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Table 1 Patient-level effects of new diagnostic algorithms

From: Modeling the patient and health system impacts of alternative xpert® MTB/RIF algorithms for the diagnosis of pulmonary tuberculosis in Addis Ababa, Ethiopia

Diagnostic algorithms Diagnosis and treatment initiation Diagnosis and treatment success
Number of visits to the diagnostic center Time to start treatment (Days) Diagnostic LTFUa rate (%) New tuberculosis cases diagnosed without a positive sputum test (%) Likelihood of a true tuberculosis patient completing diagnosis and treatment (%)
ZN-Spot-Morning-Spot 5 16.8 13.3 59.8 76.6
FN-Spot-Morning-Spot 4.9 15 12.6 52.9 78.4
Targeted-Xpert-ZN-Negative-Spot-Morning-Spot 4.4 10.5 11.8 28.6 80.9
Targeted-Xpert- MDR-HIV-ZN-Spot-Morning-Spot 4.7 15.2 12.1 52.5 79.2
Full-Xpert 3.6 9 4.3 28.9 84.9
FN-Spot-Spot 4.4 16 8.6 56.6 80.6
FN-Spot-Morning 4.9 15.5 13.1 52.4 78.2
Targeted-Xpert-MDR-HIV-FN-Spot-Spot 4.1 14.6 6.8 49.4 83.3
  1. aLTFU-Lost to follow up