{"doi":"10.1101/2020.10.16.342238","title":"Improved diagnostic prediction of the pathogenicity of bloodstream isolates of <i>Staphylococcus epidermidis</i>","abstract":"ABSTRACT With an estimated 440,000 active cases occurring each year, medical device associated infections pose a significant burden on the US healthcare system, costing about $9.8 billion in 2013. Staphylococcus epidermidis is the most common cause of these device-associated infections, which typically involve isolates that are multi-drug resistant and possess multiple virulence factors. S. epidermidis is also frequently a benign contaminant of otherwise sterile blood cultures. Therefore, tests that distinguish pathogenic from non-pathogenic isolates would improve the accuracy of diagnosis and prevent overuse/misuse of antibiotics. Attempts to use multi-locus sequence typing (MLST) with machine learning for this purpose had poor accuracy (~73%). In this study we sought to improve the diagnostic accuracy of predicting pathogenicity by focusing on phenotypic markers ( i.e ., antibiotic resistance, growth fitness in human plasma, and biofilm forming capacity) and the presence of specific virulence genes ( i.e., mecA, ses1 , and sdrF ). Commensal isolates from healthy individuals (n=23), blood culture contaminants (n=21), and pathogenic isolates considered true bacteremia (n=54) were used. Multiple machine learning approaches were applied to characterize strains as pathogenic vs non-pathogenic. The combination of phenotypic markers and virulence genes improved the diagnostic accuracy to 82.4% (sensitivity: 84.9% and specificity: 80.9%). Oxacillin resistance was the most important variable followed by growth rate in plasma. This work shows promise for the addition of phenotypic testing in clinical diagnostic applications.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":131285,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9578,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":476958,"name":"Duane W. Newton","orcid":"0000-0002-9943-2449","position":1,"is_corresponding":false},{"id":434400,"name":"J. Scott VanEpps","orcid":"0000-0002-0805-0913","position":2,"is_corresponding":false},{"id":585226,"name":"Shannon M. VanAken","orcid":null,"position":0,"is_corresponding":true}],"reference_count":58,"raw_metadata":null,"created_at":"2026-07-18T23:16:00.235845Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}