Aspartate aminotransferase to platelet ratio at admission can predict the prognosis of patients with hemorrhagic fever with renal syndrome.
Shan HeQunying HanXiaoyun WangXiaoge ZhangNa LiZhengwen LiuPublished in: Journal of medical virology (2023)
Early indicators are needed to predict the prognosis of patients with hemorrhagic fever with renal syndrome (HFRS). Aspartate aminotransferase to platelet ratio index (APRI) has been shown to be related to mortality risk of patients with various diseases. This study evaluated the prognostic value of APRI and other inflammatory scores in HFRS patients. Data of hospitalized HFRS patients from a tertiary hospital in northwest China were collected and the inflammatory scores such as APRI and neutrophil to lymphocyte count ratio (NLR) were calculated at the day of patient admission. Independent factors related to the survival of patients were determined by multivariate logistic regression. Receiver operating characteristic curve was used to analyze the predictive value, and area under the curve (AUC) and 95% confidence interval (CI) were calculated for quantification. Of the 317 HFRS patients included in study, 15 patients died. Age (OR: 1.10, 95% CI: 1.04-1.16, p = 0.001), NLR (OR: 1.11, 95% CI: 1.02-1.19, p = 0.01), and APRI (OR: 1.06, 95% CI: 1.03-1.10, p = 0.001) were quantitative objective factors independently associated with the survival of patients. APRI had an AUC of 0.95 (95% CI: 0.91-1.00, p < 0.001) for predicting the prognosis of patients, with a sensitivity of 93.3% and a specificity of 86.8%. The performance of APRI was better than that of age or NLR. Patients with an APRI ≥ 6.15 had significantly decreased survival compared with those with an APRI < 6.15. In conclusion, this simple index APRI calculated at admission can serve as a biomarker to identify HFRS patients at risk of poor prognosis.
Keyphrases
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- poor prognosis
- peritoneal dialysis
- emergency department
- prognostic factors
- type diabetes
- oxidative stress
- patient reported outcomes
- case report
- coronary artery disease
- long non coding rna
- electronic health record
- artificial intelligence
- deep learning
- data analysis