Obesity and 30-Day Outcomes Following Minimally Invasive Nephrectomy

Minimally invasive transperitoneal partial versus radical nephrectomy in obese patients: perioperative and long-term functional outcomes from a large perspective contemporary series (RECORd2 project)

BACKGROUND

The aim of this study is to evaluate the perioperative and long-term functional outcomes of laparoscopic (LPN) and robot-assisted partial nephrectomy (RAPN) in comparison to laparoscopic radical nephrectomy (LRN) in obese patients diagnosed with renal cell carcinoma.

METHODS

Clinical data of 4325 consecutive patients from The Italian REgistry of COnservative and Radical Surgery for cortical renal tumor Disease (RECORD 2 Project) were gathered. Only patients treated with transperitoneal LPN, RAPN, or LRN with Body Mass Index (BMI) ≥30 kg/m2, clinical T1 renal tumor and preoperative estimated glomerular filtration rate (eGFR) ≥60 mL/min, were included. Perioperative, and long-term functional outcomes were examined.

RESULTS

Overall, 388 patients were included, of these 123 (31.7%), 120 (30.9%) and 145 (37.4%) patients were treated with LRN, LPN, and RAPN, respectively. No significant difference was observed in preoperative characteristics. Overall, intra and postoperative complication rates were comparable among the groups. The LRN group had a significantly increased occurrence of acute kidney injury (AKI) compared to LPN and RAPN (40.6% vs. 15.3% vs. 7.6%, P=0.001). Laparoscopic RN showed a statistically significant higher renal function decline at 60-month follow-up assessment compared to LPN and RAPN. A significant renal function loss was recorded in 30.1% of patients treated with LRN compared to 16.7% and 10.3% of patients treated with LPN and RAPN (P=0.01).

CONCLUSIONS

In obese patients, both LPN and RAPN showcased comparable complication rates and higher renal function preservation than LRN. These findings highlighted the potential benefits of minimally invasive PN over radical surgery in the context of obese individuals.

Impact of Obesity on Postoperative Complications after Lateral Skull Base Surgery: A Systematic Review

INTRODUCTION

The relationship between obesity and complications after lateral skull base tumor resection is not clear. There is conflicting evidence regarding the incidence of postoperative complications in this patient population. The purpose of this study is to examine the relationship between obesity and outcomes following lateral skull base tumor resection.

DATA SOURCES

Data were extracted from PubMed, Embase, CINAHL, and Cochrane CENTRAL.

METHODS

Included studies assessed the relationship between obesity and outcomes following lateral skull base tumor removal. Studies with ≤5 patients, pediatric patients, duplicate patient populations, or insufficient data were excluded. Two independent investigators reviewed each study for inclusion. A third reviewer served as a tie-breaker for any conflicts. Extracted data includes patient demographics, tumor pathology, surgical approach, and postoperative outcomes including incidence of cerebrospinal fluid (CSF) leak and other postoperative complications, length of stay (LOS), and readmission and reoperation rates. Descriptive statistics were used to compare postoperative outcomes for obese and nonobese controls.

RESULTS

14 studies met final inclusion criteria. Nine studies evaluated the relationship between obesity and CSF leaks. Four studies found a significant increase in postoperative CSF leak in obese patients compared to nonobese controls. The remaining studies trended toward an increased incidence of CSF leak in the obese population but did not reach statistical significance. One out of seven studies found that obesity increased postoperative LOS, and one out of five studies found that obesity increased reoperation rates following tumor resection.

CONCLUSIONS

Based on the results, obesity does not appear to increase LOS, readmission, or reoperation rates after lateral skull base tumor resection. The relationship between obesity and postoperative CSF leak, however, warrants further analysis.

Machine learning prediction of postoperative unplanned 30-day hospital readmission in older adult

Background: Although unplanned hospital readmission is an important indicator for monitoring the perioperative quality of hospital care, few published studies of hospital readmission have focused on surgical patient populations, especially in the elderly. We aimed to investigate if machine learning approaches can be used to predict postoperative unplanned 30-day hospital readmission in old surgical patients.

Methods: We extracted demographic, comorbidity, laboratory, surgical, and medication data of elderly patients older than 65 who underwent surgeries under general anesthesia in West China Hospital, Sichuan University from July 2019 to February 2021. Different machine learning approaches were performed to evaluate whether unplanned 30-day hospital readmission can be predicted. Model performance was assessed using the following metrics: AUC, accuracy, precision, recall, and F1 score. Calibration of predictions was performed using Brier Score. A feature ablation analysis was performed, and the change in AUC with the removal of each feature was then assessed to determine feature importance.

Results: A total of 10,535 unique surgeries and 10,358 unique surgical elderly patients were included. The overall 30-day unplanned readmission rate was 3.36%. The AUCs of the six machine learning algorithms predicting postoperative 30-day unplanned readmission ranged from 0.6865 to 0.8654. The RF + XGBoost algorithm overall performed the best with an AUC of 0.8654 (95% CI, 0.8484–0.8824), accuracy of 0.9868 (95% CI, 0.9834–0.9902), precision of 0.3960 (95% CI, 0.3854–0.4066), recall of 0.3184 (95% CI, 0.259–0.3778), and F1 score of 0.4909 (95% CI, 0.3907–0.5911). The Brier scores of the six machine learning algorithms predicting postoperative 30-day unplanned readmission ranged from 0.3721 to 0.0464, with RF + XGBoost showing the best calibration capability. The most five important features of RF + XGBoost were operation duration, white blood cell count, BMI, total bilirubin concentration, and blood glucose concentration.

Conclusion: Machine learning algorithms can accurately predict postoperative unplanned 30-day readmission in elderly surgical patients.

T.H.R.O.B.V.S. Score – A Comprehensive Model to Predict the Surgical Complexity of Renal Cell Carcinoma With Tumor Thrombus

Background

To propose a quantitative model for predicting the surgical complexity of patients with renal cell carcinoma (RCC) and venous tumor thrombus (VTT).

Method

The clinical data of 226 cases of RCC with VTT in Peking University Third Hospital from January 2014 to August 2020 were retrospectively analyzed. Seven indicators were selected to establish the T.H.R.O.B.V.S. system, including alkaline phosphatase, tumor thrombus height, maximum tumor diameter, obesity, bland thrombus, vascular wall invasion, and side. Each indicator was assigned with 0, (1), and 2 points, and the total scores of 0~2, 3~5, and ≥6 were set as the low-, middle-, and high-risk groups, respectively. The surgical complexity was compared and validated among groups.

Results

As the risk increased, the proportion of open surgery significantly increased (P<0.001). The operation time (P<0.001), intraoperative blood loss (P<0.001), blood or plasma transfusion (P<0.001), and hospitalization (P<0.001) increased significantly. The postoperative complications (P<0.001), including notable complications (≥Clavein-Dindo II, P<0.001), were significantly different, and similar trends were shown in the validation group.

Conclusion

The T.H.R.O.B.V.S. scoring system is a quantifiable and satisfactory model to predict the surgical complexity and perioperative management of RCC with VTT.

Predicting intra-operative and postoperative consequential events using machine-learning techniques in patients undergoing robot-assisted partial nephrectomy: a Vattikuti Collective Quality Initiative database study

OBJECTIVE

To predict intra-operative (IOEs) and postoperative events (POEs) consequential to the derailment of the ideal clinical course of patient recovery.

MATERIALS AND METHODS

The Vattikuti Collective Quality Initiative is a multi-institutional dataset of patients who underwent robot-assisted partial nephectomy for kidney tumours. Machine-learning (ML) models were constructed to predict IOEs and POEs using logistic regression, random forest and neural networks. The models to predict IOEs used patient demographics and preoperative data. In addition to these, intra-operative data were used to predict POEs. Performance on the test dataset was assessed using area under the receiver-operating characteristic curve (AUC-ROC) and area under the precision-recall curve (PR-AUC).

RESULTS

The rates of IOEs and POEs were 5.62% and 20.98%, respectively. Models for predicting IOEs were constructed using data from 1690 patients and 38 variables; the best model had an AUC-ROC of 0.858 (95% confidence interval [CI] 0.762, 0.936) and a PR-AUC of 0.590 (95% CI 0.400, 0.759). Models for predicting POEs were trained using data from 1406 patients and 59 variables; the best model had an AUC-ROC of 0.875 (95% CI 0.834, 0.913) and a PR-AUC 0.706 (95% CI, 0.610, 0.790).

CONCLUSIONS

The performance of the ML models in the present study was encouraging. Further validation in a multi-institutional clinical setting with larger datasets would be necessary to establish their clinical value. ML models can be used to predict significant events during and after surgery with good accuracy, paving the way for application in clinical practice to predict and intervene at an opportune time to avert complications and improve patient outcomes.

Metabolic Syndrome Predicts Worse Perioperative Outcomes in Patients Treated With Partial Nephrectomy for Renal Cell Carcinoma

OBJECTIVE

To test the association between metabolic syndrome (MetS) and its components (high blood pressure, body mass index [BMI]  ≥ 30, altered fasting glucose, low high-density lipoprotein cholesterol and high triglycerides) on perioperative outcomes after partial nephrectomy (PN).

METHODS

Within the National Inpatient Sample database (2000-2015) we identified all PN patients. First, temporal trends of MetS were reported. Second, the effect of MetS components was tested in multivariable logistic regression models predicting overall and specific perioperative complications. Third, we tested for dose-response from the concomitant effect of multiple MetS components. All models were weighted and adjusted for clustering, as well as all available patient and hospital characteristics.

RESULTS

Of 25,875 patients: (1) 59.3% had high blood pressure, (2) 14.7% had BMI  ≥ 30, (3) 21.7% had altered fasting glucose, (4) 20.2% had high triglycerides, and (5) <0.01% had low high-density lipoprotein cholesterol. One vs 2 vs 3 vs 4 MetS components were recorded in 34.9% vs 22.9% vs 8.9% vs 2.2% patients. Of all, 11.1% exhibited  ≥ 3 components and qualified for MetS. The rates of MetS increased over time (estimated annual percentage changes: +12.0%;P <.001). The 4 tested MetS components (high blood pressure, BMI  ≥ 30, altered fasting glucose, and high triglycerides) achieved independent predictor status in multivariable models predicting overall, cardiac, miscellaneous medical, vascular, and respiratory complications, as well as transfusions. Moreover, a statistically significant dose-response was confirmed for the same endpoints.

CONCLUSION

MetS and its components consistently and strongly predict perioperative complications after PN. Moreover, the strength of the effect was directly proportional to the number of MetS components exhibited by each individual patient, even if formal MetS diagnosis of  ≥ 3 components has not been met.

Contemporary Perioperative Morbidity and Mortality Rates of Minimally Invasive vs Open Partial Nephrectomy in Obese Patients with Kidney Cancer

Aims: To compare early postoperative morbidity and mortality rates in obese patients (body mass index ≥30 kg/m²) who underwent minimally invasive partial nephrectomy (MIPN) vs open partial nephrectomy (OPN), utilizing the National Surgical Quality Improvement Program (NSQIP) database. Materials and Methods: The NSQIP database was queried to identify obese patients who underwent either MIPN or OPN between 2008 and 2016. Patient demographics, comorbidities, operative time (OT), length of stay (LOS), and 30-day postoperative complications, readmissions, and mortality rates were recorded and compared between the two groups. Multivariable logistic regression analysis was used to determine the adjusted odds of early postoperative complications in MIPN vs OPN. Results: A total of 6041 obese MIPN patients and 3064 obese OPN patients were identified. Mean OT (minutes ± standard deviation) was longer for MIPN vs OPN (197.2 ± 71.0 vs 189.6 ± 82.4, p < 0.001), while mean LOS (3.8 ± 2.8 days vs 5.8 ± 3.5 days, p < 0.001) and 30-day complications (8.5% vs 19.8%, p < 0.001) were lower. No difference in 30-day postoperative mortality rates between MIPN (0.4%) and OPN (0.5%) was observed (p = 0.426). In the adjusted analysis, the odds of any complication within 30 days in the MIPN group were 61% lower, blood transfusion 73% lower, pneumonia 38% lower, sepsis 70% lower, acute renal failure 64% lower, superficial surgical site infection 40% lower, and reoperation 47% lower, compared with OPN patients. Conclusions: When compared with OPN in obese patients, the likelihood of 30-day postoperative morbidity was significantly lower in MIPN patients. However, the odds of 30-day mortality rates were similar between the groups.

Autologous Bone Cranioplasty: A Retrospective Comparative Analysis of Frozen and Subcutaneous Bone Flap Storage Methods

OBJECTIVE

The use of autologous bone for cranioplasty offers superior cosmesis and cost-effectiveness compared with synthetic materials. The choice between 2 common autograft storage mechanisms (subcutaneous vs. frozen) remains controversial and dictated by surgeon preference. We compared surgical outcomes after autologous bone cranioplasty between patients with cryopreserved and subcutaneously stored autografts.

METHODS

Ten-year retrospective comparative analysis of patients undergoing cranioplasty with autologous bone stored subcutaneously or frozen at a tertiary academic medical center.

RESULTS

Ninety-four patients were studied, with 34 (36.2%) bone flaps stored subcutaneously and 59 (62.8%) frozen. The 2 groups were similar in demographics, comorbidities, and craniectomy indication, with only body mass index and race differing statistically. The mean operation time was greater within the subcutaneous group (P < 0.001), which also had a greater number of ventriculoperitoneal shunt (VPS) placements (P = 0.02). There were no significant differences in complications, readmissions, unplanned reoperations, or length of stay between the 2 groups. VPS placement during cranioplasty increased length of stay (P < 0.001), and placement prior to cranioplasty increased both length of stay (P = 0.009) and incidence of hospital-acquired infection (P = 0.03).

CONCLUSIONS

Subcutaneous and frozen storage of autologous bone result in similar surgical risk profiles. Cryopreservation may be preferred because of shorter operation time and avoidance of complications with the abdominal pocket, whereas the portability of subcutaneous storage remains favorable for patients undergoing cranioplasty at a different institution. VPS placement prior to cranioplasty should be avoided, if possible, due to the increased risk of hospital-acquired infection.