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Derivation of a bedside score (MASH-P) to predict 6-month mortality in tuberculous meningitis.
- Source :
-
Journal of the neurological sciences [J Neurol Sci] 2020 Aug 15; Vol. 415, pp. 116877. Date of Electronic Publication: 2020 May 05. - Publication Year :
- 2020
-
Abstract
- Background: Tuberculous meningitis is commonly associated with a poor outcome. Simple bedside prognostic scores can help immensely in predicting the outcome.<br />Materials and Method: A total of 721 patients, from 5 of our previous studies, were included. With primary outcome measure as death, a prognostic model was derived using binary logistic regression. The model was assessed using discrimination and calibration, and internally validated using the bootstrap method. A bedside prognostic score was derived by rounding of the regression coefficients to the nearest integers.<br />Results: A total of 126 (17.48%) patients died. The final model found that higher age, stage III disease, baseline MBI ≤ 12, papilledema and hydrocephalus were significant predictors of death. The final model showed good discrimination as evident by an AUC = 83.1% (95% confidence interval 79.5%-86.7%, P < .001) and good calibration (Hosmer and Lemeshow test P = .579). The model remained valid after internal validation by boot strapping. A simple bedside score with the acronym MASH-P to denote variables baseline MBI (M), age (A), stage (S), hydrocephalus (H) and papilledema (P), was thus derived. The score can range from 0 to 10. Higher the score, higher is the probability of death; a score of 0 carries a predicted probability of just 1.7% while a score of 10 corresponds to a predicted probability of 65%. An electronic ready reckoner has also been developed to aid prognostication on the go.<br />Conclusion: MASH-P is a simple prognostic scoring model that can be used at bedside and aid in decision making as well as counselling.<br /> (Copyright © 2020 Elsevier B.V. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1878-5883
- Volume :
- 415
- Database :
- MEDLINE
- Journal :
- Journal of the neurological sciences
- Publication Type :
- Academic Journal
- Accession number :
- 32408191
- Full Text :
- https://doi.org/10.1016/j.jns.2020.116877