{"doi":"10.1016/j.jss.2025.03.006","title":"Comparison of Risk Assessment Tools’ Prediction of Outcomes for Penetrating Trauma","abstract":"Introduction The Trauma and Injury Severity Score (TRISS) uses anatomic and/or physiologic variables to predict mortality; however, Injury Severity Score is less reliable for penetrating trauma. National Surgical Quality Improvement Program Surgical Risk Calculator (NSQIP-SRC) and American Society of Anesthesiologists Physical Status (ASA-PS) include functional status and comorbidities. This study evaluates the accuracy of these tools in predicting mortality, length of stay (LOS), and complications for operative penetrating trauma. Methods Adult penetrating trauma patients (≥18 y) who underwent surgery within 24 h of admission were included in this subgroup analysis of a prospective observational study at four trauma centers. The following three scoring models were compared: NSQIP-SRC, TRISS, and ASA-PS. Brier scores and area under the receiver-operating characteristic curve were used to compare mortality prediction. LOS prediction was assessed with linear regression and complications were evaluated with negative binomial regression. Likelihood ratio (LR) test was used to assess model fit. Results Of 329 penetrating trauma patients, 13 (3.9%) died. The median LOS was 4 d (interquartile range 2-9), and median number of complications was zero (interquartile range 0-1). TRISS better predicted mortality than NSQIP-SRC or ASA-PS on Brier score (0.02 versus 0.03 versus 0.03) but all had similar area under the receiver-operating characteristic curve (0.93 versus 0.93 versus 0.91, P = 0.26). NSQIP-SRC and ASA-PS better predicted LOS on adjusted R 2 (14.4% versus 14.1% versus 1.6%) and LR showed no difference between these two tools ( P = 0.16). NSQIP-SRC best predicted complications compared to TRISS and ASA-PS (Pseudo R 2 : 10.3% versus 3.8% versus 5.5%; LR: P = 0.003). Conclusions For penetrating trauma, all three models were similarly excellent at predicting mortality. NSQIP-SRC and ASA-PS better predicted LOS and NSQIP-SRC best predicted complications, suggesting these are better tools for prognostication of outcomes for penetrating trauma.","journal":"Journal of Surgical Research","year":2025,"id":550457,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9503,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":415763,"name":"Catherine M. Kuza","orcid":"0000-0001-9403-7936","position":1,"is_corresponding":false},{"id":1445670,"name":"Xi Luo","orcid":"0000-0003-3919-2495","position":2,"is_corresponding":false},{"id":1445671,"name":"Tiffany S. Moon","orcid":"0000-0001-7052-5169","position":3,"is_corresponding":false},{"id":1445672,"name":"Thomas Shoultz","orcid":"0000-0003-2655-3968","position":4,"is_corresponding":false},{"id":1446041,"name":"Anne Feeler","orcid":null,"position":5,"is_corresponding":false},{"id":1445673,"name":"Roman Dudaryk","orcid":"0000-0002-9704-7539","position":6,"is_corresponding":false},{"id":1445674,"name":"Jose R. Navas‐Blanco","orcid":"0000-0002-4026-0646","position":7,"is_corresponding":false},{"id":1445675,"name":"Georgia Vasileiou","orcid":"0000-0001-9702-1753","position":8,"is_corresponding":false},{"id":305779,"name":"Kazuhide Matsushima","orcid":"0000-0001-9625-5363","position":9,"is_corresponding":false},{"id":1446042,"name":"Matthew J. Forestiere","orcid":null,"position":10,"is_corresponding":false},{"id":1446043,"name":"Tiffany Lian","orcid":null,"position":11,"is_corresponding":false},{"id":565925,"name":"Areg Grigorian","orcid":"0000-0002-0998-796X","position":12,"is_corresponding":false},{"id":568928,"name":"Joni Ricks‐Oddie","orcid":"0000-0002-9305-7692","position":13,"is_corresponding":false},{"id":455319,"name":"Jeffry Nahmias","orcid":"0000-0003-0094-571X","position":14,"is_corresponding":false},{"id":1445669,"name":"Jeffrey Santos","orcid":"0000-0001-7345-3535","position":0,"is_corresponding":true}],"reference_count":51,"raw_metadata":null,"created_at":"2026-07-19T02:54:16.596730Z","pmid":"40220477","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":[]}