Serum Lactate Dehydrogenase Level One Week after Admission Is the Strongest Predictor of Prognosis of COVID-19: A Large Observational Study Using the COVID-19 Registry Japan.
Sho NakakuboYoko UnokiKoji KitajimaMari TeradaHiroyuki GatanagaNorio OhmagariIsao YokotaSatoshi KonnoPublished in: Viruses (2023)
Clinical features of COVID-19 are diverse, and a useful tool for predicting clinical outcomes based on clinical characteristics of COVID-19 is needed. This study examined the laboratory values and trends that influence mortality in hospitalised COVID-19 patients. Data on hospitalised patients enrolled in a registry study in Japan (COVID-19 Registry Japan) were obtained. Patients with records on basic information, outcomes, and laboratory data on the day of admission (day 1) and day 8 were included. In-hospital mortality was set as the outcome, and associated factors were identified by multivariate analysis using the stepwise method. A total of 8860 hospitalised patients were included. The group with lactate dehydrogenase (LDH) levels >222 IU/L on day 8 had a higher mortality rate compared to the group with LDH levels ≤222 IU/L. Similar results were observed in subgroups formed by age, body mass index (BMI), underlying disease, and mutation type, except for those aged <50 years. When age, sex, BMI, underlying disease, and laboratory values on days 1 and 8 were tested for factors strongly associated with in-hospital mortality, LDH on day 8 was most strongly associated with mortality. LDH level on day 8 was the strongest predictor of in-hospital mortality in hospitalised COVID-19 patients, indicating its potential usefulness in post-treatment decision-making in severe COVID-19 cases.
Keyphrases
- sars cov
- coronavirus disease
- body mass index
- end stage renal disease
- chronic kidney disease
- newly diagnosed
- emergency department
- ejection fraction
- respiratory syndrome coronavirus
- cardiovascular events
- peritoneal dialysis
- type diabetes
- randomized controlled trial
- risk factors
- prognostic factors
- cardiovascular disease
- physical activity
- community acquired pneumonia
- metabolic syndrome
- clinical trial
- coronary artery disease
- weight loss
- electronic health record
- early onset
- machine learning
- combination therapy