To clarify the demographic and clinicolaboratory features of postdialysis fatigue (PDF), we enrolled 85 patients on maintenance hemodialysis in a cross-sectional study using validated questionnaires and chart review. Forty-three patients complained of fatigue after dialysis. On formal testing using the Kidney Disease Questionnaire, the PDF group had statistically greater severity of fatigue and somatic complaints than the group of patients without subjective fatigue (P = 0.03 and 0.04, respectively). On a scale measuring intensity of fatigue (1 = least to 5 = worst), the PDF group average was 3.4 +/- 1.2. PDF subjects reported that 80% +/- 25% of dialysis treatments were followed by fatigue symptoms. In 28 (65%) of patients, the symptoms started with the first dialysis treatment. They reported needing an average of 4.8 hours of rest or sleep to overcome the fatigue symptoms (range, 0 to 24 hours). There were no significant differences between patients with and without PDF in the following parameters: age; sex; type of renal disease; presence of diabetes mellitus, heart disease (congestive, ischemic), or chronic obstructive lung disease; blood pressure response to dialysis; type or adequacy of dialysis regimen; hematocrit; electrolytes; blood urea nitrogen; creatinine; cholesterol; albumin; parathyroid hormone; ejection fraction; and use of antihistamines, benzodiazepines, and narcotics. In the fatigue group, there was significantly greater use of antihypertensive medications known to have fatigue as a side effect (P = 0.007). Depression was more common in the fatigue group by Beck Depression score (11.6 +/- 8.0 v 7.8 +/- 6.3; P = 0.02). We conclude that (1) postdialysis fatigue is a common, often incapacitating symptom in patients on chronic extracorporeal dialysis; (2) no routinely measured parameter of clinical or dialytic function appears to predict postdialysis fatigue; and (3) depression is highly associated with postdialysis fatigue, but the cause-effect relationship is unclear.

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Raman chemometric urinalysis (Rametrix™) was used to analyze 235 urine specimens from healthy individuals. The purpose of this study was to establish the “range of normal” for Raman spectra of urine specimens from healthy individuals. Ultimately, spectra falling outside of this range will be correlated with kidney and urinary tract disease. Rametrix™ analysis includes direct comparisons of Raman spectra but also principal component analysis (PCA), discriminant analysis of principal components (DAPC) models, multivariate statistics, and it is available through GitHub as the Rametrix™ LITE Toolbox for MATLAB®. Results showed consistently overlapping Raman spectra of urine specimens with significantly larger variances in Raman shifts, found by PCA, corresponding to urea, creatinine, and glucose concentrations. A 2-way ANOVA test found that age of the urine specimen donor was statistically significant (p < 0.001) and donor sex (female or male identification) was less so (p = 0.0526). With DAPC models and blind leave-one-out build/test routines using the Rametrix™ PRO Toolbox (also available through GitHub), an accuracy of 71% (sensitivity = 72%; specificity = 70%) was obtained when predicting whether a urine specimen from a healthy unknown individual was from a female or male donor. Finally, from female and male donors (n = 4) who contributed first morning void urine specimens each day for 30 days, the co-occurrence of menstruation was found statistically insignificant to Rametrix™ results (p = 0.695). In addition, Rametrix™ PRO was able to link urine specimens with the individual donor with an average of 78% accuracy. Taken together, this study established the range of Raman spectra that could be expected when obtaining urine specimens from healthy individuals and analyzed by Rametrix™ and provides the methodology for linking results with donor characteristics.

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Blood glucose, plasma sodium, bicarbonate (HCO3-), vasopressin, and hematocrit were monitored before and during treatment in patients with uncontrolled insulin-dependent diabetes mellitus (IDDM). These parameters were correlated with simultaneous serial cranial computed tomography readings of brain edema. Six of seven patients had positive computed tomography readings for brain edema on admission. Initial brain edema correlated directly with blood glucose (r = 0.79, P = 0.033) and inversely with HCO3- (r = -0.76, P = 0.047). At 6 h, brain edema still correlated with acidosis (HCO3-; r = -0.79, P = 0.033) but no longer with blood glucose. At that time, however, brain edema correlated with the rate of change in blood glucose (r = 0.915, P = 0.005). Results of interactive stepwise regression analysis suggest that the change in the calculated effective plasma osmolality plays a predominant role in the progression of brain edema during therapy (r = 0.995, P less than 0.001). Thus, although hyperglycemia and acidosis probably predispose to diabetic brain edema, osmotic factors may be major predictors of its evolution. No relationships were detected between brain edema and initiation of insulin therapy, plasma vasopressin, or changes in hematocrit. The factors responsible for initial brain edema and its progression, statistically identified in this study, require reassessment of common theories that attribute brain edema exclusively to therapy.

We performed a case-control study of acute renal failure (ARF) in patients hospitalized with sickle cell anemia (SCA). Twelve of the 116 patients (10.3%) whose records were suitable for analysis were diagnosed as having ARF based on a minimum of doubling of their serum creatinine levels (mean rise was 205 +/- 49%). ARF patients were more likely to have been admitted with infection and had a lower mean hemoglobin level than the control group. Volume depletion was the most common identifiable cause for ARF. Two of 3 patients with severe ARF received dialytic support. Ten of 12 ARF patients survived and subsequently had recovery of renal function.

Raman Chemometric Urinalysis (Rametrix TM) was used to discern differences in Raman spectra from (i) 362 urine specimens from patients receiving peritoneal dialysis (PD) therapy for end-stage kidney disease (ESKD), (ii) 395 spent dialysate specimens from those PD therapies, and (iii) 235 urine specimens from healthy human volunteers. Rametrix TM analysis includes spectral processing (e.g., truncation, baselining, and vector normalization); principal component analysis (PCA); statistical analyses (ANOVA and pairwise comparisons); discriminant analysis of principal components (DAPC); and testing DAPC models using a leave-one-out build/test validation procedure. Results showed distinct and statistically significant differences between the three types of specimens mentioned above. Further, when introducing "unknown" specimens, Rametrix TM was able to identify the type of specimen (as PD patient urine or spent dialysate) with better than 98% accuracy, sensitivity, and specificity. Rametrix TM was able to identify "unknown" urine specimens as from PD patients or healthy human volunteers with better than 96% accuracy (with better than 97% sensitivity and 94% specificity). This demonstrates that an entire Raman spectrum of a urine or spent dialysate specimen can be used to determine its identity or the presence of ESKD by the donor.