An acute kidney injury prediction nomogram based on neurosurgical intensive care unit profiles

An, Shuo; Luo, Hongliang; Wang, Jiao; Gong, Zhongying; Tian, Ye; Liu, Xuanhui; Ma, Jun; Jiang, Rongcai

doi:10.21037/atm.2020.01.60

Cited by 14 publications

(23 citation statements)

References 20 publications

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“…The special early phase of AKI development may be due to concomitant pathophysiological processes in the early phase following injury, including massive bleeding-induced hypoperfusion, systemic inflammation caused by initial and secondary brain injury, and rapid and heavy use of hyperosmotic drugs once admitted. 5,[21][22][23] This fact underlines the need to evaluate risk of developing AKI and consequently adjust treatment decisions for TBI patients as early as possible. Future studies need to be conducted to discover risk factors of AKI in the prehospital period and construct valuable risk scores based on these factors.…”

Section: Discussionmentioning

confidence: 99%

“…Many factors play complex roles in the development of AKI after TBI, such as massive release of catecholamine transmitters, systemic inflammation, and iatrogenic factors, including massive drug doses reducing intracranial pressure and nephrotoxic antibiotics. [5][6][7] The incidence of AKI in TBI patients is 9.2%-24%. [8][9][10][11] There have been few studies to investigate the details of AKI duration and AKI stage and their correlation with mortality in TBI patients.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Incidence and Burden of Acute Kidney Injury among Traumatic Brain-Injury Patients

Wang¹,

Zhang²,

Xu³

et al. 2021

RMHP

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Incidence and Burden of Acute Kidney Injury among Traumatic Brain-Injury Patients

Wang¹,

Zhang²,

Xu³

et al. 2021

RMHP

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“…Least absolute shrinkage and selection operator (LASSO) regression is of great strength for variable selection because it can efficiently address the potential association between covariates, such as collinearity. 12 Accordingly, in this study, we performed LASSO regression to select variables and built a logistic regression model to identify independent risk factors for severe AKI in patients admitted to the CSRU. We aimed to determine the risk factors for severe AKI and develop a clinical score for evaluating the probability that patients undergoing critical cardiac care will acquire severe AKI.…”

Section: Introductionmentioning

confidence: 99%

A LASSO-derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit: a large retrospective cohort study using the MIMIC database

Huang

Xie

et al. 2022

BMJ Open

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ObjectivesWe aimed to develop an effective tool for predicting severe acute kidney injury (AKI) in patients admitted to the cardiac surgery recovery unit (CSRU).DesignA retrospective cohort study.SettingData were extracted from the Medical Information Mart for Intensive Care (MIMIC)-III database, consisting of critically ill participants between 2001 and 2012 in the USA.ParticipantsA total of 6271 patients admitted to the CSRU were enrolled from the MIMIC-III database.Primary and secondary outcomeStages 2–3 AKI.ResultAs identified by least absolute shrinkage and selection operator (LASSO) and logistic regression, risk factors for AKI included age, sex, weight, respiratory rate, systolic blood pressure, diastolic blood pressure, central venous pressure, urine output, partial pressure of oxygen, sedative use, furosemide use, atrial fibrillation, congestive heart failure and left heart catheterisation, all of which were used to establish a clinical score. The areas under the receiver operating characteristic curve of the model were 0.779 (95% CI: 0.766 to 0.793) for the primary cohort and 0.778 (95% CI: 0.757 to 0.799) for the validation cohort. The calibration curves showed good agreement between the predictions and observations. Decision curve analysis demonstrated that the model could achieve a net benefit.ConclusionA clinical score built by using LASSO regression and logistic regression to screen multiple clinical risk factors was established to estimate the probability of severe AKI in CSRU patients. This may be an intuitive and practical tool for severe AKI prediction in the CSRU.

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“…AKI after TBI has been reported developing in 7.6% to 23% patients and is correlated with mortality, functional outcome and length of hospital stay in TBI patients [4][5][6][7]. Mechanisms involved in development of AKI after TBI are diversified, which included systemic inflammation response, neuroendocrine hormone release, hypoperfusion and iatrogenic factors such as blood transfusion, drugs reducing intracranial pressure and usage of nephrotoxic antibiotics [8][9][10][11]. In view of the unfavorable outcome caused by AKI, exploring novel and available biomarkers to predict the possible occurrence of AKI in early stage and consequently avoid medical treatments adverse to normal renal function is beneficial for outcome and recovery of TBI patients.…”

Section: Introductionmentioning

confidence: 99%

A nomogram based on serum cystatin C for predicting acute kidney injury in patients with traumatic brain injury

Wang

Gui

et al. 2021

Renal Failure

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Background Acute kidney injury (AKI) is a common complication in traumatic brain injury (TBI) patients and is associated with unfavorable outcome of these patients. We designed this study to explore the value of serum cystatin C, an indicator of renal function, on predicting AKI after suffering TBI. Methods Patients confirmed with TBI and hospitalized in the West China Hospital of Sichuan University between January 2015 and December 2019 were included. Patients were divided into two groups according to occurrence of AKI. Univariate and multivariate logistic regression analyses were sequentially utilized to find risk factors of AKI in included TBI patients. Nomogram composed of discovered risk factors for predicting AKI was constructed. Receiver operating characteristics (ROC) curves were drawn and area under the ROC curve (AUC) were calculated to evaluate the predictive value of cystatin C alone and the constructed nomogram. Results Among 234 included TBI patients, 55 were divided into AKI group. AKI group had shorter length of stay ( p < 0.001) and higher in-hospital mortality ( p < 0.001). Multivariate logistic regression analysis showed absolute lymphocyte count ( p = 0.034), serum creatinine ( p < 0.001), serum cystatin C ( p = 0.017) and transfusion of red blood cell ( p = 0.005) were independently associated with development of AKI after TBI. While hypertonic saline use was not associated with the development of AKI ( p = 0.067). The AUC of single cystatin C and predictive nomogram were 0.804 and 0.925, respectively. Conclusion Higher serum cystatin C is associated with development of AKI in TBI patients. Predictive nomogram incorporating cystatin C is beneficial for physicians to evaluate possibilities of AKI and consequently adjust treatment strategies to avoid occurrence of AKI.

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An acute kidney injury prediction nomogram based on neurosurgical intensive care unit profiles

Cited by 14 publications

References 20 publications

Incidence and Burden of Acute Kidney Injury among Traumatic Brain-Injury Patients

Incidence and Burden of Acute Kidney Injury among Traumatic Brain-Injury Patients

A LASSO-derived clinical score to predict severe acute kidney injury in the cardiac surgery recovery unit: a large retrospective cohort study using the MIMIC database

A nomogram based on serum cystatin C for predicting acute kidney injury in patients with traumatic brain injury

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