Analysis of the Eligibility of Patients with Sickle Cell Disease for Palliative Care
Bibliographic record
Abstract
Background: Palliative care (PC) is an approach that improves the quality of life of patients facing problems associated with any disease that leads to multiple or hard to manage symptoms, through the prevention and relief of suffering by early identification and impeccable assessment of physical, psychosocial and spiritual problems. Sickle cell diseases (SCD) consist of a group of congenital diseases characterized by the presence of the sickle hemoglobin (HbS), which can polymerize and predispose to hemolytic and ischemic events. In addition to the acute pain events, sometimes recurrent and hard to control, lesions in target organs commonly occur, leading to multiple comorbidities and serious decrease in quality of life. Aims: To evaluate clinical and demographic factors that could influence physical, psychosocial and spiritual symptom burden and confer eligibility criteria to PC in a group of SCD patients. Methods: Clinical and demographic data of SCD patients were collected by interviews using a standardized questionnaire: diagnosis, time from diagnosis, number of comorbidities and of medical specialties on regular follow up, infectious episodes, need for seeking the emergency and for hospitalization during the last 12 months, delirium events, wounds, dysphagia, recurrent falls, adverse events to medication, quality of communication with the medical team, fears regarding the disease and its complications, religious support, age, gender, monthly household income (MI), level of schooling (SCH) and profession. Specific PC scores were also applied: Edmonton Symptom Assessment Scale (ESAS) and Palliative Performance Scale (PPS). Statistical univariate and multivariate analysis were performed. P value <0.05 was considered statistically significant. This research was approved by the Institutional and National Review Board; written informed consent was obtained from all subjects. Results: Fifty-one patients were evaluated (SS, n=34; SC, n=11; SB+, n=3; SB0, n=3), with a median of age of 40 years (18-69). SB0 patients were considered in the SS subgroup for statistical analysis. According to ESAS, pain scores were worse for patients with a higher age (p=0.0039), higher number of children (p=0.0228), for individuals that had completed only primary school versus patients who received more education (p=0.0293), and for the subjects on follow up with three or more medical specialties (p=0.0411). ESAS evaluation for tiredness revealed worse scores for the patients that had no formal occupation versus those who actively work (p=0.0275). ESAS assessment for depression was worse in patients that had a lower MI (p=0.0080), lower SCH (p=0.0585) and for those who had been to emergency services three or more times during the last year (p=0.0290). ESAS for appetite scored worse for older patients (p=0.0165), for those with a lower MI (p=0.0105) and for the individuals with a higher number of comorbidities (p=0.0018). Regarding shortness of breath, ESAS was poorer for older patients (p=0.0330), for those with a lower MI (p=0.0009), for the subjects in follow up with three or more medical specialties (p=0.0064) and for the patients with lower SCH (p=0.0413). ESAS for best wellbeing was worse for the patients with a lower MI (p=0.0425) and for those with lower SCH (p=0.0314). Due to the low incidence of the following symptoms, no differences were observed between groups for ESAS evaluation of nausea, anxiety and drowsiness. PPS score was significantly better for married individuals (p=0.0490) and for those with higher MI (p=0.0121). Finally, patients with no formal occupation showed a significant higher risk of going to the emergency within twelve months (OR=14.286, CI 95% 1.475-142.857, p=0.0217). Conclusions: This study strongly indicated that SCD patients live with chronic limiting symptoms that significantly impair their quality of life, which make them a group eligible for PC. The results showed that age and a higher number of comorbidities significantly impact symptom burden. Importantly, social and economic factors, in particular a lower monthly household income and a worse level of schooling have a major adverse impact on palliative scores. These data reinforce the negative impact of social inequality in this underprivileged group of patients and the need of facing this problem when seeking global patient care. Disclosures No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".