 and lowering the threshold resulted in an improved sensitivity of 0.81.</p> <p>Conclusion</p> <p>The PHQ-9 and the PHQ-2 are useful instruments to detect major depressive disorder in primary care, provided a high score is followed by an additional diagnostic work-up. However, often recommended thresholds for the PHQ-9 and the PHQ-2 resulted in many undetected major depressive disorders.</p

Validity of a clinical model to predict influenza in patients presenting with symptoms of lower respiratory tract infection in primary care

Background. Valid clinical predictors of influenza in patients presenting with lower respiratory tract infection (LRTI) symptoms would provide adequate patient information and reassurance. Aim. Assessing the validity of an existing diagnostic model (Flu Score) to detect influenza in LRTI patients. Design and Setting. A European diagnostic study recruited 1801 adult primary care patients with LRTI-like symptoms existing ≤7 days between October and April 2007–2010. Method. History and physical examination findings were recorded and nasopharyngeal swabs taken. Polymerase chain reaction (PCR) for influenza A/B was performed as reference test. Diagnostic accuracy of the Flu Score (1× onset <48 hours + 2× myalgia + 1× chills or sweats + 2× fever and cough) was expressed as area under the curve (AUC), calibration slopes and likelihood ratios (LRs). Results. A total of 273 patients (15%) had influenza on PCR. The AUC of the Flu Score during winter months was 0.66 [95% CI (95% confidence internal) 0.63–0.70]. During peak influenza season, both influenza prevalence (24%) and AUC were higher [0.71 (95% CI 0.66–0.76], but calibration remained poor. The Flu Score assigned 64% of the patients as ‘low-risk’ (10% had influenza, LR − 0.6). About 12% were classified as ‘high risk’ of whom 32% had influenza (LR + 2.7). During peak influenza season, 60% and 14% of patients were classified as low and high risk, respectively, with influenza prevalences being 14% (LR − 0.5) and 50% (LR + 3.2). Conclusion. The Flu-Score attributes a small subgroup of patients with a high influenza risk (prevalence 32%). However, clinical usefulness is limited because this group is small and the association between predicted and observed risks is poor. Considerable diagnostic imprecision remains when it comes to differentiating those with influenza on clinical grounds from the many other causes of LRTI in primary care. New point of care tests are required that accurately, rapidly and cost effectively detect influenza in patients with respiratory tract symptoms in primary care