Clinical predictors of acute cardiac injury and normalization of troponin after hospital discharge from COVID-19: Predictors of acute cardiac injury recovery in COVID-19

Joyce Q. Lu, Justin Y. Lu, Weihao Wang, Yuhang Liu, Alexandra Buczek, Roman Fleysher, Wouter S. Hoogenboom, Wei Zhu, Wei Hou, Carlos J. Rodriguez, Tim Q. Duong

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Background: Although acute cardiac injury (ACI) is a known COVID-19 complication, whether ACI acquired during COVID-19 recovers is unknown. This study investigated the incidence of persistent ACI and identified clinical predictors of ACI recovery in hospitalized patients with COVID-19 2.5 months post-discharge. Methods: This retrospective study consisted of 10,696 hospitalized COVID-19 patients from March 11, 2020 to June 3, 2021. Demographics, comorbidities, and laboratory tests were collected at ACI onset, hospital discharge, and 2.5 months post-discharge. ACI was defined as serum troponin-T (TNT) level >99th-percentile upper reference limit (0.014ng/mL) during hospitalization, and recovery was defined as TNT below this threshold 2.5 months post-discharge. Four models were used to predict ACI recovery status. Results: There were 4,248 (39.7%) COVID-19 patients with ACI, with most (93%) developed ACI on or within a day after admission. In-hospital mortality odds ratio of ACI patients was 4.45 [95%CI: 3.92, 5.05, p<0.001] compared to non-ACI patients. Of the 2,880 ACI survivors, 1,114 (38.7%) returned to our hospitals 2.5 months on average post-discharge, of which only 302 (44.9%) out of 673 patients recovered from ACI. There were no significant differences in demographics, race, ethnicity, major commodities, and length of hospital stay between groups. Prediction of ACI recovery post-discharge using the top predictors (troponin, creatinine, lymphocyte, sodium, lactate dehydrogenase, lymphocytes and hematocrit) at discharge yielded 63.73%-75.73% accuracy. Interpretation: Persistent cardiac injury is common among COVID-19 survivors. Readily available patient data accurately predict ACI recovery post-discharge. Early identification of at-risk patients could help prevent long-term cardiovascular complications. Funding: None

Original languageEnglish (US)
Article number103821
JournalEBioMedicine
Volume76
DOIs
StatePublished - Feb 2022

Keywords

  • Machine learning
  • SARS-CoV-2
  • acute myocardial injury
  • heart failure

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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