Incidence, risk factors and 90-day mortality of patients with acute kidney injury in Finnish intensive care units: the FINNAKI study

Association between the levels of urinary cell cycle biomarkers and non-recovery of renal function among critically ill geriatric patients with acute kidney injury

The lack of early renal function recovery among geriatric patients with acute kidney injury (AKI) in the intensive care unit (ICU) is a commonly observed and acknowledged poor prognostic factor, especially for older adults. However, no reliable prognostic biomarker is available for identifying individuals at risk of renal non-recovery or mortality in older adults. In this prospective observational cohort study, we enrolled critically ill older adults (aged ≥ 60 years) with AKI from the ICU and followed their disease progression. The primary endpoint was renal non-recovery within seven days of follow-up, while the secondary endpoint was the determinants of 30-day mortality after AKI. We assessed the predictive accuracy using receiver operating characteristic curves and performed between-group comparisons using the log-rank test. Among 209 older adults, 117 (56.0%) experienced renal recovery. Multiple regression analysis revealed that urine levels of tissue inhibitor of metalloproteinase-2 (TIMP-2) multiplied by insulin-like growth factor-binding protein 7 (IGFBP7) ([TIMP-2]*[IGFBP7]), AKI stages 2-3, and the Acute Physiology and Chronic Health Evaluation (APACHE II) score were independently associated with renal non-recovery. The regression model incorporating [TIMP-2]*[IGFBP7] demonstrated a fair predictive value (AUC 0.774, p < 0.001), with the optimal threshold set at 0.81 (ng/mL)2/1000. When [TIMP-2]*[IGFBP7] was combined with AKI severity and the APACHE score, the AUC increased to 0.851. In conclusion, urine [TIMP-2]*[IGFBP7] is a reliable biomarker associated with renal non-recovery in critically ill older adults, and its predictive efficacy can be further enhanced when combined with AKI severity and the APACHE score.

Evaluating the incidence and risk factors for developing acute tubular necrosis after percutaneous coronary intervention in the management of coronary artery dissection

BACKGROUND

Coronary artery dissection is managed primarily conservatively with serial imaging or percutaneous coronary intervention (PCI). Exposure to contrast in either modality could potentially result in acute tubular necrosis (ATN). However, no data compares ATN incidence in these management strategies. This study compares the incidence of ATN and associated mortality of PCI and conservative management of coronary artery dissection.

METHODS

A retrospective analysis was performed using data from the National Inpatient Sample database, including patients with coronary artery dissection between 2016 through 2020. We analyzed the incidence of ATN and associated mortality of PCI and conservative management of coronary artery dissection.

RESULTS

We found that the odds of developing ATN were 22% lower in patients managed with PCI than those managed conservatively. There was no difference in the in-hospital mortality or hospital length of stay between the two groups but the mortality rate in patients with ATN was double that of those who did not develop ATN in both PCI and conservatively managed groups.

CONCLUSIONS

The higher incidents of ATN in patients with coronary dissection being managed with conservative measures compared to PCI suggest that the use of CTA may be harmful. Additionally, persons who developed ATN may have higher mortality. Therefore, more studies in the management of coronary artery dissection need to be done which would allow further steps to be taken to reduce this harm.

Incidence and associated factors for hypotension during continuous renal replacement therapy in critically ill patients

AIMS

This work aimed to analyse retrospective data on hypotension incidence and associated factors among patients requiring continuous renal replacement therapy.

BACKGROUND

The incidence and risk factors of continuous renal replacement therapy-related hypotension have not been adequately explored.

DESIGN

The study was designed as a retrospective analysis.

METHODS

Patients who required continuous renal replacement therapy in the ICU between January 2017 and June 2021 were reviewed. The multivariate logistic regression model was used to determine the associated factors of hypotension.

RESULTS

Hypotension occurred in 242 out of 885 circuits (27.3%) among 140 patients. The logistic regression analysis identified seven factors associated with the occurrence of hypotension during CRRT: serum albumin (OR = 0.969, 95%CI: 0.934-0.999), serum calcium (OR = 0.514, 95%CI: 0.345-0.905), CO2CP (OR = 0.933, 95%CI: 0.897-0.971), use of vasopressors (OR = 5.731, 95%CI: 4.023-8.165), hypotension before CRRT initiation (OR = 2.779, 95%CI:1.238-6.242), age (OR = 1.016, 95%CI: 1.005-1.027), and fluid removal rate (OR = 1.002, 95%CI: 1.001-1.003).

CONCLUSIONS

Hypotension frequently occurs in patients receiving continuous renal replacement therapy, especially in the early stages. Multiple factors can be associated with cardiac output or peripheral resistance changes, including excessive ultrafiltration, vasopressors, serum albumin and serum calcium levels, and carbon dioxide combining power.

Fine-grained subphenotypes in acute kidney injury populations based on deep clustering: Derivation and interpretation

BACKGROUND

Acute kidney injury (AKI) is associated with increased mortality in critically ill patients. Due to differences in the etiology and pathophysiological mechanism, the current AKI criteria put it an embarrassment to evaluate clinical therapy and prognosis.

OBJECTIVE

We aimed to identify subphenotypes based on routinely collected clinical data to expose the unique pathophysiologic patterns.

METHODS

A retrospective study was conducted based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD), and a deep clustering approach was conducted to derive subphenotypes. We conducted further analysis to uncover the underlying clinical patterns and interpret the subphenotype derivation.

RESULTS

We studied 14,189 and 19,382 patients with AKI within 48 h of ICU admission in the two datasets, respectively. Through our approach, we identified seven distinct AKI subphenotypes with mortality heterogeneity in each cohort. These subphenotypes displayed significant variations in demographics, comorbidities, levels of laboratory measurements, and survival patterns. Notably, the subphenotypes could not be effectively characterized using the Kidney Disease: Improving Global Outcomes (KDIGO) criteria alone. Therefore, we uncovered the unique underlying characteristics of each subphenotype through model-based interpretation. To assess the usability of the subphenotypes, we conducted an evaluation, which yielded a micro-Area Under the Receiver Operating Characteristic (AUROC) of 0.81 in the single-center cohort and 0.83 in the multi-center cohort within 48-hour of admission.

CONCLUSION

We derived highly characteristic, interpretable, and usable AKI subphenotypes that exhibited superior prognostic values.

Long-Term Mortality and Health-Related Quality of Life After Continuous Versus Intermittent Renal Replacement Therapy in ICU Survivors: A Secondary Analysis of the Quality of Life After ICU Study

Purpose: We assessed long-term outcomes in intensive care unit (ICU) survivors with acute kidney injury (AKI) submitted to intermittent or continuous renal replacement therapy (RRT) for comparisons between groups. Methods: The multicenter prospective cohort study included 195 adult ICU survivors with an ICU stay >72 h in 10 ICUs that had at least one episode of AKI treated with intermittent RRT (IRRT) or continuous RRT (CRRT) during ICU stay. The main outcomes were mortality and health-related quality of life (HRQoL). Hospital readmissions and physical dependence were also assessed. Results: Regarding RRT, 83 (42.6%) patients received IRRT and 112 (57.4%) received CRRT. Despite the similarity regarding sociodemographic characteristics, pre-ICU state of health and type of admission between groups, the risk of death (23.5% vs 42.7%; P < .001), the prevalence of sepsis (60.7%) and acute respiratory distress syndrome (17%) were higher at ICU admission among CRRT patients. The severity of critical illness was higher among CRRT patients, regarding the need for mechanical ventilation (75.0% vs 50.6%, P = .002) and vasopressors (91.1% vs 63.9%, P < .001). One year after ICU discharge, 67 of 195 ICU survivors died (34.4%) and, after adjustment for confounders, there were no significant differences in mortality when comparing IRRT and CRTT patients (34.9% vs 33.9%; P = .590), on HRQoL in both physical (41.9% vs 42.2%; P = .926) and mental dimensions (57.6% vs 56.6%; P = .340), and on the number of hospital readmissions and physical dependence. Conclusions: Our study suggests that among ICU survivors RRT modality (IRRT vs CRRT) in the ICU does not impact long-term outcomes after ICU discharge.

Different distances between central venous catheter tips can affect antibiotic clearance during continuous renal replacement therapy

BACKGROUND

The aim of this experimental study was to elucidate whether different distances between central venous catheter tips can affect drug clearance during continuous renal replacement therapy (CRRT). Central venous catheters (CVCs) are widely used in intensive care patients for drug infusion. If a patient receives CRRT, a second central dialysis catheter (CDC) is required. Where to insert CVCs is directed by guidelines, but recommendations regarding how to place multiple catheters are scarce. There are indications that a drug infused in a CVC with the tip close to the tip of the CDC, could be directly aspirated into the dialysis machine, with a risk of increased clearance. However, studies on whether clearance is affected by different CVC and CDC tip positions, when the two catheters are in the same vessel, are few.

METHODS

In this model with 18 piglets, gentamicin (GM) and vancomycin (VM) were infused through a CVC during CRRT. The CVC tip was placed in different positions in relation to the CDC tip from caudal, i.e., proximal to the heart, to cranial, i.e., distal to the heart. Serum and dialysate concentrations were sampled after approximately 30 min of CRRT at four different positions: when the CVC tip was 2 cm caudally (+ 2), at the same level (0), and at 2 (- 2) and 4 (- 4) cm cranially of the tip of the CDC. Clearance was calculated. A mixed linear model was performed, and level of significance was set to p < 0.05.

RESULTS

Clearance of GM had median values at + 2 cm, 0 cm, - 2 cm and - 4 cm of 17.3 (5.2), 18.6 (7.4), 20.0 (16.2) and 26.2 (12.2) ml/min, respectively (p = 0.04). Clearance of VM had median values at + 2 cm, 0 cm, - 2 cm and - 4 cm of 16.2 (4.5), 14.7 (4.9), 19.0 (10.2) and 21.2 (11.4) ml/min, respectively (p = 0.02).

CONCLUSIONS

The distance between CVC and CDC tips can affect drug clearance during CRRT. A cranial versus a caudal tip position of the CVC in relation to the tip of the CDC led to the highest clearance.

The Effects of Early-Phase, Low- or Standard-Intensity Continuous Renal Replacement Therapy on Acid-Base Control and Clinical Outcomes: An Observational Study

INTRODUCTION

The Kidney Disease Improving Global Outcomes (KDIGO) clinical practice guideline recommends administering an effluent volume of 20-25 mL/kg/h during continuous renal replacement therapy (CRRT) for acute kidney injury. Recent evidence on CRRT initiation showed that less intervention might be beneficial for renal recovery. This study aimed to explore the association between early-phase low CRRT intensity and acid-base balance corrections and clinical outcomes.

METHODS

This was a single-centre, retrospective, observational study at a tertiary intensive care unit (ICU) in Japan. All adult patients requiring CRRT in the ICU were included. Eligible patients were classified into the Low group (dialysate flow rate [QD] 10.0-19.9 mL/kg/h) and the Standard group (QD ≥20 mL/kg/h) by the intensity of CRRT at the beginning. The primary outcomes were acid-base parameters 6 h after CRRT initiation. We used an inverse probability of treatment weighting analysis to estimate the association between the intensity group and the outcomes.

RESULTS

Overall, 194 patients were classified into the Low group (n = 144) and the Standard group (n = 50). The Standard group presented with more severe acid-base disturbances, including lower pH and base excess (BE) at baseline. At 6 h after CRRT initiation, pH, BE, and strong ion difference values were comparable, even after adjusting for baseline severity. Despite the efficient correction, no evident differences were observed in clinical outcomes between the two groups.

CONCLUSIONS

The initial standard intensity appeared to be efficient in correcting acid-base imbalance at the early phase of CRRT; however, further studies are needed to assess the impact on clinical outcomes.

Ensemble Machine Learning for Predicting 90-Day Outcomes and Analyzing Risk Factors in Acute Kidney Injury Requiring Dialysis

Purpose

Our objectives were to (1) employ ensemble machine learning algorithms utilizing real-world clinical data to predict 90-day prognosis, including dialysis dependence and mortality, following the first hospitalized dialysis and (2) identify the significant factors associated with overall outcomes.

Patients and Methods

We identified hospitalized patients with Acute kidney injury requiring dialysis (AKI-D) from a dataset of the Taipei Medical University Clinical Research Database (TMUCRD) from January 2008 to December 2020. The extracted data comprise demographics, comorbidities, medications, and laboratory parameters. Ensemble machine learning models were developed utilizing real-world clinical data through the Google Cloud Platform.

Results

The Study Analyzed 1080 Patients in the Dialysis-Dependent Module, Out of Which 616 Received Regular Dialysis After 90 Days. Our Ensemble Model, Consisting of 25 Feedforward Neural Network Models, Demonstrated the Best Performance with an Auroc of 0.846. We Identified the Baseline Creatinine Value, Assessed at Least 90 Days Before the Initial Dialysis, as the Most Crucial Factor. We selected 2358 patients, 984 of whom were deceased after 90 days, for the survival module. The ensemble model, comprising 15 feedforward neural network models and 10 gradient-boosted decision tree models, achieved superior performance with an AUROC of 0.865. The pre-dialysis creatinine value, tested within 90 days prior to the initial dialysis, was identified as the most significant factor.

Conclusion

Ensemble machine learning models outperform logistic regression models in predicting outcomes of AKI-D, compared to existing literature. Our study, which includes a large sample size from three different hospitals, supports the significance of the creatinine value tested before the first hospitalized dialysis in determining overall prognosis. Healthcare providers could benefit from utilizing our validated prediction model to improve clinical decision-making and enhance patient care for the high-risk population.

Acute and long-term renal effects after iodine contrast media-enhanced computerised tomography in the critically ill-a retrospective bi-centre cohort study

OBJECTIVES

To determine if current clinical use of iodine contrast media (ICM) for computerised tomography (CT) increases the risk of acute kidney injury (AKI) and long-term decline in renal function in patients treated in intensive care.

METHODS

A retrospective bi-centre cohort study was performed with critically ill subjects undergoing either ICM-enhanced or unenhanced CT. AKI was defined and staged based on the Kidney Disease Improve Global Outcome AKI criteria, using both creatinine and urine output criteria. Follow-up plasma creatinine was recorded three to six months after CT to assess any long-term effects of ICM on renal function.

RESULTS

In total, 611 patients were included in the final analysis, median age was 65.0 years (48.0-73.0, quartile 1-quartile 3 (IQR)) and 62.5% were male. Renal replacement therapy was used post-CT in 12.9% and 180-day mortality was 31.2%. Plasma creatinine level on day of CT was 100.0 µmol/L (66.0-166.5, IQR) for non-ICM group and 77.0 µmol/L (59.0-109.0, IQR) for the ICM group. The adjusted odds ratio for developing AKI if the patient received ICM was 1.03 (95% confidence interval 0.64-1.66, p = 0.90). No significant association between ICM and increase in plasma creatinine at long-term follow-up was found, with an adjusted effect size of 2.92 (95% confidence interval - 6.52-12.36, p = 0.543).

CONCLUSIONS

The results of this study do not indicate an increased risk of AKI or long-term decline in renal function when ICM is used for enhanced CT in patients treated at intensive care units.

CLINICAL RELEVANCE STATEMENT

Patients treated in intensive care units had no increased risk of acute kidney injury or persistent decline in renal function after contrast-enhanced CT. This information underlines the need for a proper risk-reward assessment before denying patients a contrast-enhanced CT.

KEY POINTS

• Iodine contrast media is considered a risk factor for the development of acute kidney injury. • Patients receiving iodine contrast media did not have an increased incidence of acute kidney injury or persistent decline in renal function. • A more clearly defined risk of iodine contrast media helps guide clinical decisions whether to perform contrast-enhanced CTs or not.

Interorgan communication networks in the kidney-lung axis

The homeostasis and health of an organism depend on the coordinated interaction of specialized organs, which is regulated by interorgan communication networks of circulating soluble molecules and neuronal connections. Many diseases that seemingly affect one primary organ are really multiorgan diseases, with substantial secondary remote organ complications that underlie a large part of their morbidity and mortality. Acute kidney injury (AKI) frequently occurs in critically ill patients with multiorgan failure and is associated with high mortality, particularly when it occurs together with respiratory failure. Inflammatory lung lesions in patients with kidney failure that could be distinguished from pulmonary oedema due to volume overload were first reported in the 1930s, but have been largely overlooked in clinical settings. A series of studies over the past two decades have elucidated acute and chronic kidney-lung and lung-kidney interorgan communication networks involving various circulating inflammatory cytokines and chemokines, metabolites, uraemic toxins, immune cells and neuro-immune pathways. Further investigations are warranted to understand these clinical entities of high morbidity and mortality, and to develop effective treatments.

Effect of Respiratory Failure on Peripheral and Organ Perfusion Markers in Severe COVID-19: A Prospective Cohort Study

Microvascular dysfunction and inflammation caused by COVID-19 disrupt organ function. The study aimed to investigate the association between the severity of SARS-CoV-2 pneumonia and peripheral and organ perfusion as a consequence of altered microcirculation. A total of 116 patients hospitalized due to severe COVID-19 were enrolled in the study. On admission, the patients underwent a Capillary Refill Time (CRT) examination, finger oxygen saturation measurement, thermal imaging of the hand (FIT), and a kidney Doppler ultrasound. Medical data were collected from the medical history. From the evaluated perfusion parameters, only renal cortex perfusion (RCP) was substantially correlated with the CT score (p < 0.010). The peripheral perfusion parameters of Sat., FIT, CRT, and RCP correlated with the ARDS stages (p = 0.0021; p = 0.038; p < 0.0006; p < 0.0002, respectively). The Oxygenation Ratio value (p < 0.001) was significantly associated with all the perfusion parameters (saturation, CRT, FIT, and RCP) in the multivariable regression analysis model. According to the stepwise retrograde regression analysis, RCP was an independent parameter linked with the Oxygenation Ratio (p < 0.001). Severe COVID-19 can result in microvascular dysfunction influencing peripheral and organ perfusion, which can be measured with various methods. The staging of COVID-19 assessed by CT and the Oxygenation Ratio correlates with RCP, CRT, FIT, and oxygen saturation.

Diagnosis and management of autoimmune diseases in the ICU

Autoimmune diseases encompass a broad spectrum of disorders characterized by disturbed immunoregulation leading to the development of specific autoantibodies, resulting in inflammation and multiple organ involvement. A distinction should be made between connective tissue diseases (mainly systemic lupus erythematosus, systemic scleroderma, inflammatory muscle diseases, and rheumatoid arthritis) and vasculitides (mainly small-vessel vasculitis such as antineutrophil cytoplasmic antibody-associated vasculitis and immune-complex mediated vasculitis). Admission of patients with autoimmune diseases to the intensive care unit (ICU) is often triggered by disease flare-ups, infections, and organ failure and is associated with high mortality rates. Management of these patients is complex, including prompt disease identification, immunosuppressive treatment initiation, and life-sustaining therapies, and requires multi-disciplinary involvement. Data about autoimmune diseases in the ICU are limited and there is a need for multicenter, international collaboration to improve patients' diagnosis, management, and outcomes. The objective of this narrative review is to summarize the epidemiology, clinical features, and selected management of severe systemic autoimmune diseases.

Central venous pressure combined with renal venous impedance index in predicting the acute kidney injury after thoracic and abdominal (non-cardiac) surgery

BACKGROUND

In the 21st century, 13% of patients undergoing open abdominal surgery, 25% of patients undergoing heart surgery, and 57% of patients admitted to the intensive care unit (ICU) are affected by acute kidney injury (AKI).

METHODS

This prospective observational study included patients admitted directly to the ICU between June 2021 and December 2021.

RESULTS

A total of 81 patients were enrolled after thoracic and abdominal (non-cardiac) surgery; 36 patients (44.4%) were diagnosed with AKI occurred within 7 days after surgery. Six-hour postoperative central venous pressure(CVP) was a risk factor for AKI in thoracic and abdominal (non-cardiac) postoperative patients (odds ratio [OR], 1.418; 95% confidence intervals [CI], 1.106-1.819; P = 0.006). Six-hour postoperative vein impedance index(VII) and CVP were significantly positively correlated (P = 0.031). The combination of 6-h postoperative VII with CVP (VII ≥0.34, CVP ≥7.5 mmHg) showed an area under the curve (AUC) of 0.787, In the subgroup analysis of patients with 6-h postoperative CVP <7.5 mmHg, there was a significant statistical difference in 6-h postoperative VII between the groups and those without AKI (P = 0.048). At 6-h postoperative CVP <7.5 mmHg, VII of ≥0.44 had a predictive value for AKI after thoracic and abdominal (non-cardiac) surgery, with an AUC of 0.669, a sensitivity of 41.2%, and a specificity of 94.4%.

CONCLUSION

Six-hour postoperative CVP combined with VII can better predict the occurrence of AKI occurred within 7 days after thoracic and abdominal (non-cardiac) surgery but cannot predict the severity of AKI.

New biomarkers in acute kidney injury

Acute kidney injury (AKI) is a commonly encountered clinical syndrome. Although it often complicates community acquired illness, it is more common in hospitalized patients, particularly those who are critically ill or who have undergone major surgery. Approximately 20% of hospitalized adult patients develop an AKI during their hospital care, and this rises to nearly 60% in the critically ill, depending on the population being considered. In general, AKI is more common in older adults, in those with preexisting chronic kidney disease and in those with known risk factors for AKI (including diabetes and hypertension). The development of AKI is associated with an increase in both mortality and morbidity, including the development of post-AKI chronic kidney disease. Currently, AKI is defined by a rise in serum creatinine from either a known or derived baseline value and/or oliguria or anuria. However, clinicians may fail to recognize the initial development of AKI because of a delay in the rise of serum creatinine or because of inaccurate urine output monitoring. This, in turn, delays any putative measures to treat AKI or to limit its degree. Consequently, efforts have focused on new biomarkers associated with AKI that may allow early recognition of this syndrome with the intent that this will translate into improved patient outcomes. Here we outline current biomarkers associated with AKI and explore their potential in aiding diagnosis, understanding the pathophysiology and directing therapy.

A Machine Learning Algorithm Predicting Acute Kidney Injury in Intensive Care Unit Patients (NAVOY Acute Kidney Injury): Proof-of-Concept Study

BACKGROUND

Acute kidney injury (AKI) represents a significant global health challenge, leading to increased patient distress and financial health care burdens. The development of AKI in intensive care unit (ICU) settings is linked to prolonged ICU stays, a heightened risk of long-term renal dysfunction, and elevated short- and long-term mortality rates. The current diagnostic approach for AKI is based on late indicators, such as elevated serum creatinine and decreased urine output, which can only detect AKI after renal injury has transpired. There are no treatments to reverse or restore renal function once AKI has developed, other than supportive care. Early prediction of AKI enables proactive management and may improve patient outcomes.

OBJECTIVE

The primary aim was to develop a machine learning algorithm, NAVOY Acute Kidney Injury, capable of predicting the onset of AKI in ICU patients using data routinely collected in ICU electronic health records. The ultimate goal was to create a clinical decision support tool that empowers ICU clinicians to proactively manage AKI and, consequently, enhance patient outcomes.

METHODS

We developed the NAVOY Acute Kidney Injury algorithm using a hybrid ensemble model, which combines the strengths of both a Random Forest (Leo Breiman and Adele Cutler) and an XGBoost model (Tianqi Chen). To ensure the accuracy of predictions, the algorithm used 22 clinical variables for hourly predictions of AKI as defined by the Kidney Disease: Improving Global Outcomes guidelines. Data for algorithm development were sourced from the Massachusetts Institute of Technology Lab for Computational Physiology Medical Information Mart for Intensive Care IV clinical database, focusing on ICU patients aged 18 years or older.

RESULTS

The developed algorithm, NAVOY Acute Kidney Injury, uses 4 hours of input and can, with high accuracy, predict patients with a high risk of developing AKI 12 hours before onset. The prediction performance compares well with previously published prediction algorithms designed to predict AKI onset in accordance with Kidney Disease: Improving Global Outcomes diagnosis criteria, with an impressive area under the receiver operating characteristics curve (AUROC) of 0.91 and an area under the precision-recall curve (AUPRC) of 0.75. The algorithm's predictive performance was externally validated on an independent hold-out test data set, confirming its ability to predict AKI with exceptional accuracy.

CONCLUSIONS

NAVOY Acute Kidney Injury is an important development in the field of critical care medicine. It offers the ability to predict the onset of AKI with high accuracy using only 4 hours of data routinely collected in ICU electronic health records. This early detection capability has the potential to strengthen patient monitoring and management, ultimately leading to improved patient outcomes. Furthermore, NAVOY Acute Kidney Injury has been granted Conformite Europeenne (CE)-marking, marking a significant milestone as the first CE-marked AKI prediction algorithm for commercial use in European ICUs.

Impact of acute kidney injury on major adverse cardiovascular events in intensive care survivors

Background

Acute kidney injury commonly occurs in patients admitted to ICU. After acute kidney injury, kidney function may not completely recover leading to increased risk of future cardiovascular events. We sought to ascertain the rates of cardiovascular events in ICU survivors and if these rates were affected by the presence of acute kidney injury whilst in ICU.

Methods

This retrospective observational cohort study utilised routinely collected data to identify patients who had survived an admission to one of two ICUs between July 2015 and June 2018. Baseline serum creatinine and subsequent values were used to identify acute kidney injury. Major adverse cardiovascular events described were myocardial injury, coronary artery intervention, or radiological evidence of stroke.

Results

Of the 3994 ICU survivors, major adverse cardiovascular events were identified in 385 patients (9.6%; 95% confidence interval [CI] 8.8-10.6%). Presence of acute kidney injury whilst in ICU was significantly associated with future major adverse cardiovascular events (hazard ratio=1.38; 95% CI 1.12-1.70; P-value=0.003) and future biochemical myocardial injury (hazard ratio=1.48; 95% CI 1.16-1.89; P-value=0.001). Acute kidney injury did not have a statistically significant association with future coronary artery interventions or future cerebrovascular events.

Conclusions

One in 10 ICU survivors experiences a major adverse cardiovascular event after discharge. Acute kidney injury whilst in ICU was associated with an increased risk of major adverse cardiovascular events and specifically myocardial injury. Further research is warranted on whether ICU survivors with acute kidney injury merit enhanced strategies for cardiovascular protection.

Incidence, risk factors and outcome of acute kidney injury in critically ill COVID-19 patients in Tyrol, Austria: a prospective multicenter registry study

INTRODUCTION

Acute kidney injury is a frequent complication in critically ill patients with and without COVID-19. The aim of this study was to evaluate the incidence of, and risk factors for, acute kidney injury and its effect on clinical outcomes of critically ill COVID-19 patients in Tyrol, Austria.

METHODS

This multicenter prospective registry study included adult patients with a SARS-CoV-2 infection confirmed by polymerase chain reaction, who were treated in one of the 12 dedicated intensive care units during the COVID-19 pandemic from February 2020 until May 2022.

RESULTS

In total, 1042 patients were included during the study period. The median age of the overall cohort was 66 years. Of the included patients, 267 (26%) developed acute kidney injury during their intensive care unit stay. In total, 12.3% (n = 126) required renal replacement therapy with a median duration of 9 (IQR 3-18) days. In patients with acute kidney injury the rate of invasive mechanical ventilation was significantly higher with 85% (n = 227) compared to 41% (n = 312) in the no acute kidney injury group (p < 0.001). The most important risk factors for acute kidney injury were invasive mechanical ventilation (OR = 4.19, p < 0.001), vasopressor use (OR = 3.17, p < 0.001) and chronic kidney disease (OR = 2.30, p < 0.001) in a multivariable logistic regression analysis. Hospital and intensive care unit mortality were significantly higher in patients with acute kidney injury compared to patients without acute kidney injury (Hospital mortality: 52.1% vs. 17.2%, p < 0.001, ICU-mortality: 47.2% vs. 14.7%, p < 0.001).

CONCLUSION

As in non-COVID-19 patients, acute kidney injury is clearly associated with increased mortality in critically ill COVID-19 patients. Among known risk factors, invasive mechanical ventilation has been identified as an independent and strong predictor of acute kidney injury.

Epidemiology, patterns of care and prognosis of acute kidney injury in critically ill patients: A multicenter study in Argentina (The EPIRA study)

BACKGROUND

Acute kidney injury (AKI) is associated with high morbidity and mortality rates in the intensive care unit (ICU). In low- and middle-income countries (LMICs), epidemiological information about this condition is still scarce. Our main objective was to characterize its epidemiology, prognosis, and its treatment.

METHODS

This multicenter prospective cohort study included 1466 patients from 35 ICUs during 6 months in Argentina in 2018. Risk factors and outcomes in patients with and without AKI, and between AKI on admission (AKIadm) and that developed during hospitalization (AKIhosp) were analyzed.

RESULTS

AKI occurred in 61.3% of patients (900/1466); 72.6% were AKIadm and 27.3% AKIhosp. Risk factors were age, BMI, arterial hypertension, cardiovascular diseases, diabetes, SOFA, APACHE II, dehydration, sepsis, vasopressor use, radiocontrast, diuresis/h and mechanical ventilation. Independent predictors for AKI were sepsis, diabetes, dehydration, vasopressors on admission, APACHE II and radiocontrast use. Renal replacement therapies (RRT) requirement in AKI patients was 14.8%. Hospital mortality in AKI vs. non-AKI was 38.7% and 23.3% (p < 0.001); and in AKIadm vs. AKIhosp, 41.2% and 37.8% (p = 0.53).

CONCLUSIONS

ICU-acquired AKI has high incidence, complications and mortality. Risk factors for AKI and RRT utilization were similar to those described in other epidemiological studies. AKIadm was more frequent than AKIhosp, but had equal prognosis.

Personalized acute kidney injury treatment

PURPOSE OF REVIEW

Acute kidney injury (AKI) is a complex syndrome that might be induced by different causes and is associated with an increased morbidity and mortality. Therefore, it is a very heterogeneous syndrome and establishing a "one size fits all" treatment approach might not work. This review aims to examine the potential of personalized treatment strategies for AKI.

RECENT FINDINGS

The traditional diagnosis of AKI is based on changes of serum creatinine and urine output, but these two functional biomarkers have several limitations. Recent research identified different AKI phenotypes based on clinical features, biomarkers, and pathophysiological pathways. Biomarkers, such as Cystatin C, NGAL, TIMP2∗IGFBP7, CCL14, and DKK-3, have shown promise in predicting AKI development, renal recovery, and prognosis. Biomarker-guided interventions, such as the implementation of the KDIGO bundle, have demonstrated an improvement in renal outcomes in specific patient groups.

SUMMARY

A personalized approach to AKI treatment as well as research is becoming increasingly important as it allows the identification of distinct AKI phenotypes and the potential for targeted interventions. By utilizing biomarkers and clinical features, physicians might be able to stratify patients into subphenotypes, enabling more individualized treatment strategies. This review highlights the potential of personalized AKI treatment, emphasizing the need for further research and large-scale clinical trials to validate the efficacy of these approaches.

Serum Interleukin-18, Kidney Injury Molecule-1, and the Renal Resistive Index for Predicating Acute Kidney Injury in Critically Ill Patients with Sepsis

Interleukin-18 (IL-18) is a pro-inflammatory cytokine that rises approximately 24-48 h before a diagnosis of acute kidney injury (AKI). Kidney injury molecule-1 (KIM-1) is one of the most promising early biomarkers. It participates in the process of both kidney injury and healing, although the precise mechanism of the restoration of tubular integrity after injury still remains unclear. The renal resistive index (RRI) is used for evaluating changes in intrarenal perfusion occurring in renal parenchyma diseases. The study included 80 critically ill patients with sepsis, divided into 40 patients who developed AKI and 40 patients without AKI. All patients were evaluated through their history, clinical examination, laboratory investigations of serum IL-18 and KIM-1, and the RRI. Serum IL 18, serum KIM-1, and the RRI were significantly higher in critically ill patients with sepsis and AKI. Receiver operating characteristic analysis for detecting AKI 1 day after admission showed that the area under the curve (AUC) for serum IL-18 was 86.1%, the AUC for serum KIM-1 was 86%, and the AUC for the RRI was 88%, demonstrating statistical significance for the diagnosis of AKI within the next 24 h. Serum IL-18, KIM-1, and the RRI represent early predictors of AKI in critically ill septic patients.

Early acute kidney injury and transition to renal replacement therapy in critically ill patients with SARS-CoV-2 requiring veno-venous extracorporeal membrane oxygenation

BACKGROUND

Critically ill patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requiring veno-venous extracorporeal membrane oxygenation (vv-ECMO) are at risk for acute kidney injury (AKI). Currently, the incidence of AKI and progression to kidney replacement therapy (RRT) in critically ill patients with vv-ECMO for severe COVID-19 and implications on outcome are still unclear.

METHODS

Retrospective analysis at the University Medical Center Hamburg-Eppendorf (Germany) between March 1st, 2020 and July 31st, 2021. Demographics, clinical parameters, AKI, type of organ support, length of ICU stay, mortality and severity scores were assessed.

RESULTS

Ninety-one critically ill patients with SARS-CoV-2 requiring ECMO were included. The median age of the study population was 57 (IQR 49-64) years and 67% (n = 61) were male. The median SAPS II and SOFA Score on admission were 40 (34-46) and 12 (10-14) points, respectively. We observed that 45% (n = 41) developed early-AKI, 38% (n = 35) late-AKI and 16% (n = 15) no AKI during the ICU stay. Overall, 70% (n = 64) of patients required RRT during the ICU stay, 93% with early-AKI and 74% with late-AKI. Risk factors for early-AKI were younger age (OR 0.94, 95% CI 0.90-0.99, p = 0.02) and SAPS II (OR 1.12, 95% CI 1.06-1.19, p < 0.001). Patients with and without RRT were comparable regarding baseline characteristics. SAPS II (41 vs. 37 points, p < 0.05) and SOFA score (13 vs. 12 points, p < 0.05) on admission were significantly higher in patients receiving RRT. The median duration of ICU (36 vs. 28 days, p = 0.27) stay was longer in patients with RRT. An ICU mortality rate in patients with RRT in 69% (n = 44) and in patients without RRT of 56% (n = 27) was observed (p = 0.23).

CONCLUSION

Critically ill patients with severe SARS-CoV-2 related ARDS requiring vv-ECMO are at high risk of early acute kidney injury. Early-AKI is associated with age and severity of illness, and presents with high need for RRT. Mortality in patients with RRT was comparable to patients without RRT.

Initiation of continuous renal replacement therapy versus intermittent hemodialysis in critically ill patients with severe acute kidney injury: a secondary analysis of STARRT-AKI trial

BACKGROUND

There is controversy regarding the optimal renal-replacement therapy (RRT) modality for critically ill patients with acute kidney injury (AKI).

METHODS

We conducted a secondary analysis of the STandard versus Accelerated Renal Replacement Therapy in Acute Kidney Injury (STARRT-AKI) trial to compare outcomes among patients who initiated RRT with either continuous renal replacement therapy (CRRT) or intermittent hemodialysis (IHD). We generated a propensity score for the likelihood of receiving CRRT and used inverse probability of treatment with overlap-weighting to address baseline inter-group differences. The primary outcome was a composite of death or RRT dependence at 90-days after randomization.

RESULTS

We identified 1590 trial participants who initially received CRRT and 606 who initially received IHD. The composite outcome of death or RRT dependence at 90-days occurred in 823 (51.8%) patients who commenced CRRT and 329 (54.3%) patients who commenced IHD (unadjusted odds ratio (OR) 0.90; 95% confidence interval (CI) 0.75-1.09). After balancing baseline characteristics with overlap weighting, initial receipt of CRRT was associated with a lower risk of death or RRT dependence at 90-days compared with initial receipt of IHD (OR 0.81; 95% CI 0.66-0.99). This association was predominantly driven by a lower risk of RRT dependence at 90-days (OR 0.61; 95% CI 0.39-0.94).

CONCLUSIONS

In critically ill patients with severe AKI, initiation of CRRT, as compared to IHD, was associated with a significant reduction in the composite outcome of death or RRT dependence at 90-days.

VExUS Score in the Management of Patients With Acute Kidney Injury in the Intensive Care Unit: AKIVEX Study

OBJECTIVES

Venous congestion is a potential cause of acute kidney injury (AKI) and venous excess ultrasound (VExUS) score is a potentially useful tool in this scenario. The aim of this study is to verify whether the VExUS score can serve as a guide to decongestion in patients with severe AKI and whether the modification of the score can be associated with an increase in the number of renal replacement therapy (RRT)-free days in 28 days.

METHODS

This quasi-experimental study was conducted in patients admitted to the intensive care unit who developed severe AKI. The intervention was to suggest to the attending physician the use of diuretic in patients with VExUS >1. After 48 hours, a new VExUS assessment was performed. Primary outcome was RRT-free days at Day 28.

RESULTS

Ninety patients were included. Patients with a VExUS score >1 (n = 36) at enrollment had a greater use of diuretics in the following 48 hours (75.0%, n = 27) than patients with a VExUS ≤1 (n = 54) at enrollment (38.9%, n = 21), P = .001. Patients who reduced the VExUS score had a significantly greater number of RRT-free days at Day 28 (28.0; 8.0-28.0) when compared with those who did not reduce (15.0; 3.0-27.5), P = .012.

CONCLUSIONS

We found a higher diuretic use in patients with a higher VExUS score, and patients who reduced the VExUS in 48 hours had significantly more RRT-free days in 28 days.

Different Roles of Functional and Structural Renal Markers Measured at Discontinuation of Renal Replacement Therapy for Acute Kidney Injury

INTRODUCTION

Severe acute kidney injury (AKI) requiring renal replacement therapy (RRT) has been associated with an unacceptably high mortality of 50% or more. Successful discontinuation of RRT is thought to be linked to better outcomes. Although functional and structural renal markers have been evaluated in AKI, little is known about their roles in predicting outcomes at the time of RRT discontinuation.

METHODS

In this prospective single-center cohort study, we analyzed patients who received continuous RRT (CRRT) for AKI between August 2016 and March 2018 in the intensive care unit of the University of Tokyo Hospital (Tokyo, Japan). Clinical parameters and urine samples were obtained at CRRT discontinuation. Successful CRRT discontinuation was defined as neither resuming CRRT for 48 h nor receiving intermittent hemodialysis for 7 days from the CRRT termination. Major adverse kidney events (MAKEs) were defined as death, requirement for dialysis, or a decrease in the estimated glomerular filtration rate (eGFR) of more than 25% from the baseline at day 90.

RESULTS

Of 73 patients, who received CRRT for AKI, 59 successfully discontinued CRRT and 14 could not. Kinetic eGFR, urine volume, urinary neutrophil gelatinase-associated lipocalin (NGAL), and urinary L-type fatty acid binding protein were predictive for CRRT discontinuation. Of these factors, urine volume had the highest area under the curve (AUC) 0.91 with 95% confidence interval [0.80-0.96] for successful CRRT discontinuation. For predicting MAKEs at day 90, the urinary NGAL showed the highest AUC 0.76 [0.62-0.86], whereas kinetic eGFR and urine volume failed to show statistical significance (AUC 0.49 [0.35-0.63] and AUC 0.59 [0.44-0.73], respectively).

CONCLUSIONS

Our prospective study confirmed that urine volume, a functional renal marker, predicted successful discontinuation of RRT and that urinary NGAL, a structural renal marker, predicted long-term renal outcomes. These observations suggest that the functional and structural renal makers play different roles in predicting the outcomes of severe AKI requiring RRT.

Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis

The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package ( https://github.com/InfectionMedicineProteomics/BINN ).

Comparing Outcomes of Critically Ill Patients in Intensive Care Units and General Wards: A Comprehensive Analysis

Background

The admission of critically ill patients to intensive care unit (ICU) plays a crucial role in reducing mortality. However, the scarcity of available ICU beds presents a significant challenge. In resource-limited settings, the outcomes of critically ill patients, particularly those who are not accepted for ICU admission, have been a topic of ongoing debate and contention.

Objective

This study aimed to explore the outcomes and factors associated with ICU admission and mortality among critically ill patients in Thailand.

Methods

This prospective cohort study enrolled critically ill adults indicated for medical ICU admission. Patients were followed for 28 days regardless of whether they were admitted to an ICU. Data on mortality, hospital length of stay, duration of organ support, and factors associated with mortality and ICU admission were collected.

Results

Of the 180 patients enrolled, 72 were admitted to ICUs, and 108 were cared for in general wards. The ICU group had a higher 28-day mortality rate (44.4% vs 20.4%; P=0.001), but other outcomes of interest were comparable. Multivariate analysis identified alteration of consciousness, norepinephrine use, and epinephrine use as independent predictors of 28-day mortality. Higher body mass index (BMI), higher APACHE II score, and acute kidney injury were predictive factors associated with ICU acceptance.

Conclusion

Among patients indicated for ICU admission, those who were admitted had a higher 28-day mortality rate. Higher mortality was associated with alteration of consciousness and vasopressor use. Patients who were sicker and had higher BMI were more likely to be admitted to an ICU.

Development and validation of the creatinine clearance predictor machine learning models in critically ill adults

BACKGROUND

In critically ill patients, measured creatinine clearance (CrCl) is the most reliable method to evaluate glomerular filtration rate in routine clinical practice and may vary subsequently on a day-to-day basis. We developed and externally validated models to predict CrCl one day ahead and compared them with a reference reflecting current clinical practice.

METHODS

A gradient boosting method (GBM) machine-learning algorithm was used to develop the models on data from 2825 patients from the EPaNIC multicenter randomized controlled trial database. We externally validated the models on 9576 patients from the University Hospitals Leuven, included in the M@tric database. Three models were developed: a "Core" model based on demographic, admission diagnosis, and daily laboratory results; a "Core + BGA" model adding blood gas analysis results; and a "Core + BGA + Monitoring" model also including high-resolution monitoring data. Model performance was evaluated against the actual CrCl by mean absolute error (MAE) and root-mean-square error (RMSE).

RESULTS

All three developed models showed smaller prediction errors than the reference. Assuming the same CrCl of the day of prediction showed 20.6 (95% CI 20.3-20.9) ml/min MAE and 40.1 (95% CI 37.9-42.3) ml/min RMSE in the external validation cohort, while the developed model having the smallest RMSE (the Core + BGA + Monitoring model) had 18.1 (95% CI 17.9-18.3) ml/min MAE and 28.9 (95% CI 28-29.7) ml/min RMSE.

CONCLUSIONS

Prediction models based on routinely collected clinical data in the ICU were able to accurately predict next-day CrCl. These models could be useful for hydrophilic drug dosage adjustment or stratification of patients at risk.

TRIAL REGISTRATION

Not applicable.

Generalized precursor prediction boosts identification rates and accuracy in mass spectrometry based proteomics

Data independent acquisition mass spectrometry (DIA-MS) has recently emerged as an important method for the identification of blood-based biomarkers. However, the large search space required to identify novel biomarkers from the plasma proteome can introduce a high rate of false positives that compromise the accuracy of false discovery rates (FDR) using existing validation methods. We developed a generalized precursor scoring (GPS) method trained on 2.75 million precursors that can confidently control FDR while increasing the number of identified proteins in DIA-MS independent of the search space. We demonstrate how GPS can generalize to new data, increase protein identification rates, and increase the overall quantitative accuracy. Finally, we apply GPS to the identification of blood-based biomarkers and identify a panel of proteins that are highly accurate in discriminating between subphenotypes of septic acute kidney injury from undepleted plasma to showcase the utility of GPS in discovery DIA-MS proteomics.

In an endotoxaemic model, antibiotic clearance can be affected by different central venous catheter positions, during renal replacement therapy

BACKGROUND

In intensive care, different central venous catheters (CVC) are often used for infusion of drugs. If a patient is treated with continuous renal replacement therapy (CRRT) a second catheter, a central venous dialysis catheter (CVDC), is needed. Placing the catheters close together might pose a risk that a drug infused in a CVC could be directly aspirated into a CRRT machine and cleared from the blood without giving the effect intended. The purpose of this study was to elucidate if drug clearance is affected by different catheter placement, during CRRT. In this endotoxaemic animal model, an infusion of antibiotics was administered in a CVC placed in the external jugular vein (EJV). Antibiotic clearance was compared, whether CRRT was through a CVDC placed in the same EJV, or in a femoral vein (FV). To reach a target mean arterial pressure (MAP), noradrenaline was infused through the CVC and the dose was compared between the CDVDs.

RESULTS

The main finding in this study was that clearance of antibiotics was higher when both catheter tips were in the EJV, close together, compared to in different vessels, during CRRT. The clearance of gentamicin was 21.0 ± 7.3 vs 15.5 ± 4.2 mL/min (p 0.006) and vancomycin 19.3 ± 4.9 vs 15.8 ± 7.1 mL/min (p 0.021). The noradrenaline dose to maintain a target MAP also showed greater variance with both catheters in the EJV, compared to when catheters were placed in different vessels.

CONCLUSION

The results in this study indicate that close placement of central venous catheter tips could lead to unreliable drug concentration, due to direct aspiration, during CRRT.

Unfractionated heparin versus nafamostat mesylate for anticoagulation during continuous kidney replacement therapy: an observational study

BACKGROUND

Unfractionated heparin sodium and nafamostat mesylate have long been used as anticoagulants in continuous kidney replacement therapy (CKRT) where citrate is unavailable. This study aimed to determine whether heparin or nafamostat mesylate used during CKRT was associated with a longer filter life.

METHODS

In this single-centre observational study, we included adult patients who required CKRT and used heparin or nafamostat mesylate for their first CKRT in the intensive care unit from September 1, 2013, to December 31, 2020. The primary outcome was filter life (from the start to the end of using the first filter). We used propensity score matching to adjust for the imbalance in patients' characteristics and laboratory data at the start of CKRT and compared the outcomes between the two groups. We also performed restricted mean survival time analysis to compare the filter survival times.

RESULTS

We included 286 patients, 157 patients on heparin and 129 patients on nafamostat mesylate. After propensity score matching, the mean filter life with heparin was 1.58 days (N = 91, Standard deviation [SD], 1.52) and with nafamostat mesylate was 1.06 days (N = 91, SD, 0.94, p = 0.006). Multivariable regression analysis adjusted for confounding factors supported that heparin was associated with a longer filter life compared with nafamostat mesylate (regression coefficient, days, 0.52 [95% CI, 0.15, 0.89]). The between group difference of the restricted mean filter survival time in the matched cohort was 0.29 (95% CI, 0.07-0.50, p = 0.008).

CONCLUSION

Compared to nafamostat mesylate, heparin was associated with one-third to one-half a day longer filter life.

TRIAL REGISTRATION

Not applicable.

External Validation of Mortality Prediction Models for Critical Illness Reveals Preserved Discrimination but Poor Calibration

OBJECTIVES

In a recent scoping review, we identified 43 mortality prediction models for critically ill patients. We aimed to assess the performances of these models through external validation.

DESIGN

Multicenter study.

SETTING

External validation of models was performed in the Simple Intensive Care Studies-I (SICS-I) and the Finnish Acute Kidney Injury (FINNAKI) study.

PATIENTS

The SICS-I study consisted of 1,075 patients, and the FINNAKI study consisted of 2,901 critically ill patients.

MEASUREMENTS AND MAIN RESULTS

For each model, we assessed: 1) the original publications for the data needed for model reconstruction, 2) availability of the variables, 3) model performance in two independent cohorts, and 4) the effects of recalibration on model performance. The models were recalibrated using data of the SICS-I and subsequently validated using data of the FINNAKI study. We evaluated overall model performance using various indexes, including the (scaled) Brier score, discrimination (area under the curve of the receiver operating characteristics), calibration (intercepts and slopes), and decision curves. Eleven models (26%) could be externally validated. The Acute Physiology And Chronic Health Evaluation (APACHE) II, APACHE IV, Simplified Acute Physiology Score (SAPS)-Reduced (SAPS-R)' and Simplified Mortality Score for the ICU models showed the best scaled Brier scores of 0.11' 0.10' 0.10' and 0.06' respectively. SAPS II, APACHE II, and APACHE IV discriminated best; overall discrimination of models ranged from area under the curve of the receiver operating characteristics of 0.63 (0.61-0.66) to 0.83 (0.81-0.85). We observed poor calibration in most models, which improved to at least moderate after recalibration of intercepts and slopes. The decision curve showed a positive net benefit in the 0-60% threshold probability range for APACHE IV and SAPS-R.

CONCLUSIONS

In only 11 out of 43 available mortality prediction models, the performance could be studied using two cohorts of critically ill patients. External validation showed that the discriminative ability of APACHE II, APACHE IV, and SAPS II was acceptable to excellent, whereas calibration was poor.

Predicting Risk Factors of Acute Kidney Injury in the First 7 Days after Admission: Analysis of a Group of Critically Ill Patients

Background

Acute kidney injury (AKI) is a common complication in critically ill patients. Some predictive models have been reported, but the conclusions are controversial. The aim of this study was the formation of nomograms to predict risk factors for AKI in critically ill patients within the first 7 days after admission to the intensive care unit (ICU).

Methods

Data were extracted from the Medical Information Mart for Intensive Care- (MIMIC-) III database. The random forest method was used to fill in the missing values, and least absolute shrinkage and selection operator (Lasso) regression analysis was performed to screen for possible risk factors.

Results

A total of 561 patients were enrolled. Complication with AKI is significantly associated with a longer length of stay (LOS). For all patients, the predictors contained in the prediction nomogram included hypertension, coronary artery disease (CAD), cardiopulmonary bypass (CPB), coronary artery bypass grafting (CABG), Simplified Acute Physiology Score II (SAPS II), central venous pressure (CVP) measured for the first time after admission, and maximum and minimum mean artery pressure (MAP). The model showed good discrimination (C - index = 0.818, 95% CI: 0.779-0.857). In the subgroup of patients with well-controlled blood glucose levels, the significant predictors included hypertension, CABG, CPB, SAPS II, and maximum and minimum MAP. Good discrimination was also present before (C - index = 0.785, 95% CI: 0.736-0.834) and after adjustment (adjusted C - index = 0.770).

Conclusion

Hypertension, CAD, CPB, CABG, SAPS II, CVP measured for the first time after admission, and maximum and minimum MAP were independent risk factors for AKI in critically ill patients.

Cystatin C and derived measures of renal function as risk factors for mortality and acute kidney injury in sepsis - A post-hoc analysis of the FINNAKI cohort

PURPOSE

To assess the association between cystatin C-derived estimates of kidney function and mortality and acute kidney injury (AKI) in sepsis.

MATERIALS AND METHODS

Post-hoc analysis of sepsis patients in the FINNAKI-cohort (n = 802). Primary outcome was 90-day mortality. We measured plasma cystatin C and creatinine at intensive care unit (ICU) admission and estimated glomerular filtration rates (eGFR_cys, eGFR_crea) and shrunken pore syndrome (SPS; defined as eGFR_cys/eGFR_crea ratio < 0.7). Associations were assessed using Cox- or logistic regression.

RESULTS

Increased cystatin C and decreased eGFR_cys were associated with mortality in unadjusted analyses and in analyses adjusted for illness severity and creatinine. Hazard ratios (HRs) in unadjusted analyses were 3.30 (95% CI; 2.12-5.13, p < 0.001) and 3.26 (95% CI; 2.12-5.02, p < 0.001) respectively. SPS was associated with mortality in an unadjusted- (HR 1.78, 95% CI; 1.33-2.37, p < 0.001) and in an adjusted analysis (HR 1.54, 95% CI; 1.07-2.22, p = 0.021). All cystatin C-derived measures were associated with mortality also after adjustment for AKI development. Cystatin C was associated with AKI in unadjusted analyses but not in analyses adjusted for creatinine.

CONCLUSION

Cystatin C and derived measures of kidney function at ICU admission are associated with an increased 90-day mortality. Increased AKI incidence does not fully explain this association.

Acute kidney injury in critical COVID-19: a multicenter cohort analysis in seven large hospitals in Belgium

Background

Acute kidney injury (AKI) has been reported as a frequent complication of critical COVID-19. We aimed to evaluate the occurrence of AKI and use of kidney replacement therapy (KRT) in critical COVID-19, to assess patient and kidney outcomes and risk factors for AKI and differences in outcome when the diagnosis of AKI is based on urine output (UO) or on serum creatinine (sCr).

Methods

Multicenter, retrospective cohort analysis of patients with critical COVID-19 in seven large hospitals in Belgium. AKI was defined according to KDIGO within 21 days after ICU admission. Multivariable logistic regression analysis was used to explore the risk factors for developing AKI and to assess the association between AKI and ICU mortality.

Results

Of 1286 patients, 85.1% had AKI, and KRT was used in 9.8%. Older age, obesity, a higher APACHE II score and use of mechanical ventilation at day 1 of ICU stay were associated with an increased risk for AKI. After multivariable adjustment, all AKI stages were associated with ICU mortality. AKI was based on sCr in 40.1% and UO in 81.5% of patients. All AKI stages based on sCr and AKI stage 3 based on UO were associated with ICU mortality. Persistent AKI was present in 88.6% and acute kidney disease (AKD) in 87.6%. Rapid reversal of AKI yielded a better prognosis compared to persistent AKI and AKD. Kidney recovery was observed in 47.4% of surviving AKI patients.

Conclusions

Over 80% of critically ill COVID-19 patients had AKI. This was driven by the high occurrence rate of AKI defined by UO criteria. All AKI stages were associated with mortality (NCT04997915).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-04086-x.

Subphenotypes of acute kidney injury in adults

PURPOSE OF REVIEW

Acute kidney injury is a heterogeneous syndrome and as such is associated with multiple predisposing conditions and causes all of which affect outcomes. Such heterogeneity may conceal the potential benefit of therapies when generally applied to patients with acute kidney injury (AKI). The discovery of pathophysiology-based subphenotypes could be of benefit in allocating current and future therapies to specific groups.

RECENT FINDINGS

Clinical subphenotypes group patients into categories according to predisposing factors, disease severity, and trajectory. These may be helpful in assessing patient outcomes. Analyses of existing databases have revealed biological subphenotypes that are characterized by levels of biomarkers indicative of hyperinflammation and endothelial injury. Patients with increased levels of these biomarkers display higher mortality rates compared with those with lower levels and there is potential that this group might respond differently to therapies. However, challenges remain in the validation, generalizability, and application of these subphenotypes.

SUMMARY

Subphenotyping may help reduce heterogeneity under the umbrella term of acute kidney injury. Despite challenges remain, the identification of AKI subphenotypes has opened the potential of AKI research focused on better targeted therapies.

Platelet Function in Acute Kidney Injury: A Systematic Review and a Cohort Study

Acute kidney injury (AKI) patients have increased bleeding risk, which could be partially due to acquired platelet dysfunction. We conducted a systematic review and a cohort study to investigate platelet function and count in AKI and their association with AKI-related bleeding and mortality. Through a systematic literature search in PubMed and Embase, we identified 9 studies reporting platelet function and 56 studies reporting platelet count or platelet indices in AKI patients. Overall, platelet aggregation was reduced in AKI patients in nonintensive care unit (ICU) settings but not in ICU settings, except that reduced aggregation was associated with renal replacement therapy. Thrombocytopenia in AKI was frequent and often predictive of mortality. In our cohort study, we prospectively included 54 adult ICU patients who developed AKI within 24 hours of ICU admission and 33 non-AKI ICU controls. Platelet function was measured with light transmission aggregometry and flow cytometry. AKI patients bled more frequently than non-AKI patients (p = 0.04), and bleeding was associated with increased 30-day mortality in AKI (p = 0.02). However, platelet function was not different between AKI and non-AKI patients (aggregation: all p > 0.52; flow cytometry: all p > 0.07) and platelet function was not associated with bleeding in AKI. In conclusion, a reduced platelet count is frequent in AKI, but the literature on platelet function in AKI is sparse. In a cohort study, we demonstrated that patients with AKI within 24 hours of ICU admission exhibited increased bleeding tendency but this was not associated with reduced platelet function.

Subphenotypes in acute kidney injury: a narrative review

Acute kidney injury (AKI) is a frequently encountered syndrome especially among the critically ill. Current diagnosis of AKI is based on acute deterioration of kidney function, indicated by an increase in creatinine and/or reduced urine output. However, this syndromic definition encompasses a wide variety of distinct clinical features, varying pathophysiology, etiology and risk factors, and finally very different short- and long-term outcomes. Lumping all AKI together may conceal unique pathophysiologic processes specific to certain AKI populations, and discovering these AKI subphenotypes might help to develop targeted therapies tackling unique pathophysiological processes. In this review, we discuss the concept of AKI subphenotypes, current knowledge regarding both clinical and biomarker-driven subphenotypes, interplay with AKI subphenotypes and other ICU syndromes, and potential future and clinical implications.

Internal and external validation of machine learning-assisted prediction models for mechanical ventilation-associated severe acute kidney injury

BACKGROUND

Currently, very few preventive or therapeutic strategies are used for mechanical ventilation (MV)-associated severe acute kidney injury (AKI).

OBJECTIVES

We developed clinical prediction models to detect the onset of severe AKI in the first week of intensive care unit (ICU) stay during the initiation of MV.

METHODS

A large ICU database Medical Information Mart for Intensive Care IV (MIMIC-IV) was analysed retrospectively. Data were collected from the clinical information recorded at the time of ICU admission and during the initial 12 h of MV. Using univariate and multivariate analyses, the predictors were selected successively. For model development, two machine learning algorithms were compared. The primary goal was to predict the development of AKI stage 2 or 3 (AKI-23) and AKI stage 3 (AKI-3) in the first week of patients' ICU stay after initial 12 h of MV. The developed models were externally validated using another multicentre ICU database (eICU Collaborative Research Database, eICU) and evaluated in various patient subpopulations.

RESULTS

Models were developed using data from the development cohort (MIMIC-IV: 2008-2016; n = 3986); the random forest algorithm outperformed the logistic regression algorithm. In the internal (MIMIC-IV: 2017-2019; n = 1210) and external (eICU; n = 1494) validation cohorts, the incidences of AKI-23 were 154 (12.7%) and 119 (8.0%), respectively, with areas under the receiver operator characteristic curve of 0.78 (95% confidence interval [CI]: 0.74-0.82) and 0.80 (95% CI: 0.76-0.84); the incidences of AKI-3 were 81 (6.7%) and 67 (4.5%), with areas under the receiver operator characteristic curve of 0.81 (95% CI: 0.76-0.87) and 0.80 (95% CI: 0.73-0.86), respectively.

CONCLUSIONS

Models driven by machine learning and based on routine clinical data may facilitate the early prediction of MV-associated severe AKI. The validated models can be found at: https://apoet.shinyapps.io/mv_aki_2021_v2/.

Trajectory of kidney recovery in pediatric patients requiring continuous kidney replacement therapy for acute kidney injury

BACKGROUND

Acute kidney injury (AKI) is commonly seen in the PICU and is associated with poor short-term and long-term outcomes, especially in patients who required continuous kidney replacement therapy (CKRT). However, as the trajectory of kidney recovery in these patients remain uncertain, determination of the timing to convert to permanent kidney replacement therapy (KRT) remains a major challenge. We aimed to examine the frequency and timing of kidney recovery in pediatric AKI survivors that required CKRT.

METHODS

We performed a retrospective study of patients under 18 years old who received CKRT for AKI in a tertiary-care PICU over 6 years. Primary outcomes were the rate of KRT withdrawal due to kidney recovery and KRT-dependent days for those who survived to hospital discharge. Secondary outcomes were all-cause mortality, dialysis dependence, and occurrences of estimated glomerular filtration rate (eGFR) < 90 mL/min/1.73m² and eGFR < 60 mL/min/1.73m² one year after initiation of the index CKRT in survivors.

RESULTS

Thirty-nine patients were included. Of the 28 children who survived to hospital discharge, 26 (93%) withdrew from dialysis due to kidney recovery, all within 30 days. Twenty-three patients were followed up. One had died, five had an eGFR of 60 mL/min/1.73m² or more but less than 90 mL/min/1.73m², and two had an eGFR < 60 mL/min/1.73m², of which one required peritoneal dialysis.

CONCLUSIONS

Over 90% of the survivors withdrew CKRT within 30 days. However, the frequency of abnormal eGFR one year after initiation of CKRT in survivors exceeded 30% and supports the recommendation of post-AKI follow-up.

Impact of intraabdominal hypertension on kidney failure in critically ill patients: A post-hoc database analysis

PURPOSE

To assess whether intraabdominal hypertension (IAH) may influence kidney failure as well as mortality.

METHODS

This post-hoc analysis of two databases (IROI and iSOFA study) tested the independent association between IAH and kidney failure. Mortality was assessed using four prespecified groups (IAH present, kidney failure present, IAH and kidney failure present and no IAH or kidney failure present).

RESULTS

Of 825 critically ill patients, 302 (36.6%) developed kidney failure and 192 (23.7%) died during the first 90 days. Only 'Cumulative days with IAH grade II or more' was significantly associated with kidney failure (OR 1.29 (1.08-1.55), p = 0.003) while 'cumulative days with IAH grade I or more' (p = 0.135) or highest daily IAP (p = 0.062) was not. IAH combined with kidney failure was independently associated with 90-day mortality (OR 2.20 (1.20-4.05), p = 0.011), which was confirmed for higher grades of IAH (grade II or more) alone (OR 2.14 (1.07-4.30), p = 0.032) and combined with kidney failure (OR 3.25 (1.72-6.12), p < 0.001).

CONCLUSIONS

This study suggest that duration as well as higher grades of IAH are associated with kidney failure and may increase mortality.

Small, short-term, point-of-care creatinine changes as predictors of acute kidney injury in critically ill patients

PURPOSE

To assess short-term creatinine changes as predictors of acute kidney injury (AKI) when used alone and in combination with AKI risk factors.

METHODS

In this prospective cohort study, we identified all creatinine measurements from frequent point-of-care arterial blood gas measurements from ICU admission until AKI. We evaluated the predictive value of small changes between these creatinine measurements for AKI development, alone and with AKI risk factors.

RESULTS

Of 377 patients with 3235 creatinine measurements, generating 15,075 creatinine change episodes, 215 (57%) patients developed AKI, and 68 (18%) developed stage 2 or 3 AKI. In isolation, a creatinine increase over 4.1-7.3 h had a 0.65 area under the curve for predicting stage 2 or 3 AKI within 3-37.7 h. Combining creatinine increases of ≥1 μmol/L/h (≥0.0113 mg/dL/h) over 4-5.8 h with three AKI risk factors (cardiac surgery, use of vasopressors, chronic liver disease) had 83% sensitivity, 79% specificity and 0.87 area under the curve for stage 2 or 3 AKI occurring 8.7-25.6 h later.

CONCLUSION

In combination with key risk factors, frequent point-of-care creatinine assessment on arterial blood gases to detect small, short-term creatinine changes provides a robust, novel, low-cost, and rapid method for predicting AKI in critically ill patients.

Acute kidney injury in intensive care patients: Incidence, time course, and risk factors

Background

Acute kidney injury (AKI) is frequent and influences the prognosis of intensive care unit (ICU) patients. The aim of this study was to estimate the incidence, time‐course, risk factors, and mortality of AKI among unselected ICU patients.

Methods

All adult ICU patients admitted to the ICU at the University Hospital in Central Norway from 2010 to 2015 with a stay of 24 h or more were included in the study. AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. All patients were followed with respect to reversal of AKI. Risk factors for AKI were analyzed using Cox regression.

Results

Among 2325 ICU patients, 1245 developed AKI during the ICU stay, corresponding to an incidence of 53.5 % (CI, 51.5–55.5). The incidence according to KDIGO AKI stages 1, 2, and 3 was 26.2, 11.7, and 15.7%, respectively. The median duration of AKI was 24 (CI 19–24), 32 (CI 26–39), and 101 (CI 75–164) hours for AKI KDIGO stage 1, 2, and 3, respectively. AKI was transient, persistent, or AKD in 73.4, 16.5, and 10.0% of the patients with a known outcome. AKI reversal was observed in 72.9% of all AKI patients. Independent risk factors for AKI in a multivariate analysis were hypertension, diabetes, heart disease, and higher body weight. Episodes of mean arterial pressure below 73 mmHg were associated with a higher risk of AKI.

Conclusions

In our material, the incidence of AKI was comparable to what has been reported previously. Risk factors for the development of AKI were a MAP below 73, hypertension, diabetes, heart disease, chronic kidney disease, and higher body weight. Most AKI patients regain their kidney function during the ICU stay, particularly in the KDIGO AKI stages 1 and 2.

Plants with Therapeutic Potential for Ischemic Acute Kidney Injury: A Systematic Review

Acute kidney injury (AKI) is a complex condition which has an intricate pathology mostly involving hemodynamic, inflammatory, and direct toxic effects at the cellular level with high morbidity and mortality ratios. Renal ischemic reperfusion injury (RIRI) is the main factor responsible for AKI, most often observed in different types of shock, kidney transplantation, sepsis, and postoperative procedures. The RIRI-induced AKI is accompanied by increased reactive oxygen species generation together with the activation of various inflammatory pathways. In this context, plant-derived medicines have shown encouraging nephroprotective properties. Evidence provided in this systemic review leads to the conclusion that plant-derived extracts and compounds exhibit nephroprotective action against renal ischemic reperfusion induced-AKI by increasing endogenous antioxidants and decreasing anti-inflammatory cytokines. However, there is no defined biomarker or target which can be used for treating AKI completely. These plant-derived extracts and compounds are only tested in selected transgenic animal models. To develop the results obtained into a therapeutic entity, one should apply them in proper vertebrate multitransgenic animal models prior to further validation in humans.

Overview of Diagnostic Criteria and Epidemiology of Acute Kidney Injury and Acute Kidney Disease in the Critically Ill Patient

Since the description ischuria renalis by William Heberden (1), AKI has remained a prominent complication of critical illness. Beyond KRT, treatment has been limited by the capacity to phenotype this condition. Here, we chronicle the evolution of attempts to classify AKI, including the adoption of consensus definitions, the expansion of diagnosis and prognosis with novel biomarkers, and emerging tools such as artificial intelligence (AI).

Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization: Machine Learning Study

Background

As a major health hazard, the incidence of coronary heart disease has been increasing year by year. Although coronary revascularization, mainly percutaneous coronary intervention, has played an important role in the treatment of coronary heart disease, major adverse cardiovascular events (MACE) such as recurrent or persistent angina pectoris after coronary revascularization remain a very difficult problem in clinical practice.

Objective

Given the high probability of MACE after coronary revascularization, the aim of this study was to develop and validate a predictive model for MACE occurrence within 6 months based on machine learning algorithms.

Methods

A retrospective study was performed including 1004 patients who had undergone coronary revascularization at The People’s Hospital of Liaoning Province and Affiliated Hospital of Liaoning University of Traditional Chinese Medicine from June 2019 to December 2020. According to the characteristics of available data, an oversampling strategy was adopted for initial preprocessing. We then employed six machine learning algorithms, including decision tree, random forest, logistic regression, naïve Bayes, support vector machine, and extreme gradient boosting (XGBoost), to develop prediction models for MACE depending on clinical information and 6-month follow-up information. Among all samples, 70% were randomly selected for training and the remaining 30% were used for model validation. Model performance was assessed based on accuracy, precision, recall, F1-score, confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), and visualization of the ROC curve.

Results

Univariate analysis showed that 21 patient characteristic variables were statistically significant (P<.05) between the groups without and with MACE. Coupled with these significant factors, among the six machine learning algorithms, XGBoost stood out with an accuracy of 0.7788, precision of 0.8058, recall of 0.7345, F1-score of 0.7685, and AUC of 0.8599. Further exploration of the models to identify factors affecting the occurrence of MACE revealed that use of anticoagulant drugs and course of the disease consistently ranked in the top two predictive factors in three developed models.

Conclusions

The machine learning risk models constructed in this study can achieve acceptable performance of MACE prediction, with XGBoost performing the best, providing a valuable reference for pointed intervention and clinical decision-making in MACE prevention.

Acute kidney injury-attributable mortality in critically ill patients with sepsis

Background

To assess whether acute kidney injury (AKI) is independently associated with hospital mortality in ICU patients with sepsis, and estimate the excess AKI-related mortality attributable to AKI.

Methods

We analyzed adult patients from two distinct retrospective critically ill cohorts: (1) Medical Information Mart for Intensive Care IV (MIMIC IV; n = 15,610) cohort and (2) Wenzhou (n = 1,341) cohort. AKI was defined by Kidney Disease: Improving Global Outcomes (KDIGO) criteria. We applied multivariate logistic and linear regression models to assess the hospital and ICU mortality, hospital length-of-stay (LOS), and ICU LOS. The excess attributable mortality for AKI in ICU patients with sepsis was further evaluated.

Results

AKI occurred in 5,225 subjects in the MIMIC IV cohort (33.5%) and 494 in the Wenzhou cohort (36.8%). Each stage of AKI was an independent risk factor for hospital mortality in multivariate logistic regression after adjusting for baseline illness severity. The excess attributable mortality for AKI was 58.6% (95% CI [46.8%-70.3%]) in MIMIC IV and 44.6% (95% CI [12.7%-76.4%]) in Wenzhou. Additionally, AKI was independently associated with increased ICU mortality, hospital LOS, and ICU LOS.

Conclusion

Acute kidney injury is an independent risk factor for hospital and ICU mortality, as well as hospital and ICU LOS in critically ill patients with sepsis. Thus, AKI is associated with excess attributable mortality.