Causes of acute respiratory failure in the immunocompromised host
Bibliographic record
Abstract
PURPOSE OF REVIEW: A wide spectrum of heterogeneous conditions can render a patient immunocompromised. Recent years have seen an increase in the number of immunocompromised patients given the earlier detection of conditions that require immunosuppressive therapies, changes in immunosuppressive regimens leading to increased survival or novel therapeutic advancements in oncologic care. Acute respiratory failure (ARF) is the leading cause of critical illness and mortality in this population. This review highlights the spectrum of causes of ARF in immunocompromised patients with a particular focus on acute toxicities of novel oncologic treatments. RECENT FINDINGS: Recent years have seen improved survival amongst critically ill immunocompromised patients with ARF. This is likely attributable to patient selection of immunosuppressive therapy, improved noninvasive microbiologic diagnostic techniques, improved antimicrobial prophylaxis, treatment, stewardship, and advancements in supportive care including intensive care. Infectious complications remain the leading cause of ARF in this population. However, one of the greatest challenges physicians continue to face is accurate identification of the cause of ARF, given the vast (and increasing) noninfectious causes of ARF across these patients. Emerging therapies, such as immune checkpoint inhibitors (ICIs) and chimeric antigen receptor T-cell therapy (CAR T-cell) have contributed to this problem. Finally, undetermined ARF is reported in approximately 13% of immunocompromised and is associated with a worse prognosis. SUMMARY: Infectious complications are still the leading cause of ARF in immunocompromised patients. However, noninfectious complications, derived from the underlying disease or treatment, should be always considered, including novel therapies, such as ICIs and CAR T cells. Further research should focus in improving the diagnostic rate in this subgroup.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.002 |
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".