A Systematic Review and Global Meta-analysis of Secondary Fungal Infections Associated with COVID-19
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Background
The COVID-19 pandemic has exposed patients to severe secondary fungal infections, exacerbating clinical outcomes and devastating impact. This study conducts a systematic review with meta-analysis of secondary fungal infections (SFIs) associated with COVID-19 considering various significant parameters, such as the frequency of SFIs across the globe, species shift, gender-specific infection rates, the significance of medical history, efficacy of steroid and antifungal, treatment outcomes (mortality rate), and fungal- polymicrobial mortality analysis.
Methods
A literature search was conducted on COVID-19-related fungal infection studies (2020–2024) from SCOPUS and PUBMED databases, excluding preprints. The systematic data extraction captured the PMCID, country, patient demographics (age and gender), clinical outcomes, associated pathogens, medical history, and treatment details.
Findings
The global meta-analysis of COVID-19-associated SFIs yielded 10,700 cases across 58 countries, exhibiting a significant male predominance (65.6% vs. 34.3% female). Aspergillus spp., Candida spp., and Mucorales spp. emerged as the primary fungal pathogens. The predominant six countries marking 80 % of global cases include India (46.8 %), Italy (10 %), Iran (9.4 %), France (5.1 %), Spain (4.3 %), and Egypt (4.1 %). Complication rates revealed CAM as the most prevalent (59.2%), with a 28% mortality rate. CAC (21.6%) and CAPA (19.1%) had substantially higher mortality rates, at 54% and 58%, respectively. Specific populations were highly affected, including individuals with diabetes were prone to CAM, those undergone catheterization were at increased risk of CAC, and individuals with respiratory diseases or without prior medical history were susceptible to CAPA. In both CAC and CAPA, the species shift towards the non-albicans spp. and non- fumigatus spp., associated with higher mortality. In addition, polymicrobial infection with fungal pathogens ( Aspergillus spp., Candida spp., Mucorales spp.) and Multi-bacteria ( K. pneumoniae, P. aeruginosa, E. coli, S. aureus ) also increased the mortality rate. Effective treatments were identified, including combining caspofungin with corticosteroids for CAC, voriconazole with dexamethasone for CAPA, and AmBisome monotherapy for CAM.
Interpretation
In SFIs populations, CAM prevailed in high-density areas with relatively lower mortality rates, whereas CAC and CAPA exhibited higher mortality rates. Notably, polymicrobial infections significantly increased mortality across all SFIs. Underlying medical conditions primarily influenced the type of fungal pathogen, but treatment outcomes varied. Azole drugs and Amphotericin-B were ineffective against Candidiasis, except for caspofungin’s limited susceptibility. Voriconazole and AmBisome demonstrated efficacy against Aspergillosis and Mucormycosis, respectively. Additionally, steroid administration proved life-saving in CAPA and CAC cases, yet remained ineffective in CAM.
Funding
None
Research in context
Evidence before this study
Five years after the COVID-19 pandemic, a plethora of research has investigated SFIs associated with COVID-19, with a major focus on pathogenicity, immunomodulation, and the impact of steroids and tocilizumab. However, a critical knowledge gap persists that addresses meta-analysis on the frequency of SFIs by country, gender-specific infection rates, the significance of medical history, species shift within the fungal kingdom, its virulence expression, polymicrobial infection dynamics, synergistic effects of steroids and antifungals in this context remains understudied. To address these gaps, the meta-analysis comprehensively examines these critical aspects, shedding light on various aspects of SFIs.
Added value of this study
The systematic meta-analysis of COVID-19-associated SFIs revealed the emergence of non-candida spp. and non-fumigatus spp. Polymicrobial infection has been linked to alarming outcomes resulting in a 100% mortality rate.Similarly, the co- infection of C. albicans with non-albicans spp. A. fumigatus with non-fumigatus spp. increased the mortality rate to 100%. For other species-related, effective therapies such as the combination of caspofungin and corticosteroids against Candidiasis (CAC), voriconazole and dexamethasone (CAPA), and AmBisome monotherapy (CAM) to combat SFIs.
Implications of all the available evidence
The geographic distribution of fungal pathogens varies globally, with differing mortality rates. Deciphering their genomic characteristics will unveil insights into behaviour, transmission, and virulence, enabling targeted diagnostics, treatments, and prevention strategies.
Methodology
Data collection
The peer-reviewed published case studies, multicentric studies, retrospective studies, single-center studies and cohort studies represented with individuals case files were collected using search keywords “COVID-19 and Aspergillus”, COVID-19 and Candida“, COVID-19 and Mucorales”, “COVID Associated Pulmonary Aspergillosis”, “COVID Associated Mucormycosis”, COVID Associated Candidiasis”, in SCOPUS and PUBMED databases. Based on the search results, the articles from Aug 2020 to May 2024 were filtered excluding the preprint articles. A total of 1981 articles that included duplicates, articles unrelated to the study as well without abstracts were eliminated. A systematic review yielded 663 eligible publications, which were subjected to independent individual case meta- analysis. The distribution included 154 studies on CAC, 240 on CAPA, and 269 on CAM (Figure 1) . From each article, the details of PMCID, country, age, gender, treatment outcome (live/dead), pathogens, medical history, and usage of steroids, antibacterial & antifungal were systematically collected. For the global surveillance of COVID-19-associated SFIs, the information from review, cohort, and retrospective studies was included.
Meta-analysis and its statistics
A systemic global survey was conducted for the 58 reported countries with COVID- 19-associated SFIs in accordance with PRISMA guidelines. The meta-analysis surveillance was conducted considering various significant parameters, such as frequency of SFIs across the globe, species shift, gender-specific infection rates, the significance of medical history, efficacy of steroid and antifungal, treatment outcomes (mortality rate), and fungal- polymicrobial mortality analysis. For the statistical analysis, Jamovi v2.6.2 tool and other tools were used as given below.
The proportional meta-analysis was studied using a random effects model to quantify the distribution of SFIs attributed to Mucorales spp., Candida spp., and Aspergillus spp., across the countries. The analysis was considered with a 95% confidence interval (Cl). The analysis also evaluated the overall effect size and sample heterogeneity as indicated by the I² statistic. To investigate the frequency distribution of individual species within SFI, a ClinicoPath table one was employed. This statistical approach enabled the examination of the frequency range of specific species in respective SFIs. To estimate the mortality rates, a two-outcome proportion test was conducted, providing proportion values accompanied by 95 % CI. This study calculated the overall respective SFIs ( CAM , CAPA, and CAM) as well as for specific species. For instance, the mortality rate of A. fumigatus in CAPA was determined providing insight into species-specific outcomes, which enabled a detailed understanding of mortality rates across the various SFIs and their respective causative pathogens. A survival analysis was conducted to explore the interplay between gender, age, and species-specific conditions. The Long-rank, Gehan, and Tarone-Ware tests assessed differences in survival patterns. The analysis also estimated median age at risk for each species-specific condition, by gender, using cumulative hazard functions and 95% CI. A binomial logistic regression model was employed to assess the risk of treatment outcomes with species-specific pathogens. The model calculated the probability of successful treatment outcome, accompanied by standard errors (SE) and Z-scores. These metrics enabled the evaluation of the likelihood of treatment success or failure in correlation with specific pathogens. To provide the optimal treatment strategy, a Crosstable for dependent outcome analysis was performed to predict mortality rates for the treatment outcome. This final step enabled the identification of the most effective treatment approach by examining the intersection of treatment options and mortality rates, providing actionable insights for clinicians to make data-driven decisions for the management of SFIs.
