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Patient characteristics predicting prolonged length of hospital stay following robotic-assisted radical prostatectomy

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Objective: The objective of this study is to determine the preoperative patient characteristics predicting prolonged length of hospital stay (pLOS) following robotic-assisted radical prostatectomy (RARP). Methods: The National Surgical Quality… Click to show full abstract

Objective: The objective of this study is to determine the preoperative patient characteristics predicting prolonged length of hospital stay (pLOS) following robotic-assisted radical prostatectomy (RARP). Methods: The National Surgical Quality Improvement Program (NSQIP) database was used to select patients who underwent RARP without other concomitant surgeries between 2008 and 2016. Patients’ demographics, comorbidities, and laboratory markers were collected to evaluate their role in predicting pLOS. The pLOS was defined as length of stay (LOS) >2 days. A multinomial logistic regression was constructed adjusting for postoperative surgical complications to assess for the predictors of pLOS. Results: We obtained data for 31,253 patients of which 20,774 (66.5%) patients stayed ⩽1 day, 6993 (22.4%) patients stayed for 2 days, and 3486 (11.2%) patients stayed for >2 days. Demographic variables – including body mass index (BMI) <18.5: odds ratio (OR) = 2.8, 95% confidence interval (CI) = [1.7–4.8]; smoking: OR = 1.2, 95% CI = [1.1–1.4]; and dependent functional status: OR = 3.1, 95% CI = [1.6–6.0] – were predictors of pLOS. Comorbidities – such as heart failure: OR = 4.6, 95% CI = [2.0–10.8]; being dialysis dependent: OR = 2.7, 95% CI = [1.4–5.0]; and predisposition to bleeding: OR = 2.0, 95% CI =  [1.5–2.7] – were the strongest predictors of extended hospitalization. In addition, pLOS was more likely to be associated with postoperative bleeding, renal, or pulmonary complications. Conclusion: Preoperative patient characteristics and comorbidities can predict pLOS. These findings can be used preoperatively for risk assessment and patient counseling.

Keywords: characteristics predicting; length hospital; patient; prolonged length; patient characteristics; predicting prolonged

Journal Title: Therapeutic Advances in Urology
Year Published: 2022

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