There is a growing interest in research aimed at better understanding the disease status or predicting the prognosis of patients with simple blood tests associated with systemic inflammation. The neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR), platelet-lymphocyte ratio (PLR), and mean platelet volume (MPV) can be used as factors to determine the prognosis of patients in various clinical situations. However, reference values for these attributes based on large, healthy populations have yet to be determined. From January 2014 to December 2016, data from routine blood analyses were collected from healthy patients in the checkup center of a tertiary hospital in Seoul, South Korea. Retrospective data review was then performed on an electronic medical record system. Data were treated anonymously as only age, sex, body mass index, medical history including cancer diagnosis, medications, and smoking status were considered. After the initial screen, we had a collection of 12,160 samples from patients without any medical history, including cancer treatment. This patient pool consisted of 6268 (51.5%, median age 47 years) and 5892 (48.5%, median age 46 years) male and female patients, respectively. The mean NLR across all ages was 1.65 (0.79), and the values for men and women were 1.63 (0.76) and 1.66 (0.82), respectively. The mean LMR, PLR, and MPV were 5.31 (1.68), 132.40 (43.68), and 10.02 (0.79), respectively. This study provides preliminary reference data on LMR, PLR, and MPV from different age and sex groups in South Korea. The results suggest that different cutoff values should be applied to the various patient populations.

Lee Y, et al. Clin Nutr Res. 2015 Jan;4(1):32-40. http://dx.doi.org/10.7762/cnr.2015.4.1.32

Objective : Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Approach : Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. Main results : HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Significance : Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

Lim SH, et al. Yonsei Med J. 2005 Feb;46(1):21-26. http://dx.doi.org/10.3349/ymj.2005.46.1.21