Development and testing of a predictive model of symptoms for pain in community-dwelling frail older people in palliative care.

BACKGROUND

Pain assessment is a necessary step in pain management in older people in palliative care. In older people, pain assessment can be challenging due to underreporting and atypical pain manifestations by other distressing symptoms. Anxiety, fatigue, loss of appetite, nausea, insomnia, dyspnoea, and bowel problems correlate with pain in palliative care patients. Insight into these symptoms as predictors may help to identify the underlying presence of pain. This study aimed to develop and test a prediction model for pain in community-dwelling frail older people in palliative care.

METHODS

In this cross-sectional observational study, community-care nurses from multiple organizations across the Netherlands included eligible patients (life expectancy < 1 year, aged 65+, community-dwelling and frail). The outcome pain and symptoms were assessed by means of the Utrecht Symptom Diary. Also, demographic and illness information, including relevant covariates age, sex and living situation, was collected. Multivariable logistic regression and minimum Akaike Information Criterion(AIC) were used for model development and Receiver Operating Characteristics(ROC)-analysis for model performance. Additionally, predicted probability of pain are given for groups differing in age and sex.

RESULTS

A total of 157 patients were included. The final model consisted of insomnia(Odds Ratio[OR] = 2.13, 95% Confidence Interval[CI] = 1.01-1.30), fatigue(OR = 3.47, 95% CI = 1.11-1.43), sex(female)(OR = 3.83, 95% CI = 2.11-9.81) and age(OR=-1.59, 95% CI = 0.92-1.01) as predicting variables. There is an overall decreasing trend for age, older persons suffer less from pain and females have a higher probability of experiencing pain. Model performance was indicated as fair with a sensitivity of 0.74(95% CI = 0.64-0.83) and a positive predictive value of 0.80(95% CI = 0.70-0.88).

CONCLUSIONS

Insomnia and fatigue are predicting symptoms for pain, especially in women and younger patients. Further testing of the model in external cohorts is needed before clinical adoption.