The relationship between physical and psychological symptoms and health care utilization in hospitalized patients with advanced cancer
Bibliographic record
Abstract
BACKGROUND: Patients with advanced cancer often experience frequent and prolonged hospitalizations; however, the factors associated with greater health care utilization have not been described. We sought to investigate the relation between patients' physical and psychological symptom burden and health care utilization. METHODS: We enrolled patients with advanced cancer and unplanned hospitalizations from September 2014-May 2016. Upon admission, we assessed physical (Edmonton Symptom Assessment System [ESAS]) and psychological symptoms (Patient Health Questionnaire 4 [PHQ-4]). We examined the relationship between symptom burden and healthcare utilization using linear regression for hospital length of stay (LOS) and Cox regression for time to first unplanned readmission within 90 days. We adjusted all models for age, sex, marital status, comorbidity, education, time since advanced cancer diagnosis, and cancer type. RESULTS: We enrolled 1,036 of 1,152 (89.9%) consecutive patients approached. Over one-half reported moderate/severe fatigue, poor well being, drowsiness, pain, and lack of appetite. PHQ-4 scores indicated that 28.8% and 28.0% of patients had depression and anxiety symptoms, respectively. The mean hospital LOS was 6.3 days, and the 90-day readmission rate was 43.1%. Physical symptoms (ESAS: unstandardized coefficient [B], 0.06; P < .001), psychological distress (PHQ-4 total: B, 0.11; P = .040), and depression symptoms (PHQ-4 depression: B, 0.22; P = .017) were associated with longer hospital LOS. Physical (ESAS: hazard ratio, 1.01; P < .001), and anxiety symptoms (PHQ-4 anxiety: hazard ratio, 1.06; P = .045) were associated with a higher likelihood for readmission. CONCLUSIONS: Hospitalized patients with advanced cancer experience a high symptom burden, which is significantly associated with prolonged hospitalizations and readmissions. Interventions are needed to address the symptom burden of this population to improve health care delivery and utilization. Cancer 2017;123:4720-4727. © 2017 American Cancer Society.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".