ArticleJournal of personalized medicine2021
Predicting 1-Year Mortality after Hip Fracture Surgery: An Evaluation of Multiple Machine Learning Approaches.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence and Machine Learning for Outcome Prediction After Osteoporotic Hip Fracture: A Systematic Review and Meta-analysis of Prediction Model Performance.Current osteoporosis reports · 2026Pooled it
- Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis.Scientific reports · 2025Pooled it
- [Application and prospect of machine learning in orthopaedic trauma].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2023Pooled it
- Artificial Intelligence for Hip Fracture Detection and Outcome Prediction: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Secondary fragility fractures after hip fracture surgery in four thousand, four hundred and eighteen older adults: risk factors and internal validation of an interpretable machine-learning model.International orthopaedics · 2026Article
- Evaluation of short- and long-term mortality prediction in patients undergoing hip fracture surgery using a biomarker-based interpretable predictive modeling approach: a retrospective cohort analysis.BMC medical informatics and decision making · 2026Article
- Integrated nutritional-inflammatory and frailty-based model for mortality risk stratification following hip fracture surgery: a multicentre cohort study.Aging clinical and experimental research · 2026Article
- Outcomes of nonagenarians undergoing hip fracture surgery: a multicentre observational study across Asia-Oceana.BMC geriatrics · 2026Observational
- Supervised Machine Learning-Based Prediction of In-Hospital Mortality Following Hip Fracture in Older Adults.Diagnostics (Basel, Switzerland) · 2026Article
- Predicting 90-Day Mortality After Geriatric Hip Fracture Using Combined Preoperative and Perioperative Risk Factors.Geriatric orthopaedic surgery & rehabilitation · 2026Article
- Data imputation and domain-adaptive prediction of 1-year postoperative mortality in geriatric hip fracture patients following arthroplasty from multi-center study.International journal of surgery (London, England) · 2026Article
- Naïve Bayes is an interpretable and predictive machine learning algorithm in predicting osteoporotic hip fracture in-hospital mortality compared to other machine learning algorithms.PLOS digital health · 2025Article
- A nomogram for one-year risk of death after hip fracture.Frontiers in medicine · 2025Article
- Development and reporting of artificial intelligence in osteoporosis management.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- Validation of the Charlson Comorbidity Index for the prediction of 30-day and 1-year mortality among patients who underwent hip fracture surgery.Perioperative medicine (London, England) · 2024Article
- Development and Internal Validation of a Multivariable Prediction Model for Mortality After Hip Fracture with Machine Learning Techniques.Calcified tissue international · 2024Article
- Can we predict 1-year functional outcomes and mortality following hip fracture in middle-aged and geriatric patients at time of admission?Musculoskeletal surgery · 2024Article
- Machine learning applications for the prediction of extended length of stay in geriatric hip fracture patients.World journal of orthopedics · 2023Article
- Application of artificial intelligence in trauma orthopedics: Limitation and prospects.World journal of clinical cases · 2023Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Postoperative death within 1 year following hip fracture surgery is reported to be up to 27%. In the current study, we benchmarked the predictive precision and accuracy of the algorithms support vector machine (SVM), naïve Bayes classifier (NB), and random forest classifier (RF) against logistic regression (LR) in predicting 1-year postoperative mortality in hip fracture patients as well as assessed the relative importance of the variables included in the LR model. All adult patients who underwent primary emergency hip fracture surgery in Sweden, between 1 January 2008 and 31 December 2017 were included in the study. Patients with pathological fractures and non-operatively managed hip fractures, as well as those who died within 30 days after surgery, were excluded from the analysis. A LR model with an elastic net regularization were fitted and compared to NB, SVM, and RF. The relative importance of the variables in the LR model was then evaluated using the permutation importance. The LR model including all the variables demonstrated an acceptable predictive ability on both the training and test datasets for predicting one-year postoperative mortality (Area under the curve (AUC) = 0.74 and 0.74 respectively). NB, SVM, and RF tended to over-predict the mortality, particularly NB and SVM algorithms. In contrast, LR only over-predicted mortality when the predicted probability of mortality was larger than 0.7. The LR algorithm outperformed the other three algorithms in predicting 1-year postoperative mortality in hip fracture patients. The most important predictors of 1-year mortality were the presence of a metastatic carcinoma, American Society of Anesthesiologists(ASA) classification, sex, Charlson Comorbidity Index (CCI) ≤ 4, age, dementia, congestive heart failure, hypertension, surgery using pins/screws, and chronic kidney disease.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.