Citations

ABSTRACT Medically important pathogenic fungi invade vertebrate tissue and are considered primary when part of their nature life cycle is associated with an animal host and are usually able to infect immunocompetent hosts. Opportunistic fungal pathogens complete their life cycle in environmental habitats or occur as commensals within or on the vertebrate body, but under certain conditions can thrive upon infecting humans. The extent of host damage in opportunistic infections largely depends on the portal and modality of entry as well as on the host’s immune and metabolic status. Diseases caused by primary pathogens and common opportunists, causing the top approximately 80% of fungal diseases [D. W. Denning, Lancet Infect Dis, 24:e428–e438, 2024, https://doi.org/10.1016/S1473-3099(23)00692-8 ], tend to follow a predictive pattern, while those by occasional opportunists are more variable. For this reason, it is recommended that diseases caused by primary pathogens and the common opportunists are named after the etiologic agent, for example, histoplasmosis and aspergillosis, while this should not be done for occasional opportunists that should be named as [causative fungus] [clinical syndrome], for example, Alternaria alternata cutaneous infection. The addition of a descriptor that identifies the location or clinical type of infection is required, as the general name alone may cover widely different clinical syndromes, for example, “rhinocerebral mucormycosis.” A list of major recommended human and animal disease entities (nomenclature) is provided in alignment with their causative agents. Fungal disease names may encompass several genera of etiologic agents, consequently being less susceptible to taxonomic changes of the causative species, for example, mucormycosis covers numerous mucormycetous molds.

Importance Artificial intelligence (AI) tools are rapidly integrating into cancer care. Understanding stakeholder views on ethical issues associated with the implementation of AI in oncology is critical to optimal deployment. Objective To evaluate oncologists’ views on the ethical domains of the use of AI in clinical care, including familiarity, predictions, explainability (the ability to explain how a result was determined), bias, deference, and responsibilities. Design, Setting, and Participants This cross-sectional, population-based survey study was conducted from November 15, 2022, to July 31, 2023, among 204 US-based oncologists identified using the National Plan & Provider Enumeration System. Main Outcomes and Measures The primary outcome was response to a question asking whether participants agreed or disagreed that patients need to provide informed consent for AI model use during cancer treatment decisions. Results Of 387 surveys, 204 were completed (response rate, 52.7%). Participants represented 37 states, 120 (63.7%) identified as male, 128 (62.7%) as non-Hispanic White, and 60 (29.4%) were from academic practices; 95 (46.6%) had received some education on AI use in health care, and 45.3% (92 of 203) reported familiarity with clinical decision models. Most participants (84.8% [173 of 204]) reported that AI-based clinical decision models needed to be explainable by oncologists to be used in the clinic; 23.0% (47 of 204) stated they also needed to be explainable by patients. Patient consent for AI model use during treatment decisions was supported by 81.4% of participants (166 of 204). When presented with a scenario in which an AI decision model selected a different treatment regimen than the oncologist planned to recommend, the most common response was to present both options and let the patient decide (36.8% [75 of 204]); respondents from academic settings were more likely than those from other settings to let the patient decide (OR, 2.56; 95% CI, 1.19-5.51). Most respondents (90.7% [185 of 204]) reported that AI developers were responsible for the medico-legal problems associated with AI use. Some agreed that this responsibility was shared by physicians (47.1% [96 of 204]) or hospitals (43.1% [88 of 204]). Finally, most respondents (76.5% [156 of 204]) agreed that oncologists should protect patients from biased AI tools, but only 27.9% (57 of 204) were confident in their ability to identify poorly representative AI models. Conclusions and Relevance In this cross-sectional survey study, few oncologists reported that patients needed to understand AI models, but most agreed that patients should consent to their use, and many tasked patients with choosing between physician- and AI-recommended treatment regimens. These findings suggest that the implementation of AI in oncology must include rigorous assessments of its effect on care decisions as well as decisional responsibility when problems related to AI use arise.

Introduction Artificial Intelligence (AI) is an emerging technology that promises advancements across blood cancer discovery and care delivery. There are already FDA-approved uses of AI in hematology and oncology but few data available about its use or utility. Concerns have been raised over AI's potential biases, “explainability,” and who is responsible for its use. Oncologists' familiarity with AI and perspectives on these issues are not well known and are critical to understanding the ethical deployment of AI in blood cancer care. Methods From January to July 2023, we performed a national cross-sectional survey of practicing oncologists. A draft survey instrument was developed by a team with content and methodological expertise. Iterative refinement occurred through cognitive debriefing with 5 practicing hematologist/oncologists. The final instrument included 24 questions covering domains of AI familiarity, predictions, explainability, and responsibilities. A random sample of US oncologists was identified using the National Plan & Provider Enumeration System. Paper surveys were mailed with $25 incentivizing gift cards. Reminder letters and phone calls were used for non-responders with an electronic survey option provided. Responses were analyzed descriptively. X2 testing assessed bivariate associations between responses and participant practice setting, with odds ratios (OR) reported. Results Of 399 mailed surveys, 12 were undeliverable and 201 were completed for a response rate of 51.9%. Question missingness was <1%. Participants were 63.2% White, 16.9% Indian Asian, 10.0% Eastern Asian or Pacific Islander, and 4.5% Black; 6.0% were Hispanic. They represented 37 states. The majority were male (63.7%), 40-59 years old (54.2%), and had no specific training in AI (52.2%); 68.2% had at least 11 years in practice and 29.9% practiced at a primary academic hospital. Questions assessing overall familiarity with and predictions related to AI are shown in Table 1. Those at primary academic hospitals were more likely to be familiar with radiology models (OR 2.82, p<0.001) but not pathology, prognostic, or clinical decision models (all p>0.05). They were more likely to predict that AI would improve side effect management (OR 2.01, p=0.03) and end-of-life decision making (OR 2.07, p=0.02). High proportions of respondents thought that AI prognostic (67.1%) and clinical decision models (62.1%) needed to be explainable by clinicians and researchers to be used in clinic. When posed with a scenario where an FDA-approved AI model selects a different regimen than the oncologist planned to propose (Figure 1), the largest proportion reported they would present both options and let the patient decide (37.3%). Of those recommending a regimen, respondents from primary academic hospitals more frequently recommend the AI-generated regimen (56.1%), while respondents from elsewhere recommended their original regimen (69.0%; OR 2.85, p=0.007). Acceptability of some direct-to-patient AI model applications is shown in Table 1. A majority of respondents (90.5%) thought that AI developers should be responsible for medico-legal problems caused by AI use; 47.8% thought that physicians using the tool should be responsible. Though most (76.1%) agreed that it was oncologists' responsibility to protect patients from biased AI tools, few (27.3%) were confident in their ability to identify how representative the data used in an AI model was relative to the population with that cancer (including 66.7% of those who agreed it was oncologists' responsibility). More respondents thought that patients should need to consent to the use of AI tools in cancer treatment decisions (81.1%) than cancer diagnosis (55.7%); those from primary academic hospitals were less likely to respond that patients should consent for treatment or diagnosis (OR 0.43, p=0.02 and OR 0.44, p=0.009). Conclusions In this national survey of oncologists' views on AI, there was optimism about its impact on care with notable caveats related to end-of-life care, patient trust, and care disparities. Despite many approving use of AI models too complex for patients to explain, a substantial proportion deferred to patient choice when AI and physician recommendations differed. There was also dissonance between accepting responsibility to protect patients from bias and respondents' self-perceived ability to do so.

ABSTRACT The rapid pace of name changes of medically important fungi is creating challenges for clinical laboratories and clinicians involved in patient care. We describe two sources of name change which have different drivers, at the species versus the genus level. Some suggestions are made here to reduce the number of name changes. We urge taxonomists to provide diagnostic markers of taxonomic novelties. Given the instability of phylogenetic trees due to variable taxon sampling, we advocate to maintain genera at the largest possible size. Reporting of identified species in complexes or series should where possible comprise both the name of the overarching species and that of the molecular sibling, often cryptic species. Because the use of different names for the same species will be unavoidable for many years to come, an open access online database of the names of all medically important fungi, with proper nomenclatural designation and synonymy, is essential. We further recommend that while taxonomic discovery continues, the adaptation of new name changes by clinical laboratories and clinicians be reviewed routinely by a standing committee for validation and stability over time, with reference to an open access database, wherein reasons for changes are listed in a transparent way.

Background. Candida tropicalis is a virulent fungal pathogen for which echinocandins are the primary therapy. Emergence of resistance to echinocandins of C. tropicalis carries potentially ominous therapeutic implications. Methods. We describe herein two patients with breakthrough C. tropicalis fungemia during echinocandin therapy, characterize their molecular mechanism of resistance, and systematically review 13 previously reported cases of echinocandin-resistant C. tropicalis bloodstream infections (BSIs) and other diseases. Results. Among these 15 patients with echinocandin-resistant C. tropicalis infections, the median age was 61 years (ages 28–84 years) and 13 (86%) were immunocompromised. Thirteen (86%) of all patients had a history of pervious or concurrent exposure to echinocandins. Isolates of C. tropicalis from 11 cases, including the two index cases, underwent DNA sequencing of the FKS1 gene for mutations known to confer echinocandin resistance. The amino acid substitution Ser654Pro was shown in four cases, while other FKS1 mutations encoded Ser80S/Pro, Phe641Leu, Phe641Ser, Ser80S/Pro substitutions. These mutational events were not associated with collateral increases in minimum inhibitory concentrations to antifungal triazoles and amphotericin B. Overall mortality in patients with echinocandin-resistant C. tropicalis infections was 40%. Among those six patients who died, two received monotherapy with voriconazole, one was treated with fluconazole, one remained on caspofungin, and two were switched to liposomal amphotericin B. Nine patients (60%) survived after being treated with an antifungal agent other than an echinocandin. Conclusions. Emergence of resistance to echinocandins by C. tropicalis, occurs during antifungal therapy, is associated with high mortality, is mediated by a diverse range of FKS1 mutations, retains in vitro susceptibility to triazoles and amphotericin B, and constitutes an emerging threat to patients with hematological malignancies.

AbstractHyaline molds, widely distributed in nature, are an important and increasing cause of invasive fungal infections in humans, likely due to an expanding population of immunosuppressed hosts, increases in antifungal resistance, and improvements in laboratory diagnostics. Sinopulmonary disease and disseminated infection are common manifestations in neutropenic patients and transplant recipients, whereas, catheter-related and localized soft tissue infections predominate in immunocompetent hosts. These organisms are not reliably differentiated based on their morphology in tissue; rather, efforts should be made to establish a microbiologic diagnosis via culture or molecular-based methods to guide antifungal management and inform prognosis. Herein, we review the epidemiology, clinical manifestations, diagnosis, and management of these opportunistic pathogens known to cause hyalohyphomycoses: Scedosporium spp., Lomentospora prolificans, Fusarium spp., Scopulariopsis spp., Arthrographis kalrae, Trichoderma spp., Acremonium spp., Paecilomyces variotii, Purpureocillium lilacinum, and Penicillium species.

Objectives: To assess the feasibility and efficacy of implementing “Family Care Rituals” as a means of engaging family members in the care of patients admitted to the ICU with a high risk of ICU mortality on outcomes including stress-related symptoms in family members. Design: Prospective, before-and-after intervention evaluation. Setting: Two U.S. academic medical ICU’s, and one Italian academic medical/surgical ICU. Subjects: Family members of patients who had an attending predicted ICU mortality of greater than 30% within the first 24 hours of admission. Interventions: A novel intervention titled “Family Care Rituals” during which, following a baseline observation period, family members enrolled in the intervention phase were given an informational booklet outlining opportunities for engagement in care of the patient during their ICU stay. Measurements and Main Results: Primary outcome was symptoms of post-traumatic stress disorder in family members 90 days after patient death or ICU discharge. Secondary outcomes included symptoms of depression, anxiety, and family satisfaction. At 90-day follow-up, 131 of 226 family members (58.0%) responded preintervention and 129 of 226 family members (57.1%) responded postintervention. Symptoms of post-traumatic stress disorder were significantly higher preintervention than postintervention (39.2% vs 27.1%; unadjusted odds ratio, 0.58; p = 0.046). There was no significant difference in symptoms of depression (26.5% vs 25.2%; unadjusted odds ratio, 0.93; p = 0.818), anxiety (41.0% vs 45.5%; unadjusted odds ratio, 1.20; p = 0.234), or mean satisfaction scores (85.1 vs 89.0; unadjusted odds ratio, 3.85; p = 0.052) preintervention versus postintervention 90 days after patient death or ICU discharge. Conclusions: Offering opportunities such as family care rituals for family members to be involved with providing care for family members in the ICU was associated with reduced symptoms of post-traumatic stress disorder. This intervention may lessen the burden of stress-related symptoms in family members of ICU patients.

Abstract Background Bacteremia caused by extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae (ESBL-E) is associated with inadequate empirical therapy and substantial mortality in neutropenic patients. Strategies are needed to identify neutropenic patients at high risk of these infections. Methods From April 2014 to September 2016, we collected perianal swabs, both at admission and weekly thereafter, from patients undergoing hematopoietic stem cell transplantation (HSCT). Patients received prophylactic levofloxacin while neutropenic. Swabs were plated onto selective agar, colonies were identified and underwent antimicrobial susceptibility testing, and phenotypic ESBL testing and polymerase chain reaction for β-lactamase genes were performed on ceftriaxone-resistant Enterobacteriaceae. We then determined the prevalence of pre-transplant ESBL-E colonization and risk of ESBL-E bacteremia. Colonizing and bloodstream isolates from patients with ESBL-E bacteremia underwent multilocus sequence typing and pulsed-field gel electrophoresis. Results We analyzed 312 patients, including 212 allogeneic and 100 autologous HSCT recipients. Ten percent (31/312) of patients had pre-transplant ESBL-E colonization. Susceptibility rates of colonizing ESBL-E were: levofloxacin, 25%; cefepime, 9%; piperacillin-tazobactam, 84%; and meropenem, 97%. Of 31 patients colonized with ESBL-E pre-transplant, 10 (32%) developed ESBL-E bacteremia during their transplant admission, compared to 1 (0.4%) of 281 patients not colonized with ESBL-E (P < .001). All bloodstream ESBL-E were levofloxacin-resistant and colonizing and bloodstream isolates from individual patients had identical genotypic profiles. Conclusions HSCT recipients who are colonized with levofloxacin-resistant ESBL-E pre-transplant and receive levofloxacin prophylaxis have high rates of bacteremia from their colonizing strain during neutropenia. Assessing for ESBL-E colonization in neutropenic patients could lead to optimization of empirical antibacterial therapy.