Kattah, Andrea G.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) have been associated with an increased risk of chronic hypertension for both mothers and offspring. We sought to quantify the incidence of chronic hypertension in offspring from HDP-affected pregnancies in a large, population-based cohort study. Furthermore, we evaluate the association of HDP exposure in utero and maternal chronic hypertension in offspring. METHODS: We performed a population-based cohort study of 8755 individuals born during 1976 to 1982 to 7544 women who all resided in the same community at the time of delivery. HDP were identified using a previously validated algorithm. Diagnosis of chronic hypertension in mothers and their offspring was determined using diagnostic codes. Cox proportional hazards regression was used to assess the association between HDP and chronic hypertension. RESULTS: HDP exposure (hazard ratio, 1.50 [95% CI, 1.18–1.90]) and maternal chronic hypertension (hazard ratio, 1.73 [95% CI, 1.48–2.02]) were both associated with a significant increased risk for chronic hypertension in offspring. Both risk factors remained significantly associated with increased risk of hypertension in offspring when included together in a multivariate model. Having both exposures was associated with a 2.4-fold increase in the risk of hypertension in offspring, suggesting a synergistic additive interaction. CONCLUSIONS: HDP exposure in gestation and maternal hypertension are both independently associated with an increased risk of chronic hypertension in offspring. Our results suggest that HDP exposure in utero, in addition to maternal chronic hypertension, may lead to a greater risk for the development of hypertension in offspring.

Abstract Background and Aims At least 30% of acute kidney injury (AKI) survivors lack appropriate follow up after hospital discharge. AKI survivors have highly dynamic posthospital course which warrants close monitoring to prevent adverse outcomes. Digital health solutions like remote patient monitoring (RPM) could be used to improve quality and efficiency of AKI survivor care. The purpose for this study was to assess the feasibility and effectiveness of the Mayo Clinic AKI RPM program, launched in October 2021. Method The Mayo Clinic AKI RPM program enrolled individuals who experienced AKI during a hospitalization and underwent nephrology consultation (Figure 1). Feasibility was assessed as the proportion of individuals approached for AKI RPM enrollment during the first year of the program who submitted at least one set of vital signs after discharge. An effectiveness analysis compared stage 3 AKI survivors enrolled in AKI RPM with at least 30-days of follow-up to matched historical controls (3:1) sampled from before RPM was available (2018-2021). The primary endpoint was hospital readmission or emergency department (ED) visit within 30-days, assessed with the Chi-square test. Secondarily we explored time to first readmission or ED visit with a Kaplan-Meier survival curve with a non-parametric comparison between groups, as well as readmission length of stay with the Wilcoxon Rank Sum test due to right skewed data distribution. Results Of the 50 individuals approached for AKI RPM participation, 45 (90%) submitted at least one set of vitals. Among AKI RPM patients, 34 patients with stage 3 AKI were matched to 102 controls based on baseline characteristics and demographics. Dialysis during hospitalization (liberated by discharge) was used in 36 (27%) of patients. Sixty (44%) individuals required ICU level of care. Median (IQR) discharge estimated glomerular filtration rate was 15 (11, 27) mL/min/1.73m2. Through matching, groups were well balanced with respect to pertinent baseline demographics. Hospital readmission or ED visit occurred in 17 (50%) of AKI RPM patients within 30-days compared to 39 (38%) of controls within 30-days (P = .23). The endpoint appeared driven by ED visits within 30-days, not readmissions [At least one ED visit: 13 (38%) vs 21 (21%), respectively (P = .04); At least one hospital readmission: 7 (21%) vs 26 (26%), respectively (P = .56)]. Time to first readmission or ED visit within 30-days was similar between groups (P = .35; Figure 2). Among the 33 patients who were readmitted to the hospital within 30-days, readmission length of stay was similar in the AKI RPM group compared to controls [Median (IQR) 76 (10.6, 121) hours vs 108 (70, 165) hours); P = 0.33]. Conclusion In conclusion, AKI RPM was a feasible program when used to bridge the care continuum (hospital to home) in non-dialysis dependent AKI survivors. Incidence of at least one hospital readmission or ED visit within 30-days was statistically similar between AKI RPM patients and controls. More AKI RPM patients experienced ED encounters in the 30-days after discharge, but frequency of hospital readmission was similar. Digital health solutions such as RPM offer a unique opportunity to address the important gap in AKI care after discharging from the hospital. Additional research is needed to explore the impact of AKI RPM on patient outcomes.

Abstract Thyroid dysfunction in pregnancy has been associated with hypertensive disorders of pregnancy and poor maternal and fetal outcomes. In this article, we describe two cases of significant thyroid dysfunction leading to new diagnoses of chronic hypertension in pregnancy and review physiologic changes in the thyroid gland, placenta and maternal cardiovascular system during pregnancy.

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 and objectives Despite the dramatic increase in the provision of virtual nephrology care, only anecdotal reports of outcomes without comparators to usual care exist in the literature. This study aimed to provide objective determination of clinical noninferiority of hybrid (telenephrology plus face-to-face) versus standard (face-to-face) inpatient nephrology care. Design, setting, participants, & measurements This retrospective study compares objective outcomes in patients who received inpatient hybrid care versus standard nephrology care at two Mayo Clinic Health System community hospitals. Outcomes were then additionally compared with those patients receiving care at another Mayo Clinic Health System site where only standard care is available. Hospitalized adults who had nephrology consults from March 1, 2020 to February 28, 2021 were considered. Regression was used to assess 30-day mortality, length of hospitalization, readmissions, odds of being prescribed dialysis, and hospital transfers. Sensitivity analysis was performed using patients who had ≥50% of their care encounters via telenephrology. Structured surveys were used to understand the perspectives of non-nephrology hospital providers and telenephrologists. Results In total, 850 patients were included. Measured outcomes that included the number of hospital transfers (odds ratio, 1.19; 95% confidence interval, 0.37 to 3.82) and 30-day readmissions (odds ratio, 0.97; 95% confidence interval, 0.84 to 1.06), among others, did not differ significantly between controls and patients in the general cohort. Telenephrologists ( n =11) preferred video consults (82%) to phone for communication. More than half (64%) of telenephrologists spent less time on telenephrology compared with standard care. Non-nephrology hospital providers ( n =21) were very satisfied (48%) and satisfied (29%) with telenephrology response time and felt telenephrology was as safe as standard care (67%), while providing them enough information to make patient care decisions (76%). Conclusions Outcomes for in-hospital nephrology consults were not significantly different comparing hybrid care versus standard care. Non-nephrology hospital providers and telenephrologists had favorable opinions of telenephrology and most perceived it is as safe and effective as standard care. Podcast This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2022_04_11_CJN13441021.mp3

Background: Acute kidney injury (AKI) survivors are at heightened risk for poor short- and long-term health outcomes. Even among those who recover after an AKI episode, the risk for chronic kidney disease is 4- to 6-fold higher than in patients without AKI, underscoring the importance of identifying methods to improve AKI survivorship. Objective: The purpose of this report was to describe the development and feasibility of a novel multidisciplinary approach to caring for AKI survivors at care transitions (ACT). Design: Observational process improvement initiative. Setting: Single academic medical center in the United States. Patients: The studied population was adults with stage 3 AKI not discharging on dialysis who were established with a primary care provider (PCP) at our institution. Methods: An electronic health record tool was developed prior to implementation to identify AKI survivors. The ACT program encompassed engaging patients in the hospital, delivering education by nephrology-trained nurses before discharge, completing recommended laboratory testing after discharge, and conducting structured kidney-focused follow-up with a pharmacist and a PCP within 7 to 14 days after discharge. Patients could be referred for nephrology evaluation at the discretion of the PCP. Results: Preliminary data demonstrated that most AKI survivors of interest could be identified, educated, and followed up with this model. This strategy appeared feasible, scalable, and maximized the unique expertise of each member of the multidisciplinary team. Limitations: Small sample size, future assessment of process, clinical, and patient-reported outcomes needed. Conclusions: The multidisciplinary ACT workflow supported by clinical decision support was feasible and addressed gaps in existing care transition models. Team-based care delivery in primary care appears to be a mechanism to extend the capacity for kidney health monitoring for AKI survivors.

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.

ABSTRACT 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.