Incidence and Risk Factors for Nontuberculous Mycobacterial Infection after Allogeneic Hematopoietic Cell Transplantation
Bibliographic record
Abstract
Allogenic hematopoietic stem cell transplant (HCT) recipients are at risk of many infections. Nontuberculous mycobacteria (NTM) are increasingly recognized as clinically significant pathogens in this population. We investigated the incidence and risk factors for NTM infection after allogeneic HCT. This retrospective cohort study included all patients with allogeneic HCT at our institution during 2001 to 2013. Patients who developed significant NTM infection (NTM disease) were identified. Multivariable modeling was used to identify risk factors for NTM disease, and a risk score model was constructed to identify high-risk patients. Of 1097 allogeneic HCT patients, 45 (4.1%) had NTM isolated and 30 (2.7%) had NTM disease (28 [93.3%] exclusively pulmonary, 2 [6.7%] pulmonary plus another site). Incidence of NTM infection by competing risk analysis was 2.8% at 5 years (95% CI, 1.9% to 4.0%). The median time to diagnosis was 343 days (range, 19 to 1967). In Fine-Gray proportional hazards modeling, only global severity of chronic graft-versus-host disease (cGVHD) (HR, 1.99; 95% CI, 1.12 to 3.53; P = .019,) and cytomegalovirus (CMV) viremia (HR, 5.77; 95% CI, 1.71 to 19.45; P = .004) were significantly associated with NTM disease. Using these variables a risk score was calculated: 1 point for CMV viremia or moderate cGVHD and 2 points for severe cGVHD. The score divided patients into low risk (0 to 1 points, n = 820 [77.3%], 3-year NTM risk 1.2%), intermediate risk (2 points, n = 161 [15.4%], 3-year NTM risk 7.1%), and high risk (3 points, n = 56 [5.4%], 3-year NTM risk 14.3%). NTM disease after allogeneic HCT is common. Severe cGVHD and CMV viremia are associated with increased risk, permitting risk stratification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".