Impact of Infectious Disease Consultation on the Clinical and Economic Outcomes of Solid Organ Transplant Recipients Admitted for Infectious Complications
Bibliographic record
Abstract
BACKGROUND: There has been a paucity of data on the healthcare resource utilization of infectious disease-related complications in solid organ transplant recipients. The aims of this study were to report the clinical and economic burden of infectious disease-related complications, along with the impact of infectious disease consultation. METHODS: This cohort study evaluated patients requiring admission to a tertiary-care center during 2007, 2008, and 2011. Propensity score matching was used to estimate the effects of patient demographics, comorbidities, and transplant- and infection-related factors on 28-day hospital survival, length of stay (LOS), and medical costs. RESULTS: Infectious disease-related complications occurred in 603 of 1414 (43%) admissions in 306 of 531 (58%) patients. Unadjusted 28-day mortality did not differ between those who received infectious disease consultations vs those who did not (2.9% vs 3.6%, P = .820), however, after propensity score matching, infectious disease consultation resulted in significantly greater 28-day survival estimates (hazard ratio = 0.33; log-rank P = .026), and reduced 30-day rehospitalization rates (16.9% vs 23.9%, P = .036). The median LOS and hospitalization costs were significantly increased for patients receiving an infectious disease consultation than in those managed by the attending team alone (7.0 vs 5.0 days, P = .002, and $9652 vs $6192, P = .003). However, the median LOS (5.5 vs 5.1 days, P = .31) and hospitalization costs ($8106 vs $6912, P = .63) did not differ significantly among those receiving an early infectious disease consultation (<48 hours) vs no consultation, respectively. CONCLUSIONS: Infectious disease consultation in recipients of solid organ transplant is associated with increased LOS and hospitalization costs but decreased mortality and reduced rehospitalization rates. Early consultation with infectious disease specialists decreases healthcare resource utilization compared with delayed referrals.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".