Impact of Infectious Disease Consultation on Quality of Care, Mortality, and Length of Stay in Staphylococcus aureus Bacteremia: Results From a Large Multicenter Cohort Study
Bibliographic record
Abstract
BACKGROUND: We assessed the impact of infectious disease (ID) consultation on management and outcome in patients with Staphylococcus aureus bacteremia (SAB). METHODS: A retrospective cohort study examined consecutive SAB patients from 6 academic and community hospitals between 2007 and 2010. Quality measures of management including echocardiography, repeat blood culture, removal of infectious foci, and antibiotic therapy were compared between ID consultation (IDC) and no ID consultation (NIDC) groups. A competing risk model with propensity score adjustment was used to compare in-hospital mortality and time to discharge. RESULTS: Of 847 SAB patients, 506 (60%) patients received an ID consultation and 341 (40%) patients did not. Echocardiography was done for 371 (73%) IDC and 191 (56%) NIDC patients (P < .0001) in hospital. Blood cultures were repeated within 2-4 days of bacteremia in 207 (41%) IDC and 107 (31%) NIDC patients (P = .0058). The infectious foci removal rate was not statistically different between the 2 groups. For empiric therapy, 474 (94%) IDC and 297 (87%) NIDC patients received appropriate antibiotics (P = .0013). For patients who finished the planned course of antibiotics, 285 of 422 (68%) IDC and 141 of 262 (54%) NIDC patients received the appropriate duration of antibiotic therapy (P = .0004). In hospital, 204 (24%) patients died: 104 of 506 (21%) IDC and 100 of 341 (29%) NIDC patients. Matched by propensity score, ID consultation had a subdistribution hazard ratio of 0.72 (95% confidence interval [CI], .52-.99; P = .0451) for in-hospital mortality and 1.28 (95% CI, 1.06-1.56; P = .0109) for being discharged alive. CONCLUSIONS: ID consultation is associated with better adherence to quality measures, reduced in-hospital mortality, and earlier discharge in patients with SAB.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".