A Statistical Model for Early Recognition of Patients Requiring Transfer to Palliative Care (ERPAC)

Abstract

Objective: To develop a scoring system to identify patients at an early stage who will need palliative care during intensive care follow-up.Study Design: Analytical study.Place and Duration of Study: Ankara City Hospital, Neurology and Orthopaedics Hospital, General Intensive Care Unit, Ankara, Turkiye, from June 2019 to March 2020.Methodology: Intensive care patients were enrolled and divided into palliative care transfer (p1) and nontransfer groups (p2). The predicted logit value / probality score was calculated and a scoring system was developed, using the formula value, [logit=-3.275 + 0.194 (days of hospitalisation) -0.345 (SOFAmax) +1.659 (ward admission) + 2.08 (cancer)].Results: One hundred and thirty five patients were analysed. Sixty-eight (50.4%) were males. The mean age was 67.2 +/- 17.2 years. Length of hospital stay (p<0.001), highest sequential organ failure score (SOFAmax, p<0.001), previous hospitalisation (p=0.015), and cancer history (p=0.009) affect the need for palliative care significantly. Predicted probability = epredicted togit / 1+epredicted logit If predicted probabilty >0.5, patient was candidate for palliative care transfer.Conclusion: Every intensive care unit can calculate its own logit value and represent ERPAC score. ERPAC scores can predict which patients will be transferred to palliative care. Predictedlogit value will help to recognise which patients will need palliative care at an early stage.