Evaluating ChatGPT's efficacy in assessing the safety of non-prescription medications and supplements in patients with kidney disease.

Background: This study investigated the efficacy of ChatGPT-3.5 and ChatGPT-4 in assessing drug safety for patients with kidney diseases, comparing their performance to Micromedex, a well-established drug information source. Despite the perception of non-prescription medications and supplements as safe, risks exist, especially for those with kidney issues. The study's goal was to evaluate ChatGPT's versions for their potential in clinical decision-making regarding kidney disease patients. Method: The research involved analyzing 124 common non-prescription medications and supplements using ChatGPT-3.5 and ChatGPT-4 with queries about their safety for people with kidney disease. The AI responses were categorized as "generally safe," "potentially harmful," or "unknown toxicity." Simultaneously, these medications and supplements were assessed in Micromedex using similar categories, allowing for a comparison of the concordance between the two resources. Results: Micromedex identified 85 (68.5%) medications as generally safe, 35 (28.2%) as potentially harmful, and 4 (3.2%) of unknown toxicity. ChatGPT-3.5 identified 89 (71.8%) as generally safe, 11 (8.9%) as potentially harmful, and 24 (19.3%) of unknown toxicity. GPT-4 identified 82 (66.1%) as generally safe, 29 (23.4%) as potentially harmful, and 13 (10.5%) of unknown toxicity. The overall agreement between Micromedex and ChatGPT-3.5 was 64.5% and ChatGPT-4 demonstrated a higher agreement at 81.4%. Notably, ChatGPT-3.5's suboptimal performance was primarily influenced by a lower concordance rate among supplements, standing at 60.3%. This discrepancy could be attributed to the limited data on supplements within ChatGPT-3.5, with supplements constituting 80% of medications identified as unknown. Conclusion: ChatGPT's capabilities in evaluating the safety of non-prescription drugs and supplements for kidney disease patients are modest compared to established drug information resources. Neither ChatGPT-3.5 nor ChatGPT-4 can be currently recommended as reliable drug information sources for this demographic. The results highlight the need for further improvements in the model's accuracy and reliability in the medical domain.

Subtyping hospitalized patients with hypokalemia by machine learning consensus clustering and associated mortality risks.

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.

Impact of serum magnesium levels at hospital discharge and one-year mortality.

BACKGROUND: We aimed to determine the optimal range of discharge serum magnesium in hospitalized patients by evaluating one-year mortality risk according to discharge serum magnesium. METHODS: This was a single-center cohort study of hospitalized adult patients who survived until hospital discharge. We classified discharge serum magnesium, defined as the last serum magnesium within 48 hours of hospital discharge, into ≤1.6, 1.7-1.8, 1.9-2.0, 2.1-2.2, and ≥2.3 mg/dL. We assessed one-year mortality risk after hospital discharge based on discharge serum magnesium, using discharge magnesium of 2.1-2.2 mg/dL as the reference group. RESULTS: Of 39,193 eligible patients, 8%, 23%, 34%, 23%, and 12% had a serum magnesium of ≤1.6, 1.7-1.8, 1.9-2.0, 2.1-2.2, and ≥2.3 mg/dL, respectively, at hospital discharge. After the adjustment for several confounders, discharge serum magnesium of ≤1.6, 1.7-1.8, and ≥2.3 mg/dL were associated with higher one-year mortality with hazard ratio of 1.35 (95% CI 1.21-1.50), 1.14 (95% CI 1.06-1.24), and 1.17 (95% CI 1.07-1.28), respectively, compared to discharge serum magnesium of 2.1-2.2 mg/dL. There was no significant difference in one-year mortality between patients with discharge serum magnesium of 1.9-2.0 and 2.1-2.2 mg/dL. CONCLUSION: The optimal range of serum magnesium at discharge was 1.9-2.2 mg/dL. Both hypomagnesemia and hypermagnesemia at discharge were associated with higher one-year mortality.

Hypernatremia subgroups among hospitalized patients by machine learning consensus clustering with different patient survival.

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.

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia.

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.

Genomics Integration Into Nephrology Practice.

RATIONALE & OBJECTIVE: The etiology of kidney disease remains unknown in many individuals with chronic kidney disease (CKD). We created the Mayo Clinic Nephrology Genomics Clinic to improve our ability to integrate genomic and clinical data to identify the etiology of unexplained CKD. STUDY DESIGN: Retrospective study. SETTING & PARTICIPANTS: An essential component of our program is the Nephrology Genomics Board which consists of nephrologists, geneticists, pathologists, translational omics scientists, and trainees who interpret the patient's clinical and genetic data. Since September 2016, the Board has reviewed 163 cases (15 cystic, 100 glomerular, 6 congenital anomalies of kidney and urinary tract (CAKUT), 20 stones, 15 tubulointerstitial, and 13 other). ANALYTICAL APPROACH: Testing was performed with targeted panels, single gene analysis, or analysis of kidney-related genes from exome sequencing. Variant classification was obtained based on the 2015 American College of Medical Genetics and Genomics and the Association for Molecular Pathology guidelines. RESULTS: A definitive genetic diagnosis was achieved for 50 families (30.7%). The highest diagnostic yield was obtained in individuals with tubulointerstitial diseases (53.3%), followed by congenital anomalies of the kidney and urological tract (33.3%), glomerular (31%), cysts (26.7%), stones (25%), and others (15.4%). A further 20 (12.3%) patients had variants of interest, and variant segregation, and research activities (exome, genome, or transcriptome sequencing) are ongoing for 44 (40%) unresolved families. LIMITATIONS: Possible overestimation of diagnostic rate due to inclusion of individuals with variants with evidence of pathogenicity but classified as of uncertain significance by the clinical laboratory. CONCLUSIONS: Integration of genomic and research testing and multidisciplinary evaluation in a nephrology cohort with CKD of unknown etiology or suspected monogenic disease provided a diagnosis in a third of families. These diagnoses had prognostic implications, and often changes in management were implemented.

Comprehensive Genetic Analysis Reveals Complexity of Monogenic Urinary Stone Disease.

INTRODUCTION: Because of phenotypic overlap between monogenic urinary stone diseases (USD), gene-specific analyses can result in missed diagnoses. We used targeted next generation sequencing (tNGS), including known and candidate monogenic USD genes, to analyze suspected primary hyperoxaluria (PH) or Dent disease (DD) patients genetically unresolved (negative; N) after Sanger analysis of the known genes. Cohorts consisted of 285 PH (PHN) and 59 DD (DDN) families. METHODS: Variants were assessed using disease-specific and population databases plus variant assessment tools and categorized using the American College of Medical Genetics (ACMG) guidelines. Prior Sanger analysis identified 47 novel PH or DD gene pathogenic variants. RESULTS: Screening by tNGS revealed pathogenic variants in 14 known monogenic USD genes, accounting for 45 families (13.1%), 27 biallelic and 18 monoallelic, including 1 family with a copy number variant (CNV). Recurrent genes included the following: SLC34A3 (n = 13), CLDN16 (n = 8), CYP24A1 (n = 4), SLC34A1 (n = 3), SLC4A1 (n = 3), APRT (n = 2), CLDN19 (n = 2), HNF4A1 (n = 2), and KCNJ1 (n = 2), whereas ATP6V1B1, CASR, and SLC12A1 and missed CNVs in the PH genes AGXT and GRHPR accounted for 1 pedigree each. Of the 48 defined pathogenic variants, 27.1% were truncating and 39.6% were novel. Most patients were diagnosed before 18 years of age (76.1%), and 70.3% of biallelic patients were homozygous, mainly from consanguineous families. CONCLUSION: Overall, in patients suspected of DD or PH, 23.9% and 7.3% of cases, respectively, were caused by pathogenic variants in other genes. This study shows the value of a tNGS screening approach to increase the diagnosis of monogenic USD, which can optimize therapies and facilitate enrollment in clinical trials.

Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements.

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.

Subtyping Hyperchloremia among Hospitalized Patients by Machine Learning Consensus Clustering.

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.

Machine Learning Consensus Clustering of Hospitalized Patients with Admission Hyponatremia.

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.

Identification of Genetic Causes of Focal Segmental Glomerulosclerosis Increases With Proper Patient Selection.

OBJECTIVE: To increase the likelihood of finding a causative genetic variant in patients with a focal segmental glomerulosclerosis (FSGS) lesion, clinical and histologic characteristics were analyzed. PATIENTS AND METHODS: Individuals 18 years and older with an FSGS lesion on kidney biopsy evaluated at Mayo Clinic from November 1, 1999, through October 31, 2019, were divided into 4 groups based on clinical and histologic characteristics: primary FSGS, secondary FSGS with known cause, secondary FSGS without known cause, and undetermined FSGS. A targeted gene panel and a customized gene panel retrieved from exome sequencing were performed. RESULTS: The overall rate of detection of a monogenic cause was 42.9% (21/49). Individuals with undetermined FSGS had the highest rate of positivity (87.5%; 7/8) followed by secondary FSGS without an identifiable cause (61.5%; 8/13) and secondary FSGS with known cause (33.3%; 5/15). Four of 5 (80%) individuals in the latter group who had positive genetic testing results also had a family history of kidney disease. Univariate analysis showed that family history of kidney disease (odds ratio [OR], 13.8; 95% CI, 3.7 to 62.4; P<.001), absence of nephrotic syndrome (OR, 8.2; 95% CI, 1.9 to 58.1; P=.004), and female sex (OR, 5.1; 95% CI, 1.5 to 19.9; P=.01) were strong predictors of finding a causative genetic variant in the entire cohort. The most common variants were in the collagen genes (52.4%; 11/21), followed by the podocyte genes (38.1%; 8/21). CONCLUSION: In adults with FSGS lesions, proper selection of patients increases the rate of positive genetic testing significantly. The majority of individuals with undetermined FSGS in whom the clinical presentation and histologic parameters are discordant had a genetic diagnosis.

Treatment of fibrillary glomerulonephritis with rituximab: a 12-month pilot study.

BACKGROUND: Fibrillary glomerulonephritis (FGN) is a rare type of glomerulonephritis with poor prognosis, with no known effective therapies available for treatment. The objective of the study was to evaluate the efficacy and safety of rituximab in treatment of patients with FGN and to investigate the effect of rituximab on DNAJB9 levels. METHODS: This was a pilot prospective clinical trial in which patients with idiopathic FGN were treated with two courses of rituximab (1 g each) 2 weeks apart at the beginning and then again at 6 months. Primary outcome was defined as preservation of kidney function at 12 months with stable or increased creatinine clearance. Secondary outcome was defined as achieving complete remission (CR) defined as proteinuria <300 mg/24 h or partial remission (PR) with proteinuria <3 g/24 h and at least 50% reduction in the proteinuria. DNAJB9 levels were also measured in the serum at baseline, 6 and 12 months. RESULTS: The creatinine clearance did not change significantly during this time, from 47.7 mL/min/1.73 m2 at baseline to 43.7 mL/min/1.73 m2 during follow-up (P = 0.15). Proteinuria declined from 4.43 (1.6-5.53) g/24 h at baseline to 1.9 (0.46-5.26) g/24 h at 12 months but did not reach significance (P = 0.06). None of the patients reached CR, and 3 of the 11 achieved PR. There was no change in the DNAJB9 levels following treatment with rituximab. The most common adverse event was nasal congestion, fatigue and muscle cramps. CONCLUSIONS: Treatment of patients with two courses of rituximab over a span of 6 months was associated with stabilization of renal function but did not result in a significant change in proteinuria and with no change in the DNAJB9 levels.

Comparison of treatment options in adults with frequently relapsing or steroid-dependent minimal change disease.

BACKGROUND: Studies comparing all treatment options for frequently-relapsing/steroid-dependent (FR/SD) minimal change disease (MCD) in adults are lacking. METHODS: Medical records of 76 adults with FR/SD MCD who were treated with corticosteroids as the first-line therapy were reviewed. Treatment options were compared for the time to relapse, change of therapy and progression (relapse on full-dose treatment). RESULTS: Second-line treatments included rituximab (RTX; n = 13), mycophenolate mofetil (MMF; n = 12), calcineurin inhibitors (CNI; n = 26) and cyclophosphamide (CTX; n = 16). During the second-line treatments, 48 (71.6%) patients relapsed at median 17 (range 2-100) months. The majority of relapses occurred during dose tapering or off drug. Twenty of 65 (30.8%) changed therapy after the first relapse. The median time to relapse after the second line was 66 versus 28 months in RTX versus non-RTX groups (P = 0.170). The median time to change of treatment was 66 and 44 months, respectively (P = 0.060). Last-line treatment options included RTX (n = 8), MMF (n = 4), CNI (n = 3) and CTX (n = 2). Seven (41.2%) patients had a relapse during the last-line treatment at median 39 (range 5-112) months. The median time to relapse was 48 versus 34 months in the RTX versus non-RTX groups (P = 0.727). One patient in the RTX group died presumably of heart failure. No major adverse event was observed. During the median follow-up of 81 (range 9-355) months, no patients developed end-stage renal disease. CONCLUSIONS: Relapse is frequent in MCD in adults. Patients treated with RTX may be less likely to require a change of therapy and more likely to come off immunosuppressive drugs.

Hospital-Acquired Serum Ionized Calcium Derangements and Their Associations with In-Hospital Mortality.

Background: The objective of this study was to report the incidence of in-hospital serum ionized calcium derangement and its impact on mortality. Methods: We included 12,599 non-dialytic adult patients hospitalized at a tertiary medical center from January 2009 to December 2013 with normal serum ionized calcium at admission and at least 2 in-hospital serum ionized calcium values. Using serum ionized calcium of 4.60-5.40 mg/dL as the normal reference range, in-hospital serum ionized calcium levels were categorized based on the presence of hypocalcemia and hypercalcemia in hospital. We performed logistic regression to assess the relationship of in-hospital serum ionized calcium derangement with mortality. Results: Fifty-four percent of patients developed new serum ionized calcium derangements: 42% had in-hospital hypocalcemia only, 4% had in-hospital hypercalcemia only, and 8% had both in-hospital hypocalcemia and hypercalcemia. In-hospital hypocalcemia only (OR 1.28; 95% CI 1.01-1.64), in-hospital hypercalcemia only (OR 1.64; 95% CI 1.02-2.68), and both in-hospital hypocalcemia and hypercalcemia (OR 1.73; 95% CI 1.14-2.62) were all significantly associated with increased in-hospital mortality, compared with persistently normal serum ionized calcium levels. Conclusions: In-hospital serum ionized calcium derangements affect more than half of hospitalized patients and are associated with increased in-hospital mortality.

Hospital-Acquired Dysmagnesemia and In-Hospital Mortality.

Background and Objectives: This study aimed to report the incidence of hospital-acquired dysmagnesemia and its association with in-hospital mortality in adult general hospitalized patients. Materials and Methods: We studied 26,020 adult hospitalized patients from 2009 to 2013 who had normal admission serum magnesium levels and at least two serum magnesium measurements during hospitalization. The normal range of serum magnesium was 1.7-2.3 mg/dL. We categorized in-hospital serum magnesium levels based on the occurrence of hospital-acquired hypomagnesemia and/or hypermagnesemia. We assessed the association between hospital-acquired dysmagnesemia and in-hospital mortality using multivariable logistic regression. Results: 28% of patients developed hospital-acquired dysmagnesemia. Fifteen per cent had hospital-acquired hypomagnesemia only, 10% had hospital-acquired hypermagnesemia only, and 3% had both hospital-acquired hypomagnesemia and hypermagnesemia. Compared with patients with persistently normal serum magnesium levels in hospital, those with hospital-acquired hypomagnesemia only (OR 1.77; p < 0.001), hospital-acquired hypermagnesemia only (OR 2.31; p < 0.001), and both hospital-acquired hypomagnesemia and hypermagnesemia (OR 2.14; p < 0.001) were significantly associated with higher in-hospital mortality. Conclusions: Hospital-acquired dysmagnesemia affected approximately one-fourth of hospitalized patients. Hospital-acquired hypomagnesemia and hypermagnesemia were significantly associated with increased in-hospital mortality.

Impact of Admission Calcium-phosphate Product on 1-year Mortality among Hospitalized Patients.

BACKGROUND: Calcium-phosphate product is associated with mortality among patients with end-stage kidney disease on dialysis. However, clinical evidence among hospitalized patients is limited. The objective of this study was to investigate the relationship between admission calcium-phosphate product and 1-year mortality in hospitalized patients. MATERIALS AND METHODS: All adult patients admitted to a tertiary referral hospital in 2009-2013 were studied. Patients who had both available serum calcium and phosphate measurement within 24 h of hospital admission were included. Admission calcium-phosphate product (calcium × phosphate) was stratified based on its distribution into six groups: <21, 21-<27, 27-<33, 33-<39, 39-<45, and ≥45 mg2/dL2. Multivariate cox proportional hazard analysis was performed to evaluate the association between admission calcium-phosphate product and 1-year mortality, using the calcium-phosphate product of 33-<39 mg2/dL2 as the reference group. RESULTS: A total of 14,772 patients were included in this study. The mean admission calcium-phosphate product was 34.4 ± 11.3 mg2/dL2. Of these patients, 3194 (22%) died within 1 year of hospital admission. In adjusted analysis, admission calcium-phosphate product of ≥45 mg2/dL2 was significantly associated with increased 1-year mortality with hazard ratio of 1.41 (95% 95% confidence interval 1.25-1.67), whereas lower admission calcium-phosphate product was not significantly associated with 1-year mortality. CONCLUSION: Elevated calcium-phosphate product was significantly associated with increased 1-year mortality in hospitalized patients.

Serum Potassium Levels at Hospital Discharge and One-Year Mortality among Hospitalized Patients.

Background and Objectives: The optimal range of serum potassium at hospital discharge is unclear. The aim of this study was to assess the relationship between discharge serum potassium levels and one-year mortality in hospitalized patients. Materials and Methods: All adult hospital survivors between 2011 and 2013 at a tertiary referral hospital, who had available admission and discharge serum potassium data, were enrolled. End-stage kidney disease patients were excluded. Discharge serum potassium was defined as the last serum potassium level measured within 48 hours prior to hospital discharge and categorized into ≤ 2.9, 3.0-3.4, 3.5-3.9, 4.0-4.4, 4.5-4.9, 5.0-5.4 and ≥ 5.5 mEq/L. A Cox proportional hazards analysis was performed to assess the independent association between discharge serum potassium and one-year mortality after hospital discharge, using the discharge potassium range of 4.0-4.4 mEq/L as the reference group. Results: Of 57,874 eligible patients, with a mean discharge serum potassium of 4.1 ± 0.4 mEq/L, the estimated one-year mortality rate after discharge was 13.2%. A U-shaped association was observed between discharge serum potassium and one-year mortality, with the nadir mortality in the discharge serum potassium range of 4.0-4.4 mEq/L. After adjusting for clinical characteristics, including admission serum potassium, both discharge serum potassium ≤ 3.9 mEq/L and ≥ 4.5 mEq/L were significantly associated with increased one-year mortality, compared with the discharge serum potassium of 4.0-4.4 mEq/L. Stratified analysis based on admission serum potassium showed similar results, except that there was no increased risk of one-year mortality when discharge serum potassium was ≤ 3.9 mEq/L in patients with an admission serum potassium of ≥ 5.0 mEq/L. Conclusion: The association between discharge serum potassium and one-year mortality after hospital discharge had a U-shaped distribution and was independent of admission serum potassium. Favorable survival outcomes occurred when discharge serum potassium was strictly within the range of 4.0-4.4 mEq/L.

Impact of Changes in Serum Calcium Levels on In-Hospital Mortality.

Background and objectives: Calcium concentration is strictly regulated at both the cellular and systemic level, and changes in serum calcium levels can alter various physiological functions in various organs. This study aimed to assess the association between changes in calcium levels during hospitalization and mortality. Materials and Methods: We searched our patient database to identify all adult patients admitted to our hospital from January 1st, 2009 to December 31st, 2013. Patients with ≥2 serum calcium measurements during the hospitalization were included. The serum calcium changes during the hospitalization, defined as the absolute difference between the maximum and the minimum calcium levels, were categorized into five groups: 0-0.4, 0.5-0.9, 1.0-1.4, 1.5-1.9, and ≥2.0 mg/dL. Multivariable logistic regression was performed to assess the independent association between calcium changes and in-hospital mortality, using the change in calcium category of 0-0.4 mg/dL as the reference group. Results: Of 9868 patients included in analysis, 540 (5.4%) died during hospitalization. The in-hospital mortality progressively increased with higher calcium changes, from 3.4% in the group of 0-0.4 mg/dL to 14.5% in the group of ≥2.0 mg/dL (p < 0.001). When adjusted for age, sex, race, principal diagnosis, comorbidity, kidney function, acute kidney injury, number of measurements of serum calcium, and hospital length of stay, the serum calcium changes of 1.0-1.4, 1.5-1.9, and ≥2.0 mg/dL were significantly associated with increased in-hospital mortality with odds ratio (OR) of 1.55 (95% confidence interval (CI) 1.15-2.10), 1.90 (95% CI 1.32-2.74), and 3.23 (95% CI 2.39-4.38), respectively. The association remained statistically significant when further adjusted for either the lowest or highest serum calcium. Conclusion: Larger serum calcium changes in hospitalized patients were progressively associated with increased in-hospital mortality.

Impact of admission serum magnesium levels on long-term mortality in hospitalized patients.

BACKGROUND: The impact of admission serum magnesium on long-term mortality in hospitalized patients was unclear. This study aimed to assess the long-term mortality risk based on admission of serum magnesium in hospitalized patients. METHODS: This was a retrospective cohort study conducted at a tertiary referral hospital. We included all adult patients admitted to Mayo Clinic Hospital, Minnesota, between 1 January 2009 and 31 December 2013, who had available admission serum magnesium. We categorized serum magnesium into ≤1.4, 1.5-1.6, 1.7-1.8, 1.9-2.0, 2.1-2.2, ≥2.3 mg/dL. We estimated the 1-year mortality risk based on various admission serum magnesium levels using Kaplan-Meier plot and assessed the association of admission serum magnesium with 1-year mortality using Cox proportional hazard analysis. We selected serum magnesium of 1.7-1.8 mg/dL as the reference group for mortality comparison. RESULTS: We included a total of 65,974 patients, with a mean admission serum magnesium of 1.9 ± 0.3 mg/dL in this study. The 1-year mortality was 15.7%, 15.8%, 15.5%, 16.7%, 19.0%, and 25.6% in admission serum magnesium of ≤1.4, 1.5-1.6, 1.7-1.8, 1.9-2.0, 2.1-2.2, ≥2.3 mg/dL, respectively (p < 0.001). After adjustment for confounders, admission serum magnesium of 1.9-2.0, 2.1-2.2, and ≥2.3 mg/dL were significantly associated with increased 1-year mortality compared with magnesium of 1.7-1.8 mg/dL with adjusted HR of 1.09 (95% CI 1.02-1.15), 1.22 (95% CI 1.14-1.30), and 1.55 (95% CI 1.45-1.55), respectively. There was no significant difference in 1-year mortality risk between low serum magnesium ≤1.6 mg/dL and magnesium of 1.7-1.8 mg/dL. CONCLUSION: Hypermagnesemia, but not hypomagnesemia, at the time of hospital admission was associated with increased 1-year mortality among hospitalized patients.