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McBane RD, Murphree DH, Liedl D, Lopez-Jimenez F, Arruda-Olson A, Scott CG, Prodduturi N, Nowakowski SE, Rooke TW, Casanegra AI, Wysokinski WE, Houghton DE, Muthusamy K, Wennberg PW. Artificial intelligence of arterial Doppler waveforms to predict major adverse outcomes among patients with diabetes mellitus. J Vasc Surg 2024:S0741-5214(24)00401-4. [PMID: 38417709 DOI: 10.1016/j.jvs.2024.02.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 02/12/2024] [Accepted: 02/19/2024] [Indexed: 03/01/2024]
Abstract
OBJECTIVE Patients with diabetes mellitus (DM) are at increased risk for peripheral artery disease (PAD) and its complications. Arterial calcification and non-compressibility may limit test interpretation in this population. Developing tools capable of identifying PAD and predicting major adverse cardiac event (MACE) and limb event (MALE) outcomes among patients with DM would be clinically useful. Deep neural network analysis of resting Doppler arterial waveforms was used to detect PAD among patients with DM and to identify those at greatest risk for major adverse outcome events. METHODS Consecutive patients with DM undergoing lower limb arterial testing (April 1, 2015-December 30, 2020) were randomly allocated to training, validation, and testing subsets (60%, 20%, and 20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict all-cause mortality, MACE, and MALE at 5 years using quartiles based on the distribution of the prediction score. RESULTS Among 11,384 total patients, 4211 patients with DM met study criteria (mean age, 68.6 ± 11.9 years; 32.0% female). After allocating the training and validation subsets, the final test subset included 856 patients. During follow-up, there were 262 deaths, 319 MACE, and 99 MALE. Patients in the upper quartile of prediction based on deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 3.58; 95% confidence interval [CI], 2.31-5.56), MACE (HR, 2.06; 95% CI, 1.49-2.91), and MALE (HR, 13.50; 95% CI, 5.83-31.27). CONCLUSIONS An artificial intelligence enabled analysis of a resting Doppler arterial waveform permits identification of major adverse outcomes including all-cause mortality, MACE, and MALE among patients with DM.
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Affiliation(s)
- Robert D McBane
- Gonda Vascular Center, Mayo Clinic, Rochester, MN; Cardiovascular Department, Mayo Clinic, Rochester, MN.
| | - Dennis H Murphree
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | - David Liedl
- Gonda Vascular Center, Mayo Clinic, Rochester, MN
| | - Francisco Lopez-Jimenez
- Cardiovascular Department, Mayo Clinic, Rochester, MN; Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN
| | | | | | - Naresh Prodduturi
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | | | - Thom W Rooke
- Gonda Vascular Center, Mayo Clinic, Rochester, MN; Cardiovascular Department, Mayo Clinic, Rochester, MN
| | - Ana I Casanegra
- Gonda Vascular Center, Mayo Clinic, Rochester, MN; Cardiovascular Department, Mayo Clinic, Rochester, MN
| | - Waldemar E Wysokinski
- Gonda Vascular Center, Mayo Clinic, Rochester, MN; Cardiovascular Department, Mayo Clinic, Rochester, MN
| | - Damon E Houghton
- Gonda Vascular Center, Mayo Clinic, Rochester, MN; Cardiovascular Department, Mayo Clinic, Rochester, MN
| | | | - Paul W Wennberg
- Gonda Vascular Center, Mayo Clinic, Rochester, MN; Cardiovascular Department, Mayo Clinic, Rochester, MN
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McBane RD, Murphree DH, Liedl D, Lopez‐Jimenez F, Attia IZ, Arruda‐Olson AM, Scott CG, Prodduturi N, Nowakowski SE, Rooke TW, Casanegra AI, Wysokinski WE, Houghton DE, Bjarnason H, Wennberg PW. Artificial Intelligence of Arterial Doppler Waveforms to Predict Major Adverse Outcomes Among Patients Evaluated for Peripheral Artery Disease. J Am Heart Assoc 2024; 13:e031880. [PMID: 38240202 PMCID: PMC11056117 DOI: 10.1161/jaha.123.031880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 12/08/2023] [Indexed: 02/07/2024]
Abstract
BACKGROUND Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all-cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer-assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events. METHODS AND RESULTS Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle-brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all-cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow-up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78-3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49-2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43-22.39]) at 5 years. CONCLUSIONS An artificial intelligence-enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.
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Affiliation(s)
- Robert D. McBane
- Gonda Vascular CenterMayo ClinicRochesterMN
- Cardiovascular DepartmentMayo ClinicRochesterMN
| | - Dennis H. Murphree
- Department of Artificial Intelligence and InformaticsMayo ClinicRochesterMN
| | | | - Francisco Lopez‐Jimenez
- Cardiovascular DepartmentMayo ClinicRochesterMN
- Department of Artificial Intelligence and InformaticsMayo ClinicRochesterMN
| | - Itzhak Zachi Attia
- Cardiovascular DepartmentMayo ClinicRochesterMN
- Department of Artificial Intelligence and InformaticsMayo ClinicRochesterMN
| | | | | | | | | | - Thom W. Rooke
- Gonda Vascular CenterMayo ClinicRochesterMN
- Cardiovascular DepartmentMayo ClinicRochesterMN
| | - Ana I. Casanegra
- Gonda Vascular CenterMayo ClinicRochesterMN
- Cardiovascular DepartmentMayo ClinicRochesterMN
| | - Waldemar E. Wysokinski
- Gonda Vascular CenterMayo ClinicRochesterMN
- Cardiovascular DepartmentMayo ClinicRochesterMN
| | - Damon E. Houghton
- Gonda Vascular CenterMayo ClinicRochesterMN
- Cardiovascular DepartmentMayo ClinicRochesterMN
| | - Haraldur Bjarnason
- Gonda Vascular CenterMayo ClinicRochesterMN
- Vascular and Interventional RadiologyMayo ClinicRochesterMN
| | - Paul W. Wennberg
- Gonda Vascular CenterMayo ClinicRochesterMN
- Cardiovascular DepartmentMayo ClinicRochesterMN
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McBane RD, Murphree DH, Liedl D, Lopez-Jimenez F, Attia IZ, Arruda-Olson A, Scott CG, Prodduturi N, Nowakowski SE, Rooke TW, Casanegra AI, Wysokinski WE, Swanson KE, Houghton DE, Bjarnason H, Wennberg PW. Artificial intelligence for the evaluation of peripheral artery disease using arterial Doppler waveforms to predict abnormal ankle-brachial index. Vasc Med 2022; 27:333-342. [PMID: 35535982 DOI: 10.1177/1358863x221094082] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Patients with peripheral artery disease (PAD) are at increased risk for major adverse limb and cardiac events including mortality. Developing screening tools capable of accurate PAD identification is a necessary first step for strategies of adverse outcome prevention. This study aimed to determine whether machine analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with PAD. METHODS Consecutive patients (4/1/2015 - 12/31/2020) undergoing rest and postexercise ankle-brachial index (ABI) testing were included. Patients were randomly allocated to training, validation, and testing subsets (70%/15%/15%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict normal (> 0.9) or PAD (⩽ 0.9) using rest and postexercise ABI. A separate dataset of 151 patients who underwent testing during a period after the model had been created and validated (1/1/2021 - 3/31/2021) was used for secondary validation. Area under the receiver operating characteristic curves (AUC) were constructed to evaluate test performance. RESULTS Among 11,748 total patients, 3432 patients met study criteria: 1941 with PAD (mean age 69 ± 12 years) and 1491 without PAD (64 ± 14 years). The predictive model with highest performance identified PAD with an AUC 0.94 (CI = 0.92-0.96), sensitivity 0.83, specificity 0.88, accuracy 0.85, and positive predictive value (PPV) 0.90. Results were similar for the validation dataset: AUC 0.94 (CI = 0.91-0.98), sensitivity 0.91, specificity 0.85, accuracy 0.89, and PPV 0.89 (postexercise ABI comparison). CONCLUSION An artificial intelligence-enabled analysis of a resting Doppler arterial waveform permits identification of PAD at a clinically relevant performance level.
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Affiliation(s)
- Robert D McBane
- Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.,Cardiovascular Department, Mayo Clinic, Rochester, MN, USA
| | - Dennis H Murphree
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
| | - David Liedl
- Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA
| | - Francisco Lopez-Jimenez
- Cardiovascular Department, Mayo Clinic, Rochester, MN, USA.,Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Itzhak Zachi Attia
- Cardiovascular Department, Mayo Clinic, Rochester, MN, USA.,Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | - Thom W Rooke
- Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.,Cardiovascular Department, Mayo Clinic, Rochester, MN, USA
| | - Ana I Casanegra
- Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.,Cardiovascular Department, Mayo Clinic, Rochester, MN, USA
| | - Waldemar E Wysokinski
- Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.,Cardiovascular Department, Mayo Clinic, Rochester, MN, USA
| | - Keith E Swanson
- Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.,Cardiovascular Department, Mayo Clinic, Rochester, MN, USA
| | - Damon E Houghton
- Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.,Cardiovascular Department, Mayo Clinic, Rochester, MN, USA
| | - Haraldur Bjarnason
- Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.,Vascular and Interventional Radiology, Mayo Clinic, Rochester, MN, USA
| | - Paul W Wennberg
- Gonda Vascular Center, Mayo Clinic, Rochester, MN, USA.,Cardiovascular Department, Mayo Clinic, Rochester, MN, USA
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Podratz JL, Staff NP, Boesche JB, Giorno NJ, Hainy ME, Herring SA, Klennert MT, Milaster C, Nowakowski SE, Krug RG, Peng Y, Windebank AJ. An automated climbing apparatus to measure chemotherapy-induced neurotoxicity in Drosophila melanogaster. Fly (Austin) 2013; 7:187-92. [PMID: 23695893 DOI: 10.4161/fly.24789] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
We have developed a novel model system in Drosophila melanogaster to study chemotherapy-induced neurotoxicity in adult flies. Neurological deficits were measured using a manual geotactic climbing assay. The manual assay is commonly used; however, it is laborious, time-consuming, subject to human error and limited to observing one sample at a time. We have designed and built a new automated fly-counting apparatus that uses a "video capture-particle counting technology" to automatically measure 10 samples at a time, with 20 flies per sample. Climbing behavior was assessed manually, as in our previous studies, and with the automated apparatus within the same experiment yielding statistically similar results. Both climbing endpoints as well as the climbing rate can be measured in the apparatus, giving the assay more versatility than the manual assay. Automation of our climbing assay reduces variability, increases productivity and enables high throughput drug screens for neurotoxicity.
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