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Table 4 Performance characteristics of classification and regression tree models for predicting community-acquired pneumonia with a bacterial pathogen detected in patients presenting with a lower respiratory tract infection at outpatient clinics in Tanzania

From: Clinical sign and biomarker-based algorithm to identify bacterial pneumonia among outpatients with lower respiratory tract infection in Tanzania

  Prediction of community acquired pneumonia with a bacterial pathogen detected
  Patients with a lower respiratory tract infection Patients with radiological pneumonia
  All variables RR ≥ 32/min and PCT ≥ 2.9 RR ≥ 32/min and PCT ≥ 0.25 µg/l RR ≥ 32/min and PCT ≥ 0.25 µg/l
Sensitivity 88% 88% 94% 94%
Specificity 87% 88% 82% 87%
Negative likelihood ratio 0.1 0.1 0.1 0.1
Positive likelihood ratio 6.8 7.5 5.1 7.1
Negative predictive value 98% 87% 99% 93%
Positive predictive value 56% 89% 48% 89%
  1. The identified algorithm was also tested to predict community-acquired pneumonia with a bacterial pathogen detected among patients with radiological pneumonia
  2. PCT procalcitonine, RR respiratory rate