Predictive Values of Preoperative Characteristics for 30-Day Mortality in Traumatic Hip Fracture Patients

Establishment and validation of an artificial intelligence web application for predicting postoperative in-hospital mortality in patients with hip fracture: a national cohort study of 52 707 cases

BACKGROUND

In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practice. By utilizing artificial intelligence (AI) and machine learning techniques, this study aims to develop a predictive model that can assist clinicians in identifying geriatric hip fracture patients at a higher risk of in-hospital mortality.

METHODS

A total of 52 707 geriatric hip fracture patients treated with surgery from 90 hospitals were included in this study. The primary outcome was postoperative in-hospital mortality. The patients were randomly divided into two groups, with a ratio of 7:3. The majority of patients, assigned to the training cohort, were used to develop the AI models. The remaining patients, assigned to the validation cohort, were used to validate the models. Various machine learning algorithms, including logistic regression (LR), decision tree (DT), naïve bayesian (NB), neural network (NN), eXGBoosting machine (eXGBM), and random forest (RF), were employed for model development. A comprehensive scoring system, incorporating 10 evaluation metrics, was developed to assess the prediction performance, with higher scores indicating superior predictive capability. Based on the best machine learning-based model, an AI application was developed on the Internet. In addition, a comparative testing of prediction performance between doctors and the AI application.

FINDINGS

The eXGBM model exhibited the best prediction performance, with an area under the curve (AUC) of 0.908 (95% CI: 0.881-0.932), as well as the highest accuracy (0.820), precision (0.817), specificity (0.814), and F1 score (0.822), and the lowest Brier score (0.120) and log loss (0.374). Additionally, the model showed favorable calibration, with a slope of 0.999 and an intercept of 0.028. According to the scoring system incorporating 10 evaluation metrics, the eXGBM model achieved the highest score (56), followed by the RF model (48) and NN model (41). The LR, DT, and NB models had total scores of 27, 30, and 13, respectively. The AI application has been deployed online at https://in-hospitaldeathinhipfracture-l9vhqo3l55fy8dkdvuskvu.streamlit.app/ , based on the eXGBM model. The comparative testing revealed that the AI application's predictive capabilities significantly outperformed those of the doctors in terms of AUC values (0.908 vs. 0.682, P <0.001).

CONCLUSIONS

The eXGBM model demonstrates promising predictive performance in assessing the risk of postoperative in-hospital mortality among geriatric hip fracture patients. The developed AI model serves as a valuable tool to enhance clinical decision-making.

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.

Systematic review and meta-analysis of preoperative predictors for early mortality following hip fracture surgery

Hip fractures are a global health problem with a high postoperative mortality rate. Preoperative predictors for early mortality could be used to optimise and personalise healthcare strategies. This study aimed to identify predictors for early mortality following hip fracture surgery. Cohort studies examining independent preoperative predictors for mortality following hip fracture surgery were identified through a systematic search on Scopus and PubMed. Predictors for 30-day mortality were the primary outcome, and predictors for mortality within 1 year were secondary outcomes. Primary outcomes were analysed with random-effects meta-analyses. Confidence in the cumulative evidence was assessed using the GRADE criteria. Secondary outcomes were synthesised narratively. Thirty-three cohort studies involving 462,699 patients were meta-analysed. Five high-quality evidence predictors for 30-day mortality were identified: age per year (OR: 1.06, 95% CI: 1.04-1.07), ASA score ≥ 3 (OR: 2.69, 95% CI: 2.12-3.42), male gender (OR: 2.00, 95% CI: 1.85-2.18), institutional residence (OR: 1.81, 95% CI: 1.31-2.49), and metastatic cancer (OR: 2.83, 95% CI: 2.58-3.10). Additionally, six moderate-quality evidence predictors were identified: chronic renal failure, dementia, diabetes, low haemoglobin, heart failures, and a history of any malignancy. Weak evidence was found for non-metastatic cancer. This review found relevant preoperative predictors which could be used to identify patients who are at high risk of 30-day mortality following hip fracture surgery. For some predictors, the prognostic value could be increased by further subcategorising the conditions by severity.

Using machine-learning to decode postoperative hip mortality Trends: Actionable insights from an extensive clinical dataset

BACKGROUND

Hip fractures are one of the most common injuries experienced by the general population. Despite advances in surgical techniques, postoperative mortality rates remain high. identifying relevant clinical factors associated with mortality is essential to preoperative risk stratification and tailored post-surgical interventions to improve patient outcomes. The purpose of this study aimed to identify preoperative risk factors and develop predictive models for increased hip fracture-related mortality within 30 days post-surgery, using one of the largest patient cohorts to date.

METHODS

Data from the American College of Surgeons National Surgical Quality Improvement Program database, comprising 107,660 hip fracture patients treated with surgical fixation was used. A penalized regression approach, least absolute shrinkage and selection operator was employed to develop two predictive models: one using preoperative factors and the second incorporating both preoperative and postoperative factors.

RESULTS

The analysis identified 68 preoperative factor outcomes associated with 30-day mortality. The combined model revealed 84 relevant factors, showing strong predictive power for determining postoperative mortality, with an AUC of 0.83.

CONCLUSIONS

The study's comprehensive methodology provides risk assessment tools for clinicians to identify high-risk patients and optimize patient-specific care.

Systematic review of machine-learning models in orthopaedic trauma

Aims

Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool.

Methods

A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias.

Results

A total of 40 studies reported on training and internal validation; four studies performed both development and external validation, and one study performed only external validation. The most commonly reported outcomes were mortality (33%, 15/45) and length of hospital stay (9%, 4/45), and the majority of prediction models were developed in the hip fracture population (60%, 27/45). The overall median completeness for the TRIPOD statement was 62% (interquartile range 30 to 81%). The overall risk of bias in the PROBAST tool was low in 24% (11/45), high in 69% (31/45), and unclear in 7% (3/45) of the studies. High risk of bias was mainly due to analysis domain concerns including small datasets with low number of outcomes, complete-case analysis in case of missing data, and no reporting of performance measures.

Conclusion

The results of this study showed that despite a myriad of potential clinically useful applications, a substantial part of ML studies in orthopaedic trauma lack transparent reporting, and are at high risk of bias. These problems must be resolved by following established guidelines to instil confidence in ML models among patients and clinicians. Otherwise, there will remain a sizeable gap between the development of ML prediction models and their clinical application in our day-to-day orthopaedic trauma practice.

Predictors of outcomes in geriatric patients with moderate traumatic brain injury after ground level falls

Introduction

The elderly population constitutes one of the fastest-growing demographic groups globally. Within this population, mild to moderate traumatic brain injuries (TBI) resulting from ground level falls (GLFs) are prevalent and pose significant challenges. Between 50 and 80% of TBIs in older individuals are due to GLFs. These incidents result in more severe outcomes and extended recovery periods for the elderly, even when controlling for injury severity. Given the increasing incidence of such injuries it becomes essential to identify the key factors that predict complications and in-hospital mortality. Therefore, the aim of this study was to pinpoint the top predictors of complications and in-hospital mortality in geriatric patients who have experienced a moderate TBI following a GLF.

Methods

Data were obtained from the American College of Surgeons' Trauma Quality Improvement Program database. A moderate TBI was defined as a head AIS ≤ 3 with a Glasgow Coma Scale (GCS) 9-13, and an AIS ≤ 2 in all other body regions. Potential predictors of complications and in-hospital mortality were included in a logistic regression model and ranked using the permutation importance method.

Results

A total of 7,489 patients with a moderate TBI were included in the final analyses. 6.5% suffered a complication and 6.2% died prior to discharge. The top five predictors of complications were the need for neurosurgical intervention, the Revised Cardiac Risk Index, coagulopathy, the spine abbreviated injury severity scale (AIS), and the injury severity score. The top five predictors of mortality were head AIS, age, GCS on admission, the need for neurosurgical intervention, and chronic obstructive pulmonary disease.

Conclusion

When predicting both complications and in-hospital mortality in geriatric patients who have suffered a moderate traumatic brain injury after a ground level fall, the most important factors to consider are the need for neurosurgical intervention, cardiac risk, and measures of injury severity. This may allow for better identification of at-risk patients, and at the same time resulting in a more equitable allocation of resources.

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.

Application of artificial intelligence in trauma orthopedics: Limitation and prospects

The varieties and capabilities of artificial intelligence and machine learning in orthopedic surgery are extensively expanding. One promising method is neural networks, emphasizing big data and computer-based learning systems to develop a statistical fracture-detecting model. It derives patterns and rules from outstanding amounts of data to analyze the probabilities of different outcomes using new sets of similar data. The sensitivity and specificity of machine learning in detecting fractures vary from previous studies. AI may be most promising in the diagnosis of less-obvious fractures that are more commonly missed. Future studies are necessary to develop more accurate and effective detection models that can be used clinically.

Distinguishing pericarpium citri reticulatae of different origins using terahertz time-domain spectroscopy combined with convolutional neural networks

The geographical indication of pericarpium citri reticulatae (PCR) is very important in grading the quality and price of PCRs. Therefore, terahertz time-domain spectroscopy (THz-TDS) technology combined with convolutional neural networks (CNN) was proposed to distinguish PCRs of different origins without damage in this study. The one-dimensional CNN (1D-CNN) model with an accuracy of 82.99% based on spectral data processed with SNV was established. The two-dimensional image features were transformed from unprocessed spectral data using the gramian angular field (GAF), the Markov transition field (MTF) and the recurrence plot (RP), which were used to build a two-dimensional CNN (2D-CNN) model with an accuracy of 78.33%. Further, the CNN models with different fusion methods were developed for fusing spectra data and image data. In addition, the adding spectra and images based on the CNN (Add-CNN) model with an accuracy of 86.17% performed better. Eventually, the Add-CNN model based on ten frequencies extracted using permutation importance (PI) achieved the identification of PCRs from different origins. Overall, the current study would provide a new method for identifying PCRs of different origins, which was expected to be used for the traceability of PCRs products.

Artificial Intelligence for Hip Fracture Detection and Outcome Prediction: A Systematic Review and Meta-analysis

IMPORTANCE

Artificial 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.

OBJECTIVE

To 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 SOURCES

A 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 SELECTION

Studies 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 SYNTHESIS

This 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 MEASURES

Diagnostic 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.

RESULTS

Of 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 RELEVANCE

The 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.

The prediction of early mortality following hip fracture surgery in patients aged 90 years and older: the Almelo Hip Fracture Score 90 (AHFS90)

The AHFS⁹⁰ was developed for the prediction of early mortality in patients ≥ 90 years undergoing hip fracture surgery. The AHFS⁹⁰ has a good accuracy and in most risk categories a good calibration. In our study population, the AHFS⁹⁰ yielded a maximum prediction of early mortality of 64.5%.

PURPOSE

Identifying hip fracture patients with a high risk of early mortality after surgery could help make treatment decisions and information about the prognosis. This study aims to develop and validate a risk score for predicting early mortality in patients ≥ 90 years undergoing hip fracture surgery (AHFS⁹⁰).

METHODS

Patients ≥ 90 years, surgically treated for a hip fracture, were included. A selection of possible predictors for mortality was made. Missing data were subjected to multiple imputations using chained equations. Logistic regression was performed to develop the AHFS⁹⁰, which was internally and externally validated. Calibration was assessed using a calibration plot and comparing observed and predicted risks.

RESULTS

One hundred and two of the 922 patients (11.1%) died ≤ 30 days following hip fracture surgery. The AHFS⁹⁰ includes age, gender, dementia, living in a nursing home, ASA score, and hemoglobin level as predictors for early mortality. The AHFS⁹⁰ had good accuracy (area under the curve 0.72 for geographic cross validation). Predicted risks correspond with observed risks of early mortality in four risk categories. In two risk categories, the AHFS⁹⁰ overestimates the risk. In one risk category, no mortality was observed; therefore, no analysis was possible. The AHFS⁹⁰ had a maximal prediction of early mortality of 64.5% in this study population.

CONCLUSION

The AHFS⁹⁰ accurately predicts early mortality after hip fracture surgery in patients ≥ 90 years of age. Predicted risks correspond to observed risks in most risk categories. In our study population, the AHFS⁹⁰ yielded a maximum prediction of early mortality of 64.5%.

Mode of anesthesia is not associated with outcomes following emergency hip fracture surgery: a population-level cohort study

Background

Hip fractures often occur in frail patients with several comorbidities. In those undergoing emergency surgery, determining the optimal anesthesia modality may be challenging, with equipoise concerning outcomes following either spinal or general anesthesia. In this study, we investigated the association between mode of anesthesia and postoperative morbidity and mortality with subgroup analyses.

Methods

This is a retrospective study using all consecutive adult patients who underwent emergency hip fracture surgery in Orebro County, Sweden, between 2013 and 2017. Patients were extracted from the Swedish National Hip Fracture Registry, and their electronic medical records were reviewed. The association between the type of anesthesia and 30-day and 90-day postoperative mortality, as well as in-hospital severe complications (Clavien-Dindo classification ≥3a), was analyzed using Poisson regression models with robust SEs, while the association with 1-year mortality was analyzed using Cox proportional hazards models. All analyses were adjusted for potential confounders.

Results

A total of 2437 hip fracture cases were included in the study, of whom 60% received spinal anesthesia. There was no statistically significant difference in the risk of 30-day postoperative mortality (adjusted incident rate ratio (IRR) (95% CI): 0.99 (0.72 to 1.36), p=0.952), 90-day postoperative mortality (adjusted IRR (95% CI): 0.88 (0.70 to 1.11), p=0.281), 1-year postoperative mortality (adjusted HR (95% CI): 0.98 (0.83 to 1.15), p=0.773), or in-hospital severe complications (adjusted IRR (95% CI): 1.24 (0.85 to 1.82), p=0.273), when comparing general and spinal anesthesia.

Conclusions

Mode of anesthesia during emergency hip fracture surgery was not associated with an increased risk of postoperative mortality or in-hospital severe complications in the study population or any of the investigated subgroups.

Level of evidence: Therapeutic/Care Management, level III

Developing and validating a scoring system for measuring frailty in patients with hip fracture: a novel model for predicting short-term postoperative mortality

Objectives

Frailty is common among patients with hip fracture and may, in part, contribute to the increased risk of mortality and morbidity after hip fracture surgery. This study aimed to develop a novel frailty score for patients with traumatic hip fracture that could be used to predict postoperative mortality as well as facilitate further research into the role of frailty in patients with hip fracture.

Methods

The Orthopedic Hip Frailty Score (OFS) was developed using a national dataset, retrieved from the Swedish National Quality Registry for Hip Fractures, that contained all adult patients who underwent surgery for a traumatic hip fracture in Sweden between January 1, 2008 and December 31, 2017. Candidate variables were selected from the Nottingham Hip Fracture Score, Sernbo Score, Charlson Comorbidity Index, 5-factor modified Frailty Index, as well as the Revised Cardiac Risk Index and ranked based on their permutation importance, with the top 5 variables being selected for the score. The OFS was then validated on a local dataset that only included patients from Orebro County, Sweden.

Results

The national dataset consisted of 126,065 patients. 2365 patients were present in the local dataset. The most important variables for predicting 30-day mortality were congestive heart failure, institutionalization, non-independent functional status, an age ≥85, and a history of malignancy. In the local dataset, the OFS achieved an area under the receiver-operating characteristic curve (95% CI) of 0.77 (0.74 to 0.80) and 0.76 (0.74 to 0.78) when predicting 30-day and 90-day postoperative mortality, respectively.

Conclusions

The OFS is a significant predictor of short-term postoperative mortality in patients with hip fracture that outperforms, or performs on par with, all other investigated indices.

Level of evidence

Level III, Prognostic and Epidemiological.

Modifiable and non-modifiable risk factors in hip fracture mortality in Norway, 2014 to 2018 : a linked multiregistry study

Aims

This study aimed to identify risk factors (patient, healthcare system, and socioeconomic) for mortality after hip fractures and estimate their relative importance. Further, we aimed to elucidate mortality and survival patterns following fractures and the duration of excess mortality.

Methods

Data on 37,394 hip fractures in the Norwegian Hip Fracture Register from January 2014 to December 2018 were linked to data from the Norwegian Patient Registry, Statistics Norway, and characteristics of acute care hospitals. Cox regression analysis was performed to estimate risk factors associated with mortality. The Wald statistic was used to estimate and illustrate relative importance of risk factors, which were categorized in modifiable (healthcare-related) and non-modifiable (patient-related and socioeconomic). We calculated standardized mortality ratios (SMRs) comparing deaths among hip fracture patients to expected deaths in a standardized reference population.

Results

Mean age was 80.2 years (SD 11.4) and 67.5% (n = 25,251) were female. Patient factors (male sex, increasing comorbidity (American Society of Anesthesiologists grade and Charlson Comorbidity Index)), socioeconomic factors (low income, low education level, living in a healthcare facility), and healthcare factors (hip fracture volume, availability of orthogeriatric services) were associated with increased mortality. Non-modifiable risk factors were more strongly associated with mortality than modifiable risk factors. The SMR analysis suggested that cumulative excess mortality among hip fracture patients was 16% in the first year and 41% at six years. SMR was 2.48 for the six-year observation period, most pronounced in the first year, and fell from 10.92 in the first month to 3.53 after 12 months and 2.48 after six years. Substantial differences in median survival time were found, particularly for patient-related factors.

Conclusion

Socioeconomic, patient-, and healthcare-related factors all contributed to excess mortality, and non-modifiable factors had stronger association than modifiable ones. Hip fractures contributed to substantial excess mortality. Apparently small survival differences translate into substantial disparity in median survival time in this elderly population.

Cite this article: Bone Joint J 2022;104-B(7):884–893.

Dementia is a surrogate for frailty in hip fracture mortality prediction

Purpose

Among hip fracture patients both dementia and frailty are particularly prevalent. The aim of the current study was to determine if dementia functions as a surrogate for frailty, or if it confers additional information as a comorbidity when predicting postoperative mortality after a hip fracture.

Methods

All adult patients who suffered a traumatic hip fracture in Sweden between January 1, 2008 and December 31, 2017 were considered for inclusion. Pathological fractures, non-operatively treated fractures, reoperations, and patients missing data were excluded. Logistic regression (LR) models were fitted, one including and one excluding measurements of frailty, with postoperative mortality as the response variable. The primary outcome of interest was 30-day postoperative mortality. The relative importance for all variables was determined using the permutation importance. New LR models were constructed using the top ten most important variables. The area under the receiver-operating characteristic curve (AUC) was used to compare the predictive ability of these models.

Results

121,305 patients were included in the study. Initially, dementia was among the top ten most important variables for predicting 30-day mortality. When measurements of frailty were included, dementia was replaced in relative importance by the ability to walk alone outdoors and institutionalization. There was no significant difference in the predictive ability of the models fitted using the top ten most important variables when comparing those that included [AUC for 30-day mortality (95% CI): 0.82 (0.81–0.82)] and excluded [AUC for 30-day mortality (95% CI): 0.81 (0.80–0.81)] measurements of frailty.

Conclusion

Dementia functions as a surrogate for frailty when predicting mortality up to one year after hip fracture surgery. The presence of dementia in a patient without frailty does not appreciably contribute to the prediction of postoperative mortality.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00068-022-01960-9.

Mortality risk stratification in isolated severe traumatic brain injury using the revised cardiac risk index

PURPOSE

Traumatic brain injury (TBI) continues to be a significant cause of mortality and morbidity worldwide. As cardiovascular events are among the most common extracranial causes of death after a severe TBI, the Revised Cardiac Risk Index (RCRI) could potentially aid in the risk stratification of this patient population. This investigation aimed to determine the association between the RCRI and in-hospital deaths among isolated severe TBI patients.

METHODS

All adult patients registered in the TQIP database between 2013 and 2017 who suffered an isolated severe TBI, defined as a head AIS ≥ 3 with an AIS ≤ 1 in all other body regions, were included. Patients were excluded if they had a head AIS of 6. The association between different RCRI scores (0, 1, 2, 3, ≥ 4) and in-hospital mortality was analyzed using a Poisson regression model with robust standard errors while adjusting for potential confounders, with RCRI 0 as the reference.

RESULTS

259,399 patients met the study's inclusion criteria. RCRI 2 was associated with a 6% increase in mortality risk [adjusted IRR (95% CI) 1.06 (1.01-1.12), p = 0.027], RCRI 3 was associated with a 17% increased risk of mortality [adjusted IRR (95% CI) 1.17 (1.05-1.31), p = 0.004], and RCRI ≥ 4 was associated with a 46% increased risk of in-hospital mortality [adjusted IRR(95% CI) 1.46 (1.11-1.90), p = 0.006], compared to RCRI 0.

CONCLUSION

An elevated RCRI ≥ 2 is significantly associated with an increased risk of in-hospital mortality among patients with an isolated severe traumatic brain injury. The simplicity and bedside applicability of the index makes it an attractive choice for risk stratification in this patient population.

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

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.