Abnormal preoperative leukocyte counts and postoperative complications following total shoulder arthroplasty

Predictive factors for postoperative complications in nerve grafting neurorrhaphies: A multispecialty analysis using NSQIP data

BACKGROUND

To date, no large-scale research has comprehensively examined predictors of complications following neurorrhaphy with nerve grafts. This study aims to clarify the factors that can predict postoperative complications within 30 days of nerve graft surgeries.

METHODOLOGY

Data was collected from the American College of Surgeons National Quality Improvement Program (ACS NSQIP) using Current Procedural Terminology (CPT) Codes. A receiver operating characteristic (ROC) curve was created for operative time analysis. Relevant 30-day morbidities and mortality variables were run using univariate and multivariate statistical analyses. All statistical analyses were conducted using SPSS version 29.

RESULTS

The mean age of patients undergoing neurorrhaphy was 46.5 ± 16.7 years, with males comprising the majority (56.9 %). The overall 30-day complication rate was 11.1 %, with the most common complications being bleeding requiring transfusion (4.27 %) and superficial surgical site infections (2.8 %). The mean operative time was 4.6 ± 3.4 h, and the mean length of hospital stay was 2.1 ± 5.9 days. Univariate analysis identified nine preoperative variables (female sex, dialysis, disseminated cancer, steroid use, abnormal WBC, anemia, transfusions, mFI-5 score, and ASA class) and one intraoperative variable (long operative time) as significantly associated with 30-day morbidity. The multivariate model confirmed five independent predictors of 30-day morbidity: abnormal WBC (OR 2.061, p < 0.001), anemia (OR 2.233, p < 0.001), mFI-5 score ≥ 1 (OR 1.411-1.725, p = 0.011-0.023), ASA class ≥ 3 (OR 1.424, p = 0.011), and long operative time (>5.29 h, OR 5.887, p < 0.001). For 30-day mortality, univariate analysis found four significant preoperative predictors: dialysis (OR 99.875, p < 0.001), anemia (OR 6.179, p = 0.046), mFI-5 score of 1 (OR 12.571, p = 0.024), and ASA class ≥ 3 (OR 9.35, p = 0.046). Multivariate analysis suggested dialysis as a critical predictor of 30-day mortality (OR 26.513, p = 0.043). These findings highlight key preoperative and intraoperative factors influencing short-term morbidity and mortality following neurorrhaphy.

CONCLUSIONS

Complications after nerve surgery can be an additional burden on patients. Most complications occur within 30 days of surgery. Frailty, higher ASA class, leukocytosis, anemia, and operative time can predict 30-day morbidities. Dialysis is a potential predictor of 30-day mortality. Understanding the influence of preoperative factors on postoperative outcomes is necessary to mitigate risk and maximize recovery after surgery.

Preoperative Hemoglobin Level Predicts Surgical Site Infections in Trauma Orthopedic Surgery: A Cohort Study

Background: Surgical site infections resulting from trauma orthopedic surgery increase morbidity and mortality rates and generate additional costs for the healthcare system. Preoperative and postoperative blood parameters have been described as risk predictors for surgical site infection in other surgical areas. The purpose of this study was to assess the role of preoperative and postoperative hematological parameters in predicting the risk of surgical site infections in trauma orthopedic surgery. Methods: Data on patients' demographics were collected from their medical records and the operation reports. Preoperative and postoperative blood samples were collected for a complete blood count assay. The blood cell parameters as predictors of surgical site infection after trauma orthopedic surgery were determined by the Mann-Whitney U test to assess the differences in the median between the dependent and independent variables. p value < 0.05 was considered statistically significant. Results: Out of the 210 patients who were followed postsurgery, 14 (6.7%) developed surgical site infection following trauma orthopedic surgery. The mean age of the study participants was 33.08 ± 19.23 (Mean ± SD), with a range of 86 to 0.67 years old. Low preoperative hemoglobin level was identified as a predictor of surgical site infection following trauma orthopedic surgery (p=0.019). None of the postoperative blood parameters measured was significantly associated with surgical site infections after trauma orthopedic surgery in Northern Ghana. Conclusion: In conclusion, our study demonstrates that preoperative hemoglobin level is a useful hematological parameter for predicting surgical site infection following trauma orthopedic surgery. These inexpensive and common hematological parameters could assist in guiding preventive efforts to reduce surgical site infections and improve outcomes for vulnerable patients undergoing trauma orthopedic surgery. Assessing preoperative hemoglobin levels is crucial in identifying patients at increased risk of developing surgical site infections. Preoperative optimization, including incorporating hemoglobin levels into predictive risk models can help to assess these at-risk persons better. Educate patients on the need to optimize their hemoglobin levels before surgery and discuss potential interventions, including iron supplementation or transfusion.

Predicting extended hospital stay following revision total hip arthroplasty: a machine learning model analysis based on the ACS-NSQIP database

INTRODUCTION

Prolonged length of stay (LOS) following revision total hip arthroplasty (THA) can lead to increased healthcare costs, higher rates of readmission, and lower patient satisfaction. In this study, we investigated the predictive power of machine learning (ML) models for prolonged LOS after revision THA using patient data from a national-scale patient repository.

MATERIALS AND METHODS

We identified 11,737 revision THA cases from the American College of Surgeons National Surgical Quality Improvement Program database from 2013 to 2020. Prolonged LOS was defined as exceeding the 75th value of all LOSs in the study cohort. We developed four ML models: artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor, to predict prolonged LOS after revision THA. Each model's performance was assessed during training and testing sessions in terms of discrimination, calibration, and clinical utility.

RESULTS

The ANN model was the most accurate with an AUC of 0.82, calibration slope of 0.90, calibration intercept of 0.02, and Brier score of 0.140 during testing, indicating the model's competency in distinguishing patients subject to prolonged LOS with minimal prediction error. All models showed clinical utility by producing net benefits in the decision curve analyses. The most significant predictors of prolonged LOS were preoperative blood tests (hematocrit, platelet count, and leukocyte count), preoperative transfusion, operation time, indications for revision THA (infection), and age.

CONCLUSIONS

Our study demonstrated that the ML model accurately predicted prolonged LOS after revision THA. The results highlighted the importance of the indications for revision surgery in determining the risk of prolonged LOS. With the model's aid, clinicians can stratify individual patients based on key factors, improve care coordination and discharge planning for those at risk of prolonged LOS, and increase cost efficiency.

Components of metabolic syndrome as significant risk factors for postoperative complications following total shoulder arthroplasty: hypertension, diabetes, and obesity

Background

Metabolic syndrome (MetS) is a known risk factor for adverse postoperative outcomes. However, the literature surrounding the effects of MetS on orthopedic surgery outcomes following total shoulder arthroplasty (TSA) remains understudied. The purpose of this study is to investigate the effect of MetS on postoperative 30-day adverse outcomes following TSA.

Methods

The American College of Surgeons National Surgical Quality Improvement Program database was queried for all patients who underwent TSA between 2015 and 2020. After exclusion criteria, patients were divided into MetS and no MetS cohorts. MetS patients were defined as presence of hypertension, diabetes, and body mass index > 30 kg/m2. Bivariate logistic regression was used to compare patient demographics, comorbidities, and complications. Multivariate logistic regression, adjusted for all significant patient demographics and comorbidities, was used to identify the complications independently associated with MetS.

Results

A total of 26,613 patients remained after exclusion criteria, with 23,717 (89.1%) in the no MetS cohort and 2896 (10.9%) in the MetS cohort. On multivariate analysis, MetS was found to be an independent predictor of postoperative pneumonia (odds ratio [OR] 1.61, 95% confidence interval [CI] 1.02-2.55; P = .042), renal insufficiency (OR 4.09, 95% CI 1.67-10.00; P = .002), acute renal failure (OR 4.17, 95% CI 1.13-15.31; P = .032), myocardial infarction (OR 2.11, 95% CI 1.21-3.69; P = .009), nonhome discharge (OR 1.41, 95% CI 1.24-1.60; P < .001), and prolonged hospital stay > 3 days (OR 1.44, 95% CI 1.25-1.66; P < .001).

Conclusion

MetS was identified as an independent risk factor for postoperative pneumonia, renal insufficiency, acute renal failure, myocardial infarction, nonhome discharge, and prolonged hospital stay following TSA. These findings encourage physicians to medically optimize MetS patients prior to surgery to limit adverse outcomes.