Laparoscopic Partial Nephrectomy Models

The following patient parameters were recorded during the retrospective chart review (334 patients): age, sex, surgeon, body mass index, American society in anesthasiology grade, hypertension, prior pancreatitis, prior abdominal surgery, prior deep venous thrombus, peripheral vascular disease, smoking, coronary astery disease, gastic erophageal refull disease, anxiety, hyperlipidemia, depression, renal stone disease, constipation, liver cirrhosis, hepatitis C, hematuria (micro or gross), chronic obstructive pulmonary disease, alcohol use, hypothyroidism, diabetes mellitus, chronic renal insufficiency, cerebrovascular accident, gout, congestive heart failure, osteoarthritis, bleeding disorder, Crohn's disease or inflammatory bowel disease, obstructive sleep apnea, polycystic kidney disease, seizures, anemia, von Hippel-Lindau disease, simple or partial nephrectomy, planned retroperitoneal versus transperitoneal approach, tumor size (by computed tomography), solitary kidney, preoperative serum creatinine, tumor location-anterior, posterior, medial, lateral, mid, lower, upper, exophytic, parenchymal, up to renal sinus, central, peripheral, abuts the collecting system and duration of hospital stay (in days).

These "design group" data were used to create two duration of hospital stay prediction algorithms: to predict if the duration of hospital stay would be lesser or greater than two days, and to predict of the duration of hospital stay would be lesser or greater than one day.

The design group was initially divided into two groups: (i) patients with a duration of hospital stay less than or equal to two days (147 patients) and patients with a duration of hospital stay greater than two days (187 patients). Univariate and multivariate logistic regression analysis comparing the above patient parameters between these groups identified significant predictors of duration of hospital stay (lesser or greater than two days). These results were then used to generate a linear regression algorithm to predict the duration of hospital stay (lesser or greater than two days) (24,25).

The same process was repeated to design the lesser or greater than one day duration of hospital stay predictor. The design group in this case was divided into: (i) patients with a duration of hospital stay less than or equal to one day (28 patients) and patients with a duration of hospital stay greater than one day (306 patients).

These models were then prospectively tested on a separate 19 patient "test group" to assess duration of hospital stay prediction accuracy. Testing was performed on this separate group to avoid any training bias.

These "design group" data were used to create two duration of hospital stay prediction algorithms: to predict if the duration of hospital stay would be greater or lesser than two days, and to predict if the duration of hospital stay would be greater or lesser than one day.

Multiple logistic regression analysis identified chronic obstructive pulmonary disease, planned transperitoneal approach and elevated preoperative serum creatinine as independent significant predictors of a duration of hospital stay greater than two days.

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