RESUMO
BACKGROUND: C-reactive protein (CRP) is increasingly being included in the diagnostic work-up for community-acquired pneumonia in primary care. Its added diagnostic value beyond signs and symptoms, however, remains unclear. We conducted a meta-analysis of individual patient data to quantify the added value of CRP measurement. METHODS: We included studies of the diagnostic accuracy of CRP in adult outpatients with suspected lower respiratory tract infection. We contacted authors of eligible studies for inclusion of data and for additional data as needed. The value of adding CRP measurement to a basic signs-and-symptoms prediction model was assessed. Outcome measures were improvement in discrimination between patients with and without pneumonia in primary care and improvement in risk classification, both within the individual studies and across studies. RESULTS: Authors of 8 eligible studies (n = 5308) provided their data sets. In all of the data sets, discrimination between patients with and without pneumonia improved after CRP measurement was added to the prediction model (extended model), with a mean improvement in the area under the curve of 0.075 (range 0.02-0.18). In a hypothetical cohort of 1000 patients, the proportion of patients without pneumonia correctly classified at low risk increased from 28% to 36% in the extended model, and the proportion with pneumonia correctly classified at high risk increased from 63% to 70%. The number of patients with pneumonia classified at low risk did not change (n = 4). Overall, the proportion of patients assigned to the intermediate-risk category decreased from 56% to 51%. INTERPRETATION: Adding CRP measurement to the diagnostic work-up for suspected pneumonia in primary care improved the discrimination and risk classification of patients. However, it still left a substantial group of patients classified at intermediate risk, in which clinical decision-making remains challenging.
RESUMO
BACKGROUND: Pneumonia remains difficult to diagnose in primary care. Prediction models based on signs and symptoms (S&S) serve to minimize the diagnostic uncertainty. External validation of these models is essential before implementation into routine practice. In this study all published S&S models for prediction of pneumonia in primary care were externally validated in the individual patient data (IPD) of previously performed diagnostic studies. METHODS AND FINDINGS: S&S models for diagnosing pneumonia in adults presenting to primary care with lower respiratory tract infection and IPD for validation were identified through a systematical search. Six prediction models and IPD of eight diagnostic studies (N total = 5308, prevalence pneumonia 12%) were included. Models were assessed on discrimination and calibration. Discrimination was measured using the pooled Area Under the Curve (AUC) and delta AUC, representing the performance of an individual model relative to the average dataset performance. Prediction models by van Vugt et al. and Heckerling et al. demonstrated the highest pooled AUC of 0.79 (95% CI 0.74-0.85) and 0.72 (0.68-0.76), respectively. Other models by Diehr et al., Singal et al., Melbye et al., and Hopstaken et al. demonstrated pooled AUCs of 0.65 (0.61-0.68), 0.64 (0.61-0.67), 0.56 (0.49-0.63) and 0.53 (0.5-0.56), respectively. A similar ranking was present based on the delta AUCs of the models. Calibration demonstrated close agreement of observed and predicted probabilities in the models by van Vugt et al. and Singal et al., other models lacked such correspondence. The absence of predictors in the IPD on dataset level hampered a systematical comparison of model performance and could be a limitation to the study. CONCLUSIONS: The model by van Vugt et al. demonstrated the highest discriminative accuracy coupled with reasonable to good calibration across the IPD of different study populations. This model is therefore the main candidate for primary care use.
