Prognostic Models for 9-Month Mortality in Tuberculous Meningitis

Thao, Le Thi Phuong; Heemskerk, A Dorothee; Geskus, Ronald B.; Hoang, Nguyen Thi; Hà, Đặng Thị Minh; Chau, Tran Thi Hong; Phu, Nguyen Hoan; Châu, Nguyễn Văn Vĩnh; Caws, Maxine; Lân, Nguyễn Hữu; Thu, Do Dang Anh; Thuong, Nguyen Thuy Thuong; Day, Jeremy; Farrar, Jeremy; Török, M. Estée; Bang, Nguyen Duc; Thwaites, Guy; Wolbers, Marcel

doi:10.1093/cid/cix849

Cited by 73 publications

(59 citation statements)

References 31 publications

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“…1 Tuberculous meningitis, the most serious form of tuberculosis in fection, accounts for 1-5% of new cases of tuberculosis and results in death or substantial disability in more than half of those affected. 2 Outcomes are particularly poor in patients with HIV coinfection, which can triple the risk of death from tuberculous meningitis, reaching approximately 50%. 3 Another major driver of poor out comes is diagnostic delay.…”

Section: Introductionmentioning

confidence: 99%

Xpert MTB/RIF Ultra for the diagnosis of HIV-associated tuberculous meningitis: a prospective validation study

Cresswell

Tugume

Bahr

et al. 2020

The Lancet Infectious Diseases

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Introduction Tuberculous meningitis accounts for 1-5% of tuberculosis cases. Diagnostic delay contributes to poor outcomes. We evaluated the performance of the new Xpert MTB/RIF Ultra (Xpert Ultra) for tuberculous meningitis diagnosis. Methods In this prospective validation study, we tested the cerebrospinal fluid (CSF) of adults presenting with suspected meningitis (ie, headache or altered mental status with clinical signs of meningism) to the Mulago National Referral Hospital and Mbarara Regional Referral Hospital in Uganda. We centrifuged the CSF, resuspended the cell pellet in 2 mL CSF, and tested 0•5 mL aliquots with Xpert Ultra, Xpert MTB/RIF (Xpert), and mycobacterial growth indicator tube (MGIT) culture. We quantified diagnostic performance against the uniform case definition of probable or definite tuberculous meningitis and a composite microbiological reference standard. Findings From Nov 25, 2016, to Jan 24, 2019, we screened 466 adults with suspected meningitis and tested 204 for tuberculous meningitis. Uniform clinical case definition classified 51 participants as having probable or definite tuberculous meningitis. Against this uniform case definition, Xpert Ultra had 76•5% sensitivity (95% CI 62•5-87•2; 39 of 51 patients) and a negative predictive value of 92•7% (87•6-96•2; 153 of 165), compared with 55•6% sensitivity (44•0-70•4; 25 of 45; p=0•0010) and a negative predictive value of 85•8% (78•9-91•1; 121 of 141) for Xpert and 61•4% sensitivity (45•5-75•6; 27 of 44; p=0•020) and negative predictive value of 85•2% (77•4-91•1; 98 of 115) for MGIT culture. Against the composite microbiological reference standard, Xpert Ultra had sensitivity of 92•9% (80•5-98•5; 39 of 42), higher than Xpert at 65•8% (48•6-80•4; 25 of 38; p=0•0063) and MGIT culture at 72•2% (55•9-86•2; 27 of 37; p=0•092). Xpert Ultra detected nine tuberculous meningitis cases missed by Xpert and MGIT culture. Interpretation Xpert Ultra detected tuberculous meningitis with higher sensitivity than Xpert and MGIT culture in this HIV-positive population. However, with a negative predictive value of 93%, Xpert Ultra cannot be used as a ruleout test. Clinical judgment and novel highly sensitive point-of-care tests are still required.

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Section: Introductionmentioning

confidence: 99%

Xpert MTB/RIF Ultra for the diagnosis of HIV-associated tuberculous meningitis: a prospective validation study

Cresswell

Tugume

Bahr

et al. 2020

The Lancet Infectious Diseases

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“…Increased Medical Research Council TBM disease severity [5][6][7][8] Reduced consciousness 6,9 Hydrocephalus and raised ICP 8,10,11 Cerebral infarction 6,12 Seizures 12,13 HIV co-infection 9,14 Multidrug resistant, or isoniazid mono-resistant, disease 15…”

Section: Box 1 Key Predictors Of Poor Outcome In Tuberculous Meningitismentioning

confidence: 99%

“…Lower body weight 5 Younger and older age 5,16 General clinical examination and monitoring General assessment and non-invasive monitoring of vital signs may be the only tools available to guide patient management in many centres. These provide valuable information in all care settings, and are mentioned in Table 1-Table 4.…”

Section: Box 1 Key Predictors Of Poor Outcome In Tuberculous Meningitismentioning

confidence: 99%

Checklists to guide the supportive and critical care of tuberculous meningitis

et al. 2019

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The assessment and management of tuberculous meningitis (TBM) is often complex, yet no standardised approach exists, and evidence for the clinical care of patients, including those with critical illness, is limited. The roles of proformas and checklists are increasing in medicine; proformas provide a framework for a thorough approach to patient care, whereas checklists offer a priority-based approach that may be applied to deteriorating patients in time-critical situations. We aimed to develop a comprehensive assessment proforma and an accompanying ‘priorities’ checklist for patients with TBM, with the overriding goal being to improve patient outcomes. The proforma outlines what should be asked, checked, or tested at initial evaluation and daily inpatient review to assist supportive clinical care for patients, with an adapted list for patients in critical care. It is accompanied by a supporting document describing why these points are relevant to TBM. Our priorities checklist offers a useful and easy reminder of important issues to review during a time-critical period of acute patient deterioration. The benefit of these documents to patient outcomes would require investigation; however, we hope they will promote standardisation of patient assessment and care, particularly of critically unwell individuals, in whom morbidity and mortality remains unacceptably high.

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“…Our goal is to predict mortality within nine months. Results based on an internally validated Cox proportional hazards regression model can be found elsewhere (Thao et al., ). To be consistent with the statistical model used in the simulation study, we use a logistic regression model with nine‐month mortality as outcome.…”

Section: Real Data Applicationmentioning

confidence: 99%

A comparison of model selection methods for prediction in the presence of multiply imputed data

Thao

Geskus

2018

Biometrical J

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Many approaches for variable selection with multiply imputed data in the development of a prognostic model have been proposed. However, no method prevails as uniformly best. We conducted a simulation study with a binary outcome and a logistic regression model to compare two classes of variable selection methods in the presence of MI data: (I) Model selection on bootstrap data, using backward elimination based on AIC or lasso, and fit the final model based on the most frequently (e.g. ) selected variables over all MI and bootstrap data sets; (II) Model selection on original MI data, using lasso. The final model is obtained by (i) averaging estimates of variables that were selected in any MI data set or (ii) in 50% of the MI data; (iii) performing lasso on the stacked MI data, and (iv) as in (iii) but using individual weights as determined by the fraction of missingness. In all lasso models, we used both the optimal penalty and the 1‐se rule. We considered recalibrating models to correct for overshrinkage due to the suboptimal penalty by refitting the linear predictor or all individual variables. We applied the methods on a real dataset of 951 adult patients with tuberculous meningitis to predict mortality within nine months. Overall, applying lasso selection with the 1‐se penalty shows the best performance, both in approach I and II. Stacking MI data is an attractive approach because it does not require choosing a selection threshold when combining results from separate MI data sets

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Prognostic Models for 9-Month Mortality in Tuberculous Meningitis

Abstract: Prognostic models developed and validated using data from 1699 adults with tuberculous meningitis (TBM), with or without human immunodeficiency virus infection, performed well and could be used in clinical practice to identify patients with TBM at high risk of death.

Cited by 73 publications

References 31 publications

Xpert MTB/RIF Ultra for the diagnosis of HIV-associated tuberculous meningitis: a prospective validation study

Xpert MTB/RIF Ultra for the diagnosis of HIV-associated tuberculous meningitis: a prospective validation study

Checklists to guide the supportive and critical care of tuberculous meningitis

A comparison of model selection methods for prediction in the presence of multiply imputed data

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