Benchmarking taxonomic classifiers with Illumina and Nanopore sequence data for clinical metagenomic diagnostic applications.

Culture-independent metagenomic detection of microbial species has the potential to provide rapid and precise real-time diagnostic results. However, it is potentially limited by sequencing and taxonomic classification errors. We use simulated and real-world data to benchmark rates of species misclassification using 100 reference genomes for each of the ten common bloodstream pathogens and six frequent blood-culture contaminants (n=1568, only 68 genomes were available for Micrococcus luteus). Simulating both with and without sequencing error for both the Illumina and Oxford Nanopore platforms, we evaluated commonly used classification tools including Kraken2, Bracken and Centrifuge, utilizing mini (8 GB) and standard (30-50 GB) databases. Bracken with the standard database performed best, the median percentage of reads across both sequencing platforms identified correctly to the species level was 97.8% (IQR 92.7:99.0) [range 5:100]. For Kraken2 with a mini database, a commonly used combination, median species-level identification was 86.4% (IQR 50.5:93.7) [range 4.3:100]. Classification performance varied by species, with Escherichia coli being more challenging to classify correctly (probability of reads being assigned to the correct species: 56.1-96.0%, varying by tool used). Human read misclassification was negligible. By filtering out shorter Nanopore reads we found performance similar or superior to Illumina sequencing, despite higher sequencing error rates. Misclassification was more common when the misclassified species had a higher average nucleotide identity to the true species. Our findings highlight taxonomic misclassification of sequencing data occurs and varies by sequencing and analysis workflow. To account for 'bioinformatic contamination' we present a contamination catalogue that can be used in metagenomic pipelines to ensure accurate results that can support clinical decision making.

Predictors of treatment refusal in patients with colorectal cancer: A systematic review.

This systematic review was conducted to investigate predictors of treatment refusal in colorectal cancer (CRC) patients. An understanding of these predictors would inform statistical models for the identification of high-risk patients who might benefit from interventions that seek to improve treatment compliance. We performed a search of PubMed and Scopus to identify potentially relevant studies on predictors of treatment refusal in CRC patients that were published between January 1, 2000 and December 31, 2021. We screened manuscripts using predefined eligibility criteria. Information on study design, study location, patient characteristics, treatments, rates and predictors of treatment refusal, and the impact of treatment refusal on mortality or survival were collected from eligible studies. Study quality was assessed using the Newcastle-Ottawa score. The overall findings of the review process were summarized using descriptive statistics and a narrative synthesis. A total of 13 studies were included in this review. Ten studies reported on refusal of CRC surgery, refusal rate: 0.25%-3.26%; three studies reported on chemotherapy refusal (one of which reported on both surgery and chemotherapy refusal), refusal rate: 7.8%-41.5%; and one study reported on refusal of any cancer treatment, refusal rate: 8.7%. The bulk of the published literature confirmed the harmful association between treatment refusal and poor survival outcomes in CRC patients. Frequently cited predictors of treatment refusal included patient demographic characteristics (age, race, gender), clinical characteristics (disease stage, comorbidity), and factors that impact access to cancer care services (healthcare insurance, facility level). Potentially high rates of treatment refusal pose a challenge to CRC control. This review has identified several factors which must be considered when attempting to reduce treatment refusal in CRC patients. Furthermore, these factors should be tested as components of predictive risk models for this important outcome.

Metagenomic Sequencing as a Pathogen-Agnostic Clinical Diagnostic Tool for Infectious Diseases: a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies.

Metagenomic sequencing is frequently claimed to have the potential to revolutionize microbiology through rapid species identification and antimicrobial resistance (AMR) prediction. We assess the progress toward these developments. We perform a systematic review and meta-analysis of all published literature on culture-independent metagenomic sequencing for pathogen-agnostic infectious disease diagnostics up to 12 August 2020. Methodologic bias and applicability were assessed using the tool Quadas-2. (Prospero CRD42020163777). A total of 2,023 clinical samples from 13/21 eligible diagnostic test accuracy studies were included in the meta-analysis. Reference standards were culture, molecular testing, clinical decision, or a composite measure. Sensitivity and specificity in the most widely investigated sample types were 90% (95% confidence interval [CI], 78% to 96%) and 86% (45% to 98%) for blood, 75% (54% to 89%) and 96% (72% to 100%) for cerebrospinal fluid (CSF), and 84% (79% to 88%) and 67% (38% to 87%) for orthopedic samples, respectively. We identified a limited use of controls, especially negative controls which were used in only 62% (13/21) of studies. AMR prediction and comparison to phenotypic results were undertaken in four studies; categorical agreement was 88%(80% to 97%), and very major and major error rates were 24% (8% to 40%) and 5% (0% to 12%), respectively. Better human DNA depletion methods are required; a median 91% (interquartile range [IQR], 82% to 98%; range, 76% to 98%) of sequences was classified as human. The median (IQR; range) time from sample to result was 29 hours (24 to 94; 4 to 144 hours). The reported consumable cost per sample ranged from $130 to $685. There is scope for improving the quality of reporting in clinical metagenomic studies. Although our results are limited by the heterogeneity displayed, our results reflect a promising outlook for clinical metagenomics. Methodological improvements and convergence around protocols and best practices may improve performance in the future.

Precision Pandemic Preparedness: Improving Diagnostics with Metagenomics.

The threat posed by novel pandemics in the future remains active. Equipping our routine laboratory with clinical metagenomics to detect unknown threats early on offers a considerable advantage and may be feasible and scalable with the ability to identify complicated infectious diseases in routine care. Though several technical and regulatory challenges still exist, clinical metagenomics may improve individual patient outcomes and provide earlier warning signs to improve pandemic preparedness.

Clinical risk factors for in-hospital mortality in older adults with HIV infection: findings from a South African hospital administrative dataset.

INTRODUCTION: The proportion of older South African adults (aged ≥50 years old) with HIV infection requiring hospitalization is likely to increase in the near future. Clinical risk factors for in-hospital mortality (IHM) in these patients are not well described. We aimed to identify clinical risk factors associated with IHM and their overall contribution towards IHM in older South African adults with HIV infection. METHODS: Clinical data for 690 older adults with HIV infection was obtained from the hospital administrative database at the Hlabisa Hospital in KwaZulu-Natal, South Africa. Logistic regression was used to determine independent clinical risk factors for IHM. Population-attributable fractions (PAFs) were calculated for all independent clinical risk factors identified. RESULTS: Male gender (p=0.005), CD4 count <350 cells/mm3 (p=0.035), unknown CD4 count (p=0.048), tuberculosis (p=0.033) and renal failure (p=0.013) were independently associated with IHM. Male gender contributed the most to IHM (PAF=0.22), followed by unknown CD4 count (PAF=0.14), tuberculosis (PAF=0.12), renal failure (PAF=0.06) and CD4 count <350 cells/mm3 (PAF=0.01). CONCLUSION: Although further research is required to confirm our findings, there is potential for these clinical risk factors identified in our study to be used to stratify patient risk and reduce IHM in older adults with HIV infection.

A systematic review of published literature describing factors associated with tuberculosis recurrence in people living with HIV in Africa.

BACKGROUND: A summary of factors associated with recurrent tuberculosis (TB) in the African HIV-infected population is lacking. We performed a systematic review to address this. METHODS: We performed a literature search within PubMed and The WHO Global Library with specific inclusion and exclusion criteria to identify manuscripts emanating from the African continent which potentially described factors associated with recurrent TB in persons living with HIV. RESULTS: The literature search yielded 52 unique manuscripts, of which only 4 manuscripts were included in the final systematic review following application of the inclusion and exclusion criteria. Baseline CD4 count, baseline HIV viral load, a positive tuberculin skin test, prior active TB disease, cutaneous hypersensitivity reaction to treatment, having < 3 lung zones affected by prior TB disease, and anaemia were associated with recurrent TB in HIV-infected individuals, whilst age and antiretroviral status were not. CONCLUSION: The lack of studies describing recurrent TB in Africa which stratify results by HIV-status is a hindrance to understanding risk factors for recurrent TB in this population. This might be overcome by implementing guidelines related to the publishing of data from observational studies in peer-reviewed medical journals reporting recurrent TB in populations with a high-burden of HIV infection.