B. C. Craven, Lindsie M. Blencowe, Lora M. Giangregorio, Laura Carbone, Frances M. Weaver, Susan B. Jaglal, Barry Munro, Lynn Boag, Vanessa K. Noonan, S. Humphreys, Mohammad Alavinia
{"title":"海报(知识生成) ID 1985362","authors":"B. C. Craven, Lindsie M. Blencowe, Lora M. Giangregorio, Laura Carbone, Frances M. Weaver, Susan B. Jaglal, Barry Munro, Lynn Boag, Vanessa K. Noonan, S. Humphreys, Mohammad Alavinia","doi":"10.46292/sci23-1985362s","DOIUrl":null,"url":null,"abstract":"To develop a lower extremity (LE) fragility fracture risk score estimation method among adults with chronic spinal cord injury (SCI). Adults (≥18) with chronic traumatic SCI (n=90, C2-T12, AIS:A-D) participated in a 2-year prospective cohort study. We used a literature search and practice expertise to identify LE fracture predictors. Reference categories (i.e., risk score = 0) were: no prior fracture, 0-9 years post-injury, AIS-CD, no parental history of osteoporosis, and no opioid use. Using logistic regression coefficients, we calculated how far each category is from the base category and computed βi (Wij-WiREF) for each risk factor. In this model, B was the increase in risk associated with each year post injury. The point value for each fracture risk category was calculated by Pointsij=βi (Wij-WiREF)/B. The total points range from 0-21, and the probability of LE fracture is calculated for each point to determine the probability of developing a LE fracture using the formula: Most participants had an AIS-A impairment (60.0%), the mean time post-injury=15.23 years (SD=9.58). For the points system (0-17), prior fracture, years post-injury, AIS, Benzodiazepine use, Opioid use, and parental osteoporosis were defined risk factors. An individual’s risk profile can estimate LE fracture risk. A score of 11 equates to 20% or high fracture risk over a 5-year time period. We describe our preliminary model to estimate LE fracture risk among those with chronic SCI. We plan to apply statistical and machine learning algorithms using Canadian RHSCIR data and US VHA data to validate the model, and increase the model’s predictability.","PeriodicalId":46769,"journal":{"name":"Topics in Spinal Cord Injury Rehabilitation","volume":"59 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Poster (Knowledge Generation) ID 1985362\",\"authors\":\"B. C. Craven, Lindsie M. Blencowe, Lora M. Giangregorio, Laura Carbone, Frances M. Weaver, Susan B. Jaglal, Barry Munro, Lynn Boag, Vanessa K. Noonan, S. Humphreys, Mohammad Alavinia\",\"doi\":\"10.46292/sci23-1985362s\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To develop a lower extremity (LE) fragility fracture risk score estimation method among adults with chronic spinal cord injury (SCI). Adults (≥18) with chronic traumatic SCI (n=90, C2-T12, AIS:A-D) participated in a 2-year prospective cohort study. We used a literature search and practice expertise to identify LE fracture predictors. Reference categories (i.e., risk score = 0) were: no prior fracture, 0-9 years post-injury, AIS-CD, no parental history of osteoporosis, and no opioid use. Using logistic regression coefficients, we calculated how far each category is from the base category and computed βi (Wij-WiREF) for each risk factor. In this model, B was the increase in risk associated with each year post injury. The point value for each fracture risk category was calculated by Pointsij=βi (Wij-WiREF)/B. The total points range from 0-21, and the probability of LE fracture is calculated for each point to determine the probability of developing a LE fracture using the formula: Most participants had an AIS-A impairment (60.0%), the mean time post-injury=15.23 years (SD=9.58). For the points system (0-17), prior fracture, years post-injury, AIS, Benzodiazepine use, Opioid use, and parental osteoporosis were defined risk factors. An individual’s risk profile can estimate LE fracture risk. A score of 11 equates to 20% or high fracture risk over a 5-year time period. We describe our preliminary model to estimate LE fracture risk among those with chronic SCI. We plan to apply statistical and machine learning algorithms using Canadian RHSCIR data and US VHA data to validate the model, and increase the model’s predictability.\",\"PeriodicalId\":46769,\"journal\":{\"name\":\"Topics in Spinal Cord Injury Rehabilitation\",\"volume\":\"59 1\",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Topics in Spinal Cord Injury Rehabilitation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46292/sci23-1985362s\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"REHABILITATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Topics in Spinal Cord Injury Rehabilitation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46292/sci23-1985362s","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REHABILITATION","Score":null,"Total":0}
引用次数: 0
摘要
在慢性脊髓损伤(SCI)成人中开发一种下肢(LE)脆性骨折风险评分估算方法。 患有慢性创伤性 SCI 的成人(≥18 岁)(n=90,C2-T12,AIS:A-D)参加了一项为期 2 年的前瞻性队列研究。我们通过文献检索和实践经验来确定 LE 骨折的预测因素。参考类别(即风险评分 = 0)为:无骨折史、伤后 0-9 年、AIS-CD、父母无骨质疏松症史、未使用阿片类药物。利用逻辑回归系数,我们计算了每个类别与基础类别的距离,并计算了每个风险因素的 βi (Wij-WiREF)。在该模型中,B 是受伤后每一年相关风险的增加值。每个骨折风险类别的点值按 Pointsij=βi (Wij-WiREF)/B 计算。总点数范围为 0-21,计算出每个点的 LE 骨折概率,从而利用公式确定发生 LE 骨折的概率: 大多数参与者的损伤程度为 AIS-A(60.0%),受伤后的平均时间=15.23 年(SD=9.58)。在积分系统(0-17 分)中,既往骨折、受伤后年数、AIS、苯二氮卓类药物的使用、阿片类药物的使用以及父母骨质疏松症都被定义为风险因素。个人的风险概况可估算出 LE 骨折风险。得分 11 分相当于 5 年内有 20% 或很高的骨折风险。 我们介绍了用于估算慢性 SCI 患者 LE 骨折风险的初步模型。我们计划使用加拿大 RHSCIR 数据和美国退伍军人管理局数据应用统计和机器学习算法来验证模型,并提高模型的可预测性。
To develop a lower extremity (LE) fragility fracture risk score estimation method among adults with chronic spinal cord injury (SCI). Adults (≥18) with chronic traumatic SCI (n=90, C2-T12, AIS:A-D) participated in a 2-year prospective cohort study. We used a literature search and practice expertise to identify LE fracture predictors. Reference categories (i.e., risk score = 0) were: no prior fracture, 0-9 years post-injury, AIS-CD, no parental history of osteoporosis, and no opioid use. Using logistic regression coefficients, we calculated how far each category is from the base category and computed βi (Wij-WiREF) for each risk factor. In this model, B was the increase in risk associated with each year post injury. The point value for each fracture risk category was calculated by Pointsij=βi (Wij-WiREF)/B. The total points range from 0-21, and the probability of LE fracture is calculated for each point to determine the probability of developing a LE fracture using the formula: Most participants had an AIS-A impairment (60.0%), the mean time post-injury=15.23 years (SD=9.58). For the points system (0-17), prior fracture, years post-injury, AIS, Benzodiazepine use, Opioid use, and parental osteoporosis were defined risk factors. An individual’s risk profile can estimate LE fracture risk. A score of 11 equates to 20% or high fracture risk over a 5-year time period. We describe our preliminary model to estimate LE fracture risk among those with chronic SCI. We plan to apply statistical and machine learning algorithms using Canadian RHSCIR data and US VHA data to validate the model, and increase the model’s predictability.
期刊介绍:
Now in our 22nd year as the leading interdisciplinary journal of SCI rehabilitation techniques and care. TSCIR is peer-reviewed, practical, and features one key topic per issue. Published topics include: mobility, sexuality, genitourinary, functional assessment, skin care, psychosocial, high tetraplegia, physical activity, pediatric, FES, sci/tbi, electronic medicine, orthotics, secondary conditions, research, aging, legal issues, women & sci, pain, environmental effects, life care planning