Treatment Outcomes for Right-Sided Endocarditis in Intravenous Drug Users: A Systematic Review and Analysis of Outcomes in a Tertiary Centre
Bibliographic record
Abstract
BACKGROUND: The increasing prevalence of intravenous drug users (IVDU) has resulted in higher incidence of right-sided infective endocarditis (RSIE). However, treatment guidelines for RSIE in IVDU are not well defined. The aim is to evaluate efficacy of different treatment strategies in reducing mortality and to describe treatment outcomes. METHODS: We systematically reviewed the literature using PubMed, Cochrane, CENTRAL, OvidEMBASE, Web of Science, and Medline databases to include prospective studies that compare mortality rates among IVDU with RSIE receiving isolated medical treatment versus those receiving medical-surgical treatment. In conjunction, analysis of 27 RSIE patients (including IVDU) treated at authors' institution was done to supplement the findings. Kaplan-Meier survival rates following hospital admission and cumulative incidence estimates for hospital re-admission were obtained. RESULTS: A total of nine studies (all with low or marginal risk of bias) met inclusion criteria. The prevalence of RSIE among IVDU with infective endocarditis varied from 34% to 100%. Seven studies compared medical versus medical-surgical therapy with less than 30% needing surgery. Mortality was higher in patients receiving surgical therapy. There were 27 RSIE (16 non-IVDU and 11 IVDU) analyzed at the authors' institution. Survival at 30 days, 1 year, and 3 years were 89%, 82%, and 78%, respectively, and repeat hospitalization for recurrent endocarditis were 8%, 17%, and 23%, respectively. CONCLUSIONS: There is paucity around optimal RSIE management strategy for IVDU that can decrease mortality. Surgical management of RSIE may be associated with increased mortality over medical management mainly due to advanced surgical indications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".