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The objective of this study was to characterize hypernatremia patients at hospital admission into clusters using an unsupervised machine learning approach and to evaluate the mortality risk among these distinct clusters. We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 922 hospitalized adult patients with admission serum sodium of > 145 mEq/L. We calculated the standardized difference of each variable to identify each cluster’s key features. We assessed the association of each hypernatremia cluster with hospital and 1-year mortality. There were three distinct clusters of patients with hypernatremia on admission: 318 (34%) patients in cluster 1, 339 (37%) patients in cluster 2, and 265 (29%) patients in cluster 3. Cluster 1 consisted of more critically ill patients with more severe hypernatremia and hypokalemic hyperchloremic metabolic acidosis. Cluster 2 consisted of older patients with more comorbidity burden, body mass index, and metabolic alkalosis. Cluster 3 consisted of younger patients with less comorbidity burden, higher baseline eGFR, hemoglobin, and serum albumin. Compared to cluster 3, odds ratios for hospital mortality were 15.74 (95% CI 3.75–66.18) for cluster 1, and 6.51 (95% CI 1.48–28.59) for cluster 2, whereas hazard ratios for 1-year mortality were 6.25 (95% CI 3.69–11.46) for cluster 1 and 4.66 (95% CI 2.73–8.59) for cluster 2. Our cluster analysis identified three clinically distinct phenotypes with differing mortality risk in patients hospitalized with hypernatremia.

Background: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Methods: Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster’s key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. Results: In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden and lowest serum magnesium. Cluster 2 patients had the youngest age, lowest comorbidity burden, and highest kidney function. Cluster 3 patients had the oldest age and lowest kidney function. Cluster 1 and cluster 3 were associated with higher hospital and one-year mortality compared to cluster 2. In hypermagnesemia cohort (n = 4671), the analysis identified two clusters. Compared to cluster 1, the key features of cluster 2 included older age, higher comorbidity burden, more hospital admissions primarily due to kidney disease, more acute kidney injury, and lower kidney function. Compared to cluster 1, cluster 2 was associated with higher hospital mortality and one-year mortality. Conclusion: Our cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.

Background: Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes. Methods: We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to identify clinically distinct lactic acidosis subgroups. We calculated standardized mean differences to show key subgroup features. We compared outcomes among subgroups. Results: We identified 1919 patients with lactic acidosis. The algorithm revealed three best unique lactic acidosis subgroups based on patient variables. Cluster 1 (n = 554) was characterized by old age, elective admission to cardiac surgery ICU, vasopressor use, mechanical ventilation use, and higher pH and serum bicarbonate. Cluster 2 (n = 815) was characterized by young age, admission to trauma/surgical ICU with higher blood pressure, lower comorbidity burden, lower severity index, and less vasopressor use. Cluster 3 (n = 550) was characterized by admission to medical ICU, history of liver disease and coagulopathy, acute kidney injury, lower blood pressure, higher comorbidity burden, higher severity index, higher serum lactate, and lower pH and serum bicarbonate. Cluster 3 had the worst outcomes, while cluster 1 had the most favorable outcomes in terms of persistent lactic acidosis and mortality. Conclusions: Consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal clinically distinct lactic acidosis subgroups with different outcomes.

Background: We aimed to develop and validate an automated machine learning (autoML) prediction model for cardiac surgery-associated acute kidney injury (CSA-AKI). Methods: Using 69 preoperative variables, we developed several models to predict post-operative AKI in adult patients undergoing cardiac surgery. Models included autoML and non-autoML types, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and artificial neural network (ANN), as well as a logistic regression prediction model. We then compared model performance using area under the receiver operating characteristic curve (AUROC) and assessed model calibration using Brier score on the independent testing dataset. Results: The incidence of CSA-AKI was 36%. Stacked ensemble autoML had the highest predictive performance among autoML models, and was chosen for comparison with other non-autoML and multivariable logistic regression models. The autoML had the highest AUROC (0.79), followed by RF (0.78), XGBoost (0.77), multivariable logistic regression (0.77), ANN (0.75), and DT (0.64). The autoML had comparable AUROC with RF and outperformed the other models. The autoML was well-calibrated. The Brier score for autoML, RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.18, 0.18, 0.21, 0.19, 0.19, and 0.18, respectively. We applied SHAP and LIME algorithms to our autoML prediction model to extract an explanation of the variables that drive patient-specific predictions of CSA-AKI. Conclusion: We were able to present a preoperative autoML prediction model for CSA-AKI that provided high predictive performance that was comparable to RF and superior to other ML and multivariable logistic regression models. The novel approaches of the proposed explainable preoperative autoML prediction model for CSA-AKI may guide clinicians in advancing individualized medicine plans for patients under cardiac surgery.

Background: The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. Methods: We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.6 mg/dL). The standardized mean difference was applied to determine each cluster’s key features. We assessed the association of the clusters with mortality. Results: In the hypophosphatemia cohort (n = 3113), the consensus cluster analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age, a higher comorbidity burden, particularly hypertension, diabetes mellitus, coronary artery disease, lower eGFR, and more acute kidney injury (AKI) at admission. Cluster 2 had a comparable hospital mortality (3.7% vs. 2.9%, p = 0.17), but a higher one-year mortality (26.8% vs. 14.0%, p &lt, 0.001), and five-year mortality (20.2% vs. 44.3%, 0.001), compared to Cluster 1. In the hyperphosphatemia cohort (n = 7252), the analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1, included: older age, more primary admission for kidney disease, more history of hypertension, more end-stage kidney disease, more AKI at admission, and higher admission potassium, magnesium, and phosphate. Cluster 2 had a higher hospital (8.9% vs. 2.4%, 0.001) one-year mortality (32.9% vs. 14.8%, 0.001), and five-year mortality (24.5% vs. 51.1%, 0.001), compared with Cluster 1. Conclusion: Our cluster analysis classified clinically distinct phenotypes with different mortality risks among hospitalized patients with serum phosphate derangements. Age, comorbidities, and kidney function were the key features that differentiated the phenotypes.

Background and Objectives: Despite the association between hyperchloremia and adverse outcomes, mortality risks among patients with hyperchloremia have not consistently been observed among all studies with different patient populations with hyperchloremia. The objective of this study was to characterize hyperchloremic patients at hospital admission into clusters using an unsupervised machine learning approach and to evaluate the mortality risk among these distinct clusters. Materials and Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,394 hospitalized adult patients with admission serum chloride of &gt, 108 mEq/L. We calculated the standardized mean difference of each variable to identify each cluster’s key features. We assessed the association of each hyperchloremia cluster with hospital and one-year mortality. Results: There were three distinct clusters of patients with admission hyperchloremia: 3237 (28%), 4059 (36%), and 4098 (36%) patients in clusters 1 through 3, respectively. Cluster 1 was characterized by higher serum chloride but lower serum sodium, bicarbonate, hemoglobin, and albumin. Cluster 2 was characterized by younger age, lower comorbidity score, lower serum chloride, and higher estimated glomerular filtration (eGFR), hemoglobin, and albumin. Cluster 3 was characterized by older age, higher comorbidity score, higher serum sodium, potassium, and lower eGFR. Compared with cluster 2, odds ratios for hospital mortality were 3.60 (95% CI 2.33–5.56) for cluster 1, and 4.83 (95% CI 3.21–7.28) for cluster 3, whereas hazard ratios for one-year mortality were 4.49 (95% CI 3.53–5.70) for cluster 1 and 6.96 (95% CI 5.56–8.72) for cluster 3. Conclusions: Our cluster analysis identified three clinically distinct phenotypes with differing mortality risks in hospitalized patients with admission hyperchloremia.

Background: The objective of this study was to characterize patients with hyponatremia at hospital admission into clusters using an unsupervised machine learning approach, and to evaluate the short- and long-term mortality risk among these distinct clusters. Methods: We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 11,099 hospitalized adult hyponatremia patients with an admission serum sodium below 135 mEq/L. The standardized mean difference was utilized to identify each cluster’s key features. We assessed the association of each hyponatremia cluster with hospital and one-year mortality using logistic and Cox proportional hazard analysis, respectively. Results: There were three distinct clusters of hyponatremia patients: 2033 (18%) in cluster 1, 3064 (28%) in cluster 2, and 6002 (54%) in cluster 3. Among these three distinct clusters, clusters 3 patients were the youngest, had lowest comorbidity burden, and highest kidney function. Cluster 1 patients were more likely to be admitted for genitourinary disease, and have diabetes and end-stage kidney disease. Cluster 1 patients had the lowest kidney function, serum bicarbonate, and hemoglobin, but highest serum potassium and prevalence of acute kidney injury. In contrast, cluster 2 patients were the oldest and were more likely to be admitted for respiratory disease, have coronary artery disease, congestive heart failure, stroke, and chronic obstructive pulmonary disease. Cluster 2 patients had lowest serum sodium and serum chloride, but highest serum bicarbonate. Cluster 1 patients had the highest hospital mortality and one-year mortality, followed by cluster 2 and cluster 3, respectively. Conclusion: We identified three clinically distinct phenotypes with differing mortality risks in a heterogeneous cohort of hospitalized hyponatremic patients using an unsupervised machine learning approach.

The objective of this study was to characterize hypernatremia patients at hospital admission into clusters using an unsupervised machine learning approach and to evaluate the mortality risk among these distinct clusters.We performed consensus cluster analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 922 hospitalized adult patients with admission serum sodium of 145 mEq/L. We calculated the standardized difference of each variable to identify each cluster's key features. We assessed the association of each hypernatremia cluster with hospital and 1-year mortality.There were three distinct clusters of patients with hypernatremia on admission: 318 (34%) patients in cluster 1, 339 (37%) patients in cluster 2, and 265 (29%) patients in cluster 3. Cluster 1 consisted of more critically ill patients with more severe hypernatremia and hypokalemic hyperchloremic metabolic acidosis. Cluster 2 consisted of older patients with more comorbidity burden, body mass index, and metabolic alkalosis. Cluster 3 consisted of younger patients with less comorbidity burden, higher baseline eGFR, hemoglobin, and serum albumin. Compared to cluster 3, odds ratios for hospital mortality were 15.74 (95% CI 3.75-66.18) for cluster 1, and 6.51 (95% CI 1.48-28.59) for cluster 2, whereas hazard ratios for 1-year mortality were 6.25 (95% CI 3.69-11.46) for cluster 1 and 4.66 (95% CI 2.73-8.59) for cluster 2.Our cluster analysis identified three clinically distinct phenotypes with differing mortality risk in patients hospitalized with hypernatremia.

Summary Background Hospitalized patients with hyperkalemia are heterogeneous, and cluster approaches may identify specific homogenous groups. This study aimed to cluster patients with hyperkalemia on admission using unsupervised machine learning (ML) consensus clustering approach, and to compare characteristics and outcomes among these distinct clusters. Methods Consensus cluster analysis was performed in 5133 hospitalized adult patients with admission hyperkalemia, based on available clinical and laboratory data. The standardized mean difference was used to identify each cluster’s key clinical features. The association of hyperkalemia clusters with hospital and 1-year mortality was assessed using logistic and Cox proportional hazard regression. Results Three distinct clusters of hyperkalemia patients were identified using consensus cluster analysis: 1661 (32%) in cluster 1, 2455 (48%) in cluster 2 and 1017 (20%) in cluster 3. Cluster 1 was mainly characterized by older age, higher serum chloride and acute kidney injury (AKI), but lower estimated glomerular filtration rate (eGFR), serum bicarbonate and hemoglobin. Cluster 2 was mainly characterized by higher eGFR, serum bicarbonate and hemoglobin, but lower comorbidity burden, serum potassium and AKI. Cluster 3 was mainly characterized by higher comorbidity burden, particularly diabetes and end-stage kidney disease, AKI, serum potassium, anion gap, but lower eGFR, serum sodium, chloride and bicarbonate. Hospital and 1-year mortality risk was significantly different among the three identified clusters, with highest mortality in cluster 3, followed by cluster 1 and then cluster 2. Conclusion In a heterogeneous cohort of hyperkalemia patients, three distinct clusters were identified using unsupervised ML. These three clusters had different clinical characteristics and outcomes.

Background Hospitalized patients with hypokalemia are heterogeneous and cluster analysis, an unsupervised machine learning methodology, may discover more precise and specific homogeneous groups within this population of interest. Our study aimed to cluster patients with hypokalemia at hospital admission using an unsupervised machine learning approach and assess the mortality risk among these distinct clusters. Methods We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities and laboratory data among 4763 hospitalized adult patients with admission serum potassium ≤3.5 mEq/L. We calculated the standardized mean difference of each variable and used the cutoff of ±0.3 to identify each cluster's key features. We assessed the association of the hypokalemia cluster with hospital and 1-year mortality. Results Consensus cluster analysis identified three distinct clusters that best represented patients’ baseline characteristics. Cluster 1 had 1150 (32%) patients, cluster 2 had 1344 (28%) patients and cluster 3 had 1909 (40%) patients. Based on the standardized difference, patients in cluster 1 were younger, had less comorbidity burden but higher estimated glomerular filtration rate (eGFR) and higher hemoglobin; patients in cluster 2 were older, more likely to be admitted for cardiovascular disease and had higher serum sodium and chloride levels but lower eGFR, serum bicarbonate, strong ion difference (SID) and hemoglobin, while patients in cluster 3 were older, had a greater comorbidity burden, higher serum bicarbonate and SID but lower serum sodium, chloride and eGFR. Compared with cluster 1, cluster 2 had both higher hospital and 1-year mortality, whereas cluster 3 had higher 1-year mortality but comparable hospital mortality. Conclusion Our study demonstrated the use of consensus clustering analysis in the heterogeneous cohort of hospitalized hypokalemic patients to characterize their patterns of baseline clinical and laboratory data into three clinically distinct clusters with different mortality risks.

Background: Lactic acidosis is the most common cause of anion gap metabolic acidosis in the intensive care unit (ICU), associated with poor outcomes including mortality. We sought to compare machine learning (ML) approaches versus logistic regression analysis for prediction of mortality in lactic acidosis patients admitted to the ICU. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify ICU adult patients with lactic acidosis (serum lactate ≥4 mmol/L). The outcome of interest was hospital mortality. We developed prediction models using four ML approaches consisting of random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost), artificial neural network (ANN), and statistical modeling with forward stepwise logistic regression using the testing dataset. We then assessed model performance using area under the receiver operating characteristic curve (AUROC), accuracy, precision, error rate, Matthews correlation coefficient (MCC), F1 score, and assessed model calibration using the Brier score, in the independent testing dataset. Results: Of 1919 lactic acidosis ICU patients, 1535 and 384 were included in the training and testing dataset, respectively. Hospital mortality was 30%. RF had the highest AUROC at 0.83, followed by logistic regression 0.81, XGBoost 0.81, ANN 0.79, and DT 0.71. In addition, RF also had the highest accuracy (0.79), MCC (0.45), F1 score (0.56), and lowest error rate (21.4%). The RF model was the most well-calibrated. The Brier score for RF, DT, XGBoost, ANN, and multivariable logistic regression was 0.15, 0.19, 0.18, 0.19, and 0.16, respectively. The RF model outperformed multivariable logistic regression model, SOFA score (AUROC 0.74), SAP II score (AUROC 0.77), and Charlson score (AUROC 0.69). Conclusion: The ML prediction model using RF algorithm provided the highest predictive performance for hospital mortality among ICU patient with lactic acidosis.

Objective We have previously used a 12-lead, signal-processed ECG to calculate blood potassium levels. We now assess the feasibility of doing so with a smartphone-enabled single lead, to permit remote monitoring. Patients and methods Twenty-one hemodialysis patients held a smartphone equipped with inexpensive FDA-approved electrodes for three 2 min intervals during hemodialysis. Individualized potassium estimation models were generated for each patient. ECG-calculated potassium values were compared to blood potassium results at subsequent visits to evaluate the accuracy of the potassium estimation models. Results The mean absolute error between the estimated potassium and blood potassium 0.38 ± 0.32 mEq/L (9% of average potassium level) decreasing to 0.6 mEq/L using predictors of poor signal. Conclusions A single-lead ECG acquired using electrodes attached to a smartphone device can be processed to calculate the serum potassium with an error of 9% in patients undergoing hemodialysis. Summary A single-lead ECG acquired using electrodes attached to a smartphone can be processed to calculate the serum potassium in patients undergoing hemodialysis remotely.

Renal function improves early after left ventricular assist device (LVAD) implantation but later decline has been observed. We sought to determine the occurrence and evaluate possible causes for this decline. In 62 consecutive patients with HeartMateII LVAD with available calculated glomerular filtration rate (GFR, ml/min/1.73 m) 1 year after implant, GFR was assessed repeatedly and possible predictors for decline from 3 to 12 months were investigated. Post-mortem renal specimens for patients supported with an LVAD were evaluated. GFR 54.5 ± 19.5 at admission increased to 66.4 ± 22.3 preoperatively and to 79.2 ± 30.1 ~1 month after implantation. Subsequently at ~3 months GFR declined to 74.7 ± 25.4, at ~6 months to 68.8 ± 23.1, and ~1 year after implant to 63.9 ± 17.7. Glomerular filtration rate at 1 year was significantly lower (p < 0.0001, p < 0.0001, p = 0.005) than GFR 1, 3, and 6 months after implant. Early rise in GFR after surgery was not associated with late decline. Shorter bypass time (β = -0.09, p = 0.048) and higher albumin 3 months after LVAD (β = 14.4, p = 0.025) were significantly associated with less later decline in GFR. Arteriosclerosis was identified in autopsy renal specimens. In conclusion, early gains in renal function after LVAD implant are not sustained in many patients. Patient, device, and operative factors may influence long-term renal function in these patients.

Blood leak alarms are important safety features in a hemodialysis machine to protect patients from loss of blood through a rupture in the dialyzer membrane (true alarms). A false blood leak alarm can be triggered by air bubbles or detector malfunction (such as deposits of grease or scale). Hydroxocobalamin is an injectable form of vitamin B12 approved by the US Food and Drug Administration for the treatment of confirmed or suspected cyanide toxicity. Due to observations of an increase in arterial pressure after high-dose hydroxocobalamin infusion for the treatment of acute cyanide poisoning, it has recently been reported as an off-label rescue treatment for post–cardiopulmonary bypass vasoplegic syndrome. We report an 83-year-old man who received hydroxocobalamin following cardiac surgery for treatment of vasoplegic syndrome. The patient developed severe acute kidney injury with volume overload. Hydroxocobalamin interference with the blood leak detector compromised his dialysis treatment. We describe the use of continuous renal replacement therapy to overcome the hydroxocobalamin-related interference with hemodialysis. As the utility of hydroxocobalamin potentially expands, physicians must be aware of its inadvertent effect on renal replacement therapy.

Objective To report on the survival and the associations of treatments upon survival of patients with calciphylaxis seen at a single center. Patients and Methods Using the International Classification of Diseases, Ninth Revision diagnosis code of 275.49 and the keyword "calciphylaxis" in the dismissal narrative, we retrospectively identified 101 patients with calciphylaxis seen at our institution between January 1, 1999, through September 20, 2014, using a predefined, consensus-developed classification scheme. Results The average age of patients was 60 years: 81 (80.2%) were women; 68 (68.0%) were obese; 19 (18.8%) had stage 0 to 2 chronic kidney disease (CKD), 19 (18.9%) had stage 3 or 4 CKD; 63 (62.4%) had stage 5 or 5D (dialysis) CKD. Seventy-five patients died during follow-up. Six-month survival was 57%. Lack of surgical debridement was associated with insignificantly lower 6-month survival (hazard ratio [HR]=1.99; 95% CI, 0.96-4.15; P =.07) and significantly poorer survival for the entire duration of follow-up (HR=1.98; 95% CI, 1.15-3.41; P =.01), which was most pronounced in stage 5 or 5D CKD (HR=1.91; 95% CI, 1.03-3.56; P =.04). Among patients with stage 5/5D CKD, subtotal parathyroidectomy (performed only in patients with hyperparathyroidism) was associated with better 6-month (HR=0.12; 95% CI, 0.02-0.90; P =.04) and overall survival (HR= 0.37; 95% CI, 0.15-0.87; P =.02). Conclusion Calciphylaxis is associated with a high mortality rate. Significantly effective treatments included surgical debridement and subtotal parathyroidectomy in patients with stage 5/5D CKD with hyperparathyroidism. Treatments with tissue-plasminogen activator, sodium thiosulfate, and hyperbaric oxygen therapy were not associated with higher mortality.

Objective To identify coagulation risk factors in patients with calciphylaxis and the relationship between anticoagulation use and overall survival. Patients and Methods Study subjects were 101 patients with calciphylaxis seen at Mayo Clinic from 1999 to September 2014. Data including thrombophilia profiles were extracted from the medical records of each patient. Survival status was determined using patient registration data and the Social Security Death Index. Survival was estimated using the Kaplan-Meier method, and associations were evaluated using Cox proportional hazards models. Results Sixty-four of the 101 patients underwent thrombophilia testing. Of these, a complete test panel was performed in 55 and a partial panel in 9. Severe thrombophilias observed in 60% (33 of 55) of the patients included antiphospholipid antibody syndrome protein C, protein S, or antithrombin deficiencies or combined thrombophilias. Of the 55 patients, severe thrombophilia (85%, 23 of 27) was noted in patients who were not on warfarin at the time of testing (27). Nonsevere thrombophilias included heterozygous factor V Leiden (n=2) and plasminogen deficiency (n=1). For the comparison of survival, patients were divided into 3 treatment categories: Warfarin (n=63), other anticoagulants (n=20), and no anticoagulants (n=18). There was no statistically significant survival difference between treatment groups. Conclusion Laboratory testing reveals a strikingly high prevalence of severe thrombophilias in patients with calciphylaxis, underscoring the importance of congenital and acquired thrombotic propensity potentially contributing to the pathogenesis of this disease. These findings may have therapeutic implications; however, to date, survival differences did not vary by therapeutic choice.

Background Cardiovascular disease among hemodialysis (HD) patients is linked to poor outcomes. The Acute Dialysis Quality Initiative Workgroup proposed echocardiographic (ECHO) criteria for structural heart disease (SHD) in dialysis patients. The association of SHD with important patient outcomes is not well defined. Objectives This study sought to determine prevalence of ECHO-determined SHD and its association with survival among incident HD patients. Methods We analyzed patients who began chronic HD from 2001 to 2013 who underwent ECHO ≤1 month prior to or ≤3 months following initiation of HD (n = 654). Results Mean patient age was 66 ± 16 years, and 60% of patients were male. ECHO findings that met 1 or more and ≥3 of the new criteria were discovered in 87% and 54% of patients, respectively. Over a median of 2.4 years, 415 patients died: 108 (26%) died within 6 months. Five-year mortality was 62%. Age- and sex-adjusted structural heart disease variables associated with death were left ventricular ejection fraction (LVEF) ≤45% (hazard ratio [HR]: 1.48; confidence interval [CI]: 1.20 to 1.83) and right ventricular (RV) systolic dysfunction (HR: 1.68; CI: 1.35 to 2.07). An additive of higher death risk included LVEF ≤45% and RV systolic dysfunction rather than neither (HR: 2.04; CI: 1.57 to 2.67; p = 0.53 for test for interaction). Following adjustment for age, sex, race, diabetic kidney disease, and dialysis access, RV dysfunction was independently associated with death (HR: 1.66; CI 1.34 to 2.06; p Conclusions SHD was common in our HD study population, and RV systolic dysfunction independently predicted mortality.

Introduction Emerging medical technology has allowed for monitoring of heart rhythm abnormalities using smartphone compatible devices. The safety and utility of such devices have not been established in patients with cardiac implantable electronic devices (CIEDs). We sought to assess the safety and compatibility of the Food and Drug Administration-approved AliveCor Kardia device in patients with CIEDs. Methods and results We prospectively recruited patients with CIED for a Kardia recording during their routine device interrogation. A recording was obtained in paced and nonpaced states. Adverse clinical events were noted at the time of recording. Electrograms (EGMs) from the cardiac device were obtained at the time of recording to assess for any electromagnetic interference (EMI) introduced by Kardia. Recordings were analyzed for quality and given a score of 3 (interpretable rhythm, no noise), 2 (interpretable rhythm, significant noise) or 1 (uninterpretable). A total of 251 patients were recruited (59% with a pacemaker and 41% with ICD). There were no adverse clinical events noted at the time of recording and no changes to CIED settings. Review of all EGMs revealed no EMI introduced by Kardia. Recordings were correctly interpreted in 90% of paced recordings (183 had a score of 3, 43 of 2, and 25 of 1) and 94.7% of nonpaced recordings (147 of 3, 15 of 2, and 9 of 1). Conclusion The AliveCor Kardia device has an excellent safety profile when used in conjunction with most CIEDs. The quality of recordings was preserved in this population. The device, therefore, can be considered for heart rhythm monitoring in patients with CIEDs.

Background: The incidence of tuberculosis (TB) in end-stage renal disease is significantly higher than that in the general population. Among those with kidney dysfunction, anti-TB treatment is associated with increased side effects, but the effect on healthcare utilization is unknown. Methods/Aim: To assess patient-reported symptoms, adverse effects and describe changes in healthcare utilization patterns during treatment for TB, we conducted a case series (n = 12) of patients receiving maintenance hemodialysis (HD) from Mayo Clinic Dialysis Services and concurrent drug therapy for TB from January 2002 through May 2014. Healthcare utilization (hospitalizations and emergency department (ED) visits independent of hospital admission) was compared before and during treatment. Results: Patients were treated for latent (n = 7) or active (n = 5) TB. The majority of patients with latent disease were treated with isoniazid (n = 5, 71%), while active-disease patients received a 4-drug regimen. Adverse effects were reported in 83% of patients. Compared to measurements prior to drug initiation, serum albumin and dialysis weights were similar at 3 months. Commonly reported anti-TB drug toxicities were described. More than half (58%) of the patients were hospitalized at least once. No ED or hospital admissions occurred in the period prior to drug therapy, but healthcare utilization increased during treatment in the latent disease group (hospitalization rate per person-month: pre 0 vs. post 1). Conclusions: Among HD patients, anti-TB therapy is associated with frequently reported symptoms and increased healthcare utilization. Among this subset, patients receiving treatment for latent disease may be those with greatest increase in healthcare use. Careful monitoring and early complication detection may help optimize medication adherence and minimize hospitalizations.

BACKGROUND AND OBJECTIVES: Withdrawal from maintenance hemodialysis before death has become more common because of high disease and treatment burden. The study objective was to identify patient factors and examine the terminal course associated with hemodialysis withdrawal, and assess patterns of palliative care involvement before death among patients on maintenance hemodialysis. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We designed an observational cohort study of adult patients on incident hemodialysis in a midwestern United States tertiary center, from January 2001 to November 2013, with death events through to November 2015. Logistic regression models evaluated associations between patient characteristics and withdrawal status and palliative care service utilization. RESULTS: Among 1226 patients, 536 died and 262 (49% of 536) withdrew. A random sample (10%; 52 out of 536) review of Death Notification Forms revealed 73% sensitivity for withdrawal. Risk factors for withdrawal before death included older age, white race, palliative care consultation within 6 months, hospitalization within 30 days, cerebrovascular disease, and no coronary artery disease. Most withdrawal decisions were made by patients (60%) or a family member (33%; surrogates). The majority withdrew either because of acute medical complications (51%) or failure to thrive/frailty (22%). After withdrawal, median time to death was 7 days (interquartile range, 4–11). In-hospital deaths were less common in the withdrawal group (34% versus 46% nonwithdrawal, P=0.003). A third (34%; 90 out of 262) of those that withdrew received palliative care services. Palliative care consultation in the withdrawal group was associated with longer hemodialysis duration (odds ratio, 1.19 per year; 95% confidence interval, 1.10 to 1.3; P

Background: Potassium abnormalities can cause lifethreatening arrhythmias. Measuring potassium requires access to blood. We have developed methods measuring potassium noninvasively using the processed, signalaveraged ECG. Four patients, in a larger study, were found to have unexpected discrepancies between measured blood potassium and ECG-derived estimated potassium values. Methods: Of 240 patients enrolled at 17 sites in the PORTEND (REVEAL-HD) study, 200 wore a continuouslyrecording, single-lead, wireless ECG patch. Blood for chemistries was obtained once before, twice during and once after dialysis. Complete blood test and ECG data were available for 142 subjects. The general potassium pattern during dialysis was an exponential decay throughout the treatment. Four subjects, whose blood potassium values, but not ECG-based potassium values, deviated from this pattern, are the subjects of this analysis. Findings: Among 4 patients, at least one blood potassium value declined to 2.6 mmol/l or less during dialysis, and then rebounded unexpectedly, while the ECG-based potassium values were consistent with the expected exponential delay. Three of these four patients were at a single site, suggesting site-specific likelihood of pattern deviation (p=0.04). In each case, BUN and phosphorous blood levels were markedly low, with albumin and calcium unaffected. Conclusions: These results are compatible with blood drawing errors in which dialyzed blood was obtained from the venous return, rather than from the arterial tubing. A physiologic, ECG-based test that estimates potassium on the basis of the concentration of potassium in the blood surrounding the heart is free from local aberrations and might be a useful potassium monitoring tool in dialysis patients.

To determine if ECG repolarization measures can be used to detect small changes in serum potassium levels in hemodialysis patients.Signal-averaged ECGs were obtained from standard ECG leads in 12 patients before, during, and after dialysis. Based on physiological considerations, five repolarization-related ECG measures were chosen and automatically extracted for analysis: the slope of the T wave downstroke (T right slope), the amplitude of the T wave (T amplitude), the center of gravity (COG) of the T wave (T COG), the ratio of the amplitude of the T wave to amplitude of the R wave (T/R amplitude), and the center of gravity of the last 25% of the area under the T wave curve (T4 COG) (Fig. 1).The correlations with potassium were statistically significant for T right slope (P0.0001), T COG (P=0.007), T amplitude (P=0.0006) and T/R amplitude (P=0.03), but not T4 COG (P=0.13). Potassium changes as small as 0.2mmol/L were detectable.Small changes in blood potassium concentrations, within the normal range, resulted in quantifiable changes in the processed, signal-averaged ECG. This indicates that non-invasive, ECG-based potassium measurement is feasible and suggests that continuous or remote monitoring systems could be developed to detect early potassium deviations among high-risk patients, such as those with cardiovascular and renal diseases. The results of this feasibility study will need to be further confirmed in a larger cohort of patients.

Objective To understand the performance of a currently used clinical blood test with regard to the frequency and size of variation of the results. Patients and Methods From November 29, 2012, through November 29, 2013, patients were recruited at 65 sites as part of a previously reported clinical trial (ClinicalTrials.gov Identifier: NCT01737697). Eligible outpatients who had been fasting for at least 8 hours underwent venous phlebotomy at baseline, 30 minutes, and 60 minutes to measure plasma potassium levels in whole blood using a point-of-care device (i-STAT, Abbott Laboratories). We analyzed the results to assess their variability and frequency of pseudohyperkalemia and pseudonormokalemia. Results A total of 1170 patients were included in this study. Absolute differences between pairs of measurements from different time points ranged from 0 to 2.5 mmol/L, with a mean difference of 0.26 mmol/L. The mean percentage differences were approximately 5% with an SD of 5%. Approximately 12% of differences between repeated fasting potassium blood test results were above 0.5 mmol/L (33% of the normal range), and 20% of patients (234) had at least one difference greater than 0.5 mmol/L. In 44.0% of the patients with a hyperkalemic average value (true hyperkalemia) (302 of 686), at least one blood test result was in the normal range (pseudonormokalemia), and in 30.2% of the patients with a normal average value (146 of 484), at least one blood test result was elevated (pseudohyperkalemia). Conclusion Expected variability and errors exist with potassium blood tests, even when conditions are optimized. Pseudohyperkalemia and pseudonormokalemia are common, indicating a need for thoughtful clinical interpretation of unexpected test results.

SummaryBackground Hepatitis E virus (HEV) infection appears to be more common than previously thought. HEV seroprevalence in patients on maintenance haemodialysis (HD) is unclear with a range from 0% to 44%. In addition, risk factors of transmission of HEV in patients on haemodialysis are unknown. Aim To perform a systematic review and meta-analysis of HEV seroprevalence in HD patients compared with controls. Methods A systematic search of several databases identified all observational studies with comparative arms. Two reviewers extracted data and assessed the methodological quality. A random-effects model was used for pooled odds ratio (OR) and 95% confidence interval (CI) of positive anti-HEV IgG in both groups. Heterogeneity and publication bias were assessed with appropriate tests. Results We identified 31 studies from 17 countries between 1994 and 2016. Sixteen studies were judged to have adequate quality and 15 to have moderate limitations. HEV infection was more prevalent in patients on haemodialysis compared with controls (OR 2.47, 95% CI 1.79-3.40, I2 = 75.2%, P < .01). We conducted several subgroup analyses without difference in results. Egger regression test did not suggest publication bias (P = .83). Specific risk factors of HEV transmission in patients on haemodialysis were not clearly identified. Conclusion Hepatitis E virus infection is more prevalent in patients on haemodialysis compared with non-haemodialysis control groups. Further studies are needed to determine risk factors of acquisition, impact on health, and risk for chronic HEV especially among those patients going to receive organ transplantation.

Background: Overfeeding can lead to multiple metabolic and clinical complications and has been associated with increased mortality in the critically ill. Continuous venovenous hemofiltration (CVVH) represents a potential source of calories that is poorly recognized and may contribute to overfeeding complications. Objective: We aimed to quantify the systemic caloric contribution of acid-citrate-dextrose regional anticoagulation and dextrose-containing replacement fluids in the CVVH circuit. Design: This was a prospective study in 10 critically ill adult patients who received CVVH from April 2014 to June 2014. Serial pre- and postfilter blood samples (n = 4 each) were drawn and analyzed for glucose and citrate concentrations on each of 2 consecutive days. Results: Participants included 5 men and 5 women with a mean ± SEM age of 61 ± 4 y (range: 42–84 y) and body mass index (in kg/m(2)) of 28 ± 2 (range: 18.3–36.2). There was generally good agreement between data on the 2 study days (CV: 7–11%). Mean ± SEM pre- and postfilter venous plasma glucose concentrations in the aggregate group were 152 ± 10 and 178 ± 9 mg/dL, respectively. Net glucose uptake from the CVVH circuit was 54 ± 5 mg/min and provided 295 ± 28 kcal/d. Prefilter plasma glucose concentrations were higher in patients with diabetes (n = 5) than in those without diabetes (168 ± 12 compared with 140 ± 14 mg/dL; P < 0.05); however, net glucose uptake was similar (46 ± 8 compared with 61 ± 6 mg/min; P = 0.15). Mean ± SEM pre- and postfilter venous plasma citrate concentrations were 1 ± 0.1 and 3.1 ± 0.2 mmol/L, respectively. Net citrate uptake from the CVVH circuit was 60 ± 2 mg/min and provided 218 ± 8 kcal/d. Conclusions: During CVVH there was a substantial net uptake of both glucose and citrate that delivered exogenous energy and provided ∼512 kcal/d. Failure to account for this source of calories in critically ill patients receiving nutrition on CVVH may result in overfeeding.

Data regarding the long-term clinical effects of a continuous flow left ventricular assist device (CF-LVAD) on hepato-renal function is limited. Hence our aim was to assess changes in hepato-renal function over a one-year period in patients supported on a CF-LVAD.During the study period 126 patients underwent CF-LVAD implant. Changes in hepato-renal laboratory parameters were studied in 61/126 patients successfully supported on a CF-LVAD for period of one year. A separate cohort of a high-risk group (HCrB) of patients (56/126) with a serum creat1.9 mg/dL (168 μmol/L) (75th percentile) or a serum bil1.5 mg/dL (25.65 μmol/L) (75th percentile) was created. Changes in serum creatinine and bilirubin were analysed at regular intervals for this group along with the need for renal replacement therapy.Baseline creatinine and blood urea nitrogen (BUN) for the entire cohort was 1.4[1.2,1.9 mg/dL] [123.7(106,168) μmol/L) and 27[20,39.5 mg/dL] [9.6(7.1,14.1) mmol/L] respectively. After an initial reduction at the end of one month [1(0.8,1.2) mg/dL; 88(70,105) μmol/L] (p0.0001), a gradual increase was noted over the study period to reach (1.25[1.1,1.5] mg/dL; 106(97.2,132.6) μmol/L] (p=0.0003). The serum bilirubin normalised from a [1(0.7,1.55) mg/dL] [17(18.8,25.7) μmol/L) to 0.9(0.6,1.2)mg/dL [15.4(10.2,20.5) μmol/L] (p=0.0005) and continued to decline over one year. Improvement in the synthetic function of the liver was demonstrated by a rise in the serum albumin levels to reach 4.3[4.1,4.5] [43(41,45) gm/L] at the end of one year (p0.0001). The baseline serum creatinine and bilirubin for the high-risk cohort (HCrB) was 1.9(1.3,2.4) mg/dL [168(115,212) μmol/L] and 1.7(1.00,2.4) mg/dL [29(17.1,68.4) μmol/L] respectively. The high-risk cohort (HCrB) demonstrated a trend towards higher 30-day mortality (p=0.06). While the need for temporary renal replacement therapy was higher in this cohort (16% vs. 4%; p=0.03), only 3% need it permanently. A significant reduction in creatinine was apparent at the end of one month [1.1(0.8,1.4) mg/dL; 97(70.7,123.7) μmol/L] (p0.0001) and then remained stable at [1.3(1.1,1.5) mg/dL; 115(97,132.6) μmol/L]. Bilirubin demonstrated a 30% decline over one month and then remained low at [0.7(0.5,0.8) mg/dL; 62(44,70) μmol/L] p=0.0005 compared to the pre-operative baseline.Hepato-renal function demonstrates early improvement and then remains stable in the majority of patients on continuous flow left ventricular assist device support for one year. High-risk patients demonstrate a higher 30-day mortality and temporary need for renal replacement therapy. Yet even in this cohort, improvement is present over a period of one year on the device, with a minimal need for permanent haemodialysis.

Importance For patients with chronic kidney disease (CKD), hyperkalemia is common, associated with fatal arrhythmias, and often asymptomatic, while guideline-directed monitoring of serum potassium is underused. A deep-learning model that enables noninvasive hyperkalemia screening from the electrocardiogram (ECG) may improve detection of this life-threatening condition. Objective To evaluate the performance of a deep-learning model in detection of hyperkalemia from the ECG in patients with CKD. Design, setting, and participants A deep convolutional neural network (DNN) was trained using 1 576 581 ECGs from 449 380 patients seen at Mayo Clinic, Rochester, Minnesota, from 1994 to 2017. The DNN was trained using 2 (leads I and II) or 4 (leads I, II, V3, and V5) ECG leads to detect serum potassium levels of 5.5 mEq/L or less (to convert to millimoles per liter, multiply by 1) and was validated using retrospective data from the Mayo Clinic in Minnesota, Florida, and Arizona. The validation included 61 965 patients with stage 3 or greater CKD. Each patient had a serum potassium count drawn within 4 hours after their ECG was recorded. Data were analyzed between April 12, 2018, and June 25, 2018. Exposures Use of a deep-learning model. Main outcomes and measures Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity, with serum potassium level as the reference standard. The model was evaluated at 2 operating points, 1 for equal specificity and sensitivity and another for high (90%) sensitivity. Results Of the total 1 638 546 ECGs, 908 000 (55%) were from men. The prevalence of hyperkalemia in the 3 validation data sets ranged from 2.6% (n = 1282 of 50 099; Minnesota) to 4.8% (n = 287 of 6011; Florida). Using ECG leads I and II, the AUC of the deep-learning model was 0.883 (95% CI, 0.873-0.893) for Minnesota, 0.860 (95% CI, 0.837-0.883) for Florida, and 0.853 (95% CI, 0.830-0.877) for Arizona. Using a 90% sensitivity operating point, the sensitivity was 90.2% (95% CI, 88.4%-91.7%) and specificity was 63.2% (95% CI, 62.7%-63.6%) for Minnesota; the sensitivity was 91.3% (95% CI, 87.4%-94.3%) and specificity was 54.7% (95% CI, 53.4%-56.0%) for Florida; and the sensitivity was 88.9% (95% CI, 84.5%-92.4%) and specificity was 55.0% (95% CI, 53.7%-56.3%) for Arizona. Conclusions and relevance In this study, using only 2 ECG leads, a deep-learning model detected hyperkalemia in patients with renal disease with an AUC of 0.853 to 0.883. The application of artificial intelligence to the ECG may enable screening for hyperkalemia. Prospective studies are warranted.

Background The optimal treatment for multivessel coronary artery disease in patients with end-stage renal disease (ESRD) is unresolved. Aim of Study Compare clinical adverse events after percutaneous intervention with drug-eluting stents (DESs) and coronary artery bypass grafting (CABG) in patients with ESRD. Methods MEDLINE, Web of Science, and Scopus were searched for appropriate studies published in the English language (between January 2000 and August 2013). The pooled odds ratio (OR) was estimated by the Peto method with a random effect model. Data are presented with 95% confidence interval; p

Aims The number of elderly persons with end-stage renal disease is increasing with many requiring hospitalizations. This study examines the causes and predictors of hospitalization in older haemodialysis patients. Methods We reviewed hospitalizations of older (≥65 years) incident chronic haemodialysis patients initiating therapy between 1 January 2007 and 31 December 2009 under the care of a single Midwestern United States dialysis provider. Results Of 125 patients, the mean age was 76 ± 7 years and 72% were male. At first dialysis, 68% used a central venous catheter (CVC) and 51% were in the hospital. Mean follow-up was 1.8 ± 1.0 years. At least one hospitalization occurred in 89 (71%) patients and half of all patients were hospitalized once within the first 223 days. Total hospital admission rate was 1.48 per patient year with hospital days totalling 8.54 days per patient year. The three most common reasons for first admission were cardiac (33%), infection (18%) and gastrointestinal (12%). Predictors of future hospitalization included the first dialysis occurring in hospital (hazard ratios (HR) 2.1, 95% CI 1.4–3.3, P = 0.0005) and the use of a CVC at first haemodialysis (HR 2.6, CI 1.6–4.4, P

Acute kidney injury (AKI) is characterized by deterioration in kidney function resulting in multisystem abnormalities. Much of the morbidity and mortality associated with AKI result from a systemic inflammatory response syndrome (SIRS). This study described herein is a prospective, single-arm, multicenter US study designed to evaluate the safety and efficacy of the Selective Cytopheretic Device (SCD) treatment on AKI requiring continuous renal replacement therapy (CRRT) in the ICU. The study enrolled 35 subjects. The mean age was 56.3 ± 15. With regard to race, 71.4% of the subjects were Caucasian, 22.9% were Black, and 5.7% were Hispanic. Average SOFA score was 11.3 ± 3.6. Death from any cause at Day 60 was 31.4%. Renal recovery, defined as dialysis independence, was observed in all of the surviving subjects at Day 60. The results of this pilot study indicate the potential for a substantial improvement in patient outcomes over standard of care therapy, which is associated with a greater than 50% 60-day mortality in the literature. The SCD warrants further study in scientifically sound, pivotal trial to demonstrate reasonable assurance of safety and effectiveness.

Hyperkalemia is commonly seen in congestive heart failure, chronic kidney disease, diabetes, and with the medications used to treat these conditions. It is associated with significant mortality and arrhythmic risk. Electrocardiogram (ECG) changes in hyperkalemia are clearly visible only in severe

Background and objectives: Management of incidental renal artery and kidney abnormalities in patients undergoing computed tomography scans is a clinical challenge because their frequency in healthy subjects has not been precisely estimated. Therefore, the prevalence and management of these abnormalities were determined among a large cohort of potential kidney donors undergoing protocol evaluations. Design, setting, participants, & measurements: All patients at the Mayo Clinic who underwent computed tomographic angiography and urography as part of their kidney donor evaluation between 2000 and 2008 were identified. Radiographic reports were abstracted for abnormalities of the renal arteries and kidneys. The prevalence of radiographic abnormalities was stratified by age and gender, and the effect on approval for kidney donation was determined. Results: Among 1957 potential kidney donors, the mean ± SD age was 43 ± 12 years, and 58% were women. The most common abnormalities were kidney stones (11%), focal scarring (3.6%), fibromuscular dysplasia (2.8%), and other renal artery narrowing or atherosclerosis (5.3%). Fibromuscular dysplasia, focal scarring, parenchymal atrophy, and upper tract dilation were more common in women. Renal artery narrowing, focal scarring, and indeterminate masses increased with age. Overall, 25% of potential donors had at least one abnormality. However, these incidental radiographic abnormalities contributed to exclusion from donation in only 6.7% of potential donors. Conclusions: Incidental radiographic abnormalities of the renal arteries and kidneys are common. The majority of imaging findings are not perceived to be harmful enough to prevent kidney donation, but future studies are needed to determine their clinical relevance.

Background: Anemia management in chronic hemodialysis (HD) has been affected by the implementation of the prospective payment system (PPS) and changes in clinical guidelines. These factors could impact red blood cell (RBC) transfusion in HD patients. Our distinctive care system contains complete records for all RBC transfusions among our HD patients. Aims: To determine RBC transfusions in patients with prevalent chronic HD, site of administration (inpatient or outpatient), and ordering physician specialty for inpatients; compare pre- and post-PPS RBC transfusions; and compare RBC transfusions during changes in desired outpatient hemoglobin (Hb) range for patients with chronic HD. Methods: Retrospective analysis of medical and blood bank records for patients with prevalent chronic HD July 2009 through June 2013. Results: In total, 310-356 patients were studied. Mean (SD) units of RBCs per 100 patients per month for the study's 48 months were outpatient, 2.6 (1.5), and inpatient, 9.4 (4.6). Outpatient pre-PPS RBC units transfused were 2.1 (0.6) vs. post-PPS of 2.6 (1.5; p = 0.22, t test); for inpatients pre-PPS, 7.9 (4.5) RBC units per month vs. post-PPS, 11.5 (5.1; p = 0.11, t test). Inpatient RBC transfusions accounted for 75.2% (14.2%) of all RBC transfusions; 67.3% (16.3%) of inpatient transfusions were ordered by nonnephrologists. Changes in desired Hb range for outpatient HD patients did not lead to changes in RBC transfusions. Conclusions: No changes in RBC transfusions occurred among our patients with chronic HD with PPS implementation and in desired Hb range during the study period. Most transfusions were given in inpatient settings by nonnephrologists.

Background Hyper‐ and hypokalemia are clinically silent, common in patients with renal or cardiac disease, and are life threatening. A noninvasive, unobtrusive, blood‐free method for tracking potassium would be an important clinical advance. Methods and Results Two groups of hemodialysis patients (development group, n=26; validation group, n=19) underwent high‐resolution digital ECG recordings and had 2 to 3 blood tests during dialysis. Using advanced signal processing, we developed a personalized regression model for each patient to noninvasively calculate potassium values during the second and third dialysis sessions using only the processed single‐channel ECG . In addition, by analyzing the entire development group's first‐visit data, we created a global model for all patients that was validated against subsequent sessions in the development group and in a separate validation group. This global model sought to predict potassium, based on the T wave characteristics, with no blood tests required. For the personalized model, we successfully calculated potassium values with an absolute error of 0.36±0.34 mmol/L (or 10% of the measured blood potassium). For the global model, potassium prediction was also accurate, with an absolute error of 0.44±0.47 mmol/L for the training group (or 11% of the measured blood potassium) and 0.5±0.42 for the validation set (or 12% of the measured blood potassium). Conclusions The signal‐processed ECG derived from a single lead can be used to calculate potassium values with clinically meaningful resolution using a strategy that requires no blood tests. This enables a cost‐effective, noninvasive, unobtrusive strategy for potassium assessment that can be used during remote monitoring.

Lactic acidosis is associated with high morbidity and mortality in hospitalized patients. Treatment of lactic acidosis is targeted on correcting the underlying causes and optimizing adequate oxygen delivery to the tissues. Even though evidence is lacking, continuous renal replacement therapy (CRRT) and dialysis have been advocated as treatments for lactic acidosis. We report a 28-year-old Caucasian male with a history of hemophagocytic lymphohistiocytosis who presented with septic shock, severe lactic acidosis and multiple organ failure. Metabolic acidosis was corrected after bicarbonate therapy and CRRT with a hemofiltration rate of 7 L/h (58 mL/kg/h). Lactate clearance was calculated to be 79 mL/min. Compared with reported rates of lactate overproduction in septic shock, the rate of lactate clearance is quite small. Our case suggests that CRRT with high-volume hemofiltration is not effective for severe lactic acidosis. Lactic acidosis alone should not be considered as a nonrenal indication for CRRT.

The lengthy course of IgA nephropathy and the possibility of good outcomes without therapy suggest nontoxic therapies such as angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs.) Among patients with IgA nephropathy, both ACE inhibitors and ARBs reduce the transglomerular passage of large, but not small, molecules, reducing proteinuria. The antiproteinuric effects of ACE inhibitors and ARBs are probably equivalent. Dual ACE inhibitor-ARB therapy reduces proteinuria by 54% to 73% and is more effective than either agent alone. To determine whether ACE inhibitors or ARBs preserve renal function long-term, one must rely on trials studying nondiabetic, proteinuric renal diseases rather than on trials specific to IgA nephropathy. Among this group of patients, several randomized, controlled trials, including the AIPRI trial, the REIN trial, and a metaanalysis of 11 randomized, controlled trials, have established clearly that the ACE inhibitors preserve renal function. There is no reason to believe that this information is not applicable to IgA nephropathy. The COOPERATE trial, in which 50% of the subjects had IgA nephropathy, established that ACE inhibitors and ARBs preserve renal function equally, and that dual ACE inhibitor-ARB therapy preserves renal function more effectively than either therapy alone. These data suggest that most individuals with proteinuric renal diseases, including IgA nephropathy, should be treated with ACE inhibitors and ARBs, ideally in combination. Polymorphisms of the angiotensinogen gene, the ACE gene, and the angiotensin II type I receptor gene have, so far, failed to predict either susceptibility to or progression of IgA nephropathy. However, the D allele of the ID polymorphism, particularly the DD genotype, could predict a favorable response to renin-angiotensin blockade.

Objective To examine long-term outcomes, including all-cause mortality, and the likelihood and timing of renal recovery among patients requiring renal replacement therapy (RRT) for acute kidney injury after cardiac surgery. Patients and Methods This is a single-center, historical, matched cohort study of post–cardiac surgery patients who required RRT from January 1, 2007, through December 31, 2012. We matched each case with 2 controls, each of whom did not require RRT after cardiac surgery, for age, sex, and type of surgery. The patients were followed up for 1 year after the start of RRT. The main outcomes were all-cause mortality in all patients and rate of renal function recovery in patients who required RRT. Results A total of 202 patients met the inclusion criteria. The unadjusted all-cause mortality among patients requiring RRT was 64% at 1 year vs 8% for matched controls. In multivariate analysis, the hazard ratio for all-cause mortality was 12.59 (95% CI, 8.24-19.68) for cases vs controls. Increased 1-year all-cause mortality was independently associated with increased age, a history of congestive heart failure, lower preoperative creatinine level, longer interval between surgery and starting RRT, and the need for mechanical ventilation or an intra-aortic balloon pump at the time of RRT. Renal recovery occurred in 34% of cases by 90 days and in 39% by 1 year. Of those who recovered renal function, 87% were within 90 days. Only 8 (4%) of the 186 patients were alive and continued to receive RRT at 1 year. Conclusions The need for RRT after cardiac surgery is an independent risk factor for mortality. In the case of survival, the chance of renal recovery is reasonable.

Background/Aims: The incidence of adverse events (AEs) in adults who receive continuous renal replacement therapy (CRRT) is unknown. We report the incidence of mechanical, metabolic, and hemodynamic CRRT AEs. Methods: This is a retrospective study of all consecutive adult patients (≥18 years) who underwent CRRT from January 1, 2007 to December 31, 2009. Results: Out of 595 patients who underwent CRRT, 366 (62%) were male and 500 (84%) were Caucasian. Regional citrate anticoagulation was used in 98.6% of all patients. The most common clinically significant electrolyte derangements were ionized hypocalcemia (22%), ionized hypercalcemia (23%), and hyperphosphatemia (44%). Almost all (97%) patients had at least one additional AE including new onset hypotension (within the first hour after CRRT initiation) (43%), hypothermia (44%), new onset arrhythmias (29%), new onset anemia (31%) and thrombocytopenia (40%). Conclusions: ICU patients who require CRRT have a high incidence of AEs. Although the extent to which these complications are attributable to CRRT is not known, clinicians need to be cautious and aware of their high prevalence in this patient population.

Recent policy clarifications by the Centers for MedicareMedicaid Services have changed access to outpatient dialysis care at end-stage renal disease (ESRD) facilities for individuals with acute kidney injury in the United States. Tools to predict "ESRD" and "acute" status in terms of kidney function recovery among patients who previously initiated dialysis therapy in the hospital could help inform patient management decisions.Historical cohort study.Incident hemodialysis patients in the Mayo Clinic Health System who initiated in-hospital renal replacement therapy (RRT) and continued outpatient dialysis following hospital dismissal (2006 through 2009).Baseline estimated glomerular filtration rate (eGFR), acute tubular necrosis from sepsis or surgery, heart failure, intensive care unit, and dialysis access.Kidney function recovery defined as sufficient kidney function for outpatient hemodialysis therapy discontinuation.Cohort consisted of 281 patients with a mean age of 64 years, 63% men, 45% with heart failure, and baseline eGFR≥30mL/min/1.73m(2) in 46%. During a median of 8 months, 52 (19%) recovered, most (94%) within 6 months. Higher baseline eGFR (HR per 10-mL/min/1.73m(2) increase eGFR, 1.27; 95% CI, 1.16-1.39; P0.001), acute tubular necrosis from sepsis or surgery (HR, 3.34; 95% CI, 1.83-6.24; P0.001), and heart failure (HR, 0.40; 95% CI, 0.19-0.78, P=0.007) were independent predictors of recovery within 6 months, whereas first RRT in the intensive care unit and catheter dialysis access were not. There was a positive interaction between absence of heart failure and eGFR≥30mL/min/1.73m(2) for predicting kidney function recovery (P0.001).Sample size.Kidney function recovery in the outpatient hemodialysis unit following in-hospital RRT initiation is not rare. As expected, higher baseline eGFR is an important determinant of recovery. However, patients with heart failure are less likely to recover even with a higher baseline eGFR. Consideration of these factors at hospital discharge informs decisions on ESRD status designation and long-term hemodialysis care.

Patients with end-stage renal disease (ESRD) are often excluded from trials comparing off and on-pump coronary artery bypass grafting (CABG). Thus data in this cohort is limited to small retrospective studies. Hence we compared the adverse clinical events and outcome in patients with ESRD undergoing off (OPCABG) and on-pump surgery (ONCABG).Pubmed, Scopus and Web of Science were searched (inception - June 2013) to identify studies comparing clinical results of OPCABG and ONCABG in dialysis dependent patients. A random effect inverse variance weighted meta-analysis was conducted. Results are presented as risk ratios (RR) with 95% confidence intervals; p0.05 is significant.Ten retrospective studies (2762 OPCABG and 11310 ONCABG) fulfilled criteria and were pooled. Patients undergoing off-pump surgery were less than 100 in most of the articles. Early mortality [OPCABG (8.4%); ONCABG (10.4%)] was comparable [RR 0.80(0.51-1.17); p=0.35; I(2)=30%]. Re-exploration for bleeding [RR 0.81(0.47-1.39); p=0.44] and blood transfusion [RR 0.79(0.57-1.08); p=0.14] were also comparable. While patients undergoing off-pump surgery were extubated earlier (p0.01), other post-operative events like stroke (p=0.34) and atrial fibrillation (p=0.10) were similar. Mid-term survival (three to five years) was also comparable.Patients with end-stage renal disease undergoing coronary artery bypass grafting demonstrate comparable results irrespective of method. While available data is limited to retrospective studies, we failed to demonstrate any significant advantage for performing OPCABG in this group of patients.