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Ahmed HS. Advanced statistical methods for hazard modeling in cardiothoracic surgery: a comprehensive review of techniques and approaches. Indian J Thorac Cardiovasc Surg 2024; 40:633-644. [PMID: 39156066 PMCID: PMC11329482 DOI: 10.1007/s12055-024-01799-2] [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: 05/25/2024] [Revised: 07/04/2024] [Accepted: 07/04/2024] [Indexed: 08/20/2024] Open
Abstract
Hazard modeling in cardiothoracic surgery, crucial for understanding patient outcomes, utilizes survival analysis like the Cox proportional hazards model. Kaplan-Meier curves are employed in survival analysis to represent the probability of survival over time. While Cox assumes proportional hazards, the Fine-Gray model deals with competing risks. Parametric models (e.g., Weibull) specify survival distributions, unlike Cox. Bayesian analysis integrates prior knowledge with data. Machine learning, including decision trees and support vector machines, enhances risk prediction by analyzing extensive datasets. However, it is important to note that whatever new approaches one may adopt will enhance the quality of risk assessment and not the risk assessment as such. Preprocessing is vital for data quality in complex cardiovascular datasets, alongside robust validation methods like cross-validation for model reliability across patient cohorts.
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Affiliation(s)
- H. Shafeeq Ahmed
- Bangalore Medical College and Research Institute, K.R Road, Bangalore, 560002 Karnataka India
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Liu GXH, Milne T, Xu W, Varghese C, Keane C, O'Grady G, Bissett IP, Wells CI. Risk prediction algorithms for prolonged postoperative ileus: A systematic review. Colorectal Dis 2024; 26:1101-1113. [PMID: 38698504 DOI: 10.1111/codi.17010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 03/12/2024] [Accepted: 03/12/2024] [Indexed: 05/05/2024]
Abstract
AIM Prolonged postoperative ileus (PPOI) is common and is associated with a significant healthcare burden. Previous studies have attempted to predict PPOI clinically using risk prediction algorithms. The aim of this work was to systematically review and compare risk prediction algorithms for PPOI following colorectal surgery. METHOD A systematic literature search was conducted using MEDLINE, Embase, Web of Science and CINAHL Plus. Studies that developed and/or validated a risk prediction algorithm for PPOI in adults following colorectal surgery were included. Data were collected on study design, population and operative characteristics, the definition of PPOI used and risk prediction algorithm design and performance. Quality appraisal was assessed using the PROBAST tool. RESULTS Eleven studies with 87 549 participants were included in our review. Most were retrospective, single-centre analyses (6/11, 55%) and rates of PPOI varied from 10% to 28%. The most commonly used variables were sex (8/11, 73%), age (6/11, 55%) and surgical approach (5/11, 45%). Area under the curve ranged from 0.68-0.78, and only three models were validated. However, there was significant variation in the definition of PPOI used. No study reported sensitivity, specificity or positive/negative predictive values. CONCLUSION Currently available risk prediction algorithms for PPOI appear to discriminate moderately well, although there is a lack of validation data. Future studies should aim to use a standardized definition of PPOI, comprehensively report model performance and validate their findings using internal and external methodologies.
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Affiliation(s)
- Gordon Xin Hua Liu
- Department of Surgery, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Tony Milne
- Department of General Surgery, Auckland City Hospital, Te Whatu Ora Te Toka Tumai Auckland, Auckland, New Zealand
- Department of General Surgery, Middlemore Hospital, Te Whatu Ora Counties Manukau, Auckland, New Zealand
| | - William Xu
- Department of Surgery, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Chris Varghese
- Department of Surgery, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Celia Keane
- Department of Surgery, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Department of General Surgery, Whangarei Hospital, Te Whatu Ora Te Tai Tokerau, Northland, New Zealand
| | - Greg O'Grady
- Department of Surgery, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Department of General Surgery, Auckland City Hospital, Te Whatu Ora Te Toka Tumai Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Ian P Bissett
- Department of Surgery, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Department of General Surgery, Auckland City Hospital, Te Whatu Ora Te Toka Tumai Auckland, Auckland, New Zealand
| | - Cameron I Wells
- Department of Surgery, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Department of General Surgery, Auckland City Hospital, Te Whatu Ora Te Toka Tumai Auckland, Auckland, New Zealand
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Ying D, Lu M, Zhi Y, Shi P, Cao L, Wang Q, Zhang Y, Zhang J. External validation of the pediatric International IgA Nephropathy Prediction Tool in a central China cohort. Clin Exp Nephrol 2024; 28:59-66. [PMID: 37713045 DOI: 10.1007/s10157-023-02402-5] [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: 06/08/2023] [Accepted: 08/29/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND This study aimed to externally validate the pediatric International IgA Nephropathy (IgAN) Prediction Tool updated from the adult IgAN Prediction Tool. METHODS 439 children with biopsy-confirmed idiopathic IgAN were enrolled in this external validation study. The primary outcome was a 30% decline in eGFR or end-stage kidney disease. We evaluated the discrimination using Harrell's C-index, the receiver operating characteristic (ROC) curve, and Kaplan-Meier curves for four risk groups (< 16th [low risk], ∼16 to < 50th [intermediate risk], ∼50 to < 84th [high risk], and ≥ 84th percentiles [highest risk] of linear predictor). Calibration was assessed using calibration plots. RESULTS The median follow-up time of the 439 patients was 4.5 (2.7-6.8) years, and 27 patients reached the primary outcome. Compared with the reported cohorts, our cohort was more contemporary, with milder proteinuria at biopsy, and had lower proportions of S1 and T1 lesions. Harrell's C-index and area under the ROC curve at 5 years were < 0.7 for both the models with and without race. The Kaplan-Meier curves of the risk groups were not well separated for the two models, only separated completely between the highest-risk group and the others for the model without race. The two models generally overestimated the risk of the primary outcome, CONCLUSION: The model without race could accurately distinguish the highest-risk patients from patients with low, intermediate, and high risk for kidney progression. Discrimination and calibration for the full model with or without race were unsatisfactory in this contemporary cohort in central China.
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Affiliation(s)
- Daojing Ying
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Mengke Lu
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Yuanzhao Zhi
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Peipei Shi
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Lu Cao
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Qin Wang
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Yingying Zhang
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Jianjiang Zhang
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.
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Verdial F, Farjah F. A Prediction Model for Nodal Disease: Practical Considerations in the Evaluation of Model Performance: Reply. Ann Thorac Surg 2020; 110:747. [PMID: 32335019 DOI: 10.1016/j.athoracsur.2020.03.054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 03/19/2020] [Indexed: 11/29/2022]
Affiliation(s)
- Francys Verdial
- Department of Surgery, University of Washington, 1959 NE-Pacific St, Seattle, WA 98195.
| | - Farhood Farjah
- Department of Surgery, University of Washington, 1959 NE-Pacific St, Seattle, WA 98195
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Farjah F. Commentary: Predicting diagnostic outcomes may aid clinical decision making. J Thorac Cardiovasc Surg 2020; 159:2508-2509. [PMID: 32007247 DOI: 10.1016/j.jtcvs.2019.12.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 12/09/2019] [Accepted: 12/09/2019] [Indexed: 10/25/2022]
Affiliation(s)
- Farhood Farjah
- Department of Surgery, University of Washington, Seattle, Wash.
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