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

Schuijt, Henk Jan; Smeeing, Diederik P.J.; Würdemann, Franka S; Hegeman, J.H.; Geraghty, Olivia; Houwert, Roderick M.; Weaver, Michael J.; Velde, Detlef van der

doi:10.1097/bot.0000000000001851

Cited by 11 publications

(8 citation statements)

References 32 publications

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“…[10][11][12] Numerous prognostic models have been developed for mortality prediction in hip fracture patients, and new models are proposed every year. 11,[34][35][36][37][38] The NHFS is the most studied model and demonstrated adequate discrimination for 30day and 1-year mortality in different studies, although recalibration has occasionally been necessary. 12,16,23,25,39 Despite multiple validation studies, there are no reports on its calibration slope nor clinical usefulness, leaving underfitting or lack of sensitivity potentially underreported.…”

Section: Discussionmentioning

confidence: 99%

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

Karres

Zwiers

Eerenberg

et al. 2022

Journal of Orthopaedic Trauma

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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.

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Section: Discussionmentioning

confidence: 99%

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

Karres

Zwiers

Eerenberg

et al. 2022

Journal of Orthopaedic Trauma

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“…The aim of this study was to validate a previously developed prediction model (U-HIP) for in-hospital mortality in geriatric hip fracture patients [8] . In this large retrospective cohort study using NSQIP data, the model showed a fairly good predictive performance.…”

Section: Discussionmentioning

confidence: 99%

“…The primary outcome for this study was in-hospital mortality. In this study, the authors decided to validate the model in patients aged 70 years or above, even though the development cohort consisted of patients 85 year or above [8] . The cut-off of 70 years was chosen because the vast majority of patients who experience inhospital mortality after a hip fracture are aged 70 years or above.…”

Section: Methodsmentioning

confidence: 99%

“…The baseline characteristics of this external validation cohort were comparable to the development cohort in terms of sex, ASA classification, diagnosis of dementia and hemoglobin levels [8] . There were several differences between the development cohort and this external validation cohort.…”

Section: Development Cohort Versus Validation Cohortmentioning

confidence: 99%

“…After correction for optimism, this model showed good discrimination (c-statistic 0.77) at internal validation. Predictors in the model were age, sex, American Society of Anesthesiologists physical status classification system (ASA) and hemoglobin serum levels (mmol/L) upon presentation at the ED [8] . The purpose of the current study was to perform a validation study to externally validate the U-HIP prediction model for inhospital mortality in a North-American population of hip fracture patients aged 70 or above undergoing surgery [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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

Schuijt

Smeeing

Groenwold

et al. 2022

Injury

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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

Oosterhoff

Savelberg

Karhade

et al. 2022

Eur J Trauma Emerg Surg

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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.

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

Cited by 11 publications

References 32 publications

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

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

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

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

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