1
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Barry S, Wang SY. Predicting Glaucoma Surgical Outcomes Using Neural Networks and Machine Learning on Electronic Health Records. Transl Vis Sci Technol 2024; 13:15. [PMID: 38904612 PMCID: PMC11193140 DOI: 10.1167/tvst.13.6.15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 05/16/2024] [Indexed: 06/22/2024] Open
Abstract
Purpose To develop machine learning (ML) and deep learning (DL) models to predict glaucoma surgical outcomes, including postoperative intraocular pressure, use of ocular antihypertensive medications, and need for repeat surgery. Methods We identified glaucoma surgeries performed at Stanford from 2013-2024, with two or more postoperative visits with intraocular pressure (IOP) measurement. Patient features were identified from the electronic health record (EHR), including demographics, prior diagnosis and procedure codes, medications and eye exam findings. Classical ML and DL models were developed to predict which glaucoma surgeries would result in surgical failure, defined as (1) IOP not reduced by more than 20% of preoperative baseline on two consecutive postoperative visits, (2) increased classes of glaucoma medications, and (3) need for additional glaucoma surgery or revision of original surgery. Results A total of 2398 glaucoma surgeries of 1571 patients were included, of which 1677 surgeries met failure criteria. Random forest performed best for prediction of overall surgical failure, with accuracy of 75.5% and area under the receiver operator curve (AUROC) of 76.7%, similar to the deep learning model (accuracy 75.5%, AUROC 76.6%). Across all models, prediction performance was better for IOP outcomes (AUROC 86%) than need for an additional surgery (AUROC 76%) or need for additional glaucoma medication (AUC 70%). Conclusions ML and DL algorithms can predict glaucoma surgery outcomes using structured data inputs from EHRs. Translational Relevance Models that predict outcomes of glaucoma surgery may one day provide the basis for clinical decision support tools supporting surgeons in personalizing glaucoma treatment plans.
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Affiliation(s)
- Samuel Barry
- Department of Management Science & Engineering, Stanford University, Stanford, CA, USA
| | - Sophia Y. Wang
- Byers Eye Institute, Department of Ophthalmology, Stanford University, Stanford, CA, USA
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2
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Dalli J, Nguyen C, Jindal A, Epperlein J, Hardy N, Pulitano C, Warrier S, Cahill R. A feasibility study assessing quantitative indocyanine green angiographic predictors of reconstructive complications following nipple-sparing mastectomy. JPRAS Open 2024; 40:32-47. [PMID: 38425697 PMCID: PMC10904167 DOI: 10.1016/j.jpra.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 01/21/2024] [Indexed: 03/02/2024] Open
Abstract
Introduction Immediate post-mastectomy breast reconstruction offers benefits; however, complications can compromise outcomes. Intraoperative indocyanine green fluorescence angiography (ICGFA) may mitigate perfusion-related complications (PRC); however, its interpretation remains subjective. Here, we examine and develop methods for ICGFA quantification, including machine learning (ML) algorithms for predicting complications. Methods ICGFA video recordings of flap perfusion from a previous study of patients undergoing nipple-sparing mastectomy (NSM) with either immediate or staged immediate (delayed by a week due to perfusion insufficiency) reconstructions were analysed. Fluorescence intensity time series data were extracted, and perfusion parameters were interrogated for overall/regional associations with postoperative PRC. A naïve Bayes ML model was subsequently trained on a balanced data subset to predict PRC from the extracted meta-data. Results The analysable video dataset of 157 ICGFA featured females (average age 48 years) having oncological/risk-reducing NSM with either immediate (n=90) or staged immediate (n=26) reconstruction. For those delayed, peak brightness at initial ICGFA was lower (p<0.001) and significantly improved (both quicker-onset and brighter p=0.001) one week later. The overall PRC rate in reconstructed patients (n=116) was 11.2%, with such patients demonstrating significantly dimmer (overall, p=0.018, centrally, p=0.03, and medially, p=0.04) and slower-onset (p=0.039) fluorescent peaks with shallower slopes (p=0.012) than uncomplicated patients with ICGFA. Importantly, such relevant parameters were converted into a whole field of view heatmap potentially suitable for intraoperative display. ML predicted PRC with 84.6% sensitivity and 76.9% specificity. Conclusion Whole breast quantitative ICGFA assessment reveals statistical associations with PRC that are potentially exploitable via ML.
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Affiliation(s)
- J. Dalli
- UCD Centre for Precision Surgery, School of Medicine, UCD, Dublin, Ireland
| | - C.L. Nguyen
- Department of Breast Surgery, Chris O'Brien Lifehouse, Camperdown, Australia
- Department of Surgery, Royal Prince Alfred Hospital, Camperdown, Australia
- Department of Surgery, The University of Sydney, Camperdown, Australia
| | - A. Jindal
- UCD Centre for Precision Surgery, School of Medicine, UCD, Dublin, Ireland
| | | | - N.P. Hardy
- UCD Centre for Precision Surgery, School of Medicine, UCD, Dublin, Ireland
| | - C. Pulitano
- Department of Surgery, Royal Prince Alfred Hospital, Camperdown, Australia
- Department of Surgery, The University of Sydney, Camperdown, Australia
| | - S. Warrier
- Department of Breast Surgery, Chris O'Brien Lifehouse, Camperdown, Australia
- Department of Surgery, Royal Prince Alfred Hospital, Camperdown, Australia
- Department of Surgery, The University of Sydney, Camperdown, Australia
| | - R.A. Cahill
- UCD Centre for Precision Surgery, School of Medicine, UCD, Dublin, Ireland
- Department of Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
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3
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Bhattacharya K, Bhattacharya N, Kumar S, Yagnik VD, Garg P, Choudhary PR. Artificial Intelligence in Predicting Postoperative Surgical Complications. Indian J Surg 2024. [DOI: 10.1007/s12262-024-04081-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 05/06/2024] [Indexed: 07/26/2024] Open
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Zhou CM, Li H, Xue Q, Yang JJ, Zhu Y. Artificial intelligence algorithms for predicting post-operative ileus after laparoscopic surgery. Heliyon 2024; 10:e26580. [PMID: 38439857 PMCID: PMC10909660 DOI: 10.1016/j.heliyon.2024.e26580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 02/13/2024] [Accepted: 02/15/2024] [Indexed: 03/06/2024] Open
Abstract
Objective By constructing a predictive model using machine learning and deep learning technologies, we aim to understand the risk factors for postoperative intestinal obstruction in laparoscopic colorectal cancer patients, and establish an effective artificial intelligence-based predictive model to guide individualized prevention and treatment, thus improving patient outcomes. Methods We constructed a model of the artificial intelligence algorithm in Python. Subjects were randomly assigned to either a training set for variable identification and model construction, or a test set for testing model performance, at a ratio of 7:3. The model was trained with ten algorithms. We used the AUC values of the ROC curves, as well as accuracy, precision, recall rate and F1 scores. Results The results of feature engineering composited with the GBDT algorithm showed that opioid use, anesthesia duration, and body weight were the top three factors in the development of POI. We used ten machine learning and deep learning algorithms to validate the model, and the results were as follows: the three algorithms with best accuracy were XGB (0.807), Decision Tree (0.807) and Neural DecisionTree (0.807); the two algorithms with best precision were XGB (0.500) and Decision Tree (0.500); the two algorithms with best recall rate were adab (0.243) and Decision Tree (0.135); the two algorithms with highest F1 score were adab (0.290) and Decision Tree (0.213); and the three algorithms with best AUC were Gradient Boosting (0.678), XGB (0.638) and LinearSVC (0.633). Conclusion This study shows that XGB and Decision Tree are the two best algorithms for predicting the risk of developing ileus after laparoscopic colon cancer surgery. It provides new insight and approaches to the field of postoperative intestinal obstruction in colorectal cancer through the application of machine learning techniques, thereby improving our understanding of the disease and offering strong support for clinical decision-making.
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Affiliation(s)
- Cheng-Mao Zhou
- Big Data and Artificial Intelligence Research Group, Department of Anaesthesiology and Nursing, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China
| | - HuiJuan Li
- Big Data and Artificial Intelligence Research Group, Department of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Qiong Xue
- Big Data and Artificial Intelligence Research Group, Department of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Jian-Jun Yang
- Big Data and Artificial Intelligence Research Group, Department of Anesthesiology, Pain and Perioperative Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yu Zhu
- Big Data and Artificial Intelligence Research Group, Department of Anaesthesiology and Nursing, Central People's Hospital of Zhanjiang, Zhanjiang, Guangdong, China
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5
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Vannucci M, Niyishaka P, Collins T, Rodríguez-Luna MR, Mascagni P, Hostettler A, Marescaux J, Perretta S. Machine learning models to predict success of endoscopic sleeve gastroplasty using total and excess weight loss percent achievement: a multicentre study. Surg Endosc 2024; 38:229-239. [PMID: 37973639 PMCID: PMC10776503 DOI: 10.1007/s00464-023-10520-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 10/09/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND The large amount of heterogeneous data collected in surgical/endoscopic practice calls for data-driven approaches as machine learning (ML) models. The aim of this study was to develop ML models to predict endoscopic sleeve gastroplasty (ESG) efficacy at 12 months defined by total weight loss (TWL) % and excess weight loss (EWL) % achievement. Multicentre data were used to enhance generalizability: evaluate consistency among different center of ESG practice and assess reproducibility of the models and possible clinical application. Models were designed to be dynamic and integrate follow-up clinical data into more accurate predictions, possibly assisting management and decision-making. METHODS ML models were developed using data of 404 ESG procedures performed at 12 centers across Europe. Collected data included clinical and demographic variables at the time of ESG and at follow-up. Multicentre/external and single center/internal and temporal validation were performed. Training and evaluation of the models were performed on Python's scikit-learn library. Performance of models was quantified as receiver operator curve (ROC-AUC), sensitivity, specificity, and calibration plots. RESULTS Multicenter external validation: ML models using preoperative data show poor performance. Best performances were reached by linear regression (LR) and support vector machine models for TWL% and EWL%, respectively, (ROC-AUC: TWL% 0.87, EWL% 0.86) with the addition of 6-month follow-up data. Single-center internal validation: Preoperative data only ML models show suboptimal performance. Early, i.e., 3-month follow-up data addition lead to ROC-AUC of 0.79 (random forest classifiers model) and 0.81 (LR models) for TWL% and EWL% achievement prediction, respectively. Single-center temporal validation shows similar results. CONCLUSIONS Although preoperative data only may not be sufficient for accurate postoperative predictions, the ability of ML models to adapt and evolve with the patients changes could assist in providing an effective and personalized postoperative care. ML models predictive capacity improvement with follow-up data is encouraging and may become a valuable support in patient management and decision-making.
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Affiliation(s)
- Maria Vannucci
- General Surgery Department, University of Torino, Turin, Italy.
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France.
- , Turin, Italy.
| | | | - Toby Collins
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France
- Research Institute Against Digestive Cancer (IRCAD), Kigali, Rwanda
| | - María Rita Rodríguez-Luna
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France
- ICube Laboratory, Photonics Instrumentation for Health, Strasbourg, France
| | - Pietro Mascagni
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Research Group CAMMA, University of Strasbourg, Strasbourg, France
| | - Alexandre Hostettler
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France
- Research Institute Against Digestive Cancer (IRCAD), Kigali, Rwanda
| | - Jacques Marescaux
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France
- Research Institute Against Digestive Cancer (IRCAD), Kigali, Rwanda
| | - Silvana Perretta
- Research Institute Against Digestive Cancer (IRCAD), Strasbourg, France
- Department of Digestive and Endocrine Surgery, University of Strasbourg, Strasbourg, France
- IHU-Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France
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TerKonda SP, TerKonda AA, Sacks JM, Kinney BM, Gurtner GC, Nachbar JM, Reddy SK, Jeffers LL. Artificial Intelligence: Singularity Approaches. Plast Reconstr Surg 2024; 153:204e-217e. [PMID: 37075274 DOI: 10.1097/prs.0000000000010572] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
SUMMARY Artificial intelligence (AI) has been a disruptive technology within health care, from the development of simple care algorithms to complex deep-learning models. AI has the potential to reduce the burden of administrative tasks, advance clinical decision-making, and improve patient outcomes. Unlocking the full potential of AI requires the analysis of vast quantities of clinical information. Although AI holds tremendous promise, widespread adoption within plastic surgery remains limited. Understanding the basics is essential for plastic surgeons to evaluate the potential uses of AI. This review provides an introduction of AI, including the history of AI, key concepts, applications of AI in plastic surgery, and future implications.
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Affiliation(s)
- Sarvam P TerKonda
- From the Division of Plastic and Reconstructive Surgery, Mayo Clinic Florida
| | - Anurag A TerKonda
- Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in St. Louis
| | - Justin M Sacks
- Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in St. Louis
| | - Brian M Kinney
- Division of Plastic Surgery, University of Southern California
| | - Geoff C Gurtner
- Division of Plastic and Reconstructive Surgery, Stanford University
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7
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Kavian JA, Wilkey HL, Patel PA, Boyd CJ. Harvesting the Power of Artificial Intelligence for Surgery: Uses, Implications, and Ethical Considerations. Am Surg 2023; 89:5102-5104. [PMID: 37148260 DOI: 10.1177/00031348231175454] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Artificial intelligence is rapidly advancing, especially with the advent of ChatGPT technology, and its role in the world of medicine is expanding. Within surgery, AI has the capacity to improve efficiency and results in surgical treatments; however, it similarly has the potential to impose harm onto patients and undermine the role of medical providers. Its benefits may include improvements in surgical outcomes, spanning from enhanced pre-operative diagnostic capabilities to more refined intra-operative techniques, and long term patient experiences, by identifying and reducing complications. Nevertheless apprehensions revolve around laymen use potentially resulting in inappropriate therapeutic interventions, in addition to safety and ethical risks surrounding the use of patient data. Various strategies towards mitigating these harms must be considered, such as patient disclaimers and secondary review policies. While artificial intelligence brings exciting advancements to surgery, its integration must be cautiously monitored.
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Affiliation(s)
- Joseph A Kavian
- NYU Grossman School of Medicine, NYU Langone Health, New York, NY, USA
| | - Hannah L Wilkey
- Medical College of Georgia, Augusta University Medical Center, Augusta, GA, USA
| | - Parth A Patel
- Medical College of Georgia, Augusta University Medical Center, Augusta, GA, USA
| | - Carter J Boyd
- Hansjörg Wyss Department of Plastic Surgery, New York University, New York, NY, USA
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8
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Subramanian T, Shahi P, Araghi K, Mayaan O, Amen TB, Iyer S, Qureshi S. Using Artificial Intelligence to Answer Common Patient-Focused Questions in Minimally Invasive Spine Surgery. J Bone Joint Surg Am 2023; 105:1649-1653. [PMID: 37247341 DOI: 10.2106/jbjs.23.00043] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Affiliation(s)
- Tejas Subramanian
- Hospital for Special Surgery, New York, NY
- Weill Cornell Medicine, New York, NY
| | | | | | - Omri Mayaan
- Hospital for Special Surgery, New York, NY
- Weill Cornell Medicine, New York, NY
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9
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Stam WT, Ingwersen EW, Ali M, Spijkerman JT, Kazemier G, Bruns ERJ, Daams F. Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery. Surg Today 2023; 53:1209-1215. [PMID: 36840764 PMCID: PMC10520164 DOI: 10.1007/s00595-023-02662-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 02/07/2023] [Indexed: 02/26/2023]
Abstract
Complications after surgery have a major impact on short- and long-term outcomes, and decades of technological advancement have not yet led to the eradication of their risk. The accurate prediction of complications, recently enhanced by the development of machine learning algorithms, has the potential to completely reshape surgical patient management. In this paper, we reflect on multiple issues facing the implementation of machine learning, from the development to the actual implementation of machine learning models in daily clinical practice, providing suggestions on the use of machine learning models for predicting postoperative complications after major abdominal surgery.
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Affiliation(s)
- Wessel T Stam
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
- AGEM Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
| | - Erik W Ingwersen
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
- AGEM Amsterdam Gastroenterology, Endocrinology and Metabolism, Amsterdam, The Netherlands
| | - Mahsoem Ali
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Jorik T Spijkerman
- Independent Consultant in Computational Intelligence, Amsterdam, The Netherlands
| | - Geert Kazemier
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Emma R J Bruns
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands
| | - Freek Daams
- Department of Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.
- Cancer Center Amsterdam, Cancer Treatment and Quality of Life, Amsterdam, The Netherlands.
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Kovoor JG, Nann SD, Barot DD, Garg D, Hains L, Stretton B, Ovenden CD, Bacchi S, Chan E, Gupta AK, Hugh TJ. Prehabilitation for general surgery: a systematic review of randomized controlled trials. ANZ J Surg 2023; 93:2411-2425. [PMID: 37675939 DOI: 10.1111/ans.18684] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/23/2023] [Accepted: 08/27/2023] [Indexed: 09/08/2023]
Abstract
BACKGROUND Prehabilitation seeks to optimize patient health before surgery to improve outcomes. Randomized controlled trials (RCTs) have been conducted on prehabilitation, however an updated synthesis of this evidence is required across General Surgery to inform potential Supplementary discipline-level protocols. Accordingly, this systematic review of RCTs aimed to evaluate the use of prehabilitation interventions across the discipline of General Surgery. METHODS This study was registered with PROSPERO (CRD42023403289), and adhered to PRISMA 2020 and SWiM guidelines. PubMed/MEDLINE and Ovid Embase were searched to 4 March 2023 for RCTs evaluating prehabilitation interventions within the discipline of General Surgery. After data extraction, risk of bias was assessed using the Cochrane RoB 2 tool. Quantitative and qualitative data were synthesized and analysed. However, meta-analysis was precluded due to heterogeneity across included studies. RESULTS From 929 records, 36 RCTs of mostly low risk of bias were included. 17 (47.2%) were from Europe, and 14 (38.9%) North America. 30 (83.3%) investigated cancer populations. 31 (86.1%) investigated physical interventions, finding no significant difference in 16 (51.6%) and significant improvement in 14 (45.2%). Nine (25%) investigated psychological interventions: six (66.7%) found significant improvement, three (33.3%) found no significant difference. Five (13.9%) investigated nutritional interventions, finding no significant difference in three (60%), and significant improvement in two (40%). CONCLUSIONS Prehabilitation interventions showed mixed levels of effectiveness, and there is insufficient RCT evidence to suggest system-level delivery across General Surgery within standardized protocols. However, given potential benefits and non-inferiority to standard care, they should be considered on a case-by-case basis.
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Affiliation(s)
- Joshua G Kovoor
- University of Sydney, Sydney, New South Wales, Australia
- Queen Elizabeth Hospital, Adelaide, South Australia, Australia
- Health and Information, Adelaide, South Australia, Australia
| | - Silas D Nann
- Health and Information, Adelaide, South Australia, Australia
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Dwarkesh D Barot
- Health and Information, Adelaide, South Australia, Australia
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Devanshu Garg
- Health and Information, Adelaide, South Australia, Australia
- University of Adelaide, Adelaide, South Australia, Australia
| | - Lewis Hains
- Health and Information, Adelaide, South Australia, Australia
- University of Adelaide, Adelaide, South Australia, Australia
| | - Brandon Stretton
- Queen Elizabeth Hospital, Adelaide, South Australia, Australia
- Health and Information, Adelaide, South Australia, Australia
- University of Adelaide, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Christopher D Ovenden
- Health and Information, Adelaide, South Australia, Australia
- University of Adelaide, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Stephen Bacchi
- Queen Elizabeth Hospital, Adelaide, South Australia, Australia
- Health and Information, Adelaide, South Australia, Australia
- University of Adelaide, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Erick Chan
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Aashray K Gupta
- University of Sydney, Sydney, New South Wales, Australia
- Health and Information, Adelaide, South Australia, Australia
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
- University of Adelaide, Adelaide, South Australia, Australia
| | - Thomas J Hugh
- University of Sydney, Sydney, New South Wales, Australia
- Royal North Shore Hospital, Sydney, New South Wales, Australia
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11
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Ingwersen EW, Stam WT, Meijs BJV, Roor J, Besselink MG, Groot Koerkamp B, de Hingh IHJT, van Santvoort HC, Stommel MWJ, Daams F. Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy. Surgery 2023; 174:435-440. [PMID: 37150712 DOI: 10.1016/j.surg.2023.03.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 03/02/2023] [Accepted: 03/20/2023] [Indexed: 05/09/2023]
Abstract
BACKGROUND Machine learning is increasingly advocated to develop prediction models for postoperative complications. It is, however, unclear if machine learning is superior to logistic regression when using structured clinical data. Postoperative pancreatic fistula and delayed gastric emptying are the two most common complications with the biggest impact on patient condition and length of hospital stay after pancreatoduodenectomy. This study aimed to compare the performance of machine learning and logistic regression in predicting pancreatic fistula and delayed gastric emptying after pancreatoduodenectomy. METHODS This retrospective observational study used nationwide data from 16 centers in the Dutch Pancreatic Cancer Audit between January 2014 and January 2021. The area under the curve of a machine learning and logistic regression model for clinically relevant postoperative pancreatic fistula and delayed gastric emptying were compared. RESULTS Overall, 799 (16.3%) patients developed a postoperative pancreatic fistula, and 943 developed (19.2%) delayed gastric emptying. For postoperative pancreatic fistula, the area under the curve of the machine learning model was 0.74, and the area under the curve of the logistic regression model was 0.73. For delayed gastric emptying, the area under the curve of the machine learning model and logistic regression was 0.59. CONCLUSION Machine learning did not outperform logistic regression modeling in predicting postoperative complications after pancreatoduodenectomy.
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Affiliation(s)
- Erik W Ingwersen
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands; Cancer Center Amsterdam, the Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism, the Netherlands
| | - Wessel T Stam
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands; Cancer Center Amsterdam, the Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism, the Netherlands
| | - Bono J V Meijs
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands; Cancer Center Amsterdam, the Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism, the Netherlands
| | - Joran Roor
- SAS institute B.V., Huizen, the Netherlands
| | - Marc G Besselink
- Cancer Center Amsterdam, the Netherlands; Amsterdam Gastroenterology Endocrinology and Metabolism, the Netherlands; Department of Surgery, Amsterdam UMC, University of Amsterdam, the Netherlands
| | | | | | | | | | - Freek Daams
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Surgery, Amsterdam, the Netherlands; Cancer Center Amsterdam, the Netherlands.
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12
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Williams SC, Marcus HJ. Editorial: Investigations into the potential benefits of artificial intelligence and deep learning to surgical oncologists. Front Oncol 2023; 13:1243305. [PMID: 37496656 PMCID: PMC10368360 DOI: 10.3389/fonc.2023.1243305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 06/28/2023] [Indexed: 07/28/2023] Open
Affiliation(s)
- Simon C. Williams
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
- Department of Neurosurgery, St George’s University Hospital, London, United Kingdom
| | - Hani J. Marcus
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom
- Division of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
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Yu X, Wu P, Wang Z, Han W, Huang Y, Xin S, Zhang Q, Zhao S, Sun H, Lei G, Zhang T, Zhang L, Shen Y, Gu W, Li H, Jiang J. Network prediction of surgical complication clusters: a prospective multicenter cohort study. SCIENCE CHINA. LIFE SCIENCES 2023; 66:1636-1646. [PMID: 36881319 DOI: 10.1007/s11427-022-2200-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 10/11/2022] [Indexed: 03/08/2023]
Abstract
Complicated relationships exist in both occurrence and progression of surgical complications, which are difficult to account for using a separate quantitative method such as prediction or grading. Data of 51,030 surgical inpatients were collected from four academic/teaching hospitals in a prospective cohort study in China. The relationship between preoperative factors, 22 common complications, and death was analyzed. With input from 54 senior clinicians and following a Bayesian network approach, a complication grading, cluster-visualization, and prediction (GCP) system was designed to model pathways between grades of complication and preoperative risk factor clusters. In the GCP system, there were 11 nodes representing six grades of complication and five preoperative risk factor clusters, and 32 arcs representing a direct association. Several critical targets were pinpointed on the pathway. Malnourished status was a fundamental cause widely associated (7/32 arcs) with other risk factor clusters and complications. American Society of Anesthesiologists (ASA) score ⩾3 was directly dependent on all other risk factor clusters and influenced all severe complications. Grade III complications (mainly pneumonia) were directly dependent on 4/5 risk factor clusters and affected all other grades of complication. Irrespective of grade, complication occurrence was more likely to increase the risk of other grades of complication than risk factor clusters.
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Affiliation(s)
- Xiaochu Yu
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Peng Wu
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Zixing Wang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Wei Han
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Yuguang Huang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Shijie Xin
- The First Hospital of China Medical University, Shenyang, 110001, China
| | - Qiang Zhang
- Qinghai Provincial People's Hospital, Xining, 810007, China
| | - Shengxiu Zhao
- Qinghai Provincial People's Hospital, Xining, 810007, China
| | - Hong Sun
- Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Guanghua Lei
- Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Taiping Zhang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Luwen Zhang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Yubing Shen
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Wentao Gu
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Hongwei Li
- Research Department, PaodingAI, Beijing, 100083, China
| | - Jingmei Jiang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
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Ingwersen EW, van der Beek PJK, Dekker JWT, van Dieren S, Daams F. One Decade of Declining Use of Defunctioning Stomas After Rectal Cancer Surgery in the Netherlands: Are We on the Right Track? Dis Colon Rectum 2023; 66:1003-1011. [PMID: 36607894 DOI: 10.1097/dcr.0000000000002625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND The beneficial effect of a defunctioning stoma in mitigating the consequences of anastomotic leakage after rectal cancer surgery is still debated. OBJECTIVE This study aims to reflect on a decade of rectal cancer surgery in terms of stoma construction and anastomotic leakage. DESIGN Retrospective observational study. SETTING This study used data from the Dutch Colorectal Audit from 2011 to 2020. PATIENTS Patients undergoing rectal cancer surgery with a primary anastomosis. MAIN OUTCOME MEASURES Primary outcome was anastomotic leakage. Secondary outcomes were minor complications, admission to intensive care, length of stay, readmission, and patient death. RESULTS A total of 13,263 patients were included in this study. A defunctioning stoma was constructed in 7106 patients (53.6%). Patients with a defunctioning stoma were less likely to develop anastomotic leakage (7.9% vs 13.0%), and if anastomotic leakage occurred, fewer patients needed surgical reintervention (37.7% vs 81.1%). An annual decrease in the construction of a defunctioning stoma was seen (69.8% in 2011 vs 51.8% in 2015 vs 29.7% in 2020), accompanied by a 5% increase in anastomotic leakage (9.1% in 2011 vs 14.1% in 2020). A defunctioning stoma was associated with a higher occurrence of minor complications, increased admissions to the intensive care unit, longer length of stay, and more readmissions within 90 days. LIMITATION This retrospective study is susceptible to confounders by indications, and there could be risk factors for anastomotic leakage and the use of a stoma that were not regarded. CONCLUSIONS The reduction in defunctioning stomas is paralleled with an increase in anastomotic leakage. However, patients with a defunctioning stoma also showed more minor complications, a prolonged length of stay, more intensive care admissions, and more readmissions. In our opinion, the trade-offs of selective use should be individually considered. See Video Abstract at http://links.lww.com/DCR/C137 . UNA DCADA DISMINUYENDO EL USO DE ESTOMAS DISFUNCIONANTES EN LOS CASOS DE CNCER DE RECTO EN HOLANDA ESTAMOS HACIENDO LO CORRECTO ANTECEDENTES:Aún se debate el efecto benéfico de la confección de un estoma disfuncionante para limitar las consecuencias de la fuga anastomótica en los casos de cirugía por cáncer de recto.OBJETIVO:Reflexiones sobre una década de cirugía por cáncer de recto en términos de confección de estomas y de fugas anastomóticas.DISEÑO:Estudio retrospectivo y observacional.AJUSTE:El presente estudio utilizó datos de la Auditoría Colorectal Holandesa entre 2011 y 2020.PACIENTES:Todos aquellos intervenidos por cáncer de recto con anastomosis primaria.PRINCIPALES MEDIDAS DE RESULTADO:El resultado primario fue evaluar la fuga anastomótica. Los resultados secundarios fueron las complicaciones menores, la permanencia en cuidados intensivos, la duración de la hospitalización, las rehospitalizaciones y las causas de muerte en los pacientes.RESULTADOS:Un total de 13.263 pacientes fueron incluidos en el presente estudio. Se confeccionó un estoma disfuncionante en 7.106 (53,6%) pacientes. Aquellos portadores de un estoma disfuncionante tenían menos probabilidades de desarrollar una fuga anastomótica (7,9 % frente a 13,0 %) y, si ocurría una fuga anastomótica, menos pacientes necesitaban reintervención quirúrgica (37,7 % frente a 81,1 %). Se observó una disminución anual en la confección de un estoma disfuncionante (69,8 % en 2011 frente a 51,8 % en 2015 frente a 29,7 % en 2020), acompañada de un aumento del 5 % en la fuga anastomótica (9,1 % en 2011 frente a 14,1 % en 2020). Un estoma disfuncionante se asoció con una mayor incidencia de complicaciones menores, permanencia en la unidad de cuidados intensivos, una estadía más prolongada y más rehospitalizaciones dentro de los 90 días.LIMITACIÓN:Estudio retrospectivo susceptible de factores de confusión según las indicaciones, donde podrían no haber sido considerados ciertos factores de riesgo con relación a la fuga anastomótica y a la confección de un estoma disfuncionante.CONCLUSIÓN:La reducción de estomas disfuncionantes es paralela con el aumento de la fuga anastomótica. Sin embargo, los pacientes con un estoma disfuncionante también mostraron más complicaciones menores, una estadía prolongada, más admisiones a cuidados intensivos y más rehospitalizaciones. En nuestra opinión, las ventajas y desventajas del uso selectivo de estomas disfuncionantes deben ser consideradas caso por caso. Consulte Video Resumen en https://links.lww.com/DCR/C137 . (Traducción-Dr. Xavier Delgadillo ).
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Affiliation(s)
- Erik W Ingwersen
- Department of Gastrointestinal Surgery, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Treatment and Quality of Life, Amsterdam, the Netherlands
| | - Paulien J K van der Beek
- Department of Gastrointestinal Surgery, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Jan Willem T Dekker
- Department of Gastrointestinal Surgery, Reinier de Graaf Hospital, Delft, the Netherlands
| | - Susan van Dieren
- Cancer Center Amsterdam, Treatment and Quality of Life, Amsterdam, the Netherlands
- Department of Surgery, Amsterdam UMC location University of Amsterdam, Amsterdam, the Netherlands
| | - Freek Daams
- Department of Gastrointestinal Surgery, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Treatment and Quality of Life, Amsterdam, the Netherlands
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15
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Automated Capture of Intraoperative Adverse Events Using Artificial Intelligence: A Systematic Review and Meta-Analysis. J Clin Med 2023; 12:jcm12041687. [PMID: 36836223 PMCID: PMC9963108 DOI: 10.3390/jcm12041687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/08/2023] [Accepted: 02/14/2023] [Indexed: 02/22/2023] Open
Abstract
Intraoperative adverse events (iAEs) impact the outcomes of surgery, and yet are not routinely collected, graded, and reported. Advancements in artificial intelligence (AI) have the potential to power real-time, automatic detection of these events and disrupt the landscape of surgical safety through the prediction and mitigation of iAEs. We sought to understand the current implementation of AI in this space. A literature review was performed to PRISMA-DTA standards. Included articles were from all surgical specialties and reported the automatic identification of iAEs in real-time. Details on surgical specialty, adverse events, technology used for detecting iAEs, AI algorithm/validation, and reference standards/conventional parameters were extracted. A meta-analysis of algorithms with available data was conducted using a hierarchical summary receiver operating characteristic curve (ROC). The QUADAS-2 tool was used to assess the article risk of bias and clinical applicability. A total of 2982 studies were identified by searching PubMed, Scopus, Web of Science, and IEEE Xplore, with 13 articles included for data extraction. The AI algorithms detected bleeding (n = 7), vessel injury (n = 1), perfusion deficiencies (n = 1), thermal damage (n = 1), and EMG abnormalities (n = 1), among other iAEs. Nine of the thirteen articles described at least one validation method for the detection system; five explained using cross-validation and seven divided the dataset into training and validation cohorts. Meta-analysis showed the algorithms were both sensitive and specific across included iAEs (detection OR 14.74, CI 4.7-46.2). There was heterogeneity in reported outcome statistics and article bias risk. There is a need for standardization of iAE definitions, detection, and reporting to enhance surgical care for all patients. The heterogeneous applications of AI in the literature highlights the pluripotent nature of this technology. Applications of these algorithms across a breadth of urologic procedures should be investigated to assess the generalizability of these data.
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16
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Artificial Intelligence in Surgical Learning. SURGERIES 2023. [DOI: 10.3390/surgeries4010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023] Open
Abstract
(1) Background: Artificial Intelligence (AI) is transforming healthcare on all levels. While AI shows immense potential, the clinical implementation is lagging. We present a concise review of AI in surgical learning; (2) Methods: A non-systematic review of AI in surgical learning of the literature in English is provided; (3) Results: AI shows utility for all components of surgical competence within surgical learning. AI presents with great potential within robotic surgery specifically (4) Conclusions: Technology will evolve in ways currently unimaginable, presenting us with novel applications of AI and derivatives thereof. Surgeons must be open to new modes of learning to be able to implement all evidence-based applications of AI in the future. Systematic analyses of AI in surgical learning are needed.
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17
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An orthopaedic intelligence application successfully integrates data from a smartphone-based care management platform and a robotic knee system using a commercial database. INTERNATIONAL ORTHOPAEDICS 2023; 47:485-494. [PMID: 36508053 DOI: 10.1007/s00264-022-05651-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE To evaluate the feasibility of using a smartphone-based care management platform (sbCMP) and robotic-assisted total knee arthroplasty (raTKA) to collect data throughout the episode-of-care and assess if intra-operative measures of soft tissue laxity in raTKA were associated with post-operative outcomes. METHODS A secondary data analysis of 131 patients in a commercial database who underwent raTKA was performed. Pre-operative through six week post-operative step counts and KOOS JR scores were collected and cross-referenced with intra-operative laxity measures. A Kruskal-Wallis test or a Wilcoxon sign-rank was used to assess outcomes. RESULTS There were higher step counts at six weeks post-operatively in knees with increased laxity in both the lateral compartment in extension and medial compartment in flexion (p < 0.05). Knees balanced in flexion within < 0.5 mm had higher KOOS JR scores at six weeks post-operative (p = 0.034) compared to knees balanced within 0.5-1.5 mm. CONCLUSION A smartphone-based care management platform can be integrated with raTKA to passively collect data throughout the episode-of-care. Associations between intra-operative decisions regarding laxity and post-operative outcomes were identified. However, more robust analysis is needed to evaluate these associations and ensure clinical relevance to guide machine learning algorithms.
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18
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Emile SH, Ghareeb W, Elfeki H, El Sorogy M, Fouad A, Elrefai M. Development and Validation of an Artificial Intelligence-Based Model to Predict Gastroesophageal Reflux Disease After Sleeve Gastrectomy. Obes Surg 2022; 32:2537-2547. [PMID: 35596915 PMCID: PMC9273557 DOI: 10.1007/s11695-022-06112-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 05/04/2022] [Accepted: 05/11/2022] [Indexed: 12/11/2022]
Abstract
Purpose Prediction of the onset of de novo gastroesophageal reflux disease (GERD) after sleeve gastrectomy (SG) would be helpful in decision-making and selection of the optimal bariatric procedure for every patient. The present study aimed to develop an artificial intelligence (AI)-based model to predict the onset of GERD after SG to help clinicians and surgeons in decision-making. Materials and Methods A prospectively maintained database of patients with severe obesity who underwent SG was used for the development of the AI model using all the available data points. The dataset was arbitrarily split into two parts: 70% for training and 30% for testing. Then ranking of the variables was performed in two steps. Different learning algorithms were used, and the best model that showed maximum performance was selected for the further steps of machine learning. A multitask AI platform was used to determine the cutoff points for the top numerical predictors of GERD. Results In total, 441 patients (76.2% female) of a mean age of 43.7 ± 10 years were included. The ensemble model outperformed the other models. The model achieved an AUC of 0.93 (95%CI 0.88–0.99), sensitivity of 79.2% (95% CI 57.9–92.9%), and specificity of 86.1% (95%CI 70.5–95.3%). The top five ranked predictors were age, weight, preoperative GERD, size of orogastric tube, and distance of first stapler firing from the pylorus. Conclusion An AI-based model for the prediction of GERD after SG was developed. The model had excellent accuracy, yet a moderate sensitivity and specificity. Further prospective multicenter trials are needed to externally validate the model developed. Graphical Abstract ![]()
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Affiliation(s)
- Sameh Hany Emile
- General Surgery Department, Mansoura University Hospitals, Mansoura University, Mansoura, Egypt.
| | - Waleed Ghareeb
- Gastrointestinal Surgery Unit, Department of Surgery, Faculty of Medicine, Suez Canal University Hospital, Ismailia, Egypt
| | - Hossam Elfeki
- General Surgery Department, Mansoura University Hospitals, Mansoura University, Mansoura, Egypt
| | - Mohamed El Sorogy
- Gastrointestinal Surgery Centre, Mansoura University, Mansoura, Egypt
| | - Amgad Fouad
- Gastrointestinal Surgery Centre, Mansoura University, Mansoura, Egypt
| | - Mohamed Elrefai
- Gastrointestinal Surgery Centre, Mansoura University, Mansoura, Egypt
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