Upper Limb Function 3 Months Post-Stroke: How Accurate Are Physiotherapist Predictions?

IF 1.5 Q3 REHABILITATION
Sara Kjær Bastholm, Iris Charlotte Brunner, Camilla Biering Lundquist
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Abstract

Background: A frequent sequela of stroke is upper limb (UL) impairment. Accurate UL function prognosis is crucial for targeted rehabilitation.

Objective: To determine the accuracy of physiotherapists' predictions of UL function and investigate whether prediction accuracy is affected by physiotherapists' seniority within rehabilitation and/or their level of education. Physiotherapist predictions were compared with a prediction algorithm.

Methods: Data from 81 patients were included. Two weeks post-stroke, physiotherapists predicted UL function based on clinical reasoning. ARAT scores (poor, limited, good, or excellent) at 3 months post-stroke served to determine prediction accuracy. Prediction accuracy was calculated as correct classification rate (CCR). Logistic regression was used to explore the effect of seniority and education. McNemar's test was applied to compare physiotherapist predictions to an algorithm applied 2 weeks post-stroke to the same patients.

Results: The overall correct classification rate (CCR) of physiotherapist predictions was 41% (95% CI: 30-51). Predictions were most accurate for the excellent (75%) and poor (71%) categories, but lower for limited (22%) and good (30%). No association was observed between prediction accuracy and physiotherapist seniority or education. There was a tendency, but not a statistically significant superiority, in the prediction accuracy of the algorithm compared to the physiotherapist predictions (Odds ratio 2 [95% CI: 0.96-4.39], McNemar p = 0.0455, exact McNemar p = 0.0652).

Trial registration: Project number: 628213.

中风后3个月的上肢功能:物理治疗师的预测有多准确?
背景:中风的常见后遗症是上肢损伤。准确的UL功能预后对有针对性的康复至关重要。目的:确定物理治疗师对UL功能预测的准确性,并探讨预测准确性是否受到物理治疗师在康复领域的资历和/或教育水平的影响。将物理治疗师的预测与预测算法进行比较。方法:纳入81例患者的资料。中风后两周,物理治疗师根据临床推理预测UL功能。卒中后3个月的ARAT评分(差、有限、良好或优秀)用于确定预测的准确性。预测准确度以正确分类率(CCR)计算。采用Logistic回归探讨资历和学历的影响。McNemar的测试被用于比较物理治疗师的预测与中风后两周应用于同一患者的算法。结果:物理治疗师预测的总体正确分类率(CCR)为41% (95% CI: 30-51)。对优秀(75%)和差(71%)类别的预测最准确,但对有限(22%)和良好(30%)类别的预测较低。没有观察到预测准确性与物理治疗师的资历或教育之间的关联。与物理治疗师的预测相比,该算法的预测准确性有趋势,但没有统计学上显著的优势(优势比2 [95% CI: 0.96-4.39], McNemar p = 0.0455,精确McNemar p = 0.0652)。试验报名:项目编号:628213。
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来源期刊
CiteScore
3.30
自引率
5.90%
发文量
53
期刊介绍: Physiotherapy Research International is an international peer reviewed journal dedicated to the exchange of knowledge that is directly relevant to specialist areas of physiotherapy theory, practice, and research. Our aim is to promote a high level of scholarship and build on the current evidence base to inform the advancement of the physiotherapy profession. We publish original research on a wide range of topics e.g. Primary research testing new physiotherapy treatments; methodological research; measurement and outcome research and qualitative research of interest to researchers, clinicians and educators. Further, we aim to publish high quality papers that represent the range of cultures and settings where physiotherapy services are delivered. We attract a wide readership from physiotherapists and others working in diverse clinical and academic settings. We aim to promote an international debate amongst the profession about current best evidence based practice. Papers are directed primarily towards the physiotherapy profession, but can be relevant to a wide range of professional groups. The growth of interdisciplinary research is also key to our aims and scope, and we encourage relevant submissions from other professional groups. The journal actively encourages submissions which utilise a breadth of different methodologies and research designs to facilitate addressing key questions related to the physiotherapy practice. PRI seeks to encourage good quality topical debates on a range of relevant issues and promote critical reflection on decision making and implementation of physiotherapy interventions.
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