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Enregistrement 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 sur OpenAlexaff
Neil Stone, Ilan S. Schwartz

Notice bibliographique

RevueClinical Infectious Diseases · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueFungal Infections and Studies
Établissements canadiensUniversity of Alberta
Organismes subventionnairesMedical Research Council
Mots-clésMedicineCryptococcal meningitisMeningitisIntervention (counseling)Intensive care medicinePediatricsFamily medicineHuman immunodeficiency virus (HIV)Viral diseasePsychiatry

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,023
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,033

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,023
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0030,001
Communication savante0,0060,007
Science ouverte0,0020,003
Intégrité de la recherche0,0050,009
Charge utile insuffisante (le modèle a refusé de juger)0,0100,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,061
Tête enseignante GPT0,353
Écart entre enseignants0,293 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2020
Routes d'admission1
Résumé présentnon

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