Academic Journal
Computer modeling to improve measurement of stroke volume with echocardiography.
| Τίτλος: | Computer modeling to improve measurement of stroke volume with echocardiography. |
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| Συγγραφείς: | Murthi SB; School of Medicine, University of Maryland, Baltimore, USA. smurthi@som.umaryland.edu., Yang S; School of Medicine, University of Maryland, Baltimore, USA., Oliveri PP; School of Medicine, University of Maryland, Baltimore, USA., Safadi S; Departments of Nephrology and Pulmonary Critical Care , University of Minnesota, Minneapolis, USA., Fatima S; School of Medicine, University of Maryland, Baltimore, USA., Teeter W; School of Medicine, University of Maryland, Baltimore, USA. |
| Πηγή: | Cardiovascular ultrasound [Cardiovasc Ultrasound] 2026 Jul 06; Vol. 24 (1). Date of Electronic Publication: 2026 Jul 06. |
| Τύπος έκδοσης: | Journal Article; Observational Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: BioMed Central Country of Publication: England NLM ID: 101159952 Publication Model: Electronic Cited Medium: Internet ISSN: 1476-7120 (Electronic) Linking ISSN: 14767120 NLM ISO Abbreviation: Cardiovasc Ultrasound Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: London, UK : BioMed Central, 2003- |
| Ιατρικοί όροι (MeSH): | Echocardiography*/methods , Heart Ventricles*/diagnostic imaging , Heart Ventricles*/physiopathology , Image Interpretation, Computer-Assisted*/methods , Stroke Volume*/physiology , Computer Simulation* , Machine Learning*, Female ; Humans ; Male ; Middle Aged ; Prospective Studies ; Reproducibility of Results ; Aged |
| Περίληψη: | Background: The stroke volume (SV) can be measured by a human expert (HE) using the left ventricular outflow tract diameter (LVOTd) and its velocity time integral (VTI) with echo. If the SV is known, the cardiac output and systemic vascular resistance can be calculated. We have previously described a machine learning computer model (CM) to estimate the LVOTd. The accuracy of LVOTdCM vs. LVOTdHEM in estimation of the SV relative to a pulmonary artery catheter (PAC) is unknown. Method: Over 20 months, a prospective observational study was done in patients with a PAC placed for clinical indications. Participants underwent an echocardiogram, and at the same time the SV was measured by PAC. The SV with echo was calculated using the LVOTdHEM and LVOTdCM and the VTI. The bias and agreement between the metrics and PAC were determined with Bland-Altman analysis. Results: Computer modeling improves measurement of stroke volume with echocardiography. Eighty-four patients with a PAC were enrolled. With human expert measurement of the LVOTd and the VTI, the SV could be calculated in 59 (70%), and with the LVOTdCM and the VTI in 78 (92%). Bland Altman analysis comparing PAC and HEM yielded a mean bias of 3.1 with 95% limits of agreement (LOA) -28.8 and 34.9. When comparing PAC and CM, bias was 1.75 and LOA of -29 and 32.5. Conclusions: A machine learning model of the LVOTd allows accurate calculation of the SV using only the VTI, simplifying the assessment while increasing the yield by 22%. Machine learning tools can lower the bar for obtaining quantitative metrics with echo. Adding objective and repeatable measures will improve the ability of echo to guide therapy in the critically ill. (© 2026. The Author(s).) |
| Competing Interests: | Declarations. Ethics approval and consent to participate: All procedures were conducted in accordance within human ethical standards. Informed consent to participate was obtained from participants prior to their inclusion in the study. Competing interests: The authors declare no competing interests. |
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| Entry Date(s): | Date Created: 20260705 Date Completed: 20260705 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13335344 |
| DOI: | 10.1186/s12947-026-00373-7 |
| PMID: | 42402581 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1476-7120 |
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| DOI: | 10.1186/s12947-026-00373-7 |