Baseline predictors influencing the prognosis of invasive aspergillosis in adults.

Baseline predictors influencing the prognosis of invasive aspergillosis in adults.

Mycoses. 2019 May 07;:

Authors: Koehler P, Salmanton-García J, Gräfe SK, Koehler FC, Mellinghoff SC, Seidel D, Steinbach A, Cornely OA

Abstract
BACKGROUND: Invasive aspergillosis (IA) is a serious hazard to hematological and critical care patients. Impactful risk factors for developing IA have been characterized, however systematic analysis of baseline prognostic factors for treatment course of IA is missing.
OBJECTIVES: To understand prognostic variables we analyzed original articles identifying baseline factors that predict treatment outcome in patients with IA.
METHODS: PubMed database was searched for publications since database inception until May 2018. Inclusion criteria were published baseline prognostic factors present at the diagnosis of IA.
RESULTS: In total, 58 studies from 267 centers reported 7,320 patients with IA, and 40 different predictors. Unfavorable predictors in medical history were kidney (7.4%, 10/136) and liver failure (3.7%, 5/136), ICU admission (3.7%, 5/136), and uncontrolled underlying disease (3.7%, 5/136). Regarding state of immunosuppression, negative outcome predictors were prolonged neutropenia (12.5%, 17/136), corticosteroid treatment (8.1%, 11/136), and graft-versus-host disease (3.7%, 5/136). On the pathogen side, relevant predictors were galactomannan positivity (8.1%, 11/136), Aspergillus terreus infection (2.2%, 3/136), and lack of amphotericin B susceptibility (1.5%, 2/136). IA-specific predictors were disseminated disease (5.1%, 7/136) and CNS involvement (2.9%, 4/136). Imaging results associated with negative outcome were multiple consolidations (2.9%, 4/136), bipulmonary lesions (2.2%, 3/136), and pleural effusion (2.2%, 3/136).
CONCLUSION: At diagnosis of IA, most frequently identified predictors of outcome were neutropenia, corticosteroid use, elevated galactomannan, renal failure, and disseminated disease. The predictors may be used to identify patients at high risk for treatment failure, and to stratify neglected patient groups for clinical trials. This article is protected by copyright. All rights reserved.

PMID: 31066092 [PubMed – as supplied by publisher]

Source: Industry