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
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».