Abstract
Background
Few large studies have evaluated treatment responses and mortality across non-Aspergillus mold infections.
Methods
We conducted a retrospective, 21-center, observational study of non-Aspergillus mold infections in Australia and New Zealand. Cases from 2016 to 2023 were identified from each hospital's pathology system. Treatments were recorded and outcomes compared across non-Aspergillus mold infections at day 30, 90, and 180 follow-up. Multivariable analyses were performed to identify factors associated with 90-day mortality and treatment failure.
Results
Of 421 cases, 129 were Mucorales (30.6%), 89 Lomentospora prolificans (21.1%), 82 Scedosporium spp. (19.5%), 30 Fusarium spp. (7.1%), 44 other dematiaceous molds (10.5%), 27 other non-Aspergillus molds (6.4%), and 20 mixed non-Aspergillus mold infections (4.8%). Antifungal therapy was administered in 376 (89.3%), and 219 (52.0%) underwent adjunctive surgery. Among survivors at day 180, 42.6% (n = 115/270) remained on antifungals, with total duration exceeding 180 days. All-cause 90-day mortality was 31.4% (n = 132/421), highest in L. prolificans infection (n = 51/89, 57.3%). Neutropenia (hazard ratio [HR]: 2.19, 95% confidence interval [CI]: 1.30–3.69), intensive care unit admission (HR: 2.51, 95% CI: 1.60–3.92), disseminated infection (HR: 2.55, 95% CI: 1.59–4.10), Mucorales (HR: 4.87, 95% CI: 2.71–8.77), and L. prolificans infections (HR: 3.71, 95% CI: 1.97–6.98) were associated with increased 90-day mortality (all P < .001). Adjunctive surgery was associated with a lower 90-day mortality (HR: 0.34, 95% CI: .21–.54, P < .001).
Conclusions
Despite antifungal therapy, L. prolificans infections had the poorest clinical outcomes followed by Mucorales, highlighting the need for improved diagnostic methods and therapeutic strategies.
Keywords: mold infections, mortality, non-Aspergillus, outcomes, treatment
This 21-center Australian and New Zealand study of 421 non-Aspergillus mold infections found substantial mortality, with the poorest outcomes among patients with Lomentospora prolificans infections. The findings underscore the urgent need for better diagnostics, more effective therapies and timely surgical intervention, where appropriate, for these difficult-to-treat mold infections.
Mortality in invasive fungal disease (IFD) remains high in immunocompromised populations, particularly in hematological malignancy (HM) and hematopoietic stem cell transplantation [1, 2]. While Aspergillus species are the commonest mold pathogens, there has been an increase in IFDs caused by non-Aspergillus molds, including Mucorales, Lomentospora prolificans, Fusarium spp., and Scedosporium spp. [2, 3]. The World Health Organization has included several non-Aspergillus mold pathogens in the fungal priority pathogen list, as medically important fungi with high (or medium) priority [4]. Limited clinical recognition of these non-Aspergillus mold infections often delays diagnosis and initiation of targeted antifungal treatment, contributing to high mortality rates [5]. Furthermore, non-Aspergillus mold infections lead to considerable healthcare costs with prolonged hospital stay [6, 7].
While novel antifungal agents with improved in vitro and in vivo activity against certain non-Aspergillus mold pathogens are emerging [8], specific recommendations for use of these antifungal agents are yet to be proposed. Understanding the outcomes of non-Aspergillus mold infections could provide insights on identifying optimal treatment with both existing and novel antifungals. Notably, none of the few observational studies on non-Aspergillus mold infections to date have evaluated treatment responses [9–11]. Most data on treatment responses are limited to case reports, small case series, or studies focused on a single causative non-Aspergillus mold, without comparing responses across different non-Aspergillus mold pathogens [12, 13]. Conducting a randomized controlled trial to establish evidence for treating non-Aspergillus mold infections is challenging given the low numbers and high heterogeneity of patient populations [14], and thus, well-designed observational studies are important. Accordingly, we performed a large multicenter study of non-Aspergillus mold infections in Australia and New Zealand to better characterize the treatment responses and mortality rates, and to compare these clinical outcomes across various non-Aspergillus mold infections. The current study also identified factors associated with treatment failure and mortality at day 90, which could support early recognition and stratification of high-risk patients, enabling timely and targeted interventions.
METHODS
Settings and Inclusion Criteria
This retrospective cohort study included 21 sites across Australia and New Zealand (Supplementary Figure 1, MapChart, https://www.mapchart.net/). Recruitment of study sites was coordinated through the Australia and New Zealand Mycoses Interest Group under the Australasian Society for Infectious Diseases.
Adults (aged ≥18 years) who had been diagnosed with proven or probable non-Aspergillus mold infections [15] between January 2016 and December 2023 at participating sites were included. Cases were identified using microbiology and histopathology records within each hospital's pathology information system. Cases classified as colonization, those without imaging or histopathological evidence of IFDs, duplicates or incomplete records were excluded. A data review committee, comprising 4 infectious diseases experts with extensive experience in retrospective and prospective surveillance studies on IFDs, conducted case adjudication [9, 16–19].
Definitions, Data Collection, and Outcome Measures
A web-based, standardized electronic case report form hosted by REDCap was used to collect data. Electronic medical records and pharmacy records were used to retrieve clinical data and antifungal use. Data such as patient demographics, underlying medical conditions, predisposing factors up to 90 days before infection, site of infection, causative non-Aspergillus mold pathogen, antifungal prophylaxis, treatment and therapeutic drug monitoring (TDM), survival status, and treatment response at days 30, 90, and 180 were collected. The majority of definitions and data collection methods have been previously described [3]. We also include definitions and data elements in Supplementary material. Treatment approaches were not standardized across sites and decisions were made by treating clinicians at each participating site. “Initial targeted treatment” was defined as the first antifungal treatment given after the diagnosis of non-Aspergillus mold infection. “Sequential antifungal therapy” was defined as the use of 2 or more antifungal agents given at different times following initial treatment. Reason for treatment modification was categorized as treatment failure due to disease progression, intolerance to antifungals, drug–drug interaction, or other reasons (eg, end-of-life care and death). Treatment response following antifungal treatment was categorized as success or failure by the site investigators according to predefined assessment criteria (Supplementary Table 1) and the European Organization for Research and Treatment of Cancer-Mycoses Study Group Education and Research Consortium (EORTC-MSGERC) categories [20]. Cases lost to follow-up or with insufficient data to assess response were classified as having an unknown treatment response status.
All-cause mortality [9] and treatment response at day 90 were defined as the primary outcomes, enabling comparison with existing literature. Outcome measures, including response to treatment at days 30, 90, and 180 following initiation of targeted therapy, overall survival, and cause of death adjudicated by the treating physician were documented. All cases were followed for a maximum of 180 days or until death or loss to follow-up, whichever occurred first.
Statistical Analyses
All eligible cases were included, regardless of frequency. Findings were summarized as mean and standard deviation for normally distributed continuous variables, and median and interquartile range (IQR) for non-normally distributed continuous variables. As non-Aspergillus mold infections are clinically heterogeneous, we first summarized treatment patterns for the overall cohort and then reported pathogen-specific findings. To compare antifungal treatment, other treatment modalities and treatment responses across various non-Aspergillus mold pathogen subgroups [ie, Mucorales, L. prolificans, Scedosporium spp., Fusarium spp., other dematiaceous mold, other non-Aspergillus mold pathogens, and mixed infections (>1 non-Aspergillus mold pathogen)], a Pearson χ2 or Fisher's exact test was used. Given the small numbers of isolates in the categories of other hyalohyphomycetes excluding Fusarium spp. and the other non-Aspergillus mold species, these were combined for the purpose of subgroup analysis. Treatment exposure was classified according to the initial targeted antifungal regimen for analysis purposes. One-way analysis of variance or Kruskal–Wallis test was employed for continuous variables. The McNemar test was used to compare the trends in treatment responses across all follow-up time points (eg, day 30 vs day 90, day 90 vs day 180, day 30 vs day 180). Univariable and multivariable Cox proportional hazard regression analyses were performed to examine factors associated with 90-day mortality. The proportional hazards assumption was assessed using Schoenfeld residuals and visual inspection of log-minus-log survival plots. Time zero for the Cox proportional hazards analysis was defined, as the date of IFD diagnosis and antifungal treatment was modeled as a time-varying covariate in multivariable models to minimize immortal time bias arising from delayed antifungal initiation after IFD diagnosis. Factors associated with 90-day treatment failure were determined using multivariate logistic regression. Potential independent variables were identified in a univariable analysis against each outcome variable and were then included in the multivariable models. Multivariable regression analyses were used to adjust for potential confounding factors as indicated, with the significance level set at P < .05. All analyses were performed using Stata 17.0 (Stata Corp, College Station, TX, United States).
RESULTS
Species Distribution, Study Population, and Site of Infections
Of a total of 421 cases, the majority were caused by a single non-Aspergillus mold pathogen (n = 401/421, 95.2%); among these, the most frequently detected pathogens were the Mucorales (n = 129/401, 32.2%), L. prolificans (n = 89/401, 22.2%), and Scedosporium spp. (n = 82/401, 20.4%). Hematological malignancy (n = 146/421, 34.7%) was the most common underlying condition, followed by diabetes mellitus (n = 122/421, 29.0%). Of the 68 cases (16.2%) without underlying comorbidity, trauma was the predominant risk factor, observed in 54.4% (n = 37/68).
Proven infections accounted for 82.2% (n = 346/421) of cases, and 74.8% (n = 315/421) were localized infections. L. prolificans infections were more common in patients with neutropenia and those receiving chemotherapy, as were disseminated infections involving blood and lungs (P ≤ .001). Non-Aspergillus mold species appeared to have predilection to anatomical sites: Scedosporium spp. was the predominant non-Aspergillus mold in ear and bone infections, Mucorales predominated in lung/sinus infections, while skin and soft tissue infections were frequently caused by other dematiaceous molds, Scedosporium spp., and Mucorales (all P < .05). Details of this section have been previously described [3].
Antifungal Treatment and Other Treatment Modalities
A total of 376 cases received targeted antifungal therapy (89.3%), with a median duration of 89 days (IQR: 26–180), capped at 180 days for analysis (Table 1). Combination therapy was administered in 129 of 376 cases (34.3%), with voriconazole and terbinafine the most common regimen (n = 72/129, 55.8%), mainly for infections caused by L. prolificans. Posaconazole and liposomal amphotericin B (LAmB) was the second most common combination therapy (n = 23/129, 17.8%) and was used for Mucorales infections. The third most common was voriconazole and LAmB (n = 12/129, 9.3%), mainly for infections caused by Fusarium spp.
Table 1.
Antifungal Therapeutic Monitoring and Other Treatment Modalities Across Non-Aspergillus Mold Pathogen Subgroups (n = 421)
| Variablesa | Overall (n = 421) n (%) |
Stratified by Causative Fungal Pathogen | P Valuec | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Infection With Single Non-Aspergillus Mold spp. (n = 401) | Mixed Infections With 2 or 3 Non-Aspergillus Mold spp. (n = 20) | ||||||||
| Mucorales (n = 129) | Lomentospora prolificans (n = 89) | Scedosporium spp. (n = 82) | Fusarium spp. (n = 30) | Other Dematiaceous Mold (n = 44) | Otherb (n = 27) | ||||
| TDM performed (azole monotherapy)d (n = 148) | 110 (74.3) | 11 (61.1) | 4 (57.1) | 47 (85.5) | 9 (64.3) | 23 (71.9) | 9 (60.0) | 7 (100.0) | .058 |
| Voriconazole level, mg/L, median (range) (n = 78) | 3.3 (0.1–12.7) | … | 4.0 (3.3–12.7) | 3.6 (0.1–10.5) | 3.0 (1.3–12.0) | 3.1 (0.1–6.0) | 3.0 (1.3–6.3) | 3.5 (2.4–11.3) | |
| Posaconazole level, mg/L, median (range) (n = 23) | 1.6 (0.6–5.8) | 1.7 (0.6–5.8) | NA | … | … | 1.3 (0.8–2.0) | 2.1 (0.6–3.2) | 2.0 (1.1–3.1) | |
| Itraconazole level, mg/L, median (range) (n = 9) | 1.3 (0.1–1.6) | … | … | … | … | 1.3 (0.1–1.6) | NA | NA | |
| Had sequential therapy | 170 (45.2) | 66 (57.4) | 26 (35.1) | 36 (46.2) | 11 (39.3) | 11 (29.7) | 8 (33.3) | 12 (60.0) | .002 |
| Overall duration of targeted antifungal treatment, days, median (range)e (n = 376) | 89.0 (1.0–180.0) |
51.0 (1.0–180.0) |
70.0 (2.0–180.0) |
124.5 (2.0–180.0) |
88.0 (2.0–180.0) |
113.0 (12.0–180.0) |
98.5 (4.0–180.0) |
91.5 (5.0–180.0) |
<.001 |
| Adjunctive surgeryf | 219 (52.0) | 83 (64.3) | 24 (27.0) | 39 (47.6) | 15 (50.0) | 28 (63.6) | 14 (51.9) | 16 (80.0) | <.001 |
| Debridement | 155 (70.8) | 65 (78.3) | 16 (66.7) | 25 (64.1) | 10 (66.7) | 17 (60.7) | 9 (64.3) | 13 (81.3) | <.001 |
| Excision | 67 (30.6) | 24 (28.9) | 5 (20.8) | 9 (23.1) | 4 (26.7) | 15 (53.6) | 6 (42.9) | 4 (25.0) | .001 |
| Sinus drainage/washout | 31 (14.2) | 16 (19.3) | 3 (12.5) | 4 (10.3) | 2 (13.3) | 3 (10.7) | 1 (7.1) | 2 (12.5) | .224 |
| Otherg | 86 (39.3) | 38 (45.8) | 12 (50.0) | 16 (41.0) | 6 (40.0) | 5 (17.9) | 3 (21.4) | 6 (37.5) | .033 |
| Immunomodulatorh | 36 (8.6) | 15 (11.6) | 14 (15.8) | 5 (6.1) | 1 (3.3) | 0 (0) | 1 (3.7) | 0 (0) | .015 |
Abbreviations: LAmB, liposomal amphotericin B; NA, not applicable (as n < 3); TDM, therapeutic drug monitoring.
aVariables are reported in n (%) unless stated otherwise.
bOther included other hyalohyphomycete fungi excluding Fusarium spp. (eg, Paecilomyces spp., Purpureocillium spp., Cladosporium spp., Neoscytalidium dimidiatum, Pleurostoma richardsiae, non-marneffei Talaromyces sp., Trematosphaeria spp., Trichoderma sp., Arthrinium phaeospermum, and Aureobasidium sp.) and other non-Aspergillus mold fungi (eg, Colletotrichum gloeosporioides, Falciformispora sp., Lindgomycetaceae family, Meanderella rijsii, Paraconiothyrium cyclothyrioides, Periconia sp., Rhytidhysteron rufulum, Schizophyllum commune, Tintelnotia destructans, and Westerdykella angulata).
c P value denotes comparison analyses (Pearson χ2 or Fisher's exact test or Kruskal–Wallis test) across the subgroups of causative fungal pathogen.
dMissing data (n = 1) for posaconazole level in Mucorales subgroup.
eDuration of antifungal therapy was capped at 180 days to align with the predefined 180-day follow-up period. Of those who survived by day 180, 42.6% (n = 115/270) received antifungal treatment for >180 days.
fSurgery performed as treatment of non-Aspergillus mold infections. Enucleation, mastoidectomy, and vitrectomy have relatively small sample size (n < 20), and no significant differences were found between the subgroups stratified by fungal spp. (P > .05) and can have more than one encounter.
gSee Supplementary Table 2 for details.
hImmunomodulators mainly were granulocyte colony-stimulating factor (n = 31).
Among the cases that received monotherapy (n = 247/376, 65.7%), voriconazole was the most frequently prescribed agent for Scedosporium spp. infections (54/58, 93.1%), while LAmB monotherapy was used in 70 of 89 cases (78.7%) of infections caused by Mucorales (Table 1). Twelve received olorofim mainly for infections caused by L. prolificans (n = 10). Mixed infections were analyzed as a separate subgroup, with various antifungal treatments used in these cases are detailed in Supplementary Table 2. Therapeutic drug monitoring was performed in 74.3% of patients on azole monotherapy (n = 110/148), primarily measuring voriconazole levels in the Scedosporium subgroup (n = 47), with a median plasma concentration of 3.3 mg/L (IQR: 1.6–5.4).
Sequential antifungal therapy was administered in 45.2% of cases (n = 170), predominantly among those with Mucorales infections (Table 1), mostly because of treatment failure or intolerance to antifungal therapy (n = 32/66, 48.5%). Of those who survived by day 180, 42.6% (n = 115/270) received antifungal treatment for more than 180 days, more than a quarter of these cases had Scedosporium spp. infections (n = 32/115, 27.8%).
Fifty-two percent (n = 219/421) received adjunctive surgery, with the majority undergoing debridement (n = 155/219, 70.8%; Table 1). Details of other adjunctive surgeries are included in Supplementary Table 3. Adjunctive surgery was more commonly encountered in infections caused by Mucorales than other non-Aspergillus mold infections (P < .001). Adjunctive granulocyte colony-stimulating factor was administered to 8.6% (n = 36/421), primarily with Mucorales and L. prolificans infections.
Treatment Response
More than half of the non-Aspergillus mold cases (n = 233/421, 55.3%) experienced treatment failure at initial (30-day) follow-up, while 37.8% (n = 159/421) achieved treatment success. The remaining 6.9% (n = 29/421) had unknown treatment responses, primarily due to loss to follow-up. Excluding cases with an unknown treatment response status, L. prolificans had the highest failure rates (79.1%–86.1%) across all 3 time points, followed by Mucorales (59.0%–63.5%; Figure 1A–C).
Figure 1.
Treatment responses at (A) 30-day, (B) 90-day, and (C) 180-day follow-up across non-Aspergillus mold pathogen subgroups (excluding unknown status).
Other dematiaceous mold infections demonstrated significant improvement between day 30 and day 180 (treatment failure 38.9% vs 13.2%, P = .016; Figure 1A–C). Similar observation was noted in mixed non-Aspergillus mold infections, mostly occurring in immunocompetent patients with localized infections (treatment failure 65.0% at day 30 vs 29.4% at day 180, P = .049). No significant improvement in treatment failure rates across all time points was observed for infections caused by Fusarium spp. (39.3%–44.4%) and other non-Aspergillus mold species (36.4%–37.5%; all P > .05). Regardless of the type of non-Aspergillus mold infection, immunosuppressed patients were more likely to experience treatment failure at all time points (P < .001).
When stable response was re-categorized as treatment success (Supplementary Figure 2A–C), high failure rates persisted across all time points for L. prolificans (66.3%–75.6%) and Mucorales (54.0%–56.4%). No significant changes in treatment failure rates were observed in the overall cohort or within other individual fungal subgroups (all P > .05). When considering only non-Aspergillus mold cases with localized skin and subcutaneous infections (n = 40), most of which were due to other dematiaceous molds, treatment success rates were generally high, reaching 78.8% at day 30, 81.6% at day 90, and 87.2% at day 180.
Factors Associated With Mortality and Treatment Failure
The 90-day all-cause mortality was 31.4% (n = 132/421), with most deaths occurring in the L. prolificans (n = 51/89, 57.3%) and Mucorales (n = 56/129, 43.4%) subgroups (P < .001) [3]. Multivariate Cox regression analyses further demonstrated that neutropenia [hazard ratio (HR): 2.19, 95% confidence interval (CI): 1.30–3.69], intensive care unit (ICU) admission (HR: 2.51, 95% CI: 1.60–3.92), disseminated infection (HR: 2.55, 95% CI: 1.59–4.10), Mucorales (HR: 4.87, 95% CI: 2.71–8.77), and L. prolificans infections (HR: 3.71, 95% CI: 1.97–6.98) were independently associated with an increased rate of 90-day mortality (all P < .005; Table 2). After adjusting for potential confounders, receipt of adjunctive surgery was associated with a lower 90-day mortality following non-Aspergillus mold infections (HR: 0.34, 95% CI: .21–.54, P < .001). In the hematological malignancy cohort, neutropenia (HR: 2.12, 95% CI: 1.17–3.85, P = .013), ICU admission (HR: 2.60, 95% CI: 1.53–4.41, P < .001), and disseminated infection (HR: 2.05, 95% CI: 1.22–3.45, P = .007) were associated with increased 90-day mortality (Table 2). Conversely, adjunctive surgery (HR: 0.24, 95% CI: .12–.49, P < .001) and Fusarium infection (HR: 0.11, 95% CI: .03–.47, P = .003) were associated with lower 90-day mortality (Table 2).
Table 2.
Univariate and Multivariate Analysis of All-cause 90-Day Mortality for the Entire Cohort and for the Hematological Malignancy Cohort
| Entire Cohort | Hematologic Malignancy Cohort | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Univariable Analysis | Multivariable Analysisa | Univariable Analysis | Multivariable Analysisa | |||||||||
| HR | 95%CI | P value | HR | 95%CI | P value | HR | 95%CI | P value | HR | 95%CI | P value | |
| Age | 1.01 | 1.00–1.02 | .263 | … | … | … | 1.01 | 1.00–1.03 | .146 | … | … | … |
| Male | 1.11 | .77–1.58 | .579 | … | … | … | 0.63 | .39–1.01 | .057 | … | … | … |
| Underlying condition/risk factors | ||||||||||||
| HM | 3.60 | 2.53–5.13 | <.001 | … | … | … | NA | NA | NA | … | … | … |
| HCT | 2.70 | 1.78–4.08 | <.001 | 1.08 | .68–1.72 | .743 | 1.14 | .71–1.82 | .592 | … | … | … |
| SOT | 0.66 | .39–1.10 | .108 | … | … | … | 0.97 | .31–3.08 | .958 | … | … | … |
| Diabetes | 0.63 | .42–.96 | .033 | 1.00 | .58–1.71 | .994 | 1.63 | .88–3.02 | .120 | … | … | … |
| Chronic lung disease | 0.57 | .31–1.03 | .061 | … | … | … | 0.65 | .20–2.05 | .459 | … | … | … |
| Chronic liver disease | 2.07 | 1.05–4.08 | .035 | … | … | … | 5.29 | 1.25–22.32 | .023 | … | … | … |
| Neutropenia | 4.30 | 3.03–6.10 | <.001 | 2.19 | 1.30–3.69 | .003 | 2.18 | 1.25–3.79 | .006 | 2.12 | 1.17–3.85 | .013 |
| Lymphopenia | 3.51 | 2.39–5.14 | <.001 | 1.53 | .99–2.37 | .058 | 1.25 | .75–2.11 | .395 | 1.33 | .86–2.07 | .203 |
| Duration of lymphopenia | 1.01 | .98–1.03 | .513 | … | … | … | 0.94 | .91–.97 | <.001 | … | … | … |
| Trauma | 0.29 | .12–.70 | .006 | 0.66 | .17–2.55 | .543 | NA | NA | NA | … | … | … |
| Immunosuppressive agents | 3.75 | 2.41–5.85 | <.001 | … | … | … | 1.06 | .49–2.31 | .881 | … | … | … |
| Prolonged corticosteroid use | 1.71 | 1.20–2.43 | .003 | 1.22 | .80–1.87 | .350 | 0.87 | .55–1.38 | .561 | … | … | … |
| Cancer chemotherapy | 3.40 | 2.40–4.81 | <.001 | … | … | … | 1.35 | .81–2.25 | .249 | … | … | … |
| Calcineurin inhibitors | 1.17 | .79–1.73 | .434 | … | … | … | 1.25 | .73–2.14 | .418 | … | … | … |
| Cyclosporine | 2.34 | 1.40–3.89 | .001 | … | … | … | 1.11 | .60–2.05 | .745 | … | … | … |
| ATG | 2.88 | 1.06–7.79 | .038 | … | … | … | 1.53 | .38–6.26 | .552 | … | … | … |
| Mono/polyclonal antibodies | 1.90 | 1.14–3.17 | .014 | … | … | … | 0.96 | .53–1.74 | .892 | … | … | … |
| Anti-CD20 | 3.22 | 1.73–5.98 | <.001 | … | … | … | 1.98 | 1.04–3.76 | .037 | … | … | … |
| Zanubrutinib | 7.12 | 1.74–29.14 | .006 | … | … | … | 3.34 | .81–13.83 | .096 | … | … | … |
| Venetoclax | 4.01 | 1.95–8.23 | <.001 | … | … | … | 1.93 | .93–4.04 | .079 | … | … | … |
| ICU admission | 2.59 | 1.80–3.73 | <.001 | 2.51 | 1.60–3.92 | <.001 | 3.11 | 1.91–5.07 | <.001 | 2.60 | 1.53–4.41 | <.001 |
| COVID-19 | 0.70 | .50–.97 | .034 | … | … | … | 0.94 | .56–1.58 | .818 | … | … | … |
| Site of infection/co-infection | ||||||||||||
| Disseminated infection | 5.02 | 3.54–7.13 | <.001 | 2.55 | 1.59–4.10 | <.001 | 2.44 | 1.52–3.93 | <.001 | 2.05 | 1.22–3.45 | .007 |
| Blood | 5.56 | 3.84–8.03 | <.001 | … | … | … | 2.89 | 1.83–4.55 | <.001 | … | … | … |
| Bone | 0.27 | .12–.57 | .001 | … | … | … | 0.19 | .05–.77 | .020 | … | … | … |
| CNS | 2.37 | 1.51–3.73 | <.001 | … | … | … | 1.49 | .84–2.62 | .169 | … | … | … |
| Lung | 2.55 | 1.80–3.61 | <.001 | … | … | … | 1.50 | .94–2.39 | .090 | … | … | … |
| Joint | 0.11 | .01–.76 | .026 | … | … | … | NA | NA | NA | … | … | … |
| Sinus | 1.12 | .67–1.87 | .653 | … | … | … | 0.45 | .23–.91 | .026 | … | … | … |
| Skin soft tissue | 0.38 | .24–.59 | <.001 | 0.72 | .44–1.16 | .178 | 0.61 | .32–1.20 | .153 | … | … | … |
| Receipt of treatment | ||||||||||||
| Combination antifungal therapy | 1.58 | 1.06–2.34 | .024 | 0.77 | .44–1.35 | .368 | 1.08 | .64–1.82 | .765 | … | … | … |
| Adjunctive surgery | 0.30 | .21–.44 | <.001 | 0.34 | .21–.54 | <.001 | 0.25 | .13–.48 | <.001 | 0.24 | .12–.49 | <.001 |
| Immunomodulator | 2.11 | 1.30–3.44 | .003 | … | … | … | 1.37 | .78–2.41 | .274 | … | … | … |
| Causative fungal pathogen | ||||||||||||
| Mucorales | 1.82 | 1.28–2.59 | .001 | 4.87 | 2.71–8.77 | <.001 | 1.09 | .67–1.79 | .717 | … | … | … |
| Lomentospora prolificans | 3.18 | 2.22–4.55 | <.001 | 3.71 | 1.97–6.98 | <.001 | 2.96 | 1.87–4.68 | <.001 | 1.43 | .86–2.39 | .166 |
| Scedosporium spp. | 0.37 | .20–.67 | .001 | … | … | … | 0.24 | .08–.78 | .017 | … | … | … |
| Fusarium spp. | 0.37 | .14–1.00 | .050 | … | … | … | 0.16 | .04–.64 | .010 | 0.11 | .03–.47 | .003 |
| Other dematiaceous mold | NA | NA | NA | … | … | … | NA | NA | NA | … | … | … |
| Mixed non-Aspergillus mold | 0.42 | .13–1.31 | .133 | … | … | … | 0.83 | .20–3.39 | .797 | … | … | … |
Abbreviations: ATG, antithymocyte globulin; CI, confidence interval; CNS, central nervous system; COVID-19, coronavirus disease-19; HM, hematological malignancy; HR, hazard ratio; HCT, hematopoietic stem cell transplant; ICU, intensive care unit; NA, not applicable because n = 0 or far too few observations to model; SOT, solid organ transplant.
aCox regression model: The proportional hazards assumption was assessed using Schoenfeld residuals and log-minus-log plots. Although the Schoenfeld test suggested possible nonproportionality driven by ICU status, log-minus-log plots showed noncrossing curves; therefore, Cox model estimates were retained. Variables shown in the multivariable analysis columns were the covariates/confounders included in the adjusted models. Multivariable models were rerun with antifungal treatment modeled as a time-varying covariate to minimize immortal time bias related to delayed treatment initiation after IFD diagnosis. Variables from the following categories/domains (ie, underlying condition/risk factor, site of infection, receipt of treatment, and causative fungal pathogen) in the univariable analysis were chosen based on either the narrower CI or clinical grounds. The number of variables included in each adjusted model was based on a minimum of 10 outcomes per explanatory variable to avoid overfittings.
After multivariate analyses, neutropenia [odds ratio (OR): 4.28, 95% CI: 1.61–11.39, P = .004], prolonged corticosteroid use (OR: 1.91, 95% CI: 1.04–3.53, P = .038), solid cancer (OR: 4.31, 95% CI: 1.53–12.16, P = .006), disseminated infection (OR: 2.68, 95% CI: 1.18–6.10, P = .019), central nervous system (CNS) infection (OR: 10.41, 95% CI: 2.99–36.24, P < .001), and L. prolificans infections (OR: 3.64, 95% CI: 1.41–9.40, P = .007) were independent risk factors for increased treatment failure rate at day 90 (Table 3). No collinearity was observed between the variables. Whereas for the hematology malignancy cohort (Table 3), L. prolificans infections (OR: 4.99, 95% CI: 1.05–23.62, P = .043) remained significantly associated with a higher 90-day treatment failure rate.
Table 3.
Univariate and Multivariate Analysis of Treatment Failure 90-Day for the Entire Cohort and for the Hematological Malignancy Cohort
| Entire Cohort | Hematologic Malignancy Cohort | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Univariable Analysis | Multivariable Analysisa | Univariable Analysis | Multivariable Analysisa | |||||||||
| OR | 95%CI | P value | OR | 95%CI | P value | OR | 95%CI | P value | OR | 95%CI | P value | |
| Age | 1.01 | .99–1.02 | .361 | … | … | … | 1.01 | .98–1.03 | .670 | … | … | … |
| Male | 0.97 | .64–1.46 | .869 | … | … | … | 0.54 | .25–1.19 | .127 | … | … | … |
| Underlying condition/risk factors | ||||||||||||
| HM | 4.51 | 2.84–7.16 | <.001 | 0.52 | .18–1.47 | .216 | NA | NA | NA | … | … | … |
| HCT | 5.57 | 2.54–12.21 | <.001 | 1.09 | .37–3.21 | .876 | 2.00 | .83–4.85 | .125 | … | … | … |
| Solid tumor | 3.28 | 1.45–7.42 | .004 | 4.31 | 1.53–12.16 | .006 | NA | NA | NA | … | … | … |
| SOT | 0.96 | .57–1.61 | .868 | … | … | … | NA | NA | NA | … | … | … |
| Diabetes | 0.80 | .51–1.25 | .331 | … | … | … | 1.25 | .39–4.06 | .710 | … | … | … |
| Chronic lung disease | 1.72 | .95–3.09 | .072 | … | … | … | 2.36 | .28–19.88 | .431 | … | … | … |
| Chronic liver disease | 2.75 | .87–8.69 | .084 | … | … | … | NA | NA | NA | … | … | … |
| Neutropenia | 6.38 | 3.69–11.05 | <.001 | 4.28 | 1.61–11.39 | .004 | 2.57 | 1.15–5.73 | .021 | 2.52 | .91–6.95 | .075 |
| Lymphopenia | 3.66 | 2.40–5.57 | <.001 | 1.68 | .91–3.09 | .097 | 1.66 | .72–3.81 | .234 | … | … | … |
| Trauma | 0.20 | .09–.43 | <.001 | 0.36 | .11–1.16 | .088 | NA | NA | NA | … | … | … |
| Surgery | 0.48 | .26–.87 | .017 | 1.25 | .48–3.28 | 0.651 | 0.80 | .15–4.32 | 0.795 | … | … | … |
| Immunosuppressive agents | 4.23 | 2.74–6.51 | <.001 | … | … | … | 1.42 | .41–4.95 | .580 | … | … | … |
| Cancer chemotherapy | 4.56 | 2.72–7.66 | <.001 | 0.88 | .33–2.39 | .806 | 1.20 | .52–2.75 | .675 | … | … | … |
| Calcineurin inhibitors | 1.49 | .93–2.37 | .098 | … | … | … | 1.63 | .57–4.68 | .366 | … | … | … |
| Cyclosporine | 4.89 | 1.83–13.06 | .002 | … | … | … | 1.04 | .35–3.10 | .940 | … | … | … |
| Prolonged corticosteroid | 2.68 | 1.71–4.19 | <.001 | 1.91 | 1.04–3.53 | .038 | 1.26 | .57–2.78 | .568 | … | … | … |
| ATG | 4.50 | .52–38.92 | .171 | … | … | … | NA | NA | NA | … | … | … |
| Mono/polyclonal antibodies | 3.93 | 1.67–9.24 | .002 | 3.33 | .95–11.68 | .061 | 1.66 | .52–5.26 | .392 | … | … | … |
| BTK inhibitors | 3.59 | .40–32.38 | .255 | … | … | … | 1.31 | .14–12.11 | .814 | … | … | … |
| BCL2 inhibitors | 3.64 | .76–17.36 | .105 | … | … | … | 1.32 | .27–6.54 | .734 | … | … | … |
| ICU admission | 1.63 | 1.00–2.63 | .049 | 1.32 | .64–2.71 | 0.454 | 6.70 | 1.51–29.71 | .012 | 8.44 | .99–71.94 | .051 |
| COVID-19 | 0.89 | .58–1.37 | .609 | … | … | … | 1.36 | .60–3.08 | .466 | … | … | … |
| Site of infection/co-infection | ||||||||||||
| Disseminated infection | 10.54 | 5.52–20.12 | <.001 | 2.68 | 1.18–6.10 | .019 | 5.28 | 2.18–12.80 | <.001 | 2.26 | .63–8.13 | .213 |
| Blood | 12.56 | 4.90–32.21 | <.001 | … | … | … | 9.04 | 2.60–31.42 | .001 | 1.13 | .19–6.86 | .891 |
| Bone | 0.65 | .37–1.14 | .133 | … | … | … | 1.68 | .35–8.10 | .515 | … | … | … |
| CNS | 7.24 | 2.77–18.92 | <.001 | 10.41 | 2.99–36.24 | <.001 | NA | NA | NA | … | … | … |
| Eye | 2.11 | .85–5.24 | .109 | … | … | … | 3.86 | .48–31.07 | .204 | … | … | … |
| Lung | 3.59 | 2.29–5.61 | <.001 | 1.92 | .92–4.01 | .082 | 1.18 | .54–2.59 | .672 | … | … | … |
| Joint | 1.25 | .54–2.90 | .595 | … | … | … | NA | NA | NA | … | … | … |
| Sinus | 1.27 | .69–2.35 | .437 | … | … | … | 0.46 | .19–1.14 | .095 | … | … | … |
| Skin soft tissue | 0.31 | .20–.49 | <.001 | 0.84 | .42–1.66 | .609 | 1.55 | .49–4.95 | .458 | … | … | … |
| Receipt of treatment | ||||||||||||
| Combination antifungal therapy | 2.71 | 1.71–4.29 | <.001 | 1.09 | .55–2.16 | .812 | 3.39 | 1.41–8.16 | .007 | 1.44 | .48–4.35 | .515 |
| Adjunctive surgery | 0.37 | .25–.57 | <.001 | 1.28 | .66–2.46 | .466 | 0.92 | .40–2.10 | .837 | … | … | … |
| Immunomodulator | 8.18 | 2.83–23.62 | <.001 | … | … | … | 2.43 | .68–8.76 | .174 | … | … | … |
| Causative fungal pathogen | ||||||||||||
| Mucorales | 1.44 | .93–2.23 | .104 | … | … | … | 0.96 | .41–2.25 | .925 | … | … | … |
| Lomentospora prolificans | 6.44 | 3.48–11.92 | <.001 | 3.64 | 1.41–9.40 | .007 | 10.94 | 3.15–38.01 | <.001 | 4.99 | 1.05–23.62 | .043 |
| Scedosporium spp. | 0.50 | .30–.84 | .009 | 0.52 | .25–1.07 | .077 | 0.23 | .08–.70 | .009 | 0.68 | .18–2.54 | .567 |
| Fusarium spp. | 0.69 | .31–1.51 | .354 | … | … | … | 0.32 | .11–.96 | .041 | … | … | … |
| Other dematiaceous mold | 0.17 | .07–.40 | <.001 | 0.40 | .14–1.16 | .091 | NA | NA | NA | … | … | … |
| Mixed non-Aspergillus mold | 0.43 | .16–1.16 | .094 | … | … | … | 0.97 | .10–9.65 | .980 | … | … | … |
Abbreviations: ATG, antithymocyte globulin; BTK, Bruton tyrosine kinase; CI, confidence interval; CNS, central nervous system; COVID-19, coronavirus disease-19; HM, hematological malignancy; HR, hazard ratio; HCT, hematopoietic stem cell transplant; ICU, intensive care unit; NA, not applicable because n = 0 or far too few observations to model; SOT, solid organ transplant.
aLogistic regression model: Variables shown in the multivariable analysis columns were the covariates/confounders included in the adjusted models. Variables from the following categories/domains (ie, underlying condition/risk factor, site of infection, receipt of treatment, and causative fungal pathogen) in the univariable analysis were chosen based on either the narrower CI or clinical grounds. The number of variables included in each adjusted model was based on a minimum of 10 outcomes per explanatory variable to avoid overfittings.
DISCUSSION
This large multicenter study is the first to provide a comprehensive, longitudinal analysis of treatment responses across distinct non-Aspergillus mold subgroups at 30, 90, and 180 days, offering valuable insights into real-world clinical outcomes. These findings reflect real-world antifungal treatment practices over the past decade, highlight the limitations of current management and support the need for new antifungal agents and well-designed studies to guide the use of existing therapies. L. prolificans exhibited the highest treatment failure rates at all time points, followed by Mucorales, reflecting the significant therapeutic challenges associated with these infections.
While our findings align with previous studies reporting high treatment failure rates in L. prolificans infections among patients with HM or disseminated disease [12], a key difference is the definition of treatment response. In one earlier study [12], stable disease was classified as treatment success, whereas our analysis used the current EORTC-MSGERC definitions [20]. Recent discussions have proposed that stable disease may represent a favorable outcome, potentially preceding partial or complete response [21]. The natural history of many non-Aspergillus mold infections remains poorly understood, where divergence between radiological or mycological findings and clinical improvement is often noted (eg, delays in imaging resolution or normalization of biomarkers) [21]. Despite re-categorizing stable disease as treatment success in our cohort, treatment failure rates remained unacceptably high, reaching 76% for the L. prolificans subgroup and 56% in Mucorales. This underscores the limitations of existing treatment options and the pressing need for more effective antifungal strategies to improve patient outcomes in these difficult-to-treat non-Aspergillus mold infections. Beyond the angio-invasiveness of these molds, suboptimal outcomes may also reflect diagnostic limitations as current consensus definitions rely substantially on culture and histopathology from tissue specimens, requiring invasive procedures that may be delayed or precluded in those with HM, especially thrombocytopenic patients.
We have also identified the factors associated with treatment failure in the context of non-Aspergillus mold infections. Our study demonstrated that L. prolificans infections were significantly associated with increased odds of treatment failure at 90-day follow-up, both in the overall and the hematology malignancy cohorts. Disseminated infection, CNS involvement, neutropenia, prolonged corticosteroid use, and solid tumor cancer were also identified as a factor associated with treatment failure at day 90. Collectively, these results underpin the compounded risks of treatment failure in the setting of severe immunosuppression and deep-seated disease, highlighting the need for early diagnosis before disease progression and dissemination. In addition, L. prolificans and Mucorales infections were independently associated with a 4- to 5-fold increased risk of death, consistent with their intrinsic antifungal resistance and rapid angioinvasion that leading to poor prognosis, particularly in immunocompromised hosts [22–24]. While adjunctive surgery was associated with improved survival in line with existing literature [14, 25], the observational nature of the study precludes any conclusions being drawn. In the current study, most patients who died without receiving antifungal therapy had L. prolificans or Mucorales infections, with a median time to death of only 2–4 days post–non-Aspergillus mold infection diagnosis. These findings underscore the importance of timely diagnosis to enable prompt initiation of targeted antifungal therapy, particularly in high-risk patients, in order to improve outcomes. A particularly notable finding was the significantly lower risk of death in Fusarium infections among HM patients in our study, even after adjusting for confounders. This could be due to the more frequent use of voriconazole or combination therapies in recent years that led to an improved survival as reported elsewhere [26].
The management of non-Aspergillus mold infections in our study was largely aligned with current Australasian and global clinical practice guidelines [2, 25, 27, 28]. However, significant challenges were noted in the treatment of Mucorales infections, where nearly 50% of the cases required sequential therapies due to either intolerance to antifungals or treatment failure. Given these complexities, adjunctive surgical interventions were frequently required, particularly for Mucorales infections, reinforcing the critical role of debridement in improving patient outcomes [25, 27, 28]. While several promising novel antifungal agents are in late phase of clinical trials, including olorofim which has shown excellent activity against L. prolificans [29–31], there remains a significant unmet need for effective treatment given the limited in vitro activity of these novel agents against Mucorales [32–34]. Accordingly, exploring LAmB in combination with newer antifungals such as ibrexafungerp or fosmanogepix [35] may offer a potential alternative for the treatment of Mucorales. Likewise, fosmanogepix/LAmB combination therapy appears to be a promising option for invasive fusariosis based on animal studies [35]. Well-designed clinical studies are warranted to establish the efficacy of combination antifungal therapy while accounting for baseline patient severity and treatment-selection bias.
The current study provides important insights into the complexity of management of non-Aspergillus mold infections, emphasizing the necessity for prolonged antifungal therapy and adjunctive surgery where appropriate. Slow treatment response was evident in patients with Scedosporium spp., accounting for nearly one-third of our cases who survived and required antifungal therapy beyond 180 days, reflecting the persistent disease burden. In our study, Scedosporium spp. frequently involved the ear and bone, mostly presenting as skull base osteomyelitis in immunocompetent hosts. These deep-seated infections often required extended therapy due to slow tissue healing and delayed radiological resolution [21]. Hence, evaluating treatment responses beyond 3 months may provide a more comprehensive view of infection progression and therapeutic efficacy given the slow response. This is pertinent given that recommendations regarding optimal duration of therapy for most non-Aspergillus mold infections remain poorly defined [25, 27].
Scant data are available on mixed non-Aspergillus mold infections, with existing literature largely restricted to case reports and small case series, primarily involving HM patients with pulmonary infections, often in the context of co-existing aspergillosis [36, 37]. Interestingly, the mixed non-Aspergillus mold infections in our cohort were typically seen in less immunosuppressed hosts (eg, trauma) with predominance of localized infections, which is likely contributing to favorable outcomes by day 90 or 180. Despite the overall improvement in treatment response with time, a substantial proportion of mixed infections cases (65%) experienced treatment failure at day 30, highlighting the challenges in managing non-Aspergillus mold infections during the early phase. Likewise, infections caused by other dematiaceous molds, which primarily affected the skin and soft tissue, were associated with significantly lower rates of mortality and treatment failure. Prior reports of non-Aspergillus mold infections [9] did not assess treatment response or factors associated with treatment failure, therefore, limiting direct comparison with the current study.
Study limitations include retrospective nature of the study and the potential risk of variability in outcome assessments as treatment responses were determined by individual site investigators without central adjudication. We sought to maintain uniformity of assessment through provision of clear definitions. Although our non-Aspergillus infection management was generally informed by contemporary guidelines, treatment selection was clinician-directed and may have been influenced by disease severity, underlying immunosuppression and toxicity profile. Therefore, analyses of treatment response should be interpreted as associations rather than causal treatment effects given the potential for confounding by indication. We were unable to confirm whether TDM concentrations obtained were true trough levels, which would affect interpretation of antifungal exposure. Given the small number of other non-Aspergillus mold pathogens, these were combined into a single heterogeneous subgroup, which limits meaningful conclusions. Further, our analysis was not stratified by species or even genus, and therefore, we were unable to evaluate species-specific outcomes.
In conclusion, we demonstrate the therapeutic complexity in managing non-Aspergillus mold infections, particularly among immunocompromised patients. The significant impact of immunosuppression and extent of infection on treatment failure and mortality provide a strong rationale for aggressive management strategies. Adjunctive surgery was associated with a lower 90-day mortality, supporting its potential role in the management of non-Aspergillus mold infections. Prolonged antifungal therapy is often required, with delayed responses often observed in Scedosporium spp. infections. L. prolificans and Mucorales infections are associated with persistently high mortality and failure rates despite antifungal therapy, reinforcing the shortcomings of current treatment options and diagnostics in infection management.
Supplementary Material
Notes
Acknowledgments. The authors thank the pathology services at all study sites, the Australia and New Zealand Mycoses Interest Group (ANZMIG) and Dr Julien Coussement. Preliminary findings of this study were presented as an oral presentation at the 35th Congress of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID Global), Vienna, Austria, 11–15 April 2025.
Ethics approval and patient consent statement. The study obtained Human Research Ethics Committee approvals (HREC/85674/PMCC and HDEC/2022EXP/13381). Waiver of patient consent was granted.
Data availability . The data underlying this article (ie, individual de-identified patient-level data) cannot be shared publicly as the ethics approval allows only the publication of pooled, aggregated data.
Financial support. This investigator-initiated study is partially funded by Gilead Fellowship Grant 2022.
Members of the Australian and New Zealand study group for non-Aspergillus mold infections (by alphabetical order): Amy Crowe (St Vincent's Hospital, VIC, Australia), Jane Davies (Royal Darwin Hospital, NT, Australia), Michelle England (Fiona Stanley Hospital, WA, Australia), Dianne Gardam (Fiona Stanley Hospital, WA, Australia), Rupert Handy (Auckland City Hospital, New Zealand), May E. Leitch (Royal Darwin Hospital, NT, Australia), Catherine Marshall (Royal Darwin Hospital, NT, Australia), Sarah Lynar (Royal Darwin Hospital, NT, Australia), Owen Robinson (Royal Perth Hospital, WA, Australia), Philip Selby (Royal Adelaide Hospital, SA, Australia), Olivia Smibert (Austin Health, VIC, Australia), Peter Simos (Gold Coast Hospital, QLD, Australia), Michelle K. Yong (Peter MacCallum Cancer Centre, VIC, Australia), and Kar Yee Yong (Peter MacCallum Cancer Centre, VIC, Australia).
Contributor Information
Chin Fen Neoh, National Centre for Infections in Cancer, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; Department of Infectious Diseases, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia.
Sharon C A Chen, Clinical Mycology Reference Laboratory, Centre for Infectious Diseases and Microbiology Laboratory Services, New South Wales Health Pathology, Westmead Hospital, Sydney, New South Wales, Australia; The University of Sydney Institute of Infectious Diseases, Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia.
Arthur J Morris, Department of Clinical Microbiology, New Zealand Mycology Reference Laboratory, LabPlus, Auckland City Hospital, Auckland, New Zealand.
Christopher H Heath, Department of Microbiology, PathWest Laboratory Medicine, Fiona Stanley Hospital, Murdoch, Western Australia, Australia; Department of Infectious Diseases, Fiona Stanley Hospital, Murdoch, Western Australia, Australia; School of Medicine, University of Western Australia, Crawley, Western Australia, Australia.
Shu Jin Tan, Department of Infectious Diseases, Royal Perth Hospital, Perth, Western Australia, Australia.
Rebekah Lane, Department of Infectious Diseases, Te Toka Tumai, Auckland City Hospital, Auckland, New Zealand.
Shio Yen Tio, National Centre for Infections in Cancer, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; Department of Infectious Diseases, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; Victorian Infectious Diseases Service, Royal Melbourne Hospital, Melbourne, Victoria, Australia.
Sze Yen Tay, Department of Infectious Diseases, Royal Darwin Hospital, Darwin, Northern Territory, Australia; ACT Pathology, Canberra Health Services, Canberra, Australian Capital Territory, Australia.
Matthew B Roberts, Department of Infectious Diseases, Royal Adelaide Hospital, Adelaide, South Australia, Australia.
Karina Kennedy, ACT Pathology, Canberra Health Services, Canberra, Australian Capital Territory, Australia.
Sebastiaan J van Hal, The University of Sydney Institute of Infectious Diseases, Sydney Medical School, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia; Department of Infectious Diseases, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia.
Hugh C Murray, Department of Infectious Diseases, St Vincent’s Hospital Melbourne, Melbourne, Victoria, Australia.
Robert Pickles, Department of Infectious Diseases, John Hunter Hospital, Newcastle, New South Wales, Australia; School of Medicine and Public Health, University of Newcastle, Newcastle, Australia.
Natasha Marcella Vaselli, Department of Infectious Diseases, Gold Coast Hospital and Health Service, Southport, Queensland, Australia.
Abby Douglas, National Centre for Infections in Cancer, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; Department of Infectious Diseases, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia; Department of Infectious Diseases, Austin Health, Heidelberg, Victoria, Australia.
Karen Urbancic, National Centre for Infections in Cancer, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; Department of Infectious Diseases, Austin Health, Heidelberg, Victoria, Australia.
Mahesh Menon, Department of Infectious Diseases, Wesley Hospital, Brisbane, Queensland, Australia.
Adam Stewart, Department of Infectious Diseases, Royal Brisbane and Women’s Hospital, Brisbane, Queensland, Australia.
Julia C Howard, Health New Zealand Te Whatu Ora Waitaha Canterbury, Christchurch, New Zealand.
Caitlin Keighley, Southern.IML Pathology, a Sonic Healthcare practice, Coniston, New South Wales, Australia; Department of Infectious Diseases, Wollongong Hospital, Wollongong, New South Wales, Australia; Graduate School of Medicine, University of Wollongong, Wollongong, New South Wales, Australia.
Spiros Miyakis, Department of Infectious Diseases, Wollongong Hospital, Wollongong, New South Wales, Australia.
Rohan Beresford, Department of Infectious Diseases, Concord Repatriation General Hospital, Concord, New South Wales, Australia.
Louise Cooley, Department of Microbiology and Infectious Diseases, Royal Hobart Hospital, Hobart, Tasmania, Australia.
Marjoree Sehu, Department of Infectious Diseases, Peninsula Health, Melbourne, Victoria, Australia.
Catriona L Halliday, Clinical Mycology Reference Laboratory, Centre for Infectious Diseases and Microbiology Laboratory Services, New South Wales Health Pathology, Westmead Hospital, Sydney, New South Wales, Australia.
Sarah Kidd, National Mycology Reference Centre, SA Pathology, Adelaide, Australia; School of Biological Sciences, Faculty of Sciences, University of Adelaide, Adelaide, Australia.
David C M Kong, Centre for Medicine Use and Safety, Monash Institute of Pharmaceutical Sciences, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia; The National Centre for Antimicrobial Stewardship, The Peter Doherty Institute for Infections and Immunity, Melbourne, Australia.
Tim Spelman, Department of Health Services Research, Peter MacCallum Cancer Centre, Melbourne, Australia.
Leon J Worth, National Centre for Infections in Cancer, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; Department of Infectious Diseases, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia.
Monica A Slavin, National Centre for Infections in Cancer, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; Department of Infectious Diseases, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, Victoria, Australia; Victorian Infectious Diseases Service, Royal Melbourne Hospital, Melbourne, Victoria, Australia.
the Australian and New Zealand Study Group for non-Aspergillus mold infections:
Amy Crowe, Jane Davies, Michelle England, Dianne Gardam, Rupert Handy, May E Leitch, Catherine Marshall, Sarah Lynar, Owen Robinson, Philip Selby, Olivia Smibert, Peter Simos, Michelle K Yong, and Kar Yee Yong
Supplementary Data
Supplementary materials are available at Open Forum Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.
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