Development and validation of a nomogram for predicting difficult radial artery cannulation in adult surgical patients

开发和验证用于预测成人外科患者桡动脉插管困难的列线图

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Abstract

BACKGROUND: Radial artery cannulation is an invasive procedure commonly performed in patients in the perioperative time, in the intensive care unit, and in other critical care settings. The current study aimed to explore the preoperative risk factors associated with difficult radial artery cannulation and develop a nomogram model for adult patients undergoing major surgery. This nomogram may optimize preoperative clinical decision-making, thereby reducing the number of puncture attempts and preventing associated complications. METHODS: This was a single-center prospective cohort study. Between December 2021 and May 2022, 530 adult surgical patients were enrolled. The patients were randomized into the training and validation cohorts at a ratio of 8:2. Radial artery cannulation was performed before the induction of anesthesia. Univariate and multivariate logistic regression analyses were performed to identify variables that were significantly associated with difficult radial artery cannulation. These variables were then incorporated into the nomogram. The discrimination and calibration abilities of the nomogram were assessed. RESULTS: One hundred and seventy-three (41.7 %) patients in the training cohort had difficult radial artery cannulation. Based on multivariate analysis, the independent risk factors were wrist circumference, anatomical abnormalities, BMI <18.5 kg/m(2), grade II hypertension, hypotension, and history of chemotherapy and stroke. The concordance indices were 0.765 (95 % confidence interval [CI]: 0.719-0.812) and 0.808 (95 % CI: 0.725-0.890) in the training and validation cohorts, respectively. The calibration curve showed good agreement between the actual and predicted risks. CONCLUSIONS: A preoperative predictive model for difficult radial artery cannulation in adult patients undergoing surgery was developed and validated. This model can provide reliable data for optimizing preoperative clinical decision-making.

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