Incidence and Risk Factors for Nontuberculous Mycobacteria Infection after Allogeneic Hematopoietic Cell Transplantation
Bibliographic record
Abstract
Abstract Introduction: Nontuberculous mycobacteria (NTM) are ubiquitous environmental organisms that are increasingly recognized as clinically significant pathogens in the allogenic hematopoietic cell transplanted (alloHCT) population. The incidence of NTM infection post alloHCT has increased from 0.49-1.0% in early studies to 2.8-8.7% in more recent investigations, possibly due to improvements in NTM detection, varying pre-transplant conditioning regimens and regional epidemiology of different NTM species. We investigated incidence and risk factors of NTM infection after alloHCT. Methods & Patients: Medical records for 1097 consecutive patients who underwent alloHCT at Princess Margaret Cancer Centre from 2000 to 2013 were reviewed to determine the frequency, risk factors and outcomes associated with NTM infections. Clinically significant NTM infection was differentiated from colonization according to the American Thoracic Society guidelines, and was classified as pulmonary, non-pulmonary, or disseminated. Acute and chronic graft versus host disease (aGVHD and cGVHD) were diagnosed and graded using established and NIH consensus criteria respectively. The cumulative incidence of NTM was calculated considering competing risks of death. Multivariate analysis comprised Cox proportional hazards regression, modeling NTM risk. Statistical analyses were performed using EZR software (Saitama, Japan). Results: Of 1097 patients, NTM were isolated in 45 (4.1%) and judged clinically significant in 30 (2.7%). The incidence of NTM infection by competing risk analysis was 2.8% at 5 years (95% CI, 1.9-4.0%). The median (range) time to diagnosis was 343 (19-1967) days, and in 83% of patients, was diagnosed within 2 years of alloHCT. Of the 30 clinically significant NTM infections, 28 (93.3%) were pulmonary and 2 (6.7%) were disseminated. With respect to the latter group, one patient had NTM isolated from blood, while the second case was presumed disseminated based on characteristic skin findings, but with no confirmed microbiologic diagnosis. The most common species/groups isolated were Mycobacterium avium complex (n=11, 36.7%), M. xenopi (n=5, 16.7%), and M. fortuitum (n=5, 16.7%). 22/30 patients (73.3%) were on systemic immunosuppression at the time of diagnosis, and 95.7% had concurrent infections (30.4% pulmonary, 17.3% extra-pulmonary, and 47.8% both), with fungal infections occurring most frequently (53.3%). Significant risk factors (HR 95% CI) for NTM included aGVHD grades 2-4 (3.25 [1.33-7.96] p=0.036), cGVHD (3.20 [1.06-9.68] p=0.010), age (1.05 [1.02-1.07], p <0.001), and CMV viremia (4.64 [1.90-11.37] p=0.001). 76.7% of patients with clinically significant NTM had a diagnosis of cGVHD (23/30), in comparison to 47.4% (520/1097) of patients without a diagnosis of NTM infection (p=0.003), and cGVHD severity by NIH global score correlated with NTM risk. Among all patients with cGVHD, severe cGVHD was present in 39% (9/23) of NTM patients, versus 17% (89/520) of non-NTM patients (p=0.012). Pre-alloHCT diagnosis (p=0.34), conditioning regimen (p=0.81), T-cell depletion (p=0.66), HLA matching (p=0.62), or donor type (p=0.63), did not reach statistical significance. Median survival duration after a diagnosis of clinically significant NTM was 398 (range, 20-764) days, with a survival rate of 40.8±10.8% at 2 years. Conclusion: Clinically significant NTM infection after alloHCT was relatively common in our study population. GVHD (acute and chronic), age, and CMV bacteremia were significant risk factors. Given a median survival of approximately 1 year following diagnosis, NTM infection may be of greater clinical significance than previously thought. A high index of suspicion for NTM infection in patients with pulmonary symptoms, particularly within 2 years after HCT and in the presence of cGVHD, may lead to prompt diagnosis and treatment, and potentially better outcomes. Disclosures Lipton: Pfizer: Consultancy, Research Funding; Ariad: Consultancy, Research Funding; Teva: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Novartis Pharmaceuticals: Consultancy, Research Funding. Kim:Novartis Pharmaceuticals: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".