Successful treatment of a kidney transplant patient with COVID-19 and late-onset Pneumocystis jirovecii pneumonia
Bibliographic record
Abstract
BACKGROUND: Solid transplant patients are susceptible to Pneumocystis jirovecii pneumonia (PJP). While the vast majority of PJP cases occur within the first 6 months after transplantation, very few PJP cases are seen beyond 1 year post-transplantation (late-onset PJP). PJP and coronavirus disease 2019 (COVID-19, caused by infection with SARS-CoV-2) share quite a few common clinical manifestations and imaging findings, making the diagnosis of PJP often underappreciated during the current COVID-19 pandemic. To date, only 1 case of kidney transplantation who developed COVID-19 and late-onset PJP has been reported, but this patient also suffered from many other infections and died from respiratory failure and multiple organ dysfunction syndrome. A successful treatment of kidney patients with COVID-19 and late-onset PJP has not been reported. CASE PRESENTATION: We present a case of a 55-year-old male kidney transplant patient with COVID-19 who also developed late-onset PJP. He received a combined treatment strategy, including specific anti-pneumocystis therapy, symptomatic supportive therapy, adjusted immunosuppressive therapy, and use of antiviral drugs/antibiotics, ending with a favorable outcome. CONCLUSIONS: This case highlights the importance of prompt and differential diagnosis of PJP in kidney transplant patients with SARS-CoV-2 infection. Further studies are required to clarify if kidney transplant patients with COVID-19 could be prone to develop late-onset PJP and how these patients should be treated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".