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Telenephrology has become an important health care delivery modality during the COVID-19 pandemic. However, little is known about patient perspectives on the quality of care provided via telenephrology compared to face-to-face visits. We aimed to use objective data to study patients' perspectives on outpatient nephrology care received via telenephrology (phone and video) versus face-to-face visits. We retrospectively studied adults who received care in the outpatient Nephrology & Hypertension division at Mayo Clinic, Rochester, from March to July 2020. We used a standardized survey methodology to evaluate patient satisfaction. The primary outcome was the percent of patients who responded with a score of good (4) or very good (5) on a 5-point Likert scale on survey questions that asked their perspectives on access to their nephrologist, relationship with care provider, their opinions on the telenephrology technology, and their overall assessment of the care received. Wilcoxon rank sum tests and chi-square tests were used as appropriate to compare telenephrology versus face-to-face visits. 3,486 of the patient encounters were face-to-face, 808 phone and 317 video visits. 443 patients responded to satisfaction surveys, and 21% of these had telenephrology encounters. Established patients made up 79.6% of telenephrology visits and 60.9% of face-to-face visits. There was no significant difference in patient perceived access to health care, satisfaction with their care provider, or overall quality of care between patients cared for via telenephrology versus face-to-face. Patient satisfaction was also equally high. Patient satisfaction was equally high amongst those patients seen face-to-face or via telenephrology.

Purpose of ReviewPreeclampsia and chronic kidney disease have a complex, bidirectional relationship. Women with kidney disease, with even mild reductions in glomerular filtrate rate, have an increased risk of developing preeclampsia. Preeclampsia, in turn, has been implicated in the subsequent development of albuminuria, chronic kidney disease, and end-stage kidney disease. We will discuss observational evidence and mechanisms linking the two disease processes.Recent FindingsPreeclampsia is characterized by an imbalance in angiogenic factors that causes systemic endothelial dysfunction. Chronic kidney disease may predispose to the development of preeclampsia due to comorbid conditions, such as hypertension, but is also associated with impaired glycocalyx integrity and alterations in the complement and renin-angiotensin-aldosterone systems. Preeclampsia may lead to kidney disease by causing acute kidney injury, endothelial damage, and podocyte loss.SummaryPreeclampsia may be an important sex-specific risk factor for chronic kidney disease. Understanding how chronic kidney disease increases the risk of preeclampsia from a mechanistic standpoint may open the door to future biomarkers and therapeutics for all women.

BackgroundEvidence suggests that upregulation of tumor necrosis factor-alpha (TNF-α) plays a role in immune dysregulation in both preeclampsia and inflammatory bowel disease (IBD).AimsWe aimed to investigate whether anti-TNF therapy during pregnancy decreases the risk of preeclampsia in women with IBD.MethodsThe study population included women with IBD and pregnancies who were followed at a tertiary care center from 2007 to 2021. Cases of preeclampsia were compared with controls with a normotensive pregnancy. Data on patient demographics, disease type and activity, pregnancy complications, and additional risk factors for preeclampsia were collected. The association between anti-TNF therapy and preeclampsia was analyzed using univariate analysis and multivariate logistic regression.ResultsWomen with preeclampsia were more likely to have a preterm delivery (44% vs. 12%, p < 0.001). More women without preeclampsia were exposed to anti-TNF therapy during pregnancy than women with preeclampsia (55% vs. 30%, p = 0.029). The majority of women (32/44) on anti-TNF therapy, either adalimumab or infliximab, continued to have some degree of exposure during the third trimester. Though not significant, multivariate analysis showed a trend towards a protective effect of anti-TNF therapy against developing preeclampsia if exposed during the third trimester (OR 0.39; 95% CI 0.14–1.12, p = 0.08).ConclusionsIn this study, anti-TNF therapy exposure was higher in IBD patients who did not develop preeclampsia than in those who did. While not significant, there was a trend towards a protective effect of anti-TNF therapy against preeclampsia if exposed during the third trimester.

Background COVID-19 is associated with increased morbidity and mortality in patients with end-stage kidney disease on dialysis. Efficacy of SARS-CoV-2 vaccination to prevent severe COVID-19 disease in end-stage kidney disease patients remains limited. We compared the incidence of COVID-19-related hospitalization and death in dialysis patients based on SARS-CoV-2 vaccine status. Methods Retrospective study of adults on chronic dialysis within Mayo Clinic Dialysis System in the Midwest (USA) between April 1st, 2020 and October 31st, 2022, who had a laboratory test positive for SARS-CoV-2 by PCR. Incidence of both COVID-19-related hospitalization and death were compared between vaccinated and unvaccinated patients. Results SARS-CoV-2 infection was identified in 309 patients, including 183 vaccinated and 126 unvaccinated. The incidence of death (11.1% vs 3.8%, p  = 0.02) and hospitalization (55.6% vs 23.5%, p  < 0.001) was significantly higher in unvaccinated compared to vaccinated patients. Age at infection, sex, Charlson comorbidity index, dialysis modality, and hospital stays did not differ between the two groups. The incidence of hospitalization was significantly higher in partially vaccinated (63.6% vs 20.9%, p  = 0.004) and unboosted (32% vs 16.4%, p  = 0.04) patients compared to fully vaccinated and boosted, respectively. Among the 21 patients who died in the whole cohort, 47.6% ( n  = 10) died during the pre-vaccine period. The composite risk of death or hospitalization was lower among vaccinated patients after adjusting for age, sex and Charlson comorbidity index (OR 0.24, 95% CI 0.15–0.40). Conclusions This study supports the use of SARS-CoV-2 vaccination to improve COVID-19 outcomes in patients on chronic dialysis. Graphical Abstract

A 59-year-old man with a history of bipolar disorder was admitted to the hospital after a fall and was incidentally found to have numerous small renal cysts, a finding consistent with previous lithium treatment.

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 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. Methods 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. Results 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. Conclusion Our cluster analysis identified three clinically distinct phenotypes with differing mortality risk in patients hospitalized with hypernatremia. Graphic abstract

Acute kidney injury (AKI) survivors have a dynamic posthospital course which warrants close monitoring. Remote patient monitoring (RPM) could be used to improve quality and efficiency of AKI survivor care. Objective: The objective of this report was to describe the development and preliminary feasibility of an AKI RPM program launched in October 2021. Setting: Academic medical center. Patients: Patients enrolled in the AKI RPM program were those who experienced AKI during a hospitalization and underwent nephrology consultation. Measurements/Methods: At enrollment, patients were provided with home monitoring technology and underwent weekly laboratory assessments. Nurses evaluated the data daily and adhered to prespecified protocols for management and escalation of care if needed. Results: Twenty patients were enrolled in AKI RPM in the first 5 months. Median duration of program participation was 36 (31, 40) days. Eight patients (40%) experienced an unplanned readmission, or an emergency department visit, half (N = 4) of which were attributed to AKI and related circumstances. Of the 9 postgraduation survey respondents, all were satisfied with the RPM program and 89% would recommend RPM to other patients with similar health conditions. Limitations: Acute kidney injury RPM was made possible by the existing infrastructure in our integrated health system and the robust resources available in the Mayo Clinic Center for Digital Health. Such infrastructure may not be universally available which could limit scale and generalizability of such a program. Conclusions: Remote patient monitoring can offer a unique opportunity to bridge the care transition from hospital to home and increase access to quality care for the AKI survivors.

To develop and validate criteria for the retrospective diagnoses of hypertensive disorders of pregnancy that would be amenable to the development of an electronic algorithm, and to compare the accuracy of diagnoses based on both the algorithm and diagnostic codes with the gold standard, of physician-made diagnoses based on a detailed review of medical records using accepted clinical criteria. An algorithm for hypertensive disorders of pregnancy was developed by first defining a set of criteria for retrospective diagnoses, which included relevant clinical variables and diagnosis of hypertension that required blood pressure elevations in greater than 50% of readings (“the 50% rule”). The algorithm was validated using the Rochester Epidemiology Project (Rochester, Minnesota). A stratified random sample of pregnancies and deliveries between January 1, 1976, and December 31, 1982, with the algorithm-based diagnoses was generated for review and physician-made diagnoses (normotensive, gestational hypertension, and preeclampsia), which served as the gold standard; the targeted cohort size for analysis was 25 per diagnosis category according to the gold standard. Agreements between (1) algorithm-based diagnoses and (2) diagnostic codes and the gold standard were analyzed. Sensitivities of the algorithm for 25 normotensive pregnancies, 25 with gestational hypertension, and 25 with preeclampsia were 100%, 88%, and 100%, respectively, and specificities were 94%, 100%, and 100%, respectively. Diagnostic code sensitivities were 96% for normotensive pregnancies, 32% for gestational hypertension, and 96% for preeclampsia, and specificities were 78%, 96%, and 88%, respectively. The electronic diagnostic algorithm was highly sensitive and specific in identifying and classifying hypertensive disorders of pregnancy and was superior to diagnostic codes.

IntroductionObservational studies have linked slower and faster net ultrafiltration (UFNET) rates during kidney replacement therapy (KRT) with mortality in critically ill patients with acute kidney injury (AKI) and fluid overload. To inform the design of a larger randomised trial of patient-centered outcomes, we conduct a feasibility study to examine restrictive and liberal approaches to UFNET during continuous KRT (CKRT).Methods and analysisThis study is an investigator-initiated, unblinded, 2-arm, comparative-effectiveness, stepped-wedged, cluster randomised trial among 112 critically ill patients with AKI treated with CKRT in 10 intensive care units (ICUs) across 2 hospital systems. In the first 6 months, all ICUs started with a liberal UFNET rate strategy. Thereafter, one ICU is randomised to the restrictive UFNET rate strategy every 2 months. In the liberal group, the UFNET rate is maintained between 2.0 and 5.0 mL/kg/hour; in the restrictive group, the UFNET rate is maintained between 0.5 and 1.5 mL/kg/hour. The three coprimary feasibility outcomes are (1) between-group separation in mean delivered UFNET rates; (2) protocol adherence; and (3) patient recruitment rate. Secondary outcomes include daily and cumulative fluid balance, KRT and mechanical ventilation duration, organ failure-free days, ICU and hospital length of stay, hospital mortality and KRT dependence at hospital discharge. Safety endpoints include haemodynamics, electrolyte imbalance, CKRT circuit issues, organ dysfunction related to fluid overload, secondary infections and thrombotic and haematological complications.Ethics and disseminationThe University of Pittsburgh Human Research Protection Office approved the study, and an independent Data and Safety Monitoring Board monitors the study. A grant from the United States National Institute of Diabetes and Digestive and Kidney Diseases sponsors the study. The trial results will be submitted for publication in peer-reviewed journals and presented at scientific conferences.Trial registration numberThis trial has been prospectively registered with clinicaltrials.gov (NCT05306964). Protocol version identifier and date: 1.5; 13 June 2023.