Brand Jeff, Hardy Rich, Tori Aerika, Fuchs Hannah, Sungur Engin, Monroe Emily
Sports Medicine Department, Heartland Orthopedic Specialists, 111 17th Ave E #101, Alexandria, MN 56308, USA.
Department of Biology, Division of Science and Math, University of Minnesota Morris, Morris, MN, USA.
J Hip Preserv Surg. 2020 Feb 12;7(1):57-61. doi: 10.1093/jhps/hnaa002. eCollection 2020 Jan.
To determine if scores of the International Hip Outcome Tool-12 (iHOT12) and the Hip Outcome Score (HOS) correlate with one another in hip pain patients. Patients reporting to an orthopedic clinic for their scheduled appointment for hip pain were given a paper survey consisting of the iHOT12 and the HOS. Demographic information [age, weight, height and body mass index (BMI)] was obtained by chart review. Overall, 114 patients were invited to voluntarily complete the surveys of which 23 declined. Our sample consisted of 91 (57 female and 34 male) patients (80% response rate). The HOS (iHOT12) explained 62% of the variation in iHOT12 (HOS) by using a linear model (Pearson's correlation() is 0.79, < 0.001). Age, weight, BMI, gender and arthritis did not show a statistically significant predictive power explaining HOS. However, only gender had a 'statistically' significant predictive power explaining iHOT12 ( = 0.007). The relationship between the two scores are stronger for males ( = 0.81, < 0.001) compared with females ( = 0.77, < 0.001). The proportion of variations explained on one of the scores by the other are 0.66 for males and 0.59 for females. HOS score together with gender explained 64% of the variation in iHOT12 by using a linear model. iHOT12 together with the non-statistically significant gender term explained 62% of the variation in HOS by using a linear model. It may not be necessary to collect both the iHOT12 and HOS, since the predictive power of one on the other is high. Collecting HOS together with information on gender is preferable compared with collecting iHOT12. : Level III.
为确定国际髋关节疗效评估工具 -12(iHOT12)评分与髋关节疗效评分(HOS)在髋关节疼痛患者中是否相互关联。向一家骨科诊所前来预约治疗髋关节疼痛的患者发放了一份包含iHOT12和HOS的纸质调查问卷。通过查阅病历获取人口统计学信息(年龄、体重、身高和体重指数(BMI))。总体而言,邀请了114名患者自愿完成调查,其中23名拒绝。我们的样本包括91名患者(57名女性和34名男性)(应答率为80%)。通过线性模型,HOS(iHOT12)解释了iHOT12(HOS)中62%的变异(Pearson相关系数为0.79,P < 0.001)。年龄、体重、BMI、性别和关节炎对HOS均未显示出具有统计学意义的预测能力。然而,只有性别对iHOT12具有“统计学”意义的预测能力(P = 0.007)。与女性(r = 0.77,P < 0.001)相比,男性的两个评分之间的关系更强(r = 0.81,P < 0.001)。一个评分对另一个评分所解释的变异比例,男性为0.66,女性为0.59)。通过线性模型,HOS评分与性别共同解释了iHOT12中64%的变异。iHOT12与无统计学意义的性别项共同通过线性模型解释了HOS中62%的变异。由于一个对另一个的预测能力较高,可能无需同时收集iHOT12和HOS。与收集iHOT12相比,收集HOS及性别信息更可取。:三级。