Accurate estimation of epidural depth is critical for safe epidural anesthesia during percutaneous nephrolithotomy (PCNL). Although various imaging modalities can predict epidural depth, they increase healthcare burden and are not always feasible in emergency or bedside settings. Identifying simple, easily obtainable clinical parameters-such as sex, age, and body mass index (BMI)-for predicting epidural depth has become a research priority. However, systematic investigations specifically targeting the Chinese population remain scarce. This retrospective study aims to identify independent factors influencing epidural depth in PCNL patients and to develop simple, level-specific predictive models for different puncture sites, thereby providing a practical reference for clinical epidural anesthesia.
Percutaneous nephrolithotomy (PCNL) is the standard minimally invasive surgical procedure for removal of kidney stones larger than 2 cm and is established as the gold standard for the management of complex renal calculi. This procedure may be performed under either general anesthesia or regional anesthesia, including epidural or spinal anesthesia. Accumulating meta-analyses have demonstrated that, compared with general anesthesia, regional anesthesia is associated with reduced postoperative pain scores and lower total hospital costs, without compromising the stone-free rate. Furthermore, in awake patients undergoing regional anesthesia, respiratory movements may generate a so-called "respiratory-synchronous stone fragmentation effect," which facilitates the expulsion of stone debris. Successful epidural anesthesia relies critically on accurate identification of the epidural space and precise control of puncture depth. Insufficient puncture depth may result in improper catheter placement and subsequent block failure, whereas excessive puncture depth increases the risk of dural puncture, leading to cerebrospinal fluid leakage and post-dural puncture headache. In severe cases, inadvertent injection of a large dose of local anesthetics into the subarachnoid space can cause life-threatening cardiovascular and respiratory depression. Therefore, preprocedural estimation of the skin-to-epidural space distance is of considerable clinical importance. Although racial differences in epidural depth have been reported, systematic investigations specifically targeting the Chinese population remain scarce. Various imaging modalities, including computed tomography, magnetic resonance imaging, and ultrasound, can be used to predict epidural depth. However, these examinations increase healthcare burden and are not always feasible in emergency or bedside settings. Consequently, identifying simple, easily obtainable clinical parameters-such as sex, age, and body mass index (BMI)-for predicting epidural depth has become a research priority. In this context, the present study aimed to identify factors influencing epidural depth through a retrospective analysis of clinical data from PCNL patients and to develop simple, level-specific predictive models for different puncture sites, thereby providing a practical reference for clinical epidural anesthesia.
Study Type
OBSERVATIONAL
Enrollment
3,278
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, China
Epidural depth
Epidural depth, defined as the vertical distance from the skin surface to the epidural space measured at the time of puncture using the loss-of-resistance technique via the midline approach. Depth was recorded from the needle shaft markings to the nearest 0.1 cm.
Time frame: Perioperative/Periprocedural
Adjusted R²
The coefficient of determination adjusted for the number of predictors in the model, reflecting the proportion of variance in epidural depth explained by the predictive model at each puncture level. Higher values indicate better model fit.
Time frame: through study completion, an average of 1 month
Root mean square error (RMSE)
The square root of the average squared difference between the model-predicted epidural depth and the actual measured depth, expressed in cm. Lower values indicate better predictive accuracy of the model at each puncture level.
Time frame: through study completion, an average of 1 month
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