Prediction model of stress ulcer after laparoscopic surgery for colorectal cancer established by machine learning algorithm.

Abstract

Patients with colorectal cancer (CRC) are prone to stress ulcer after laparoscopic surgery. The analysis of risk factors for stress ulcer (SU) in patients with CRC is important to reduce mortality and improve patient prognosis. To identify risk factors for SU after laparoscopic surgery for CRC, and develop a nomogram model to predict the risk of SU in these patients. Among the 135 patients with CRC, 23 patients had postoperative SU, with an incidence of 17.04%. The SU group had higher levels of heat shock protein (HSP) 70, HSP90, and gastrin (GAS) than the non-SU group. Age, lymph node metastasis, HSP70, HSP90, and GAS levels were statistically different between the two groups, but other indicators were not statistically different. Logistic regression analysis showed that age ≥ 65 years, lymph node metastasis, and increased levels of HSP70, HSP90 and GAS were all risk factors for postoperative SU in patients with CRC (P < 0.05). According to these five risk factors, the area under the ROC curve for the nomogram model was 0.988 (95%CI: 0.971-1.0); the calibration curve demonstrated excellent agreement between predicted and actual probabilities, and the Hosmer-Lemeshow goodness of fit test revealed that the difference was not statistically significant (χ2 = 0.753, P = 0.999), suggesting that the nomogram model had good discrimination, calibration, and stability. Patients with CRC aged ≥ 65 years, with lymph node metastasis and elevated HSP70, HSP90, GAS levels, are prone to post-laparoscopic surgery SU. Our nomogram model shows good predictive value.