Personalised risk prediction tools for cryptococcal meningitis mortality to guide treatment stratification; a pooled analysis of two randomised-controlled trials
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Abstract
Background
Cryptococcal meningitis is a leading cause of adult community-acquired meningitis in sub-Saharan Africa with high mortality rates in the first 10 weeks post diagnosis. Practical tools to stratify mortality risk may help to tailor effective treatment strategies.
Methods
We pooled individual-level data from two randomised-controlled trials of HIV-associated cryptococcal meningitis across eight sub-Saharan African countries (ACTA, ISRCTN45035509 ; Ambition-cm, ISRCTN72509687 ). We used this pooled dataset to develop and validate multivariable logistic regression models for 2-week and 10-week mortality. Candidate predictor variables were specified a priori . ‘Basic’ models were developed using only predictors available in resource-limited settings; ‘Research’ models were developed from all available predictors. We used internal-external cross-validation to evaluate performance across countries within the development cohort, before validation of discrimination, calibration and net benefit in held-out data from Malawi (Ambition-cm trial). We also evaluated whether treatment effects in the trials were heterogenous by predicted mortality risk.
Findings
We included 1488 participants, of whom 236 (15.9%) and 469 (31.5%) met the 2-week and 10-week mortality outcomes, respectively. In the development cohort (n=1263), five variables were selected into the basic model (haemoglobin, neutrophil count, Eastern Cooperative Oncology Group performance status, Glasgow coma scale and treatment regimen), with two additional variables in the research model (cerebrospinal fluid quantitative culture and opening pressure) for 2-week mortality. During internal-external cross-validation, both models showed consistent discrimination across countries (pooled areas under the receiver operating characteristic curves (AUROCs) 0.75 (95% CI 0.68-0.82) and 0.78 (0.75-0.82) for the ‘Basic’ and ‘Research’ 2-week mortality models, respectively), with some variation in calibration between sites. Performance was similar in held-out validation (n=225), with the models demonstrating higher net benefit to inform decision-making than alternative approaches including a pre-existing comparator model. In exploratory analyses, treatment effects varied by predicted mortality risk, with a trend towards lower absolute and relative mortality for a single high-dose liposomal Amphotericin B-based regimen (in comparison to 1-week Amphotericin B deoxycholate plus flucytosine) among lower risk participants in the Ambition-cm trial.
Interpretation
Both models accurately predict mortality, were generalisable across African trial settings, and have potential to be incorporated into future treatment stratification approaches in low and middle-income settings.
Funding
MRC, United Kingdom (100504); ANRS, France (ANRS12275); SIDA, Sweden (TRIA2015-1092); Wellcome/MRC/UKAID Joint Global Health Trials (MR/P006922/1); European DCCT Partnership; NIHR, United Kingdom through a Global Health Research Professorship to JNJ (RP-2017-08-ST2-012) and a personal Fellowship to RKG (NIHR302829).
RESEARCH IN CONTEXT
Evidence before this study
There is an urgent need to improve clinical management for HIV-associated cryptococcal meningitis in resource limited settings across Africa. Cryptococcal meningitis accounts for ∼112,000 AIDS-related deaths per year globally, with over 75% in Africa, despite widespread antiretroviral therapy roll-out. The development of practical tools to identify patients at highest risk of death could help to tailor management strategies and stratify therapy. We searched PubMed for studies published between database inception and Jan 12, 2024, using the terms “cryptococcal meningitis”, “HIV”, “human immunodeficiency virus”, “immunocompromised”, “predict*”, and “model*”, with no language restrictions. Three previous studies, all conducted in China, have developed prognostic models for cryptococcal meningitis mortality. Of these, two used statistical methods while the third used machine learning but focused on persons without HIV only. No studies conducted in Africa, specifically targeting people living with HIV, or using both statistical and machine learning approaches in parallel, were identified. Well-developed and validated tools to predict risk of cryptococcal meningitis mortality and guide treatment stratification are thus lacking for resource limited settings in Africa.
Added value of this study
To our knowledge, this is the largest study to date to develop and validate prediction models for HIV-associated cryptococcal meningitis mortality. We combined high-quality data from the two largest randomised-controlled clinical trials conducted to date for cryptococcal meningitis treatment, with a total sample size of 1488 participants of whom 236 (15.9%) and 469 (31.5%) met the 2-week and 10-week mortality outcomes, respectively. We developed two models, ‘basic’ and ‘research’, to enable use in both resource-limited and research settings (where additional prognostic markers such as measurements of cerebrospinal fluid (CSF) opening pressure and CSF fungal burden may also be available). In the 2-week mortality models, five variables were included in the ‘basic’ model, with two additional variables included in the ‘research’ model. Both models predicted risk of mortality with consistent discrimination and calibration across sub-Saharan African settings. Head-to-head statistical (logistic regression) and machine learning (XGBoost) methods revealed no added value of the machine learning approach. In exploratory analyses, treatment effects varied by predicted 2-week mortality risk, thus providing proof-of-concept for future treatment stratification approaches. Specifically, there was a trend towards lower mortality for a single high-dose liposomal Amphotericin B-based regimen (in comparison to 1-week Amphotericin B deoxycholate plus flucytosine) among lower risk participants in the Ambition-cm trial.
Implications of all the available evidence
The personalised risk predictor for cryptococcal meningitis (PERISKOPE-CM) models accurately predicted mortality risk among patients with HIV-associated cryptococcal meningitis and demonstrated generalisable performance across trial settings in Africa. Predictions from the models could be utilised to direct treatment stratification approaches in future clinical trials, with patients at lowest predicted risk receiving less intensive and less toxic therapy. The models have been made available for future research use on an open access online interface.
Article activity feed
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Tihana Bicanic
Review 1: "Personalised Risk Prediction Tools for Cryptococcal Meningitis Mortality to Guide Treatment Stratification; A Pooled Analysis of Two Randomised-controlled Trials"
The reviewer commented on the robustness of the statistical analysis providing credibility to the model's performance and its conclusions which can be informative in future clinical trials to further validate this type of predictive model for this illness.
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Tihana Bicanic
Review of "Personalised Risk Prediction Tools for Cryptococcal Meningitis Mortality to Guide Treatment Stratification; A Pooled Analysis of Two Randomised-controlled Trials"
Reviewer: T Bicanic (St George's, University of London) | 📘📘📘📘📘
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