Predicting Acute Kidney Injury After Cardiac Surgery Using a Simpler Model

Coulson, Tim G; Bailey, Michael; Pilcher, D.; Reid, Christopher M.; Seevanayagam, Siven; Williams-Spence, Jenni; Bellomo, Rinaldo

doi:10.1053/j.jvca.2020.06.072

Cited by 19 publications

(42 citation statements)

References 26 publications

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“…A radiomics signature using LASSO has been developed to evaluate survival in patients with non-small-cell lung cancer 24. LASSO has been used to predict AKI in patients with haematologic tumours, patients suffering from cardiac surgery or patients hospitalised in the neurosurgical ICU 12 22 25. In the present study, based on clinical profiles, LASSO was performed to select relevant coefficients from a multitude of variables, simultaneously removing all unrelated variables.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…Twenty-two studies recruited patients from a single centre; eight studies reported results from two or more centres [5 ▪▪ ,6,7 ▪▪ ,8,9,10 ▪▪ ,11,12]. The most common source of patient data was Medical Information Mart for Intensive Care (MIMIC) III, which was used in 15 studies [5 ▪▪ ,7 ▪▪ ,10 ▪▪ ,13–15,16 ▪▪ ,17 ▪▪ ,18,19 ▪▪ –22 ▪▪ ,23 ▪ ,24].…”

Section: Acute Kidney Injury Algorithmsmentioning

confidence: 99%

“…Methods for determining the baseline serum creatinine (sCr) varied across studies, with some using the first sCr measured within 3 days of ICU admission [14,19 ▪▪ ,27,29,31,34] and others using the lowest value during ICU admission [5 ▪▪ ,9,18]. Studies also established baseline sCr from the lowest sCr prior to admission (ranging from 365 days to 1 week prior [25 ▪▪ ,29,30,32 ▪▪ ] or within 7 days prior [24]), mean sCr from 180 to 7 days prior to admission [21 ▪▪ ,26 ▪ ], preoperative sCr [6,31] or the Modified Diet and Renal Disease (MDRD) equation if a measured sCr was not available [9,12,26 ▪ ,30]. Eleven studies did not define baseline sCr [7 ▪▪ ,10 ▪▪ ,11–13,15,20 ▪▪ ,22 ▪▪ ,23 ▪ ,28,33].…”

Section: Acute Kidney Injury Algorithmsmentioning

confidence: 99%

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Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury

Ozrazgat‐Baslanti

Loftus

Ren

et al. 2021

Current Opinion in Critical Care

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Purpose of reviewAcute kidney injury (AKI) affects nearly 60% of all patients admitted to ICUs. Large volumes of clinical, monitoring and laboratory data produced in ICUs allow the application of artificial intelligence analytics. The purpose of this article is to assimilate and critically evaluate recently published literature regarding artificial intelligence applications for predicting, diagnosing and subphenotyping AKI among critically ill patients. Recent findingsAmong recent studies regarding artificial intelligence implementations for predicting, diagnosing and subphenotyping AKI among critically ill patients, there are many promising models, but few had external validation, clinical interpretability and high predictive performance. Deep learning techniques leveraging multimodal clinical data show great potential to provide continuous, accurate, early predictions of AKI risk, which could be implemented clinically to optimize preventive and early therapeutic management strategies.

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“…Perioperative risk factors include prolonged CPB and aortic cross-clamp time, hypotension, low-cardiac-output state, and hemorrhage. 1,2 AKI is diagnosed using the Kidney Disease: Improving Global Outcome (KDIGO) criteria based on oliguria and elevation in serum creatinine (SCr) concentration relative to baseline. 3 As creatinine is freely filtered at the glomerulus, and neither secreted nor reabsorbed, an increasing SCr concentration represents a reduction in glomerular filtration rate (GFR).…”

Section: Is Serum Phosphate Concentration a Biomarker Formentioning

confidence: 99%

Is Serum Phosphate Concentration a Biomarker for Renal Injury and Recovery in Cardiac Surgery?

Desai

Hwang

2020

Journal of Cardiothoracic and Vascular Anesthesia

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Predicting Acute Kidney Injury After Cardiac Surgery Using a Simpler Model

Cited by 19 publications

References 26 publications

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 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

Advances in artificial intelligence and deep learning systems in ICU-related acute kidney injury

Is Serum Phosphate Concentration a Biomarker for Renal Injury and Recovery in Cardiac Surgery?

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