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Assessment of risk factors and establishment of a nomogram model to predict early high-output ileostomy.
Langenbeck's Archives of Surgery 2023 Februrary 26
OBJECTIVE: This study evaluated the prevalence and perioperative risk factors for early high-output ileostomy (EHOI) and developed a precise nomogram model to predict the occurrence of EHOI.
METHODS: 140 patients who underwent ileostomy surgery at three hospitals in Wuhan, Hubei Province, between January 2022 and May 2022 were enrolled in this prospective cohort study. By using univariate and multifactorial logistic regression, independent risk variables for the development of EHOI were examined, and the nomogram model for predicting the risk of EHOI was created by using R software. The calibration curve and area under the receiver operating characteristic curve (ROC AUC) were used to evaluate the calibration and discrimination of the prediction model, Hosmer-Lemeshow to verify the fit of the model. Clinical impact curve (CIC) and decision curve analysis (DCA) were used to assess the model's clinical efficacy.
RESULTS: A total of 132 patients participated in the study, and the incidence of EHOI was 25.8% (34/132). The nomogram model incorporated 3 risk factors hypertension, drinking habits, and high white blood cell (WBC) count after surgery. The AUC was 0.742, and the nomogram showed great calibration and clinical validity by comparing the calibration curve, DCA, and CIC.
CONCLUSIONS: Hypertension, drinking, and high WBC was significantly correlated with EHOI. The nomogram model has great clinical value in predicting the EHOI.
METHODS: 140 patients who underwent ileostomy surgery at three hospitals in Wuhan, Hubei Province, between January 2022 and May 2022 were enrolled in this prospective cohort study. By using univariate and multifactorial logistic regression, independent risk variables for the development of EHOI were examined, and the nomogram model for predicting the risk of EHOI was created by using R software. The calibration curve and area under the receiver operating characteristic curve (ROC AUC) were used to evaluate the calibration and discrimination of the prediction model, Hosmer-Lemeshow to verify the fit of the model. Clinical impact curve (CIC) and decision curve analysis (DCA) were used to assess the model's clinical efficacy.
RESULTS: A total of 132 patients participated in the study, and the incidence of EHOI was 25.8% (34/132). The nomogram model incorporated 3 risk factors hypertension, drinking habits, and high white blood cell (WBC) count after surgery. The AUC was 0.742, and the nomogram showed great calibration and clinical validity by comparing the calibration curve, DCA, and CIC.
CONCLUSIONS: Hypertension, drinking, and high WBC was significantly correlated with EHOI. The nomogram model has great clinical value in predicting the EHOI.
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