Research
Print page Print page
Switch language
The Capital Region of Denmark - a part of Copenhagen University Hospital
Published

Predicting mortality and incident immobility in older Belgian men by characteristics related to sarcopenia and frailty

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

APA

CBE

MLA

Vancouver

Author

Bibtex

@article{81c136a5c296408fb94935e7f402a119,
title = "Predicting mortality and incident immobility in older Belgian men by characteristics related to sarcopenia and frailty",
abstract = "There is an increasing awareness of sarcopenia in older people. We applied machine learning principles to predict mortality and incident immobility in older Belgian men through sarcopenia and frailty characteristics. Mortality could be predicted with good accuracy. Serum 25-hydroxyvitamin D and bone mineral density scores were the most important predictors.INTRODUCTION: Machine learning principles were used to predict 5-year mortality and 3-year incident severe immobility in a population of older men by frailty and sarcopenia characteristics.METHODS: Using prospective data from 1997 on 264 older Belgian men (n = 152 predictors), 29 statistical models were developed and tuned on 75{\%} of data points then validated on the remaining 25{\%}. The model with the highest test area under the curve (AUC) was chosen as the best. From these, ranked predictor importance was extracted.RESULTS: Five-year mortality could be predicted with good accuracy (test AUC of .85 [.73; .97], sensitivity 78{\%}, specificity 89{\%} at a probability cut-off of 22.3{\%}) using a Bayesian generalized linear model. Three-year incident severe immobility could be predicted with fair accuracy (test AUC .74 [.57; .91], sensitivity 67{\%}, specificity 78{\%} at a probability cut-off of 14.2{\%}) using a multivariate adaptive regression splines model. Serum 25-hydroxyvitamin D levels and hip bone mineral density scores were the most important predictors of mortality, while biochemical androgen markers and Short-Form 36 Physical Domain questions were the most important predictors of immobility. Sarcopenia assessed by lean mass estimates was relevant to mortality prediction but not immobility prediction.CONCLUSIONS: Using advanced statistical models and a machine learning approach 5-year mortality can be predicted with good accuracy using a Bayesian generalized linear model and 3-year incident severe immobility with fair accuracy using a multivariate adaptive regression splines model.",
author = "C Kruse and S Goemaere and {De Buyser}, S and B Lapauw and P Eiken and P Vestergaard",
year = "2018",
doi = "10.1007/s00198-018-4467-z",
language = "English",
volume = "29",
pages = "1437--1445",
journal = "Osteoporosis International",
issn = "0937-941X",
publisher = "Springer U K",
number = "6",

}

RIS

TY - JOUR

T1 - Predicting mortality and incident immobility in older Belgian men by characteristics related to sarcopenia and frailty

AU - Kruse, C

AU - Goemaere, S

AU - De Buyser, S

AU - Lapauw, B

AU - Eiken, P

AU - Vestergaard, P

PY - 2018

Y1 - 2018

N2 - There is an increasing awareness of sarcopenia in older people. We applied machine learning principles to predict mortality and incident immobility in older Belgian men through sarcopenia and frailty characteristics. Mortality could be predicted with good accuracy. Serum 25-hydroxyvitamin D and bone mineral density scores were the most important predictors.INTRODUCTION: Machine learning principles were used to predict 5-year mortality and 3-year incident severe immobility in a population of older men by frailty and sarcopenia characteristics.METHODS: Using prospective data from 1997 on 264 older Belgian men (n = 152 predictors), 29 statistical models were developed and tuned on 75% of data points then validated on the remaining 25%. The model with the highest test area under the curve (AUC) was chosen as the best. From these, ranked predictor importance was extracted.RESULTS: Five-year mortality could be predicted with good accuracy (test AUC of .85 [.73; .97], sensitivity 78%, specificity 89% at a probability cut-off of 22.3%) using a Bayesian generalized linear model. Three-year incident severe immobility could be predicted with fair accuracy (test AUC .74 [.57; .91], sensitivity 67%, specificity 78% at a probability cut-off of 14.2%) using a multivariate adaptive regression splines model. Serum 25-hydroxyvitamin D levels and hip bone mineral density scores were the most important predictors of mortality, while biochemical androgen markers and Short-Form 36 Physical Domain questions were the most important predictors of immobility. Sarcopenia assessed by lean mass estimates was relevant to mortality prediction but not immobility prediction.CONCLUSIONS: Using advanced statistical models and a machine learning approach 5-year mortality can be predicted with good accuracy using a Bayesian generalized linear model and 3-year incident severe immobility with fair accuracy using a multivariate adaptive regression splines model.

AB - There is an increasing awareness of sarcopenia in older people. We applied machine learning principles to predict mortality and incident immobility in older Belgian men through sarcopenia and frailty characteristics. Mortality could be predicted with good accuracy. Serum 25-hydroxyvitamin D and bone mineral density scores were the most important predictors.INTRODUCTION: Machine learning principles were used to predict 5-year mortality and 3-year incident severe immobility in a population of older men by frailty and sarcopenia characteristics.METHODS: Using prospective data from 1997 on 264 older Belgian men (n = 152 predictors), 29 statistical models were developed and tuned on 75% of data points then validated on the remaining 25%. The model with the highest test area under the curve (AUC) was chosen as the best. From these, ranked predictor importance was extracted.RESULTS: Five-year mortality could be predicted with good accuracy (test AUC of .85 [.73; .97], sensitivity 78%, specificity 89% at a probability cut-off of 22.3%) using a Bayesian generalized linear model. Three-year incident severe immobility could be predicted with fair accuracy (test AUC .74 [.57; .91], sensitivity 67%, specificity 78% at a probability cut-off of 14.2%) using a multivariate adaptive regression splines model. Serum 25-hydroxyvitamin D levels and hip bone mineral density scores were the most important predictors of mortality, while biochemical androgen markers and Short-Form 36 Physical Domain questions were the most important predictors of immobility. Sarcopenia assessed by lean mass estimates was relevant to mortality prediction but not immobility prediction.CONCLUSIONS: Using advanced statistical models and a machine learning approach 5-year mortality can be predicted with good accuracy using a Bayesian generalized linear model and 3-year incident severe immobility with fair accuracy using a multivariate adaptive regression splines model.

U2 - 10.1007/s00198-018-4467-z

DO - 10.1007/s00198-018-4467-z

M3 - Journal article

VL - 29

SP - 1437

EP - 1445

JO - Osteoporosis International

JF - Osteoporosis International

SN - 0937-941X

IS - 6

ER -

ID: 54746887