Comprehensive Summary
This study evaluates the economic and clinical impact of postoperative complications in patients undergoing total knee arthroplasty (TKA). Using data from 2,299,979 patients from the Nationwide Inpatient Sample (NIS) database (2016-2019), researchers applied a neural network model to identify patients at high risk for increased hospital charges and prolonged length of stay (LOS) due to complications. The model focused on patients in the top 25% for both hospital charges and LOS after experiencing complications such as sepsis, surgical site infections (SSI), and acute kidney injury (AKI). The most common complications were blood loss anemia (14.6%), AKI (1.6%), and urinary tract infection (0.9%). Patients who experienced complications had significantly higher total hospital charges ($66,804 vs. $58,545, p < 0.0001) and longer hospital stays (2.9 vs. 2.1 days, p < 0.0001) compared to those without complications. The greatest financial and clinical burdens were observed in sepsis (hospital charges: $156,707, LOS: 10.1 days) and SSI (hospital charges: $126,132, LOS: 8.7 days). A neural network model trained on 80% of the dataset and tested on 20% of cases demonstrated strong predictive accuracy, achieving an AUC of 0.83 in the training set and 0.78 in the testing set. The model significantly outperformed traditional logistic regression and decision tree models in identifying high-risk patients. A 57-year-old patient with diabetes and sepsis had a 100% probability of being in the highest cost and LOS quartile, while a 75-year-old patient with chronic kidney disease, heart failure, and UTI had an 89% probability. The study highlights the importance of preoperative risk assessment and targeted patient management strategies to mitigate complications that drive up healthcare costs and extend recovery periods.
Outcomes and Implications
The findings emphasize the substantial financial and clinical impact of postoperative complications in TKA, reinforcing the need for early identification of high-risk patients. The study revealed that patients with complications incurred 13.6% higher costs ($66,804 vs. $58,545, p < 0.0001) and experienced 38% longer hospital stays (2.9 vs. 2.1 days, p < 0.0001). Sepsis and SSIs had the most profound impact, increasing LOS by 5.3 and 8.7 days, respectively, and raising hospital charges by $68,769 and $126,132, respectively. The neural network model demonstrated significant predictive power, allowing healthcare providers to identify high-risk patients before surgery. By implementing proactive interventions such as enhanced infection control measures, aggressive postoperative monitoring, and tailored rehabilitation protocols, hospitals can reduce complications, minimize prolonged hospital stays, and lower healthcare costs. This study also underscores the value of machine learning in orthopedic care, particularly in resource allocation, patient stratification, and clinical decision-making. By integrating AI-driven risk prediction models into surgical workflows, hospitals can improve patient outcomes, optimize ICU resources, and prevent avoidable complications. Future research should focus on enhancing the neural network model with additional clinical parameters, including genomic risk factors, inflammatory markers, and real-time patient monitoring data. Expanding predictive models to include multicenter validation studies and long- term cost-benefit analyses will further refine AI-driven patient management strategies. By leveraging AI-powered risk assessment tools, healthcare institutions can reduce the burden of postoperative complications, improve hospital efficiency, and enhance the quality of care for TKA patients.