Souwer ETD, Bastiaannet E, Steyerberg EW, Dekker JWT, van den Bos F, Portielje JEA. Risk prediction models for postoperative outcomes of colorectal cancer surgery in the older population - a systematic review.
J Geriatr Oncol 2020;
11:1217-1228. [PMID:
32414672 DOI:
10.1016/j.jgo.2020.04.006]
[Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 01/17/2020] [Accepted: 04/16/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND
An increasing number of patients with Colorectal Cancer (CRC) is 65 years or older. We aimed to systematically review existing clinical prediction models for postoperative outcomes of CRC surgery, study their performance in older patients and assess their potential for preoperative decision making.
METHODS
A systematic search in Pubmed and Embase for original studies of clinical prediction models for outcomes of CRC surgery. Bias and relevance for preoperative decision making with older patients were assessed using the CHARMS guidelines.
RESULTS
26 prediction models from 25 publications were included. The average age of included patients ranged from 61 to 76. Two models were exclusively developed for 65 and older. Common outcomes were mortality (n = 10), anastomotic leakage (n = 7) and surgical site infections (n = 3). No prediction models for quality of life or physical functioning were identified. Age, gender and ASA score were common predictors; 12 studies included intraoperative predictors. For the majority of the models, bias for model development and performance was considered moderate to high.
CONCLUSIONS
Prediction models are available that address mortality and surgical complications after CRC surgery. Most models suffer from methodological limitations, and their performance for older patients is uncertain. Models that contain intraoperative predictors are of limited use for preoperative decision making. Future research should address the predictive value of geriatric characteristics to improve the performance of prediction models for older patients.
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