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Namavarian A, Gabinet-Equihua A, Deng Y, Khalid S, Ziai H, Deutsch K, Huang J, Gilbert RW, Goldstein DP, Yao CMKL, Irish JC, Enepekides DJ, Higgins KM, Rudzicz F, Eskander A, Xu W, de Almeida JR. Length of Stay Prediction Models for Oral Cancer Surgery: Machine Learning, Statistical and ACS-NSQIP. Laryngoscope 2024; 134:3664-3672. [PMID: 38651539 DOI: 10.1002/lary.31443] [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: 01/27/2024] [Revised: 03/17/2024] [Accepted: 03/27/2024] [Indexed: 04/25/2024]
Abstract
OBJECTIVE Accurate prediction of hospital length of stay (LOS) following surgical management of oral cavity cancer (OCC) may be associated with improved patient counseling, hospital resource utilization and cost. The objective of this study was to compare the performance of statistical models, a machine learning (ML) model, and The American College of Surgeons National Surgical Quality Improvement Program's (ACS-NSQIP) calculator in predicting LOS following surgery for OCC. MATERIALS AND METHODS A retrospective multicenter database study was performed at two major academic head and neck cancer centers. Patients with OCC who underwent major free flap reconstructive surgery between January 2008 and June 2019 surgery were selected. Data were pooled and split into training and validation datasets. Statistical and ML models were developed, and performance was evaluated by comparing predicted and actual LOS using correlation coefficient values and percent accuracy. RESULTS Totally 837 patients were selected with mean patient age being 62.5 ± 11.7 [SD] years and 67% being male. The ML model demonstrated the best accuracy (validation correlation 0.48, 4-day accuracy 70%), compared with the statistical models: multivariate analysis (0.45, 67%) and least absolute shrinkage and selection operator (0.42, 70%). All were superior to the ACS-NSQIP calculator's performance (0.23, 59%). CONCLUSION We developed statistical and ML models that predicted LOS following major free flap reconstructive surgery for OCC. Our models demonstrated superior predictive performance to the ACS-NSQIP calculator. The ML model identified several novel predictors of LOS. These models must be validated in other institutions before being used in clinical practice. LEVEL OF EVIDENCE 3 Laryngoscope, 134:3664-3672, 2024.
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Affiliation(s)
- Amirpouyan Namavarian
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
| | | | - Yangqing Deng
- Department of Biostatistics, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Shuja Khalid
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Hedyeh Ziai
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Konrado Deutsch
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Jingyue Huang
- Department of Biostatistics, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Ralph W Gilbert
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - David P Goldstein
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Christopher M K L Yao
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Jonathan C Irish
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
| | - Danny J Enepekides
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Kevin M Higgins
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Frank Rudzicz
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- International Centre for Surgical Safety, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
| | - Antoine Eskander
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
- Department of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - John R de Almeida
- Department of Otolaryngology-Head & Neck Surgery, University of Toronto, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Princess Margaret Cancer Center-University Health Network, Toronto, Ontario, Canada
- Department of Otolaryngology-Head & Neck Surgery, Sinai Health System, Toronto, Ontario, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, Ontario, Canada
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Liu M, Fan Z, Gao Y, Mubonanyikuzo V, Wu R, Li W, Xu N, Liu K, Zhou L. A two-tier feature selection method for predicting mortality risk in ICU patients with acute kidney injury. Sci Rep 2024; 14:16794. [PMID: 39039115 PMCID: PMC11263702 DOI: 10.1038/s41598-024-63793-3] [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: 12/11/2023] [Accepted: 06/03/2024] [Indexed: 07/24/2024] Open
Abstract
Acute kidney injury (AKI) is one of the most important lethal factors for patients admitted to intensive care units (ICUs), and timely high-risk prognostic assessment and intervention are essential to improving patient prognosis. In this study, a stacking model using the MIMIC-III dataset with a two-tier feature selection approach was developed to predict the risk of in-hospital mortality in ICU patients admitted for AKI. External validation was performed using separate MIMIC-IV and eICU-CRD. The area under the curve (AUC) was calculated using the stacking model, and features were selected using the Boruta and XGBoost feature selection methods. This study compares the performance of a stacking model using two-tier feature selection with a model using single-tier feature selection (XGBoost: 85; Boruta: 83; two-tier: 0.91). The predictive effectiveness of the stacking model was further validated by using different datasets (Validation 1: 0.83; Validation 2: 0.85) and comparing it with a simpler model and traditional clinical scores (SOFA: 0.65; APACH IV: 0.61). In addition, this study combined interpretable techniques and causal inference to analyze the causal relationship between features and predicted outcomes.
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Affiliation(s)
- Mengqing Liu
- College of Health Science and Engineering University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Zhiping Fan
- Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China
| | - Yu Gao
- Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China
| | - Vivens Mubonanyikuzo
- College of Health Science and Engineering University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Ruiqian Wu
- College of Health Science and Engineering University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Wenjin Li
- College of Health Science and Engineering University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Naiyue Xu
- College of Health Science and Engineering University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Kun Liu
- College of Health Science and Engineering University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Liang Zhou
- Jiading District Central Hospital Affiliated to Shanghai University of Medicine & Health Sciences, Shanghai, 201899, China.
- Research Center for Medical Intelligent Development, China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, 200025, China.
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Kovoor JG, Nann SD, Chambers C, Mishra K, Goel S, Thompson I, Koh D, Litwin P, Bacchi S, Harford PJ, Stretton B, Gupta AK. Prehabilitation before general surgery: Worth the effort? J Perioper Pract 2024; 34:219-225. [PMID: 38149496 DOI: 10.1177/17504589231214395] [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] [Indexed: 12/28/2023]
Abstract
Prehabilitation, or interventions before surgery aimed at improving preoperative health and postoperative outcomes, has various forms. Although it may confer benefit to patients undergoing general surgery, this is not certain. Furthermore, although it may yield a net monetary gain, it is also likely to require substantial monetary and non-monetary investment. The impact of prehabilitation is highly variable and dependent on multiple factors. Physical function and pulmonary outcomes are likely to be improved by most forms of prehabilitation involving physical and multimodal exercise programmes. However, other surgical outcomes have demonstrated mixed results from prehabilitation. Within this issue, the measures used for evaluating baseline patient biopsychosocial health are important, and collecting sufficient data to accurately inform patient-centred prehabilitation programmes is only possible through thorough clinical and laboratory investigation and synthesised metrics such as cardiopulmonary exercise testing. Although a multimodal approach to prehabilitation is the current gold standard, societal factors may affect engagement with programmes that require a significant in-person activity. However, this is weighed against the substantial financial and non-financial investment that accompanies many programmes. The overall effectiveness and optimal mode of intervention across the discipline of general surgery remains unclear, and further research is needed to prove prehabilitation's full worth.
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Affiliation(s)
- Joshua G Kovoor
- Discipline of Surgery, The University of Adelaide, Adelaide, SA, Australia
- The Queen Elizabeth Hospital, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Health and Information, Adelaide, SA, Australia
| | - Silas D Nann
- Health and Information, Adelaide, SA, Australia
- Gold Coast University Hospital, Gold Coast, QLD, Australia
| | - Courtney Chambers
- The Queen Elizabeth Hospital, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Kritika Mishra
- The Queen Elizabeth Hospital, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Sahil Goel
- The Queen Elizabeth Hospital, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Isabella Thompson
- Gold Coast University Hospital, Gold Coast, QLD, Australia
- Bond University, Gold Coast, QLD, Australia
| | - Dong Koh
- The Queen Elizabeth Hospital, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Peter Litwin
- The Queen Elizabeth Hospital, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Health and Information, Adelaide, SA, Australia
| | - Stephen Bacchi
- Discipline of Surgery, The University of Adelaide, Adelaide, SA, Australia
- The Queen Elizabeth Hospital, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Health and Information, Adelaide, SA, Australia
| | - Philip J Harford
- The Queen Elizabeth Hospital, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
| | - Brandon Stretton
- Discipline of Surgery, The University of Adelaide, Adelaide, SA, Australia
- The Queen Elizabeth Hospital, Adelaide, SA, Australia
- Royal Adelaide Hospital, Adelaide, SA, Australia
- Health and Information, Adelaide, SA, Australia
| | - Aashray K Gupta
- Discipline of Surgery, The University of Adelaide, Adelaide, SA, Australia
- Health and Information, Adelaide, SA, Australia
- Gold Coast University Hospital, Gold Coast, QLD, Australia
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Sanaiha Y, Verma A, Ng AP, Hadaya J, Ko CY, deVirgilio C, Benharash P. Development and preliminary assessment of a machine learning model to predict myocardial infarction and cardiac arrest after major operations. Resuscitation 2024; 200:110241. [PMID: 38759719 DOI: 10.1016/j.resuscitation.2024.110241] [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: 01/10/2024] [Revised: 04/22/2024] [Accepted: 05/08/2024] [Indexed: 05/19/2024]
Abstract
INTRODUCTION Accurate prediction of complications often informs shared decision-making. Derived over 10 years ago to enhance prediction of intra/post-operative myocardial infarction and cardiac arrest (MI/CA), the Gupta score has been criticized for unreliable calibration and inclusion of a wide spectrum of unrelated operations. In the present study, we developed a novel machine learning (ML) model to estimate perioperative risk of MI/CA and compared it to the Gupta score. METHODS Patients undergoing major operations were identified from the 2016-2020 ACS-NSQIP. The Gupta score was calculated for each patient, and a novel ML model was developed to predict MI/CA using ACS NSQIP-provided data fields as covariates. Discrimination (C-statistic) and calibration (Brier score) of the ML model were compared to the existing Gupta score within the entire cohort and across operative subgroups. RESULTS Of 2,473,487 patients included for analysis, 25,177 (1.0%) experienced MI/CA (55.2% MI, 39.1% CA, 5.6% MI and CA). The ML model, which was fit using a randomly selected training cohort, exhibited higher discrimination within the testing dataset compared to the Gupta score (C-statistic 0.84 vs 0.80, p < 0.001). Furthermore, the ML model had significantly better calibration in the entire cohort (Brier score 0.0097 vs 0.0100). Model performance was markedly improved among patients undergoing thoracic, aortic, peripheral vascular and foregut surgery. CONCLUSIONS The present ML model outperformed the Gupta score in the prognostication of MI/CA across a heterogenous range of operations. Given the growing integration of ML into healthcare, such models may be readily incorporated into clinical practice and guide benchmarking efforts.
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Affiliation(s)
- Yas Sanaiha
- Cardiovascular Outcomes Research Laboratories (CORELAB), University of California Los Angeles, Los Angeles, CA, USA
| | - Arjun Verma
- Cardiovascular Outcomes Research Laboratories (CORELAB), University of California Los Angeles, Los Angeles, CA, USA
| | - Ayesha P Ng
- Cardiovascular Outcomes Research Laboratories (CORELAB), University of California Los Angeles, Los Angeles, CA, USA
| | - Joseph Hadaya
- Cardiovascular Outcomes Research Laboratories (CORELAB), University of California Los Angeles, Los Angeles, CA, USA
| | - Clifford Y Ko
- Department of Surgery, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, CA, USA; Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL, USA; The Healthcare Improvement Studies Institute, University of Cambridge, Cambridge, UK
| | - Christian deVirgilio
- Department of Surgery, Harbor-University of California, Los Angeles Medical Center, Torrance, California, USA
| | - Peyman Benharash
- Cardiovascular Outcomes Research Laboratories (CORELAB), University of California Los Angeles, Los Angeles, CA, USA; Department of Surgery, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, CA, USA.
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Yoon HK, Kim HJ, Kim YJ, Lee H, Kim BR, Oh H, Park HP, Lee HC. Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery. Br J Anaesth 2024; 132:1304-1314. [PMID: 38413342 DOI: 10.1016/j.bja.2024.01.030] [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: 06/26/2023] [Revised: 01/01/2024] [Accepted: 01/26/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, defined as prolonged (>48 h) mechanical ventilation or reintubation after surgery. METHODS Easily extractable electronic health record (EHR) variables that do not require subjective assessment by clinicians were used. From EHR data of 307,333 noncardiac surgical cases, the model, trained with a gradient boosting algorithm, utilised a derivation cohort of 99,025 cases from Seoul National University Hospital (2013-9). External validation was performed using three separate cohorts A-C from different hospitals comprising 208,308 cases. Model performance was assessed by area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC), a measure of sensitivity and precision at different thresholds. RESULTS The model included eight variables: serum albumin, age, duration of anaesthesia, serum glucose, prothrombin time, serum creatinine, white blood cell count, and body mass index. Internally, the model achieved an AUROC of 0.912 (95% confidence interval [CI], 0.908-0.915) and AUPRC of 0.113. In external validation cohorts A, B, and C, the model achieved AUROCs of 0.879 (95% CI, 0.876-0.882), 0.872 (95% CI, 0.870-0.874), and 0.931 (95% CI, 0.925-0.936), and AUPRCs of 0.029, 0.083, and 0.124, respectively. CONCLUSIONS Utilising just eight easily extractable variables, this machine learning model demonstrated excellent discrimination in both internal and external validation for predicting postoperative respiratory failure. The model enables personalised risk stratification and facilitates data-driven clinical decision-making.
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Affiliation(s)
- Hyun-Kyu Yoon
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Hyun Joo Kim
- Department of Anesthesiology and Pain Medicine, Anesthesia and Pain Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Yi-Jun Kim
- Institute of Convergence Medicine, Ewha Womans University Mokdong Hospital, Seoul, South Korea
| | - Hyeonhoon Lee
- Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea
| | - Bo Rim Kim
- Department of Anesthesiology and Pain Medicine, Korea University Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Hyongmin Oh
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Hee-Pyoung Park
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Hyung-Chul Lee
- Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea.
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Danilatou V, Dimopoulos D, Kostoulas T, Douketis J. Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review. Thromb Haemost 2024. [PMID: 38574756 DOI: 10.1055/a-2299-4758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
BACKGROUND Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment. METHODS Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included. RESULTS Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison. CONCLUSION ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.
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Affiliation(s)
- Vasiliki Danilatou
- School of Medicine, European University of Cyprus, Nicosia, Cyprus
- Healthcare Division, Sphynx Technology Solutions, Nicosia, Cyprus
| | - Dimitrios Dimopoulos
- School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
| | - Theodoros Kostoulas
- School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
| | - James Douketis
- Department of Medicine, McMaster University, Hamilton, Canada
- Department of Medicine, St. Joseph's Healthcare Hamilton, Ontario, Canada
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Gairola S, Solanki SL, Patkar S, Goel M. Artificial Intelligence in Perioperative Planning and Management of Liver Resection. Indian J Surg Oncol 2024; 15:186-195. [PMID: 38818006 PMCID: PMC11133260 DOI: 10.1007/s13193-024-01883-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 01/16/2024] [Indexed: 06/01/2024] Open
Abstract
Artificial intelligence (AI) is a speciality within computer science that deals with creating systems that can replicate the intelligence of a human mind and has problem-solving abilities. AI includes a diverse array of techniques and approaches such as machine learning, neural networks, natural language processing, robotics, and expert systems. An electronic literature search was conducted using the databases of "PubMed" and "Google Scholar". The period for the search was from 2000 to June 2023. The search terms included "artificial intelligence", "machine learning", "liver cancers", "liver tumors", "hepatectomy", "perioperative" and their synonyms in various combinations. The search also included all MeSH terms. The extracted articles were further reviewed in a step-wise manner for identification of relevant studies. A total of 148 articles were identified after the initial literature search. Initial review included screening of article titles for relevance and identifying duplicates. Finally, 65 articles were reviewed for this review article. The future of AI in liver cancer planning and management holds immense promise. AI-driven advancements will increasingly enable precise tumour detection, location, and characterisation through enhanced image analysis. ML algorithms will predict patient-specific treatment responses and complications, allowing for tailored therapies. Surgical robots and AI-guided procedures will enhance the precision of liver resections, reducing risks and improving outcomes. AI will also streamline patient monitoring, better hemodynamic management, enabling early detection of recurrence or complications. Moreover, AI will facilitate data-driven research, accelerating the development of novel treatments and therapies. Ultimately, AI's integration will revolutionise liver cancer care, offering personalised, efficient and effective solutions, improving patients' quality of life and survival rates.
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Affiliation(s)
- Shruti Gairola
- Department of Anaesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra India
| | - Sohan Lal Solanki
- Department of Anaesthesiology, Critical Care and Pain, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra India
| | - Shraddha Patkar
- Division of Hepatobiliary Surgical Oncology, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra India
| | - Mahesh Goel
- Division of Hepatobiliary Surgical Oncology, Department of Surgical Oncology, Tata Memorial Hospital, Homi Bhabha National Institute, Mumbai, Maharashtra India
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Dixon D, Sattar H, Moros N, Kesireddy SR, Ahsan H, Lakkimsetti M, Fatima M, Doshi D, Sadhu K, Junaid Hassan M. Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review. Cureus 2024; 16:e59954. [PMID: 38854327 PMCID: PMC11161909 DOI: 10.7759/cureus.59954] [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] [Accepted: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.
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Affiliation(s)
- Diny Dixon
- Medicine, Jubilee Mission Medical College and Research Institute, Thrissur, IND
| | - Hina Sattar
- Medicine, Dow University of Health Sciences, Karachi, PAK
| | - Natalia Moros
- Medicine, Pontifical Javeriana University Medical School, Bogotá, COL
| | | | - Huma Ahsan
- Medicine, Jinnah Postgraduate Medical Centre, Karachi, PAK
| | | | - Madiha Fatima
- Medicine, Fatima Jinnah Medical University, Lahore, PAK
| | - Dhruvi Doshi
- Medicine, Gujarat Cancer Society Medical College, Hospital & Research Centre, Ahmedabad, IND
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Lopez-Lopez V, Morise Z, Albaladejo-González M, Gavara CG, Goh BKP, Koh YX, Paul SJ, Hilal MA, Mishima K, Krürger JAP, Herman P, Cerezuela A, Brusadin R, Kaizu T, Lujan J, Rotellar F, Monden K, Dalmau M, Gotohda N, Kudo M, Kanazawa A, Kato Y, Nitta H, Amano S, Valle RD, Giuffrida M, Ueno M, Otsuka Y, Asano D, Tanabe M, Itano O, Minagawa T, Eshmuminov D, Herrero I, Ramírez P, Ruipérez-Valiente JA, Robles-Campos R, Wakabayashi G. Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study. Surg Endosc 2024; 38:2411-2422. [PMID: 38315197 PMCID: PMC11078826 DOI: 10.1007/s00464-024-10681-6] [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: 08/07/2023] [Accepted: 01/02/2024] [Indexed: 02/07/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8. METHODS We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open. RESULTS Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables "resection type" and "largest tumor size" for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables "tumor location," "blood loss," "complications," and "operation time." CONCLUSION We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.
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Affiliation(s)
- Victor Lopez-Lopez
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Zeniche Morise
- Department of Surgery, Fujita Health University School of Medicine Okazaki Medical Center, Okazaki, Aichi, Japan
| | | | - Concepción Gomez Gavara
- Department of HPB Surgery and Transplants, Vall d'Hebron University Hospital, Barcelona Autonomic University, Barcelona, Spain
| | - Brian K P Goh
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore
- Surgery Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Ye Xin Koh
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore
- Surgery Academic Clinical Programme, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Sijberden Jasper Paul
- Department of Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Mohammed Abu Hilal
- Department of Surgery, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
- Department of Surgery, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Kohei Mishima
- Department of Surgery, Ageo Central General Hospital, Ageo, Japan
| | - Jaime Arthur Pirola Krürger
- Serviço de Cirurgia do Fígado, Divisão de Cirurgia do Aparelho Digestivo, Departamento de Gastroenterologia, Faculdade de Medicina, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Paulo Herman
- Serviço de Cirurgia do Fígado, Divisão de Cirurgia do Aparelho Digestivo, Departamento de Gastroenterologia, Faculdade de Medicina, Hospital das Clínicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Alvaro Cerezuela
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Roberto Brusadin
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Takashi Kaizu
- Department of General, Pediatric and Hepatobiliary-Pancreatic Surgery, Kitasato University School of Medicine, Sagamihara, Japan
| | - Juan Lujan
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
- Department of General Surgery, School of Medicine, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Fernando Rotellar
- Department of General Surgery, School of Medicine, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Kazuteru Monden
- Department of Surgery, Fukuyama City Hospital, Hiroshima, Japan
| | - Mar Dalmau
- Department of HPB Surgery and Transplants, Vall d'Hebron University Hospital, Barcelona Autonomic University, Barcelona, Spain
| | - Naoto Gotohda
- Department of Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Masashi Kudo
- Department of Surgery, National Cancer Center Hospital East, Kashiwa, Japan
| | - Akishige Kanazawa
- Department of Hepato-Biliary-Pancreatic Surgery, Osaka City General Hospital, Osaka, Japan
| | - Yutaro Kato
- Department of Surgery, Fujita Health University, Toyoake, Japan
| | - Hiroyuki Nitta
- Department of Surgery, Iwate Medical University, Iwate, Japan
| | - Satoshi Amano
- Department of Surgery, Iwate Medical University, Iwate, Japan
| | | | - Mario Giuffrida
- General Surgery Unit, Parma University Hospital, Parma, Italy
| | - Masaki Ueno
- Second Department of Surgery, Wakayama Medical University, 811-1 Kimiidera, Wakayama City, Wakayama, Japan
| | | | - Daisuke Asano
- Department of Hepatobiliary and Pancreatic Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Minoru Tanabe
- Department of Hepatobiliary and Pancreatic Surgery, Graduate School of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Osamu Itano
- Department of Hepato-Biliary-Pancreatic and Gastrointestinal Surgery, School of Medicine, International University of Health and Welfare, Chiba, Japan
| | - Takuya Minagawa
- Department of Hepato-Biliary-Pancreatic and Gastrointestinal Surgery, School of Medicine, International University of Health and Welfare, Chiba, Japan
| | - Dilmurodjon Eshmuminov
- Department of Surgery and Transplantation, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Irene Herrero
- Department of Surgery, Getafe University Hospital, Madrid, Spain
| | - Pablo Ramírez
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | | | - Ricardo Robles-Campos
- Department of General, Visceral and Transplantation Surgery, Clinic and University Hospital Virgen de La Arrixaca, IMIB-ARRIXACA, El Palmar, Murcia, Spain
| | - Go Wakabayashi
- Department of Surgery, Ageo Central General Hospital, Ageo, Japan
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Varghese C, Harrison EM, O'Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med 2024; 30:1257-1268. [PMID: 38740998 DOI: 10.1038/s41591-024-02970-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.
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Affiliation(s)
- Chris Varghese
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Greg O'Grady
- Department of Surgery, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Eric J Topol
- Scripps Research Translational Institute, La Jolla, CA, USA.
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11
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Nwaiwu CA, Rivera Perla KM, Abel LB, Sears IJ, Barton AT, Peterson RC, Liu YZ, Khatri IS, Sarkar IN, Shah N. Predicting Colonic Neoplasia Surgical Complications: A Machine Learning Approach. Dis Colon Rectum 2024; 67:700-713. [PMID: 38319746 DOI: 10.1097/dcr.0000000000003166] [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: 02/08/2024]
Abstract
BACKGROUND A range of statistical approaches have been used to help predict outcomes associated with colectomy. The multifactorial nature of complications suggests that machine learning algorithms may be more accurate in determining postoperative outcomes by detecting nonlinear associations, which are not readily measured by traditional statistics. OBJECTIVE The aim of this study was to investigate the utility of machine learning algorithms to predict complications in patients undergoing colectomy for colonic neoplasia. DESIGN Retrospective analysis using decision tree, random forest, and artificial neural network classifiers to predict postoperative outcomes. SETTINGS National Inpatient Sample database (2003-2017). PATIENTS Adult patients who underwent elective colectomy with anastomosis for neoplasia. MAIN OUTCOME MEASURES Performance was quantified using sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve to predict the incidence of anastomotic leak, prolonged length of stay, and inpatient mortality. RESULTS A total of 14,935 patients (4731 laparoscopic, 10,204 open) were included. They had an average age of 67 ± 12.2 years, and 53% of patients were women. The 3 machine learning models successfully identified patients who developed the measured complications. Although differences between model performances were largely insignificant, the neural network scored highest for most outcomes: predicting anastomotic leak, area under the receiver operating characteristic curve 0.88/0.93 (open/laparoscopic, 95% CI, 0.73-0.92/0.80-0.96); prolonged length of stay, area under the receiver operating characteristic curve 0.84/0.88 (open/laparoscopic, 95% CI, 0.82-0.85/0.85-0.91); and inpatient mortality, area under the receiver operating characteristic curve 0.90/0.92 (open/laparoscopic, 95% CI, 0.85-0.96/0.86-0.98). LIMITATIONS The patients from the National Inpatient Sample database may not be an accurate sample of the population of all patients undergoing colectomy for colonic neoplasia and does not account for specific institutional and patient factors. CONCLUSIONS Machine learning predicted postoperative complications in patients with colonic neoplasia undergoing colectomy with good performance. Although validation using external data and optimization of data quality will be required, these machine learning tools show great promise in assisting surgeons with risk-stratification of perioperative care to improve postoperative outcomes. See Video Abstract . PREDICCIN DE LAS COMPLICACIONES QUIRRGICAS DE LA NEOPLASIA DE COLON UN ENFOQUE DE MODELO DE APRENDIZAJE AUTOMTICO ANTECEDENTES:Se han utilizado una variedad de enfoques estadísticos para ayudar a predecir los resultados asociados con la colectomía. La naturaleza multifactorial de las complicaciones sugiere que los algoritmos de aprendizaje automático pueden ser más precisos en determinar los resultados posoperatorios al detectar asociaciones no lineales, que generalmente no se miden en las estadísticas tradicionales.OBJETIVO:El objetivo de este estudio fue investigar la utilidad de los algoritmos de aprendizaje automático para predecir complicaciones en pacientes sometidos a colectomía por neoplasia de colon.DISEÑO:Análisis retrospectivo utilizando clasificadores de árboles de decisión, bosques aleatorios y redes neuronales artificiales para predecir los resultados posoperatorios.AJUSTE:Base de datos de la Muestra Nacional de Pacientes Hospitalizados (2003-2017).PACIENTES:Pacientes adultos sometidos a colectomía electiva con anastomosis por neoplasia.INTERVENCIONES:N/A.PRINCIPALES MEDIDAS DE RESULTADO:El rendimiento se cuantificó utilizando la sensibilidad, especificidad, precisión y la característica operativa del receptor del área bajo la curva para predecir la incidencia de fuga anastomótica, duración prolongada de la estancia hospitalaria y mortalidad de los pacientes hospitalizados.RESULTADOS:Se incluyeron un total de 14.935 pacientes (4.731 laparoscópicos, 10.204 abiertos). Presentaron una edad promedio de 67 ± 12,2 años y el 53% eran mujeres. Los tres modelos de aprendizaje automático identificaron con éxito a los pacientes que desarrollaron las complicaciones medidas. Aunque las diferencias entre el rendimiento del modelo fueron en gran medida insignificantes, la red neuronal obtuvo la puntuación más alta para la mayoría de los resultados: predicción de fuga anastomótica, característica operativa del receptor del área bajo la curva 0,88/0,93 (abierta/laparoscópica, IC del 95%: 0,73-0,92/0,80-0,96); duración prolongada de la estancia hospitalaria, característica operativa del receptor del área bajo la curva 0,84/0,88 (abierta/laparoscópica, IC del 95%: 0,82-0,85/0,85-0,91); y mortalidad de pacientes hospitalizados, característica operativa del receptor del área bajo la curva 0,90/0,92 (abierto/laparoscópico, IC del 95%: 0,85-0,96/0,86-0,98).LIMITACIONES:Los pacientes de la base de datos de la Muestra Nacional de Pacientes Hospitalizados pueden no ser una muestra precisa de la población de todos los pacientes sometidos a colectomía por neoplasia de colon y no tienen en cuenta factores institucionales y específicos del paciente.CONCLUSIONES:El aprendizaje automático predijo con buen rendimiento las complicaciones postoperatorias en pacientes con neoplasia de colon sometidos a colectomía. Aunque será necesaria la validación mediante datos externos y la optimización de la calidad de los datos, estas herramientas de aprendizaje automático son muy prometedoras para ayudar a los cirujanos con la estratificación de riesgos de la atención perioperatoria para mejorar los resultados posoperatorios. (Traducción-Dr. Fidel Ruiz Healy ).
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Affiliation(s)
- Chibueze A Nwaiwu
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Krissia M Rivera Perla
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Logan B Abel
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Isaac J Sears
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Andrew T Barton
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | | | - Yao Z Liu
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Ishaani S Khatri
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
| | - Indra N Sarkar
- Center for Biomedical Informatics, Brown University, Providence, Rhode Island
- Rhode Island Quality Institute, Providence, Rhode Island
| | - Nishit Shah
- Department of Surgery, Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, Rhode Island
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Sun R, Li S, Wei Y, Hu L, Xu Q, Zhan G, Yan X, He Y, Wang Y, Li X, Luo A, Zhou Z. Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study. Int J Surg 2024; 110:2950-2962. [PMID: 38445452 DOI: 10.1097/js9.0000000000001237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 02/15/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND Early identification of patients at high-risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. The authors also evaluated the predictive performance of models that included only preoperative variables or only important predictors. MATERIALS AND METHODS Adult patients undergoing noncardiac surgery were retrospectively included in the study (76 457 patients in the discovery cohort and 11 910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees. The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation. RESULTS The patients in the discovery cohort had a median age of 52 years (IQR: 42-61 years), and 1179 patients (1.5%) developed AKI after surgery. The gradient boosting decision trees algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI: 0.835-0.863) and 0.828 (95% CI: 0.813-0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine, and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, the authors observed a similar pattern regarding the models' predictive performance. CONCLUSIONS The machine learning models the authors developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Furthermore, the authors found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.
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Affiliation(s)
- Rao Sun
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Shiyong Li
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Yuna Wei
- Yidu Cloud Technology Inc, Beijing, People's Republic of China
| | - Liu Hu
- Health Management Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei
| | - Qiaoqiao Xu
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Gaofeng Zhan
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Xu Yan
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Yuqin He
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Yao Wang
- Yidu Cloud Technology Inc, Beijing, People's Republic of China
| | - Xinhua Li
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Ailin Luo
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
| | - Zhiqiang Zhou
- Department of Anesthesiology and Pain Medicine, Hubei Key Laboratory of Geriatric Anesthesia and Perioperative Brain Health, and Wuhan Clinical Research Center for Geriatric Anesthesia
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Tan HJ, Spratte BN, Deal AM, Heiling HM, Nazzal EM, Meeks W, Fang R, Teal R, Vu MB, Bennett AV, Blalock SJ, Chung AE, Gotz D, Nielsen ME, Reuland DS, Harris AH, Basch E. Clinical Decision Support for Surgery: A Mixed Methods Study on Design and Implementation Perspectives From Urologists. Urology 2024:S0090-4295(24)00307-8. [PMID: 38697362 DOI: 10.1016/j.urology.2024.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/08/2024] [Accepted: 04/20/2024] [Indexed: 05/05/2024]
Abstract
OBJECTIVE To assess urologist attitudes toward clinical decision support (CDS) embedded into the electronic health record (EHR) and define design needs to facilitate implementation and impact. With recent advances in big data and artificial intelligence (AI), enthusiasm for personalized, data-driven tools to improve surgical decision-making has grown, but the impact of current tools remains limited. METHODS A sequential explanatory mixed methods study from 2019 to 2020 was performed. First, survey responses from the 2019 American Urological Association Annual Census evaluated attitudes toward an automatic CDS tool that would display risk/benefit data. This was followed by the purposeful sampling of 25 urologists and qualitative interviews assessing perspectives on CDS impact and design needs. Bivariable, multivariable, and coding-based thematic analysis were applied and integrated. RESULTS Among a weighted sample of 12,366 practicing urologists, the majority agreed CDS would help decision-making (70.9%, 95% CI 68.7%-73.2%), aid patient counseling (78.5%, 95% CI 76.5%-80.5%), save time (58.1%, 95% CI 55.7%-60.5%), and improve patient outcomes (42.9%, 95% CI 40.5%-45.4%). More years in practice was negatively associated with agreement (P <.001). Urologists described how CDS could bolster evidence-based care, personalized medicine, resource utilization, and patient experience. They also identified multiple implementation barriers and provided suggestions on form, functionality, and visual design to improve usefulness and ease of use. CONCLUSION Urologists have favorable attitudes toward the potential for clinical decision support in the EHR. Smart design will be critical to ensure effective implementation and impact.
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Affiliation(s)
- Hung-Jui Tan
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC; Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC.
| | - Brooke N Spratte
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Allison M Deal
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Hillary M Heiling
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Elizabeth M Nazzal
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - William Meeks
- American Urological Association Data Management and Statistical Services
| | - Raymond Fang
- American Urological Association Data Management and Statistical Services
| | - Randall Teal
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Connected Health Applications and Interventions Core, University of North Carolina, Chapel Hill, NC
| | - Maihan B Vu
- Connected Health Applications and Interventions Core, University of North Carolina, Chapel Hill, NC; Center for Health Promotion and Disease Prevention, University of North Carolina, Chapel Hill, NC
| | - Antonia V Bennett
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC
| | - Susan J Blalock
- Pharmaceutical Outcomes & Policy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC
| | - Arlene E Chung
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Bioinformatics, Duke University, Durham, NC
| | - David Gotz
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; School of Information and Library Science, University of North Carolina, Chapel Hill, NC
| | - Matthew E Nielsen
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC; Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC
| | - Daniel S Reuland
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Alex Hs Harris
- Department of Surgery, School of Medicine, Stanford University, Palo Alto, CA
| | - Ethan Basch
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC
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Luțenco V, Țocu G, Guliciuc M, Moraru M, Candussi IL, Dănilă M, Luțenco V, Dimofte F, Mihailov OM, Mihailov R. New Horizons of Artificial Intelligence in Medicine and Surgery. J Clin Med 2024; 13:2532. [PMID: 38731061 PMCID: PMC11084145 DOI: 10.3390/jcm13092532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/06/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024] Open
Abstract
Background: Ideas about Artificial intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided are becoming a bigger pool and we need artificial intelligence that will help us with its superhuman powers. Its interaction with medicine is improving more and more, with medicine being a domain that continues to be perfected. Materials and Methods: The most important databases were used to perform this detailed search that addresses artificial intelligence in the medical and surgical fields. Discussion: Machine learning, deep learning, neural networks and computer vision are some of the mechanisms that are becoming a trend in healthcare worldwide. Developed countries such as Japan, France and Germany have already implemented artificial intelligence in their medical systems. The help it gives is in medical diagnosis, patient monitoring, personalized therapy and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques and to choose the best surgical techniques. Conclusions: The goal is to predict complications, reduce diagnostic times, diagnose complex pathologies, guide surgeons intraoperatively and reduce medical errors. We are at the beginning of this, and the potential is enormous, but we must not forget the impediments that may appear and slow down its implementation.
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Affiliation(s)
- Valerii Luțenco
- Surgery I Clinic, Emergency Hospital “Sf. Ap. Andrei”, 800578 Galați, Romania; (V.L.); (R.M.)
| | - George Țocu
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Mădălin Guliciuc
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Monica Moraru
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Iuliana Laura Candussi
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
- Clinical Children Emergency Hospital “Sf. Ioan”, 060011 Galați, Romania;
| | - Marius Dănilă
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
- Clinical Children Emergency Hospital “Sf. Ioan”, 060011 Galați, Romania;
| | - Verginia Luțenco
- Clinical Children Emergency Hospital “Sf. Ioan”, 060011 Galați, Romania;
| | - Florentin Dimofte
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Oana Mariana Mihailov
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
| | - Raul Mihailov
- Surgery I Clinic, Emergency Hospital “Sf. Ap. Andrei”, 800578 Galați, Romania; (V.L.); (R.M.)
- Faculty of Medicine and Pharmacy, “Dunărea de Jos” University of Galati, 800008 Galați, Romania; (M.G.); (M.M.); (I.L.C.); (M.D.); (F.D.)
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15
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Bassi M, Vaz Sousa R, Zacchini B, Centofanti A, Ferrante F, Poggi C, Carillo C, Pecoraro Y, Amore D, Diso D, Anile M, De Giacomo T, Venuta F, Vannucci J. Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment. Healthcare (Basel) 2024; 12:803. [PMID: 38610225 PMCID: PMC11011722 DOI: 10.3390/healthcare12070803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
The general world population is aging and patients are often diagnosed with early-stage lung cancer at an advanced age. Several studies have shown that age is not itself a contraindication for lung cancer surgery, and therefore, more and more octogenarians with early-stage lung cancer are undergoing surgery with curative intent. However, octogenarians present some peculiarities that make surgical treatment more challenging, so an accurate preoperative selection is mandatory. In recent years, new artificial intelligence techniques have spread worldwide in the diagnosis, treatment, and therapy of lung cancer, with increasing clinical applications. However, there is still no evidence coming out from trials specifically designed to assess the potential of artificial intelligence in the preoperative evaluation of octogenarian patients. The aim of this narrative review is to investigate, through the analysis of the available international literature, the advantages and implications that these tools may have in the preoperative assessment of this particular category of frail patients. In fact, these tools could represent an important support in the decision-making process, especially in octogenarian patients in whom the diagnostic and therapeutic options are often questionable. However, these technologies are still developing, and a strict human-led process is mandatory.
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Affiliation(s)
- Massimiliano Bassi
- Division of Thoracic Surgery, Department of General Surgery and Surgical Specialties “Paride Stefanini”, Policlinico Umberto I, Sapienza University of Rome, 00161 Rome, Italy
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Zhuang Y, Dyas A, Meguid RA, Henderson WG, Bronsert M, Madsen H, Colborn KL. Preoperative Prediction of Postoperative Infections Using Machine Learning and Electronic Health Record Data. Ann Surg 2024; 279:720-726. [PMID: 37753703 DOI: 10.1097/sla.0000000000006106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
OBJECTIVE To estimate preoperative risk of postoperative infections using structured electronic health record (EHR) data. BACKGROUND Surveillance and reporting of postoperative infections is primarily done through costly, labor-intensive manual chart reviews on a small sample of patients. Automated methods using statistical models applied to postoperative EHR data have shown promise to augment manual review as they can cover all operations in a timely manner. However, there are no specific models for risk-adjusting infectious complication rates using EHR data. METHODS Preoperative EHR data from 30,639 patients (2013-2019) were linked to the American College of Surgeons National Surgical Quality Improvement Program preoperative data and postoperative infection outcomes data from 5 hospitals in the University of Colorado Health System. EHR data included diagnoses, procedures, operative variables, patient characteristics, and medications. Lasso and the knockoff filter were used to perform controlled variable selection. Outcomes included surgical site infection, urinary tract infection, sepsis/septic shock, and pneumonia up to 30 days postoperatively. RESULTS Among >15,000 candidate predictors, 7 were chosen for the surgical site infection model and 6 for each of the urinary tract infection, sepsis, and pneumonia models. Important variables included preoperative presence of the specific outcome, wound classification, comorbidities, and American Society of Anesthesiologists physical status classification. The area under the receiver operating characteristic curve for each model ranged from 0.73 to 0.89. CONCLUSIONS Parsimonious preoperative models for predicting postoperative infection risk using EHR data were developed and showed comparable performance to existing American College of Surgeons National Surgical Quality Improvement Program risk models that use manual chart review. These models can be used to estimate risk-adjusted postoperative infection rates applied to large volumes of EHR data in a timely manner.
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Affiliation(s)
- Yaxu Zhuang
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
- Department of Biostatistics and Informatics, Colorado School of Public Health
| | - Adam Dyas
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
- Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus
| | - Robert A Meguid
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
- Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus
- Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - William G Henderson
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
| | - Michael Bronsert
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
- Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Helen Madsen
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
- Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus
| | - Kathryn L Colborn
- Department of Surgery, Surgical Outcomes and Applied Research Program, University of Colorado Anschutz Medical Campus
- Department of Biostatistics and Informatics, Colorado School of Public Health
- Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus
- Adult and Child Consortium for Health Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO
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17
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Oliver CM, Wagstaff D, Bedford J, Moonesinghe SR. Systematic development and validation of a predictive model for major postoperative complications in the Peri-operative Quality Improvement Project (PQIP) dataset. Anaesthesia 2024; 79:389-398. [PMID: 38369686 DOI: 10.1111/anae.16248] [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] [Accepted: 01/04/2024] [Indexed: 02/20/2024]
Abstract
Complications are common following major surgery and are associated with increased use of healthcare resources, disability and mortality. Continued reliance on mortality estimates risks harming patients and health systems, but existing tools for predicting complications are unwieldy and inaccurate. We aimed to systematically construct an accurate pre-operative model for predicting major postoperative complications; compare its performance against existing tools; and identify sources of inaccuracy in predictive models more generally. Complete patient records from the UK Peri-operative Quality Improvement Programme dataset were analysed. Major complications were defined as Clavien-Dindo grade ≥ 2 for novel models. In a 75% train:25% test split cohort, we developed a pipeline of increasingly complex models, prioritising pre-operative predictors using Least Absolute Shrinkage and Selection Operators (LASSO). We defined the best model in the training cohort by the lowest Akaike's information criterion, balancing accuracy and simplicity. Of the 24,983 included cases, 6389 (25.6%) patients developed major complications. Potentially modifiable risk factors (pain, reduced mobility and smoking) were retained. The best-performing model was highly complex, specifying individual hospital complication rates and 11 patient covariates. This novel model showed substantially superior performance over generic and specific prediction models and scores. We have developed a novel complications model with good internal accuracy, re-prioritised predictor variables and identified hospital-level variation as an important, but overlooked, source of inaccuracy in existing tools. The complexity of the best-performing model does, however, highlight the need for a step-change in clinical risk prediction to automate the delivery of informative risk estimates in clinical systems.
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Affiliation(s)
- C M Oliver
- Centre for Peri-operative Medicine, University College London, UK
- Department of Anaesthesia and Peri-operative Medicine, UCL Hospitals, London, UK
| | - D Wagstaff
- Department of Anaesthesia and Peri-operative Medicine, UCL Hospitals, London, UK
- Centre for Peri-operative Medicine, University College London, UK
| | - J Bedford
- Department of Anaesthesia and Peri-operative Medicine, UCL Hospitals, London, UK
- Centre for Peri-operative Medicine, University College London, UK
| | - S R Moonesinghe
- Department of Anaesthesia and Peri-operative Medicine, UCL Hospitals, London, UK
- Centre for Peri-operative Medicine, University College London, UK
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18
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Hernandez MC, Chen C, Nguyen A, Choong K, Carlin C, Nelson RA, Rossi LA, Seth N, McNeese K, Yuh B, Eftekhari Z, Lai LL. Explainable Machine Learning Model to Preoperatively Predict Postoperative Complications in Inpatients With Cancer Undergoing Major Operations. JCO Clin Cancer Inform 2024; 8:e2300247. [PMID: 38648576 PMCID: PMC11161247 DOI: 10.1200/cci.23.00247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/24/2024] [Accepted: 03/06/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE Preoperative prediction of postoperative complications (PCs) in inpatients with cancer is challenging. We developed an explainable machine learning (ML) model to predict PCs in a heterogenous population of inpatients with cancer undergoing same-hospitalization major operations. METHODS Consecutive inpatients who underwent same-hospitalization operations from December 2017 to June 2021 at a single institution were retrospectively reviewed. The ML model was developed and tested using electronic health record (EHR) data to predict 30-day PCs for patients with Clavien-Dindo grade 3 or higher (CD 3+) per the CD classification system. Model performance was assessed using area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), and calibration plots. Model explanation was performed using the Shapley additive explanations (SHAP) method at cohort and individual operation levels. RESULTS A total of 988 operations in 827 inpatients were included. The ML model was trained using 788 operations and tested using a holdout set of 200 operations. The CD 3+ complication rates were 28.6% and 27.5% in the training and holdout test sets, respectively. Training and holdout test sets' model performance in predicting CD 3+ complications yielded an AUROC of 0.77 and 0.73 and an AUPRC of 0.56 and 0.52, respectively. Calibration plots demonstrated good reliability. The SHAP method identified features and the contributions of the features to the risk of PCs. CONCLUSION We trained and tested an explainable ML model to predict the risk of developing PCs in patients with cancer. Using patient-specific EHR data, the ML model accurately discriminated the risk of developing CD 3+ complications and displayed top features at the individual operation and cohort level.
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Affiliation(s)
| | - Chen Chen
- Department of Applied AI and Data Science, City of Hope National Medical Center, Duarte, CA
| | - Andrew Nguyen
- Department of Surgery, City of Hope National Medical Center, Duarte, CA
| | - Kevin Choong
- Department of Surgery, Division of Oncology, Primas Health, University of South Carolina Medical School, Greeneville, SC
| | - Cameron Carlin
- Department of Applied AI and Data Science, City of Hope National Medical Center, Duarte, CA
| | - Rebecca A. Nelson
- Department of Computational and Quantitative Medicine, Division of Biostatistics, City of Hope National Medical Center, Duarte, CA
| | - Lorenzo A. Rossi
- Department of Applied AI and Data Science, City of Hope National Medical Center, Duarte, CA
| | - Naini Seth
- Department of Clinical Informatics, City of Hope National Medical Center, Duarte, CA
| | - Kathy McNeese
- Department of Surgery, University of New Mexico, Albuquerque, NM
| | - Bertram Yuh
- Department of Surgery, University of New Mexico, Albuquerque, NM
| | - Zahra Eftekhari
- Department of Applied AI and Data Science, City of Hope National Medical Center, Duarte, CA
| | - Lily L. Lai
- Department of Surgery, University of New Mexico, Albuquerque, NM
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Kambale M, Jadhav S. Applications of artificial intelligence in anesthesia: A systematic review. Saudi J Anaesth 2024; 18:249-256. [PMID: 38654854 PMCID: PMC11033896 DOI: 10.4103/sja.sja_955_23] [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: 12/12/2023] [Revised: 12/18/2023] [Accepted: 12/26/2023] [Indexed: 04/26/2024] Open
Abstract
This review article examines the utility of artificial intelligence (AI) in anesthesia, with a focus on recent developments and future directions in the field. A total of 19,300 articles were available on the given topic after searching in the above mentioned databases, and after choosing the custom range of years from 2015 to 2023 as an inclusion component, only 12,100 remained. 5,720 articles remained after eliminating non-full text. Eighteen papers were identified to meet the inclusion criteria for the review after applying the inclusion and exclusion criteria. The applications of AI in anesthesia after studying the articles were in favor of the use of AI as it enhanced or equaled human judgment in drug dose decision and reduced mortality by early detection. Two studies tried to formulate prediction models, current techniques, and limitations of AI; ten studies are mainly focused on pain and complications such as hypotension, with a P value of <0.05; three studies tried to formulate patient outcomes with the help of AI; and three studies are mainly focusing on how drug dose delivery is calculated (median: 1.1% ± 0.5) safely and given to the patients with applications of AI. In conclusion, the use of AI in anesthesia has the potential to revolutionize the field and improve patient outcomes. AI algorithms can accurately predict patient outcomes and anesthesia dosing, as well as monitor patients during surgery in real time. These technologies can help anesthesiologists make more informed decisions, increase efficiency, and reduce costs. However, the implementation of AI in anesthesia also presents challenges, such as the need to address issues of bias and privacy. As the field continues to evolve, it will be important to carefully consider the ethical implications of AI in anesthesia and ensure that these technologies are used in a responsible and transparent manner.
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Affiliation(s)
- Monika Kambale
- Symbiosis Institute of Health Sciences, Pune, Maharashtra, India
| | - Sammita Jadhav
- Symbiosis Institute of Health Sciences, Pune, Maharashtra, India
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20
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Kowadlo G, Mittelberg Y, Ghomlaghi M, Stiglitz DK, Kishore K, Guha R, Nazareth J, Weinberg L. Development and validation of 'Patient Optimizer' (POP) algorithms for predicting surgical risk with machine learning. BMC Med Inform Decis Mak 2024; 24:70. [PMID: 38468330 DOI: 10.1186/s12911-024-02463-w] [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: 02/21/2023] [Accepted: 02/20/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Pre-operative risk assessment can help clinicians prepare patients for surgery, reducing the risk of perioperative complications, length of hospital stay, readmission and mortality. Further, it can facilitate collaborative decision-making and operational planning. OBJECTIVE To develop effective pre-operative risk assessment algorithms (referred to as Patient Optimizer or POP) using Machine Learning (ML) that predict the development of post-operative complications and provide pilot data to inform the design of a larger prospective study. METHODS After institutional ethics approval, we developed a base model that encapsulates the standard manual approach of combining patient-risk and procedure-risk. In an automated process, additional variables were included and tested with 10-fold cross-validation, and the best performing features were selected. The models were evaluated and confidence intervals calculated using bootstrapping. Clinical expertise was used to restrict the cardinality of categorical variables (e.g. pathology results) by including the most clinically relevant values. The models were created with logistic regression (LR) and extreme gradient-boosted trees using XGBoost (Chen and Guestrin, 2016). We evaluated performance using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC). Data was obtained from a metropolitan university teaching hospital from January 2015 to July 2020. Data collection was restricted to adult patients undergoing elective surgery. RESULTS A total of 11,475 adult admissions were included. The performance of XGBoost and LR was very similar across endpoints and metrics. For predicting the risk of any post-operative complication, kidney failure and length-of-stay (LOS), POP with XGBoost achieved an AUROC (95%CI) of 0.755 (0.744, 0.767), 0.869 (0.846, 0.891) and 0.841 (0.833, 0.847) respectively and AUPRC of 0.651 (0.632, 0.669), 0.336 (0.282, 0.390) and 0.741 (0.729, 0.753) respectively. For 30-day readmission and in-patient mortality, POP with XGBoost achieved an AUROC (95%CI) of 0.610 (0.587, 0.635) and 0.866 (0.777, 0.943) respectively and AUPRC of 0.116 (0.104, 0.132) and 0.031 (0.015, 0.072) respectively. CONCLUSION The POP algorithms effectively predicted any post-operative complication, kidney failure and LOS in the sample population. A larger study is justified to improve the algorithm to better predict complications and length of hospital stay. A larger dataset may also improve the prediction of additional specific complications, readmission and mortality.
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Affiliation(s)
| | | | | | - Daniel K Stiglitz
- Atidia Health, Melbourne, Australia
- Department of Anaesthesiology and Perioperative Medicine, Alfred Health, Melbourne, Australia
| | - Kartik Kishore
- Data Analytics Research and Evaluation Centre, Austin Health, Melbourne, Australia
| | - Ranjan Guha
- Department of Anaesthesia, Austin Health, Heidelberg, Australia
| | - Justin Nazareth
- Department of Anaesthesia, Austin Health, Heidelberg, Australia
| | - Laurence Weinberg
- Department of Anaesthesia, Austin Health, Heidelberg, Australia
- Department of Critical Care, The University of Melbourne, Austin Health, Heidelberg, Australia
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21
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Feinstein M, Katz D, Demaria S, Hofer IS. Remote Monitoring and Artificial Intelligence: Outlook for 2050. Anesth Analg 2024; 138:350-357. [PMID: 38215713 PMCID: PMC10794024 DOI: 10.1213/ane.0000000000006712] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Abstract
Remote monitoring and artificial intelligence will become common and intertwined in anesthesiology by 2050. In the intraoperative period, technology will lead to the development of integrated monitoring systems that will integrate multiple data streams and allow anesthesiologists to track patients more effectively. This will free up anesthesiologists to focus on more complex tasks, such as managing risk and making value-based decisions. This will also enable the continued integration of remote monitoring and control towers having profound effects on coverage and practice models. In the PACU and ICU, the technology will lead to the development of early warning systems that can identify patients who are at risk of complications, enabling early interventions and more proactive care. The integration of augmented reality will allow for better integration of diverse types of data and better decision-making. Postoperatively, the proliferation of wearable devices that can monitor patient vital signs and track their progress will allow patients to be discharged from the hospital sooner and receive care at home. This will require increased use of telemedicine, which will allow patients to consult with doctors remotely. All of these advances will require changes to legal and regulatory frameworks that will enable new workflows that are different from those familiar to today's providers.
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Affiliation(s)
- Max Feinstein
- Department of Anesthesiology Pain and Perioperative Medicine, Icahn School of Medicine at Mount Sinai
| | - Daniel Katz
- Department of Anesthesiology Pain and Perioperative Medicine, Icahn School of Medicine at Mount Sinai
| | - Samuel Demaria
- Department of Anesthesiology Pain and Perioperative Medicine, Icahn School of Medicine at Mount Sinai
| | - Ira S. Hofer
- Department of Anesthesiology Pain and Perioperative Medicine, Icahn School of Medicine at Mount Sinai
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22
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Abid R, Hussein AA, Guru KA. Artificial Intelligence in Urology: Current Status and Future Perspectives. Urol Clin North Am 2024; 51:117-130. [PMID: 37945097 DOI: 10.1016/j.ucl.2023.06.005] [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] [Indexed: 11/12/2023]
Abstract
Surgical fields, especially urology, have shifted increasingly toward the use of artificial intelligence (AI). Advancements in AI have created massive improvements in diagnostics, outcome predictions, and robotic surgery. For robotic surgery to progress from assisting surgeons to eventually reaching autonomous procedures, there must be advancements in machine learning, natural language processing, and computer vision. Moreover, barriers such as data availability, interpretability of autonomous decision-making, Internet connection and security, and ethical concerns must be overcome.
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Affiliation(s)
- Rayyan Abid
- Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA
| | - Ahmed A Hussein
- Department of Urology, Roswell Park Comprehensive Cancer Center
| | - Khurshid A Guru
- Department of Urology, Roswell Park Comprehensive Cancer Center.
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23
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Talwar AA, Desai AA, McAuliffe PB, Broach RB, Hsu JY, Liu T, Udupa JK, Tong Y, Torigian DA, Fischer JP. Optimal computed tomography-based biomarkers for prediction of incisional hernia formation. Hernia 2024; 28:17-24. [PMID: 37676569 PMCID: PMC11235401 DOI: 10.1007/s10029-023-02835-7] [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: 03/29/2023] [Accepted: 07/04/2023] [Indexed: 09/08/2023]
Abstract
PURPOSE Unstructured data are an untapped source for surgical prediction. Modern image analysis and machine learning (ML) can harness unstructured data in medical imaging. Incisional hernia (IH) is a pervasive surgical disease, well-suited for prediction using image analysis. Our objective was to identify optimal biomarkers (OBMs) from preoperative abdominopelvic computed tomography (CT) imaging which are most predictive of IH development. METHODS Two hundred and twelve rigorously matched colorectal surgery patients at our institution were included. Preoperative abdominopelvic CT scans were segmented to derive linear, volumetric, intensity-based, and textural features. These features were analyzed to find a small subset of OBMs, which are maximally predictive of IH. Three ML classifiers (Ensemble Boosting, Random Forest, SVM) trained on these OBMs were used for prediction of IH. RESULTS Altogether, 279 features were extracted from each CT scan. The most predictive OBMs found were: (1) abdominopelvic visceral adipose tissue (VAT) volume, normalized for height; (2) abdominopelvic skeletal muscle tissue volume, normalized for height; and (3) pelvic VAT volume to pelvic outer aspect of body wall skeletal musculature (OAM) volume ratio. Among ML prediction models, Ensemble Boosting produced the best performance with an AUC of 0.85, accuracy of 0.83, sensitivity of 0.86, and specificity of 0.81. CONCLUSION These OBMs suggest increased intra-abdominopelvic volume/pressure as the salient pathophysiologic driver and likely mechanism for IH formation. ML models using these OBMs are highly predictive for IH development. The next generation of surgical prediction will maximize the utility of unstructured data using advanced image analysis and ML.
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Affiliation(s)
- A A Talwar
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania Health System, 3400 Civic Center Boulevard, 14th floor South Tower, Philadelphia, PA, 19104, USA
| | - A A Desai
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania Health System, 3400 Civic Center Boulevard, 14th floor South Tower, Philadelphia, PA, 19104, USA
| | - P B McAuliffe
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania Health System, 3400 Civic Center Boulevard, 14th floor South Tower, Philadelphia, PA, 19104, USA
| | - R B Broach
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania Health System, 3400 Civic Center Boulevard, 14th floor South Tower, Philadelphia, PA, 19104, USA
| | - J Y Hsu
- Division of Biostatistics, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - T Liu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - J K Udupa
- Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Y Tong
- Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - D A Torigian
- Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - J P Fischer
- Division of Plastic Surgery, Department of Surgery, University of Pennsylvania Health System, 3400 Civic Center Boulevard, 14th floor South Tower, Philadelphia, PA, 19104, USA.
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24
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Filiberto AC, Adiyeke E, Ozrazgat-Baslanti T, Jacobs CR, Fazzone B, Bihorac A, Upchurch GR, Cooper M. Persistent Acute Kidney Injury is Associated with Poor Outcomes and Increased Hospital Cost in Vascular Surgery. Ann Vasc Surg 2024; 98:342-349. [PMID: 37423327 PMCID: PMC10964738 DOI: 10.1016/j.avsg.2023.06.023] [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: 02/24/2023] [Revised: 06/15/2023] [Accepted: 06/15/2023] [Indexed: 07/11/2023]
Abstract
BACKGROUND Postoperative acute kidney injury (AKI) is common after major surgery and is associated with increased morbidity, mortality, and cost. Additionally, there are recent studies demonstrating that time to renal recovery may have a substantial impact on clinical outcomes. We hypothesized that patients with delayed renal recovery after major vascular surgery will have increased complications, mortality, and hospital cost. METHODS A single-center retrospective cohort of patients undergoing nonemergent major vascular surgery between 6/1/2014 and 10/1/2020 was analyzed. Development of postoperative AKI (defined using Kidney Disease Improving Global Outcomes (KDIGO) criteria: >50% or > 0.3 mg/dl absolute increase in serum creatinine relative to reference after surgery and before discharge) was evaluated. Patients were divided into 3 groups: no AKI, rapidly reversed AKI (<48 hours), and persistent AKI (≥48 hours). Multivariable generalized linear models were used to evaluate the association between AKI groups and postoperative complications, 90-day mortality, and hospital cost. RESULTS A total of 1,881 patients undergoing 1,980 vascular procedures were included. Thirty five percent of patients developed postoperative AKI. Patients with persistent AKI had longer intensive care unit and hospital stays, as well as more mechanical ventilation days. In multivariable logistic regression analysis, persistent AKI was a major predictor of 90-day mortality (odds ratio 4.1, 95% confidence interval 2.4-7.1). Adjusted average cost was higher for patients with any type of AKI. The incremental cost of having any AKI ranged from $3,700 to $9,100, even after adjustment for comorbidities and other postoperative complications. The adjusted average cost for patients stratified by type of AKI was higher among patients with persistent AKI compared to those with no or rapidly reversed AKI. CONCLUSIONS Persistent AKI after vascular surgery is associated with increased complications, mortality, and cost. Strategies to prevent and aggressively treat AKI, specifically persistent AKI, in the perioperative setting are imperative to optimize care for this population.
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Affiliation(s)
| | - Esra Adiyeke
- Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL; Department of Medicine, University of Florida, Gainesville, FL
| | - Tezcan Ozrazgat-Baslanti
- Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL; Department of Medicine, University of Florida, Gainesville, FL
| | | | - Brian Fazzone
- Department of Surgery, University of Florida, Gainesville, FL
| | - Azra Bihorac
- Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL; Department of Medicine, University of Florida, Gainesville, FL
| | | | - Michol Cooper
- Department of Surgery, University of Florida, Gainesville, FL.
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25
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Arina P, Kaczorek MR, Hofmaenner DA, Pisciotta W, Refinetti P, Singer M, Mazomenos EB, Whittle J. Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools. Anesthesiology 2024; 140:85-101. [PMID: 37944114 PMCID: PMC11146190 DOI: 10.1097/aln.0000000000004764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. METHODS A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. RESULTS A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. CONCLUSIONS The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Pietro Arina
- Bloomsbury Institute of Intensive Care Medicine and Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Maciej R. Kaczorek
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Daniel A. Hofmaenner
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom; and Institute of Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Walter Pisciotta
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Patricia Refinetti
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
| | - Mervyn Singer
- Bloomsbury Institute of Intensive Care Medicine, University College London, London, United Kingdom
| | - Evangelos B. Mazomenos
- Wellcome/EPSRC Centre of Interventional and Surgical Sciences and Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - John Whittle
- Human Physiology and Performance Laboratory, Centre for Perioperative Medicine, Department of Targeted Intervention, University College London, London, United Kingdom
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Jiang J, Liu X, Cheng Z, Liu Q, Xing W. Interpretable machine learning models for early prediction of acute kidney injury after cardiac surgery. BMC Nephrol 2023; 24:326. [PMID: 37936067 PMCID: PMC10631004 DOI: 10.1186/s12882-023-03324-w] [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: 04/07/2023] [Accepted: 09/05/2023] [Indexed: 11/09/2023] Open
Abstract
OBJECTIVE Postoperative acute kidney injury (PO-AKI) is a common complication after cardiac surgery. We aimed to evaluate whether machine learning algorithms could significantly improve the risk prediction of PO-AKI. METHODS The retrospective cohort study included 2310 adult patients undergoing cardiac surgery in a tertiary teaching hospital, China. Postoperative AKI and severe AKI were identified by the modified KDIGO definition. The sample was randomly divided into a derivation set and a validation set based on a ratio of 4:1. Exploiting conventional logistic regression (LR) and five ML algorithms including decision tree, random forest, gradient boosting classifier (GBC), Gaussian Naive Bayes and multilayer perceptron, we developed and validated the prediction models of PO-AKI. We implemented the interpretation of models using SHapley Additive exPlanation (SHAP) analysis. RESULTS Postoperative AKI and severe AKI occurred in 1020 (44.2%) and 286 (12.4%) patients, respectively. Compared with the five ML models, LR model for PO-AKI exhibited the largest AUC (0.812, 95%CI: 0.756, 0.860, all P < 0.05), sensitivity (0.774, 95%CI: 0.719, 0.813), accuracy (0.753, 95%CI: 0.719, 0.781) and Youden index (0.513, 95%CI: 0.451, 0.573). Regarding severe AKI, GBC algorithm showed a significantly higher AUC than the other four ML models (all P < 0.05). Although no significant difference (P = 0.173) was observed in AUCs between GBC (0.86, 95%CI: 0.808, 0.902) and conventional logistic regression (0.803, 95%CI: 0.746, 0.852), GBC achieved greater sensitivity, accuracy and Youden index than conventional LR. Notably, SHAP analyses showed that preoperative serum creatinine, hyperlipidemia, lipid-lowering agents and assisted ventilation time were consistently among the top five important predictors for both postoperative AKI and severe AKI. CONCLUSION Logistic regression and GBC algorithm demonstrated moderate to good discrimination and superior performance in predicting PO-AKI and severe AKI, respectively. Interpretation of the models identified the key contributors to the predictions, which could potentially inform clinical interventions.
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Affiliation(s)
- Jicheng Jiang
- Department of Big Data Center for Cardiovascular Disease, Heart Center of Henan Provincial People's Hospital, Fuwai Central China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China
| | - Xinyun Liu
- Department of Big Data Center for Cardiovascular Disease, Heart Center of Henan Provincial People's Hospital, Fuwai Central China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhaoyun Cheng
- Department of Cardiovascular Surgery, Heart Center of Henan Provincial People's Hospital, Fuwai Central China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China.
| | - Qianjin Liu
- Department of Cardiovascular Surgery, Heart Center of Henan Provincial People's Hospital, Fuwai Central China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China
| | - Wenlu Xing
- Department of Big Data Center for Cardiovascular Disease, Heart Center of Henan Provincial People's Hospital, Fuwai Central China Cardiovascular Hospital, Central China Fuwai Hospital of Zhengzhou University, Zhengzhou, China
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Huang RW, Tsai TY, Hsieh YH, Hsu CC, Chen SH, Lee CH, Lin YT, Kao HK, Lin CH. Reliability of Postoperative Free Flap Monitoring with a Novel Prediction Model Based on Supervised Machine Learning. Plast Reconstr Surg 2023; 152:943e-952e. [PMID: 36790782 DOI: 10.1097/prs.0000000000010307] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
BACKGROUND Postoperative free flap monitoring is a critical part of reconstructive microsurgery. Postoperative clinical assessments rely heavily on specialty-trained staff. Therefore, in regions with limited specialist availability, the feasibility of performing microsurgery is restricted. This study aimed to apply artificial intelligence in postoperative free flap monitoring and validate the ability of machine learning in predicting and differentiating types of postoperative free flap circulation. METHODS Postoperative data from 176 patients who received free flap surgery were prospectively collected, including free flap photographs and clinical evaluation measures. Flap circulation outcome variables included normal, arterial insufficiency, and venous insufficiency. The Synthetic Minority Oversampling Technique plus Tomek Links (SMOTE-Tomek) was applied for data balance. Data were divided into 80%:20% for model training and validation. Shapley Additive Explanations were used for prediction interpretations of the model. RESULTS Of 805 total included flaps, 555 (69%) were normal, 97 (12%) had arterial insufficiency, and 153 (19%) had venous insufficiency. The most effective prediction model was developed based on random forest, with an accuracy of 98.4%. Temperature and color differences between the flap and the surrounding skin were the most significant contributing factors to predict a vascular compromised flap. CONCLUSIONS This study demonstrated the reliability of a machine-learning model in differentiating various types of postoperative flap circulation. This novel technique may reduce the burden of free flap monitoring and encourage the broader use of reconstructive microsurgery in regions with a limited number of staff specialists.
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Affiliation(s)
- Ren-Wen Huang
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Tzong-Yueh Tsai
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Yun-Huan Hsieh
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Chung-Chen Hsu
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Shih-Heng Chen
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Che-Hsiung Lee
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Yu-Te Lin
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Huang-Kai Kao
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Cheng-Hung Lin
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
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Xu Z, Yao S, Jiang Z, Hu L, Huang Z, Zeng Q, Liu X. Development and validation of a prediction model for postoperative intensive care unit admission in patients with non-cardiac surgery. Heart Lung 2023; 62:207-214. [PMID: 37567008 DOI: 10.1016/j.hrtlng.2023.08.001] [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: 03/07/2023] [Revised: 08/01/2023] [Accepted: 08/01/2023] [Indexed: 08/13/2023]
Abstract
BACKGROUND Accurately forecasting patients admitted to the intensive care units (ICUs) after surgery may improve clinical outcomes and guide the allocation of expensive and limited ICU resources. However, studies on predicting postoperative ICU admission in non-cardiac surgery have been limited. OBJECTIVE To develop and validate a prediction model combining pre- and intraoperative variables to predict ICU admission after non-cardiac surgery. METHODS This study is based on data from the Vital Signs DataBase (VitalDB) database. Predictors were selected using the least absolute shrinkage and selection operator regression method and logistic regression to develop a nomogram and an online web calculator. The model was internally verified by 1000-Bootstrap resampling. Performance of model was evaluated using area under the receiver operating characteristic curve (AUC), calibration curve and Brier score. The Youden's index was used to find the optimal nomogram's probability threshold. Clinical utility was assessed by decision curve analysis. RESULTS This study included 5216 non-cardiac surgery patients; of these, 812 (15.6%) required postoperative ICU admission. Potential predictors included age, ASA classification, surgical department, emergency surgery, preoperative albumin level, preoperative urea nitrogen level, intraoperative crystalloid, intraoperative transfusion, intraoperative catheterization, and surgical time. A nomogram was constructed with an AUC of 0.917 (95% CI: 0.907-0.926) and a Brier score of 0.077. The Bootstrap-adjusted AUC was 0.914; the adjusted Brier score was 0.078. The calibration curve showed good agreement between predicted and actual probabilities; and the decision curve indicated clinical usefulness. Finally, we established an online web calculator for clinical application (https://xuzhikun.shinyapps.io/postopICUadmission1/). CONCLUSION We developed and internally validated an easy-to-use nomogram for predicting ICU admission after non-cardiac surgery.
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Affiliation(s)
- Zhikun Xu
- Department of Critical Care Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, The Second Affiliated Hospital of Jinan University, Shenzhen 518020, China
| | - Shihua Yao
- Division of Cardiovascular Surgery, Cardiac and Vascular Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen 518053, China
| | - Zhongji Jiang
- Department of Biology, School of Medicine, Shenzhen Center, Cancer Hospital Chinese Academy of Medical Sciences, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Linhui Hu
- Department of Critical Care Medicine, Maoming People's Hospital, The Affiliated Maoming Hospital of Southern Medical University, Maoming 525000, China
| | - Zijun Huang
- Department of Anesthesiology, Maoming People's Hospital, The Affiliated Maoming Hospital of Southern Medical University, Maoming 525000, China
| | - Quanjun Zeng
- Department of Anesthesiology, University of Chinese Academy of Sciences Shenzhen Hospital, Shenzhen 518107, China
| | - Xueyan Liu
- Department of Critical Care Medicine, Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, The Second Affiliated Hospital of Jinan University, Shenzhen 518020, China.
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Singhal M, Gupta L, Hirani K. A Comprehensive Analysis and Review of Artificial Intelligence in Anaesthesia. Cureus 2023; 15:e45038. [PMID: 37829964 PMCID: PMC10566398 DOI: 10.7759/cureus.45038] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2023] [Indexed: 10/14/2023] Open
Abstract
In the field of anaesthesia, artificial intelligence (AI) has become a game-changing technology. Applications of AI include keeping records, monitoring patients, calculating and administering drugs, and carrying out mechanical procedures. This article explores the current uses, challenges, and prospective applications of AI in anaesthesia practices. This review discusses AI-supported systems like anaesthesia information management systems (AIMS), mechanical robots for carrying out procedures, and pharmacological models for drug delivery. AIMS has helped in automated record-keeping, predicting bad events, and monitoring the vital signs of the patient. Their application has a vital role in improving the efficacy of anaesthesia management and patient safety. The application of AI in anaesthesia comes with its own unique difficulties. Noteworthy obstacles include issues with data quantity and quality, technical limitations, and moral and legal dilemmas. The key to overcoming these barriers is to set guidelines for the ethical use of AI in healthcare, improve the reliability and comprehension of AI systems, and certify the health data precision and security. AI has very bright potential. Exciting future directions include developments in AI and machine learning thus development of new applications, and the possible enhancement in training and education. Potential research areas include the application of AI to chronic disease management, pain management, and the reinforcement of anaesthesiologists' education. AI could be used to design authentic lifelike training simulations and individualized student feedback systems, hence transforming anaesthesia education and training methodology. For this review, we conducted a PubMed, Google Scholar, and Cochrane Database search in 2022-2023 and retrieved articles on AI and its uses in anaesthesia. Recommendations for future research and development include strengthening the safety and reliability of health data, building a better understanding of AI systems, and looking into new areas of use. The power of AI can be used to innovate anaesthesia practices by concentrating on these areas.
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Affiliation(s)
- Meghna Singhal
- Department of Anesthesiology and Critical Care, Maulana Azad Medical College, Delhi, IND
| | - Lalit Gupta
- Department of Anesthesiology and Critical Care, Maulana Azad Medical College, Delhi, IND
| | - Kshitiz Hirani
- Department of Anesthesiology and Critical Care, University College of Medical Sciences and Guru Teg Bahadur Hospital, Delhi, IND
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Okada Y, Mertens M, Liu N, Lam SSW, Ong MEH. AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges. Resusc Plus 2023; 15:100435. [PMID: 37547540 PMCID: PMC10400904 DOI: 10.1016/j.resplu.2023.100435] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023] Open
Abstract
Aim Artificial intelligence (AI) and machine learning (ML) are important areas of computer science that have recently attracted attention for their application to medicine. However, as techniques continue to advance and become more complex, it is increasingly challenging for clinicians to stay abreast of the latest research. This overview aims to translate research concepts and potential concerns to healthcare professionals interested in applying AI and ML to resuscitation research but who are not experts in the field. Main text We present various research including prediction models using structured and unstructured data, exploring treatment heterogeneity, reinforcement learning, language processing, and large-scale language models. These studies potentially offer valuable insights for optimizing treatment strategies and clinical workflows. However, implementing AI and ML in clinical settings presents its own set of challenges. The availability of high-quality and reliable data is crucial for developing accurate ML models. A rigorous validation process and the integration of ML into clinical practice is essential for practical implementation. We furthermore highlight the potential risks associated with self-fulfilling prophecies and feedback loops, emphasizing the importance of transparency, interpretability, and trustworthiness in AI and ML models. These issues need to be addressed in order to establish reliable and trustworthy AI and ML models. Conclusion In this article, we overview concepts and examples of AI and ML research in the resuscitation field. Moving forward, appropriate understanding of ML and collaboration with relevant experts will be essential for researchers and clinicians to overcome the challenges and harness the full potential of AI and ML in resuscitation.
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Affiliation(s)
- Yohei Okada
- Duke-NUS Medical School, National University of Singapore, Singapore
- Preventive Services, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mayli Mertens
- Antwerp Center for Responsible AI, Antwerp University, Belgium
- Centre for Ethics, Department of Philosophy, Antwerp University, Belgium
| | - Nan Liu
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Sean Shao Wei Lam
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Marcus Eng Hock Ong
- Duke-NUS Medical School, National University of Singapore, Singapore
- Department of Emergency Medicine, Singapore General Hospital
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Bellini V, Russo M, Lanza R, Domenichetti T, Compagnone C, Maggiore SM, Cammarota G, Pelosi P, Vetrugno L, Bignami EG. Artificial intelligence and "the Art of Kintsugi" in Anesthesiology: ten influential papers for clinical users. Minerva Anestesiol 2023; 89:804-811. [PMID: 37194240 DOI: 10.23736/s0375-9393.23.17279-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the present review we chose ten influential papers from the last five years and through Kintsugi, shed the light on recent evolution of artificial intelligence in anesthesiology. A comprehensive search in in Medline, Embase, Web of Science and Scopus databases was conducted. Each author searched the databases independently and created a list of six articles that influenced their clinical practice during this period, with a focus on their area of competence. During a subsequent step, each researcher presented his own list and most cited papers were selected to create the final collection of ten articles. In recent years purely methodological works with a cryptic technology (black-box) represented by the intact and static vessel, translated to a "modern artificial intelligence" in clinical practice and comprehensibility (glass-box). The purposes of this review are to explore the ten most cited papers about artificial intelligence in anesthesiology and to understand how and when it should be integrated in clinical practice.
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Affiliation(s)
- Valentina Bellini
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Michele Russo
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Roberto Lanza
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Tania Domenichetti
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Christian Compagnone
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Salvatore M Maggiore
- Department of Anesthesiology, Critical Care Medicine and Emergency, SS. Annunziata Hospital, Chieti, Italy
- University Department of Innovative Technologies in Medicine and Dentistry, Gabriele D'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Gianmaria Cammarota
- Department of Anesthesia and Intensive Care Medicine, University of Perugia, Perugia, Italy
| | - Paolo Pelosi
- Department of Anesthesia and Intensive Care, IRCCS San Martino Polyclinic Hospital, University of Genoa, Genoa, Italy
- Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Luigi Vetrugno
- Department of Medical, Oral and Biotechnological Sciences, University of Chieti-Pescara, Chieti, Italy
| | - Elena G Bignami
- Unit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy -
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Filiberto AC, Miao S, Ren Y, Ozrazgat-Baslanti T, Hensley SE, Jacobs CR, Weaver ML, Upchurch GR, Bihorac A, Cooper M. Bilateral renal artery stenosis impacts postoperative complications after major vascular surgery. Surg Open Sci 2023; 14:17-21. [PMID: 37409074 PMCID: PMC10319299 DOI: 10.1016/j.sopen.2023.06.001] [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: 05/31/2023] [Accepted: 06/04/2023] [Indexed: 07/07/2023] Open
Abstract
Background Incidental atherosclerotic renal artery stenosis (RAS) is common in patients undergoing vascular surgery and has been shown to be associated with postoperative AKI among patients undergoing major non-vascular surgeries. We hypothesized that patients with RAS undergoing major vascular procedures would have a higher incidence of AKI and postoperative complications than those without RAS. Methods A single-center retrospective cohort study of 200 patients who underwent elective open aortic or visceral bypass surgery (100 with postoperative AKI; 100 without AKI) were identified. RAS was then evaluated by review of pre-surgery CTAs with readers blinded to AKI status. RAS was defined as ≥50 % stenosis. Univariate and multivariable logistic regression was used to assess association of unilateral and bilateral RAS with postoperative outcomes. Results 17.4 % (n = 28) of patients had unilateral RAS while 6.2 % (n = 10) of patients had bilateral RAS. Patients with bilateral RAS had similar preadmission creatinine and GFR as compared to unilateral RAS or no RAS. 100 % (n = 10) of patients with bilateral RAS had postoperative AKI compared with 45 % (n = 68) of patients with unilateral or no RAS (p < 0.05). In adjusted logistic regression models, bilateral RAS predicted severe AKI (OR 5.82; CI 1.33, 25.53; p = 0.02), in-hospital mortality (OR 5.71; CI 1.03, 31.53; p = 0.05), 30-day mortality (OR 10.56; CI 2.03, 54.05; p = 0.005) and 90-day mortality (OR 6.88; CI 1.40, 33.87; p = 0.02). Conclusions Bilateral RAS is associated with increased incidence of AKI as well as in-hospital, 30-day, and 90-day mortality suggesting it is a marker of poor outcomes and should be considered in preoperative risk stratification.
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Affiliation(s)
- Amanda C. Filiberto
- Department of Surgery, University of Florida, Gainesville, FL, United States of America
| | - Shunshun Miao
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States of America
- Department of Medicine, University of Florida, Gainesville, FL, United States of America
| | - Yuanfang Ren
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States of America
- Department of Medicine, University of Florida, Gainesville, FL, United States of America
| | - Tezcan Ozrazgat-Baslanti
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States of America
- Department of Medicine, University of Florida, Gainesville, FL, United States of America
| | - Sara E. Hensley
- Department of Surgery, University of Florida, Gainesville, FL, United States of America
| | - Christopher R. Jacobs
- Department of Surgery, University of Florida, Gainesville, FL, United States of America
| | - M. Libby Weaver
- Department of Surgery, University of Florida, Gainesville, FL, United States of America
| | - Gilbert R. Upchurch
- Department of Surgery, University of Florida, Gainesville, FL, United States of America
| | - Azra Bihorac
- Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL, United States of America
- Department of Medicine, University of Florida, Gainesville, FL, United States of America
| | - Michol Cooper
- Department of Surgery, University of Florida, Gainesville, FL, United States of America
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Henn J, Hatterscheidt S, Sahu A, Buness A, Dohmen J, Arensmeyer J, Feodorovici P, Sommer N, Schmidt J, Kalff JC, Matthaei H. Machine Learning for Decision-Support in Acute Abdominal Pain - Proof of Concept and Central Considerations. Zentralbl Chir 2023; 148:376-383. [PMID: 37562397 DOI: 10.1055/a-2125-1559] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used here as a decision-support and relieve the time and personnel resource shortage.Patients with acute abdominal pain presenting to the Department of Surgery at Bonn University Hospital in 2020 and 2021 were retrospectively analyzed. Clinical parameters as well as laboratory values were used as predictors. After randomly splitting into a training and test data set (ratio 80 to 20), three ML algorithms were comparatively trained and validated. The entire procedure was repeated 20 times.A total of 1357 patients were identified and included in the analysis, with one in five (n = 276, 20.3%) requiring emergency abdominal surgery within 24 hours. Patients operated on were more likely to be male (p = 0.026), older (p = 0.006), had more gastrointestinal symptoms (nausea: p < 0.001, vomiting p < 0.001) as well as a more recent onset of pain (p < 0.001). Tenderness (p < 0.001) and guarding (p < 0.001) were more common in surgically treated patients and blood analyses showed increased inflammation levels (white blood cell count: p < 0.001, CRP: p < 0.001) and onset of organ dysfunction (creatinine: p < 0.014, quick p < 0.001). Of the three trained algorithms, the tree-based methods (h2o random forest and cforest) showed the best performance. The algorithms classified patients, i.e., predicted surgery, with a median AUC ROC of 0.81 and 0.79 and AUC PRC of 0.56 in test sets.A proof-of-concept was achieved with the development of an ML model for predicting timely surgical therapy for acute abdomen. The ML algorithm can be a valuable tool in decision-making. Especially in the context of heavily used medical resources, the algorithm can help to use these scarce resources more effectively. Technological progress, especially regarding artificial intelligence, increasingly enables evidence-based approaches in surgery but requires a strictly interdisciplinary approach. In the future, the use and handling of ML should be integrated into surgical training.
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Affiliation(s)
- Jonas Henn
- Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Simon Hatterscheidt
- Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Anshupa Sahu
- Institute for Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
| | - Andreas Buness
- Institute for Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
| | - Jonas Dohmen
- Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Jan Arensmeyer
- Division of Thoracic Surgery, Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Philipp Feodorovici
- Division of Thoracic Surgery, Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Nils Sommer
- Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Joachim Schmidt
- Division of Thoracic Surgery, Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
- Department of Thoracic Surgery, Helios Hospital Bonn Rhein-Sieg, Bonn, Germany
| | - Jörg C Kalff
- Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
| | - Hanno Matthaei
- Department of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Bonn, Germany
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Affiliation(s)
- Nikhil R Sahni
- From the Department of Economics, Harvard University, Cambridge (N.R.S.), and the Center for U.S. Healthcare Improvement, McKinsey and Company, Boston (N.R.S., B.C.) - both in Massachusetts
| | - Brandon Carrus
- From the Department of Economics, Harvard University, Cambridge (N.R.S.), and the Center for U.S. Healthcare Improvement, McKinsey and Company, Boston (N.R.S., B.C.) - both in Massachusetts
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Ding Y, Yang F, Han M, Li C, Wang Y, Xu X, Zhao M, Zhao M, Yue M, Deng H, Yang H, Yao J, Liu Y. Multi-center study on predicting breast cancer lymph node status from core needle biopsy specimens using multi-modal and multi-instance deep learning. NPJ Breast Cancer 2023; 9:58. [PMID: 37443117 DOI: 10.1038/s41523-023-00562-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The objective of our study is to develop a deep learning model based on clinicopathological data and digital pathological image of core needle biopsy specimens for predicting breast cancer lymph node metastasis. We collected 3701 patients from the Fourth Hospital of Hebei Medical University and 190 patients from four medical centers in Hebei Province. Integrating clinicopathological data and image features build multi-modal and multi-instance (MMMI) deep learning model to obtain the final prediction. For predicting with or without lymph node metastasis, the AUC was 0.770, 0.709, 0.809 based on the clinicopathological features, WSI and MMMI, respectively. For predicting four classification of lymph node status (no metastasis, isolated tumor cells (ITCs), micrometastasis, and macrometastasis), the prediction based on clinicopathological features, WSI and MMMI were compared. The AUC for no metastasis was 0.770, 0.709, 0.809, respectively; ITCs were 0.619, 0.531, 0.634, respectively; micrometastasis were 0.636, 0.617, 0.691, respectively; and macrometastasis were 0.748, 0.691, 0.758, respectively. The MMMI model achieved the highest prediction accuracy. For prediction of different molecular types of breast cancer, MMMI demonstrated a better prediction accuracy for any type of lymph node status, especially in the molecular type of triple negative breast cancer (TNBC). In the external validation sets, MMMI also showed better prediction accuracy in the four classification, with AUC of 0.725, 0.757, 0.525, and 0.708, respectively. Finally, we developed a breast cancer lymph node metastasis prediction model based on a MMMI model. Through all cases tests, the results showed that the overall prediction ability was high.
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Affiliation(s)
- Yan Ding
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China
| | - Fan Yang
- AI Lab, Tencent, 518057, Shenzhen, China
| | - Mengxue Han
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China
| | - Chunhui Li
- Department of Pathology, Chengde Medical University Affiliated Hospital, 067000, Chengde, Hebei, China
| | - Yanan Wang
- Department of Pathology, Affiliated Hospital of Hebei University, 071000, Baoding, Hebei, China
| | - Xin Xu
- Department of Pathology, Xingtai People's Hospital, 054000, Xingtai, Hebei, China
| | - Min Zhao
- Department of Pathology, First Hospital of Qinhuangdao, 066000, Qinhuangdao, Hebei, China
| | - Meng Zhao
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China
| | - Meng Yue
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China
| | - Huiyan Deng
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China
| | - Huichai Yang
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China
| | | | - Yueping Liu
- Department of Pathology, The Fourth Hospital of Hebei Medical University, 050011, Shijiazhuang, Hebei, China.
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Nolde JM, Schlaich MP, Sessler DI, Mian A, Corcoran TB, Chow CK, Chan MTV, Borges FK, McGillion MH, Myles PS, Mills NL, Devereaux PJ, Hillis GS. Machine learning to predict myocardial injury and death after non-cardiac surgery. Anaesthesia 2023; 78:853-860. [PMID: 37070957 DOI: 10.1111/anae.16024] [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] [Accepted: 03/17/2023] [Indexed: 04/19/2023]
Abstract
Myocardial injury due to ischaemia within 30 days of non-cardiac surgery is prognostically relevant. We aimed to determine the discrimination, calibration, accuracy, sensitivity and specificity of single-layer and multiple-layer neural networks for myocardial injury and death within 30 postoperative days. We analysed data from 24,589 participants in the Vascular Events in Non-cardiac Surgery Patients Cohort Evaluation study. Validation was performed on a randomly selected subset of the study population. Discrimination for myocardial injury by single-layer vs. multiple-layer models generated areas (95%CI) under the receiver operating characteristic curve of: 0.70 (0.69-0.72) vs. 0.71 (0.70-0.73) with variables available before surgical referral, p < 0.001; 0.73 (0.72-0.75) vs. 0.75 (0.74-0.76) with additional variables available on admission, but before surgery, p < 0.001; and 0.76 (0.75-0.77) vs. 0.77 (0.76-0.78) with the addition of subsequent variables, p < 0.001. Discrimination for death by single-layer vs. multiple-layer models generated areas (95%CI) under the receiver operating characteristic curve of: 0.71 (0.66-0.76) vs. 0.74 (0.71-0.77) with variables available before surgical referral, p = 0.04; 0.78 (0.73-0.82) vs. 0.83 (0.79-0.86) with additional variables available on admission but before surgery, p = 0.01; and 0.87 (0.83-0.89) vs. 0.87 (0.85-0.90) with the addition of subsequent variables, p = 0.52. The accuracy of the multiple-layer model for myocardial injury and death with all variables was 70% and 89%, respectively.
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Affiliation(s)
- J M Nolde
- Dobney Hypertension Centre, Royal Perth Hospital Research Foundation, Perth, Australia
| | - M P Schlaich
- Dobney Hypertension Centre, Royal Perth Hospital Research Foundation, Perth, Australia
| | - D I Sessler
- Department of Outcomes Research, Cleveland Clinic, Cleveland, OH, USA
| | - A Mian
- School of Computer Science and Software Engineering, University of Western Australia, Perth, Australia
| | - T B Corcoran
- Department of Anaesthesia and Pain Medicine, Royal Perth Hospital and Medical School, University of Western Australia and Department of Anaesthesiology and Peri-operative Medicine, Alfred Hospital and Monash University, Melbourne, Australia
| | - C K Chow
- Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, and Department of Cardiology, Westmead Hospital, Sydney, Australia
| | - M T V Chan
- Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | - F K Borges
- McMaster University, Faculty of Health Sciences and Population Health Research Institute, Hamilton, ON, Canada
| | - M H McGillion
- McMaster University, Faculty of Health Sciences and Population Health Research Institute, Hamilton, ON, Canada
| | - P S Myles
- Department of Anaesthesiology and Peri-operative Medicine, Alfred Hospital and Monash University, Melbourne, Australia
| | - N L Mills
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh and Usher Institute, Edinburgh, UK
| | - P J Devereaux
- McMaster University, Faculty of Health Sciences and Population Health Research Institute, Hamilton, ON, Canada
| | - G S Hillis
- Medical School, University of Western Australia and Department of Cardiology, Royal Perth Hospital, Perth, Australia
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Wu C, Zhang Y, Nie S, Hong D, Zhu J, Chen Z, Liu B, Liu H, Yang Q, Li H, Xu G, Weng J, Kong Y, Wan Q, Zha Y, Chen C, Xu H, Hu Y, Shi Y, Zhou Y, Su G, Tang Y, Gong M, Wang L, Hou F, Liu Y, Li G. Predicting in-hospital outcomes of patients with acute kidney injury. Nat Commun 2023; 14:3739. [PMID: 37349292 PMCID: PMC10287760 DOI: 10.1038/s41467-023-39474-6] [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: 12/25/2022] [Accepted: 06/15/2023] [Indexed: 06/24/2023] Open
Abstract
Acute kidney injury (AKI) is prevalent and a leading cause of in-hospital death worldwide. Early prediction of AKI-related clinical events and timely intervention for high-risk patients could improve outcomes. We develop a deep learning model based on a nationwide multicenter cooperative network across China that includes 7,084,339 hospitalized patients, to dynamically predict the risk of in-hospital death (primary outcome) and dialysis (secondary outcome) for patients who developed AKI during hospitalization. A total of 137,084 eligible patients with AKI constitute the analysis set. In the derivation cohort, the area under the receiver operator curve (AUROC) for 24-h, 48-h, 72-h, and 7-day death are 95·05%, 94·23%, 93·53%, and 93·09%, respectively. For dialysis outcome, the AUROC of each time span are 88·32%, 83·31%, 83·20%, and 77·99%, respectively. The predictive performance is consistent in both internal and external validation cohorts. The model can predict important outcomes of patients with AKI, which could be helpful for the early management of AKI.
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Affiliation(s)
- Changwei Wu
- Department of Nephrology and Nephrology Institute, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China
| | - Yun Zhang
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China
| | - Sheng Nie
- National Clinical Research Center for Kidney Disease, State Laboratory of Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China
| | - Daqing Hong
- Department of Nephrology and Nephrology Institute, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China
| | - Jiajing Zhu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China
| | - Zhi Chen
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China
| | - Bicheng Liu
- Institute of Nephrology, Zhongda Hospital, Southeast University School of Medicine, 210000, Nanjing, China
| | - Huafeng Liu
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Institute of Nephrology, Affiliated Hospital of Guangdong Medical University, 524000, Zhanjiang, China
| | - Qiongqiong Yang
- Department of Nephrology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510515, Guangzhou, China
| | - Hua Li
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Gang Xu
- Division of Nephrology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430000, Wuhan, China
| | - Jianping Weng
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230000, Hefei, China
| | - Yaozhong Kong
- Department of Nephrology, the First People's Hospital of Foshan, 528000, Foshan, China
| | - Qijun Wan
- The Second People's Hospital of Shenzhen, Shenzhen University, 518000, Shenzhen, China
| | - Yan Zha
- Guizhou Provincial People's Hospital, Guizhou University, 550000, Guiyang, China
| | - Chunbo Chen
- Department of Critical Care Medicine, Maoming People's Hospital, 525000, Maoming, China
| | - Hong Xu
- Children's Hospital of Fudan University, 200000, Shanghai, China
| | - Ying Hu
- The Second Affiliated Hospital of Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Yongjun Shi
- Huizhou Municipal Central Hospital, Sun Yat-Sen University, 516000, Huizhou, China
| | - Yilun Zhou
- Department of Nephrology, Beijing Tiantan Hospital, Capital Medical University, 100000, Beijing, China
| | - Guobin Su
- Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital, The Second Clinical College, Guangzhou University of Chinese Medicine, 510000, Guangzhou, China
| | - Ying Tang
- The Third Affiliated Hospital of Southern Medical University, 510000, Guangzhou, China
| | - Mengchun Gong
- Institute of Health Management, Southern Medical University, 510000, Guangzhou, China
- DHC Technologies, 100000, Beijing, China
| | - Li Wang
- Department of Nephrology and Nephrology Institute, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China
| | - Fanfan Hou
- National Clinical Research Center for Kidney Disease, State Laboratory of Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China.
| | - Yongguo Liu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China.
| | - Guisen Li
- Department of Nephrology and Nephrology Institute, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China.
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Reich DA, Adiyeke E, Ozrazgat-Baslanti T, Rabley AK, Bozorgmehri S, Bihorac A, Bird VG. Clinical Considerations for Patients Experiencing Acute Kidney Injury Following Percutaneous Nephrolithotomy. Biomedicines 2023; 11:1712. [PMID: 37371807 PMCID: PMC10296554 DOI: 10.3390/biomedicines11061712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/06/2023] [Accepted: 06/08/2023] [Indexed: 06/29/2023] Open
Abstract
Acute kidney injury (AKI) is a common postoperative outcome in urology patients undergoing surgery for nephrolithiasis. The objective of this study was to determine the prevalence of postoperative AKI and its degrees of severity, identify risk factors, and understand the resultant outcomes of AKI in patients with nephrolithiasis undergoing percutaneous nephrolithotomy (PCNL). A cohort of patients admitted between 2012 and 2019 to a single tertiary-care institution who had undergone PCNL was retrospectively analyzed. Among 417 (n = 326 patients) encounters, 24.9% (n = 104) had AKI. Approximately one-quarter of AKI patients (n = 18) progressed to Stage 2 or higher AKI. Hypertension, peripheral vascular disease, chronic kidney disease, and chronic anemia were significant risk factors of post-PCNL AKI. Corticosteroids and antifungals were associated with increased odds of AKI. Cardiovascular, neurologic complications, sepsis, and prolonged intensive care unit (ICU) stay percentages were higher in AKI patients. Hospital and ICU length of stay was greater in the AKI group. Provided the limited literature regarding postoperative AKI following PCNL, and the detriment that AKI can have on clinical outcomes, it is important to continue studying this topic to better understand how to optimize patient care to address patient- and procedure-specific risk factors.
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Affiliation(s)
- Daniel A. Reich
- University of Florida College of Medicine, Gainesville, FL 32610, USA; (D.A.R.); (E.A.); (T.O.-B.); (S.B.); (A.B.)
| | - Esra Adiyeke
- University of Florida College of Medicine, Gainesville, FL 32610, USA; (D.A.R.); (E.A.); (T.O.-B.); (S.B.); (A.B.)
- Division of Nephrology, Hypertension, and Renal Transplantation, University of Florida College of Medicine, Gainesville, FL 32610, USA
- Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL 32610, USA
| | - Tezcan Ozrazgat-Baslanti
- University of Florida College of Medicine, Gainesville, FL 32610, USA; (D.A.R.); (E.A.); (T.O.-B.); (S.B.); (A.B.)
- Division of Nephrology, Hypertension, and Renal Transplantation, University of Florida College of Medicine, Gainesville, FL 32610, USA
- Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL 32610, USA
| | - Andrew K. Rabley
- Department of Urology, University of Florida College of Medicine, Gainesville, FL 32610, USA;
| | - Shahab Bozorgmehri
- University of Florida College of Medicine, Gainesville, FL 32610, USA; (D.A.R.); (E.A.); (T.O.-B.); (S.B.); (A.B.)
| | - Azra Bihorac
- University of Florida College of Medicine, Gainesville, FL 32610, USA; (D.A.R.); (E.A.); (T.O.-B.); (S.B.); (A.B.)
- Division of Nephrology, Hypertension, and Renal Transplantation, University of Florida College of Medicine, Gainesville, FL 32610, USA
- Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL 32610, USA
| | - Vincent G. Bird
- University of Florida College of Medicine, Gainesville, FL 32610, USA; (D.A.R.); (E.A.); (T.O.-B.); (S.B.); (A.B.)
- Department of Urology, University of Florida College of Medicine, Gainesville, FL 32610, USA;
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Cascella M, Tracey MC, Petrucci E, Bignami EG. Exploring Artificial Intelligence in Anesthesia: A Primer on Ethics, and Clinical Applications. SURGERIES 2023; 4:264-274. [DOI: 10.3390/surgeries4020027] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023] Open
Abstract
The field of anesthesia has always been at the forefront of innovation and technology, and the integration of Artificial Intelligence (AI) represents the next frontier in anesthesia care. The use of AI and its subtypes, such as machine learning, has the potential to improve efficiency, reduce costs, and ameliorate patient outcomes. AI can assist with decision making, but its primary advantage lies in empowering anesthesiologists to adopt a proactive approach to address clinical issues. The potential uses of AI in anesthesia can be schematically grouped into clinical decision support and pharmacologic and mechanical robotic applications. Tele-anesthesia includes strategies of telemedicine, as well as device networking, for improving logistics in the operating room, and augmented reality approaches for training and assistance. Despite the growing scientific interest, further research and validation are needed to fully understand the benefits and limitations of these applications in clinical practice. Moreover, the ethical implications of AI in anesthesia must also be considered to ensure that patient safety and privacy are not compromised. This paper aims to provide a comprehensive overview of AI in anesthesia, including its current and potential applications, and the ethical considerations that must be considered to ensure the safe and effective use of the technology.
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Affiliation(s)
- Marco Cascella
- Pain Unit and Research, Istituto Nazionale Tumori IRCCS Fondazione Pascale, 80100 Napoli, Italy
| | - Maura C. Tracey
- Rehabilitation Medicine Unit, Strategic Health Services Department, Istituto Nazionale Tumori-IRCCS-Fondazione Pascale, 80100 Naples, Italy
| | - Emiliano Petrucci
- Department of Anesthesia and Intensive Care Unit, San Salvatore Academic Hospital of L’Aquila, 67100 L’Aquila, Italy
| | - Elena Giovanna Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, 43126 Parma, Italy
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Neto PCS, Rodrigues AL, Stahlschmidt A, Helal L, Stefani LC. Developing and validating a machine learning ensemble model to predict postoperative delirium in a cohort of high-risk surgical patients: A secondary cohort analysis. Eur J Anaesthesiol 2023; 40:356-364. [PMID: 36860180 DOI: 10.1097/eja.0000000000001811] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
BACKGROUND Postoperative delirium (POD) has a negative impact on prognosis, length of stay and the burden of care. Although its prediction and identification may improve postoperative care, this need is largely unmet in the Brazilian public health system. OBJECTIVE To develop and validate a machine-learning prediction model and estimate the incidence of delirium. We hypothesised that an ensemble machine-learning prediction model that incorporates predisposing and precipitating features could accurately predict POD. DESIGN A secondary analysis nested in a cohort of high-risk surgical patients. SETTING An 800-bed, quaternary university-affiliated teaching hospital in Southern Brazil. We included patients operated on from September 2015 to February 2020. PATIENTS We recruited 1453 inpatients with an all-cause postoperative 30-day mortality risk greater than 5% assessed preoperatively by the ExCare Model. MAIN OUTCOME MEASURE The incidence of POD classified by the Confusion Assessment Method, up to 7 days postoperatively. Predictive model performance with different feature scenarios were compared with the area under the receiver operating characteristic curve. RESULTS The cumulative incidence of delirium was 117, giving an absolute risk of 8.05/100 patients. We developed multiple machine-learning nested cross-validated ensemble models. We selected features through partial dependence plot analysis and theoretical framework. We treated the class imbalance with undersampling. Different feature scenarios included: 52 preoperative, 60 postoperative and only three features (age, preoperative length of stay and the number of postoperative complications). The mean areas (95% confidence interval) under the curve ranged from 0.61 (0.59 to 0.63) to 0.74 (0.73 to 0.75). CONCLUSION A predictive model composed of three indicative readily available features performed better than those with numerous perioperative features, pointing to its feasibility as a prognostic tool for POD. Further research is required to test the generalisability of this model. TRIAL REGISTRATION Institutional Review Board Registration number 04448018.8.0000.5327 (Brazilian CEP/CONEP System, available in https://plataformabrasil.saude.gov.br/ ).
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Affiliation(s)
- Paulo C S Neto
- From the Programa de Pós-graduação em Medicina: Ciências Médicas, Universidade Federal do Rio Grande do Sul (PCSN), Universidade Federal do Rio Grande do Sul (ALR), Programa de Pós-graduação em Medicina: Ciências Médicas, Universidade Federal do Rio Grande do Sul (AS), Hospital de Clínicas de Porto Alegre and Universidade Federal do Rio Grande do Sul (LH), Programa de Pós-graduação em Medicina: Ciências Médicas, Professor at Surgical Department -Universidade Federal do Rio Grande do Sul and Chief of Teaching Division of Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil (LCS)
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Feng Y, Wang AY, Jun M, Pu L, Weisbord SD, Bellomo R, Hong D, Gallagher M. Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis. JAMA Netw Open 2023; 6:e2313359. [PMID: 37184837 DOI: 10.1001/jamanetworkopen.2023.13359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/16/2023] Open
Abstract
Importance Despite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes. Objective To systematically review published AKI prediction models across all clinical subsettings. Data Sources MEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models. Study Selection All studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs. Data Extraction and Synthesis Two authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias. Main Outcomes and Measures C statistic was used to measure the discrimination of prediction models. Results Of the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium-associated AKI: I2 = 99.9%; P < .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool. Conclusions and Relevance In this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.
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Affiliation(s)
- Yunlin Feng
- Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Amanda Y Wang
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- Concord Clinical School, University of Sydney, Sydney, Australia
- The Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia
| | - Min Jun
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
| | - Lei Pu
- Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Steven D Weisbord
- Renal Section, Medicine Service, Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
- Renal-Electrolyte Division, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Rinaldo Bellomo
- Department of Critical Care, University of Melbourne, Melbourne, Australia
| | - Daqing Hong
- Department of Nephrology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Martin Gallagher
- The George Institute for Global Health, University of New South Wales, Sydney, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, Australia
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Kalt F, Mayr H, Gero D. Classification of Adverse Events in Adult Surgery. Eur J Pediatr Surg 2023; 33:120-128. [PMID: 36720250 DOI: 10.1055/s-0043-1760821] [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: 02/02/2023]
Abstract
Successful surgery combines quality (achievement of a positive outcome) with safety (avoidance of a negative outcome). Outcome assessment serves the purpose of quality improvement in health care by establishing performance indicators and allowing the identification of performance gaps. Novel surgical quality metric tools (benchmark cutoffs and textbook outcomes) provide procedure-specific ideal surgical outcomes in a subgroup of well-defined low-risk patients, with the aim of setting realistic and best achievable goals for surgeons and centers, as well as supporting unbiased comparison of surgical quality between centers and periods of time. Validated classification systems have been deployed to grade adverse events during the surgical journey: (1) the ClassIntra classification for the intraoperative period; (2) the Clavien-Dindo classification for the gravity of single adverse events; and the (3) Comprehensive Complication Index (CCI) for the sum of adverse events over a defined postoperative period. The failure to rescue rate refers to the death of a patient following one or more potentially treatable postoperative adverse event(s) and is a reliable proxy of the institutional safety culture and infrastructure. Complication assessment is undergoing digital transformation to decrease resource-intensity and provide surgeons with real-time pre- or intraoperative decision support. Standardized reporting of complications informs patients on their chances to realize favorable postoperative outcomes and assists surgical centers in the prioritization of quality improvement initiatives, multidisciplinary teamwork, surgical education, and ultimately, in the enhancement of clinical standards.
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Affiliation(s)
- Fabian Kalt
- Department of Surgery and Transplantation, University Hospital Zurich, University of Zurich, Switzerland
| | - Hemma Mayr
- Department of Surgery and Transplantation, University Hospital Zurich, University of Zurich, Switzerland
| | - Daniel Gero
- Department of Surgery and Transplantation, University Hospital Zurich, University of Zurich, Switzerland
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Srinivas S, Young AJ. Machine Learning and Artificial Intelligence in Surgical Research. Surg Clin North Am 2023; 103:299-316. [PMID: 36948720 DOI: 10.1016/j.suc.2022.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
Machine learning, a subtype of artificial intelligence, is an emerging field of surgical research dedicated to predictive modeling. From its inception, machine learning has been of interest in medical and surgical research. Built on traditional research metrics for optimal success, avenues of research include diagnostics, prognosis, operative timing, and surgical education, in a variety of surgical subspecialties. Machine learning represents an exciting and developing future in the world of surgical research that will not only allow for more personalized and comprehensive medical care.
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Affiliation(s)
- Shruthi Srinivas
- Department of Surgery, The Ohio State University, 370 West 9th Avenue, Columbus, OH 43210, USA
| | - Andrew J Young
- Division of Trauma, Critical Care, and Burn, The Ohio State University, 181 Taylor Avenue, Suite 1102K, Columbus, OH 43203, USA.
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44
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Yu X, Wu R, Ji Y, Feng Z. Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide. Front Public Health 2023; 11:1136939. [PMID: 37006534 PMCID: PMC10063840 DOI: 10.3389/fpubh.2023.1136939] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 03/01/2023] [Indexed: 03/19/2023] Open
Abstract
Background Acute kidney injury (AKI) is a serious clinical complication associated with adverse short-term and long-term outcomes. In recent years, with the rapid popularization of electronic health records and artificial intelligence machine learning technology, the detection rate and treatment of AKI have been greatly improved. At present, there are many studies in this field, and a large number of articles have been published, but we do not know much about the quality of research production in this field, as well as the focus and trend of current research. Methods Based on the Web of Science Core Collection, studies reporting machine learning-based AKI research that were published from 2013 to 2022 were retrieved and collected after manual review. VOSviewer and other software were used for bibliometric visualization analysis, including publication trends, geographical distribution characteristics, journal distribution characteristics, author contributions, citations, funding source characteristics, and keyword clustering. Results A total of 336 documents were analyzed. Since 2018, publications and citations have increased dramatically, with the United States (143) and China (101) as the main contributors. Regarding authors, Bihorac, A and Ozrazgat-Baslanti, T from the Kansas City Medical Center have published 10 articles. Regarding institutions, the University of California (18) had the most publications. Approximately 1/3 of the publications were published in Q1 and Q2 journals, of which Scientific Reports (19) was the most prolific journal. Tomašev et al.'s study that was published in 2019 has been widely cited by researchers. The results of cluster analysis of co-occurrence keywords suggest that the construction of AKI prediction model related to critical patients and sepsis patients is the research frontier, and XGBoost algorithm is also popular. Conclusion This study first provides an updated perspective on machine learning-based AKI research, which may be beneficial for subsequent researchers to choose suitable journals and collaborators and may provide a more convenient and in-depth understanding of the research basis, hotspots and frontiers.
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Affiliation(s)
- Xiang Yu
- State Key Laboratory of Kidney Diseases, Department of Nephrology, Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Institute of Nephrology, National Clinical Research Center of Kidney Diseases, Beijing, China
| | - RiLiGe Wu
- Medical Big Data Research Center, Chinese People's Liberation Army General Hospital, Beijing, China
| | - YuWei Ji
- State Key Laboratory of Kidney Diseases, Department of Nephrology, Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Institute of Nephrology, National Clinical Research Center of Kidney Diseases, Beijing, China
| | - Zhe Feng
- State Key Laboratory of Kidney Diseases, Department of Nephrology, Chinese People's Liberation Army General Hospital, Chinese People's Liberation Army Institute of Nephrology, National Clinical Research Center of Kidney Diseases, Beijing, China
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Chen KA, Joisa CU, Stem J, Guillem JG, Eng SMG, Kapadia MR. Improved Prediction of Surgical-Site Infection After Colorectal Surgery Using Machine Learning. Dis Colon Rectum 2023; 66:458-466. [PMID: 36538699 PMCID: PMC10069984 DOI: 10.1097/dcr.0000000000002559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND Surgical-site infection is a source of significant morbidity after colorectal surgery. Previous efforts to develop models that predict surgical-site infection have had limited accuracy. Machine learning has shown promise in predicting postoperative outcomes by identifying nonlinear patterns within large data sets. OBJECTIVE This study aimed to seek usage of machine learning to develop a more accurate predictive model for colorectal surgical-site infections. DESIGN Patients who underwent colorectal surgery were identified in the American College of Surgeons National Quality Improvement Program database from years 2012 to 2019 and were split into training, validation, and test sets. Machine-learning techniques included random forest, gradient boosting, and artificial neural network. A logistic regression model was also created. Model performance was assessed using area under the receiver operating characteristic curve. SETTINGS A national, multicenter data set. PATIENTS Patients who underwent colorectal surgery. MAIN OUTCOME MEASURES The primary outcome (surgical-site infection) included patients who experienced superficial, deep, or organ-space surgical-site infections. RESULTS The data set included 275,152 patients after the application of exclusion criteria. Of all patients, 10.7% experienced a surgical-site infection. Artificial neural network showed the best performance with area under the receiver operating characteristic curve of 0.769 (95% CI, 0.762-0.777), compared with 0.766 (95% CI, 0.759-0.774) for gradient boosting, 0.764 (95% CI, 0.756-0.772) for random forest, and 0.677 (95% CI, 0.669-0.685) for logistic regression. For the artificial neural network model, the strongest predictors of surgical-site infection were organ-space surgical-site infection present at time of surgery, operative time, oral antibiotic bowel preparation, and surgical approach. LIMITATIONS Local institutional validation was not performed. CONCLUSIONS Machine-learning techniques predict colorectal surgical-site infections with higher accuracy than logistic regression. These techniques may be used to identify patients at increased risk and to target preventive interventions for surgical-site infection. See Video Abstract at http://links.lww.com/DCR/C88 . PREDICCIN MEJORADA DE LA INFECCIN DEL SITIO QUIRRGICO DESPUS DE LA CIRUGA COLORRECTAL MEDIANTE EL APRENDIZAJE AUTOMTICO ANTECEDENTES:La infección del sitio quirúrgico es una fuente de morbilidad significativa después de la cirugía colorrectal. Los esfuerzos anteriores para desarrollar modelos que predijeran la infección del sitio quirúrgico han tenido una precisión limitada. El aprendizaje automático se ha mostrado prometedor en la predicción de los resultados posoperatorios mediante la identificación de patrones no lineales dentro de grandes conjuntos de datos.OBJETIVO:Intentamos utilizar el aprendizaje automático para desarrollar un modelo predictivo más preciso para las infecciones del sitio quirúrgico colorrectal.DISEÑO:Los pacientes que se sometieron a cirugía colorrectal se identificaron en la base de datos del Programa Nacional de Mejoramiento de la Calidad del Colegio Estadounidense de Cirujanos de los años 2012 a 2019 y se dividieron en conjuntos de capacitación, validación y prueba. Las técnicas de aprendizaje automático incluyeron conjunto aleatorio, aumento de gradiente y red neuronal artificial. También se creó un modelo de regresión logística. El rendimiento del modelo se evaluó utilizando el área bajo la curva característica operativa del receptor.CONFIGURACIÓN:Un conjunto de datos multicéntrico nacional.PACIENTES:Pacientes intervenidos de cirugía colorrectal.PRINCIPALES MEDIDAS DE RESULTADO:El resultado primario (infección del sitio quirúrgico) incluyó pacientes que experimentaron infecciones superficiales, profundas o del espacio de órganos del sitio quirúrgico.RESULTADOS:El conjunto de datos incluyó 275.152 pacientes después de la aplicación de los criterios de exclusión. El 10,7% de los pacientes presentó infección del sitio quirúrgico. La red neuronal artificial mostró el mejor rendimiento con el área bajo la curva característica operativa del receptor de 0,769 (IC del 95 %: 0,762 - 0,777), en comparación con 0,766 (IC del 95 %: 0,759 - 0,774) para el aumento de gradiente, 0,764 (IC del 95 %: 0,756 - 0,772) para conjunto aleatorio y 0,677 (IC 95% 0,669 - 0,685) para regresión logística. Para el modelo de red neuronal artificial, los predictores más fuertes de infección del sitio quirúrgico fueron la infección del sitio quirúrgico del espacio del órgano presente en el momento de la cirugía, el tiempo operatorio, la preparación intestinal con antibióticos orales y el abordaje quirúrgico.LIMITACIONES:No se realizó validación institucional local.CONCLUSIONES:Las técnicas de aprendizaje automático predicen infecciones del sitio quirúrgico colorrectal con mayor precisión que la regresión logística. Estas técnicas se pueden usar para identificar a los pacientes con mayor riesgo y para orientar las intervenciones preventivas para la infección del sitio quirúrgico. Consulte Video Resumen en http://links.lww.com/DCR/C88 . (Traducción-Dr Yolanda Colorado ).
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Affiliation(s)
- Kevin A Chen
- Department of Surgery, University of North Carolina, Chapel Hill, NC 100 Manning Drive, Burnett Womack Building, Suite 4038, Chapel Hill, NC 27599
| | - Chinmaya U Joisa
- Joint Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 10202C Mary Ellen Jones Building, Chapel Hill, NC, 27599
| | - Jonathan Stem
- Department of Surgery, University of North Carolina, Chapel Hill, NC 100 Manning Drive, Burnett Womack Building, Suite 4038, Chapel Hill, NC 27599
| | - Jose G Guillem
- Department of Surgery, University of North Carolina, Chapel Hill, NC 100 Manning Drive, Burnett Womack Building, Suite 4038, Chapel Hill, NC 27599
| | - Shawn M Gomez Eng
- Joint Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 10202C Mary Ellen Jones Building, Chapel Hill, NC, 27599
| | - Muneera R Kapadia
- Department of Surgery, University of North Carolina, Chapel Hill, NC 100 Manning Drive, Burnett Womack Building, Suite 4038, Chapel Hill, NC 27599
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Prediction of postoperative infection in elderly using deep learning-based analysis: an observational cohort study. Aging Clin Exp Res 2023; 35:639-647. [PMID: 36598653 PMCID: PMC10014765 DOI: 10.1007/s40520-022-02325-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/09/2022] [Indexed: 01/05/2023]
Abstract
Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly. This was an observational cohort study with 2014 elderly patients who had elective surgery from 28 hospitals in China from April to June 2014. We aimed to develop and validate deep learning-based predictive models for postoperative infections in the elderly. 1510 patients were randomly assigned to be training dataset for establishing deep learning-based models, and 504 patients were used to validate the effectiveness of these models. The conventional model predicted postoperative infections was 0.728 (95% CI 0.688-0.768) with the sensitivity of 66.2% (95% CI 58.2-73.6) and specificity of 66.8% (95% CI 64.6-68.9). The deep learning model including risk factors relevant to baseline clinical characteristics predicted postoperative infections was 0.641 (95% CI 0.545-0.737), and sensitivity and specificity were 34.2% (95% CI 19.6-51.4) and 88.8% (95% CI 85.6-91.6), respectively. Including risk factors relevant to baseline variables and surgery, the deep learning model predicted postoperative infections was 0.763 (95% CI 0.681-0.844) with the sensitivity of 63.2% (95% CI 46-78.2) and specificity of 80.5% (95% CI 76.6-84). Our feasibility study indicated that a deep learning model including risk factors for the prediction of postoperative infections can be achieved in elderly. Further study is needed to assess whether this model can be used to guide clinical practice to improve surgical outcomes in elderly.
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Behnoush AH, Khalaji A, Rezaee M, Momtahen S, Mansourian S, Bagheri J, Masoudkabir F, Hosseini K. Machine learning-based prediction of 1-year mortality in hypertensive patients undergoing coronary revascularization surgery. Clin Cardiol 2023; 46:269-278. [PMID: 36588391 PMCID: PMC10018097 DOI: 10.1002/clc.23963] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/12/2022] [Accepted: 12/19/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Machine learning (ML) has shown promising results in all fields of medicine, including preventive cardiology. Hypertensive patients are at higher risk of mortality after coronary artery bypass graft (CABG) surgery; thus, we aimed to design and evaluate five ML models to predict 1-year mortality among hypertensive patients who underwent CABG. HYOTHESIS ML algorithms can significantly improve mortality prediction after CABG. METHODS Tehran Heart Center's CABG data registry was used to extract several baseline and peri-procedural characteristics and mortality data. The best features were chosen using random forest (RF) feature selection algorithm. Five ML models were developed to predict 1-year mortality: logistic regression (LR), RF, artificial neural network (ANN), extreme gradient boosting (XGB), and naïve Bayes (NB). The area under the curve (AUC), sensitivity, and specificity were used to evaluate the models. RESULTS Among the 8,493 hypertensive patients who underwent CABG (mean age of 68.27 ± 9.27 years), 303 died in the first year. Eleven features were selected as the best predictors, among which total ventilation hours and ejection fraction were the leading ones. LR showed the best prediction ability with an AUC of 0.82, while the least AUC was for the NB model (0.79). Among the subgroups, the highest AUC for LR model was for two age range groups (50-59 and 80-89 years), overweight, diabetic, and smoker subgroups of hypertensive patients. CONCLUSIONS All ML models had excellent performance in predicting 1-year mortality among CABG hypertension patients, while LR was the best regarding AUC. These models can help clinicians assess the risk of mortality in specific subgroups at higher risk (such as hypertensive ones).
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Affiliation(s)
- Amir Hossein Behnoush
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Amirmohammad Khalaji
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Malihe Rezaee
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Shahram Momtahen
- Department of Surgery, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Soheil Mansourian
- Department of Surgery, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Jamshid Bagheri
- Department of Surgery, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Masoudkabir
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Kaveh Hosseini
- Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.,Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
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Vernooij JEM, Koning NJ, Geurts JW, Holewijn S, Preckel B, Kalkman CJ, Vernooij LM. Performance and usability of pre-operative prediction models for 30-day peri-operative mortality risk: a systematic review. Anaesthesia 2023; 78:607-619. [PMID: 36823388 DOI: 10.1111/anae.15988] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/06/2023] [Indexed: 02/25/2023]
Abstract
Estimating pre-operative mortality risk may inform clinical decision-making for peri-operative care. However, pre-operative mortality risk prediction models are rarely implemented in routine clinical practice. High predictive accuracy and clinical usability are essential for acceptance and clinical implementation. In this systematic review, we identified and appraised prediction models for 30-day postoperative mortality in non-cardiac surgical cohorts. PubMed and Embase were searched up to December 2022 for studies investigating pre-operative prediction models for 30-day mortality. We assessed predictive performance in terms of discrimination and calibration. Risk of bias was evaluated using a tool to assess the risk of bias and applicability of prediction model studies. To further inform potential adoption, we also assessed clinical usability for selected models. In all, 15 studies evaluating 10 prediction models were included. Discrimination ranged from a c-statistic of 0.82 (MySurgeryRisk) to 0.96 (extreme gradient boosting machine learning model). Calibration was reported in only six studies. Model performance was highest for the surgical outcome risk tool (SORT) and its external validations. Clinical usability was highest for the surgical risk pre-operative assessment system. The SORT and risk quantification index also scored high on clinical usability. We found unclear or high risk of bias in the development of all models. The SORT showed the best combination of predictive performance and clinical usability and has been externally validated in several heterogeneous cohorts. To improve clinical uptake, full integration of reliable models with sufficient face validity within the electronic health record is imperative.
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Affiliation(s)
- J E M Vernooij
- Department of Anaesthesia, Rijnstate Hospital, the Netherlands
| | - N J Koning
- Department of Anaesthesia, Rijnstate Hospital, the Netherlands
| | - J W Geurts
- Department of Anaesthesia, Rijnstate Hospital, the Netherlands
| | - S Holewijn
- Department of Vascular Surgery, Rijnstate Hospital, the Netherlands
| | - B Preckel
- Department of Anaesthesia, Amsterdam UMC, Amsterdam, the Netherlands
| | - C J Kalkman
- University Medical Centre, Utrecht, the Netherlands
| | - L M Vernooij
- Department of Anaesthesia, University Medical Centre Utrecht, the Netherlands
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Balch JA, Ruppert MM, Shickel B, Ozrazgat-Baslanti T, Tighe PJ, Efron PA, Upchurch GR, Rashidi P, Bihorac A, Loftus TJ. Building an automated, machine learning-enabled platform for predicting post-operative complications. Physiol Meas 2023; 44:024001. [PMID: 36657179 PMCID: PMC9910093 DOI: 10.1088/1361-6579/acb4db] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/29/2022] [Accepted: 01/19/2023] [Indexed: 01/21/2023]
Abstract
Objective. In 2019, the University of Florida College of Medicine launched theMySurgeryRiskalgorithm to predict eight major post-operative complications using automatically extracted data from the electronic health record.Approach. This project was developed in parallel with our Intelligent Critical Care Center and represents a culmination of efforts to build an efficient and accurate model for data processing and predictive analytics.Main Results and Significance. This paper discusses how our model was constructed and improved upon. We highlight the consolidation of the database, processing of fixed and time-series physiologic measurements, development and training of predictive models, and expansion of those models into different aspects of patient assessment and treatment. We end by discussing future directions of the model.
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Affiliation(s)
- Jeremy A Balch
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States of America
- Department of Surgery, University of Florida, Gainesville, Florida, United States of America
| | - Matthew M Ruppert
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States of America
- Department of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Benjamin Shickel
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States of America
- Department of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Tezcan Ozrazgat-Baslanti
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States of America
- Department of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Patrick J Tighe
- Department of Anesthesiology, University of Florida, Gainesville, Florida, United States of America
| | - Philip A Efron
- Department of Surgery, University of Florida, Gainesville, Florida, United States of America
| | - Gilbert R Upchurch
- Department of Surgery, University of Florida, Gainesville, Florida, United States of America
| | - Parisa Rashidi
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States of America
- Department of Biomedical Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Azra Bihorac
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States of America
- Department of Medicine, University of Florida, Gainesville, Florida, United States of America
| | - Tyler J Loftus
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, United States of America
- Department of Surgery, University of Florida, Gainesville, Florida, United States of America
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50
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Loftus TJ, Ruppert MM, Shickel B, Ozrazgat-Baslanti T, Balch JA, Hu D, Javed A, Madbak F, Skarupa DJ, Guirgis F, Efron PA, Tighe PJ, Hogan WR, Rashidi P, Upchurch GR, Bihorac A. Overtriage, Undertriage, and Value of Care after Major Surgery: An Automated, Explainable Deep Learning-Enabled Classification System. J Am Coll Surg 2023; 236:279-291. [PMID: 36648256 PMCID: PMC9993068 DOI: 10.1097/xcs.0000000000000471] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND In single-institution studies, overtriaging low-risk postoperative patients to ICUs has been associated with a low value of care; undertriaging high-risk postoperative patients to general wards has been associated with increased mortality and morbidity. This study tested the reproducibility of an automated postoperative triage classification system to generating an actionable, explainable decision support system. STUDY DESIGN This longitudinal cohort study included adults undergoing inpatient surgery at two university hospitals. Triage classifications were generated by an explainable deep learning model using preoperative and intraoperative electronic health record features. Nearest neighbor algorithms identified risk-matched controls. Primary outcomes were mortality, morbidity, and value of care (inverted risk-adjusted mortality/total direct costs). RESULTS Among 4,669 ICU admissions, 237 (5.1%) were overtriaged. Compared with 1,021 control ward admissions, overtriaged admissions had similar outcomes but higher costs ($15.9K [interquartile range $9.8K to $22.3K] vs $10.7K [$7.0K to $17.6K], p < 0.001) and lower value of care (0.2 [0.1 to 0.3] vs 1.5 [0.9 to 2.2], p < 0.001). Among 8,594 ward admissions, 1,029 (12.0%) were undertriaged. Compared with 2,498 control ICU admissions, undertriaged admissions had longer hospital length-of-stays (6.4 [3.4 to 12.4] vs 5.4 [2.6 to 10.4] days, p < 0.001); greater incidence of hospital mortality (1.7% vs 0.7%, p = 0.03), cardiac arrest (1.4% vs 0.5%, p = 0.04), and persistent acute kidney injury without renal recovery (5.2% vs 2.8%, p = 0.002); similar costs ($21.8K [$13.3K to $34.9K] vs $21.9K [$13.1K to $36.3K]); and lower value of care (0.8 [0.5 to 1.3] vs 1.2 [0.7 to 2.0], p < 0.001). CONCLUSIONS Overtriage was associated with low value of care; undertriage was associated with both low value of care and increased mortality and morbidity. The proposed framework for generating automated postoperative triage classifications is reproducible.
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Affiliation(s)
- Tyler J Loftus
- From the University of Florida Intelligent Critical Care Center, Gainesville, FL (Loftus, Ruppert, Shickel, Ozrazgat-Baslanti, Balch, Hu, Rashidi, Bihorac)
- Departments of Surgery (Loftus, Balch, Hu, Efron, Upchurch, Bihorac), University of Florida Health, Gainesville, FL
| | - Matthew M Ruppert
- From the University of Florida Intelligent Critical Care Center, Gainesville, FL (Loftus, Ruppert, Shickel, Ozrazgat-Baslanti, Balch, Hu, Rashidi, Bihorac)
- Medicine (Ruppert, Shickel, Ozrazgat-Baslanti, Bihorac), University of Florida Health, Gainesville, FL
| | - Benjamin Shickel
- From the University of Florida Intelligent Critical Care Center, Gainesville, FL (Loftus, Ruppert, Shickel, Ozrazgat-Baslanti, Balch, Hu, Rashidi, Bihorac)
- Medicine (Ruppert, Shickel, Ozrazgat-Baslanti, Bihorac), University of Florida Health, Gainesville, FL
| | - Tezcan Ozrazgat-Baslanti
- From the University of Florida Intelligent Critical Care Center, Gainesville, FL (Loftus, Ruppert, Shickel, Ozrazgat-Baslanti, Balch, Hu, Rashidi, Bihorac)
- Medicine (Ruppert, Shickel, Ozrazgat-Baslanti, Bihorac), University of Florida Health, Gainesville, FL
| | - Jeremy A Balch
- From the University of Florida Intelligent Critical Care Center, Gainesville, FL (Loftus, Ruppert, Shickel, Ozrazgat-Baslanti, Balch, Hu, Rashidi, Bihorac)
- Departments of Surgery (Loftus, Balch, Hu, Efron, Upchurch, Bihorac), University of Florida Health, Gainesville, FL
- Biomedical Engineering (Balch, Rashidi), University of Florida, Gainesville, FL
- Computer and Information Science and Engineering (Balch, Rashidi), University of Florida, Gainesville, FL
- Electrical and Computer Engineering (Balch, Rashidi), University of Florida, Gainesville, FL
| | - Die Hu
- From the University of Florida Intelligent Critical Care Center, Gainesville, FL (Loftus, Ruppert, Shickel, Ozrazgat-Baslanti, Balch, Hu, Rashidi, Bihorac)
- Departments of Surgery (Loftus, Balch, Hu, Efron, Upchurch, Bihorac), University of Florida Health, Gainesville, FL
| | - Adnan Javed
- Departments of Emergency Medicine (Javed, Guirgis), University of Florida College of Medicine, Jacksonville, FL
- Critical Care Medicine (Javed), University of Florida College of Medicine, Jacksonville, FL
| | - Firas Madbak
- Surgery (Madbak, Skarupa), University of Florida College of Medicine, Jacksonville, FL
| | - David J Skarupa
- Surgery (Madbak, Skarupa), University of Florida College of Medicine, Jacksonville, FL
| | - Faheem Guirgis
- Departments of Emergency Medicine (Javed, Guirgis), University of Florida College of Medicine, Jacksonville, FL
| | - Philip A Efron
- Departments of Surgery (Loftus, Balch, Hu, Efron, Upchurch, Bihorac), University of Florida Health, Gainesville, FL
| | - Patrick J Tighe
- Anesthesiology (Tighe), University of Florida Health, Gainesville, FL
- Orthopedics (Tighe), University of Florida Health, Gainesville, FL
- Information Systems/Operations Management (Tighe), University of Florida Health, Gainesville, FL
| | - William R Hogan
- Department of Health Outcomes and Biomedical Informatics, College of Medicine (Hogan), University of Florida, Gainesville, FL
| | - Parisa Rashidi
- From the University of Florida Intelligent Critical Care Center, Gainesville, FL (Loftus, Ruppert, Shickel, Ozrazgat-Baslanti, Balch, Hu, Rashidi, Bihorac)
- Biomedical Engineering (Balch, Rashidi), University of Florida, Gainesville, FL
- Computer and Information Science and Engineering (Balch, Rashidi), University of Florida, Gainesville, FL
- Electrical and Computer Engineering (Balch, Rashidi), University of Florida, Gainesville, FL
| | - Gilbert R Upchurch
- Departments of Surgery (Loftus, Balch, Hu, Efron, Upchurch, Bihorac), University of Florida Health, Gainesville, FL
| | - Azra Bihorac
- From the University of Florida Intelligent Critical Care Center, Gainesville, FL (Loftus, Ruppert, Shickel, Ozrazgat-Baslanti, Balch, Hu, Rashidi, Bihorac)
- Departments of Surgery (Loftus, Balch, Hu, Efron, Upchurch, Bihorac), University of Florida Health, Gainesville, FL
- Medicine (Ruppert, Shickel, Ozrazgat-Baslanti, Bihorac), University of Florida Health, Gainesville, FL
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