The added value of C-reactive protein to clinical signs and symptoms in patients with obstructive airway disease: results of a diagnostic study in primary care

BACKGROUND: To evaluate the diagnostic accuracy of clinical signs and symptoms, C-reactive protein (CRP) and spirometric parameters and determine their interrelation in patients suspected to have an obstructive airway disease (OAD) in primary care. METHODS: In a cross sectional diagnostic study, 60 adult patients coming to the general practitioner (GP) for the first-time with complaints suspicious for obstructive airway disease (OAD) underwent spirometry. Peak expiratory flow (PEF)-variability within two weeks was determined in patients with inconspicuous spirometry. Structured medical histories were documented and CRP was measured. The reference standard was the Tiffeneau ratio (FEV(1)/VC) in spirometry and the PEF-variability. OAD was diagnosed when FEV(1)/VC ≤ 70% or PEF-variability > 20%. RESULTS: 37 (62%) patients had OAD. The best cut-off value for CRP was found at 2 mg/l with a diagnostic odds ratio (OR) of 4.4 (95% CI 1.4–13.8). Self-reported wheezing was significantly related with OAD (OR 3.4; CI 1.1–10.3), whereas coughing was inversely related (OR 0.2; CI 0.1–0.7). The diagnostic OR of CRP increased when combined with dyspnea (OR 8.5; 95% CI 1.7–42.3) or smoking history (OR 8.4; 95% CI 1.5–48.9). CRP (p = 0.004), FEV(1 )(p = 0.001) and FIV(1 )(p = 0.023) were related with the severity of dyspnea. CRP increased with the number of cigarettes, expressed in pack years (p = 0.001). CONCLUSION: The diagnostic accuracy of clinical signs and symptoms was low. The diagnostic accuracy of CRP improved in combination with dyspnea and smoking history. Due to their coherence with the severity of dyspnea and number of cigarettes respectively, CRP and spirometry might allow risk stratification of patients with OAD in primary care. Further studies need to be done to confirm these findings

Uniting statistical and individual-based approaches for animal movement modelling

<div><p>The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.</p></div