Risk factors for mortality among kidney transplant recipients with COVID-19 in Saudi Arabia: a case-control study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has profoundly impacted global health, leading to over 74 million confirmed cases and 1.67 million deaths by December 2020. In Saudi Arabia, extensive measures were implemented to mitigate the spread of the virus. Kidney transplant recipients, due to their immunosuppressed status, are particularly vulnerable to severe COVID-19 outcomes. This study aims to identify risk factors associated with mortality in COVID-19-infected kidney transplant patients in Saudi Arabia. The primary objective is to identify mortality risk factors among COVID-19-infected kidney transplant patients. The secondary objective is to compare clinical management and outcomes between deceased and recovered patients. METHODOLOGY: This case-control study matched 82 deceased kidney transplant patients (cases) with 151 survivors (controls). Data were collected from the National Registry for COVID-19 Mortality and King Faisal Specialist Hospital and Research Centre (KFSH&RC) for patients diagnosed between March 2020 and January 2021. Key variables included demographic information, comorbidities, clinical symptoms, and treatment details. Statistical analyses involved chi-square tests and multivariable logistic regression to assess associations with mortality. RESULTS: Among cases, 93.9% required ICU admission, and 95.1% were intubated. Males constituted 73.2% of cases, with 53.7% aged over 60. Cardiovascular comorbidities were more prevalent among cases (97% vs. 87.4%, p = 0.01). and presented more frequently with fever, cough, and respiratory distress. In multivariable analysis, fever, shortness of breath, and desaturation were associated with increased mortality odds. Notably, patients who discontinued immunosuppressive therapy had higher mortality odds (OR = 63.2, p = 0.083), whereas those who held or adjusted their therapy had significantly lower odds (OR = 0.1, p = 0.042; OR = 0.0, p = 0.007). Bacterial infections also increased mortality risk (OR = 56.6, p = 0.009). CONCLUSION: This study identifies critical risk factors for mortality among kidney transplant patients infected with COVID-19 in Saudi Arabia. The findings underscore the need for tailored clinical management strategies to improve outcomes in this vulnerable population. Further research is warranted to explore long-term implications and effective treatment protocols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".