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Record W3104194717 · doi:10.1093/cid/ciaa1756

Identifying the Patients Most Likely to Die from Cryptococcal Meningitis: Time to Move from Recognition to Intervention

2020· article· en· W3104194717 on OpenAlexaff
Neil Stone, Ilan S. Schwartz

Bibliographic record

VenueClinical Infectious Diseases · 2020
Typearticle
Languageen
FieldMedicine
TopicFungal Infections and Studies
Canadian institutionsUniversity of Alberta
FundersMedical Research Council
KeywordsMedicineCryptococcal meningitisMeningitisIntervention (counseling)Intensive care medicinePediatricsFamily medicineHuman immunodeficiency virus (HIV)Viral diseasePsychiatry

Abstract

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Our knowledge of cryptococcal meningitis (CM), one of the leading causes of AIDS-related mortality globally, has improved substantially through the years, resulting in significant progress in the diagnosis and treatment of the disease. Diagnosis of CM is now straightforward, with the global availability of cryptococcal antigen lateral flow assays that are rapid, simple, affordable, and have sensitivity and specificity as high as any test for acute infection [1]. Treatment strategies are becoming shorter, simpler, and more accessible. The Advancing Cryptococcal Treatment for Africa (ACTA) study was a groundbreaking trial that demonstrated noninferiority of an entirely oral treatment regimen and showed that, where available, a 1-week amphotericin B–based induction regimen was not only an acceptable alternative to the standard 2-week induction treatment but was associated with lower toxicity and cost [2]. The ongoing AMBIsome Therapy Induction OptimisatioN for Cryptococcal Meningitis (AMBITION-CM) phase 3 trial is investigating a single dose of liposomal amphotericin B as an adjunct to oral combination therapy for induction treatment of CM [3]. These trials, which focus on the evaluation of noninferiority of cheaper, safer, and more accessible treatment options, are critically important, particularly in resource-limited settings. However, these advances have not significantly reduced the overall mortality in CM, which remains unacceptably high. Most vexing is improving the outcomes for the “final fifth,” that is, the approximately 20% of patients who will not survive even the first 2 weeks of therapy. For example, the ACTA trial had a 2-week mortality of approximately 20% across all treatment arms, and phase 2 of the AMBITION-CM trial had a 2- and 10-week mortality of 15% and 29%, respectively [4]. The Adjunctive Sertraline for the Treatment of HIV-Associated Cryptococcal Meningitis (ASTRO-CM) trial reported that approximately half of all patients had died by week 18 in a Ugandan setting [5]. This is despite the fact that outcomes are likely to be better in clinical trial settings due to increased monitoring and scrupulous adherence to best practices. Even in high-resource settings such as the United States, mortality remains stubbornly high at greater than 10% inpatient mortality and approaching 20% mortality at 3 months [6]. How can we improve outcomes for this final fifth of patients at highest risk of death? The first challenges are to prospectively identify these patients and understand the pathogenesis. In this edition of Clinical Infectious Diseases, Abassi et al describe their use of samples collected in the ASTRO-CM trial to identify cerebrospinal fluid (CSF) lactate as an independent risk factor for poor outcomes in CM [7]. CSF lactate can thus be added to altered mental status, reduced CSF inflammatory response, high fungal burden, and an array of clinical and laboratory markers that can help stratify patients at the very highest risk of death [8]. This finding may also provide a glimpse into the pathophysiology of CM and, ultimately, the cause of death, which remains incompletely understood. Raised intracranial pressure, which results from impaired resorption of CSF by arachnoid villi blocked by the infecting organism, has been considered a major contributor [9]. Management of raised intracranial pressure is therefore mandatory in the management of CM. High CSF lactate suggests that impairment of cerebral perfusion may contribute to severe CM. As Abassi et al discuss, supportive measures such as oxygenation and aggressive correction of anemia may be beneficial in such cases. These are not simple interventions in many resource-limited settings and will require further evaluation. Alternatively, increased CSF lactate may be a reflection of seizure activity, a known complication of advanced CM, and more aggressive and proactive control of seizures may be required in this patient group. Other central nervous system infections, such as tuberculous meningitis, can also raise CSF lactate. Given that CM is primarily found in immunocompromised patients, coinfections in these patients with additional opportunistic infections are possible and require prompt recognition and management [10]. To date, therapeutic interventions have focused on the optimization of antifungal drug therapy. However, drug therapy alone is unlikely to be the key to treatment of the most extremely unwell patients. Amphotericin B combined with flucytosine, the current gold standard, is a rapidly fungicidal combination that quickly reduces CNS fungal burden. Improving on this will be difficult. There are a number of new drugs progressing along the antifungal pipeline; unfortunately, few have significant anticryptococcal activity [11]. Those that do, such as fosmanogepix and VT-1129, are welcome additions and may well find a role in the treatment of CM; however, they are unlikely to substantially improve outcomes of patients at the most extreme end of the spectrum of this disease. An increased focus on a package of optimizing supportive care is therefore likely to be a more fruitful strategy. The study of adjunctive therapies has to date been limited. Adjunctive treatments that have been evaluated in CM include steroids [12], mannitol [13], and sertraline [5], none of which have been shown to provide significant mortality benefit. More recently, adjunctive neurapheresis, that is, extracorporeal filtration of CSF, has been described [14]. Ideally, we should aim to prevent patients from reaching a state of extremis with CM. Early recognition and management of subclinical cryptococcosis can prevent progression to advanced CM. There is growing evidence that screening with point-of-care cryptococcal antigen testing, even in asymptomatic individuals, is a clinically valuable and cost-effective intervention [15]. CM can also largely be prevented by early and effective treatment of human immunodeficiency virus (HIV), the dominant risk factor for the disease. Despite spectacular treatment advances in HIV, there remains a substantial minority of people living with HIV with uncontrolled infection, resulting in substantial risk for opportunistic infections. In summary, despite general advances in diagnosis and therapy, the mortality in CM remains horrific: approximately 20% of patients will die even within 2 weeks of diagnosis, regardless of the treatment provided. We know how to identify those at greatest risk; the challenge now is to find new, effective interventions for them. We owe it to the “final fifth” to do better. Potential conflicts of interest. I. S. S. has received personal fees for consulting on an advisory board for AVIR Pharma outside the submitted work. N. R. H. S. has received personal feels for consulting from Gilead on treatment of coronavirus disease 2019–related fungal infection outside the submitted work. Both authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0060.007
Open science0.0020.003
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0100.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.061
GPT teacher head0.353
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2020
Admission routes1
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