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Severity predictors for multisystemic inflammatory syndrome in children after SARS-CoV-2 infection in Vietnam

  • Dien. M. Tran,
  • Dem. V. Pham,
  • Tung. V. Cao,
  • Canh. N. Hoang,
  • Ha. T. T. Nguyen,
  • Giang. D. Nguyen,
  • Cuong. N. Le,
  • Quan. Q. Thieu,
  • Tuan. A. Ta,
  • Hung. V. Dau,
  • Chi. Q. Le,
  • Quang. H. Le,
  • Nghiem. T. Luong,
  • Mai. T. Tran,
  • Phu. H. Nguyen,
  • Nhung. T. Nguyen,
  • Phuc. H. Phan

摘要

Multisystemic inflammatory syndrome in children (MIS-C) might manifest in a broad spectrum of clinical scenarios, ranging from mild features to multi-organ dysfunction and mortality. However, this novel entity has a heterogenicity of data regarding prognostic factors associated with severe outcomes. The present study aimed to identify independent predictors for severity by using multivariate regression models. A total of 391 patients (255 boys and 136 girls) were admitted to Vietnam National Children’s Hospital from January 2022 to June 2023. The median age was 85 (range: 2–188) months, and only 12 (3.1%) patients had comorbidities. 161 (41.2%) patients required PICU admission, and the median PICU LOS was 4 (2–7) days. We observed independent factors related to PICU admission, including CRP \(\ge \) 50 (mg/L) (OR 2.52, 95% CI 1.39–4.56, p = 0.002), albumin \(\le \) 30 (g/L) (OR 3.18, 95% CI 1.63–6.02, p = 0.001), absolute lymphocyte count \(\le \) 2 (× 109/L) (OR 2.18, 95% CI 1.29–3.71, p = 0.004), ferritin ≥ 300 (ng/mL) (OR 2.35, 95% CI 1.38–4.01), p = 0.002), and LVEF < 60 (%) (OR 2.48, 95% CI 1.28–4.78, p = 0.007). Shock developed in 140 (35.8%) patients, especially for those decreased absolute lymphocyte \(\le \) 2 (× 109/L) (OR 2.48, 95% CI 1.10–5.61, p = 0.029), albumin \(\le \) 30 (g/L) (OR 2.53, 95% CI 1.22–5.24, p = 0.013), or LVEF < 60 (%) (OR 2.24, 95% CI 1.12–4.51, p = 0.022). In conclusion, our study emphasized that absolute lymphocyte count, serum albumin, CRP, and LVEF were independent predictors for MIS-C severity. Further well-designed investigations are required to validate their efficacy in predicting MIS-C severe cases, especially compared to other parameters. As MIS-C is a new entity and severe courses may progress aggressively, identifying high-risk patients optimizes clinicians' follow-up and management to improve disease outcomes.