Predictors of follow-up of pediatric stone patients after surgical intervention

Objective

We investigated if socioeconomic and demographic factors predict post-surgical follow-up in pediatric stone patients.

Materials and methods

All patients having kidney stone surgery at a single academic pediatric hospital over a 5 year period (2016–2020) were identified through the use of CPT® codes specific to ureteroscopy, shock wave lithotripsy, and percutaneous nephrolithotomy. Electronic charts were reviewed for patient demographics, stone characteristics, and characteristics of intervention. Unique patients with a scheduled post-operative follow-up office visit following a first time stone surgery were included in the patient cohort (Figure 1). Primary outcome was attendance of a scheduled provider visit within 6 months after surgery. Secondary outcomes included completion of scheduled post-operative imaging and 24-h urine study. Univariable analysis was performed.

Results

195 pediatric patients, a median age of 15.4 years (IQR: 11.34, 17.14) at surgery, were identified. The majority were non-Hispanic white (86.2%) and female (62.1%). Most had undergone ureteroscopy alone (85.6%). Of the 195 patients, 146 (74.9%) attended a post-operative office visit. Indicators of lower socioeconomic status, such as having public insurance and being from a single-parent home, were associated with not attending a scheduled follow-up visit (p < 0.01 and p = 0.02, respectively). Patients with a pre-operative urology clinic visit were more likely to follow-up with a clinic visit (p = 0.02), while those with a larger total stone burden treated were more likely to undergo ordered imaging (p < 0.01).

Discussion

We found that indicators of lower socioeconomic status, such as having public insurance status and being in a single-parent household, were associated with lower pediatric follow-up compliance after kidney stone surgery, while patients seen in the urology clinic preoperatively were more likely to attend follow-up.

Conclusion

Identifying factors that may predict non-compliance could be used to help at-risk patient populations.