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To Identify Relatives at Risk for Prolonged Grief Disorder Symptomatology: A Cross-Sectional Study
Charlotte Boven1, Nele Van Den Noortgate1, Let Dillen2
1Department of Geriatric Medicine (C.B., N.V.D.N., L.V.H., R.P.), Ghent University Hospital, Ghent, Belgium.
Context:
While most bereaved can navigate a death-related loss, some face persistent and disabling challenges, a condition known as Prolonged Grief Disorder (PGD). Early identification of PGD can prevent adverse health outcomes. The effectiveness of healthcare providers' clinical intuition versus relatives' self-report surveys in estimating the risk of developing PGD remains uncertain.
Objectives:
The study evaluates the accuracy of healthcare providers' subjective risk estimates and self-reported surveys from relatives, conducted close to the cancer patient's death, in predicting ICD-11 PGD symptomatology seven months postloss.
Methods:
The study involved specialist palliative care healthcare providers and bereaved relatives of oncology patients in ten hospital and five home settings. Subjective risk estimates from healthcare providers (N = 75) and relatives' self-report surveys (N = 75) using the PreLoss Grief-12 (PG-12) were compared with Traumatic Grief Inventory-Self Report Plus (TGI-SR+) total scores seven months postloss. Diagnostic efficacy was evaluated using the Receiver Operating Characteristic and Area Under Curve.
Results:
In the sample of 75 relatives, 8% had ICD-11 PGD symptomatology. The Receiver Operating Characteristic analysis of the PG-12 (N = 75) yielded an Area Under Curve of 0.891, 95% CI [0.782-1], while the healthcare providers' subjective estimates (N = 75) resulted in an Area Under Curve of 0.549, 95% CI [0.299-0.799]. The optimal PreLoss Grief-12 cut-off score for identifying relatives at risk for PGD was 30 with a sensitivity of 100% and a specificity of 64.7%.
Conclusion:
The PG-12 accurately predicted PGD symptomatology seven months postloss, while clinical intuition did not perform better than chance. Future research should compare the predictive value of self-report data with clinical interviews.
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