{"doi":"10.2460/ajvr.24.09.0256","title":"Predicting blood loss volume in a canine model of hemorrhagic shock using arterial waveform machine learning analysis","abstract":"OBJECTIVE: To determine if the compensatory reserve algorithm validated in humans can be applied to canines. Our secondary objective was to determine if a simpler waveform analysis could predict the percentage of blood loss volume. METHODS: 6 purpose-bred, anesthetized dogs underwent 5 rounds of controlled hemorrhage and resuscitation while continuously recording invasive arterial blood pressure waveforms in this prospective, experimental study. We calculated human compensatory reserve using deep learning (hCRM-DL) and machine learning (hCRM-ML) models previously developed with human data. We trained a metric to track blood loss volume using features extracted from canine (c) arterial waveforms as an input. RESULTS: When applied to the 6 dogs, the hCRM-DL model (R2 = 0.38) more poorly fit a linear regression model against mean arterial pressure and had lower area under the receiver operating characteristic (AUROC; 0.60) compared to the hCRM-ML model (R2 = 0.61; AUROC, 0.73). Conversely, the arterial waveform analysis for canine blood loss volume metric (cBLVM) predicted blood loss in dogs experiencing controlled hemorrhagic shock more accurately (R2 = 0.74). The cBLVM model for predicting blood loss volume had the highest AUROC score (0.81) and was the earliest indicator of hemorrhage onset. CONCLUSIONS: The hCRM-ML and hCRM-DL algorithms did not translate to accurate prediction of the onset of hemorrhagic shock in dogs. However, the arterial waveform feature analysis-derived cBLVM might provide decision support to resuscitate dogs with hemorrhagic shock. CLINICAL RELEVANCE: Canine BLVM may be useful in estimating blood loss in dogs, which can guide resuscitation strategies for these patients.","journal":"American Journal of Veterinary Research","year":2024,"id":482601,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9298,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":899770,"name":"Jose M. Gonzalez","orcid":"0000-0002-4325-409X","position":1,"is_corresponding":false},{"id":884641,"name":"Sofía I. Hernández Torres","orcid":"0000-0002-0764-519X","position":2,"is_corresponding":false},{"id":1286737,"name":"Emilee C. Venn","orcid":"0000-0002-9158-1677","position":3,"is_corresponding":false},{"id":1086194,"name":"Rebekah Ford","orcid":null,"position":4,"is_corresponding":false},{"id":1086195,"name":"Nicole Ewer","orcid":null,"position":5,"is_corresponding":false},{"id":389895,"name":"Guillaume L. Hoareau","orcid":"0000-0002-8635-3960","position":6,"is_corresponding":false},{"id":1237893,"name":"Lawrence Holland","orcid":"0000-0003-0210-6856","position":7,"is_corresponding":false},{"id":1323121,"name":"Victor A. Convertino","orcid":"0000-0003-4627-136X","position":8,"is_corresponding":false},{"id":884640,"name":"Eric J. Snider","orcid":"0000-0002-0293-4937","position":9,"is_corresponding":false},{"id":740141,"name":"Thomas H. Edwards","orcid":"0000-0002-1706-9536","position":0,"is_corresponding":true}],"reference_count":26,"raw_metadata":null,"created_at":"2026-07-19T02:07:18.280369Z","pmid":"39662033","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":[]}