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

Development and validation of a prognostic scoring system for 1-year mortality in older patients with hip fractures

BACKGROUND

Hip fractures in older people result in increased mortality.

OBJECTIVE

We developed and validated an accurate and simple prognostic scoring system for hip fractures that can be used preoperatively.

DESIGN

Retrospective study.

SETTING

Multicenter.

PARTICIPANTS

Patients aged ≥65 years with hip fractures who underwent surgery between 2011 and 2021 were enrolled.

METHODS

The significant factors were determined with logistic regression analysis, and a scoring system was developed. The patients were classified into three groups, and a log-rank test was performed to evaluate 1-year survival rates. The model was internally and externally validated using the 5-fold cross-validation and data from another hospital, respectively.

RESULTS

We included 1026 patients. The analysis revealed eight significant prognostic factors: sex, body mass index, history of chronic heart failure and malignancy, activities of daily living (ADLs) before injury, hemoglobin and the prognostic nutritional index (PNI) at injury, and the American Society of Anesthesiologists Physical Status. The area under the receiver operating characteristic curve (AUC) after internal validation was 0.853. The external validation data consisted of 110 patients. The AUC of the model for the validation data was 0.905, showing outstanding discrimination. Sensitivity and specificity were 88.7% vs. 100% and 93.3% vs. 95.2% for the development and validation data, respectively.

CONCLUSIONS

We developed and validated an accurate and simple prognostic scoring system for hip fractures using only preoperative factors. Our findings highlight PNI as an important predictor of prognosis in hip fracture patients.

Predictors of mortality over time in geriatric patients with hip fracture

Objectives

The aim of this study was to determine the variations in effect for predictors of mortality over time and risk of in-hospital complications in geriatric patients with a hip fracture. Many studies have investigated risk factors of short-term and long-term mortality separately. In current literature, little is known about the variations in effect of risk factors over time and no comparison with the general population is made.

Methods

All patients with a hip fracture aged 70 years or older admitted to our hospital between January 1, 2016, and May 1, 2018, were included in this retrospective study. Patients who had undergone total hip arthroplasty (THA) were not included. The primary outcome was mortality after 1 year. Secondary outcomes were mortality after 30 days, 90 days, 2 years, and complications. Kaplan-Meier (KM) curves for risk factors were generated to visualize survival over time. Data were compared with data extracted from the national health records.

Results

A total of 685 geriatric patients with hip fractures were included with a 1-year mortality of 27%. The adjusted odds ratios (AOR) found differed over time. Five risk factors for mortality were investigated in this study using KM curves: age, prefracture living situation, dementia, sex, and ASA classification.

Conclusions

Over time, the variation of 5 risk factors for mortality were visualized in geriatric patients with a hip fracture: age, prefracture living situation, dementia, sex, and ASA classification. An elevated risk of mortality was discovered compared with the general population. The variation in effect observed in risk factors plays a vital role in prognosis. This insight will help guide accurate medical decision-making for a tailored treatment plan for geriatric patients with a hip fracture.

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.

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.

Risk factors of mortality and second fracture after elderly hip fracture surgery in Shanghai, China

INTRODUCTION

Hip fracture is one of the leading causes of death and disability in the elderly. We analyzed the risk factors of mortality and second fracture within 2 years after hip fracture surgery in elderly Chinese patients.

MATERIALS AND METHODS

A total of 613 elderly patients after hip fracture surgery were selected, including 181 males and 432 females, and the patients were followed for at least 24 months. Information about patients and surgery was collected from medical records. Information on death, secondary fracture, and postoperative activities of daily living (ADL) was obtained by telephone follow-up. Cox regression was performed to identify risk factors associated with mortality and second fracture, measured by hazard ratio (HR).

RESULTS

The 1-year and 2-year mortality rates after hip fracture were 13.4% and 20.7%, respectively. The second fracture rate within 2 years was 9.5%. Male gender (HR 1.51, P = 0.035), increased age (HR 1.07, P < 0.001), preoperative hypoalbuminemia (HR 1.79, P = 0.004), preoperative pneumonia (HR 2.60, P = 0.005) and poor ADL (P = 0.048) were independent risk factors for 2-year mortality, while high preoperative hemoglobin (HR 0.98, P = 0.002), high preoperative eGFR (HR 0.99, P = 0.031), high preoperative LVEF (HR 0.92, P = 0.048) were protective factors for 2-year mortality. Poor ADL (P = 0.002) was the independent risk factor for second fracture within 2 years.

CONCLUSIONS

The 2-year mortality rate and second fracture rate after hip fracture in elderly remained high, which was related to old age and complications exists. Postoperative rehabilitation and improving ADL were very important to reduce mortality and second fracture.

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.

Development and internal validation of a clinical prediction model using machine learning algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above

Purpose

Preoperative prediction of mortality in femoral neck fracture patients aged 65 years or above may be valuable in the treatment decision-making. A preoperative clinical prediction model can aid surgeons and patients in the shared decision-making process, and optimize care for elderly femoral neck fracture patients. This study aimed to develop and internally validate a clinical prediction model using machine learning (ML) algorithms for 90 day and 2 year mortality in femoral neck fracture patients aged 65 years or above.

Methods

A retrospective cohort study at two trauma level I centers and three (non-level I) community hospitals was conducted to identify patients undergoing surgical fixation for a femoral neck fracture. Five different ML algorithms were developed and internally validated and assessed by discrimination, calibration, Brier score and decision curve analysis.

Results

In total, 2478 patients were included with 90 day and 2 year mortality rates of 9.1% (n = 225) and 23.5% (n = 582) respectively. The models included patient characteristics, comorbidities and laboratory values. The stochastic gradient boosting algorithm had the best performance for 90 day mortality prediction, with good discrimination (c-statistic = 0.74), calibration (intercept = − 0.05, slope = 1.11) and Brier score (0.078). The elastic-net penalized logistic regression algorithm had the best performance for 2 year mortality prediction, with good discrimination (c-statistic = 0.70), calibration (intercept = − 0.03, slope = 0.89) and Brier score (0.16). The models were incorporated into a freely available web-based application, including individual patient explanations for interpretation of the model to understand the reasoning how the model made a certain prediction: https://sorg-apps.shinyapps.io/hipfracturemortality/

Conclusions

The clinical prediction models show promise in estimating mortality prediction in elderly femoral neck fracture patients. External and prospective validation of the models may improve surgeon ability when faced with the treatment decision-making.

Level of evidence

Prognostic Level II.

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.

Decision curve analysis to evaluate the clinical benefit of prediction models

There is increased interest in the use of prediction models to guide clinical decision-making in orthopedics. Prediction models are typically evaluated in terms of their accuracy: discrimination (area-under-the-curve [AUC] or concordance index) and calibration (a plot of predicted vs. observed risk). But it can be hard to know how high an AUC has to be in order to be "high enough" to warrant use of a prediction model, or how much miscalibration would be disqualifying. Decision curve analysis was developed as a method to determine whether use of a prediction model in the clinic to inform decision-making would do more good than harm. Here we give a brief introduction to decision curve analysis, explaining the critical concepts of net benefit and threshold probability. We briefly review some prediction models reported in the orthopedic literature, demonstrating how use of decision curves has allowed conclusions as to the clinical value of a prediction model. Conversely, papers without decision curves were unable to address questions of clinical value. We recommend increased use of decision curve analysis to evaluate prediction models in the orthopedics literature.

A Culture Change in Geriatric Traumatology: Holistic and Patient-Tailored Care for Frail Patients with Fractures

Medical decision-making for frail geriatric trauma patients is complex, especially toward the end of life. The goal of this paper is to review aspects of end-of-life decision-making, such as frailty, cognitive impairment, quality of life, goals of care, and palliative care. Additionally, we make recommendations for composing a patient-tailored treatment plan. In doing so, we seek to initiate the much-needed discussion regarding end-of-life care for frail geriatric patients.

Biodegradable cement augmentation of gamma nail osteosynthesis reduces migration in pertrochanteric fractures, a biomechanical in vitro study

BACKGROUND

Cut-out of gamma nail often results from poor primary bone stability, suboptimal reduction (varus) and excentric placement of the head element which may lead to "instability" and frequently requires revision. Various studies have shown that augmentation with polymethylmethacrylate cement increases the primary stability of osteosynthesis. However, it has not yet been widely used in fracture treatment due to certain disadvantages, e.g., the lack of osteointegration, the formation of an interface membrane or the presence of toxic monomers. Few studies show that biodegradable bone cements increase the stability of osteosynthesis in different anatomical regions and therefore could be an alternative to polymethylmethacrylate cement in the treatment of pertrochanteric fractures.

METHODS

Two biomechanical situations were simulated using 24 Sawbones (simple and multifragmentary pertrochanteric fractures; AO-classification 31-A1 and 31-A2. Both groups were stabilized using the Gamma3® nailing system with and without biodegradable bone cement. Sawbones underwent the same cyclic loading test, simulating 10.000 gait cycles loading the bones with three times body weight. Migration was determined by comparing computed tomography scans recorded before and after the mechanical testing. The three-dimensional migration of the lag screw was calculated, and the rotation of the head around the longitudinal axis was determined.

FINDINGS

Biodegradable cement reduced migration by approximately 35% in 31-A1 fractures (25.4% in 31-A2 fractures) and the rotation of the head around the lag screw by approximately 37% in 31-A1 fractures (17.8%, 31-A2).

INTERPRETATION

Use of biodegradable bone cement improved the primary stability of gamma nail osteosynthesis in the biomechanical model.