Kattah, Andrea G.

Background: We aimed to cluster patients with acute kidney injury at hospital admission into clinically distinct subtypes using an unsupervised machine learning approach and assess the mortality risk among the distinct clusters. Methods: We performed consensus clustering analysis based on demographic information, principal diagnoses, comorbidities, and laboratory data among 4289 hospitalized adult patients with acute kidney injury at admission. The standardized difference of each variable was calculated to identify each cluster’s key features. We assessed the association of each acute kidney injury cluster with hospital and one-year mortality. Results: Consensus clustering analysis identified four distinct clusters. There were 1201 (28%) patients in cluster 1, 1396 (33%) patients in cluster 2, 1191 (28%) patients in cluster 3, and 501 (12%) patients in cluster 4. Cluster 1 patients were the youngest and had the least comorbidities. Cluster 2 and cluster 3 patients were older and had lower baseline kidney function. Cluster 2 patients had lower serum bicarbonate, strong ion difference, and hemoglobin, but higher serum chloride, whereas cluster 3 patients had lower serum chloride but higher serum bicarbonate and strong ion difference. Cluster 4 patients were younger and more likely to be admitted for genitourinary disease and infectious disease but less likely to be admitted for cardiovascular disease. Cluster 4 patients also had more severe acute kidney injury, lower serum sodium, serum chloride, and serum bicarbonate, but higher serum potassium and anion gap. Cluster 2, 3, and 4 patients had significantly higher hospital and one-year mortality than cluster 1 patients (p < 0.001). Conclusion: Our study demonstrated using machine learning consensus clustering analysis to characterize a heterogeneous cohort of patients with acute kidney injury on hospital admission into four clinically distinct clusters with different associated mortality risks.

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 < 0.001), and five-year mortality (20.2% vs. 44.3%; p < 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%; p < 0.001) one-year mortality (32.9% vs. 14.8%; p < 0.001), and five-year mortality (24.5% vs. 51.1%; p < 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 >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.

AbstractPeritoneal–mediastinal communication is a rare complication of peritoneal dialysis (PD). We report the first case of peritoneal–mediastinal communication complication in a 36‐year‐old Caucasian man on continuous cycler peritoneal dialysis (CCPD) after undergoing cardiac surgery. He developed end‐stage kidney disease (ESKD) due to calcineurin inhibitor nephrotoxicity and BK virus nephropathy in the setting of prior heart transplantation. He was initially started on intermittent hemodialysis (iHD) and was switched to CCPD 1 year later. He presented with increased drainage from his sternal incision site and reduced ultrafiltration. A contrast‐enhanced chest computed tomography scan revealed an anterior chest wall subcutaneous fluid collection. He was found to have a peritoneal–mediastinal communication intraoperatively. He was successfully managed with “low‐volume” PD by using reduced fill volumes for all his exchanges and did not require transition to iHD. He also had no vascular access options because of multiple prior thromboses, which would have made transitioning to iHD not feasible. This case further highlights the complex management of an ESKD patient who cannot do iHD and only do low‐volume PD because of a surgical complication and the need for a multidisciplinary approach to ensure appropriate patient care.

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.

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.

AbstractBackgroundThis study aimed to assess outcomes of delivery hospitalizations, including acute kidney injury (AKI), obstetric and foetal events and resource utilization among pregnant women with kidney transplants compared with pregnant women with no known kidney disease and those with chronic kidney disease (CKD) Stages 3–5.MethodHospitalizations for delivery in the US were identified using the enhanced delivery identification method in the National Inpatient Sample dataset from the years 2009 to 2014. Diagnoses of CKD Stages 3–5, kidney transplantation, along with obstetric events, delivery methods and foetal events were identified using ICD‐9‐CM diagnosis and procedure codes. Patients with no known kidney disease group were identified by excluding any diagnoses of CKD, end stage kidney disease, and kidney transplant. Multivariable logistic regression accounting for the survey weights and matched regression was conducted to investigate the risk of maternal and foetal complications in women with kidney transplants, compared with women with no kidney transplants and no known kidney disease, and to women with CKD Stages 3–5.ResultA total of 5, 408, 215 hospitalizations resulting in deliveries were identified from 2009 to 2014, including 405 women with CKD Stages 3–5, 295 women with functioning kidney transplants, and 5, 405, 499 women with no known kidney disease. Compared with pregnant women with no known kidney disease, pregnant kidney transplant recipients were at higher odds of hypertensive disorders of pregnancy (OR = 3.11, 95% CI [2.26, 4.28]), preeclampsia/eclampsia/HELLP syndrome (OR = 3.42, 95% CI [2.54, 4.60]), preterm delivery (OR = 2.46, 95% CI [1.75, 3.45]), foetal growth restriction (OR = 1.74, 95% CI [1.01, 3.00]) and AKI (OR = 10.46, 95% CI [5.33, 20.56]). There were no significant differences in rates of gestational diabetes or caesarean section. Pregnant women with kidney transplants had 1.30‐times longer lengths of stay and 1.28‐times higher costs of hospitalization. However, pregnant women with CKD Stages 3–5 were at higher odds of AKI (OR = 5.29, 95% CI [2.41, 11.59]), preeclampsia/eclampsia/HELLP syndrome (OR = 1.72, 95% CI [1.07, 2.76]) and foetal deaths (OR = 3.20, 95% CI [1.06, 10.24]), and had 1.28‐times longer hospital stays and 1.37‐times higher costs of hospitalization compared with pregnant women with kidney transplant.ConclusionPregnant women with kidney transplant were more likely to experience adverse events during delivery and had longer lengths of stay and higher total charges when compared with women with no known kidney disease. However, pregnant women with moderate to severe CKD were more likely to experience serious complications than kidney transplant recipients.

Abstract Background: Accurate interpretation of laboratory values with sex-specific reference ranges presents a challenge in transgender individuals on gender affirming hormone therapy (GAHT). Creatinine (Cr), the most common marker used for kidney function, varies significantly with body mass and composition. Both Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi) equations account for sex in estimating glomerular filtration rate. GAHT can effect Cr values in 2 potential ways: 1) by causing changes in muscle mass and body fat redistribution as early as 3 months after GAHT initiation and 2) by direct effects of sex hormones on kidney function. Previous studies have shown Cr values approaching affirmed gender identity as early as 6 months when on GAHT without mention of sex steroid levels. In this study we sought to describe the changes in serum Cr after initiation of GAHT in an effort to better understand expected changes and interpretation of lab data in TG individuals. Methods: A retrospective chart analysis on all adult TG patients initiated on GAHT at our institution from January 2011 to 2020 was completed. We reviewed demographics, baseline health information, body mass index, and lab values including Cr, sex hormone levels, A1C, and fasting blood glucose. Lab values were obtained prior to GAHT, at the start of GAHT, at 3, 6, and 12 months after GAHT. Matched pair testing was conducted with sex steroid levels and Cr values in transgender men (TM) on testosterone and transgender women (TW) on estradiol in order to compare the median pre GAHT Cr to median Cr levels at 3, 6, and 12 months. Results: 84 TW with a median age of 30 and 24 TM with a median age of 23 were included for analysis. TW and TM had a low rate of existing kidney disease (4.9%, 0%), diabetes mellitus (4.8%, 0%), and hypertension (10.8%, 4.5%) respectively. TW on GAHT achieved a goal estradiol level (≥100 pg/ml) at a rate of 37.3%, 51.7%, and 71.1% and suppressed testosterone to a goal level (&lt;60ng/ml) at a rate of 44.4%, 54.7%, and 76.5% at 3, 6, and 12 months respectively. There was no significant change in Cr values at 3 months, but significantly decreased on average by -0.07 (p&lt;0.001) at 6 months, and by -0.09 (p&lt;0.001) at 12 months. TM on GAHT achieved a goal testosterone level (≥240 ng/dl) at a rate of 64.3%, 80.0%, and 72.3% at 3, 6, and 12 months respectively. Cr values increased significantly on average by 0.14 (p=0.036) at 3 months, by 0.21 (p=0.004) at 6 months, and by 0.15 (p=0.003) at 12 months. Conclusions: In TW on GAHT, clinicians can consider using affirmed gender Cr reference ranges as early as 6 months. Similarly in TM on GAHT, affirmed gender Cr reference ranges can be used as early at 3 months. It remains to be seen whether changes in Cr levels reflect changes in sex steroid levels or sex steroid direct effects. Additionally, research is needed to determine if change in Cr levels reflect true changes in GFR.

Abstract Objective: Hypertensive disorders of pregnancy and menopausal symptoms, specifically vasomotor symptoms, have both been associated with cardiovascular disease risk in women. However, data are sparse on the association between these two female-specific cardiovascular risk factors. This study was conducted to investigate the association between a history of a hypertensive disorder of pregnancy and menopausal symptoms. Methods: This was a cross-sectional study of women aged 40 to 65 years seen for specialty consultation in women's health clinics at Mayo Clinic Rochester, MN and Scottsdale, AZ, between May, 2015 and September, 2019. A self-reported history of hypertensive disorders of pregnancy served as the independent variable, and menopause symptoms as assessed by the Menopause Rating Scale were the primary outcome measure. Results: Of 2,684 women included in the analysis, 180 had a self-reported history of a hypertensive disorder of pregnancy. The total menopausal symptom scores as well as somatic and psychological domain scores were higher in women with a history of a hypertensive disorder of pregnancy compared to women without a history of a hypertensive disorder of pregnancy or to women without a pregnancy. On multivariable analysis, women with a hypertensive disorder of pregnancy using hormone therapy had significantly higher total menopause symptom scores than women with no such history. Conclusions: In this large cross-sectional study, a history of hypertensive disorders of pregnancy was associated with more bothersome menopausal symptoms. Additional study is needed to determine the strength of this association, underlying mechanisms of the association, and clinical implications for cardiovascular risk prediction in women.