The association between pre-operative sepsis and 30-day mortality in hip fracture patients-A cohort study

Risk factors for short-term mortality in elderly hip fracture patients with complicated heart failure in the ICU: A MIMIC-IV database analysis using nomogram

BACKGROUND

Hip fracture is a prevalent and hazardous injury among the elderly population that often results in intensive care unit (ICU) admission due to various complications, despite advanced medical science. One common complication experienced in the ICU by elderly hip fracture patients is heart failure, which significantly impacts short-term survival rates. Currently, there is a deficit of adequate predictive models to forecast the short-term risk of death following heart failure for elderly hip fracture patients in the ICU. This study aims to identify independent risk factors for all-cause mortality within 30 days for elderly patients with hip fractures and heart failure while in the ICU in order to develop a predictive model.

METHOD

A total of 641 elderly patients with hip fractures combined with heart failure were recruited from the Medical Information Mart for Intensive Care IV dataset and randomized to the training and validation sets. The primary outcome was all-cause mortality within 30 days. The least absolute shrinkage and selection operator regression was used to reduce data dimensionality and select features. Multivariate logistic regression was used to build predictive models. Consistency index (C-index), receiver operating characteristic curve, and decision curve analysis (DCA) were used to measure the predictive performance of the nomogram.

RESULT

Our results showed that these variables including MCH, MCV, INR, monocyte percentage, neutrophils percentage, creatinine, and combined sepsis were independent factors for death within 30 days in elderly patients with hip fracture combined with heart failure in the ICU. The C-index was 0.869 (95% CI 0.823-0.916) and 0.824 (95% CI 0.749-0.900) for the training and validation sets, respectively. The results of the area under the curve and decision curve analysis (DCA) confirmed that the nomogram performed well in predicting elderly patients with hip fractures combined with heart failure in the ICU.

CONCLUSION

We developed a new nomogram model for predicting 30-day all-cause mortality in elderly patients with hip fractures combined with heart failure in the ICU, which could be a valid and useful clinical tool for clinicians for targeted treatment and prognosis prediction.

A Machine Learning-Based Prediction of Hospital Mortality in Patients With Postoperative Sepsis

Introduction: The incidence of postoperative sepsis is continually increased, while few studies have specifically focused on the risk factors and clinical outcomes associated with the development of sepsis after surgical procedures. The present study aimed to develop a mathematical model for predicting the in-hospital mortality among patients with postoperative sepsis. Materials and Methods: Surgical patients in Medical Information Mart for Intensive Care (MIMIC-III) database who simultaneously fulfilled Sepsis 3.0 and Agency for Healthcare Research and Quality (AHRQ) criteria at ICU admission were incorporated. We employed both extreme gradient boosting (XGBoost) and stepwise logistic regression model to predict the in-hospital mortality among patients with postoperative sepsis. Consequently, the model performance was assessed from the angles of discrimination and calibration. Results: We included 3,713 patients who fulfilled our inclusion criteria, in which 397 (10.7%) patients died during hospitalization, and 3,316 (89.3%) patients survived through discharge. Fluid-electrolyte disturbance, coagulopathy, renal replacement therapy (RRT), urine output, and cardiovascular surgery were important features related to the in-hospital mortality. The XGBoost model had a better performance in both discriminatory ability (c-statistics, 0.835 vs. 0.737 and 0.621, respectively; AUPRC, 0.418 vs. 0.280 and 0.237, respectively) and goodness of fit (visualized by calibration curve) compared to the stepwise logistic regression model and baseline model. Conclusion: XGBoost model has a better performance in predicting hospital mortality among patients with postoperative sepsis in comparison to the stepwise logistic regression model. Machine learning-based algorithm might have significant application in the development of early warning system for septic patients following major operations.

Development of a Risk Score to Predict Postoperative Delirium in Patients With Hip Fracture

BACKGROUND

Post-hip fracture surgery delirium (PHFD) is a significant clinical problem in older patients, but an adequate, simple risk prediction model for use in the preoperative period has not been developed.

METHODS

The 2016 American College of Surgeons National Surgical Quality Improvement Program Hip Fracture Procedure Targeted Participant Use Data File was used to obtain a cohort of patients ≥60 years of age who underwent hip fracture surgery (n = 8871; randomly assigned to derivation [70%] or validation [30%] cohorts). A parsimonious prediction model for PHFD was developed in the derivation cohort using stepwise multivariable logistic regression with further removal of variables by evaluating changes in the area under the receiver operator characteristic curve (AUC). A risk score was developed from the final multivariable model.

RESULTS

Of 6210 patients in the derivation cohort, PHFD occurred in 1816 (29.2%). Of 32 candidate variables, 9 were included in the final model: (1) preoperative delirium (adjusted odds ratio [aOR], 8.32 [95% confidence interval {CI}, 6.78-10.21], 8 risk score points); (2) preoperative dementia (aOR, 2.38 [95% CI, 2.05-2.76], 3 points); (3) age (reference, 60-69 years of age) (age 70-79: aOR, 1.60 [95% CI, 1.20-2.12], 2 points; age 80-89: aOR, 2.09 [95% CI, 1.59-2.74], 2 points; and age ≥90: aOR, 2.43 [95% CI, 1.82-3.23], 3 points); (4) medical comanagement (aOR, 1.43 [95% CI, 1.13-1.81], 1 point); (5) American Society of Anesthesiologists (ASA) physical status III-V (aOR, 1.40 [95% CI, 1.14-1.73], 1 point); (6) functional dependence (aOR, 1.37 [95% CI, 1.17-1.61], 1 point); (7) smoking (aOR, 1.36 [95% CI, 1.07-1.72], 1 point); (8) systemic inflammatory response syndrome/sepsis/septic shock (aOR, 1.34 [95% CI, 1.09-1.65], 1 point); and (9) preoperative use of mobility aid (aOR, 1.32 [95% CI, 1.14-1.52], 1 point), resulting in a risk score ranging from 0 to 20 points. The AUCs of the logistic regression and risk score models were 0.77 (95% CI, 0.76-0.78) and 0.77 (95% CI, 0.76-0.78), respectively, with similar results in the validation cohort.

CONCLUSIONS

A risk score based on 9 preoperative risk factors can predict PHFD in older adult patients with fairly good accuracy.