Relationship Between Lactate Levels and Length of Hospital Stay in Infants with Lower Respiratory Tract Infection.
Emek Uyur YalçınFurkan ErdoganEsra TopalSelda SeçimRabia Gönül Sezer YamanelPublished in: Pediatric allergy, immunology, and pulmonology (2022)
Background: Increased lactate concentrations are directly related to the severity of shock and mortality rates. There are limited data regarding the prognostic value of lactate among lower respiratory tract infections. We aimed to investigate the impact of lactate levels on admission on the clinical outcomes of children with lower respiratory tract infections. Methods: We performed a retrospective study of hospitalized patients aged 1-12 months. We recorded data on patient demographics, clinical, laboratory, treatment, and outcomes. The primary outcome measure was the length of hospital stay, and the secondary outcome was transfer to the pediatric intensive care unit (PICU) and/or mortality rates. Results: A total of 304 infants were included in the study. There were 198 infants with lactate levels of >2 mmol/L. Lactic acidosis was present in 6 infants, with a mean hospital stay of 8 ± 3 days. Only 1 (0.3%) patient required intubation, and 5 (1.6%) were transferred to the PICU. The overall mortality rate was 0%. Lactate levels (≤2 and >2 mmol/L) were not related to the length of hospital stay, transfer to PICU/discharge, and the need for intubation (P = 0.16, 0.8, and 0.46, respectively). The length of hospital stay was not correlated with lactate levels on admission (r = 0.01, P = 0.84), pCO2 (r = 0.03, P = 0.52), pH (r = 0.07, P = 0.19), C-reactive protein (r = 0.06, P = 0.28), and oxygen saturation (r = -0.02, P = 0.72). Conclusions: Lactate levels on admission did not predict the length of hospital stay in children with lower respiratory infections and were not related to the need for transfer to the intensive care unit. We suggest using lactate levels in combination with clinical, laboratory, and physical examination findings as predictors of disease severity.
Keyphrases
- respiratory tract
- healthcare
- intensive care unit
- emergency department
- acute care
- young adults
- cardiac arrest
- case report
- type diabetes
- metabolic syndrome
- machine learning
- coronary artery disease
- big data
- cardiovascular disease
- skeletal muscle
- deep learning
- adipose tissue
- insulin resistance
- extracorporeal membrane oxygenation
- artificial intelligence
- smoking cessation
- electron transfer