Na KR, et al. J Korean Med Sci. 2020 Jun;35(28):e257. https://doi.org/10.3346/jkms.2020.35.e257

For reducing the high mortality rate of severe acute kidney injury (AKI) patients initiating continuous renal replacement therapy (CRRT), diagnosing sepsis and predicting prognosis are essential. However, with reduced renal function, biomarkers for diagnosing sepsis and predicting prognosis are unclear. This study aimed to assess whether C-reactive protein (CRP), procalcitonin, and presepsin could be used to diagnose sepsis and predict mortality in patients with impaired renal function initiating CRRT. This was a single-center, retrospective study involving 127 patients who initiated CRRT. Patients were divided into sepsis and non-sepsis groups according to the SEPSIS-3 criteria. Of the 127 patients, 90 were in the sepsis group and 37 were in the non-sepsis group. Cox regression analysis was performed to determine the association between the biomarkers (CRP, procalcitonin, and presepsin) and survival. CRP and procalcitonin were superior to presepsin for diagnosing sepsis. Presepsin was closely related to the estimated glomerular filtration rate (eGFR) (r = −0.251, p = 0.004). These biomarkers were also evaluated as prognostic markers. Procalcitonin levels ≥3 ng/mL and CRP levels ≥31 mg/L were associated with higher all-cause mortality using Kaplan–Meier curve analysis. (log-rank test p = 0.017 and p = 0.014, respectively). In addition, procalcitonin levels ≥3 ng/mL and CRP levels ≥31 mg/L were associated with higher mortality in univariate Cox proportional hazards model analysis. In conclusion, a higher lactic acid, sequential organ failure assessment score, eGFR, and a lower albumin level have prognostic value to predict mortality in patients with sepsis initiating CRRT. Moreover, among these biomarkers, procalcitonin and CRP are significant factors for predicting the survival of AKI patients with sepsis-initiating CRRT.

Background and aims: Knowledge of the proper dry weight plays a critical role in the efficiency of dialysis and the survival of hemodialysis patients. Recently, bioimpedance spectroscopy(BIS) has been widely used for set dry weight in hemodialysis patients. However, BIS is often misrepresented in clinical healthy weight. In this study, we tried to predict the clinically proper dry weight (DW_CP) using machine learning for patient’s clinical information including BIS. We then analyze the factors that influence the prediction of the clinical dry weight.Methods: As a retrospective, single center study, data of 1672 hemodialysis patients were reviewed. DW_CPdata were collected when the dry weight was measured using the BIS (DW_BIS). The gap between the two (Gap_DW) was calculated and then grouped and analyzed based on gaps of 1 kg and 2 kg.Results: Based on the gap between DW_BISand DW_CP, 972, 303, and 384 patients were placed in groups with gaps of <1 kg, ≧1kg and <2 kg, and ≧2 kg, respectively. For less than 1 kg and 2 kg of GapDW, It can be seen that the average accuracies for the two groups are 83% and 72%, respectively, in usign XGBoost machine learning. As Gap_DWincreases, it is more difficult to predict the target property. As Gap_DWincrease, the mean values of hemoglobin, total protein, serum albumin, creatinine, phosphorus, potassium, and the fat tissue index tended to decrease. However, the height, total body water, extracellular water (ECW), and ECW to intracellular water ratio tended to increase.Conclusions: Machine learning made it slightly easier to predict DW_CPbased on DW_BISunder limited conditions and gave better insights into predicting DW_CP. Malnutrition-related factors and ECW were important in reflecting the differences between DW_BISand DW_CP.

Renal disease associated with type 2 diabetes mellitus (T2DM) has become the leading cause of chronic kidney disease (CKD). Renal ultrasonography is an imaging examination required in the work-up of renal disease. This study aimed to identify the differences in renal ultrasonographic findings between patients with and without DM, and to evaluate the relationship between renal ultrasound findings and renal prognosis in patients with DM. A total of 252 patients who underwent renal ultrasonography at Chungnam National University Hospital were included. Kidney disease progression was defined as a ≥10% decline in the annual estimated glomerular filtration rate (eGFR), which, in this paper, is referred to as ΔeGFR/year, or the initiation of renal replacement therapy after follow-up. The renal scoring system was evaluated by summing up the following items: the value of renal parenchymal echogenicity (0: normal; 1: mildly increased; and 2: increased) and the shape of the cortical margin (0: normal and 1: irregular; right kidney length/height (RH—0 or 1), mean cortical thickness/renal length/height (CKH—0 or 1), and cortical thickness/parenchymal thickness (CK/PK—0 or 1) based on the median: 0—above median, and 1—below median). Patients with DM had thicker renal PKH than those without, despite having lower eGFRs (0.91 ± 0.15, 0.86 ± 0.14, p = 0.006). In the progression group, the renal scores were significantly higher than those from the non-progression group. In the multivariate logistic regression analysis, the higher renal scores, presence of DM, and younger age were independently predicted for renal disease progression after adjusting for confounding variables, such as the presence of hypertension, serum hemoglobin and albumin levels, and UPCR. In conclusion, patients with high renal scores were significantly associated with renal disease progression. Our results suggest that renal ultrasonography at the time of diagnosis provides useful prognostic information in patients with kidney disease.

Kim HR, et al. Restor Dent Endod. 2017 Feb;42(1):27-33. https://doi.org/10.5395/rde.2017.42.1.27

Hyun Jin Kim, M.D., Ph.D., Manmyung Kim, Ph.D., Hyunjae Zhang, M.E., Hae Ri Kim, M.D., Ph.D., Jae Wan Jeon, M.D., Ph.D., Yuri Seo, M.D., Ph.D., and Qute Choi, M.D., Ph.D.. Ann Lab Med 2025;45:178-84. https://doi.org/10.3343/alm.2024.0304