The surgical treatment of small renal masses has shifted from open to minimally invasive approaches. Preoperative blood typing and product orders often mirror the practices of the open era. We aim to define the rate of transfusion after robot-assisted partial laparoscopic nephrectomy (RAPN) at an academic medical center and the costs associated with current practice.
A retrospective review of an institutional database was utilized to identify patients who underwent RAPN and transfusion of blood products. Patient, tumor and operative variables were identified.
From 2008 to 2021, 804 patients underwent RAPN, with 9 (1.1%) patients requiring a transfusion. Comparison of the transfused group with nontransfused patients yielded a significant difference in mean operative blood loss (527.8 ml vs 162.5 ml, p <0.0001), R.E.N.A.L. (for radius, exophytic/endophytic, nearness of tumor to collecting system, anterior/posterior, location relative to polar line) nephrometry score (7.1 vs 5.9, p <0.05), hemoglobin (11.3 gm/dl vs 13.9 gm/dl, p <0.05) and hematocrit (34.2% vs 41.4%, p <0.05). The variables associated with transfusion on univariate analysis were examined for predictive capacity using logistic regression. Operative blood loss (p <0.05), nephrometry score (p=0.05), hemoglobin (p <0.05) and hematocrit (p <0.05) remained associated with a transfusion. The hospital charge for blood typing and crossmatching was $1,320 USD per patient.
With the maturity of RAPN techniques and outcomes, the extent of preoperative testing related to blood products should evolve to better reflect current procedural risks. Prioritizing testing resources for patients at increased complication risk can be based on predictive factors.
Urology practice. 2022 Jun 17 [Epub]
Matthew M Banti, Atiyeh Samadi, Mihaela E Sardiu, Hadley Wyre, Moben Mirza, David Duchene, Jeffrey M Holzbeierlein, Eugene K Lee
Department of Urology, University of Kansas Health System, Kansas City, Kansas., University of Kansas School of Medicine, Kansas City, Kansas., Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, Kansas.