Impact of admission serum ionized calcium levels on risk of acute kidney injury in hospitalized patients

Cheungpasitporn, Wisit; Chewcharat, Api; Mao, Michael A; Vallabhajosyula, Saraschandra; Thirunavukkarasu, Sorkko; Kashani, Kianoush

doi:10.1038/s41598-020-69405-0

Cited by 14 publications

(22 citation statements)

References 25 publications

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“…Hypercalcemia in the hospitalized setting is commonly seen in patients with cancers or endocrine/metabolic disorders [ 3 , 13 , 21 ]. An increase in calcium levels can cause renal vasoconstriction and nephrogenic diabetes insipidus, resulting in volume depletion and leading to a pre-renal AKI or exacerbating its severity [ 4 ]. AKI has been clearly linked with worse patient outcomes, including hospital and long-term mortality [ 22 , 23 , 24 , 25 ].…”

Section: Discussionmentioning

confidence: 99%

“…The remaining total body calcium (approximately 10 in adults) exists outside of the skeletal system and participates in a myriad of essential functions, including neurotransmitter release, muscle contraction, and cellular signaling. Approximately 50% of circulating calcium ions are bound to protein, such as albumin or globulin, whereas the rest exists as unbound ionized (free) calcium [ 4 , 5 , 6 , 7 , 8 ]. Measuring ionized calcium is important, as Payne’s formula [ 6 ] for corrected total calcium level tends to overestimate ionized calcium, especially in patients with hypoalbuminemia [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…Both reduced and elevated admission serum ionized calcium were associated with increased 1 year mortality with a U-shape relationship [ 19 ]. While the effects of calcium disturbances at hospital admission on poor outcomes among hospitalized patients have been demonstrated [ 4 , 5 , 19 ], the incidence of in-hospital calcium disturbances and their association with mortality are currently unknown.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…We collected pertinent information, including clinical characteristics and laboratory data, from our hospital's electronic database using previously validated methods [20][21][22][23][24]. We aimed to categorize patients with CA-AKI into clusters based on their admission demographic information, principal diagnoses, comorbidities, and laboratory data.…”

Section: Data Collectionmentioning

confidence: 99%

Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering

et al. 2021

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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.

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“…Acute kidney injury (AKI) is a common condition among hospitalised critically ill patients, especially in intensive care units (ICUs), and has been a major healthcare burden worldwide. [1][2][3][4] AKI is also associated with serious complications, increased healthcare costs, length of stay and mortality. More than 1.7 million deaths have been reported indirectly due to AKI annually related to chronic kidney disease (CKD), cardiovascular and cerebrovascular events.…”

Section: Introductionmentioning

confidence: 99%

Prediction models for acute kidney injury in critically ill patients: a protocol for systematic review and critical appraisal

et al. 2021

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IntroductionAcute kidney injury (AKI) has high morbidity and mortality in intensive care units, which can lead to chronic kidney disease, more costs and longer hospital stay. Early identification of AKI is crucial for clinical intervention. Although various risk prediction models have been developed to identify AKI, the overall predictive performance varies widely across studies. Owing to the different disease scenarios and the small number of externally validated cohorts in different prediction models, the stability and applicability of these models for AKI in critically ill patients are controversial. Moreover, there are no current risk-classification tools that are standardised for prediction of AKI in critically ill patients. The purpose of this systematic review is to map and assess prediction models for AKI in critically ill patients based on a comprehensive literature review.Methods and analysisA systematic review with meta-analysis is designed and will be conducted according to the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Three databases including PubMed, Cochrane Library and EMBASE from inception through October 2020 will be searched to identify all studies describing development and/or external validation of original multivariable models for predicting AKI in critically ill patients. Random-effects meta-analyses for external validation studies will be performed to estimate the performance of each model. The restricted maximum likelihood estimation and the Hartung-Knapp-Sidik-Jonkman method under a random-effects model will be applied to estimate the summary C statistic and 95% CI. 95% prediction interval integrating the heterogeneity will also be calculated to pool C-statistics to predict a possible range of C-statistics of future validation studies. Two investigators will extract data independently using the CHARMS checklist. Study quality or risk of bias will be assessed using the Prediction Model Risk of Bias Assessment Tool.Ethics and disseminationEthical approval and patient informed consent are not required because all information will be abstracted from published literatures. We plan to share our results with clinicians and publish them in a general or critical care medicine peer-reviewed journal. We also plan to present our results at critical care international conferences.OSF registration number10.17605/OSF.IO/X25AT.

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Impact of admission serum ionized calcium levels on risk of acute kidney injury in hospitalized patients

Cited by 14 publications

References 25 publications

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

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

Clinically Distinct Subtypes of Acute Kidney Injury on Hospital Admission Identified by Machine Learning Consensus Clustering

Prediction models for acute kidney injury in critically ill patients: a protocol for systematic review and critical appraisal

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