Development and Validation of a Model to Predict Ureteral Stent Placement Following Ureteroscopy: Results From a Statewide Collaborative.

OBJECTIVE: To develop and validate a model to predict whether patients undergoing ureteroscopy (URS) will receive a stent. METHODS: Using registry data obtained from the Michigan Urological Surgery Improvement Collaborative Reducing Operative Complications from Kidney Stones initiative, we identified patients undergoing URS from 2016 to 2020. We used patients' age, sex, body mass index, size and location of the largest stone, current stent in place, history of any kidney stone procedure, procedure type, and acuity to fit a multivariable logistic regression model to a derivation cohort consisting of a random two-thirds of episodes. Model discrimination and calibration were evaluated in the validation cohort. A sensitivity analysis examined urologist variation using generalized mixed-effect models. RESULTS: We identified 15,048 URS procedures, of which 11,471 (76%) had ureteral stents placed. Older age, male sex, larger stone size, the largest stone being in the ureteropelvic junction, no prior stone surgery, no stent in place, a planned procedure type of laser lithotripsy, and urgent procedure were associated with a higher risk of stent placement. The model achieved an area under the receiver operating characteristic curve of 0.69 (95% CI 0.67, 0.71). Incorporating urologist-level variation improved the area under the receiver operating characteristic curve to 0.83 (95% CI 0.82, 0.84). CONCLUSION: Using a large clinical registry, we developed a multivariable regression model to predict ureteral stent placement following URS. Though well-calibrated, the model had modest discrimination due to heterogeneity in practice patterns in stent placement across urologists.

Intestinal tumor suppression in ApcMin/+ mice by prostaglandin D2 receptor PTGDR.

Our earlier work showed that knockout of hematopoietic prostaglandin D synthase (HPGDS, an enzyme that produces prostaglandin D2) caused more adenomas in Apc(Min/+) mice. Conversely, highly expressed transgenic HPGDS allowed fewer tumors. Prostaglandin D2 (PGD2) binds to the prostaglandin D2 receptor known as PTGDR (or DP1). PGD2 metabolites bind to peroxisome proliferator-activated receptor γ (PPARG). We hypothesized that Ptgdr or Pparg knockouts may raise numbers of tumors, if these receptors take part in tumor suppression by PGD2. To assess, we produced Apc(Min/+) mice with and without Ptgdr knockouts (147 mice). In separate experiments, we produced Apc(Min/+) mice expressing transgenic lipocalin-type prostaglandin D synthase (PTGDS), with and without heterozygous Pparg knockouts (104 mice). Homozygous Ptgdr knockouts raised total numbers of tumors by 30-40% at 6 and 14 weeks. Colon tumors were not affected. Heterozygous Pparg knockouts alone did not affect tumor numbers in Apc(Min/+) mice. As mentioned above, our Pparg knockout assessment also included mice with highly expressed PTGDS transgenes. Apc(Min/+) mice with transgenic PTGDS had fewer large adenomas (63% of control) and lower levels of v-myc avian myelocytomatosis viral oncogene homolog (MYC) mRNA in the colon. Heterozygous Pparg knockouts appeared to blunt the tumor-suppressing effect of transgenic PTGDS. However, tumor suppression by PGD2 was more clearly mediated by receptor PTGDR in our experiments. The suppression mechanism did not appear to involve changes in microvessel density or slower proliferation of tumor cells. The data support a role for PGD2 signals acting through PTGDR in suppression of intestinal tumors.