Antifungal Prophylaxis for Prevention of COVID-19-Associated Pulmonary Aspergillosis in Critically Ill Patients

An Observational Study

Stefan Hatzl; Alexander C. Reisinger; Florian Posch; Juergen Prattes; Martin Stradner; Stefan Pilz; Philipp Eller; Michael Schoerghuber; Wolfgang Toller; Gregor Gorkiewicz; Philipp Metnitz; Martin Rief; Florian Prüller; Alexander R. Rosenkranz; Thomas Valentin; Robert Krause; Martin Hoenigl; Gernot Schilcher

Disclosures

Crit Care. 2021;25(335)

Abstract and Introduction

Abstract

Background: Coronavirus disease 19 (COVID-19)-associated pulmonary aspergillosis (CAPA) emerged as important fungal complications in patients with COVID-19-associated severe acute respiratory failure (ARF). Whether mould active antifungal prophylaxis (MAFP) can prevent CAPA remains elusive so far.

Methods: In this observational study, we included all consecutive patients admitted to intensive care units with COVID-19-associated ARF between September 1, 2020, and May 1, 2021. We compared patients with versus without antifungal prophylaxis with respect to CAPA incidence (primary outcome) and mortality (secondary outcome). Propensity score adjustment was performed to account for any imbalances in baseline characteristics. CAPA cases were classified according to European Confederation of Medical Mycology (ECMM)/International Society of Human and Animal Mycoses (ISHAM) consensus criteria.

Results: We included 132 patients, of whom 75 (57%) received antifungal prophylaxis (98% posaconazole). Ten CAPA cases were diagnosed, after a median of 6 days following ICU admission. Of those, 9 CAPA cases were recorded in the non-prophylaxis group and one in the prophylaxis group, respectively. However, no difference in 30-day ICU mortality could be observed. Thirty-day CAPA incidence estimates were 1.4% (95% CI 0.2–9.7) in the MAFP group and 17.5% (95% CI 9.6–31.4) in the group without MAFP (p = 0.002). The respective subdistributional hazard ratio (sHR) for CAPA incidence comparing the MAFP versus no MAFP group was of 0.08 (95% CI 0.01–0.63; p = 0.017).

Conclusion: In ICU patients with COVID-19 ARF, antifungal prophylaxis was associated with significantly reduced CAPA incidence, but this did not translate into improved survival. Randomized controlled trials are warranted to evaluate the efficacy and safety of MAFP with respect to CAPA incidence and clinical outcomes.

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease 2019 (COVID-19)-associated acute respiratory failure contributes to a highly permissive inflammatory environment. This in turn favours fungal pathogenesis due to the release of danger-associated molecular patterns and collateral effects of host recognition pathways required for the activation of antiviral immunity.^[1] In this context, COVID-19-associated pulmonary aspergillosis (CAPA) has been emerging as an important fungal complication of COVID-19,^[1] affecting an average of 3.1% (range 0.7–7.7%)^[2–5] of patients hospitalized with COVID-19, 8.9% (range 2.5–39%) of patients admitted to the intensive care unit (ICU),^[3,6–11] and an average of 20.1% (range 3.2–38%) of those requiring invasive ventilation.^{[6,7,12–14]}

Diagnosis of CAPA is challenging in patients with COVID-19-associated ARDS, as clinical picture and radiological findings of CAPA resemble those of severe COVID-19,^[15,16] and blood tests lack sensitivity due to the primarily airway invasive growth of Aspergillus in non-neutropenic patients.^[15–17] Testing of bronchoalveolar lavage (BAL) with fungal culture, galactomannan (GM), Aspergillus polymerase chain reaction (PCR), or the Aspergillus GM lateral flow assay (LFA) is therefore preferred,^[18,19] but due to the presumed risk of COVID-19 transmission through bronchoscopies, sampling of the primary infection site is still not performed consistently across ICUs.

The high prevalence rates of CAPA in critically ill patients requiring invasive ventilation together with the difficulties in diagnosis and the devastating overall mortality rates of over 50%^{[2–4,6,9–12,14,15,20,21]} could justify clinical trials evaluating antifungal prophylaxis in COVID-19 patients with acute respiratory failure. One retrospective single-centre case series from Belgium has reported the successful use of prophylaxis in terms of CAPA case reduction with inhaled liposomal Amphotericin B in a cohort of ICU patients with severe COVID-19;^[6] however, studies evaluating systemic antifungal prophylaxis are lacking.

The objective of this observational single-centre study was to evaluate the effectiveness of mould-active antifungal prophylaxis in preventing CAPA in critical care patients with COVID-19-associated acute respiratory failure. Secondary objectives included the evaluation of a potential survival benefit associated with antifungal prophylaxis as well as the impact of CAPA on overall survival.

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Crit Care. 2021;25(335) © 2021 BioMed Central, Ltd.
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Abstract and Introduction
Methods
Results
Discussion
Conclusion
References

Table 1. Baseline characteristics of the study population—distribution at ICU admission
Variable	n (%miss)	Overall (n = 132)	Antifungal prophylaxis (n = 75)	No antifungal prophylaxis (n = 57)	p	p_IPTW
Demographic variables
Age (years)	132 (0%)	65 [55–75]	67 [55–74]	63 [56–75]	0.879	0.375
Female Gender	132 (0%)	48 (36%)	24 (32%)	24 (42%)	0.232	0.632
BMI (kg/m²)	127 (4%)	28.6 [25.4–33.2]	27.8 [24.5–32.8]	29.6 [25.9–33.7]	0.139	0.285
Coexisting conditions
Number of coexisting conditions	132 (0%)	2 [1–4]	2 [1–3]	2 [1–4]	0.629	0.640
Hypertension	132 (0%)	93 (71%)	49 (65%)	44 (77%)	0.186	0.562
Diabetes	132 (0%)	31 (23%)	20 (27%)	11 (19%)	0.323	0.454
Atrial fibrillation	132 (0%)	22 (17%)	10 (13%)	12 (21%)	0.238	0.400
Coronary artery disease^$	132 (0%)	21 (16%)	9 (12%)	12 (21%)	0.159	0.686
Congestive heart failure	132 (0%)	20 (15%)	9 (12%)	11 (19%)	0.247	0.763
Peripheral arterial disease	132 (0%)	17 (13%)	7 (9%)	10 (17%)	0.163	0.171
Thromboembolic disease	132 (0%)	19 (14%)	10 (13%)	9 (16%)	0.690	0.230
Chronic kidney disease	132 (0%)	35 (26%)	15 (20%)	20 (35%)	0.052	0.892
Dialysis	132 (0%)	11 (8%)	2 (3%)	9 (16%)	0.007	0.656
COPD	132 (0%)	16 (12%)	13 (17%)	3 (5%)	0.035	0.867
Asthma	132 (0%)	15 (11%)	9 (12%)	6 (11%)	0.792	0.702
Prior cancer in complete remission	132 (0%)	11 (8%)	6 (8%)	5 (8%)	0.874	0.917
Active malignancy	132 (0%)	9 (7%)	6 (8%)	3 (5%)	0.537	0.644
Dementia	132 (0%)	4 (3%)	2 (3%)	2 (4%)	0.780	0.844
Prior transplantation	132 (0%)	10 (8%)	7 (9%)	3 (5%)	0.381	0.821
Immunosuppression †	132 (0%)	17 (13%)	13 (17%)	4 (8%)	0.008	0.921
ICU risk stratification
SOFA (points)	132 (0%)	5 [4–8]	4 [4–7]	5 [4–10]	0.057	0.987
paO₂/FiO₂	128 (3%)	95 [75–146]	90 [75–130]	107 [75–152]	0.302	0.749
PEEP (mmHg)—maximum	111 (16%)	11 [9–12]	10 [9–12]	11 [10–12]	0.599	0.464
Acute respiratory failure grade (inspired by ARDS Berlin 2015 -classification)	132 (0%)	–	–	–	0.660	0.636
–severe		76 (58%)	46 (61%)	30 (54%)	–
–moderate		45 (34%)	24 (32%)	21 (37%)	–
–mild		10 (8%)	5 (7%)	6 (9%)	–
Ventilation	132 (0%)	–	–	–	0.146	0.776
–Intubated		59 (45%)	35 (47%)	24 (42%)	–
–vvECMO		13 (10%)	6 (8%)	7 (12%)	–
–NIV		49 (37%)	31 (41%)	18 (32%)	–
–HFNC		4 (3%)	2 (3%)	2 (3%)	–
–Mask oxygen supply		7 (5%)	1 (1%)	6 (11%)	–
Any invasive ventilation		72 (56%)	41 (55%)	31 (54%)	0.974	0.947
Antimycotic substances
Duration form diagnosis to initiation (days)	75 (0%)	–	4 [1–7]	–	–
Antimycotic drug used	75 (0%)	–	–	–	–
–Posaconazole		–	73 (98%)	–	–
–Isavuconazole		–	1(1%)	–	–
–Caspofungin		–	1(1%)	–	–
Laboratory values
Lactate (mmol/l)	127 (4%)	1.2 [0.9–1.9]	1.2 [0.9–1.9]	1.4 [0.9–1.9]	0.980	0.462
IL-6 (pg/ml)	114 (14%)	83 [28–258]	76 [28–185]	89 [29–258]	0.448	0.907
CRP (mg/l)	130 (2%)	112 [70–177]	113 [76–196]	112[58–151]	0.237	0.573
Ferritin (ng/ml)	113 (13%)	1334 [635–2196]	1459 [755–2164]	1128 [493–2306]	0.361	0.240
hs-TnT (pg/ml)	119 (10%)	24 [11–61]	22 [11–58]	27 [11–92]	0.488	0.889
D-Dimer (mg/l)	121 (8%)	1.8 [0.9–4.7]	1.5 [0.9–3.4]	2.4 [1.1–7.7]	0.134	0.343
Creatine (mg/dl)	132 (0%)	1.1 [0.81–1.42]	1.0 [0.8–1.4]	1.2 [0.8–1.7]	0.139	0.998
Bilirubin total (mg/dl)	132 (0%)	0.5 [0.4–0.7]	0.5 [0.4–0.7]	0.5 [0.4–0.7]	0.774	0.784
SARS-CoV-2-Antibody status at ICU admission	132 (0%)	–	–	–	0.612	0.617
–positive		35 (27%)	20 (27%)	15 (26%)	–	–
–negative		33 (25%)	21 (27%)	12 (21%)	–	–
–not available		64 (48%)	34 (46%)	30 (52%)	–	–
Blood counts
Leukocytes [G/l]	132 (0%)	9.4 [6.8–13.3]	9.4 [7.0–13.4]	9.2 [6.4–13.3]	0.646	0.606
Neutrophils [G/l]	132 (0%)	8.4 [6.0–11.6]	8.3 [6.0–11.6]	9.2 [6.0–11.9]	0.834	0.322
Lymphocytes [G/l]	131 (1%)	0.7 [0.5–0.9]	0.8 [0.5–1.0]	0.6 [0.5–0.9]	0.299	0.340
Thrombocytes [G/l]	132 (0%)	221 [158–309]	240 [161–335]	208 [152–293]	0.164	0.834
Specific Medication
Glucocorticoids §	132 (0%)	132 (100%)	75 (100%)	57 (100%)	1.000	1.000
Remdesivir	132 (0%)	53 (40%)	36 (48%)	17 (30%)	0.035	0.555
Tocilizumab	132 (0%)	1 (1%)	1 (1%)	0 (0%)	0.382	0.326
Convalescent Plasma	132 (0%)	50 (38%)	32 (43%)	18 (32%)	0.193	0.871
Outcomes
CAPA	132 (0%)				0.008	0.015
–probable CAPA	–	9 (7%)	1 (1%)	8 (14%)
–possible CAPA	–	1 (1%)	0 (0%)	1 (2%)
Deceased at data cut off	132 (0%)	65 (49%)	39 (52%)	26 (46%)	0.467	–
Length of ICU stay	132 (0%)	13 [5–25]	13 [6–24]	12 [4–25]	0.700	–

Data are reported as medians [25th-75th percentile] or as absolute counts (%)
p denotes p values before ITPW weighting, p _IPTW denotes p values after IPTW adjustment
p values are either from rank-sum tests, χ²-tests, or Fisher's exact tests, as appropriate
The discrepancy between missing variable regarding pO₂/FiO₂ and ARDS classification inspired by the Berlin 2015 classification arises from missing exact pO₂/FiO₂ values; however, ARDS was classified
Abbreviations: BMI body mass index; ICU intensive care unit; COPD chronic obstructive pulmonary disease; SOFA sequential organ failure assessment; PEEP positive end expiratory pressure; vvECMO veno-venous extracorporeal membrane oxygenation, NIV non-invasive ventilation, HFNC high flow nasal cannula; IL-6 interleukin 6; CRP C- reactive protein; hs-TnT high sensitive troponin T, SARS-CoV2 severe acute respiratory syndrome corona virus 2; CAPA coronavirus disease-associated pulmonary aspergillosis
^$documented coronary heart disease either by specific coronary imaging or coronary angiography
^†comprises immunosuppressive medication (low dose of glucocorticoids are excluded) as well as diseases with severe immunosuppression
^§Glucocorticoids included low-dose dexamethasone or equivalent doses of other glucocorticoids

Authors and Disclosures

Stefan Hatzl^1,2†, Alexander C. Reisinger^1†, Florian Posch³, Juergen Prattes⁴, Martin Stradner⁵, Stefan Pilz⁶, Philipp Eller¹, Michael Schoerghuber⁷, Wolfgang Toller⁷, Gregor Gorkiewicz⁸, Philipp Metnitz⁸, Martin Rief⁸, Florian Prüller⁹, Alexander R. Rosenkranz¹⁰, Thomas Valentin⁵, Robert Krause^4*, Martin Hoenigl^4,11† and Gernot Schilcher^1†

¹Intensive Care Unit, Department of Internal Medicine, Medical University of Graz, Graz, Austria. ²Division of Haematology, Department of Internal Medicine, Medical University of Graz, Graz, Austria. ³Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria. ⁴Division of Infectious Diseases, Department of Internal Medicine, Medical University of Graz, Graz, Austria. ⁵Division of Rheumatology and Immunology, Department of Internal Medicine, Medical University of Graz, Graz, Austria. ⁶Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria. ⁷Department of Anaesthesiology and Intensive Care Medicine, Medical University Graz, Graz, Austria. ⁸Institute of Pathology, Medical University of Graz, Graz, Austria. ⁹Clinical Institute for Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria. ¹⁰Division of Nephrology, Department of Internal Medicine, Medical University of Graz, Graz, Austria. ¹¹Division of Infectious Diseases, University of California San Diego, San Diego, USA.

*Correspondence
robert.krause@medunigraz.at

^†Stefan Hatzl, Alexander C. Reisinger, Martin Hoenigl and Gernot Schilcher contributed equally to this paper.

Author's contributions
SH, RK and GS planned and designed the study. SH, ACR, MR, TV, RK and GS collected the data. SH and FP analysed the data. SH and FP developed the methodology and the analysis plan. SH administered the project. RK, MH and GS supervised the project. SH, ACR, MH, TV, RK and GS validated the data. SH visualized the data. SH, ACR and MH wrote the original draft. SH, ACR, FP, JP, MS, SP, PE, MS, WT, GG, PM, MR, FPr, ARR, TV, RK, MH and GS critically revised the manuscript. SH, ACR and FP verified the underlying data. All authors read and approved the final manuscript.

Conflict of interest
S. Hatzl declares no conflict of interest. A.C. Reisinger declares no conflict of interest. F. Posch declares no conflict of interest. J. Prattes received grants from Pfizer, MSD, Gilead; honoraria from Menarini, MSD, Angelini, Pfizer, Gilead; participated in advisory boards from Pfizer and holds stocks and stock-options from AbbVie Inc., NovoNordisk, Stryker. M. Stradner declares no conflict of interest. S. Pilz declares no conflict of interest. P. Eller declares no conflict of interest. M. Schoerghuber declares no conflict of interest. W. Toller declares no conflict of interest. G. Gorkiewicz declares no conflict of interest. P. Metnitz declares no conflict of interest. M. Rief declares no conflict of interest. F. Prüller declares no conflict of interest. A.R. Rosenkranz declares no conflict of interest. T. Valentin declares no conflict of interest. R. Krause declares no conflict of interest. M. Hoenigl received grants from Astellas, MSD, Pfizer, Gilead. G. Schilcher declares no conflict of interest. S. Hatzl and M. Hoenigl received independent (non-industrial affiliated) grant from the National Institute of Health (M. Hoenigl) and the City of Graz (S. Hatzl).