Curves of expected recovery and their predictive validity for identifying treatment failure

Abstract

De auteurs van dit artikel presenteren 'verwacht-herstelgrafieken' (expected recovery curves) als maatstaf om de voortgang respectievelijk achteruitgang tijdens een psychotherapeutische behandeling van jongeren te 'monitoren'. Kinderen (of hun ouders) en adolescenten die in psychotherapie waren, vulden daartoe regelmatig de Youth outcome questionnaire-30 (YOG-30) in. De scores van alle patie¨nten werden samengenomen in een dataset, waaruit grafieken voor het te verwachten herstel werden afgeleid, uitgaande van de ernst van de symptomen bij intake. Vervolgens werd onderzocht of in het bijzonder verslechtering op deze manier goed te voorspellen was. De resultaten lieten zien dat de toegepaste statistische methode 72% van de patie¨nten met een negatief therapieresultaat betrouwbaar kon identificeren. Daarmee lijkt de YOG-30 een nuttig instrument om therapeuten in hun dagelijkse praktijk regelmatig van feedback over de vorderingen van hun patie¨nten te voorzien, met het oog op verbetering van de behandelresultaten. (Samenvatting door de redactievan dit tijdschrift)

Identifying Youth at Risk for Treatment Failure in Outpatient Community Mental Health Services

“…Psychotherapy research suggests that 5-10% of adult psychotherapy clients can be classified as experiencing deterioration or treatment failure-leaving treatment significantly worse off than when they entered (Lambert and Bergin 1994;Mohr 1995). Similar estimates of deterioration have been found for child and adolescent populations in managed care settings (Bishop et al 2005;Bybee et al 2007), and rates may be even higher for children and adolescents in traditional community mental health settings (Weisz et al 1995). In a related vein, Lilienfeld (2007) asserted that greater emphasis should be placed on research identifying potentially harmful treatments than on identifying empiricallysupported therapies.…”

“…Utilizing empirically-derived change trajectories based on multilevel modeling (MLM), Bybee et al (2007) tested the accuracy of a similar warning system using a large archival database of children and adolescents served in a managed care setting. In this study, the warning system accurately identified 72% of youth patients who ultimately ended treatment with a negative outcome.…”

Predicting patient deterioration in youth mental health services: community mental health vs. managed care settings

“…We used cutoff scores at each measurement occasion to identify at-risk cases (c.f. Bybee et al, 2007;Cannon, Warren, Nelson, & Burlingame, 2010;Finch et al, 2001). We intended these cutoffs to identify the most extreme portion of scores that were worse than expected, on any measurement occasion.…”

“…For example, Lambert, Whipple, Bishop et al (2002) reported overall hit rates with adult patients of .79 and .83 for rationally derived and empirically derived methods, respectively. Similarly, Bishop et al (2005) reported an overall hit rate of .81 for a combined sample of youth served in residential and outpatient settings, and Bybee et al (2007) achieved an overall hit rate of .88 for youth in outpatient managed care settings. Relative to these studies, the overall hit rate for the present study (.75) may be a conservative estimate due to the methodology we employed for including scores that could be used as an alert signal.…”

“…In addition, comparable research in adults suggests that empirically derived algorithms for predicting treatment failure (those using empirical outcome data and average change trajectories for patients with similar symptom levels) tend to be more accurate than those that use rationally derived methods (Lambert, Whipple, Bishop et al, 2002;Spielmans, Masters, & Lambert, 2006). Subsequently, Bybee, Lambert, and Eggett (2007) tested an empirically derived method (based on multilevel modeling of empirically based change trajectories) for identifying youth at risk for treatment failure in an outpatient managed care setting, accurately identifying 72% of deteriorators. However, several factors limited the interpretability of these findings, such as the combination of parent and youth self-report data in the analyses and the neglect of potentially important predictors in the models such as patient age, diagnosis, and treatment setting variables.…”