Effects of advance care planning in care dependent community-dwelling older persons (STADPLAN): A cluster-randomised controlled trial

Measuring patients' medical treatment preferences in advance care planning: development and validation of the Treat-Me-ACP instrument - a secondary analysis of a cluster-randomized controlled trial

BACKGROUND

Advance Care Planning interventions should be evaluated as broadly as possible to gain a holistic understanding of the Advance Care Planning process. However, validated early stage outcome instruments are lacking. Therefore, the Treatment-Preference-Measure-Advance Care Planning (Treat-Me-ACP) instrument was developed and validated as part of the cluster-randomized controlled trial STADPLAN (Study on Advance Care Planning in care-dependent community-dwelling older persons) to assess the effects of Advance Care Planning interventions on patients' medical treatment preferences.

METHODS

The design of Treat-Me-ACP is based on the Emanuel Medical Directive and the Life Support Preferences Questionnaires. Using a multi-stage team approach a preliminary version of the Treat-Me-ACP was developed and pre-tested. The pre-tested instrument consists of one global medical care goal-item, five hypothetical scenarios with five hypothetical treatments, and one how would you feel-item within each scenario. A total of five scenario preference scores and five treatment preference scores can be formed. This version was subsequently applied to a subsample of the STADPLAN project (n = 80) to assess patient's preferences at baseline (T0) and at 12-month follow-up (T2). The further validation steps were based on this subsample and included: (1) acceptance by using completion rate and frequencies of missing data, (2) internal consistency by using Cronbach's α to test whether it was possible to create preference scores by scenario and treatment, (3) concurrent validation examining the association between the global medical care goal-item and the preference scores and the association between the how would you feel-items and the scenario preference scores, and (4) responsiveness of the instrument to changes in preferences for life-sustaining treatments by comparing preference scores from T0 to T2 between study groups.

RESULTS

Acceptance of the instrument was high. Results of concurrent validation indicate that the five scenarios represent the global medical care goal well. The preference scores showed an average tendency for decreasing preferences for life-sustaining treatments across all scales for the intervention group during study follow-up.

CONCLUSIONS

The Treat-Me-ACP can be used to evaluate the dynamics of patients' medical treatment preferences in Advance Care Planning. It has been validated for care-dependent community-dwelling older persons and can be used as an additional outcome measure in evaluating the effectiveness of ACP interventions.

TRIAL REGISTRATION

German Clinical Trials Register: DRKS00016886 on 04/06/2019.

Complex advance care planning intervention in general practice (ACP-GP): a cluster-randomised controlled trial

BACKGROUND

Advance care planning (ACP) is an iterative communication process about patients' preferences for future care. In general practice, there are barriers to ACP at patient, GP, and healthcare-system levels. A complex intervention may be necessary to reduce barriers.

AIM

To evaluate the effects of a complex ACP intervention for patients with chronic, life-limiting illness in general practice (ACP-GP).

DESIGN AND SETTING

A cluster-randomised controlled trial was undertaken in Belgian general practice.

METHOD

ACP-GP included a patient workbook, GP training, ACP conversations, and a documentation template. The control group received usual care. Outcomes were the 15-item ACP Engagement Survey for patients and the ACP Self-Efficacy scale for GPs. Linear mixed models evaluated differences at 3 months (T1, effectiveness evaluation) and 6 months (T2) post-baseline. Analysis was intention-to-treat.

RESULTS

In total, 35 GPs and 95 patients were randomised. Patient ACP engagement did not differ between the intervention and control group at T1 (baseline-adjusted mean difference = 0.34; 95% confidence interval [CI] = -0.02 to 0.69; P = 0.062) or T2 (baseline-adjusted mean difference = 0.20; 95% CI = -0.17 to 0.57; P = 0.28). For GP ACP self-efficacy, there were no significant differences between groups at T1 (baseline-adjusted mean difference = 0.16; 95% CI = -0.04 to 0.35; P = 0.11) or at T2 (baseline-adjusted mean difference = 0.11; 95% CI = -0.09 to 0.31; P = 0.27).

CONCLUSION

ACP-GP did not improve patient engagement and GP self-efficacy more than usual care. Both groups showed patterns of increase from baseline. Trial procedures and the COVID-19 pandemic may have increased awareness about ACP.