The kinetic estimated glomerular filtration rate ratio predicts acute kidney injury

The work-recovery cycle of kidney strain and inflammation in sugarcane workers following repeat heat exposure at work and at home

PURPOSE

To examine heat exposure at work and home and the work-recovery cycle and temporal variation of kidney strain, muscle injury and inflammation biomarkers in sugarcane workers.

METHODS

20 male sugarcane workers (age: 33 ± 7 years) with a workplace Rest.Shade.Hydration (RSH) intervention were observed over 4 days, at the end (18 h post-shift recovery) and beginning of a work week (42 h post-shift recovery). Measures included work intensity (heart rate), gastro-intestinal temperature, estimated body core temperature (using heart rate), fluid consumption, pre- and post-work blood and urine samples, physical activity (accelerometery) away from work, plus ambient heat exposure at work and home.

RESULTS

On workdays, workers awakened at approx. 02:40 after 5 h sleep in ~ 30 °C. Across work shifts, daily average WBGT ranged from 26 to 29 °C (cooler than normal) and average workload intensity ranged from 55 to 58%HRmax. Workers reported consuming ~ 8 L of water and ~ 4 × 300 mL bags of electrolyte fluid each day. Serum creatinine, cystatin C and creatine phosphokinase markedly increased post-work and decreased during recovery; serum potassium did the opposite (all p < 0.01). Biomarker concentration changes were similar between recovery periods (18 h vs. 42 h; all p > 0.27). C-reactive protein was the highest at the end of the work week (p = 0.01).

CONCLUSION

Despite RSH intervention, cross-shift kidney strain was marked (recovering overnight) and systemic inflammation increased over the work week. Thus, biomonitoring of kidney function in occupational populations should be performed before a work shift at any point in the work week. This is essential knowledge for field studies and surveillance.

A clinical model to predict successful renal replacement therapy (RRT) discontinuation in patients with Acute Kidney Injury (AKI)

INTRODUCTION

Ideal timing of Renal Replacement Therapy (RRT) discontinuation in Acute Kidney Injury (AKI) is still unknown. We aimed to study the role of creatinine-related variables in predicting RRT successful discontinuation and to propose a clinical predictive score.

METHODS

In this single-centre retrospective study, we evaluated all AKI patients in whom RRT was interrupted for at least 48 hours. Patients who were still RRT-independent 7 days after initial RRT cessation were included in the "Success" group and opposed to the "Failure" group. We evaluated baseline characteristics and variables collected at the time of RRT interruption, as well as the Kinetic estimated Glomerular Filtration Rate (KeGFR), the simple variation in serum Creatinine (ΔsCr), and the incremental creatinine ratio on the first three days after RRT interruption. Multivariable analysis was performed to evaluate prediction of success. Internal validation using a simple binomial generalized regression model with Lasso estimation and 5-fold cross validation method was performed.

RESULTS

We included 124 patients, 49 in the "Failure" group and 75 in the "Success" group. All creatinine-related variables predicted success in simple and multiple logistic regression models. The best model generated a clinical score based on the odds ratio obtained for each variable and included urine output, non-renal SOFA score, fluid balance, serum urea, serum potassium, blood pH, and the variation in sCr values after RRT discontinuation. The score presented an area under the ROC of 0.86 (95% CI 0.76‒1.00).

CONCLUSION

Creatinine variation between the first 2 consecutive days after RRT discontinuation might predict success in RRT discontinuation. The developed clinical score based on these variables might be a useful clinical decision tool to guide hemodialysis catheter safe removal.

Electronic alerts and a care bundle for acute kidney injury-an Australian cohort study

BACKGROUND

Early recognition of hospital-acquired acute kidney injury (AKI) may improve patient management and outcomes.

METHODS

This multicentre study was conducted at three hospitals (H1-intervention; H2 and H3-controls) served by a single laboratory. The intervention bundle [an interruptive automated alerts (aAlerts) showing AKI stage and baseline creatinine in the eMR, a management guide and junior medical staff education] was implemented only at H1. Outcome variables included length-of-stay (LOS), all-cause in-hospital mortality and management quality.

RESULTS

Over 6 months, 639 patients developed AKI (265 at H1 and 374 at controls), with 94.7% in general wards; 537 (84%) patients developed Stage 1, 58 (9%) Stage 2 and 43 (7%) Stage 3 AKI. Median LOS was 9 days (IQR 4-17) and was not different between intervention and controls. However, patients with AKI stage 1 had shorter LOS at H1 [median 8 versus 10 days (P = 0.021)]. Serum creatinine had risen prior to admission in most patients. Documentation of AKI was better in H1 (94.8% versus 83.4%; P = 0.001), with higher rates of nephrology consultation (25% versus 19%; P = 0.04) and cessation of nephrotoxins (25.3 versus 18.8%; P = 0.045). There was no difference in mortality between H1 versus controls (11.7% versus 13.0%; P = 0.71).

CONCLUSIONS

Most hospitalized patients developed Stage 1 AKI and developed AKI in the community and remained outside the intensive care unit (ICU). The AKI eAlert bundle reduced LOS in most patients with AKI and increased AKI documentation, nephrology consultation rate and cessation of nephrotoxic medications.

Diagnosing acute kidney injury ahead of time in critically ill septic patients using kinetic estimated glomerular filtration rate

INTRODUCTION

Accurate and actionable diagnosis of Acute Kidney Injury (AKI) ahead of time is important to prevent or mitigate renal insufficiency. The purpose of this study was to evaluate the performance of Kinetic estimated Glomerular Filtration Rate (KeGFR) in timely predicting AKI in critically ill septic patients.

METHODS

We conducted a retrospective analysis on septic ICU patients who developed AKI in AmsterdamUMCdb, the first freely available European ICU database. The reference standard for AKI was the Kidney Disease: Improving Global Outcomes (KDIGO) classification based on serum creatinine and urine output (UO). Prediction of AKI was based on stages defined by KeGFR and UO. Classifications were compared by length of ICU stay (LOS), need for renal replacement therapy and 28-day mortality. Predictive performance and time between prediction and diagnosis were calculated.

RESULTS

Of 2492 patients in the cohort, 1560 (62.0%) were diagnosed with AKI by KDIGO and 1706 (68.5%) by KeGFR criteria. Disease stages had agreement of kappa = 0.77, with KeGFR sensitivity 93.2%, specificity 73.0% and accuracy 85.7%. Median time to recognition of AKI Stage 1 was 13.2 h faster for KeGFR, and 7.5 h and 5.0 h for Stages 2 and 3. Outcomes revealed a slight difference in LOS and 28-day mortality for Stage 1.

CONCLUSIONS

Predictive performance of KeGFR combined with UO criteria for diagnosing AKI is excellent. Compared to KDIGO, deterioration of renal function was identified earlier, most prominently for lower stages of AKI. This may shift the actionable window for preventing and mitigating renal insufficiency.

Definitions, phenotypes, and subphenotypes in acute kidney injury-Moving towards precision medicine

The current definition of acute kidney injury (AKI) is generic and, based only on markers of function, is unsuitable for guiding individualized treatment. AKI is a complex syndrome with multiple presentations and causes. Targeted AKI management will only be possible if different phenotypes and subphenotypes of AKI are recognised, based on causation and related pathophysiology. Molecular signatures to identify subphenotypes are being recognised, as specific biomarkers reveal activated pathways. Assessment of individual clinical risk needs wider dissemination to allow identification of patients at high risk of AKI. New and more timely markers for glomerular filtration rate (GFR) are available. However, AKI diagnosis and classification should not be limited to GFR, but include tubular function and damage. Combining damage and stress biomarkers with functional markers enhances risk prediction, and identifies a population enriched for clinical trials targeting AKI. We review novel developments and aim to encourage implementation of these new techniques into clinical practice as a strategy for individualizing AKI treatment akin to a precision medicine-based approach.

Opportunities in digital health and electronic health records for acute kidney injury care

PURPOSE OF REVIEW

The field of digital health is evolving rapidly with applications relevant to the prediction, detection and management of acute kidney injury (AKI). This review will summarize recent publications in these areas.

RECENT FINDINGS

Machine learning (ML) approaches have been applied predominantly for AKI prediction, but also to identify patients with AKI at higher risk of adverse outcomes, and to discriminate different subgroups (subphenotypes) of AKI. There have been multiple publications in this area, but a smaller number of ML models have robust external validation or the ability to run in real-time in clinical systems. Recent studies of AKI alerting systems and clinical decision support systems continue to demonstrate variable results, which is likely to result from differences in local context and implementation strategies. In the design of AKI alerting systems, choice of baseline creatinine has a strong effect on performance of AKI detection algorithms.

SUMMARY

Further research is required to overcome barriers to the validation and implementation of ML models for AKI care. Simpler electronic systems within the electronic medical record can lead to improved care in some but not all settings, and careful consideration of local context and implementation strategy is recommended.

Acute Changes in Serum Creatinine and Kinetic Glomerular Filtration Rate Estimation in Early Phase of Acute Pancreatitis

In patients with acutely changing kidney function, equations used to estimate glomerular filtration rate (eGFR) must be adjusted for dynamic changes in the concentrations of filtration markers (kinetic eGFR, KeGFR). The aim of our study was to evaluate serum creatinine-based KeGFR in patients in the early phase of acute pancreatitis (AP) as a marker of changing renal function and as a predictor of AP severity. We retrospectively calculated KeGFR on day 2 and 3 of the hospital stay in a group of 147 adult patients admitted within 24 h from the onset of AP symptoms and treated in two secondary-care hospitals. In 34 (23%) patients, changes in serum creatinine during days 1-3 of the hospital stay exceeded 26.5 µmol/L; KeGFR values almost completely differentiated those with increasing and decreasing serum creatinine (area under receiver operating characteristic curve, AUROC: 0.990 on day 3). In twelve (8%) patients, renal failure was diagnosed during the first three days of the hospital stay according to the modified Marshall scoring system, which was associated with significantly lower KeGFR values. KeGFR offered good diagnostic accuracy for renal failure (area under receiver operating characteristic-AUROC: 0.942 and 0.950 on days 2 and 3). Fourteen (10%) patients developed severe AP. KeGFR enabled prediction of severe AP with moderate diagnostic accuracy (AUROC: 0.788 and 0.769 on days 2 and 3), independently of age, sex, comorbidities and study center. Lower KeGFR values were significantly associated with mortality. Significant dynamic changes in renal function are common in the early phase of AP. KeGFR may be useful in the assessment of kidney function in AP and the prediction of AP severity.