Predicting 1-Year Mortality after Hip Fracture Surgery: An Evaluation of Multiple Machine Learning Approaches

Forssten, Maximilian Peter; Bass, Gary Alan; Ismail, Ahmad Mohammad; Mohseni, Shahin; Cao, Yang

doi:10.3390/jpm11080727

Cited by 28 publications

(29 citation statements)

References 48 publications

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“…Various machine learning models for mortality prediction in hip fracture patients have been proposed [ 17 – 21 , 23 , 24 , 66 ]. Unlike most previous studies, we investigated only hip fractures from low-energy trauma, and we did not exclude patients aged less than 65 years because osteoporotic hip fracture can occur at the age of 50 [ 67 ].…”

Section: Discussionmentioning

confidence: 99%

“…Artificial neural networks and logistic regression are well-known methods and have been extensively studied [ 17 – 22 ]. Support Vector Machine [ 23 , 24 ], Naive Bayes [ 20 , 24 ] and Random Forests [ 22 – 24 ] have also been used to predict mortality after hip fracture. However, there are other novel methods that demonstrate good performance with high accuracy in predicting death [ 15 , 25 , 26 ], such as Gradient Boosting, which have not yet been thoroughly explored for use in patients with hip fracture.…”

Section: Introductionmentioning

confidence: 99%

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Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture

et al. 2022

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Background Fragility hip fracture increases morbidity and mortality in older adult patients, especially within the first year. Identification of patients at high risk of death facilitates modification of associated perioperative factors that can reduce mortality. Various machine learning algorithms have been developed and are widely used in healthcare research, particularly for mortality prediction. This study aimed to develop and internally validate 7 machine learning models to predict 1-year mortality after fragility hip fracture. Methods This retrospective study included patients with fragility hip fractures from a single center (Siriraj Hospital, Bangkok, Thailand) from July 2016 to October 2018. A total of 492 patients were enrolled. They were randomly categorized into a training group (344 cases, 70%) or a testing group (148 cases, 30%). Various machine learning techniques were used: the Gradient Boosting Classifier (GB), Random Forests Classifier (RF), Artificial Neural Network Classifier (ANN), Logistic Regression Classifier (LR), Naive Bayes Classifier (NB), Support Vector Machine Classifier (SVM), and K-Nearest Neighbors Classifier (KNN). All models were internally validated by evaluating their performance and the area under a receiver operating characteristic curve (AUC). Results For the testing dataset, the accuracies were GB model = 0.93, RF model = 0.95, ANN model = 0.94, LR model = 0.91, NB model = 0.89, SVM model = 0.90, and KNN model = 0.90. All models achieved high AUCs that ranged between 0.81 and 0.99. The RF model also provided a negative predictive value of 0.96, a positive predictive value of 0.93, a specificity of 0.99, and a sensitivity of 0.68. Conclusions Our machine learning approach facilitated the successful development of an accurate model to predict 1-year mortality after fragility hip fracture. Several machine learning algorithms (eg, Gradient Boosting and Random Forest) had the potential to provide high predictive performance based on the clinical parameters of each patient. The web application is available at www.hipprediction.com. External validation in a larger group of patients or in different hospital settings is warranted to evaluate the clinical utility of this tool. Trial registration Thai Clinical Trials Registry (22 February 2021; reg. no. TCTR20210222003).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture

et al. 2022

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“…Variable selection or stopping rules were not used, because these methods provide regression coefficients that are too high and confidence intervals that are too small. Neural networks, such as support vector machines, naive Bayes classifiers, and random forest classifiers, have been applied to hip fracture data sets, but were no better than logistic regression in predicting outcomes after surgery [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

Pilot Project for a Web-Based Dynamic Nomogram to Predict Survival 1 Year After Hip Fracture Surgery: Retrospective Observational Study

McLeod¹,

Kennedy²,

Simpson³

et al. 2022

Interact J Med Res

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Background Hip fracture is associated with high mortality. Identification of individual risk informs anesthetic and surgical decision-making and can reduce the risk of death. However, interpreting mathematical models and applying them in clinical practice can be difficult. There is a need to simplify risk indices for clinicians and laypeople alike. Objective Our primary objective was to develop a web-based nomogram for prediction of survival up to 365 days after hip fracture surgery. Methods We collected data from 329 patients. Our variables included sex; age; BMI; white cell count; levels of lactate, creatinine, hemoglobin, and C-reactive protein; physical status according to the American Society of Anesthesiologists Physical Status Classification System; socioeconomic status; duration of surgery; total time in the operating room; side of surgery; and procedure urgency. Thereafter, we internally calibrated and validated a Cox proportional hazards model of survival 365 days after hip fracture surgery; logistic regression models of survival 30, 120, and 365 days after surgery; and a binomial model. To present the models on a laptop, tablet, or mobile phone in a user-friendly way, we built an app using Shiny (RStudio). The app showed a drop-down box for model selection and horizontal sliders for data entry, model summaries, and prediction and survival plots. A slider represented patient follow-up over 365 days. Results Of the 329 patients, 24 (7.3%) died within 30 days of surgery, 65 (19.8%) within 120 days, and 94 (28.6%) within 365 days. In all models, the independent predictors of mortality were age, BMI, creatinine level, and lactate level. The logistic model also incorporated white cell count as a predictor. The Cox proportional hazards model showed that mortality differed as follows: age 80 vs 60 years had a hazard ratio (HR) of 0.6 (95% CI 0.3-1.1), a plasma lactate level of 2 vs 1 mmol/L had an HR of 2.4 (95% CI 1.5-3.9), and a plasma creatinine level of 60 vs 90 mol/L had an HR of 2.3 (95% CI 1.3-3.9). Conclusions In conclusion, we provide an easy-to-read web-based nomogram that predicts survival up to 365 days after hip fracture. The Cox proportional hazards model and logistic models showed good discrimination, with concordance index values of 0.732 and 0.781, respectively.

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“…After fracture, the mortality rate can reach 50.0%, and the 5-year survival rate is only 20.0% [ 15 , 16 ], indicating poor prognosis of the disease. In current practice, surgery is advocated for patients without surgical contraindications because according to the clinical data, nonsurgical treatment will further elevate patient mortality [ 17 , 18 ], while surgery can shorten patients' bed time and accelerate their limb recovery. However, elderly patients with hip fracture usually have poor body tolerance and a high rate of perioperative complications, among which cerebrovascular accidents are one of the most serious ones and also the leading cause of postoperative death [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

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

Wang¹,

Gao

2021

Journal of Healthcare Engineering

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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.

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Predicting 1-Year Mortality after Hip Fracture Surgery: An Evaluation of Multiple Machine Learning Approaches

Cited by 28 publications

References 48 publications

Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture

Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture

Pilot Project for a Web-Based Dynamic Nomogram to Predict Survival 1 Year After Hip Fracture Surgery: Retrospective Observational Study

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

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