Development of a prognostic model for 1-year survival after fragile hip fracture in Chinese

Development and validation of a prognostic scoring system for 1-year mortality in older patients with hip fractures

BACKGROUND

Hip fractures in older people result in increased mortality.

OBJECTIVE

We developed and validated an accurate and simple prognostic scoring system for hip fractures that can be used preoperatively.

DESIGN

Retrospective study.

SETTING

Multicenter.

PARTICIPANTS

Patients aged ≥65 years with hip fractures who underwent surgery between 2011 and 2021 were enrolled.

METHODS

The significant factors were determined with logistic regression analysis, and a scoring system was developed. The patients were classified into three groups, and a log-rank test was performed to evaluate 1-year survival rates. The model was internally and externally validated using the 5-fold cross-validation and data from another hospital, respectively.

RESULTS

We included 1026 patients. The analysis revealed eight significant prognostic factors: sex, body mass index, history of chronic heart failure and malignancy, activities of daily living (ADLs) before injury, hemoglobin and the prognostic nutritional index (PNI) at injury, and the American Society of Anesthesiologists Physical Status. The area under the receiver operating characteristic curve (AUC) after internal validation was 0.853. The external validation data consisted of 110 patients. The AUC of the model for the validation data was 0.905, showing outstanding discrimination. Sensitivity and specificity were 88.7% vs. 100% and 93.3% vs. 95.2% for the development and validation data, respectively.

CONCLUSIONS

We developed and validated an accurate and simple prognostic scoring system for hip fractures using only preoperative factors. Our findings highlight PNI as an important predictor of prognosis in hip fracture patients.

Systematic review of multivariable prognostic models for outcomes at least 30 days after hip fracture finds 18 mortality models but no nonmortality models warranting validation

OBJECTIVES

Prognostic models have the potential to aid clinical decision-making after hip fracture. This systematic review aimed to identify, critically appraise, and summarize multivariable prediction models for mortality or other long-term recovery outcomes occurring at least 30 days after hip fracture.

STUDY DESIGN AND SETTING

MEDLINE, Embase, Scopus, Web of Science, and CINAHL databases were searched up to May 2023. Studies were included that aimed to develop multivariable models to make predictions for individuals at least 30 days after hip fracture. Risk of bias (ROB) was dual-assessed using the Prediction model Risk Of Bias ASsessment Tool. Study and model details were extracted and summarized.

RESULTS

From 5571 records, 80 eligible studies were identified. They predicted mortality in n = 55 studies/81 models and nonmortality outcomes (mobility, function, residence, medical, and surgical complications) in n = 30 studies/45 models. Most (n = 46; 58%) studies were published since 2020. A quarter of studies (n = 19; 24%) reported using 'machine-learning methods', while the remainder used logistic regression (n = 54; 68%) and other statistical methods (n = 11; 14%) to build models. Overall, 15 studies (19%) presented 18 low ROB models, all predicting mortality. Common concerns were sample size, missing data handling, inadequate internal validation, and calibration assessment. Many studies with nonmortality outcomes (n = 11; 37%) had clear data complexities that were not correctly modeled.

CONCLUSION

This review has comprehensively summarized and appraised multivariable prediction models for long-term outcomes after hip fracture. Only 15 studies of 55 predicting mortality were rated as low ROB, warranting further development of their models. All studies predicting nonmortality outcomes were high or unclear ROB. Careful consideration is required for both the methods used and justification for developing further nonmortality prediction models for this clinical population.

Predictive Value of Machine Learning Models in Postoperative Mortality of Older Adults Patients with Hip Fracture: A Systematic Review and Meta-analysis

BACKGROUND

Some researchers have used machine learning to predict mortality in old patients with hip fracture, but its application value lacks an evidence-based basis. Hence, we conducted this meta-analysis to explore the predictive accuracy of machine learning for mortality in old patients with hip fracture.

METHODS

We systematically retrieved PubMed, Cochrane, Embase, and Web of Science for relevant studies published before July 15, 2022. The PROBAST assessment tool was used to assess the risk of bias in the included studies. A random-effects model was used for the meta-analysis of C-index, whereas a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. The meta-analysis was performed on R and Stata.

RESULTS

Eighteen studies were included, involving 8 machine learning models and 398,422 old patients undergoing hip joint surgery, of whom 60,457 died. According to the meta-analysis, the pooled C-index for machine learning models was 0.762 (95% CI: 0.691 ∼ 0.833) in the training set and 0.838 (95% CI: 0.783 ∼ 0.892) in the validation set, which is better than the C-index of the main clinical scale (Nottingham Hip Fracture Score), that is, 0.702 (95% CI: 0.681 ∼ 0.723). Among different machine learning models, ANN and Bayesian belief network had the best predictive performance.

CONCLUSION

Machine learning models are more accurate in predicting mortality in old patients after hip joint surgery than current mainstream clinical scoring systems. Subsequent research could focus on updating clinical scoring systems and improving their predictive performance by relying on machine learning models.

Association between General Anesthesia and the Occurrence of Cerebrovascular Accidents in Hip Fracture Patients

Background

General anesthesia is an important factor leading to postoperative complications, and cerebrovascular accidents take the first place in the causes of postoperative death. Therefore, it is extremely important to explore the correlation between general anesthesia and the occurrence of cerebrovascular accidents in hip fracture patients.

Objective

To explore the association between general anesthesia and the occurrence of cerebrovascular accidents in hip fracture patients.

Methods

The data of 240 hip fracture patients treated in our hospital from February 2017 to February 2021 were retrospectively analyzed, and the patients were divided into the general anesthesia group (n = 120) and nongeneral anesthesia group (n = 120) according to whether or not they received general anesthesia, so as to compare their incidence rate of cerebrovascular accidents between the two groups, record their hemodynamic changes, and analyze the association between various risk factors under general anesthesia and the occurrence of cerebrovascular accidents.

Results

No statistical differences in patients' general information such as age and gender between the two groups were observed (P > 0.05); compared with the nongeneral anesthesia group, the incidence rate of cerebrovascular accidents was significantly higher in the general anesthesia group (P < 0.001); between the two groups, the heart rates and mean arterial pressure (MAP) at 15 min after anesthesia, at the time of skin incision, and 15 min before the end of surgery were significantly different (P < 0.05); according to the multiple logistic regression analysis, general anesthesia was a risk factor affecting the occurrence of cerebrovascular accidents in hip fracture patients, and under general anesthesia, age ≥80 years, BMI ≥23 kg/m², types of anesthetic drugs ≥4, intraoperative blood pressure ≥140 mmHg, and intraoperative heart rate ≥80 bpm were also regarded as the risk factors.

Conclusion

General anesthesia is a risk factor affecting the occurrence of cerebrovascular accidents in hip fracture patients, and under general anesthesia, age ≥80 years, BMI ≥23 kg/m², types of anesthetic drugs ≥4, intraoperative blood pressure ≥140 mmHg, and intraoperative heart rate ≥80 bpm will further increase the possibility of cerebrovascular accidents.