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Ioannou LJ, Maharaj AD, Zalcberg JR, Loughnan JT, Croagh DG, Pilgrim CH, Goldstein D, Kench JG, Merrett ND, Earnest A, Burmeister EA, White K, Neale RE, Evans SM. Prognostic models to predict survival in patients with pancreatic cancer: a systematic review. HPB (Oxford) 2022; 24:1201-1216. [PMID: 35289282 DOI: 10.1016/j.hpb.2022.01.011] [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] [Received: 05/20/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) has poor survival. Current treatments offer little likelihood of cure or long-term survival. This systematic review evaluates prognostic models predicting overall survival in patients diagnosed with PDAC. METHODS We conducted a comprehensive search of eight electronic databases from their date of inception through to December 2019. Studies that published models predicting survival in patients with PDAC were identified. RESULTS 3297 studies were identified; 187 full-text articles were retrieved and 54 studies of 49 unique prognostic models were included. Of these, 28 (57.1%) were conducted in patients with advanced disease, 17 (34.7%) with resectable disease, and four (8.2%) in all patients. 34 (69.4%) models were validated, and 35 (71.4%) reported model discrimination, with only five models reporting values >0.70 in both derivation and validation cohorts. Many (n = 27) had a moderate to high risk of bias and most (n = 33) were developed using retrospective data. No variables were unanimously found to be predictive of survival when included in more than one study. CONCLUSION Most prognostic models were developed using retrospective data and performed poorly. Future research should validate instruments performing well locally in international cohorts and investigate other potential predictors of survival.
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Affiliation(s)
- Liane J Ioannou
- Public Health and Preventive Medicine, Monash University, Victoria, Australia.
| | - Ashika D Maharaj
- Public Health and Preventive Medicine, Monash University, Victoria, Australia
| | - John R Zalcberg
- Public Health and Preventive Medicine, Monash University, Victoria, Australia
| | - Jesse T Loughnan
- Public Health and Preventive Medicine, Monash University, Victoria, Australia
| | - Daniel G Croagh
- Department of Surgery, Monash Health, Monash University, Victoria, Australia
| | - Charles H Pilgrim
- Department of Surgery, Alfred Health, Monash University, Victoria, Australia
| | - David Goldstein
- Prince of Wales Clinical School, UNSW Medicine, NSW, Australia
| | - James G Kench
- Royal Prince Alfred Hospital, Camperdown, NSW, Australia; Central Clinical School, University of Sydney, NSW, Australia
| | - Neil D Merrett
- School of Medicine, Western Sydney University, NSW, Australia
| | - Arul Earnest
- Public Health and Preventive Medicine, Monash University, Victoria, Australia
| | | | - Kate White
- Sydney Nursing School, University of Sydney, NSW, Australia
| | - Rachel E Neale
- QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
| | - Sue M Evans
- Public Health and Preventive Medicine, Monash University, Victoria, Australia
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Hang J, Wu L, Zhu L, Sun Z, Wang G, Pan J, Zheng S, Xu K, Du J, Jiang H. Prediction of overall survival for metastatic pancreatic cancer: Development and validation of a prognostic nomogram with data from open clinical trial and real-world study. Cancer Med 2018; 7:2974-2984. [PMID: 29856121 PMCID: PMC6051216 DOI: 10.1002/cam4.1573] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 04/29/2018] [Accepted: 05/01/2018] [Indexed: 12/20/2022] Open
Abstract
It is necessary to develop prognostic tools of metastatic pancreatic cancer (MPC) for optimizing therapeutic strategies. Thus, we tried to develop and validate a prognostic nomogram of MPC. Data from 3 clinical trials (NCT00844649, NCT01124786, and NCT00574275) and 133 Chinese MPC patients were used for analysis. The former 2 trials were taken as the training cohort while NCT00574275 was used as the validation cohort. In addition, 133 MPC patients treated in China were taken as the testing cohort. Cox regression model was used to investigate prognostic factors in the training cohort. With these factors, we established a nomogram and verified it by Harrell's concordance index (C‐index) and calibration plots. Furthermore, the nomogram was externally validated in the validation cohort and testing cohort. In the training cohort (n = 445), performance status, liver metastasis, Carbohydrate antigen 19‐9 (CA19‐9) log‐value, absolute neutrophil count (ANC), and albumin were independent prognostic factors for overall survival (OS). A nomogram was established with these factors to predict OS and survival probabilities. The nomogram showed an acceptable discrimination ability (C‐index: .683) and good calibration, and was further externally validated in the validation cohort (n = 273, C‐index: .699) and testing cohort (n = 133, C‐index: .653).The nomogram total points (NTP) had the potential to stratify patients into 3‐risk groups with median OS of 11.7, 7.0 and 3.7 months (P < .001), respectively. In conclusion, the prognostic nomogram with NTP can predict OS for patients with MPC with considerable accuracy.
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Affiliation(s)
- Junjie Hang
- Department of Oncology, Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou, China
| | - Lixia Wu
- Department of Oncology, Shanghai JingAn District ZhaBei Central Hospital, Shanghai, China
| | - Lina Zhu
- Department of Oncology, Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou, China
| | - Zhiqiang Sun
- Department of Oncology, Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou, China
| | - Ge Wang
- Department of Oncology, Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou, China
| | - Jingjing Pan
- Department of Oncology, Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou, China
| | - Suhua Zheng
- Department of Oncology, Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou, China
| | - Kequn Xu
- Department of Oncology, Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou, China
| | - Jiadi Du
- Center of Data Mining and Business Analytics, Rutgers Business School, Newark, NJ, USA
| | - Hua Jiang
- Department of Oncology, Changzhou No.2 People's Hospital, the Affiliated Hospital of Nanjing Medical University, Changzhou, China
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