Improving acute kidney injury diagnostics using predictive analytics

Novel renal injury markers in dogs with ehrlichiosis

Canine monocytic ehrlichiosis (CME) has been observed to impact renal function. Currently, the recognition of acute kidney injury is through the nonspecific biomarker serum creatinine (sCr). Novel markers of renal injury such as urinary clusterin (uClust) and urinary cystatin B (uCysB) may increase our understanding of the relationship between ehrlichiosis and renal cellular injury. The aim of this study was to evaluate novel renal injury biomarkers in dogs with acute CME. Twenty healthy dogs were enrolled in the control group (CG), and 16 dogs naturally infected with Ehrlichia canis were included in the Ehrlichia Group (EG). All dogs were followed for 45 days. EG dogs were treated with doxycycline twice daily for the first 30 days. Urine and serum were collected at: 0, 0.5, 1, 15, 30, and 45 days after start of treatment. Urine concentrations of uClust and uCysB were determined using a research ELISA immunoassay. A linear mixed model was used to estimate population mean of renal injury markers with patient as the random effect, and day and treatment as fixed effects. EG was observed to have higher uClust values compared to CG (estimated population mean EG: 213 ng/dL vs. CG: 84 ng/dL, P < 0.001). EG was observed to have higher uCysB values compared to CG (estimated population mean EG: 248 ng/dL vs. CG: 38 ng/dL, P < 0.001). Increases in uCysB and uClust suggest the presence of renal injury and a possible mechanism for the observed predisposition to chronic kidney disease in dogs with ehrlichiosis.

Predictive value of plasma proenkephalin and neutrophil gelatinase-associated lipocalin in acute kidney injury and mortality in cardiogenic shock

BACKGROUND

Acute kidney injury (AKI) is a frequent form of organ injury in cardiogenic shock. However, data on AKI markers such as plasma proenkephalin (P-PENK) and neutrophil gelatinase-associated lipocalin (P-NGAL) in cardiogenic shock populations are lacking. The objective of this study was to assess the ability of P-PENK and P-NGAL to predict acute kidney injury and mortality in cardiogenic shock.

RESULTS

P-PENK and P-NGAL were measured at different time points between baseline and 48 h in 154 patients from the prospective CardShock study. The outcomes assessed were AKI defined by an increase in creatinine within 48 h and all-cause 90-day mortality. Mean age was 66 years and 26% were women. Baseline levels of P-PENK and P-NGAL (median [interquartile range]) were 99 (71-150) pmol/mL and 138 (84-214) ng/mL. P-PENK > 84.8 pmol/mL and P-NGAL > 104 ng/mL at baseline were identified as optimal cut-offs for AKI prediction and independently associated with AKI (adjusted HRs 2.2 [95% CI 1.1-4.4, p = 0.03] and 2.8 [95% CI 1.2-6.5, p = 0.01], respectively). P-PENK and P-NGAL levels at baseline were also associated with 90-day mortality. For patients with oliguria < 0.5 mL/kg/h for > 6 h before study enrollment, 90-day mortality differed significantly between patients with low and high P-PENK/P-NGAL at baseline (5% vs. 68%, p < 0.001). However, the biomarkers provided best discrimination for mortality when measured at 24 h. Identified cut-offs of P-PENK_24h > 105.7 pmol/L and P-NGAL_24h > 151 ng/mL had unadjusted hazard ratios of 5.6 (95% CI 3.1-10.7, p < 0.001) and 5.2 (95% CI 2.8-9.8, p < 0.001) for 90-day mortality. The association remained significant despite adjustments with AKI and two risk scores for mortality in cardiogenic shock.

CONCLUSIONS

High levels of P-PENK and P-NGAL at baseline were independently associated with AKI in cardiogenic shock patients. Furthermore, oliguria before study inclusion was associated with worse outcomes only if combined with high baseline levels of P-PENK or P-NGAL. High levels of both P-PENK and P-NGAL at 24 h were found to be strong and independent predictors of 90-day mortality.

TRIAL REGISTRATION

NCT01374867 at www.clinicaltrials.gov , registered 16 Jun 2011-retrospectively registered.

Clinical risk factors and inflammatory biomarkers of post-traumatic acute kidney injury in combat patients

BACKGROUND

Post-traumatic acute kidney injury has occurred in every major military conflict since its initial description during World War II. To ensure the proper treatment of combat casualties, early detection is critical. This study therefore aimed to investigate combat-related post-traumatic acute kidney injury in recent military conflicts, used machine learning algorithms to identify clinical and biomarker variables associated with the development of post-traumatic acute kidney injury, and evaluated the effects of post-traumatic acute kidney injury on wound healing and nosocomial infection.

METHODS

We conducted a retrospective clinical cohort review of 73 critically injured US military service members who sustained major combat-related extremity wounds and had collected injury characteristics, assayed serum and tissue biopsy samples for the expression of protein and messenger ribonucleic acid biomarkers. Bivariate analyses and random forest recursive feature elimination classification algorithms were used to identify associated injury characteristics and biomarker variables.

RESULTS

The incidence of post-traumatic acute kidney injury was 20.5%. Of that, 86% recovered baseline renal function and only 2 (15%) of the acute kidney injury group required renal replacement therapy. Random forest recursive feature elimination algorithms were able to estimate post-traumatic acute kidney injury with the area under the curve of 0.93, sensitivity of 0.91, and specificity of 0.91. Post-traumatic acute kidney injury was associated with injury severity score, serum epidermal growth factor, and tissue activin A type receptor 1, matrix metallopeptidase 10, and X-C motif chemokine ligand 1 expression. Patients with post-traumatic acute kidney injury exhibited poor wound healing and increased incidence of nosocomial infections.

CONCLUSION

The occurrence of acute kidney injury in combat casualties may be estimated using injury characteristics and serum and tissue biomarkers. External validations of these models are necessary to generalize for all trauma patients.

AKI!Now Initiative: Recommendations for Awareness, Recognition, and Management of AKI

The American Society of Nephrology has established a new initiative, AKI!Now, with the goal of promoting excellence in the prevention and treatment of AKI by building a foundational program that transforms education and delivery of AKI care, aiming to reduce morbidity and associated mortality and to improve long-term outcomes. In this article, we describe our current efforts to improve early recognition and management involving inclusive interdisciplinary collaboration between providers, patients, and their families; discuss the ongoing need to change some of our current AKI paradigms and diagnostic methods; and provide specific recommendations to improve AKI recognition and care. In the hospital and the community, AKI is a common and increasingly frequent condition that generates risks of adverse events and high costs. Unfortunately, patients with AKI may frequently have received less than optimal quality of care. New classifications have facilitated understanding of AKI incidence and its impact on outcomes, but they are not always well aligned with AKI pathophysiology. Despite ongoing research efforts, treatments to promote or hasten kidney recovery remain ineffective. To avoid progression, the current approach to AKI emphasizes the promotion of early recognition and timely response. However, a lack of awareness of the importance of early recognition and treatment among health care team members and the heterogeneity of approaches within the health care teams assessing the patient remains a major challenge. Early identification is further complicated by differences in settings where AKI occurs (the community or the hospital), and by differences in patient populations and cultures between the intensive care unit and ward environments. To address these obstacles, we discuss the need to improve education at all levels of care and to generate specific guidance on AKI evaluation and management, including the development of a widely applicable education and an AKI management toolkit, engaging hospital administrators to incorporate AKI as a quality initiative, and raising awareness of AKI as a complication of other disease processes.

Development of Deployable Predictive Models for Minimal Clinically Important Difference Achievement Across the Commonly Used Health-related Quality of Life Instruments in Adult Spinal Deformity Surgery

STUDY DESIGN

Retrospective analysis of prospectively-collected, multicenter adult spinal deformity (ASD) databases.

OBJECTIVE

To predict the likelihood of reaching minimum clinically important differences in patient-reported outcomes after ASD surgery.

SUMMARY OF BACKGROUND DATA

ASD surgeries are costly procedures that do not always provide the desired benefit. In some series only 50% of patients achieve minimum clinically important differences in patient-reported outcomes (PROs). Predictive modeling may be useful in shared-decision making and surgical planning processes. The goal of this study was to model the probability of achieving minimum clinically important differences change in PROs at 1 and 2 years after surgery.

METHODS

Two prospective observational ASD cohorts were queried. Patients with Scoliosis Research Society-22, Oswestry Disability Index , and Short Form-36 data at preoperative baseline and at 1 and 2 years after surgery were included. Seventy-five variables were used in the training of the models including demographics, baseline PROs, and modifiable surgical parameters. Eight predictive algorithms were trained at four-time horizons: preoperative or postoperative baseline to 1 year and preoperative or postoperative baseline to 2 years. External validation was accomplished via an 80%/20% random split. Five-fold cross validation within the training sample was performed. Precision was measured as the mean average error (MAE) and R values.

RESULTS

Five hundred seventy patients were included in the analysis. Models with the lowest MAE were selected; R values ranged from 20% to 45% and MAE ranged from 8% to 15% depending upon the predicted outcome. Patients with worse preoperative baseline PROs achieved the greatest mean improvements. Surgeon and site were not important components of the models, explaining little variance in the predicted 1- and 2-year PROs.

CONCLUSION

We present an accurate and consistent way of predicting the probability for achieving clinically relevant improvement after ASD surgery in the largest-to-date prospective operative multicenter cohort with 2-year follow-up. This study has significant clinical implications for shared decision making, surgical planning, and postoperative counseling.

LEVEL OF EVIDENCE

4.

Routine adoption of TIMP2 and IGFBP7 biomarkers in cardiac surgery for early identification of acute kidney injury

Background and purpose

Acute Kidney Injury (AKI) is a severe complication affecting many hospitalized patients after cardiac surgery, with negative impacts on short- and long-term clinical outcomes and on healthcare costs. Recently, clinical interest has been aimed at defining and classifying AKI, identifying risk factors and developing diagnostic strategies to identify patients at risk early on. Achieving an early and accurate diagnosis of AKI is a crucial issue, because prevention and timely detection may help to prevent negative clinical outcomes and avoid AKI-associated costs. In this retrospective study, we evaluate the NephroCheck Test as a diagnostic tool for early detection of AKI in a high-risk population of patients undergoing cardiac surgery at the San Bortolo Hospital of Vicenza.

Methods

We assessed the ability of the NephroCheck Test to predict the probability of developing CSA-AKI (cardiac surgery-associated AKI) and evaluated its accuracy as a diagnostic test, by building a multivariate logistic regression model for CSA-AKI prediction.

Results

Based on our findings, when the results of the NephroCheck Test are included in a multivariate model its performance is substantially improved, as compared to the benchmark model, which only accounts for the other clinical factors. We also define a rule – in terms of a probability cut-off – for discriminating cases that are at higher risk of developing AKI of any stage versus those in which AKI is less likely.

Conclusions

Our study has implications in clinical practice: when a Nephrocheck Test result is >0.3 ng/dL, an automated electronic alert prompts the physician to intervene by following a checklist of preventive measures.

Development of a Prediction Model of Early Acute Kidney Injury in Critically Ill Children Using Electronic Health Record Data

OBJECTIVE

Acute kidney injury is independently associated with poor outcomes in critically ill children. However, the main biomarker of acute kidney injury, serum creatinine, is a late marker of injury and can cause a delay in diagnosis. Our goal was to develop and validate a data-driven multivariable clinical prediction model of acute kidney injury in a general PICU using electronic health record data.

DESIGN

Derivation and validation of a prediction model using retrospective data.

PATIENTS

All patients 1 month to 21 years old admitted between May 2003 and March 2015 without acute kidney injury at admission and alive and in the ICU for at least 24 hours.

SETTING

A multidisciplinary, tertiary PICU.

INTERVENTION

The primary outcome was early acute kidney injury, which was defined as new acute kidney injury developed in the ICU within 72 hours of admission. Multivariable logistic regression was performed to derive the Pediatric Early AKI Risk Score using electronic health record data from the first 12 hours of ICU stay.

MEASUREMENTS AND MAIN RESULTS

A total of 9,396 patients were included in the analysis, of whom 4% had early acute kidney injury, and these had significantly higher mortality than those without early acute kidney injury (26% vs 3.3%; p < 0.001). Thirty-three candidate variables were tested. The final model had seven predictors and had good discrimination (area under the curve 0.84) and appropriate calibration. The model was validated in two validation sets and maintained good discrimination (area under the curves, 0.81 and 0.86).

CONCLUSION

We developed and validated the Pediatric Early AKI Risk Score, a data-driven acute kidney injury clinical prediction model that has good discrimination and calibration in a general PICU population using only electronic health record data that is objective, available in real time during the first 12 hours of ICU care and generalizable across PICUs. This prediction model was designed to be implemented in the form of an automated clinical decision support system and could be used to guide preventive, therapeutic, and research strategies.