Longitudinal assessment of post-ureteroscopic laser lithotripsy pain and opioid consumption using text messaging: Tailoring pain management to the patient


Introduction: We identified patterns of postoperative pain and opioid consumption and associated factors following ureteroscopy for kidney stones by acquiring real-time data through automated text messages.

Methods: Adult patients undergoing ureteroscopy for kidney stones were prospectively enrolled to receive postoperative pain assessments and opioid consumption inquiries through daily automated text messages. Patients were prompted for pain levels (0 to 10) twice daily and opioid consumption nightly. Univariable and multivariable analyses were performed to identify factors associated with decreased time to pain resolution and increased opioid consumption.

Results: Of 62 patients enrolled 46 (74%) completed the study. Median time to pain resolution was 7 days and 75% of patients reported pain of 4 or less by postoperative day 3. Median opioid consumption was 10 pills, 25% of patients consumed no pills and 63% of pills went unused. Higher pain immediately preceding surgery (HR 0.7, p <0.001) and preoperative opioid consumption (HR 0.36, p=0.004) were predictive of increased time to pain resolution. Increased postoperative opioid consumption was associated with increased pain immediately preceding surgery (p <0.001), consumption of opioids at the time of surgery (p=0.001) and increased quantity of opioid consumption at the time of surgery (p <0.001). Preoperative renal drainage was associated with faster pain resolution (HR 2.29, p=0.017) and decreased opioid use (p=0.018).

Conclusions: Pain following ureteroscopy peaks on postoperative day 0 and decreases to zero by postoperative day 7, with patients taking a median of 10 opioids in the postoperative period. Preoperative identification of at-risk populations allows for patient specific dose escalation of opioids, which may limit future opioid overprescription.

Urology Practice
Jacob Simmering
Jacob Simmering
Assistant Professor of Internal Medicine

Health, data, and statistics.