Prediction Model of In-Hospital Mortality After Hip Fracture Surgery

The real time to surgery: Limited delay after medical optimization does not impact hip fracture surgery outcomes

INTRODUCTION

Current U.S./Canadian guidelines recommend hip fracture surgery within 48 h of injury to decrease morbidity/mortality. Multiple studies have identified medical optimization as the key component of time to surgery, but have inherent bias as patients with multiple co-morbidities often take longer to optimize. This study aimed to evaluate time from medical optimization to surgery (TMOS) to determine if "real surgical delay" is associated with: 1) mortality and 2) complications for geriatric hip fracture patients.

METHODS

A retrospective chart review of geriatric hip fractures treated from 2015-2018 at a single, level-1 trauma center was conducted. Univariate logistic regression was performed to identify association between TMOS and post-operative complication rates. For mortality, the Wilcoxon test was used to compare TMOS for patients discharged following surgery to those who were not.

RESULTS

A total of 884 hip fractures were treated operatively, with median TMOS 16.2 h (5.0-22.5, 1st-3rd quartiles). Univariate logistic regression models did not identify an association between TMOS and complication rates. For patients successfully discharged, median TMOS was 16.2 h (5.0-22.3, 1st-3rd quartiles). For the cohort of patients not successfully discharged, median TMOS was 19.1 h (10.1-25.9, 1st-3rd quartiles, p = 0.16).

CONCLUSION

"Real surgical delay", or TMOS is not associated with increased complications or with inpatient mortality for geriatric hip fracture patients. With few exceptions, our institution adhered to the 48-hour time window from injury to hip surgery. We maintain the belief timely surgery following optimization plays a crucial role in the geriatric hip fracture patient outcomes.

Risk prediction models of mortality after hip fracture surgery in older individuals: a systematic review

OBJECTIVE

This study aimed to critically assess existing risk prediction models for postoperative mortality in older individuals with hip fractures, with the objective of offering substantive insights for their clinical application.

DESIGN

A comprehensive search was conducted across prominent databases, including PubMed, Embase, Cochrane Library, SinoMed, CNKI, VIP, and Wanfang, spanning original articles in both Chinese and English up until 1 December 2023. Two researchers independently extracted pertinent research characteristics, such as predictors, model performance metrics, and modeling methodologies. Additionally, the bias risk and applicability of the incorporated risk prediction models were systematically evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

Within the purview of this investigation, a total of 21 studies were identified, constituting 21 original risk prediction models. The discriminatory capacity of the included risk prediction models, as denoted by the minimum and maximum areas under the subject operating characteristic curve, ranged from 0.710 to 0.964. Noteworthy predictors, recurrent across various models, included age, sex, comorbidities, and nutritional status. However, among the models assessed through the PROBAST framework, only one was deemed to exhibit a low risk of bias. Beyond this assessment, the principal limitations observed in risk prediction models pertain to deficiencies in data analysis, encompassing insufficient sample size and suboptimal handling of missing data.

CONCLUSION

Subsequent research endeavors should adopt more stringent experimental designs and employ advanced statistical methodologies in the construction of risk prediction models. Moreover, large-scale external validation studies are warranted to rigorously assess the generalizability and clinical utility of existing models, thereby enhancing their relevance as valuable clinical references.

The value of preoperative diagnostic testing and geriatric assessment in frail institutionalized elderly with a hip fracture; a secondary analysis of the FRAIL-HIP study

PURPOSE

The aim of this study was to provide a comprehensive overview of (preoperative and geriatric) diagnostic testing, abnormal diagnostic tests and their subsequent interventions, and clinical relevance in frail older adults with a hip fracture.

METHODS

Data on clinical consultations, radiological, laboratory, and microbiological diagnostics were extracted from the medical files of all patients included in the FRAIL-HIP study (inclusion criteria: hip fracture, > 70 years, living in a nursing home with malnourishment/cachexia and/or impaired mobility and/or severe co-morbidity). Data were evaluated until hospital discharge in nonoperatively treated patients and until surgery in operatively treated patients.

RESULTS

A total of 172 patients (88 nonoperative and 84 operative) were included, of whom 156 (91%) underwent laboratory diagnostics, 126 (73%) chest X-rays, and 23 (13%) CT-scans. In 153/156 (98%) patients at least one abnormal result was found in laboratory diagnostics. In 82/153 (50%) patients this did not result in any additional diagnostics or (pharmacological) intervention. Abnormal test results were mentioned as one of the deciding arguments for operative delay (> 24 h) for 10/84 (12%) patients and as a factor in the decision between nonoperative and operative treatment in 7/172 (4%) patients.

CONCLUSION

A large number and variety of diagnostics were performed in this patient population. Abnormal test results in laboratory diagnostics were found for almost all patients and, in majority, appear to have no direct clinical consequences. To prevent unnecessary diagnostics, prospective research is required to evaluate the clinical consequences and added value of the separate elements of preoperative diagnostic testing and geriatric assessment in frail hip fracture patients.

No Difference in Short-Term Complications following Treatment of Closed Tibial Shaft Fractures with Intramedullary Nailing versus Plate Fixation

Objectives

Tibial shaft fractures are treated with both intramedullary nailing (IMN) and plate fixation (ORIF). Using a large national database, we aimed to explore the differences in thirty-day complication rates between IMN and ORIF.

Methods

Patients in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database who had undergone either tibial IMN or ORIF for closed fractures from 2010 to 2018 were identified using current procedural terminology (CPT) codes. After excluding all patients with open fractures, the propensity score was matching. Univariate and multivariate logistic regressions were used to identify risk factors associated with the thirty-day incidence of complications in the two cohorts.

Results

A total of 5,400 patients were identified with 3,902 (72.3%) undergoing IMN and 1,498 (27.7%) ORIF. After excluding any ICD-10 diagnosis codes not pertaining to closed, traumatic tibial shaft fractures, 2,136 IMN and 621 ORIF cases remained. After matching, the baseline demographics were not significantly different between the cohorts. Following matching, the rate of any adverse event (aae) did not differ significantly between the IMN (7.08% (n = 44)) and ORIF (8.86% (n = 55)) cohorts (p=0.13). There was also no significant difference in operative time (IMN = 98.5 min, ORIF = 100 min; p=0.3) or length of stay (IMN = 3.7 days, ORIF = 3.3 days; p=0.08) between the cohorts.

Conclusion

There were no significant differences in short-term complications between cohorts. These are important data for the surgeon when considering surgical management of closed tibial shaft fractures.

Ninety-day mortality of extremely elderly patients undergoing hip fracture surgery and its association with preoperative cardiac function: a single-center retrospective study

PURPOSE

We investigated the 90-day mortality rate in elderly patients who underwent hip fracture surgery and the association of preoperative cardiac function with mortality.

METHODS

We retrospectively enrolled 133 consecutive patients aged 80 years or older who underwent hip fracture surgery. We obtained information for patient sex, age, comorbidities, medications, anesthesia method, left ventricular systolic and diastolic functions assessed by echocardiography, and preoperative brain natriuretic peptide (BNP) levels. Multivariate logistic regression analysis was performed.

RESULTS

The 90-day mortality rate in patients with a mean age of 88.9 years was 7.5% (10/133). More than half of the patients had diastolic dysfunction of the left ventricle. There were no significant differences in preoperative cardiac systolic and diastolic functions between the mortality group and non-mortality group. The preoperative BNP level in the mortality group was significantly higher than that in the non-mortality group (p = 0.038). Preoperative BNP level was not an independent risk factor for 90-day mortality (p = 0.081) in the primary multivariate logistic regression analysis but was an independent risk factor (p = 0.039) with an odds ratio of 1.004 (95% CI 1.000-1.008) in the sensitivity analysis with different explanatory variables.

CONCLUSION

The 90-day mortality rate in patients over 80 years old after hip fracture surgery was 7.5%. There were no significant differences in preoperative cardiac function assessed by echocardiography between the mortality and non-mortality groups. Our results suggest that there is no association or only a weak association of high BNP level with 90-day mortality in this age population.

Assessing the predictive capacity of machine learning models using patient-specific variables in determining in-hospital outcomes after THA

Background

Machine learning is a subset of artificial intelligence using algorithmic modeling to progressively learn and create predictive models. Clinical application of machine learning can aid physicians through identification of risk factors and implications of predicted patient outcomes.

Aims

The aim of this study was to compare patient-specific and situation perioperative variables through optimized machine learning models to predict postoperative outcomes.

Methods

Data from 2016 to 2017 from the National Inpatient Sample was used to identify 177,442 discharges undergoing primary total hip arthroplasty, which were included in the training, testing, and validation of 10 machine learning models. 15 predictive variables consisting of 8 patient-specific and 7 situational specific variables were utilized to predict 3 outcome variables: length of stay, discharge, and mortality. The machine learning models were assessed in responsiveness via area under the curve and reliability.

Results

For all outcomes, Linear Support Vector Machine had the highest responsiveness among all models when using all variables. When utilizing patient-specific variables only, responsiveness of the top 3 models ranged between 0.639 and 0.717 for length of stay, 0.703-0.786 for discharge disposition, and 0.887-0.952 for mortality. The top 3 models utilizing situational variables only produced responsiveness of 0.552-0.589 for length of stay, 0.543-0.574 for discharge disposition, and 0.469-0.536 for mortality.

Conclusions

Linear Support Vector Machine was the most responsive machine learning model of the 10 algorithms trained, while decision list was most reliable. Responsiveness was observed to be consistently higher with patient-specific variables than situational variables, emphasizing the predictive capacity and value of patient-specific variables. The current practice in machine learning literature generally deploys a single model, it is suboptimal to develop optimized models for application into clinical practice. The limitation of other algorithms may prohibit potential more reliable and responsive models.Level of Evidence III.

Prediction of long-term mortality following hip fracture surgery: evaluation of three risk models

INTRODUCTION

Several prognostic models have been developed for mortality in hip fracture patients, but their accuracy for long-term prediction is unclear. This study evaluates the performance of three models assessing 30-day, 1-year and 8-year mortality after hip fracture surgery: the Nottingham Hip Fracture Score (NHFS), the model developed by Holt et al. and the Hip fracture Estimator of Mortality Amsterdam (HEMA).

MATERIALS AND METHODS

Patients admitted with a fractured hip between January 2012 and June 2013 were included in this retrospective cohort study. Relevant variables used by the three models were collected, as were mortality data. Predictive performance was assessed in terms of discrimination with the area under the receiver operating characteristic curve and calibration with the Hosmer-Lemeshow goodness-of-fit test. Clinical usefulness was evaluated by determining risk groups for each model, comparing differences in mortality using Kaplan-Meier curves, and by assessing positive and negative predictive values.

RESULTS

A total of 344 patients were included for analysis. Observed mortality rates were 6.1% after 30 days, 19.1% after 1 year and 68.6% after 8 years. The NHFS and the model by Holt et al. demonstrated good to excellent discrimination and adequate calibration for both short- and long-term mortality prediction, with similar clinical usefulness measures. The HEMA demonstrated inferior prediction of 30-day and 8-year mortality, with worse discriminative abilities and a significant lack of fit.

CONCLUSIONS

The NHFS and the model by Holt et al. allowed for accurate identification of low- and high-risk patients for both short- and long-term mortality after a fracture of the hip. The HEMA performed poorly. When considering predictive performance and ease of use, the NHFS seems most suitable for implementation in daily clinical practice.

Evaluation of a 30-day-mortality risk calculator for patients undergoing surgical fixation of fragility hip fractures

INTRODUCTION

Hip fractures often occur in medically complex patients and can be associated with high perioperative mortality. Mortality risk assessment tools that are specific to hip fracture patients have not been extensively studied. The objective of this study is to evaluate a recently published 30-day mortality risk calculator (Hip Fracture Estimator of Mortality Amsterdam [HEMA]) in a group of patients treated at a university health system.

MATERIALS & METHODS

625 patients treated surgically for hip fractures between 2015 and 2020 at our institution were retrospectively reviewed. Patients younger than age 65, periprosthetic fractures, revision procedures, and fractures treated non-operatively were excluded. Univariate and multivariate analyses were used to determine significant relationships between variables and 30-day mortality after surgery. Additional patient-specific risk factors not included in the original risk calculator were also evaluated.

RESULTS

The observed 30-day mortality was 5.6%. HEMA score was significantly associated with 30-mortality, though our cohort had significantly lower mortality rates in high-risk patients than expected based on the HEMA tool. In analyzing patient characteristics not included in HEMA score, history of dementia and elevated troponin were significantly associated with 30-day mortality.

DISCUSSION

The HEMA score reliably stratifies risk for 30-day mortality after hip fracture, though overestimates mortality in high-risk patients treated at a tertiary care center with a multidisciplinary team. The HEMA score may be enhanced by considering additional variables, including troponin level and history of dementia.

LEVEL OF EVIDENCE

IV.

Multidimensional prognostic index predicts short- and long-term mortality and rehospitalizations in older patients with hip fracture

BACKGROUND

Multidimensional Prognostic Index (MPI), calculated on cognitive, functional, nutritional, social, pharmacological and comorbidity domains, strongly correlates with mortality in older patients. Hip fractures are a major health problem and are associated with adverse outcomes in those affected by frailty.

AIM

We aimed at evaluating whether MPI is a predictor of mortality and rehospitalization in hip fracture older patients.

METHODS

We investigated the associations of MPI with all-cause 3- and 6-month mortality and rehospitalization in 1259 older patients admitted for hip fracture surgical treatment and managed by an orthogeriatric team [age 85 years (65-109); male gender: 22%].

RESULTS

Overall mortality was 11,4%, 17% and 23,5% at 3, 6 and 12 months from surgery (rehospitalizations: 15, 24,5 and 35,7%). MPI was associated (p < 0.001) with 3-, 6- and 12- month mortality and readmissions; Kaplan-Meier estimate for rehospitalization and survival according to MPI risk classes confirmed these results. In multiple regression analyses these associations were independent (p < 0.05) of mortality and rehospitalization-associated factors not included in the MPI, such as gender, age and post-surgical complications. Similar MPI predictive value was observed in patients undergoing endoprosthesis or other surgeries. ROC analysis confirmed that MPI was a predictor (p < 0.001) of both 3- and 6- month mortality and rehospitalization.

CONCLUSIONS

In hip fracture older patients, MPI is a strong predictor of 3-, 6- and 12- months mortality and rehospitalization, independently of surgical treatment and post-surgical complications. Therefore, MPI should be considered a valid pre-surgical tool to identify patients with higher clinical risk of adverse outcomes.

Factors Associated with Readmission within 30 Days after Discharge and In-Hospital Mortality after Proximal Femoral Fracture Surgery in the Elderly: Retrospective Cohort

Objective  To evaluate the factors associated with readmission within 30 days after discharge (R30) and in-hospital mortality (IHM) in elderly patients undergoing proximal femur fracture surgery (PFF). Methods  Retrospective cohort with data from 896 medical records of elderly (≥ 60 years) patients submitted to PFF surgery in a Brazilian hospital between November 2014 and December, 2019. The patients included were followed-up from the date of hospitalization for surgery up to 30 days after discharge. As independent variables, we evaluated gender, age, marital status, pre- and postoperative hemoglobin (Hb), international normalized ratio, time of hospitalization related to the surgery, door-surgery time, comorbidities, previous surgeries, use of medications, and the American Society of Anesthesiologists (ASA) score. Results The incidence of R30 was 10.2% (95% confidence interval [CI]: 8.3-12.3%), and the incidence of IHM was 5.7% (95%CI: 4.3-7.4%). Regarding R30, hypertension (odds ratio [OR]: 1.71; 95%CI: 1.03-2.96), and regular use of psychotropic drugs (OR: 1.74; 95%CI: 1.12-2.72) were associated in the adjusted model. In the case of IHM, higher chances were associated with chronic kidney disease (CKD) (OR: 5.80; 95%CI: 2.64-12.31), longer hospitalization time (OR: 1.06; 95%CI: 1.01-1.10), and R30 (OR: 3.60; 95%CI: 1.54-7.96). Higher preoperative Hb values were associated with a lower chance of mortality (OR: 0.73; 95%CI: 0.61-0.87). Conclusion Findings suggest that the occurrence of these outcomes is associated with comorbidities, medications, and Hb.

The Geriatrics at Risk Score (GeRi-Score) for mortality prediction in geriatric patients with proximal femur fracture - a development and validation study from the Registry for Geriatric Trauma (ATR-DGU)

This study developed an easy-to-use mortality prediction tool, which showed an acceptable discrimination and no significant lack of fit. The GeRi-Score was able to predict mortality and could distinguish between mild, moderate and high risk groups. Therefore, the GeRi-Score might have the potential to distribute the intensity of medical care.

PURPOSE

Several mortality-predicting tools for hip fracture patients are available, but all consist of a high number of variables, require a time-consuming evaluation and/or are difficult to calculate. The aim of this study was to develop and validate an easy-to-use score, which depends mostly on routine data.

METHODS

Patients from the Registry for Geriatric Trauma were divided into a development and a validation group. Logistic regression models were used to build a model for in-house mortality and to obtain a score. Candidate models were compared using Akaike information criteria (AIC) and likelihood ratio tests. The quality of the model was tested using the area under the curve (AUC) and the Hosmer-Lemeshow test.

RESULTS

38,570 patients were included, almost equal distributed to the development and to the validation dataset. The AUC was 0.727 (95% CI 0.711 - 0.742) for the final model, AIC resulted in a significant reduction in deviance compared to the basic model, and the Hosmer-Lemeshow test showed no significant lack of fit (p = 0.07). The GeRi-Score predicted an in-house mortality of 5.3% vs. 5.3% observed mortality in the development dataset and 5.4% vs. 5.7% in the validation dataset. The GeRi-Score was able to distinguish between mild, moderate and high risk groups.

CONCLUSIONS

The GeRi-Score is an easy-to-use mortality-predicting tool with an acceptable discrimination and no significant lack of fit. The GeRi-Score might have the potential to distribute the intensity of perioperative medical care in hip fracture surgery and can be used in quality management programs as benchmark tool.

A machine learning-based prediction model for in-hospital mortality among critically ill patients with hip fracture: An internal and external validated study

INTRODUCTION

Few studies have investigated the in-hospital mortality among critically ill patients with hip fracture. This study aimed to develop and validate a model to estimate the risk of in-hospital mortality among critically ill patients with hip fracture.

METHODS

For this study, data from the Medical Information Mart for Intensive Care III (MIMIC-III) Database and electronic Intensive Care Unit (eICU) Collaborative Research Database were evaluated. Enrolled patients (n=391) in the MIMIC-III database were divided into a training (2/3, n=260) and a validation (1/3, n=131) group at random. Using machine learning algorithms such as random forest, gradient boosting machine, decision tree, and eXGBoosting machine approach, the training group was utilized to train and optimize models. The validation group was used to internally validate models and the optimal model could be obtained in terms of discrimination (area under the receiver operating characteristic curve, AUROC) and calibration (calibration curve). External validation was done in the eICU Collaborative Research Database (n=165). To encourage practical use of the model, a web-based calculator was developed according to the eXGBoosting machine approach.

RESULTS

The in-hospital death rate was 13.81% (54/391) in the MIMIC-III database and 10.91% (18/165) in the eICU Collaborative Research Database. Age, gender, anemia, mechanical ventilation, cardiac arrest, and chronic airway obstruction were the six model parameters which were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) method combined with 10-fold cross-validation. The model established using the eXGBoosting machine approach showed the highest area under curve (AUC) value (0.797, 95% CI: 0.696-0.898) and the best calibrating ability, with a calibration slope of 0.999 and intercept of -0.019. External validation also revealed favorable discrimination (AUC: 0.715, 95% CI: 0.566-0.864; accuracy: 0.788) and calibration (calibration slope: 0.805) in the eICU Collaborative Research Database. The web-based calculator could be available at https://doctorwangsj-webcalculator-main-yw69yd.streamlitapp.com/.

CONCLUSION

The model has the potential to be a pragmatic risk prediction tool that is able to identify hip fracture patients who are at a high risk of in-hospital mortality in ICU settings, guide patient risk counseling, and simplify prognosis bench-marking by controlling for baseline risk.

Effect Modification on Death by Age and Sex in Elderly Hip Fracture

BACKGROUND

This study compared the effects of hip fractures on mortality according to sex and age in a nationwide cohort of elderly patients with hip fractures and controls.

METHODS

Patients with hip fractures and matched controls were selected from the National Health Insurance Service-Senior cohort. Time-dependent propensity score matching was estimated from a Cox proportional hazards model with January 1, 2005, as the baseline and hip fracture as an event. Patients were matched by age and sex to participants at risk of developing a hip fracture at time zero. The effect size is presented as hazard ratio (HR) using a Cox proportional hazards model with a robust variance estimator that accounts for clustering within the matched pairs.

RESULTS

Altogether, 14,283 patients with incident hip fractures and 28,566 matched controls were identified. The HR of male sex in hip fractures was 1.31 (95% confidence interval [CI], 1.22-1.40; Pinteraction<0.01). Moreover, the HR of age group in hip fractures was 0.73 (95% CI, 0.66-0.80; Pinteraction<0.01) between the 65 to 74 and 75 to 84 years groups, 0.76 (95% CI, 0.71-0.81; Pinteraction<0.01) between the 75 to 84 and ≥85 years groups, and 0.55 (95% CI, 0.50-0.61; Pinteraction<0.01) between the 65 to 74 and ≥85 years groups.

CONCLUSIONS

Male sex increases the risk of death in elderly patients with hip fractures versus matched controls, but the increased risk of death with age in hip fractures was decreased compared to that in matched controls.

Mortality Prediction in Hip Fracture Patients: Physician Assessment Versus Prognostic Models

OBJECTIVES

To evaluate 2 prognostic models for mortality after a fracture of the hip, the Nottingham Hip Fracture Score and Hip Fracture Estimator of Mortality Amsterdam and to compare their predictive performance to physician assessment of mortality risk in hip fracture patients.

DESIGN

Prospective cohort study.

SETTING

Two level-2 trauma centers located in the Netherlands.

PATIENTS

Two hundred forty-four patients admitted to the Emergency Departments of both hospitals with a fractured hip.

INTERVENTION

Data used in both prediction models were collected at the time of admission for each individual patient, as well as predictions of mortality by treating physicians.

MAIN OUTCOME MEASURES

Predictive performances were evaluated for 30-day, 1-year, and 5-year mortality. Discrimination was assessed with the area under the curve (AUC); calibration with the Hosmer-Lemeshow goodness-of-fit test and calibration plots; clinical usefulness in terms of accuracy, sensitivity, and specificity.

RESULTS

Mortality was 7.4% after 30 days, 22.1% after 1 year, and 59.4% after 5 years. There were no statistically significant differences in discrimination between the prediction methods (AUC 0.73-0.80). The Nottingham Hip Fracture Score demonstrated underfitting for 30-day mortality and failed to identify the majority of high-risk patients (sensitivity 33%). The Hip fracture Estimator of Mortality Amsterdam showed systematic overestimation and overfitting. Physicians were able to identify most high-risk patients for 30-day mortality (sensitivity 78%) but with some overestimation. Both risk models demonstrated a lack of fit when used for 1-year and 5-year mortality predictions.

CONCLUSIONS

In this study, prognostic models and physicians demonstrated similar discriminating abilities when predicting mortality in hip fracture patients. Although physicians overestimated mortality, they were better at identifying high-risk patients and at predicting long-term mortality.

LEVEL OF EVIDENCE

Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Comparative Analysis of the Ability of Machine Learning Models in Predicting In-hospital Postoperative Outcomes After Total Hip Arthroplasty

BACKGROUND

Machine learning (ML) methods have shown promise in a wide range of applications including the development of patient-specific predictive models before surgical interventions. The purpose of this study was to develop, test, and compare four distinct ML models to predict postoperative parameters after primary total hip arthroplasty.

METHODS

Data from the Nationwide Inpatient Sample were used to identify patients undergoing total hip arthroplasty from 2016 to 2017. Linear support vector machine (LSVM), random forest (RF), neural network (NN), and extreme gradient boost trees (XGBoost) predictive of mortality, length of stay, and discharge disposition were developed and validated using 15 predictive patient-specific and hospital-specific factors. Area under the curve of the receiver operating characteristic (AUCROC) curve and accuracy were used as validity metrics, and the strongest predictive variables under each model were assessed.

RESULTS

A total of 177,442 patients were included in this analysis. For mortality, the XGBoost, NN, and LSVM models all had excellent responsiveness during validation while RF had fair responsiveness. LSVM had the highest responsiveness with an AUCROC of 0.973 during validation. For the length of stay, the LSVM and NN models had fair responsiveness while the XGBoost and random forest models had poor responsiveness. LSVM had the highest responsiveness with an AUCROC of 0.744 during validation. For the discharge disposition outcome, LSVM had good responsiveness while the XGBoost, NN, and RF models all had fair responsiveness. LSVM had the highest responsiveness with an AUCROC of 0.801.

DISCUSSION

The ML methods tested demonstrated a range of poor-to-excellent responsiveness and accuracy in the prediction of the assessed metrics, with LSVM being the best performer. Such models should be further developed, with eventual integration into clinical practice to inform patient discussions and management decision making, with the potential for integration into tiered bundled payment models.

Differences between intertrochanteric and femoral neck fractures in resuscitative status and mortality rates

INTRODUCTION

Hip fracture mortality remains a challenge for orthopedic surgeons. The purpose of this study was to compare resuscitative mean arterial pressures (MAPs), intravenous fluid (IVF) administration, and mortality rates between intertrochanteric (IT) and femoral neck (FN) fracture patients.

HYPOTHESIS

We hypothesized that IT fracture patients would receive less aggressive fluid resuscitation than FNF patients given the perceived less invasive nature of intra-medullary nails compared with hemiarthroplasty.

MATERIALS AND METHODS

An institutional database was queried to identify all hip fractures managed surgically over a 2-year period. Preoperative and intraoperative MAPs and IVF administration, as measures of resuscitation, were compared between IT fracture patients treated with open reduction internal fixation and FN fracture patients treated with hemiarthroplasty.

RESULTS

Six hundred and ninety-eight hip fractures, including 531 IT and 167 FN fractures, were analyzed. There were no differences between IT and FN fracture cohorts for age, sex distribution, or Charlson Comorbidity Index scores. IT fracture patients were found to have lower MAP upon admission (103.7±20.1 vs. 107.8±18.4mmHg; p=0.026), and lower average, minimum, and maximum MAP values preoperatively and intraoperatively. Despite lower MAPs, IT fracture patients received less total IVF (581.9±472.5 vs. 832.9±496.5cc; p<0.001) and lower IVF rates intraoperatively (306.5±256.8 vs. 409.8±251.0 cc/h; p<0.001). IT fracture patients experienced higher 30-day (7.9% vs. 3.6%; p=0.040) and 90-day (10.6% vs. 5.4%; p=0.035) mortality rates and trended towards higher inpatient mortality (3.0% vs. 0.6%; p=0.088). Multivariate regression demonstrated IT pattern to be independently predictive of 30-day mortality with 2.459 increased odds relative to FN fracture (p=0.039).

DISCUSSION

IT fracture patterns are associated with decreased perioperative MAP values, yet received lower perioperative IVF rates. IT fracture patients suffered higher 30- and 90-day mortality rates, despite similar age and comorbidities.

LEVEL OF EVIDENCE

III; retrospective cohort study.

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

Background

Post-operative mortality risk assessment for geriatric patients with hip fractures (HF) is a challenge for clinicians. Early identification of geriatric HF patients with a high risk of post-operative death is helpful for early intervention and improving clinical prognosis. However, a single significant risk factor of post-operative death cannot accurately predict the prognosis of geriatric HF patients. Therefore, our study aims to utilize a machine learning approach, random forest algorithm, to fabricate a prediction model for post-operative death of geriatric HF patients.

Methods

This retrospective study enrolled consecutive geriatric HF patients who underwent treatment for surgery. The study cohort was divided into training and testing datasets at a 70:30 ratio. The random forest algorithm selected or excluded variables according to the feature importance. Least absolute shrinkage and selection operator (Lasso) was utilized to compare feature selection results of random forest. The confirmed variables were used to create a simplified model instead of a full model with all variables. The prediction model was then verified in the training dataset and testing dataset. Additionally, a prediction model constructed by logistic regression was used as a control to evaluate the efficiency of the new prediction model.

Results

Feature selection by random forest algorithm and Lasso regression demonstrated that seven variables, including age, time from injury to surgery, chronic obstructive pulmonary disease (COPD), albumin, hemoglobin, history of malignancy, and perioperative blood transfusion, could be used to predict the 1-year post-operative mortality. The area under the curve (AUC) of the random forest algorithm-based prediction model in training and testing datasets were 1.000, and 0.813, respectively. While the prediction tool constructed by logistic regression in training and testing datasets were 0.895, and 0.797, respectively.

Conclusions

Compared with logistic regression, the random forest algorithm-based prediction model exhibits better predictive ability for geriatric HF patients with a high risk of death within post-operative 1 year.

Predictive capacity of four machine learning models for in-hospital postoperative outcomes following total knee arthroplasty

Background

Machine learning (ML) methods have shown promise in the development of patient-specific predictive models prior to surgical interventions. The purpose of this study was to develop, test, and compare four distinct ML models to predict postoperative parameters following primary total knee arthroplasty (TKA).

Methods

Data from the Nationwide Inpatient Sample was used to identify patients undergoing TKA during 2016-2017. Four distinct ML models predictive of mortality, length of stay (LOS), and discharge disposition were developed and validated using 15 predictive patient and hospital-specific factors. Area under the curve of the receiver operating characteristic curve (AUCROC) and accuracy were used as validity metrics, and the strongest predictive variables under each model were assessed.

Results

A total of 305,577 patients were included. For mortality, the XGBoost, neural network (NN), and LSVM models all had excellent responsiveness during validation, while random forest (RF) had fair responsiveness. For predicting LOS, all four models had poor responsiveness. For the discharge disposition outcome, the LSVM, NN, and XGBoost models had good responsiveness, while the RF model had poor responsiveness. LSVM and XGBoost had the highest responsiveness for predicting discharge disposition with an AUCROC of 0.747.

Discussion

The ML models tested demonstrated a range of poor to excellent responsiveness and accuracy in the prediction of the assessed metrics, with considerable variability noted in the predictive precision between the models. The continued development of ML models should be encouraged, with eventual integration into clinical practice in order to inform patient discussions, management decision making, and health policy.

Implementation of a machine learning application in preoperative risk assessment for hip repair surgery

Background

This study aims to develop a machine learning-based application in a real-world medical domain to assist anesthesiologists in assessing the risk of complications in patients after a hip surgery.

Methods

Data from adult patients who underwent hip repair surgery at Chi-Mei Medical Center and its 2 branch hospitals from January 1, 2013, to March 31, 2020, were analyzed. Patients with incomplete data were excluded. A total of 22 features were included in the algorithms, including demographics, comorbidities, and major preoperative laboratory data from the database. The primary outcome was a composite of adverse events (in-hospital mortality, acute myocardial infarction, stroke, respiratory, hepatic and renal failure, and sepsis). Secondary outcomes were intensive care unit (ICU) admission and prolonged length of stay (PLOS). The data obtained were imported into 7 machine learning algorithms to predict the risk of adverse outcomes. Seventy percent of the data were randomly selected for training, leaving 30% for testing. The performances of the models were evaluated by the area under the receiver operating characteristic curve (AUROC). The optimal algorithm with the highest AUROC was used to build a web-based application, then integrated into the hospital information system (HIS) for clinical use.

Results

Data from 4,448 patients were analyzed; 102 (2.3%), 160 (3.6%), and 401 (9.0%) patients had primary composite adverse outcomes, ICU admission, and PLOS, respectively. Our optimal model had a superior performance (AUROC by DeLong test) than that of ASA-PS in predicting the primary composite outcomes (0.810 vs. 0.629, p < 0.01), ICU admission (0.835 vs. 0.692, p < 0.01), and PLOS (0.832 vs. 0.618, p < 0.01).

Conclusions

The hospital-specific machine learning model outperformed the ASA-PS in risk assessment. This web-based application gained high satisfaction from anesthesiologists after online use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-022-01648-y.

External validation of the U-HIP prediction model for in-hospital mortality in geriatric hip fracture patients

INTRODUCTION

Identification of high-risk hip fracture patients in an early stage is vital for guiding surgical management and shared decision making. To objective of this study was to perform an external international validation study of the U-HIP prediction model for in-hospital mortality in geriatric patients with a hip fracture undergoing surgery.

MATERIALS AND METHODS

In this retrospective cohort study, data were used from The American College of Surgeons National Surgical Quality Improvement Program. Patients aged 70 years or above undergoing hip fracture surgery were included. The discrimination (c-statistic) and calibration of the model were investigated.

RESULTS

A total of 25,502 patients were included, of whom 618 (2.4%) died. The mean predicted probability of in-hospital mortality was 3.9% (range 0%-55%). The c-statistic of the model was 0.74 (95% CI 0.72-0.76), which was comparable to the c-statistic of 0.78 (95% CI 0.71-0.85) that was found in the development cohort. The calibration plot indicated that the model was slightly overfitted, with a calibration-in-the-large of 0.015 and a calibration slope of 0.780. Within the subgroup of patients aged between 70 and 85, however, the c-statistic was 0.78 (95% CI 0.75-0.81), with good calibration (calibration slope 0.934).

DISCUSSION AND CONCLUSION

The U-HIP model for in-hospital mortality in geriatric hip fractures was externally validated in a large international cohort, and showed a good discrimination and fair calibration. This model is freely available online and can be used to predict the risk of mortality, identify high-risk patients and aid clinical decision making.

Early Detection of Pneumonia with the Help of Dementia in Geriatric Hip Fracture Patients

Objective

To investigate the role of dementia in pneumonia among geriatric patients with hip fracture and further develop an algorithm for stratifying risk of developing postoperative pneumonia.

Methods

The algorithm was developed after retrospectively analyzing 1344 hip fracture patients in the National Clinical Research Center for Orthopedics, Sports Medicine, and Rehabilitation from 1992 to 2012. Twenty‐eight variables were analyzed for evaluating the ability to predict postoperative pneumonia. The validation of the algorithm was performed in the MIMIC‐III database after enrolling 235 patients.

Results

One thousand five hundred and seventy‐nine patients were enrolled, 4.69% patients had postoperative pneumonia in our hospital, and 17.02% suffered pneumonia in the MIMIC‐III database. Dementia patients had more postoperative pneumonia (12.68% vs 4.24%, P = 0.0075), as compared with patients without dementia. The algorithm included nine predictors: dementia, age, coronary heart disease, the American Society of Anesthesiologists score, surgical method, mechanical ventilation, anemia, hypoproteinemia, and high creatinine. Internal validation showed the algorithm with dementia could improve predictive performance, while external validation found the algorithm with or without dementia both had similar and good predictive ability.

Conclusions

The algorithm has the potential to be a pragmatic risk prediction tool to calculate risk of pneumonia in clinical practice and it may also be applicable in critically ill hip fracture patients with dementia.

Predictors of In-Hospital Mortality in Older Adults Undergoing Hip Fracture Surgery: A Case-Control Study

Introduction: Although surgery has been proven to improve the long-term survival of older adults with hip fracture, in-hospital mortality directly resulting from repair of hip fracture is undesirable. This study aimed to identify potential prognostic factors that predict in-hospital mortality risk in elderly patients following hip fracture surgery. Materials and Methods: This case–control study comprehensively collected data from older adults with hip fracture admitted to a single medical centre. Age was selected as the cross-matching factor. Univariate and binary multivariate logistic regression models were used to estimate the odds ratios with 95% confidence intervals. A receiver operating characteristic curve was constructed to quantify the discrimination power of the model. Results: Among a total of 841 older adults who received hip fracture surgery, 17 died during hospitalisation, yielding a 2.0% in-hospital mortality rate. Using a binary multivariate logistic regression model to perform a comparison with 51 age-matched patients in survival groups, the model revealed that estimated glomerular filtration rate (eGFR) and malignant cancer history were the only 2 factors significantly correlated with in-hospital mortality. The prognostic values for the eGFR and malignant cancer history were acceptable, with areas under the curve of .76 and .67, respectively. Conclusion: The prevalence of in-hospital mortality following hip fracture is low. After adjustment for age, eGFR and malignant cancer history were identified as factors significantly correlated with in-hospital mortality. The findings of this study could assist in the early screening and detection of patients with high in-hospital mortality risks.

Predicting Survival in Thai Patients After Low Impact Hip Fracture Using Flexible Parametric Modelling: A Retrospective Cohort Study

Predictive post-hip fracture mortality models have been presented for specific time points (in-hospital, 30-days or 1-year) and most provide marginal predictions based on the patient's risk group. However, the predictive model for individual survival probability following hip fracture is not available. This study aimed to develop a flexible parametric model for predicting individual survival probability for hip fracture patients. In this retrospective study, the medical charts of 765 Thai patients admitted to hospital with a hip fracture resulting from low-impact injury from January 2014 to December 2018 were reviewed. Predictors for all-cause mortality were identified using flexible parametric survival analysis and were used to develop the predictive model. The model was calibrated using a calibration graph and discrimination performance was evaluated using the C-statistic. Internal validity was assessed using bootstrapping. The overall mortality rate of the hip fracture patients was 14%. Predictors significantly associated with survival after hip fracture were age, active malignancy, dementia or Alzheimer's disease, chronic obstructive pulmonary disorder, diabetes mellitus, hemoglobin concentration, eGFR<30 mL/min/1.73m² and operative treatments. The model-predicted survival was similar to that actually observed in the very low survival group in the first year after hip fracture. In bootstrapping, the apparent C-statistic and the test C-statistic of the reduced model were 0.79 (95% CI 0.77-0.81) and 0.79 (95% CI 0.78-0.80), respectively. The flexible survival model provides good predictive power for individual survival probability at any given time point within the first year after hip fracture and would be an easy to use tool in clinical practice.

A Case-Control Study of Hip Fracture Surgery Timing and Mortality at an Academic Hospital: Day Surgery May Be Safer than Night Surgery

Time from hospital admission to operative intervention has been consistently demonstrated to have a significant impact on mortality. Nonetheless, the relationship between operative start time (day versus night) and associated mortality has not been thoroughly investigated. Methods: All patients who underwent hip fracture surgery at a single academic institution were retrospectively analyzed. Operative start times were dichotomized: (1) day operation—7 a.m. to 4 p.m.; (2) night operation—4 p.m. to 7 a.m. Outcomes between the two groups were evaluated. Results: Overall, 170 patients were included in this study. The average admission to operating room (OR) time was 26.0 ± 18.0 h, and 71.2% of cases were performed as a day operation. The overall 90-day mortality rate was 7.1% and was significantly higher for night operations (18.4% vs. 2.5%; p = 0.001). Following multivariable logistic regression analysis, only night operations were independently associated with 90-day mortality (aOR 8.91, 95% confidence interval 2.19–33.22; p = 0.002). Moreover, these patients were significantly more likely to return to the hospital within 50 days (34.7% vs. 19.0%; p = 0.029) and experience mortality prior to discharge (8.2% vs. 0.8%; p = 0.025). Notably, admission to OR time was not associated with in-hospital mortality (29.22 vs. 25.90 h; p = 0.685). Hip fracture surgery during daytime operative hours may minimize mortalities.

Trauma Risk Score Also Predicts Blood Transfusion Requirements in Hip Fracture Patients

Introduction

The purpose of this study is to determine if the risk of receiving a blood transfusion during hip fracture hospitalization can be predicted by a validated risk profiling score (Score for Trauma Triage in Geriatric and Middle Aged (STTGMA)).

Materials and Methods

A consecutive series of 1449 patients 55 years and older admitted for a hip fracture at one academic medical center were identified from a trauma database. The STTGMA risk score was calculated for each patient. Patients were stratified into risk groups based on their STTGMA score quantile: minimal risk (0–50%), low risk (50–80%), moderate risk (80–95%), and high risk (95–100%). Incidence and volume of blood transfusions were compared between risk groups.

Results

There were 562 (38.8%) patients who received a transfusion during their admission. 58.3% of patients in the high risk group received a transfusion during admission compared to 31.2% of minimal risk group patients, 42.6% of low risk group patients, and 50.0% of moderate risk group patients (p < 0.001). STTGMA was predictive of first transfusion incidence in both the preoperative and postoperative periods. There was no difference in mean total transfusion volume between the four risk groups.

Conclusion

The STTGMA model is capable of risk stratifying hip fracture patients more likely to receive blood transfusions during hospitalization. Surgeons can use this tool to anticipate transfusion requirements.

Transfusion timing relative to surgery does not impact outcomes in hip fracture patients

PURPOSE

The purpose of this study is to determine the effects of blood transfusion timing in hip fracture patients.

METHODS

A consecutive series of hip fracture patients 55 years and older who required a blood transfusion during hospitalization were reviewed for demographic, injury, clinical outcome, and cost information. A validated risk predictive score (STTGMA) was calculated for each patient. Patients were stratified to preoperative, intraoperative, or postoperative first transfusion cohorts. The intraoperative and postoperative cohorts were matched by STTGMA, sex, and procedure to the preoperative cohort. Baseline patient characteristics and outcomes were compared before and after matching.

RESULTS

Prior to matching, the preoperative cohort was more often male (p < 0.001) with increased Charlson comorbidity index (p = 0.012), ASA class (p < 0.002), STTGMA (p < 0.001), total transfused volume (p = 0.002), incidence of inpatient mortality (p = 0.045), myocardial infarction (p = 0.005) and cardiac arrest (p = 0.014). After matching, the preoperative cohort had increased total transfused volume (p = 0.015) and decreased pneumonia incidence (p = 0.040).

CONCLUSION

Matching STTGMA score, sex, and procedure results in non-inferior outcomes among hip fracture patients receiving preoperative first blood transfusions compared to intraoperative and postoperative transfusions.

A retrospective pilot study of preoperative mobilization program for older adults with hip fracture

OBJECTIVE

To investigate the feasible effect of a preoperative mobilization program for older hip fracture patients who waited more than two days before surgery.

METHODS

A total of 38 patients with hip fracture were analyzed in this retrospective descriptive pilot study. The modified Barthel index (MBI) was used to measure functional outcome. Visual analogue scale (VAS) was used to evaluate the changes of pain degree during preoperative mobilization. The perioperative complications were recorded.

RESULTS

After the preoperative mobilization program was implemented, the MBI score was improved immediately and further improved after surgery until 3 months after discharge. On the premise of analgesia, no patient experienced severe pain during preoperative mobilization. Perioperative complications occurred in 2 (5.3%) patients.

CONCLUSION

For older patients with hip fracture, the preoperative mobilization program may be a feasible method, which may have a positive effect on promoting functional recovery and preventing perioperative complications.

Patient Factors That Matter in Predicting Hip Arthroplasty Outcomes: A Machine-Learning Approach

BACKGROUND

Despite the success of total hip arthroplasty (THA), approximately 10%-15% of patients will be dissatisfied with their outcome. Identifying patients at risk of not achieving meaningful gains postoperatively is critical to pre-surgical counseling and clinical decision support. Machine learning has shown promise in creating predictive models. This study used a machine-learning model to identify patient-specific variables that predict the postoperative functional outcome in THA.

METHODS

A prospective longitudinal cohort of 160 consecutive patients undergoing total hip replacement for the treatment of degenerative arthritis completed self-reported measures preoperatively and at 3 months postoperatively. Using four types of independent variables (patient demographics, patient-reported health, cognitive appraisal processes and surgical approach), a machine-learning model utilizing Least Absolute Shrinkage Selection Operator (LASSO) was constructed to predict postoperative Hip Disability and Osteoarthritis Outcome Score (HOOS) at 3 months.

RESULTS

The most predictive independent variables of postoperative HOOS were cognitive appraisal processes. Variables that predicted a worse HOOS consisted of frequent thoughts of work (β = -0.34), frequent comparison to healthier peers (β = -0.26), increased body mass index (β = -0.17), increased medical comorbidities (β = -0.19), and the anterior surgical approach (β = -0.15). Variables that predicted a better HOOS consisted of employment at the time of surgery (β = 0.17), and thoughts related to family interaction (β = 0.12), trying not to complain (β = 0.13), and helping others (β = 0.22).

CONCLUSIONS

This clinical prediction model in THA revealed that the factors most predictive of outcome were cognitive appraisal processes, demonstrating their importance to outcome-based research.

LEVEL OF EVIDENCE

Prognostic Level 1.

Perioperative Transfusion Associated With Increased Morbidity and Mortality in Geriatric Patients Undergoing Hip Fracture Surgery

Introduction:

Both conservative and liberal transfusion thresholds, in regard to hematocrit and hemoglobin levels, have been widely studied with varying outcomes. The aim of this study was to evaluate if transfusion administered peri- (anytime during the admission), pre-, intra-, or postoperatively an its association with morbidity and mortality in the geriatric population undergoing hip surgery.

Methods:

This study was an institutional review board approved retrospective analysis of data collected from 841 patients at a single urban institution who underwent surgical repairs for hip fractures from 2008 to 2010.

Results:

Our analysis included data from 841 surgical patients. Mean patient age was 83, 74% were female, 48% received spinal anesthesia while 52% underwent general anesthesia. Out of 841 patients, 425 were transfused during the perioperative period. Most transfusions occurred postoperatively. Perioperative, intraoperative and postoperative transfusion was associated with an increase in post-operative AKI. Intraoperative blood transfusion was associated with an increase in morbidity (11.6% increased to 22.2%) by 1.9 fold, AKI (3.9% increased to 11.1%) by 2.8 fold, as well as an increase in mortality (5.2 increased to 15.6%) within 60 days by 3 fold.

Conclusions:

This may suggest that patients transfused prior to surgery, despite having met a specific trigger hemoglobin level earlier, may have been treated before deteriorating to a point that would cause future systemic implications.

Merits of Surgical Comanagement of Patients With Hip Fracture by Dedicated Orthopaedic Hospitalists

Rotating medical consultants, hospitalists or geriatricians, are involved in the care of patients with hip fracture, often after medical complications have already occurred. In August 2012, we implemented a unique surgical comanagement (SCM) model in which the same Internal Medicine hospitalists are dedicated year-round to the orthopaedic surgery service. We examine whether this SCM model was associated with a decrease in medical complications, length of stay, and inpatient mortality in patients with hip fracture admitted at our institution, compared with the previous model.

Impact of Heart Failure on In-Hospital Outcomes after Surgical Femoral Neck Fracture Treatment

BACKGROUND

Femoral neck fracture (FNF) is a common condition with a rising incidence, partly due to aging of the population. It is recommended that FNF should be treated at the earliest opportunity, during daytime hours, including weekends. However, early surgery shortens the available time for preoperative medical examination. Cardiac evaluation is critical for good surgical outcomes as most of these patients are older and frail with other comorbid conditions, such as heart failure. The aim of this study was to determine the impact of heart failure on in-hospital outcomes after surgical femoral neck fracture treatment.

METHODS

We performed a retrospective study using the Spanish National Hospital Discharge Database, 2007-2015. We included patients older than 64 years treated for reduction and internal fixation of FNF. Demographic characteristics of patients, as well as administrative variables, related to patient's diseases and procedures performed during the episode were evaluated.

RESULTS

A total of 234,159 episodes with FNF reduction and internal fixation were identified from Spanish National Health System hospitals during the study period; 986 (0.42%) episodes were excluded, resulting in a final study population of 233,173 episodes. Mean age was 83.7 (±7) years and 179,949 (77.2%) were women (p < 0.001). In the sample, 13,417 (5.8%) episodes had a main or secondary diagnosis of heart failure (HF) (p < 0.001). HF patients had a mean age of 86.1 (±6.3) years, significantly older than the rest (p < 0.001). All the major complications studied showed a higher incidence in patients with HF (p < 0.001). Unadjusted in-hospital mortality was 4.1%, which was significantly higher in patients with HF (18.2%) compared to those without HF (3.3%) (p < 0.001). The average length of stay (LOS) was 11.9 (±9.1) and was also significantly higher in the group with HF (16.5 ± 13.1 vs. 11.6 ± 8.7; p < 0.001).

CONCLUSIONS

Patients with HF undergoing FNF surgery have longer length of stay and higher rates of both major complications and mortality than those without HF. Although their average length of stay has decreased in the last few years, their mortality rate has remained unchanged.

Effect of time-to-surgery on in-house mortality during orthogeriatric treatment following hip fracture: A retrospective analysis of prospectively collected data from 16,236 patients of the AltersTraumaRegister DGU®

BACKGROUND

Time-to-surgery in geriatric hip fractures remains of interest. The majority of the literature reports a significantly decreased mortality rate after early surgery. Nevertheless, there are some studies presenting no effect of time-to-surgery on mortality. The body of literature addressing the effect of an orthogeriatric co-management is growing. Here we investigate the effect of time-to-surgery on in-house mortality in a group of patients treated under the best possible conditions in certified orthogeriatric treatment units.

METHODS

We conducted a retrospective cohort registry analysis from prospectively collected data of the AltersTraumaRegister DGU®. Data were analyzed univariably, and the association of early surgery with in-house mortality was assessed with multivariable logistic regression while controlling for specified patient characteristics. Additionally, propensity score matching for time-to-surgery was applied to examine its effect on the in-house mortality rate.

FINDINGS

A total of 15,099 patients met the inclusion criteria. The median age was 85 years (IQR 80-89), and 72.1% were female. The overall in-house mortality rate was 5.5%. Most (71.2%) of the patients were treated within 24 h, and 91.6% within 48 h. Neither the multivariable logistic regression model nor the propensity score matching indicated that early surgery was associated with a decreased mortality rate. The most important indicators for mortality were ASA ≥ 3 [Odds ratio (OR) 3.4, 95% confidence interval (CI) 2.35-5.11], fracture event during inpatient stay (OR 2.6, 95% CI 1.48-4.3), ISAR ≥ 2 (OR 1.88, 95% CI 1.33-2.76), and male gender (OR 1.71, 95% CI 1.39-2.09).

INTERPRETATION

Our results suggest that for those patients, who were treated in an orthogeriatric co-management under the best possible conditions, there are no significant differences regarding in-house mortality rate between the time-to-surgery intervals of 24 and 48 h or slightly above. This and the comparatively small number of patients who underwent surgery after 24 h show that an extension of the pre-surgery interval, justified by an orthogeriatric treatment team, will not be detrimental to the affected patients.

Predictors of hip fracture mortality in Ghana: a single-center prospective study

To determine risk factors influencing mortality in patients with proximal femur fractures in a Ghanaian hospital over a 4-year period.

METHODS

Incidence of mortality was assessed among 76 participants with proximal femur fractures from January to December 2014 and followed up for 4 years. Outcomes of interest were mortality at 1 month, 6 months, 1 year, and 4 years. Hazard ratios (HRs) were calculated using Cox proportional hazards regression, adjusting for mortality risk factors.

RESULTS

Among the 76 participants (mean age 75.8 years [SD = 12.02], 36 (47.4%) males), there were 21 death cases. The mean time of injury to surgery was 16.4 (SD = 16.2) days. Hip fractures comprised of 38 (50%) intertrochanteric, 35 (46.05%) transcervical, and 3 (3.95%) basicervical. Mortality at 1 month, 6 months, 1 year, and 4 years were 6.6%, 13.2%, 19.7%, and 27.6%, respectively. Multiple regression analysis showed a yearly increase in age that was associated with a 1.03-fold increase in the risk of death (p = 0.17). Comparing males to females, there was a significant difference in mortality (HR = 5.24, p = 0.03). Participants with basicervical hip fracture versus those with transcervical hip fracture were at higher risk of dying (HR = 28.88, p = 0.01). Patients with abnormal/low creatinine as compared to those with normal creatinine were at higher risk of dying (HR = 5.64, p = 0.005). Also, participants with an American Society of Anesthesiologists (ASA) score of III or IV were 2.73 times more likely to experience death than those with an ASA score of I or II (95% CI: 0.93-8.89, p = 0.08). Additionally, a higher risk of death was associated with patients with chronic obstructive pulmonary disease (COPD) (HR = 53.45, p = 0.001) and osteoporosis (HR = 8.75, p = 0.006).

CONCLUSION

Being male, having basicervical hip fracture, abnormal/low creatinine, and a history of COPD and osteoporosis were the main predictors of mortality in the study population. These findings could serve as a guide when managing patients with proximal femur fractures to improve the outcome.

Cost Determinants of Continuum-Care Episodes for Hip Fracture

Many factors affect the healthcare costs and outcomes in patients with hip fracture (HF). Through the construction of a Continuum-Care Episode (CCE), we investigated the costs of CCEs for HF and their determinants. We used data extracted from administrative databases of 5094 consecutive elderly patients hospitalized in 2017 in Emilia Romagna, Italy, to evaluate the overall costs of the CCE. We calculated the acute and post-acute costs from the date of the hospital admission to the end of the CCE. The determinants of costs by type of surgical intervention (total hip replacement, partial hip replacement, open reduction, and internal fixation) were investigated using generalized linear regression models. Regardless of the type of surgical intervention, hospital bed-based rehabilitation in public or private healthcare facilities either followed by rehabilitation in a community hospital/temporary nursing home beds or not were the strongest determinants of costs, while rehabilitation in intermediate care facilities alone was associated with lower costs. CCE's cost and its variability is mainly related to the rehabilitation setting. Cost-wise, intermediate care resulted to be an appropriate setting for providing post-acute rehabilitation for HF, representing the one associated with lower overall costs. Intermediate care organizational setting should be privileged when planning integrated care HF pathways.

Mortality Risk Assessment at the Admission in Patient With Proximal Femur Fractures: Electrolytes and Renal Function

In patients over 65y.o. who were surgically treated for a hip fracture, electrolytes have not been specifically studied as predictors of mortality. The main purpose of this study was to assess whether electrolytes and chronic kidney disease (CKD) stages, evaluated at admission, could represent a pre-operative prognostic factor in this population. Moreover, the role of epidemiological and clinical parameters was analyzed with and without a surgical timing stratification. This retrospective study included 746 patients. For each patient, their age, gender, fracture classification, Hb value, comorbidities, ASA class, chronic kidney disease, creatinine levels, electrolytes and surgical timing were collected. CKD-epi, MDRD, modified MDRD and BIS1 were used to obtain eGFR and CKD stages. All parameters were analyzed individually and in relation to the different surgical timing. Descriptive statistics, Chi-square test and survivability analysis with Kaplan Meier curve were used. In patients with a hip fracture non-significant association with increased mortality was shown for the following variables: Hb value, sodium values, calcium values, CKD stages and creatinine values. Otherwise altered kalemia was associated with a statistically significant increase in mortality as well as male gender, two or more comorbid medical conditions, advanced age (>75 years), higher ASA class. Surgery performed within 72h resulted in a statistically significant reduction in mortality at 6 months and, when performed in 24h-48h, a further reduction at 4 years. Age and ASA class statistically significant increased mortality regardless the surgical timing. Male patients operated after 48h from hospitalization were associated with a statistically significant increase in mortality rate. Two or more comorbidities were related to a statistically significant increased number of deaths when patients were treated after 96h. Altered kalemia values at hospitalization are associated with a statistically significant increase in mortality in patients operated after 72h from admission.

Development and Internal Validation of a Prediction Model for In-Hospital Mortality in Geriatric Patients With a Hip Fracture

OBJECTIVE

To develop and validate a prediction model for in-hospital mortality in patients with hip fracture 85 years of age or older undergoing surgery.

DESIGN

A multicenter prospective cohort study.

SETTING

Six Dutch trauma centers, level 2 and 3.

PARTICIPANTS

Patients with hip fracture 85 years of age or older undergoing surgery.

INTERVENTION

Hip fracture surgery.

MAIN OUTCOME MEASUREMENTS

In-hospital mortality.

RESULTS

The development cohort consisted of 1014 patients. In-hospital mortality was 4%. Age, male sex, American Society of Anesthesiologists classification, and hemoglobin levels at presentation were independent predictors of in-hospital mortality. The bootstrap adjusted performance showed good discrimination with a c-statistic of 0.77.

CONCLUSION

Age, male sex, higher American Society of Anesthesiologists classification, and lower hemoglobin levels at presentation are robust independent predictors of in-hospital mortality in patients with geriatric hip fracture and were incorporated in a simple prediction model with good accuracy and no lack of fit.

LEVEL OF EVIDENCE

Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Factors Associated With Mortality After Surgical Management of Femoral Neck Fractures

BACKGROUND

Hip fractures are recognized as one of the most devastating injuries impacting older adults because of the complications that follow. Mortality rates postsurgery can range from 14% to 58% within one year of fracture. We aimed to identify factors associated with increased risk of mortality within 24 months of a femoral neck fracture in patients aged ≥50 years enrolled in the FAITH and HEALTH trials.

METHODS

Two multivariable Cox proportional hazards regressions were used to investigate potential prognostic factors that may be associated with mortality within 90 days and 24 months of hip fracture.

RESULTS

Ninety-one (4.1%) and 304 (13.5%) of 2247 participants died within 90 days and 24 months of suffering a femoral neck fracture, respectively. Older age (P < 0.001), lower body mass index (P = 0.002), American Society of Anesthesiologists (ASA) class III/IV/V (P = 0.004), use of an ambulatory aid before femoral neck fracture (P < 0.001), and kidney disease (P < 0.001) were associated with a higher risk of mortality within 24 months of femoral neck fracture. Older age (P = 0.03), lower body mass index (P = 0.02), use of an ambulatory aid before femoral neck fracture (P < 0.001), and having a comorbidity (P = 0.04) were associated with a higher risk of mortality within 90 days of femoral neck fracture.

CONCLUSIONS

Our analysis found that factors that are indicative of a poorer health status were associated with a higher risk of mortality within 24 months of femoral neck fracture. We did not find a difference in treatment methods (internal fixation vs. joint arthroplasty) on the risk of mortality.

LEVEL OF EVIDENCE

Therapeutic Level II. See Instructions for Authors for a complete description of levels of evidence.

Postoperative Physical Therapy to Prevent Hospital-acquired Pneumonia in Patients Over 80 Years Undergoing Hip Fracture Surgery-A Quasi-experimental Study

Background

Hip fracture requiring surgical fixation is a common condition with high mortality and morbidity in the geriatric population. The patients are usually frail, and vulnerable to postoperative complications and delayed recovery. Few studies have investigated physical therapy methods to prevent hospital-acquired pneumonia (HAP) after hip fracture surgery.

Objective

To explore whether an intensified physical therapy regimen can prevent HAP and reduce hospital length of stay in patients aged 80 and older, following hip fracture surgery.

Patients and Methods

The inclusion criterion was patients aged 80 or older who had undergone hip fracture surgery at Örebro University Hospital, Sweden during eight months in 2015–2016 (the “physical therapy group”) (n=69). The study has a quasi-experimental design with a historical control group (n=64) who had received routine physical therapy treatment. The physical therapy group received intensified postoperative physical therapy treatment, which included daily supervised early mobilization and coached deep breathing exercises with positive expiratory pressure (PEP). The patients were instructed to take deep breaths, and then exhale through the PEP-valve in three sessions of 10 deep breaths, at least four times daily. Early mobilization to a sitting position and walking was advised as soon as possible after surgery.

Results

There was a significantly lower incidence of HAP in the physical therapy group; 2/69 (3%, 95%CI: 1– 10) compared to the historical control group 13/64 (20%, 95%CI: 12–32%) (p=0.002). Patients in the physical therapy group had a significantly shorter length of stay than the control group (10.6±4 vs 13.4±9 days, p=0.022).

Conclusion

Intensified physical therapy treatment after hip fracture surgery may be of benefit to reduce the incidence of HAP in patients over 80 years; however, the results need to be confirmed in randomized controlled trials.

Validation of a prospective mortality prediction score for hip fracture patients

PURPOSE

Although mortality prediction tools are the subject of significant interest as components of comprehensive hip fracture protocols, few have been applied or validated to prospectively inform ongoing patient management. Five regional hospitals are currently generating real-time mortality risk scores for all adults at the time of admission using available laboratory and comorbidity data (Cowen et al. J Hosp Med 9(11):720-726, 2014). Although results for aggregated conditions have been published, the primary aim of this study is to determine how well prospectively calculated scores predict mortality for hip fracture patients specifically.

METHODS

Using a five-hospital database, 1376 patients who were prospectively scored on admission were identified from January 2013 to April 2017, cross-referencing ICD9/10 diagnosis and procedure codes for AO/OTA 31A1 through 31B3 fractures. Prospective mortality scores have been previously divided into 5 risk categories to facilitate ease of clinical use. Vital status was determined from hospital data, Social Security and Michigan Death Indices.

RESULTS

Prospective scores demonstrated good mortality prediction, with AUCs of 0.80, 0.73, 0.74 and 0.74 for in hospital, 30-, 60- and 90-day mortality, respectively. Patients in the top 2 mortality risk categories represented 30% (410/1376) of the cohort and accounted for 78% (25/32) of the inpatient and 59% (57/97) of the 30 day deaths.

CONCLUSIONS

Implementation of this real-time mortality risk tool is feasible and valid for the prediction of short- to medium-term mortality risk for hip fracture patients, and potentially offers valuable information to guide ongoing patient management decisions such as admitting service or level of care.

Is the number of previous hospitalizations associated with increased in-hospital mortality after hip fracture in a developing country?

Purpose

We aimed to examine whether the number of previous hospitalizations and the main diagnoses of those hospitalizations are associated with increased in-hospital hip fracture mortality for older people. That assessment is relevant because if those variables are shown to be associated with increased mortality, that finding could support their use as proxies for comorbidity burden for case-mix adjustment in statistical models seeking to compare the performance of hospitals regarding hip fracture mortality in settings with limited hospital information systems.

Methods

In this retrospective cohort study of all public hospital admissions for older adults with hip fractures in the city of Rio de Janeiro between 2010 and 2011, we used data from the Hospital Admission Information System database to examine the association between in-hospital mortality and the number of hospitalizations in the previous two years and their main diagnoses through logistic regression.

Results

Among 1938 patients included in the study there were 103 (5.3%) in-hospital deaths. Although the presence of hospitalization episodes within the two years preceding the index hip fracture was associated with increased mortality (OR: 1.78, 95%CI: 1.07 to 2.97) we did not find evidence of a gradient of increased mortality with a growing number of previous hospitalizations. Additionally, several diseases recorded as main diagnoses of previous hospitalizations were not associated with increased mortality rates, as was expected based on existing knowledge on risk factors for decreased survival in older adults with hip fractures.

Conclusions

Our results suggest that, in settings where local hospital information systems have limited access to secondary diagnoses, the use of the number of previous hospitalizations or the main diagnoses associated with those hospitalizations as proxies for the profile of comorbidities of older adults with hip fractures may not be an effective way to adjust for case-mix when comparing in-hospital mortality rates among hospitals.

What Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?

BACKGROUND

Machine learning techniques can identify complex relationships in large healthcare datasets and build prediction models that better inform physicians in ways that can assist in patient treatment decision-making. In the domain of shoulder arthroplasty, machine learning appears to have the potential to anticipate patients' results after surgery, but this has not been well explored.

QUESTIONS/PURPOSES

(1) What is the accuracy of machine learning to predict the American Shoulder and Elbow Surgery (ASES), University of California Los Angeles (UCLA), Constant, global shoulder function, and VAS pain scores, as well as active abduction, forward flexion, and external rotation at 1 year, 2 to 3 years, 3 to 5 years, and more than 5 years after anatomic total shoulder arthroplasty (aTSA) or reverse total shoulder arthroplasty (rTSA)? (2) What is the accuracy of machine learning to identify whether a patient will achieve clinical improvement that exceeds the minimum clinically important difference (MCID) threshold for each outcome measure? (3) What is the accuracy of machine learning to identify whether a patient will achieve clinical improvement that exceeds the substantial clinical benefit threshold for each outcome measure?

METHODS

A machine learning analysis was conducted on a database of 7811 patients undergoing shoulder arthroplasty of one prosthesis design to create predictive models for multiple clinical outcome measures. Excluding patients with revisions, fracture indications, and hemiarthroplasty resulted in 6210 eligible primary aTSA and rTSA patients, of whom 4782 patients with 11,198 postoperative follow-up visits had sufficient preoperative, intraoperative, and postoperative data to train and test the predictive models. Preoperative clinical data from 1895 primary aTSA patients and 2887 primary rTSA patients were analyzed using three commercially available supervised machine learning techniques: linear regression, XGBoost, and Wide and Deep, to train and test predictive models for the ASES, UCLA, Constant, global shoulder function, and VAS pain scores, as well as active abduction, forward flexion, and external rotation. Our primary study goal was to quantify the accuracy of three machine learning techniques to predict each outcome measure at multiple postoperative timepoints after aTSA and rTSA using the mean absolute error between the actual and predicted values. Our secondary study goals were to identify whether a patient would experience clinical improvement greater than the MCID and substantial clinical benefit anchor-based thresholds of patient satisfaction for each outcome measure as quantified by the model classification parameters of precision, recall, accuracy, and area under the receiver operating curve.

RESULTS

Each machine learning technique demonstrated similar accuracy to predict each outcome measure at each postoperative point for both aTSA and rTSA, though small differences in prediction accuracy were observed between techniques. Across all postsurgical timepoints, the Wide and Deep technique was associated with the smallest mean absolute error and predicted the postoperative ASES score to ± 10.1 to 11.3 points, the UCLA score to ± 2.5 to 3.4, the Constant score to ± 7.3 to 7.9, the global shoulder function score to ± 1.0 to 1.4, the VAS pain score to ± 1.2 to 1.4, active abduction to ± 18 to 21°, forward elevation to ± 15 to 17°, and external rotation to ± 10 to 12°. These models also accurately identified the patients who did and did not achieve clinical improvement that exceeded the MCID (93% to 99% accuracy for patient-reported outcome measures (PROMs) and 85% to 94% for pain, function, and ROM measures) and substantial clinical benefit (82% to 93% accuracy for PROMs and 78% to 90% for pain, function, and ROM measures) thresholds.

CONCLUSIONS

Machine learning techniques can use preoperative data to accurately predict clinical outcomes at multiple postoperative points after shoulder arthroplasty and accurately risk-stratify patients by preoperatively identifying who may and who may not achieve MCID and substantial clinical benefit improvement thresholds for each outcome measure.

CLINICAL RELEVANCE

Three different commercially available machine learning techniques were used to train and test models that predicted clinical outcomes after aTSA and rTSA; this device-type comparison was performed to demonstrate how predictive modeling techniques can be used in the near future to help answer unsolved clinical questions and augment decision-making to improve outcomes after shoulder arthroplasty.

The SIFK score: a validated predictive model for arthroplasty progression after subchondral insufficiency fractures of the knee

PURPOSE

The purpose of this study was to create a predictive model utilizing baseline demographic and radiographic characteristics for the likelihood that a patient with subchondral insufficiency fracture of the knee will progress to knee arthroplasty with emphasis on clinical interpretability and usability.

METHODS

A retrospective review of baseline and final radiographs in addition to MRIs were reviewed for evaluation of insufficiency fractures and associated injuries. Patient and radiographic factors were used in building predictive models for progression to arthroplasty with Train: Validation: Test subsets. Multiple models were compared with emphasis on clinical utility.

RESULTS

Total of 249 patients with a mean age of 64.6 (SD 10.5) years were included. Knee arthroplasty rate was 27% at mean of 4 years of follow-up. Lasso Regression was non-inferior to other models and was chosen for ease of interpretability. In order of importance, predictors for progression to arthroplasty included lateral meniscus extrusion, Kellgren-Lawrence Grade 4, SIFK on MFC, lateral meniscus root tear, and medial meniscus extrusion. The final SIFK Score stratified patients into low-, medium-, and high-risk categories with arthroplasty rates of 8.8%, 40.4%, and 78.9% (p < 0.001) and an area under the curve of 82.5%.

CONCLUSION

In this validated model, lateral meniscus extrusion, K-L Grade 4, SIFK on MFC, lateral meniscus root tear, and medial meniscus extrusion were the most important factors in predicting progression to arthroplasty (in that order). This model assists in patient treatment and counseling in providing prognostic information based on patient-specific risk factors by classifying them into a low-, medium-, and high-risk categories. This model can be used both by medical professionals treating musculoskeletal injuries in guiding patient decision making.

LEVEL OF EVIDENCE

Level III.

Effect of weekend admission on geriatric hip fractures

BACKGROUND

The care discrepancy for patients presenting to a hospital on the weekend relative to the work week is well documented. With respect to hip fractures, however, there is no consensus about the presence of a so-called “weekend effect”. This study sought to determine the effects, if any, of weekend admission on care of geriatric hip fractures admitted to a large tertiary care hospital. It was hypothesized that geriatric hip fracture patients admitted on a weekend would have longer times to medical optimization and surgery and increased complication and mortality rates relative to those admitted on a weekday.

AIM

To determine if weekend admission of geriatric hip fractures is associated with poor outcome measures and surgical delay.

METHODS

A retrospective chart review of operative geriatric hip fractures treated from 2015-2017 at a large tertiary care hospital was conducted. Two cohorts were compared: patients who arrived at the emergency department on a weekend, and those that arrived at the emergency department on a weekday. Primary outcome measures included mortality rate, complication rate, transfusion rate, and length of stay. Secondary outcome measures included time from emergency department arrival to surgery, time from emergency department arrival to medical optimization, and time from medical optimization to surgery.

RESULTS

There were no statistically significant differences in length of stay (P = 0.2734), transfusion rate (P = 0.9325), or mortality rate (P = 0.3460) between the weekend and weekday cohorts. Complication rate was higher in patients who presented on a weekend compared to patients who presented on a weekday (13.3% vs 8.3%; P = 0.044). Time from emergency department arrival to medical optimization (22.7 h vs 20.0 h; P = 0.0015), time from medical optimization to surgery (13.9 h vs 10.8 h; P = 0.0172), and time from emergency department arrival to surgery (42.7 h vs 32.5 h; P < 0.0001) were all significantly longer in patients who presented to the hospital on a weekend compared to patients who presented to the hospital on a weekday.

CONCLUSION

This study provided insight into the “weekend effect” for geriatric hip fractures and found that day of presentation has a clinically significant impact on delivered care.

Prognostic Factors for All-Cause Mortality in Thai Patients with Fragility Fracture of Hip: Comorbidities and Laboratory Evaluations

Background and Objectives: Although the types of comorbidities and laboratory evaluations are major factors associated with mortality after hip fractures, there have been no studies of the association of these factors and mortality in Thai hip-fracture patients. This study aimed to identify prognostic factors associated with mortality after a hip fracture in the Thai population, including types of comorbidities, treatment-related factors, and laboratory evaluations. Materials and Methods: This five-year retrospective study was conducted in a tertiary care hospital in Thailand. A total of 775 Thai patients who had been admitted with a hip fracture resulting from a simple fall were identified using the International Classification of Disease 10 codes, and a review of their medical charts was conducted. Associations between general factors, comorbidities, laboratory evaluations, treatment factors including type of treatment, and time to death were analyzed using the Cox proportional hazard regression and the hazard ratio (HR). Results: The overall mortality rate of hip fracture patients was 13.94%. Independent prognostic factors found to be significantly associated with mortality were nonoperative treatment (HR = 3.29, p < 0.001), admission glomerular filtration rate (GFR) < 30 mL/min/1.73 m² (HR = 3.40, p < 0.001), admission hemoglobin concentration <10 g/dL. (HR = 2.31, p < 0.001), chronic obstructive pulmonary disorder (HR = 2.63, p < 0.001), dementia or Alzheimer’s disease (HR = 4.06, p < 0.001), and active malignancy (HR = 6.80, p < 0.001). Conclusion: The types of comorbidities and laboratory evaluation findings associated with mortality in Thai patients with hip fractures include chronic obstructive pulmonary disorder, dementia or Alzheimer’s disease, active malignancy, admission GFR < 30 mL/min/1.73 m², and admission hemoglobin concentration <10 g/dL. The risks of mortality for Thai hip-fracture patients with these comorbidities or laboratory evaluation findings were 2.5, 4, 7, 3.5, and 2.5 times higher, respectively, than patients without those factors.

POSSUM and P-POSSUM Scoring in Hip Fracture Mortalities

Introduction:

Physiological and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) and Portsmouth POSSUM (P-POSSUM) are general surgical tools used to efficiently assess mortality and morbidity risk. Data suggest that these tools can be used in hip fracture patients to predict morbidity and mortality; however, it is unclear what score indicates a significant risk on a case-by-case basis. We examined the POSSUM and P-POSSUM scores in a group of hip fracture mortalities in order to assess their accuracy in identification of similar high-risk patients.

Materials and Methods:

Retrospective analysis of all consecutive mortalities in hip fracture patients at a single tertiary care center over 2 years was performed. Patient medical records were examined for baseline demographics, fracture characteristics, surgical interventions, and cause of death. Twelve physiological and 6 operative variables were used to retrospectively calculate POSSUM and P-POSSUM scores at the time of injury.

Results:

Forty-seven hip fracture mortalities were reviewed. Median patient age was 88 years (range: 56-99). Overall, 68.1% (32) underwent surgical intervention. Mean predicted POSSUM morbidity and mortality rates were 73.9% (28%-99%) and 31.1% (5%-83%), respectively. The mean predicted P-POSSUM mortality rate was 26.4% (1%-91%) and 53.2% (25) had a P-POSSUM predicted mortality of >20%. Subgroup analysis demonstrated poor agreement between predicted mortality and observed mortality rate for POSSUM in operative (χ² = 127.5, P < .00001) and nonoperative cohorts (χ² = 14.6, P < .00001), in addition to P-POSSUM operative (χ² = 101.9, P < .00001) and nonoperative (χ² = 11.9, P < .00001) scoring.

Discussion/Conclusion:

Hip fracture patients are at significant risk of both morbidity and mortality. A reliable, replicable, and accurate tool to represent the expected risk of such complications could help facilitate clinical decision-making to determine the optimal level of care. Screening tools such as POSSUM and P-POSSUM have limitations in accurately identifying high-risk hip fracture patients.

Severe aortic stenosis is associated with perioperative mortality in proximal femur fracture patients

Objectives:

Evaluate the correlation between aortic stenosis and perioperative mortality in patients following surgical fixation of proximal femur fractures.

Design:

Retrospectively reviewed case series.

Setting:

Two Academic, Level 1 Trauma Centers.

Patients/Participants:

One hundred fifty-eight patients, definitively diagnosed with aortic stenosis by means of echocardiogram, who underwent surgical fixation for an isolated proximal femur fracture (OTA/AO 31-A, 31-B, 32-A, 32-B, and 32-C fractures) between January 2000 and June 2015. The severity of the aortic stenosis was based upon accepted echocardiographic hemodynamic parameters designated by the 2014 American Heart Association guidelines.

Main Outcome Measures:

Post Injury mortality, 30-day mortality, and 1-year mortality.

Secondary Outcome Measures:

Postoperative mortality stratified by severity of aortic stenosis based on aortic valve area (AVA) and ejection fracture (EF) as determined by preoperative echocardiography.

Results:

One hundred fifty-eight patients were available for final analysis. Kaplan–Meier survival analysis revealed a significantly longer time to mortality among Non-severe aortic stenosis patients compared to Severe aortic stenosis patients, P value .006. Twenty-three percent of patients with Severe aortic stenosis and 10% of patients with Non-severe aortic stenosis died within 30 days of surgery. No significant difference was observed in mean survival among AS patients who underwent surgery within 48 hours of injury (34.5 months) and those delayed more than 48 hours after injury (25.0 months), P value .116. Among the commonly measured hemodynamic parameters of aortic stenosis, only AVA and EF were significantly associated with mortality, P value .015, and P value < .001, respectively. There were no significant effects for Aortic Vmax, Peak ΔP, and Mean ΔP. An AVA of 0.8 cm² or less is associated with a significantly shorter (22 months) postinjury mortality than patients with an AVA > 0.8 cm^2. (37 months), P value .009.

Conclusions:

Severe aortic stenosis is associated with a shorter postoperative time to mortality after surgical fixation of hip fractures compared to patients with Non-severe stenosis. Aortic valve area and Ejection Fraction are the only hemodynamic parameters significantly associated with postoperative mortality.

Level of Evidence: Prognostic Level III