Browsing by Author "Poka-Mayap, Virginie"

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    Development and validation of a prognostic score during tuberculosis treatment
    (2017) Pefura-Yone, Eric Walter; Balkissou, Adamou Dodo; Poka-Mayap, Virginie; Fatime-Abaicho, Hadja Koté; Enono-Edende, Patrick Thierry; Kengne, André Pascal
    BACKGROUND: Death under care is a major challenge for tuberculosis (TB) treatment programs. We derived and validated a simple score to predict mortality during tuberculosis treatment in high endemicity areas. METHODS: We used data for patients aged ≥15 years, diagnosed and treated for tuberculosis at the Yaounde Jamot Hospital between January 2012 and December 2013. Baseline characteristics associated with mortality were investigated using logistic regressions. A simple prognosis score (CABI) was constructed with regression coefficients for predictors in the final model. Internal validation used bootstrap resampling procedures. Models discrimination was assessed using c-statistics and calibration assessed via calibration plots and the Hosmer and Lemeshwow (H-L) statistics. The optimal score was based on the Youden's index. RESULTS: A total of 2250 patients (men 57.2%) with a mean age of 35.8 years were included; among whom 213 deaths (cumulative incidence 9.5%) were recorded. Clinical form of tuberculosis (C), age (A, years), adjusted body mass index (B, BMI, kg/m2) and status for HIV (Human immunodefiency virus) infection (I) were significant predictors in the final model (p < 0.0001) which was of the form Death risk = 1/(1 + e - (-1.3120 + 0.0474 ∗ age - 0.1866 ∗ BMI + 1.1637 (if smear negative TB) + 0.5418(if extra - pulmonary TB) + 1.3820(if HIV+))). The c-statistic was 0.812 in the derivation sample and 0.808 after correction for optimism. The calibration was good [H-Lχ2 = 6.44 (p = 0.60)]. The optimal absolute risk threshold was 4.8%, corresponding to a sensitivity of 81% and specificity of 67%. CONCLUSIONS: The preliminary promising findings from this study require confirmation through independent external validation studies. If confirmed, the model derived could facilitate the stratification of TB patients for mortality risk and implementation of additional monitoring and management measures in vulnerable patients.