Hospital admissions and mortality due to complications of injection drug use in two hospitals in Regina, Canada: retrospective chart review
Bibliographic record
Abstract
BACKGROUND: Infectious complications of injection drug use (IDU) often require lengthy inpatient treatment. Our objective was to identify the number of admissions related to IDU in Regina, Canada, as well as describe patient demographics and comorbidities, yearly mortality, readmission rate, and cumulative cost of these hospitalizations between January 1 and December 31, 2018. Additionally, we sought to identify factors that increased risk of death or readmission. METHODS: This study is a retrospective chart review conducted at the two hospitals in Regina. Eligible study cases were identified by querying the discharge database for predetermined International Classification of Diseases code combinations. Electronic medical records were reviewed to assess whether each admission met inclusion criteria, and hospitalization and patient data were subsequently extracted for all included admissions. Mortality data were gleaned from hospital and Ministry of Health databases. Data were analyzed using Excel and IBM SPSS Statistics to identify common comorbidities, admission diagnoses, and costs, as well as to compare patients with a single admission during the study period to those with multiple admissions. Logistic regression analysis was used to identify the relationship between individual variables and in- and out-of-hospital annual mortality. RESULTS: One hundred and forty-nine admissions were included, with 102 unique patients identified. Common comorbidities included hepatitis C (47%), human immunodeficiency virus (HIV) (25%), and comorbid psychiatric disorders (19%). In 23% of all admissions, patients left hospital prior to treatment completion, and 27% of patients experienced multiple admissions. Female patients and those with chronic pain were more likely to be readmitted (p = 0.024 and p = 0.029, respectively). Patients admitted with infective endocarditis were more likely to die during hospitalization (p = 0.0001). The overall mortality was 15% in our cohort. The estimated cumulative cost of inpatient treatment of complications of IDU in Regina was $3.7 million CAD in 2018. CONCLUSION: Patients with history of IDU and hospital admission experience high mortality rates in Regina, a city with paucity of inpatient supports for persons who use injection drugs. Needle syringe programs, opioid agonist therapy, and safe consumption sites have been shown to improve outcomes as well as reduce healthcare costs for this patient population. We will use our findings to advocate for increased access to these harm reduction strategies in Regina, particularly for inpatients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".