This case highlights the limitations of current prognostication and communication in clinical practice.

We report a case of a 50 year old patient with metastatic melanoma following admission to intensive care unit and later transferred to palliative care unit for end-of-life care.

The patient had clinical improvement despite signs of predictors of death and was later transferred back to care of oncology team.

Physicians frequently overestimate or underestimate survival time which can be distressing to patients and families. There is need for further research to improve the accuracy of these tools for the sake of our patients and their families.

Prognosticating outcomes for traumatic brain injury (TBI) patients is challenging due to the required specialized skills and variability among clinicians. Recent attempts to standardize TBI prognosis have leveraged machine learning (ML) methodologies. This study evaluates the necessity and influence of ML-assisted TBI prognostication through healthcare professionals’ perspectives via focus group discussions.

Two virtual focus groups included ten key TBI care stakeholders (one neurosurgeon, two emergency clinicians, one internist, two radiologists, one registered nurse, two researchers in ML and healthcare and one patient representative). They answered six open-ended questions about their perceptions and potential ML use in TBI prognostication. Transcribed focus group discussions were thematically analyzed using qualitative data analysis software.

The study captured diverse perceptions and interests in TBI prognostication across clinical specialties. Notably, certain clinicians who currently do not prognosticate expressed an interest in doing so independently provided they had access to ML support. Concerns included ML’s accuracy and the need for proficient ML researchers in clinical settings. The consensus suggested using ML as a secondary consultation tool and promoting collaboration with internal or external research resources. Participants believed ML prognostication could enhance disposition planning and standardize care regardless of clinician expertise or injury severity. There was no evidence of perceived bias or interference during the discussions.

Our findings revealed an overall positive attitude toward ML-based prognostication. Despite raising multiple concerns, the focus group discussions were particularly valuable in underscoring the potential of ML in democratizing and standardizing TBI prognosis practices.

Accurate prognostication is important for patients and their families to prepare for the end of life. Objective Prognostic Score (OPS) is an easy-to-use tool that does not require the clinicians’ prediction of survival (CPS), whereas Palliative Prognostic Score (PaP) needs CPS. Thus, inexperienced clinicians may hesitate to use PaP. We aimed to evaluate the accuracy of OPS compared with PaP in inpatients in palliative care units (PCUs) in three East Asian countries.

This study was a secondary analysis of a cross-cultural, multicenter cohort study. We enrolled inpatients with far-advanced cancer in PCUs in Japan, Korea, and Taiwan from 2017 to 2018. We calculated the area under the receiver operating characteristics (AUROC) curve to compare the accuracy of OPS and PaP.

A total of 1,628 inpatients in 33 PCUs in Japan and Korea were analyzed. OPS and PaP were calculated in 71.7% of the Japanese patients and 80.0% of the Korean patients. In Taiwan, PaP was calculated for 81.6% of the patients. The AUROC for 3-week survival was 0.74 for OPS in Japan, 0.68 for OPS in Korea, 0.80 for PaP in Japan, and 0.73 for PaP in Korea. The AUROC for 30-day survival was 0.70 for OPS in Japan, 0.71 for OPS in Korea, 0.79 for PaP in Japan, and 0.74 for PaP in Korea.

Both OPS and PaP showed good performance in Japan and Korea. Compared with PaP, OPS could be more useful for inexperienced physicians who hesitate to estimate CPS.

The use of palliative care (PC) screening criteria to trigger PC consultations may optimize the utilization of PC services, improve patient comfort, and reduce invasive and futile end-of-life care. The aim of the present study was to assess the criterion validity and inter-rater reliability of a PC screening tool for patients admitted to an emergency department intensive care unit (ED-ICU).

Observational retrospective study evaluating PC screening criteria based on the presence of advanced diagnosis and the use of two “surprise questions” (traditional and modified). Patients were classified at ED-ICU admission in four categories according to the proposed algorithm.

A total of 510 patients were included in the analysis. From these, 337 (66.1%) were category 1, 0 (0.0%) category 2, 63 (12.4%) category 3, and 110 (21.6%) category 4. Severity of illness (Simplified Acute Physiology Score III score and mechanical ventilation), mortality (ED-ICU and intrahospital), and PC-related measures (order for a PC consultation, time between admission and PC consultation, and transfer to a PC bed) were significantly different across groups, more evidently between categories 4 and 1. Category 3 patients presented similar outcomes to patients in category 1 for severity of illness and mortality. However, category 3 patients had a PC consultation ordered more frequently than did category 1 patients. The screening criteria were assessed by two independent raters (n = 100), and a substantial interrater reliability was found, with 80% of agreement and a kappa coefficient of 0.75 (95% confidence interval = 0.62, 0.88).

This study is the first step toward the implementation of a PC screening tool in the ED-ICU. The tool was able to discriminate three groups of patients within a spectrum of increasing severity of illness, risk of death, and PC needs, presenting substantial inter-rater reliability. Future research should investigate the implementation of these screening criteria into routine practice of an ED-ICU.

There is no standardized and universally accepted pain classification system for the assessment and management of cancer pain in both clinical practice and research studies. The Edmonton Classification System for Cancer Pain (ECS–CP) is an assessment tool that has demonstrated value in assessing pain characteristics and response. The purpose of our study was to determine the relationship between negative ECS–CP features and some pain-related variables like pain intensity and opioid use. We also explored whether the number of negative ECS–CP features was associated with higher pain intensity.

The electronic charts of 100 patients at an outpatient supportive care clinic in a comprehensive cancer center were reviewed for variables like patient characteristics, initial ECS–CP assessment, morphine equivalent daily dose (MEDD), opioid rotation, Edmonton Symptom Assessment Score (ESAS), and use of adjuvant analgesics.

Some 91 of the 100 charts were eligible for analysis. The most common primary cancer type was gastrointestinal (22.1%). The median pain intensity was 6, and the median MEDD was 45 mg. Neuropathic pain was associated with higher median pain intensity (7 vs. 5, p = 0.007) and median MEDD requirement (83 vs. 30, p = 0.013). Psychological distress was associated with higher median pain intensity (7 vs. 5, p = 0.042). Incident pain was also associated with a trend toward higher pain intensity (6 vs. 5, p = 0.06). A higher number of negative ECS–CP features was associated with higher pain intensity (p = 0.01).

The ECS–CP was successfully completed in the majority of patients, demonstrating its utility in routine clinical practice. Neuropathic pain and psychological distress were associated with higher pain intensity. Also, neuropathic pain was associated with a higher MEDD. A higher sum of negative ECS–CP features was associated with higher pain intensity. Further studies will be needed to verify and explore these observations.