Predictors of Immune Thrombocytopenic Purpura in COVID-19 Positive Elderly Admitted in the Pre-Vaccination Era of the Pandemic; An Analysis Via the 2020 National Inpatient Sample
Bibliographic record
Abstract
Introduction: Immune thrombocytopenia (ITP) involves an autoimmune destruction of platelets secondary to factors such as lupus erythematosus or viral conditions, e.g., COVID-19. With the onset of the pandemic in 2020, several reports of ITP in COVID-19 patients were published. However, to date, very limited information exists on the factors influencing the odds of ITP among the elderly who were COVID-19-positive before the introduction of mass vaccination campaigns. Hereby, we seek to bridge the knowledge gap via an in-depth analysis of hospitalization records. Methods: Our retrospective analysis via the 2020 National Inpatient Sample included admissions of patients ages≥60 years with a primary diagnosis of COVID-19. As the COVID-19 vaccination began on a large scale in December 2020 in the US and the ICD-10 code ‘U071‘ was introduced in April, we restricted our sample to admissions between April to November 2020 for better accuracy of results. Logistic regression models were used to estimate various patient characteristics that affected the odds of a diagnosis of ITP. Results: Between 1st April 2020 and 30th November 2020, there were 507860 patients of 60 and more years hospitalized primarily for COVID-19. An estimated 830 cases developed ITP, with an incidence rate of 163 cases per 100,000 COVID-19-positive patients. Multiple comorbidities such as a history of autoimmune conditions (aOR 1.982, 95% CI 1.483-2.648, p<0.01), chronic kidney disease (aOR 1.652, 95% CI 1.375-1.984, p<0.01), and cirrhosis (aOR 4.119, 95% CI 2.964-5.724, p<0.01) showed higher odds of ITP. Racial disparities were also noted as compared to Whites, Blacks (aOR 0.523, 95% CI 0.417-0.657, p<0.01) and Hispanics (aOR 0.461, 95% CI 0.355-0.600, p<0.01) showed reduced odds. Furthermore, Medicaid insurers also had lower odds (aOR 0.411, 95% CI 0.244-0.694, p<0.01) than those insured by Medicare. Hypertension (aOR 0.747, 95% CI 0.624-0.894, p<0.01) and long-term aspirin use(aOR 0.700, 95% CI 0.572-0.856, p<0.01) also showed fewer events of ITP (Table 1, Figure 1). The mean Charlson Comorbidity Index (CCI) score was higher among those who had ITP (mean score of 4.20 vs. 3.08, p<0.01). Furthermore, patients with ITP also experienced a longer stay (12.29 days vs. 8.34 days, p<0.01). Events of ITP among the elderly with COVID-19 were linked with a poorer prognosis as 27.1% of all patients died (vs. 15.2% in the non-ITP group, aOR mortality 1.880, 95% CI 1.585-2.230, p<0.01). Conclusion: Our results provide the first report of factors linked with the risk of ITP among the elderly with COVID-19. Various conditions such as autoimmunity and racial disparities have previously been reported to influence events of ITP, as also seen in our study. We further report higher odds among patients with cirrhosis and chronic kidney disease, while those with hypertension and long term aspirin use had lower odds. ITP also led to a higher mortality rate. Broader studies can help understand the impact of our findings during the pre-vaccination era, which can help set a template for protocols among the elderly in future pandemics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".