A Predictive Model for Determining Patients Not Requiring Prolonged Hospital Length of Stay After Elective Primary Total Hip Arthroplasty.

BACKGROUND: Hospital length of stay (LOS) is an important quality metric for total hip arthroplasty. Accurately predicting LOS is important to expectantly manage bed utilization and other hospital resources. We aimed to develop a predictive model for determining patients who do not require prolonged LOS. METHODS: This was a retrospective single-institution study analyzing patients undergoing elective unilateral primary total hip arthroplasty from 2014 to 2016. The primary outcome of interest was LOS less than or equal to the expected duration, defined as ≤3 days. Multivariable logistic regression was performed to generate a model for this outcome, and a point-based calculator was designed. The model was built on a training set, and performance was assessed on a validation set. The area under the receiver operating characteristic curve and the Hosmer-Lemeshow test were calculated to determine discriminatory ability and goodness-of-fit, respectively. Predictive models using other machine learning techniques (ridge regression, Lasso, and random forest) were created, and model performances were compared. RESULTS: The point-based score calculator included 9 variables: age, opioid use, metabolic equivalents score, sex, anemia, chronic obstructive pulmonary disease, hypertension, obesity, and primary anesthesia type. The area under the receiver operating characteristic curve of the calculator on the validation set was 0.735 (95% confidence interval, 0.675-0.787) and demonstrated adequate goodness-of-fit (Hosmer-Lemeshow test, P = .37). When using a score of 12 as a threshold for predicting outcome, the positive predictive value was 86.1%. CONCLUSIONS: A predictive model that can help identify patients at higher odds for not requiring a prolonged hospital LOS was developed and may aid hospital administrators in strategically planning bed availability to reduce both overcrowding and underutilization when coordinating with surgical volume.

Preoperative Renal Insufficiency: Underreporting and Association With Readmission and Major Postoperative Morbidity in an Academic Medical Center.

BACKGROUND: Making a formal diagnosis of chronic kidney disease (CKD) in the preoperative setting may be challenging because of lack of longitudinal data. We explored the predictive value of a single reduced preoperative estimated glomerular filtration rate (eGFR) value on adverse patient outcomes in the first 30 days after elective surgery. We compared the rate of major postoperative adverse events, including 30-day readmission rate, hospital length of stay, infection, acute kidney injury (AKI), and myocardial infarction across patients with declining preoperative eGFR values. We hypothesized that there is an association between decreasing preoperative eGFR values and major postoperative morbidity including readmission within 30 days of discharge and that the reasons for unplanned readmissions may be associated with poor preoperative renal function. METHODS: This was a retrospective analysis of the electronic health record of 39 989 adult patients who underwent elective surgery between June 2011 and July 2013 at our institution. Patients with reduced eGFR (<60 mL/min/1.73 m) were identified and categorized by the stages of CKD that correlated with the preoperative eGFR value. Odds of readmission to our hospital within 30 days, as well as new diagnosis of AKI, myocardial infarction, and infection, were determined with multivariate logistic regression. The subset of patients who were readmitted within 30 days also were subdivided further into patients who had an eGFR <60 mL/min/1.73 m and those with an eGFR ≥60 mL/min/1.73 m, as well as whether the readmission was planned or unplanned. RESULTS: Of the 4053 patients with eGFR <60 mL/min/1.73 m, 3290 (81.2%) did not carry a preoperative diagnosis of CKD. Adjusted odds ratios of being readmitted were 1.48 (99% confidence interval [CI], 1.18-1.87; P < .001) for eGFR 30 to 44 mL/min/1.73 m to 2.06 (99% CI, 1.32-3.23; P < .001) for eGFR <15 mL/min/1.73 m compared with patients with a preoperative eGFR value ≥60 mL/min/1.73 m. Patients with a lower eGFR also demonstrated increasing odds of AKI from 2.78 (99% CI, 1.86-4.17; P < .001) for eGFR 45 to 59 mL/min/1.73 m to 3.81 (99% CI, 1.68-8.16; P < .001) for eGFR <15 mL/min/1.73 m. CONCLUSIONS: This study highlights that preoperative renal insufficiency may be underreported and appears to be significantly associated with postoperative complications. It extends the association between a single low preoperative eGFR and postoperative morbidity to a broader range of surgical populations than previously described. Our results suggest that preoperative calculation of eGFR may be a relatively low-cost, readily available tool to identify patients who are at an increased risk of readmission within 30 days of surgery and postoperative morbidity in patients presenting for elective surgery.

Predicting patients requiring discharge to post-acute care facilities following primary total hip replacement: Does anesthesia type play a role?

STUDY OBJECTIVE: We sought to develop a predictive model for discharge to post-acute care facilities in patients undergoing unilateral total hip replacement (THR). Furthermore, we sought to determine if the use of neuraxial anesthesia was an important covariate for the predictive model. DESIGN: Retrospective observational study. SETTING: Preoperative care and operating room at a single institution. PATIENTS: Patients (n = 960) who underwent an elective primary THR between 2014 and 2016. INTERVENTIONS: No intervention was performed. MEASUREMENTS: We collected variables that were known preoperatively including age, sex, body mass index (BMI), preoperative opioid use, functional status based on metabolic equivalents (METS), preoperative anemia, thrombocytopenia, osteoarthritis and contralateral osteoarthritis grade, anesthesia type, comorbidities and surgical approach. We then performed multivariable logistic regression to develop a predictive model. MAIN RESULTS: Female sex, preoperative opioid use, older age, general anesthesia, anemia, hypertension, a psychiatric diagnosis, use of dialysis, metabolic equivalents <4 and obesity are all risk factors for a post-acute facility discharge. The use of general anesthesia compared to neuraxial anesthesia was associated with increased odds (odds ratio 1.98, 95% confidence interval 1.31-3.00, p = 0.001) for post-acute facility discharge. Model performance was assessed using ten-fold cross-validation - the average area under the receiver operating characteristic curve calculated was 0.794. CONCLUSIONS: We developed a predictive model for post-acute care facility discharge following THR. The use of neuraxial anesthesia was associated with decreased odds for post-acute care facility discharge.