Trends in Heparin-Induced Thrombocytopenia in the Pre-Pandemic and Peak-Pandemic Era, and Impact of COVID-19 on Mortality; An Analysis Via the National Inpatient Sample
Notice bibliographique
Résumé
Introduction: The recent pandemic of COVID-19 caused a significant upheaval in access to healthcare and resources across the world. The COVID-19 infection has also been associated with a hypercoagulable state that can alter multiple coagulation pathways and cause various cerebrovascular and cardiovascular insults, thus requiring the need for heparin products. While a recent study found a decreasing trend of Heparin-induced thrombocytopenia(HIT) between 2011-2014, there have yet to be any recent studies investigating the pandemic's role on HIT trends and the overall effect of COVID-19 on the mortality of these patients. We, therefore, used the National Inpatient Sample (NIS) to assess the impact of the pandemic on HIT. Methods: The 2016-2020 NIS, a set of yearly discharge records released in de-identified form with various patient characteristics, discharge and procedural codes, was used for our study. We included cases with a diagnosis for HIT via the International Classification of Diseases, Tenth Revision (ICD-10) code “D7582”. Patients with COVID-19 and other comorbidities that can influence the outcomes were also identified via their respective ICD-10 codes from past studies. The incidence of HIT per 100,000 hospitalizations between 2016-2020 was compared and linear-by-linear analysis was used to assess trends, as in previous studies. We further proceeded to compare the trends in mortality rates among HIT patients. Finally, the odds of mortality among HIT patients with and without COVID-19, restricting the sample to HIT patients from 2020, were estimated via multivariable regression models. SPSS 29.0 was used for our analyses, and a p-value <0.05 was set for statistical significance. Results: Our study included a total of 72930 cases of HIT between January 1st 2016- December 31st 2020, with 16780 cases in 2016, 16450 cases in 2017, 14590 cases in 2018, 12980 cases in 2019, and 12130 cases in 2020. The overall adjusted incidence of HIT dropped between 2016-2019 from 55.6 cases per 100,000 (in 2016) hospitalizations to 43.0 cases per 100,000 hospitalizations (in 2019). However, a rise was seen in 2020 with 43.9 cases per 100,000 hospitalizations (ptrend<0.01). On average, HIT was reported in 49.1 cases per 100,000 adult hospitalizations. However, in 2020, among the 12130 cases of HIT, there were 1745(14.4%) COVID-19 positive patients. The adjusted incidence of HIT in 2020 without COVID-19 cases of HIT was 37.6 cases per 100,000 hospitalizations. The adjusted analysis (excluding COVID-19 positive HIT cases), showed a continuous decrease in incidence of HIT between 2016-2020 (Figure 1a). Meanwhile, the mortality rates among HIT patients were 10.2% in 2016, 11.9% in 2017, 11.0% in 2018, 10.5% in 2019 and 16.2% in 2020 (ptrend<0.01). However, in 2020 the mortality rate among non-COVID-19 positive HIT patients was 10.4%, with no statistical differences between the years (ptrend=0.440) (Figure 1b). Finally, among the HIT cases of 2020, concurrent presence of COVID-19 was linked with a higher odds of mortality (50.4% vs. 10.4%, aOR 8.471, 95% CI 7.405-9.690, p<0.01). Conclusion: Our study provides a fresh analysis of the burden of the COVID-19 pandemic on the incidence and outcomes of HIT in the United States. While a decreasing incidence of HIT was seen between 2016-2020 for cases without COVID-19, in 2020, patients with the virus contributed to a rise in the overall incidence and mortality rates. COVID-19 infection has a massive impact on coagulation profiles, and while our data covers the pre-vaccination period, we strongly advocate for research via future NIS databases to better understand the progress of the pandemic on HIT in 2021 and onwards.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 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 ».