Characterisation of Acute Kidney Injury in Critically Ill Patients with Severe Coronavirus Disease-2019 (COVID-19)

AbstractBackgroundCOVID-19-associated acute kidney injury frequency, severity and characterisation in critically ill patients has not been reported.MethodsSingle-center cohort performed from March 3, 2020, to April 14, 2020 in 4 intensive care units in Bordeaux University Hospital, France. All patients with COVID19 and pulmonary severity criteria were included. AKI was defined using KDIGO criteria. A systematic urinary analysis was performed. The incidence, severity, clinical presentation, biological characterisation (transient vs. persistent acute kidney injury; proteinuria, hematuria and glycosuria), and short-term outcomes was evaluated.Results71 patients were included, with basal serum creatinine of 69 ± 21 µmol/L. At admission, AKI was present in 8/71 (11%) patients. Median follow-up was 17 [12–23] days. AKI developed in a total of 57/71 (80%) patients with 35% Stage 1, 35% Stage 2, and 30% Stage 3 acute kidney injury; 10/57 (18%) required renal replacement therapy. Transient AKI was present in only 4/55 (7%) patients and persistent AKI was observed in 51/55 (93%). Patients with persistent AKI developed a median urine protein/creatinine of 82 [54–140] (mg/mmol) with an albuminuria/proteinuria ratio of 0.23 ± 20 indicating predominant tubulo-interstitial injury. Only 2 (4%) patients had glycosuria. At Day 7 onset of after AKI, six (11%) patients remained dependent on renal replacement therapy, nine (16%) had SCr > 200 µmol/L, and four (7%) died. Day 7 and day 14 renal recovery occurred in 28% and 52 % respectively.ConclusionCOVID-19-associated AKI is frequent, persistent severe and characterised by an almost exclusive tubulo-interstitial injury without glycosuria.

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Characterization of acute kidney injury in critically ill patients with severe coronavirus disease 2019

Clinical Kidney Journal ◽

10.1093/ckj/sfaa099 ◽

2020 ◽

Cited By ~ 3

Author(s):

Sébastien Rubin ◽

Arthur Orieux ◽

Renaud Prevel ◽

Antoine Garric ◽

Marie-Lise Bats ◽

...

Keyword(s):

Acute Kidney Injury ◽

Critically Ill ◽

Critically Ill Patients ◽

Kidney Injury ◽

University Hospital ◽

Tubulointerstitial Injury ◽

Urinary Analysis ◽

Stage 1

Abstract Background Coronavirus disease 2019 (COVID-19)-associated acute kidney injury (AKI) frequency, severity and characterization in critically ill patients has not been reported. Methods Single-centre cohort performed from 3 March 2020 to 14 April 2020 in four intensive care units in Bordeaux University Hospital, France. All patients with COVID-19 and pulmonary severity criteria were included. AKI was defined using Kidney Disease: Improving Global Outcomes (KDIGO) criteria. A systematic urinary analysis was performed. The incidence, severity, clinical presentation, biological characterization (transient versus persistent AKI; proteinuria, haematuria and glycosuria) and short-term outcomes were evaluated. Results Seventy-one patients were included, with basal serum creatinine (SCr) of 69 ± 21 µmol/L. At admission, AKI was present in 8/71 (11%) patients. Median [interquartile range (IQR)] follow-up was 17 (12–23) days. AKI developed in a total of 57/71 (80%) patients, with 35% Stage 1, 35% Stage 2 and 30% Stage 3 AKI; 10/57 (18%) required renal replacement therapy (RRT). Transient AKI was present in only 4/55 (7%) patients and persistent AKI was observed in 51/55 (93%). Patients with persistent AKI developed a median (IQR) urine protein/creatinine of 82 (54–140) (mg/mmol) with an albuminuria/proteinuria ratio of 0.23 ± 20, indicating predominant tubulointerstitial injury. Only two (4%) patients had glycosuria. At Day 7 after onset of AKI, six (11%) patients remained dependent on RRT, nine (16%) had SCr >200 µmol/L and four (7%) had died. Day 7 and Day 14 renal recovery occurred in 28% and 52%, respectively. Conclusion Severe COVID-19-associated AKI is frequent, persistent, severe and characterized by an almost exclusive tubulointerstitial injury without glycosuria.

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Safety of Argatroban in critically ill patients with acute kidney injury and renal replacement therapy after cardiac surgery

The Thoracic and Cardiovascular Surgeon ◽

10.1055/s-0030-1269141 ◽

2011 ◽

Vol 59 (S 01) ◽

Author(s):

M Klingele ◽

A Lerner-Gräber ◽

D Fliser ◽

HJ Schäfers ◽

H Groesdonk

Keyword(s):

Acute Kidney Injury ◽

Cardiac Surgery ◽

Renal Replacement Therapy ◽

Replacement Therapy ◽

Critically Ill ◽

Critically Ill Patients ◽

Kidney Injury

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Renal replacement therapy in critically ill patients with acute kidney injury: 2020 nephrologist’s perspective

Nefrología (English Edition) ◽

10.1016/j.nefroe.2021.05.003 ◽

2021 ◽

Author(s):

María Valdenebro ◽

Leyre Martín-Rodríguez ◽

Blanca Tarragón ◽

Paula Sánchez-Briales ◽

Jose Portolés

Keyword(s):

Acute Kidney Injury ◽

Renal Replacement Therapy ◽

Replacement Therapy ◽

Critically Ill ◽

Critically Ill Patients ◽

Kidney Injury

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Comparison of Approaches for Prediction of Renal Replacement Therapy-Free Survival in Patients with Acute Kidney Injury

Blood Purification ◽

10.1159/000513700 ◽

2021 ◽

pp. 1-7

Author(s):

Pattharawin Pattharanitima ◽

Akhil Vaid ◽

Suraj K. Jaladanki ◽

Ishan Paranjpe ◽

Ross O’Hagan ◽

...

Keyword(s):

Acute Kidney Injury ◽

Renal Replacement Therapy ◽

Replacement Therapy ◽

Critically Ill ◽

Critically Ill Patients ◽

Kidney Injury ◽

Support Vector ◽

Adaptive Boosting ◽

Free Survival ◽

Extreme Gradient Boosting

Background/Aims: Acute kidney injury (AKI) in critically ill patients is common, and continuous renal replacement therapy (CRRT) is a preferred mode of renal replacement therapy (RRT) in hemodynamically unstable patients. Prediction of clinical outcomes in patients on CRRT is challenging. We utilized several approaches to predict RRT-free survival (RRTFS) in critically ill patients with AKI requiring CRRT. Methods: We used the Medical Information Mart for Intensive Care (MIMIC-III) database to identify patients ≥18 years old with AKI on CRRT, after excluding patients who had ESRD on chronic dialysis, and kidney transplantation. We defined RRTFS as patients who were discharged alive and did not require RRT ≥7 days prior to hospital discharge. We utilized all available biomedical data up to CRRT initiation. We evaluated 7 approaches, including logistic regression (LR), random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), multilayer perceptron (MLP), and MLP with long short-term memory (MLP + LSTM). We evaluated model performance by using area under the receiver operating characteristic (AUROC) curves. Results: Out of 684 patients with AKI on CRRT, 205 (30%) patients had RRTFS. The median age of patients was 63 years and their median Simplified Acute Physiology Score (SAPS) II was 67 (interquartile range 52–84). The MLP + LSTM showed the highest AUROC (95% CI) of 0.70 (0.67–0.73), followed by MLP 0.59 (0.54–0.64), LR 0.57 (0.52–0.62), SVM 0.51 (0.46–0.56), AdaBoost 0.51 (0.46–0.55), RF 0.44 (0.39–0.48), and XGBoost 0.43 (CI 0.38–0.47). Conclusions: A MLP + LSTM model outperformed other approaches for predicting RRTFS. Performance could be further improved by incorporating other data types.

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