# Identification of Prognostic Factors Related to Morbidity, Mortality, and Increased Healthcare Expenditure Following Surgery for Femoral Fracture or Hip Arthroplasty

## TL;DR

This study identifies risk factors for complications and higher healthcare costs after hip surgery, helping doctors improve patient care and outcomes.

## Contribution

The study provides a predictive model for adverse outcomes and resource use after hip surgery, highlighting key modifiable and non-modifiable factors.

## Key findings

- Higher ASA classification and underweight BMI were significant predictors of adverse outcomes.
- Overweight and obese BMI were protective against adverse events and increased resource use.
- The predictive model achieved a mean AUC of 0.73 through cross-validation.

## Abstract

Introduction

Postoperative outcomes following hip arthroplasty and femoral fracture surgeries significantly impact patient care and healthcare resources. This study aimed to identify modifiable and non-modifiable prognostic factors that independently predict major postoperative complications and increased hospital resource utilization in these patients.

Methods

We conducted a retrospective cohort study using the 2019 National Surgical Quality Improvement Program (NSQIP) database, including adult patients who underwent hip arthroplasty or femoral fracture treatment. Patients with incomplete data were excluded. The primary outcome was a composite of major adverse events, including mortality and 11 complications; the secondary outcome was healthcare resource utilization, assessed by length of stay and readmissions. We used stepwise backward multivariable logistic regression for analysis.

Results

Out of 176,801 cases, 12,146 (6.87%) experienced adverse outcomes. Significant predictors of adverse events included higher American Society of Anesthesiologists (ASA) classification, age ≥65 years, underweight body mass index (BMI), male sex, use of general anesthesia, and comorbidities such as COPD, insulin-dependent diabetes, ascites, congestive heart failure (CHF), hypertension, dialysis requirement, steroid use, bleeding disorders, and sepsis. Overweight and obese BMI were protective against adverse events. Increased resource utilization was associated with higher ASA classification, underweight BMI, use of general anesthesia, and comorbidities like insulin and non-insulin-dependent diabetes, COPD, CHF, hypertension, dialysis, steroid use, bleeding disorders, and SIRS. Again, overweight and obese BMIs were protective. The predictive model achieved a mean area under the curve (AUC) of 0.73 through 10-fold cross-validation.

Conclusions

Key predictors of adverse outcomes and increased hospital resource use include specific comorbidities and surgical factors, notably underweight BMI and higher ASA classification. Targeted interventions to optimize perioperative care for high-risk patients are necessary to minimize complications. These findings can guide clinical practice and surgical decision-making. Further research should explore these associations and refine preoperative risk stratification models.