In Reply

Perioperative Risk Factors Associated With Acute Kidney Injury in Patients After Brain Tumor Resection

BACKGROUND

Acute kidney injury (AKI) is a serious complication after surgery. The aim of this study is to identify risk factors for postoperative AKI in patients undergoing brain tumor surgery.

METHODS

This single-center, retrospective, matched case-control study included patients undergoing elective brain tumor surgery between January 2016 and December 2018 at Beijing Tiantan Hospital, Capital Medical University, China. Patients developing postoperative AKI were compared with controls without AKI matched by age, sex, and date of surgery in a ratio of 1:3. AKI was defined using the Kidney Disease Improving Global Outcomes criteria.

RESULTS

A total of 9933 patients were identified for review, of which 115 (1.16%) developed AKI; 345 matched patients were included in the control group. AKI occurred most commonly within the first 24 hours (41/97, 42.3%) and 48 hours (33/94, 35.1%) after surgery. Preoperative administration of mannitol (odds ratio [OR], 1.64; 95% confidence interval [CI], 1.04-2.60; P= 0.034), American Society of Anesthesiologists physical status III or higher (OR, 5.50; 95% CI, 2.23-13.59; P<0.001), preoperative blood glucose (OR, 2.53; 95% CI, 1.23-5.22; P=0.012), craniopharyngioma (OR, 8.96; 95% CI, 3.55-22.63; P<0.001), nonsteroidal anti-inflammatory drug administration (OR, 3.74; 95% CI, 1.66-8.42; P<0.001), and intraoperative hypotension (OR, 2.13; 95% CI, 1.21-3.75; P=0.009) were independent risk factors for postoperative AKI.

CONCLUSION

Multiple factors, including preoperative administration of mannitol, are independently associated with the development of postoperative AKI in patients undergoing brain tumor surgery.

Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics

BACKGROUND

Acute kidney injury (AKI) is a common complication after surgery that is associated with increased morbidity and mortality. The majority of existing perioperative AKI risk prediction models are limited in their generalizability and do not fully utilize intraoperative physiological time-series data. Thus, there is a need for intelligent, accurate, and robust systems to leverage new information as it becomes available to predict the risk of developing postoperative AKI.

METHODS

A retrospective single-center cohort of 2,911 adults who underwent surgery at the University of Florida Health between 2000 and 2010 was utilized for this study. Machine learning and statistical analysis techniques were used to develop perioperative models to predict the risk of developing AKI during the first three days after surgery, first seven days after surgery, and overall (after surgery during the index hospitalization). The improvement in risk prediction was examined by incorporating intraoperative physiological time-series variables. Our proposed model enriched a preoperative model that produced a probabilistic AKI risk score by integrating intraoperative statistical features through a machine learning stacking approach inside a random forest classifier. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and Net Reclassification Improvement (NRI).

RESULTS

The predictive performance of the proposed model is better than the preoperative data only model. The proposed model had an AUC of 0.86 (accuracy of 0.78) for the seven-day AKI outcome, while the preoperative model had an AUC of 0.84 (accuracy of 0.76). Furthermore, by integrating intraoperative features, the algorithm was able to reclassify 40% of the false negative patients from the preoperative model. The NRI for each outcome was AKI at three days (8%), seven days (7%), and overall (4%).

CONCLUSIONS

Postoperative AKI prediction was improved with high sensitivity and specificity through a machine learning approach that dynamically incorporated intraoperative data.