Comparison of intuitive assessment and palliative care screening tool in the early identification of patients needing palliative care

Abstract

The intuitive assessment of palliative care (PC) needs and Palliative Care Screening Tool (PCST) are the assessment tools used in the early detection of patients requiring PC. However, the comparison of their prognostic accuracies has not been extensively studied. This cohort study aimed to compare the validity of intuitive assessment and PCST in terms of recognizing patients nearing end-of-life (EOL) and those appropriate for PC. All adult patients admitted to Taipei City Hospital from 2016 through 2019 were included in this prospective study. We used both the intuitive assessment of PC and PCST to predict patients’ 6-month mortality and identified those appropriate for PC. The c-statistic value was calculated to indicate the predictive accuracies of the intuition and PCST. Of 111,483 patients, 4.5% needed PC by the healthcare workers’ intuitive assessment, and 6.7% had a PCST score ≥ 4. After controlling for other covariates, a positive response ‘yes’ to intuitive assessment of PC needs [adjusted odds ratio (AOR) = 9.89; 95% confidence interval (CI) 914–10.71] and a PCST score ≥ 4 (AOR = 6.59; 95%CI 6.17–7.00) were the independent predictors of 6-month mortality. Kappa statistics showed moderate concordance between intuitive assessment and PCST in predicting patients' 6-month mortality (k = 0.49). The c-statistic values of the PCST at recognizing patients’ 6-month mortality was significantly higher than intuition (0.723 vs. 0.679; p < 0.001). As early identification of patients in need of PC could improve the quality of EOL care, our results suggest that it is imperative to screen patients’ palliative needs by using a highly accurate screening tool of PCST.

Calling emergency medical services for terminally ill patients: a qualitative study exploring reasons why informal caregivers make the call

“…The novel screening tool, Rapid-PCST, was based on a validated palliative care screening tool designed for hospital settings [25,26]. Using the Rapid-PCST, medical dispatchers can label incoming calls as a patient with palliative care needs based on the information about the patient provided by the caller.…”

Indian Society of Critical Care Medicine and Indian Association of Palliative Care Expert Consensus and Position Statements for End-of-life and Palliative Care in the Intensive Care Unit

“… 108 , 109 In a large cohort of patients, the superiority of PC screening tool over intuitive assessment was demonstrated. 110 In a meta-analysis, SQ performs poorly to modestly predicting death, performing worse in noncancer illnesses. 111 Combining SQ with objective prediction scores appears advantageous.…”

“… 111 Combining SQ with objective prediction scores appears advantageous. 110 , 112 Integrating patient's values and preferences is the model of SDM. 113 Recently, the use of large patient databases [Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC II)] 114 and machine learning, mining all of the patients’ clinical data promises greater prognostic precision.…”

Predicting Patient Mortality for Earlier Palliative Care Identification in Medicare Advantage Plans: Features of a Machine Learning Model

“…After these parameters were tuned, we returned to training split 1 to tune the number of estimators (n_estimators) using early stopping (early_stopping). Key parameters included learning_rate (0.05), n_estimators (550), num_leaves (16), max_depth (no limit), min_child_samples (10), and early_stopping_rounds (200). Both the training and holdout partitions had similar mortality rates of 4% in 2018, indicating the mortality outcome was not biased nor skewed in either the training or validation step.…”

“…One major cause of lower uptake involves unreliability in provider identification of patients who are appropriate for palliative care. Research shows a clinician's intuition alone is not the most effective method for recognizing individuals in general practice who could benefit from palliative services [10][11][12]. Standardized screening tools that rely primarily on diagnostic criteria, medical record information, and patient-reported needs can promote better reliability in clinician identification of palliative patients [13][14][15][16][17][18][19][20].…”