A Commentary on Albumin in Acidosis

Acidic urine is associated with poor prognosis in patients with bladder cancer undergoing radical cystectomy

Purpose

To assess the prognostic value of acidic urine (low urine pH) in patients with bladder cancer undergoing radical cystectomy.

Materials and methods

We reviewed patients enrolled in the Seoul National University Prospectively Enrolled Registry for Urothelial Cancer-Cystectomy (SUPER-UC-Cx) who underwent radical cystectomy for bladder cancer between March 2016 and December 2020 at the Seoul National University Hospital. During this period, 368 patients were registered in our database. To eliminate confounding factors, we excluded patients diagnosed with non-urothelial cancer and end-stage renal disease.

Results

A total of 351 patients with a mean age of 69.8 ± 10.5 years and median follow-up of 16.0 months were eligible for the analysis. The mean preoperative urine pH was 6.0. The patients were divided into low (pH ≤ 5.5) and high (pH≥6.0) urine pH groups for comparison. All clinicopathological features, including the tumor size, grade, and stage were comparable between the low and high urine pH groups. A Cox regression analysis was performed to assess the independent effect of acidic urine on patient survival. A multivariate analysis showed that high T stage (T3-4) (hazard ratio (HR) 5.18, P<0.001), decreased renal function (estimated glomerular filtration rate <60 mL/min/1.73 m²) (HR 2.29, P=0.003), and low urine pH (≤5.5) (HR 1.69, P=0.05) were associated with shortened recurrence-free survival (RFS). Regarding the overall survival (OS), high T stage (T3-4) (HR 7.15, P<0.001) and low urine pH (≤5.5) (HR 2.66, P=0.029) were significantly associated with shortened survival. A Kaplan–Meier analysis demonstrated that the acidic urine group showed shorter RFS (P=0.04) and OS (P=0.028) than the other groups.

Conclusions

Acidic urine was independently associated with reduced RFS and OS in patients with bladder cancer undergoing radical cystectomy. Acidic urine contributing to an acidic tumor environment may promote aggressive behavior in bladder cancer.

Development of a prognostic model for mortality in COVID-19 infection using machine learning

Coronavirus disease 2019 (COVID-19) is a novel disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has quickly risen since the beginning of 2020 to become a global pandemic. As a result of the rapid growth of COVID-19, hospitals are tasked with managing an increasing volume of these cases with neither a known effective therapy, an existing vaccine, nor well-established guidelines for clinical management. The need for actionable knowledge amidst the COVID-19 pandemic is dire and yet, given the urgency of this illness and the speed with which the healthcare workforce must devise useful policies for its management, there is insufficient time to await the conclusions of detailed, controlled, prospective clinical research. Thus, we present a retrospective study evaluating laboratory data and mortality from patients with positive RT-PCR assay results for SARS-CoV-2. The objective of this study is to identify prognostic serum biomarkers in patients at greatest risk of mortality. To this end, we develop a machine learning model using five serum chemistry laboratory parameters (c-reactive protein, blood urea nitrogen, serum calcium, serum albumin, and lactic acid) from 398 patients (43 expired and 355 non-expired) for the prediction of death up to 48 h prior to patient expiration. The resulting support vector machine model achieved 91% sensitivity and 91% specificity (AUC 0.93) for predicting patient expiration status on held-out testing data. Finally, we examine the impact of each feature and feature combination in light of different model predictions, highlighting important patterns of laboratory values that impact outcomes in SARS-CoV-2 infection.