Brorson Hanne H, Arnevik Espen Ajo, Rand Kim
Department of Psychology, University of Oslo, Norway, Oslo.
Department of Substance use Disorder Treatment, Oslo University Hospital, Oslo.
Subst Abuse. 2019 Aug 15;13:1178221819866181. doi: 10.1177/1178221819866181. eCollection 2019.
There is an urgent need for tools allowing therapists to identify patients at risk of dropout. The OQ-Analyst, an increasingly popular computer-based system, is used to track patient progress and predict dropout. However, we have been unable to find empirical documentation regarding the ability of OQ-Analyst to predict dropout. The aim of the present study was to perform the first direct test of the ability of the OQ-Analyst to predict dropout.
Patients were consecutively enlisted in a naturalistic, prospective, longitudinal clinical trial. As interventions based on feedback from the OQ-Analyst could alter the outcome and potentially render the prediction wrong, feedback was withheld from patients and therapists.
The study was carried out during 2011-2013 in an inpatient substance use disorder clinic in Oslo, Norway.
Patients aged 18 to 28 years who met criteria for a principal diagnosis of mental or behavioural disorder due to psychoactive substance use (ICD 10; F10.2-F19.2).
Red signal (predictions of high risk) from the Norwegian version of the OQ-Analyst were compared with dropouts identified using patient medical records as the standard for predictive accuracy.
A total of 40 patients completed 647 OQ assessments resulting in 46 red signals. There were 27 observed dropouts, only one of which followed after a red signal. Patients indicated by the OQ-Analyst as being at high risk of dropping out were no more likely to do so than those indicated as being at low risk. Random intercept logistic regression predicting dropout from a red signal was statistically nonsignificant. Bayes factor supports no association.
The study does not support the predictive ability of the OQ-Analyst for the present patient population. In the absence of empirical evidence of predictive ability, it may be better not to assume such ability.
迫切需要能够帮助治疗师识别有退出治疗风险患者的工具。OQ-分析师是一种越来越受欢迎的基于计算机的系统,用于跟踪患者进展并预测退出情况。然而,我们尚未找到关于OQ-分析师预测退出能力的实证文献。本研究的目的是首次直接测试OQ-分析师预测退出的能力。
患者连续纳入一项自然主义、前瞻性、纵向临床试验。由于基于OQ-分析师反馈的干预可能会改变结果并可能使预测出错,因此未向患者和治疗师提供反馈。
该研究于2011年至2013年在挪威奥斯陆的一家住院物质使用障碍诊所进行。
年龄在18至28岁之间,符合因使用精神活性物质导致精神或行为障碍主要诊断标准的患者(国际疾病分类第10版;F10.2-F19.2)。
将挪威版OQ-分析师的红色信号(高风险预测)与以患者病历为预测准确性标准确定的退出者进行比较。
共有40名患者完成了647次OQ评估,产生了46个红色信号。观察到27例退出,其中只有1例是在红色信号之后。OQ-分析师指出有高退出风险的患者退出的可能性并不比低风险患者更高。从红色信号预测退出的随机截距逻辑回归在统计学上无显著意义。贝叶斯因子支持无关联。
该研究不支持OQ-分析师对当前患者群体的预测能力。在缺乏预测能力的实证证据的情况下,最好不要假定有这种能力。