Work

Diagnosis and risk factors for invasive mould disease—a round table discussion on directing antifungal therapy according to the available evidence

Year: 2026

Type: article

Abstract: Virtual roundtable. When choosing a preferred antifungal therapy strategy various factors need to be considered (Table 1 and Figures 1a and b). Understanding the current and evolving risk predisposing patients to invasive fungal disease (IFD) is critical to underpinning the overall need for antifungals, and how these are best applied (i.e. prophylactically, empirically, pre-emptively and targeted) requires an understanding of the benefits/limitations or each approach.1 Despite an increasing range of options to aid in the diagnosis of IFD, there remains a heavy reliance on therapeutic antifungal strategies lacking mycological confirmation of IFD (e.g. empirical therapy). Such strategies were introduced when diagnostic options were limited, but this is no longer the case, although access to mycological tests with timely reporting remains limited in many countries. Nevertheless, the diagnosis of IFD is typically a multicomponent process involving assessment of clinical, radiological and mycological evidence. A keen understanding of the many factors influencing test performance is required, along with the capacity to identify potential antifungal resistance.2 Antifungal options according to risk of invasive fungal (IFD) and diagnostic testing for (a) a low incidence of and (b) a moderate/high incidence of IFD. AFT, antifungal therapy; Abx, antibacterials; PK/PD, pharmacokinetics/pharmacodynamics; Dx, diagnosis; CT, computed tomography. Factors influencing antifungal therapy Knowledge of IFD within established, novel and evolving cohorts. The perceived risk of IFD drives antifungal use and provides an evidence base to direct antifungal therapy/strategies. Identifies cohorts where diagnostic test performance may differ. Knowledge of likely fungal species and their resistance profiles can direct AFT (See Supplementary Table, available as Supplementary data at JAC Online). Organism epidemiology can vary geographically. Co-infections with two fungal species can occur. Many AFT are only licenced for specific clinical scenarios. The PK/PD profiles of many antifungals are complex. Drug–drug interactions (e.g. particularly inducers/inhibitors of Cytochrome P450 enzymes). Adverse effects. Underlying patient issues (e.g. renal insufficiency, hepatic failure). Route of administration (oral/IV) and bioavailability (tissue penetration and distribution) and focus of infection. Clinical interventions (e.g. ECMO), patient genetics (e.g. azole hyper-metabolizers), age (i.e. adults versus paediatrics) and morbidity. The duration of therapy and need for out-patient management. Patients at high risk of IFD can be managed by established clinical algorithms involving prophylactic, empirical or pre-emptive antifungal strategies. When risk of IFD is high (e.g. >10%) prophylactic strategies are widely used. Prophylaxis is often complemented by empirical AFT to manage refractory fever despite broad spectrum antibiotics in patients lacking a diagnosis, due to fear of breakthrough IFD. When timely diagnostics are available pre-emptive/diagnostic driven strategies can be employed as a safe alternative to mould-active prophylaxis and empirical AFT in high-risk haematology patients and this limits the use of unnecessary AFT. Based on the strength of recommendation (front-line, secondary and tertiary therapeutic options, or against use) guidelines will underpin many local clinical protocols and daily clinical decisions but may not be sufficiently comprehensive to cover every scenario faced in routine clinical service, and thus requiring decisions based on local expertise and knowledge. Perfect tests in clinical mycology are limited (e.g. CrAg LFA). Results interpretation is complex: clinical risk/presentation radiology histology (if available). Timing/frequency of sampling (e.g. IAPA patients may have infection on admission to ICU). Type of test and its performance according to: sample type/patient (e.g. BAL fluid versus blood/neutropenic versus non-neutropenic) current medication (including AFT and impact on test performance) additional mycological evidence and alternative diagnoses, including non-infective causes of symptoms. Most IFD classifications/diagnoses are multicomponent, collating risk, clinical and mycological evidence. Using international definitions (e.g. EORTC/MSGERC classifications) provides a high diagnostic specificity, although the influence of conflicting results is not considered. Not reaching a classification proven or probable IFD should not be used to exclude IFD in clinical practice. Timely access to diagnostics is required to provide optimal patient outcomes. Artificial intelligence (AI)/machine learning has the capacity to provide a diagnostic role, although validation for interpreting multicomponent diagnosis is yet to be definitively demonstrated. AI interrogation or radiological images/reports, histopathological biopsies/slides may prove useful. Species level identification of clinically relevant isolates is beneficial when AFT is being considered. Understanding intrinsic resistance [all strains of a species are resistant to a particular antifungal(s)] versus acquired resistance [a proportion of the species population are resistant to a particular antifungal(s)]. Antifungal resistance is a ‘One Health’ issues and can be driven by both clinical and non-clinical applications of antifungal agents. Knowledge of rates of antifungal resistance in both clinical and environmental fungi with the potential to be human pathogens is important when considering front-line therapy. Limitations with the diagnosis of many IFD can hamper the identification of resistance. Knowledge of local antifungal resistance rates remains critical when considering treatment. Phenotypic resistance testing may have a poor turn-around time (>7 days) limiting clinical utility of the result. The identification of genetic markers associated with antifungal resistance can improve turn-around time and sensitivity but may detect only a limited number of resistance mechanisms. A single nucleotide polymorphism does not necessarily infer phenotypic resistance. If resistance is suspected or identified, then a switch to another class of antifungal (for IA: L-Amb), combination therapy (for IA: mould-active azole plus an echinocandin) or a novel antifungal (Fosmanogepix or Olorofim) can be considered under compassionate use. Refractory disease (worsening symptoms, new or increasing radiological presentation or persistent/increasing or new mycological evidence) is not always related to resistance. PK/PD issues leading to sub-threshold therapeutic antifungal levels need to be considered. TDM (a pre-requisite for voriconazole) is required to determine if drug is and or a in therapy is A to antifungal therapy is not always in a patient with can disease in the patient should not always be considered are in to the (e.g. if or despite is critical to the optimal of underpinning antifungal and diagnostic and international can provide on the of IFD and antifungal resistance. are limited and in of fungal (e.g. is by the of diagnosis and typically of the potential the disease is to both timely diagnostics and antifungals remains a particularly in and will when patients at risk with IFD. Knowledge of IFD within established, novel and evolving cohorts. The perceived risk of IFD drives antifungal use and provides an evidence base to direct antifungal therapy/strategies. Identifies cohorts where diagnostic test performance may differ. Knowledge of likely fungal species and their resistance profiles can direct AFT (See Supplementary Table, available as Supplementary data at JAC Online). Organism epidemiology can vary geographically. Co-infections with two fungal species can occur. Many AFT are only licenced for specific clinical scenarios. The PK/PD profiles of many antifungals are complex. Drug–drug interactions (e.g. particularly inducers/inhibitors of Cytochrome P450 enzymes). Adverse effects. Underlying patient issues (e.g. renal insufficiency, hepatic failure). Route of administration (oral/IV) and bioavailability (tissue penetration and distribution) and focus of infection. Clinical interventions (e.g. ECMO), patient genetics (e.g. azole hyper-metabolizers), age (i.e. adults versus paediatrics) and morbidity. The duration of therapy and need for out-patient management. Patients at high risk of IFD can be managed by established clinical algorithms involving prophylactic, empirical or pre-emptive antifungal strategies. When risk of IFD is high (e.g. >10%) prophylactic strategies are widely used. Prophylaxis is often complemented by empirical AFT to manage refractory fever despite broad spectrum antibiotics in patients lacking a diagnosis, due to fear of breakthrough IFD. When timely diagnostics are available pre-emptive/diagnostic driven strategies can be employed as a safe alternative to mould-active prophylaxis and empirical AFT in high-risk haematology patients and this limits the use of unnecessary AFT. Based on the strength of recommendation (front-line, secondary and tertiary therapeutic options, or against use) guidelines will underpin many local clinical protocols and daily clinical decisions but may not be sufficiently comprehensive to cover every scenario faced in routine clinical service, and thus requiring decisions based on local expertise and knowledge. Perfect tests in clinical mycology are limited (e.g. CrAg LFA). Results interpretation is complex: clinical risk/presentation radiology histology (if available). Timing/frequency of sampling (e.g. IAPA patients may have infection on admission to ICU). Type of test and its performance according to: sample type/patient (e.g. BAL fluid versus blood/neutropenic versus non-neutropenic) current medication (including AFT and impact on test performance) additional mycological evidence and alternative diagnoses, including non-infective causes of symptoms. Most IFD classifications/diagnoses are multicomponent, collating risk, clinical and mycological evidence. Using international definitions (e.g. EORTC/MSGERC classifications) provides a high diagnostic specificity, although the influence of conflicting results is not considered. Not reaching a classification proven or probable IFD should not be used to exclude IFD in clinical practice. Timely access to diagnostics is required to provide optimal patient outcomes. Artificial intelligence (AI)/machine learning has the capacity to provide a diagnostic role, although validation for interpreting multicomponent diagnosis is yet to be definitively demonstrated. AI interrogation or radiological images/reports, histopathological biopsies/slides may prove useful. Species level identification of clinically relevant isolates is beneficial when AFT is being considered. Understanding intrinsic resistance [all strains of a species are resistant to a particular antifungal(s)] versus acquired resistance [a proportion of the species population are resistant to a particular antifungal(s)]. Antifungal resistance is a ‘One Health’ issues and can be driven by both clinical and non-clinical applications of antifungal agents. Knowledge of rates of antifungal resistance in both clinical and environmental fungi with the potential to be human pathogens is important when considering front-line therapy. Limitations with the diagnosis of many IFD can hamper the identification of resistance. Knowledge of local antifungal resistance rates remains critical when considering treatment. Phenotypic resistance testing may have a poor turn-around time (>7 days) limiting clinical utility of the result. The identification of genetic markers associated with antifungal resistance can improve turn-around time and sensitivity but may detect only a limited number of resistance mechanisms. A single nucleotide polymorphism does not necessarily infer phenotypic resistance. If resistance is suspected or identified, then a switch to another class of antifungal (for IA: L-Amb), combination therapy (for IA: mould-active azole plus an echinocandin) or a novel antifungal (Fosmanogepix or Olorofim) can be considered under compassionate use. Refractory disease (worsening symptoms, new or increasing radiological presentation or persistent/increasing or new mycological evidence) is not always related to resistance. PK/PD issues leading to sub-threshold therapeutic antifungal levels need to be considered. TDM (a pre-requisite for voriconazole) is required to determine if drug is and or a in therapy is A to antifungal therapy is not always in a patient with can disease in the patient should not always be considered are in to the (e.g. if or despite is critical to the optimal of underpinning antifungal and diagnostic and international can provide on the of IFD and antifungal resistance. are limited and in of fungal (e.g. is by the of diagnosis and typically of the potential the disease is to both timely diagnostics and antifungals remains a particularly in and will when patients at risk with IFD. AFT, antifungal therapy; for and of and associated Factors influencing antifungal therapy Knowledge of IFD within established, novel and evolving cohorts. The perceived risk of IFD drives antifungal use and provides an evidence base to direct antifungal therapy/strategies. Identifies cohorts where diagnostic test performance may differ. Knowledge of likely fungal species and their resistance profiles can direct AFT (See Supplementary Table, available as Supplementary data at JAC Online). Organism epidemiology can vary geographically. Co-infections with two fungal species can occur. Many AFT are only licenced for specific clinical scenarios. The PK/PD profiles of many antifungals are complex. Drug–drug interactions (e.g. particularly inducers/inhibitors of Cytochrome P450 enzymes). Adverse effects. Underlying patient issues (e.g. renal insufficiency, hepatic failure). Route of administration (oral/IV) and bioavailability (tissue penetration and distribution) and focus of infection. Clinical interventions (e.g. ECMO), patient genetics (e.g. azole hyper-metabolizers), age (i.e. adults versus paediatrics) and morbidity. The duration of therapy and need for out-patient management. Patients at high risk of IFD can be managed by established clinical algorithms involving prophylactic, empirical or pre-emptive antifungal strategies. When risk of IFD is high (e.g. >10%) prophylactic strategies are widely used. Prophylaxis is often complemented by empirical AFT to manage refractory fever despite broad spectrum antibiotics in patients lacking a diagnosis, due to fear of breakthrough IFD. When timely diagnostics are available pre-emptive/diagnostic driven strategies can be employed as a safe alternative to mould-active prophylaxis and empirical AFT in high-risk haematology patients and this limits the use of unnecessary AFT. Based on the strength of recommendation (front-line, secondary and tertiary therapeutic options, or against use) guidelines will underpin many local clinical protocols and daily clinical decisions but may not be sufficiently comprehensive to cover every scenario faced in routine clinical service, and thus requiring decisions based on local expertise and knowledge. Perfect tests in clinical mycology are limited (e.g. CrAg LFA). Results interpretation is complex: clinical risk/presentation radiology histology (if available). Timing/frequency of sampling (e.g. IAPA patients may have infection on admission to ICU). Type of test and its performance according to: sample type/patient (e.g. BAL fluid versus blood/neutropenic versus non-neutropenic) current medication (including AFT and impact on test performance) additional mycological evidence and alternative diagnoses, including non-infective causes of symptoms. Most IFD classifications/diagnoses are multicomponent, collating risk, clinical and mycological evidence. Using international definitions (e.g. EORTC/MSGERC classifications) provides a high diagnostic specificity, although the influence of conflicting results is not considered. Not reaching a classification proven or probable IFD should not be used to exclude IFD in clinical practice. Timely access to diagnostics is required to provide optimal patient outcomes. Artificial intelligence (AI)/machine learning has the capacity to provide a diagnostic role, although validation for interpreting multicomponent diagnosis is yet to be definitively demonstrated. AI interrogation or radiological images/reports, histopathological biopsies/slides may prove useful. Species level identification of clinically relevant isolates is beneficial when AFT is being considered. Understanding intrinsic resistance [all strains of a species are resistant to a particular antifungal(s)] versus acquired resistance [a proportion of the species population are resistant to a particular antifungal(s)]. Antifungal resistance is a ‘One Health’ issues and can be driven by both clinical and non-clinical applications of antifungal agents. Knowledge of rates of antifungal resistance in both clinical and environmental fungi with the potential to be human pathogens is important when considering front-line therapy. Limitations with the diagnosis of many IFD can hamper the identification of resistance. Knowledge of local antifungal resistance rates remains critical when considering treatment. Phenotypic resistance testing may have a poor turn-around time (>7 days) limiting clinical utility of the result. The identification of genetic markers associated with antifungal resistance can improve turn-around time and sensitivity but may detect only a limited number of resistance mechanisms. A single nucleotide polymorphism does not necessarily infer phenotypic resistance. If resistance is suspected or identified, then a switch to another class of antifungal (for IA: L-Amb), combination therapy (for IA: mould-active azole plus an echinocandin) or a novel antifungal (Fosmanogepix or Olorofim) can be considered under compassionate use. Refractory disease (worsening symptoms, new or increasing radiological presentation or persistent/increasing or new mycological evidence) is not always related to resistance. PK/PD issues leading to sub-threshold therapeutic antifungal levels need to be considered. TDM (a pre-requisite for voriconazole) is required to determine if drug is and or a in therapy is A to antifungal therapy is not always in a patient with can disease in the patient should not always be considered are in to the (e.g. if or despite is critical to the optimal of underpinning antifungal and diagnostic and international can provide on the of IFD and antifungal resistance. are limited and in of fungal (e.g. is by the of diagnosis and typically of the potential the disease is to both timely diagnostics and antifungals remains a particularly in and will when patients at risk with IFD. Knowledge of IFD within established, novel and evolving cohorts. The perceived risk of IFD drives antifungal use and provides an evidence base to direct antifungal therapy/strategies. Identifies cohorts where diagnostic test performance may differ. Knowledge of likely fungal species and their resistance profiles can direct AFT (See Supplementary Table, available as Supplementary data at JAC Online). Organism epidemiology can vary geographically. Co-infections with two fungal species can occur. Many AFT are only licenced for specific clinical scenarios. The PK/PD profiles of many antifungals are complex. Drug–drug interactions (e.g. particularly inducers/inhibitors of Cytochrome P450 enzymes). Adverse effects. Underlying patient issues (e.g. renal insufficiency, hepatic failure). Route of administration (oral/IV) and bioavailability (tissue penetration and distribution) and focus of infection. Clinical interventions (e.g. ECMO), patient genetics (e.g. azole hyper-metabolizers), age (i.e. adults versus paediatrics) and morbidity. The duration of therapy and need for out-patient management. Patients at high risk of IFD can be managed by established clinical algorithms involving prophylactic, empirical or pre-emptive antifungal strategies. When risk of IFD is high (e.g. >10%) prophylactic strategies are widely used. Prophylaxis is often complemented by empirical AFT to manage refractory fever despite broad spectrum antibiotics in patients lacking a diagnosis, due to fear of breakthrough IFD. When timely diagnostics are available pre-emptive/diagnostic driven strategies can be employed as a safe alternative to mould-active prophylaxis and empirical AFT in high-risk haematology patients and this limits the use of unnecessary AFT. Based on the strength of recommendation (front-line, secondary and tertiary therapeutic options, or against use) guidelines will underpin many local clinical protocols and daily clinical decisions but may not be sufficiently comprehensive to cover every scenario faced in routine clinical service, and thus requiring decisions based on local expertise and knowledge. Perfect tests in clinical mycology are limited (e.g. CrAg LFA). Results interpretation is complex: clinical risk/presentation radiology histology (if available). Timing/frequency of sampling (e.g. IAPA patients may have infection on admission to ICU). Type of test and its performance according to: sample type/patient (e.g. BAL fluid versus blood/neutropenic versus non-neutropenic) current medication (including AFT and impact on test performance) additional mycological evidence and alternative diagnoses, including non-infective causes of symptoms. Most IFD classifications/diagnoses are multicomponent, collating risk, clinical and mycological evidence. Using international definitions (e.g. EORTC/MSGERC classifications) provides a high diagnostic specificity, although the influence of conflicting results is not considered. Not reaching a classification proven or probable IFD should not be used to exclude IFD in clinical practice. Timely access to diagnostics is required to provide optimal patient outcomes. Artificial intelligence (AI)/machine learning has the capacity to provide a diagnostic role, although validation for interpreting multicomponent diagnosis is yet to be definitively demonstrated. AI interrogation or radiological images/reports, histopathological biopsies/slides may prove useful. Species level identification of clinically relevant isolates is beneficial when AFT is being considered. Understanding intrinsic resistance [all strains of a species are resistant to a particular antifungal(s)] versus acquired resistance [a proportion of the species population are resistant to a particular antifungal(s)]. Antifungal resistance is a ‘One Health’ issues and can be driven by both clinical and non-clinical applications of antifungal agents. Knowledge of rates of antifungal resistance in both clinical and environmental fungi with the potential to be human pathogens is important when considering front-line therapy. Limitations with the diagnosis of many IFD can hamper the identification of resistance. Knowledge of local antifungal resistance rates remains critical when considering treatment. Phenotypic resistance testing may have a poor turn-around time (>7 days) limiting clinical utility of the result. The identification of genetic markers associated with antifungal resistance can improve turn-around time and sensitivity but may detect only a limited number of resistance mechanisms. A single nucleotide polymorphism does not necessarily infer phenotypic resistance. If resistance is suspected or identified, then a switch to another class of antifungal (for IA: L-Amb), combination therapy (for IA: mould-active azole plus an echinocandin) or a novel antifungal (Fosmanogepix or Olorofim) can be considered under compassionate use. Refractory disease (worsening symptoms, new or increasing radiological presentation or persistent/increasing or new mycological evidence) is not always related to resistance. PK/PD issues leading to sub-threshold therapeutic antifungal levels need to be considered. TDM (a pre-requisite for voriconazole) is required to determine if drug is and or a in therapy is A to antifungal therapy is not always in a patient with can disease in the patient should not always be considered are in to the (e.g. if or despite is critical to the optimal of underpinning antifungal and diagnostic and international can provide on the of IFD and antifungal resistance. are limited and in of fungal (e.g. is by the of diagnosis and typically of the potential the disease is to both timely diagnostics and antifungals remains a particularly in and will when patients at risk with IFD. AFT, antifungal therapy; for and of and associated The of IFD refractory to antifungals necessarily by a resistant can be the limited number of these drug a of antifungal profiles is important (Table the of patients both at risk of IFD or with IFD, a is understanding of the clinical impact of IFD is to the of IFD is based on with limited and in of patients with IFD in and is critical both diagnostic capacity and is in these A involving in the of mycology to the evidence on how these factors influence antifungal therapy. the these and of the is based on is by of the of is to and influence decisions on antifungal therapy and patients at risk or with IFD and is not to are not or are in the of to a on the understanding of IFD, a for and the by the and by the and the were on the of their the of mycology with the to cover clinical, and IFD has typically associated with established and clinical risk factors (e.g. these patients to IFD of (e.g. prophylactic the epidemiology of IFD has to invasive disease in is typically by are in (e.g. in and increasing rates of in antifungal use can for breakthrough with resistant species (e.g. azole resistant and and (e.g. and local mycological epidemiology species and of antifungal is critical to the antifungal therapy. IFD is of established cohorts (e.g. including patients or and with invasive in IFD is and in lacking established factors but requiring or with a infection (e.g. or of patients with requiring will secondary associated on or on admission to critical the rates of secondary IFD in patients are the range of fungal pathogens is and of these and potential pathogens is critical for clinical strategies both diagnostics and antifungal therapy. is routine clinical probable and of are managed on the of clinical the of international definitions with clinical a of mycological and conflicting results with the use of and of patients with radiology of but lacking mycology confirmation due to the risk, poor and of testing the of and probable with radiology and of mycological evidence be considered to proven IFD, the need for a the of timely diagnostic results a reliance on empirical therapy access to radiology is in and is to have a with a computed is a for antifungal therapy. is important to with typically the patient is being not the test result. antifungal resistance in the can be limited by the of and access to but is critical to treatment. can be driven by non-clinical and azole resistance in the antifungal are faced with patients with refractory to antifungal therapy (e.g. clinical or radiological despite or the is If resistance is suspected or and antifungal levels then a switch to another class of antifungal is options are limited by antifungal the of administration and the of and combination or novel are often clinical disease in an patient should not always be considered particularly with a the the may improve the of IFD. can be used to patients according to perceived of IFD is on the risk of where the incidence the number to to or a of IFD. If there is a high risk of IFD (e.g. >10%) within a particular then antifungal prophylaxis can be for the of IFD. prophylaxis will rates of IFD, will in unnecessary to antifungals in with the potential to and influence antifungal therapy. can antifungal the and of therapeutic drug should be particularly when mould-active antifungal therapy is widely used in the or diagnostic to fever refractory to broad spectrum in high-risk (e.g. fever despite of empirical will patients with IFD but will in by an antifungal in the of an When considering empirical antifungal therapy for refractory fever in patients on mould-active a in antifungal class should be considered in of pre-emptive or diagnostic driven antifungal strategies are being employed to antifungal therapy in patients (e.g. there is an of the use of these strategies may be by the impact of mould-active on diagnostic test When a diagnosis has therapy to the should be in with international the evidence base for the therapeutic strategies is limited the where both risk of IFD, and diagnostic test performance can the of patients IFD, there is a need for to the clinical presentation and risk, the diagnostic test to treatment. the diagnostic tests are the results are thus the diagnosis with therapy being and by TDM and local clinical Antifungal need not be in but the spectrum of patient and can of a understanding of of IFD, in the of both IFD and antifungal resistance are required and many a mycology of and underpinning are required to on the of IFD the of diagnostic and therapeutic and for the of the to diagnostics is critical to improve particularly but not the likely of IFD in diagnostics need to be and access to antifungals to patient and the of antifungal resistant fungal species with the capacity to patients and in the clinical (e.g. and increasing rates of fungal the established cohorts (e.g. in the infection is relevant in the risk of IFD and how to the to fungi is required for at The patient at risk of IFD to with the risk of antifungal resistance. there remains a range of therapeutic strategies available to manage IFD, their may on of local patient cohorts and fungal and access to timely diagnostic The use of antifungals in the and in the may influence fungal epidemiology and rates of resistance. the current limited number of antifungal drug and in an of increasing antifungal every should be to a diagnosis to direct antifungal therapy will improve is limited and and a understanding of IFD including within novel A diagnosis of proven is with diagnosis typically based on a of fungal tests with radiology the of is and requires with use of is in patients on mould-active is critical are to improve both the diagnosis and of IFD in is an need to on the and improve and on IFD. and the is for the and of the by the is not in of the of the a and on an for and with the for and for to the for or and for on and and and for or and and for on and and as or at the of to and on to to and on to and and the of to of to and and to the has diagnostic and of and and and and and The Supplementary is available as Supplementary data at JAC

Source: Journal of Antimicrobial Chemotherapy

DOI: 10.1093/jac/dkag062

Authors: Ana Alastruey-Izqueirdo, Johan Maertens, Monica A Slavin, Paul E Verweij, P Lewis White