Dynamic prediction of death in patients with tuberculous meningitis using time-updated Glasgow coma score and plasma sodium measurements.
Thao LTP., Wolbers M., Heemskerk AD., Thi Hoang Mai N., Thi Minh Ha D., Thi Hong Chau T., Hoan Phu N., Van Vinh Chau N., Caws M., Huu Lan N., Dang Anh Thu D., Thuy Thuong Thuong N., Day J., Torok ME., Duc Bang N., Thwaites GE., Geskus RB.
BACKGROUND: Pre-treatment predictors of death from tuberculous meningitis (TBM) are well-established, but whether outcome can be predicted more accurately after the start of treatment by updated clinical variables is unknown. Hence, we developed and validated models that dynamically predict mortality using time-updated Glasgow coma score (GCS) and plasma sodium measurements, together with patient baseline characteristics. METHODS: We included 1048 adults from four TBM studies conducted in southern Vietnam from 2004-2016. We used a landmarking approach to predict death within 120 days after treatment initiation using time-updated data during the first 30 days of treatment. Separate models were built for patients with and without human immunodeficiency virus (HIV) infection. We used the area under the receiver operating characteristic curve (AUC) to evaluate performance of the models at day 10, 20 and 30 of treatment to predict mortality by 60, 90 and 120 days. Our internal validation was corrected for over-optimism using bootstrap. We provide a web-based application that computes mortality risk within 120 days. RESULTS: Higher GCS indicated better prognosis in all patients. In HIV-infected patients, higher plasma sodium was uniformly associated with good prognosis, whereas in HIV-uninfected patients the association was heterogeneous over time. The bias-corrected AUC of the models ranged from 0.82-0.92 in HIV-uninfected, and 0.81-0.85 in HIV-infected individuals. The models outperformed the previously published baseline models. CONCLUSIONS: Time-updated GCS and plasma sodium measurements improved predictions based solely on information obtained at diagnosis. Our models may be used in practice to define those with poor prognosis during treatment.