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Background: The Surprise Question (SQ) identifies patients with palliative care needs. The NECPAL CCOMS-ICO© (NECPAL) tool combines the Surprise Question with additional clinical parameters for a more comprehensive assessment. The capacity of these screening tools to predict mortality is still unknown Aim: To explore the predictive validity of the NECPAL and SQ to determine 12- to 24-month mortality Design: Longitudinal, prospective and observational cohort study. Setting/participants: Three primary care centres, one general hospital, one intermediate care centre, and four nursing homes. Population cohort with advanced chronic conditions and limited life prognosis. Patients were classified according to SQ and NECPAL criteria and followed for 24 months Results: Data available to assess 1059 of 1064 recruited patients (99.6%) at 12 and 24 months: 837 patients were SQ+ and 780 were NECPAL+. Mortality rates at 24 months were as follows: 44.6% (SQ+) versus 15.8% (SQ-) and 45.8% (NECPAL+) versus 18.3% (NECPAL-) (p = 0.000). SQ+ and NECPAL+ identification was significantly correlated with 24-month mortality risk (hazard ratios: 2.719 and 2.398, respectively). Both tools were highly sensitive (91.4, CI: 88.7-94.1 and 87.5, CI: 84.3-90.7) with high negative predictive values (84.2, CI: 79.4-89.0 and 81.7, CI: 77.2-86.2), with low specificity and positive predictive value. The prognostic accuracy of SQ and NECPAL was 52.9% and 55.2%, respectively. The predictive validity was slightly better for NECPAL Conclusion: SQ and NECPAL are valuable screening instruments to identify patients with limited life prognosis who may require palliative care. More research is needed to increase its prognostic utility in combination with other parameters. [ABSTRACT FROM AUTHOR]

The article discusses a study regarding the prevalence and characteristics among advanced chronically ill patients, who are in need of palliative care (PC). Topics include the use of cross-sectional methods and the estimation of needed palliative care, mortality due to chronic diseases, and the focus of PC's predominant model on terminal cancer.