A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures

Xing, Fei; Luo, Rong; Liu, Ming; Zhou, Zongke; Xiang, Zhou; Duan, Xin

doi:10.3389/fmed.2022.829977

Cited by 23 publications

(14 citation statements)

References 49 publications

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“…Similar findings were noted in another study conducted in patients over 60 years old who underwent hip fracture surgery ( 35 ). These results are in line with a recent report ( 36 ) in which hemoglobin was selected as a predictor for 1-year post-operative mortality in geriatric patients with hip fractures using an artificial intelligence approach. According to the aforementioned findings, pre-operative anemia should be evaluated carefully in aging surgical populations.…”

Section: Discussionsupporting

confidence: 91%

Pre-operative anemia was associated with all-cause mortality in patients with vertebral fracture who underwent percutaneous vertebroplasty

Lin

et al. 2022

Front. Med.

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PurposeWe investigated the association between pre-operative anemia and long-term all-cause mortality in patients with vertebral fracture who underwent a vertebroplasty.Materials and methodsWe retrospectively selected patients who were admitted for vertebroplasty for vertebral compression fracture between 2013 and 2020. Patients who had pathologic fractures or had no assessment of bone mineral density were excluded. Relevant information was collected from electronic medical records. Patients’ survival status was confirmed at the end of March 2021. Cox-proportional hazard models were conducted to examine the effects of anemia (<12 g/dL vs. ≥12 g/dL) and pre-operative hemoglobin levels (as a continuous variable) on all-cause mortality with multivariate adjustments.ResultsA total of 167 patients were analyzed (mean age 75.8 ± 9.3 years, male 25.7%). After a median follow-up duration of 2.1 years, pre-operative anemia (hemoglobin <12 g/dL vs. ≥12 g/dL) was independently associated with a higher risk of all-cause mortality (hazard ratio 2.762, 95% CI 1.184 to 6.442, p = 0.019). An increase in pre-operative hemoglobin was associated with a lower risk of all-cause mortality after multivariate adjustment (hazard ratio 0.775, 95% CI 0.606 to 0.991, p = 0.042).ConclusionPre-operative anemia (<12 g/dL) was independently associated with survival outcome among patients with vertebral compression fractures who underwent vertebroplasty. Our findings highlight anemia as a risk factor of long-term mortality in this elderly surgical population.

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Section: Discussionsupporting

confidence: 91%

Pre-operative anemia was associated with all-cause mortality in patients with vertebral fracture who underwent percutaneous vertebroplasty

Lin

et al. 2022

Front. Med.

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“…Of 39 studies that met all criteria and were included in this analysis, 18 studies (46.2%) used AI models to diagnose hip fractures on plain radiographs and 21 studies (53.8%) used AI models to predict patient outcomes following hip fracture surgery. A PRISMA flowchart of included studies is displayed in eFigure 1 in Supplement 1.…”

Section: Resultsmentioning

confidence: 99%

“…Included Studies on Application of Artificial Intelligence for Diagnosis of Hip FracturesArtificial Intelligence for Hip Fracture Detection and Outcome Prediction Age (18 of 21 studies47,[49][50][51][52][54][55][56][57][58][59][60][61][63][64][65][66][67] [85.7%]) and sex (17 of 21 studies [80.9%][47][48][49][50][51][52][54][55][56][57][58]60,61,[64][65][66][67] …”

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confidence: 99%

Artificial Intelligence for Hip Fracture Detection and Outcome Prediction

et al. 2023

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ImportanceArtificial intelligence (AI) enables powerful models for establishment of clinical diagnostic and prognostic tools for hip fractures; however the performance and potential impact of these newly developed algorithms are currently unknown.ObjectiveTo evaluate the performance of AI algorithms designed to diagnose hip fractures on radiographs and predict postoperative clinical outcomes following hip fracture surgery relative to current practices.Data SourcesA systematic review of the literature was performed using the MEDLINE, Embase, and Cochrane Library databases for all articles published from database inception to January 23, 2023. A manual reference search of included articles was also undertaken to identify any additional relevant articles.Study SelectionStudies developing machine learning (ML) models for the diagnosis of hip fractures from hip or pelvic radiographs or to predict any postoperative patient outcome following hip fracture surgery were included.Data Extraction and SynthesisThis study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses and was registered with PROSPERO. Eligible full-text articles were evaluated and relevant data extracted independently using a template data extraction form. For studies that predicted postoperative outcomes, the performance of traditional predictive statistical models, either multivariable logistic or linear regression, was recorded and compared with the performance of the best ML model on the same out-of-sample data set.Main Outcomes and MeasuresDiagnostic accuracy of AI models was compared with the diagnostic accuracy of expert clinicians using odds ratios (ORs) with 95% CIs. Areas under the curve for postoperative outcome prediction between traditional statistical models (multivariable linear or logistic regression) and ML models were compared.ResultsOf 39 studies that met all criteria and were included in this analysis, 18 (46.2%) used AI models to diagnose hip fractures on plain radiographs and 21 (53.8%) used AI models to predict patient outcomes following hip fracture surgery. A total of 39 598 plain radiographs and 714 939 hip fractures were used for training, validating, and testing ML models specific to diagnosis and postoperative outcome prediction, respectively. Mortality and length of hospital stay were the most predicted outcomes. On pooled data analysis, compared with clinicians, the OR for diagnostic error of ML models was 0.79 (95% CI, 0.48-1.31; P = .36; I2 = 60%) for hip fracture radiographs. For the ML models, the mean (SD) sensitivity was 89.3% (8.5%), specificity was 87.5% (9.9%), and F1 score was 0.90 (0.06). The mean area under the curve for mortality prediction was 0.84 with ML models compared with 0.79 for alternative controls (P = .09).Conclusions and RelevanceThe findings of this systematic review and meta-analysis suggest that the potential applications of AI to aid with diagnosis from hip radiographs are promising. The performance of AI in diagnosing hip fractures was comparable with that of expert radiologists and surgeons. However, current implementations of AI for outcome prediction do not seem to provide substantial benefit over traditional multivariable predictive statistics.

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“…The results show that the random forest model has the best performance, with a sensitivity of 81.86% (test set) and a specificity of 87.06% (test set), which is more suitable for the risk assessment of COPD in coal workers. The random forest model is an improvement on the decision tree model that is widely used in the medical field and outperforms other models in some studies [ 37 , 38 ]. In this study, the CNN is better than the logistic model but not as good as the random forest model.…”

Section: Discussionmentioning

confidence: 99%

Development and Internal Validation of Risk Assessment Models for Chronic Obstructive Pulmonary Disease in Coal Workers

Wang

Meng

Wang

et al. 2023

IJERPH

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Coal workers are more likely to develop chronic obstructive pulmonary disease due to exposure to occupational hazards such as dust. In this study, a risk scoring system is constructed according to the optimal model to provide feasible suggestions for the prevention of chronic obstructive pulmonary disease in coal workers. Using 3955 coal workers who participated in occupational health check-ups at Gequan mine and Dongpang mine of Hebei Jizhong Energy from July 2018 to August 2018 as the study subjects, random forest, logistic regression, and convolutional neural network models are established, and model performance is evaluated to select the optimal model, and finally a risk scoring system is constructed according to the optimal model to achieve model visualization. The training set results show that the logistic, random forest, and CNN models have sensitivities of 78.55%, 86.89%, and 77.18%; specificities of 85.23%, 92.32%, and 87.61%; accuracies of 81.21%, 85.40%, and 83.02%; Brier scores of 0.14, 0.10, and 0.14; and AUCs of 0.76, 0.88, and 0.78, respectively, and similar results are obtained for the test set and validation set, with the random forest model outperforming the other two models. The risk scoring system constructed according to the importance ranking of random forest predictor variables has an AUC of 0.842; the evaluation results of the risk scoring system shows that its accuracy rate is 83.7% and the AUC is 0.827, and the established risk scoring system has good discriminatory ability. The random forest model outperforms the CNN and logistic regression models. The chronic obstructive pulmonary disease risk scoring system constructed based on the random forest model has good discriminatory power.

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A New Random Forest Algorithm-Based Prediction Model of Post-operative Mortality in Geriatric Patients With Hip Fractures

Cited by 23 publications

References 49 publications

Pre-operative anemia was associated with all-cause mortality in patients with vertebral fracture who underwent percutaneous vertebroplasty

Pre-operative anemia was associated with all-cause mortality in patients with vertebral fracture who underwent percutaneous vertebroplasty

Artificial Intelligence for Hip Fracture Detection and Outcome Prediction

Development and Internal Validation of Risk Assessment Models for Chronic Obstructive Pulmonary Disease in Coal Workers

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