External validation of the Almelo Hip Fracture Score, a prediction model for early mortality following hip fracture surgery

Validation of the Geriatrics at Risk Score (GeRi-Score) on 120-day follow-up, the influence of preoperative geriatric visits, and the time to surgery on the outcome of hip fracture patients: an analysis from the Registry for Geriatric Trauma (ATR-DGU)

A validation of the GeRi-Score on 120-day mortality, the impact of a pre-operative visit by a geriatrician, and timing of surgery on the outcome was conducted. The score has predictive value for 120-day mortality. No advantage was found for surgery within 24 h or a preoperative geriatric visit.

PURPOSE

Numerous tools predict mortality among patients with hip fractures, but they include many variables, require time-consuming assessment, and are difficult to calculate. The GeRi-Score provides a quick method of pre-operative assessment. The aim of this study is to validate the score in the 120-day follow-up and determine the impact of a pre-operative visit by a geriatrician and timing of surgery on the patient outcome.

METHODS

A retrospective analysis of the AltersTraumaRegister DGU® from 2017 to 2021 was conducted, including all proximal femur fractures. The patients were divided into low-, moderate-, and high-risk groups based on the GeRi-Score. Mortality was analyzed using logistic regression. To determine the influence of the time to surgery and the preoperative visit by a geriatrician, matching was performed using the exact GeRi-Score, preoperative walking ability, type of fracture, and the time to surgery.

RESULTS

The study included 38,570 patients, divided into 12,673 low-risk, 18,338 moderate-risk, and 7,559 high-risk patients. The moderate-risk group had three times the mortality risk of the low-risk group (OR 3.19 (95% CI 2.68-3.79; p<0.001)), while the high-risk group had almost eight times the mortality risk than the low-risk group (OR 7.82 (95% CI 6.51-9.93; p<0.001)). No advantage was found for surgery within the first 24 h across all groups. There was a correlation of a preoperative geriatric visit and mortality showing an increase in the moderate and high-risk group on in-house mortality.

CONCLUSIONS

The GeRi-Score has predictive value for 120-day mortality. No advantage was found for surgery within 24 h. The analysis did not demonstrate a benefit of the preoperative geriatric visit, but more data are needed.

AtoG: A simple score to predict complications and death after hip fractures, in line with the comprehensive geriatric assessment

INTRODUCTION

Proximal Femur Fractures (PFFs) are a significant public health issue and occur in the context of global frailty and aging. Recent literature identifies new patient-related prognostic factors that focus on socioeconomic environment, patient well-being, or nutrition status. Specific scores have been developed, but in most cases, they fail to be in line with the comprehensive geriatric assessment, or do not assess the newly identified prognostic factors, contain multitude collinearities, or are too complex to be used in the daily practice. Hypothesis A comprehensive score with equal representation of the patient's dimensions does at least as good as the Charlson score (CCI), to predict complications and mortality.

OBJECTIVE

To develop a new comprehensive prognostic score, predicting inpatient complications and mortality up to 5-year after PFF.

MATERIAL AND METHODS

The patients treated surgically for PFF on a native hip, between 2005 and 2017 were selected from a French national database. The variables were the gender, age, the type of treatment (osteosynthesis or arthroplasty), and the CCI. The outcomes were the medical and surgical complications as inpatient and the mortality (up to 5-year). Variables were grouped into dimensions with similar clinical significance, using a Principal Component Analysis, for instance, bedsores and malnutrition. The dimensions were tested for 90-day mortality and complications, in regressions models. Two scores were derived from the coefficients: SCOREpond (strict ponderation), and SCORE (with loose ponderation: 1 point/risk factors, -1 point/protective factors). Calibration, discrimination (ROC curves with Area Under Curves AUC), and cross-validation were assessed for SCOREpond, SCORE, and CCI.

RESULTS

Analyses were performed on 7756 fractures. The factorial analysis identified seven dimensions: age; brain-related conditions (including dementia): 1738/7756; severe chronic conditions (for instance, organ failures) 914/7756; undernutrition: 764/7756; environment, including social issues or housing difficulties: 659/7756; associated trauma: 814/7756; and gender. The seven dimensions were selected for the prognostic score named AtoG (ABCDEFG, standing for Age, Brain, Comorbidities, unDernutrition, Environment, other Fractures, Gender). The median survival rate was 50.8 months 95% CI [49-53]. Anaemia and urologic complications were the most prevalent medical complications (1674/7756, 21%, and 1109/7756, 14.2%). A total of 149/7756 patients (1.9%) developed a mechanical inpatient complication (fractures or dislocations), with a slightly higher risk for arthroplasties. The AUCs were 0.69, 0.68, and 0.67 for AtoGpond, AtoG, and CCI, respectively, for 90-day mortality, and 0.64, 0.63, and 0.56 for complications. Compared to patients with AtoG=0, Hazard Ratios for 90-day mortality were 2.3 95% CI [1.7-2.9], 4.2 95% CI [3.1-5.4], 6 95% CI [4.5-8.1], 8.3 95% CI [6.5-12.9], and 13.7 95% CI [8-24], from AtoG=1 to AtoG≥5, respectively (p<10-4); the 90-day survival decreased by 5%/point, roughly. The sur-risk of mortality associated with AtoG was up to 5-year: HR=1.51 (95% CI [1.46-1.55], p<10-4). Compared to AtoG=0, from AtoG=1 to AtoG≥5, the pooled Odd Ratios were 1.14 95% CI [1.06-1.2], 1.53 95% CI [1.4-1.7], 2.17 95% CI [1.9-2.4], 2.9 95% CI [2.4-3.4], and 4.9 95% CI [3.3-7.4] for any complication (p<10-4).

CONCLUSION

AtoG is a multidimensional score in line with the concept of comprehensive geriatric assessment. It had good discrimination and performance in predicting 90-day mortality and complications. Performances were as good as CCI for 90-day mortality, and better than it for the complications.

LEVEL OF PROOF

IV; retrospective cohort study.

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.

Predictive Value of Machine Learning Models in Postoperative Mortality of Older Adults Patients with Hip Fracture: A Systematic Review and Meta-analysis

BACKGROUND

Some researchers have used machine learning to predict mortality in old patients with hip fracture, but its application value lacks an evidence-based basis. Hence, we conducted this meta-analysis to explore the predictive accuracy of machine learning for mortality in old patients with hip fracture.

METHODS

We systematically retrieved PubMed, Cochrane, Embase, and Web of Science for relevant studies published before July 15, 2022. The PROBAST assessment tool was used to assess the risk of bias in the included studies. A random-effects model was used for the meta-analysis of C-index, whereas a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. The meta-analysis was performed on R and Stata.

RESULTS

Eighteen studies were included, involving 8 machine learning models and 398,422 old patients undergoing hip joint surgery, of whom 60,457 died. According to the meta-analysis, the pooled C-index for machine learning models was 0.762 (95% CI: 0.691 ∼ 0.833) in the training set and 0.838 (95% CI: 0.783 ∼ 0.892) in the validation set, which is better than the C-index of the main clinical scale (Nottingham Hip Fracture Score), that is, 0.702 (95% CI: 0.681 ∼ 0.723). Among different machine learning models, ANN and Bayesian belief network had the best predictive performance.

CONCLUSION

Machine learning models are more accurate in predicting mortality in old patients after hip joint surgery than current mainstream clinical scoring systems. Subsequent research could focus on updating clinical scoring systems and improving their predictive performance by relying on machine learning models.

Validation and updating of the Older Person's Emergency Risk Assessment (OPERA) score to predict outcomes for hip fracture patients

INTRODUCTION

Hip fractures are associated with significant morbidity and mortality in older people. Accurate risk stratification is important for planning of care, informed decision-making and communication with patients and relatives. The Older Persons' Emergency Risk Assessment (OPERA) score is a risk stratification score for older people admitted to hospital. Our aims were to validate OPERA in hip fracture patients, update the score and compare performance with the Nottingham Hip Fracture Score (NHFS).

METHODS

This dual-centre 3-year observational study (2016-2018) included acutely admitted hip fracture patients managed surgically aged ⩾65 years. The primary outcome was 30-day mortality. Secondary outcomes included residence at 120 days and 1-year mortality. Model performance was assessed using area under the curve (AUC) analysis and Brier scores (discrimination) and calibration curves. The OPERA score was updated using regression analysis with additional independent predictors and validated using bootstrap analysis.

RESULTS

2142 patients (median age 86 [80-91] years) were included with a 30-day mortality of 5.2% and a 1-year mortality of 31.4%. 30-day mortality AUC for OPERA was 0.75 (95% CI, 0.73-0.77) and for NHFS 0.68 (0.65-0.70). For 1-year mortality AUC for OPERA was 0.74 (0.73-0.75) and for NHFS 0.70 (0.69-0.71). The OPERA Score was updated to Hip-OPERA, including ASA grade. Hip-OPERA demonstrated an AUC for 30-day mortality of 0.77 (0.73-0.81) and an AUC for 1-year mortality of 0.76 (0.75-0.77). AUC for new residential care status at 120 days was 0.79 (0.78-0.80).

CONCLUSIONS

Hip-OPERA demonstrated superior discrimination to the NHFS and OPERA for 30-day mortality, 1-year mortality and residence at 120 days following hip fracture. External validation is desirable.

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.