A New Predictive Nomogram for the Risk of Delayed Incision Healing After Open Posterior Lumbar Surgery: A...

This was a primary research study.

A risk nomogram was established and externally validated by exploring the related risk factors for delayed incision healing in patients undergoing open posterior lumbar surgery.

The use of a nomogram model to predict prognosis in patients with delayed incision healing is an evolving field given the complex presentation of patients with this condition.

This study reviewed 954 patients with data collected from January 2017 to December 2021 who were randomized into a training set and a validation set (7:3). We built a prediction model based on a training set of 616 patients. The "least absolute shrinkage and selection operator" regression model was applied to screen out the optimal prediction features, and binary logistic regression was used to develop a prediction model. The discrimination, calibration, and clinical applicability of the prediction model were assessed by using the area under the curve, C -index, calibration curve, and decision curve analysis.

Postoperative delayed incision healing occurred in 214 (24.4%) patients. The least absolute shrinkage and selection operator regression model showed that smoking, white blood cell count, infection, diabetes, and obesity were involved in delayed incision healing ( P ≠ 0). A binary logistic regression model confirmed that smoking [odds ratio (OR) = 3.854, 95% CI: 1.578~9.674, P = 0.003], infection (OR = 119.524, 95% CI: 59.430~263.921, P < 0.001), diabetes (OR = 3.935, 95% CI: 1.628~9.703, P = 0.003), and obesity (OR = 9.906, 95% CI: 4.435~23.266, P < 0.001) were predictors of delayed incision healing, and a nomogram model was established. The area under the curve was 0.917 (95% CI: 0.876-0.959). The calibration curve showed good consistency. Decision curve analysis showed that when the risk threshold of delayed incision healing was >5%, the use of this nomogram was more clinically valuable.

Smoking, infection, diabetes, and obesity are risk factors for delayed incision healing. The nomogram model could be used to predict the risk of delayed incision healing and could provide a reference for early clinical intervention.