Development and internal validation of a clinical prediction model for acute adjacent vertebral fracture after vertebral augmentation

2022 ◽  
Vol 104-B (1) ◽  
pp. 97-102
Author(s):  
Yasukazu Hijikata ◽  
Tsukasa Kamitani ◽  
Masayuki Nakahara ◽  
Shinji Kumamoto ◽  
Tsubasa Sakai ◽  
...  

Aims To develop and internally validate a preoperative clinical prediction model for acute adjacent vertebral fracture (AVF) after vertebral augmentation to support preoperative decision-making, named the after vertebral augmentation (AVA) score. Methods In this prognostic study, a multicentre, retrospective single-level vertebral augmentation cohort of 377 patients from six Japanese hospitals was used to derive an AVF prediction model. Backward stepwise selection (p < 0.05) was used to select preoperative clinical and imaging predictors for acute AVF after vertebral augmentation for up to one month, from 14 predictors. We assigned a score to each selected variable based on the regression coefficient and developed the AVA scoring system. We evaluated sensitivity and specificity for each cut-off, area under the curve (AUC), and calibration as diagnostic performance. Internal validation was conducted using bootstrapping to correct the optimism. Results Of the 377 patients used for model derivation, 58 (15%) had an acute AVF postoperatively. The following preoperative measures on multivariable analysis were summarized in the five-point AVA score: intravertebral instability (≥ 5 mm), focal kyphosis (≥ 10°), duration of symptoms (≥ 30 days), intravertebral cleft, and previous history of vertebral fracture. Internal validation showed a mean optimism of 0.019 with a corrected AUC of 0.77. A cut-off of ≤ one point was chosen to classify a low risk of AVF, for which only four of 137 patients (3%) had AVF with 92.5% sensitivity and 45.6% specificity. A cut-off of ≥ four points was chosen to classify a high risk of AVF, for which 22 of 38 (58%) had AVF with 41.5% sensitivity and 94.5% specificity. Conclusion In this study, the AVA score was found to be a simple preoperative method for the identification of patients at low and high risk of postoperative acute AVF. This model could be applied to individual patients and could aid in the decision-making before vertebral augmentation. Cite this article: Bone Joint J 2022;104-B(1):97–102.

Author(s):  
Joost Velzel ◽  
Ewoud Schuit ◽  
Floortje Vlemmix ◽  
Jan F.M. Molkenboer ◽  
Joris A.M. Van der Post ◽  
...  

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0173461 ◽  
Author(s):  
Amanda S. Trudell ◽  
Methodius G. Tuuli ◽  
Graham A. Colditz ◽  
George A. Macones ◽  
Anthony O. Odibo

2020 ◽  
Vol 28 ◽  
pp. S254-S255
Author(s):  
W.H. van der Gaag ◽  
A. Chiarotto ◽  
M.W. Heymans ◽  
W.T. Enthoven ◽  
P.A. Luijsterburg ◽  
...  

2015 ◽  
Vol 41 (6) ◽  
pp. 1029-1036 ◽  
Author(s):  
Michael Coslovsky ◽  
Jukka Takala ◽  
Aristomenis K. Exadaktylos ◽  
Luca Martinolli ◽  
Tobias M. Merz

2016 ◽  
Vol 11 (1) ◽  
pp. 64-68 ◽  
Author(s):  
Sharmin Jahan ◽  
Mohammad Ali

Introduction: The healthcare delivery challenges in Bangladesh are phenomenal. Improving maternal and child health, reducing the high maternal and infant mortality & morbidity are challenging. Arrangement of additional expenditure for GDM screening is again challenging. The efficiency of screening could be enhanced by considering women’s risks of gestational diabetes on the basis of their clinical characteristics.Objectives: To find out the use of the clinical prediction model of gestational diabetes mellitus (GDM) is valid for Bangladeshi pregnant women and to assess the risk of gestational diabetes by using clinical prediction model based on maternal characteristics.Materials and Methods: A cross sectional study was carried out from July 2011 to June 2012 among purposively selected 217 pregnant women of ?24 weeks of gestation in the Gynae and Obstetric outpatient department of Combined Military Hospital, Dhaka. Data were collected by face to face interview, anthropometric measurement and record review. Two step oral glucose tests were done for diagnosis of GDM.Results: According to Chadakaran clinical prediction model 84 (38.7%) respondents were at high risk, 92 (42.4%) were at intermediate risk and 41(18.9%) found at low risk of gestational diabetes but only 24(11.05%) developed gestational diabetes. Highest occurrence of gestational diabetes was found in high risk group 17 (20.2%) with zero occurrence in low risk group. Risk score performance at the level of ?380, sensitivity was 100% and specificity 21.8%, 13.6% positive predictive value, 100% negative predictive value and area under curve was 0.385. At the level of 460 score the sensitivity and specificity was found closest (70.8% and 65.3%, respectively) and area under curve was highest 0.657. The receiver operating characteristics curve of the risk score in the study sample for predicting women with glucose tolerance test demonstrated an area 0.763 (95%, 0.682 – 0.845).Conclusion: The use of clinical prediction model is a simple, non invasive, cost effective useful method to identify women at increased risk of gestational diabetes mellitus and could be short listed for further testing.Journal of Armed Forces Medical College Bangladesh Vol.11(1) 2015: 64-68


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