Impact of a computerized decision support tool deployed in two intensive care units on acute kidney injury progression and guideline compliance: a prospective observational study

Urine output is an early and strong predictor of acute kidney injury and associated mortality: a systematic literature review of 50 clinical studies

BACKGROUND

Although the present diagnosis of acute kidney injury (AKI) involves measurement of acute increases in serum creatinine (SC) and reduced urine output (UO), measurement of UO is underutilized for diagnosis of AKI in clinical practice. The purpose of this investigation was to conduct a systematic literature review of published studies that evaluate both UO and SC in the detection of AKI to better understand incidence, healthcare resource use, and mortality in relation to these diagnostic measures and how these outcomes may vary by population subtype.

METHODS

The systematic literature review was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Data were extracted from comparative studies focused on the diagnostic accuracy of UO and SC, relevant clinical outcomes, and resource usage. Quality and validity were assessed using the National Institute for Health and Care Excellence (NICE) single technology appraisal quality checklist for randomized controlled trials and the Newcastle-Ottawa Quality Assessment Scale for observational studies.

RESULTS

A total of 1729 publications were screened, with 50 studies eligible for inclusion. A majority of studies (76%) used the Kidney Disease: Improving Global Outcomes (KDIGO) criteria to classify AKI and focused on the comparison of UO alone versus SC alone, while few studies analyzed a diagnosis of AKI based on the presence of both UO and SC, or the presence of at least one of UO or SC indicators. Of the included studies, 33% analyzed patients treated for cardiovascular diseases and 30% analyzed patients treated in a general intensive care unit. The use of UO criteria was more often associated with increased incidence of AKI (36%), than was the application of SC criteria (21%), which was consistent across the subgroup analyses performed. Furthermore, the use of UO criteria was associated with an earlier diagnosis of AKI (2.4-46.0 h). Both diagnostic modalities accurately predicted risk of AKI-related mortality.

CONCLUSIONS

Evidence suggests that the inclusion of UO criteria provides substantial diagnostic and prognostic value to the detection of AKI.

Fluid balance, biomarkers of renal function and mortality in critically ill patients with AKI diagnosed before, or within 24 h of intensive care unit admission: a prospective study

BACKGROUND

To evaluate fluid balance, biomarkers of renal function and its relation to mortality in patients with acute kidney injury (AKI) diagnosed before, or within 24 h of intensive care unit admission.

METHODS

A prospective cohort study considered 773 critically ill patients observed over six years. Pre-intensive care unit-onset AKI was defined as AKI diagnosed before, or within 24 h of intensive care unit admission. Body weight-adjusted fluid balance and fluid balance-adjusted biomarkers of renal function were measured daily for the first three days of intensive care unit admission. Primary outcome was mortality in the intensive care unit.

RESULTS

Prevalence of pre-intensive care unit-onset AKI was 55.1%, of which 55.6% of cases were hospital-acquired and 44.4% were community-acquired. Fluid balance was higher in AKI patients than in non-AKI patients (p < 0.001) and had a negative correlation with urine output (p < 0.01). Positive fluid balance and biomarkers of renal function were independently related to mortality. Multivariate analysis identified the following AKI-related variables associated with increased mortality: (1) In AKI patients: type 1 cardiorenal syndrome (OR 2.00), intra-abdominal hypertension (OR 1.71), AKI stage 3 (OR 2.15) and increase in AKI stage (OR 4.99); 2) In patients with community-acquired AKI: type 1 cardiorenal syndrome (OR 5.16), AKI stage 2 (OR 2.72), AKI stage 3 (OR 4.95) and renal replacement therapy (OR 3.05); and 3) In patients with hospital-acquired AKI: intra-abdominal hypertension (OR 2.31) and increase in AKI stage (OR 4.51).

CONCLUSIONS

In patients with pre-intensive care unit-onset AKI, positive fluid balance is associated with worse renal outcomes. Positive fluid balance and decline in biomarkers of renal function are related to increased mortality, thus in this subpopulation of critically ill patients, positive fluid balance is not recommended and renal function must be closely monitored.

Processes of Care After Hospital Discharge for Survivors of Acute Kidney Injury: A Population-Based Cohort Study

RATIONALE & OBJECTIVE

Survivors of acute kidney injury (AKI) are at high risk of adverse outcomes. Monitoring of kidney function, screening for proteinuria, use of statins and renin-angiotensin-aldosterone system (RAAS) inhibitors, and nephrology follow-up among survivors have not been fully characterized. We examined these processes of care after discharge in survivors of hospitalized AKI.

STUDY DESIGN

Population-based retrospective cohort study.

SETTING & PARTICIPANTS

Adults in Alberta, Canada, admitted to the hospital between 2009 and 2017, then followed from their discharge date until 2019 for a median follow-up of 2.7 years.

EXPOSURE

Hospital-acquired AKI diagnostically conforming to Kidney Disease: Improving Global Outcomes (KDIGO) serum creatinine criteria for stage 2 or stage 3 disease, or the need for acute dialysis.

OUTCOME

Outcomes after hospital discharge included the proportion of participants who had evaluation of kidney function, were seen by a specialist or general practitioner, and received postdischarge prescriptions for recommended medications for chronic kidney disease (CKD).

ANALYTICAL APPROACH

Cumulative incidence curves to characterize the proportion of participants who received each process of care outcome within the first 90 days and subsequent 1-year follow-up period after hospital discharge. To avoid risks associated with multiple hypothesis testing, differences were not statistically compared across groups.

RESULTS

The cohort (n=23,921) included 50.2% men (n=12,015) with a median age of 68.1 [IQR, 56.9-78.8] years. Within 90 days after discharge, 21.2% and 8.6% of patients with and without pre-existing CKD, respectively, were seen by a nephrologist; 60.1% of AKI survivors had at least 1 serum creatinine measured, but only 25.5% had an assessment for albuminuria within 90 days after discharge; 52.7% of AKI survivors with pre-existing CKD, and 51.6% with de novo CKD were prescribed a RAAS inhibitor within 4-15 months after discharge.

LIMITATIONS

Retrospective data were collected as part of routine clinical care.

CONCLUSIONS

The proportion of patients receiving optimal care after an episode of AKI in Alberta was low and may represent a target for improving long-term outcomes for this population.

PLAIN-LANGUAGE SUMMARY

A study in Alberta, Canada, examined the care received by patients with acute kidney disease (AKI) during hospitalization and after discharge between 2007 and 2019. The results showed that a low proportion of patients with moderate to severe AKI were seen by a kidney specialist during hospitalization or within 90 days after discharge. Fewer than 25% of AKI patients had their kidney function monitored with both blood and urine tests within 90 days of discharge. Additionally, about half of AKI survivors with chronic kidney disease (CKD) were prescribed guideline recommended medications for CKD within 15 months after discharge. There is potential to improve health care delivery to these patients both in hospital and after hospital discharge.

Furosemide Responsiveness Predicts Acute Kidney Injury Progression After Cardiac Surgery

BACKGROUND

As patients with acute kidney injury (AKI) progress to a higher stage, the risk for poor outcomes dramatically rises. Early identification of patients at high risk for AKI progression remains a major challenge. This study aimed to evaluate the value of furosemide responsiveness (FR) for predicting AKI progression in patients with initial mild and moderate AKI after cardiac surgery.

METHODS

We performed 2 separate exploratory analyses. The Zhongshan cohort was a single-center, prospective, observational cohort, whereas the Beth Israel Deaconess Medical Center cohort was a single-center, retrospective cohort. We calculated 2 FR parameters for each patient, namely the FR index and modified FR index, defined as 2-hour urine output divided by furosemide dose (FR index, mL/mg/2 h) and by furosemide dose and body weight (modified FR index, mL/[mg·kg]/2 h), respectively. The primary outcome was AKI progression within 7 days.

RESULTS

AKI progression occurred in 80 (16.0%) and 359 (11.3%) patients in the Zhongshan and Beth Israel Deaconess Medical Center cohorts, respectively. All FR parameters (considered continuously or in quartiles) were inversely associated with risk of AKI progression in both cohorts (all adjusted P < .01). The addition of FR parameters significantly improved prediction for AKI progression based on baseline clinical models involving C-index, net reclassification improvement, and integrated discrimination improvement index in both cohorts (all P < .01).

CONCLUSIONS

FR parameters were inversely associated with risk of AKI progression in patients with mild and moderate AKI after cardiac surgery. The addition of FR parameters significantly improved prediction for AKI progression based on baseline clinical models.

From digital health to learning health systems: four approaches to using data for digital health design

Digital health technologies, powered by digital data, provide an opportunity to improve the efficacy and efficiency of health systems at large. However, little is known about different approaches to the use of data for digital health design, or about their possible relations to system-level dynamics. In this contribution, we identify four existing approaches to the use of data for digital health design, namely the silent, the overt, the data-enabled, and the convergent. After characterising the approaches, we provide real-life examples of each. Furthermore, we compare the approaches in terms of selected desirable characteristics of the design process, highlighting relative advantages and disadvantages. Finally, we reflect on the system-level relevance of the differentiation between the approaches and point towards future research directions. Overall, the contribution provides researchers and practitioners with a broad conceptual framework to examine data-related challenges and opportunities in digital health design.

Chinese experts' consensus on the application of intensive care big data

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review

OBJECTIVE

Surveillance algorithms that predict patient decompensation are increasingly integrated with clinical workflows to help identify patients at risk of in-hospital deterioration. This scoping review aimed to identify the design features of the information displays, the types of algorithm that drive the display, and the effect of these displays on process and patient outcomes.

MATERIALS AND METHODS

The scoping review followed Arksey and O'Malley's framework. Five databases were searched with dates between January 1, 2009 and January 26, 2022. Inclusion criteria were: participants-clinicians in inpatient settings; concepts-intervention as deterioration information displays that leveraged automated AI algorithms; comparison as usual care or alternative displays; outcomes as clinical, workflow process, and usability outcomes; and context as simulated or real-world in-hospital settings in any country. Screening, full-text review, and data extraction were reviewed independently by 2 researchers in each step. Display categories were identified inductively through consensus.

RESULTS

Of 14 575 articles, 64 were included in the review, describing 61 unique displays. Forty-one displays were designed for specific deteriorations (eg, sepsis), 24 provided simple alerts (ie, text-based prompts without relevant patient data), 48 leveraged well-accepted score-based algorithms, and 47 included nurses as the target users. Only 1 out of the 10 randomized controlled trials reported a significant effect on the primary outcome.

CONCLUSIONS

Despite significant advancements in surveillance algorithms, most information displays continue to leverage well-understood, well-accepted score-based algorithms. Users' trust, algorithmic transparency, and workflow integration are significant hurdles to adopting new algorithms into effective decision support tools.

Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup

Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes. However, despite great progress, the potential benefits of using digital technology to manage AKI has not yet been fully explored or implemented in clinical practice. Digital health studies in AKI have shown variable evidence of benefits, and the digital divide means that access to digital technologies is not equitable. Upstream research and development costs, limited stakeholder participation and acceptance, and poor scalability of digital health solutions have hindered their widespread implementation and use. Here, we provide recommendations from the Acute Disease Quality Initiative consensus meeting, which involved experts in adult and paediatric nephrology, critical care, pharmacy and data science, at which the use of digital health for risk prediction, prevention, identification and management of AKI and its consequences was discussed.

Digital health utilities in acute kidney injury management

PURPOSE OF REVIEW

Acute kidney injury (AKI) is a highly prevalent clinical syndrome that substantially impacts patient outcomes. It is accepted by the clinical communities that the management of AKI is time-sensitive. Unfortunately, despite growing proof of its preventability, AKI management remains suboptimal in community, acute care, and postacute care settings. Digital health solutions comprise various tools and models to improve care processes and patient outcomes in multiple medical fields. AKI development, progression, recovery, or lack thereof, offers tremendous opportunities for developing, validating, and implementing digital health solutions in multiple settings. This article will review the definitions and components of digital health, the characteristics of AKI that allow digital health solutions to be considered, and the opportunities and threats in implementing these solutions.

RECENT FINDINGS

Over the past two decades, the academic output related to the use of digital health solutions in AKI has exponentially grown. While this indicates the growing interest in the topic, most topics are primarily related to clinical decision support by detecting AKI within hospitals or using artificial intelligence or machine learning technologies to predict AKI within acute care settings. However, recently, projects to assess the impact of digital health solutions in more complex scenarios, for example, managing nephrotoxins among adults of pediatric patients who already have AKI, is increasing. Depending on the type of patients, chosen digital health solution intervention, comparator groups, and selected outcomes, some of these studies showed benefits, while some did not indicate additional gain in care processes or clinical outcomes.

SUMMARY

Careful needs assessment, selection of the correct digital health solution, and appropriate clinical validation of the benefits while avoiding additional health disparities are moral, professional, and ethical obligations for all individuals using these healthcare tools, including clinicians, data scientists, and administrators.

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.

A Machine Learning Based Discharge Prediction of Cardiovascular Diseases Patients in Intensive Care Units

This paper targets a major challenge of how to effectively allocate medical resources in intensive care units (ICUs). We trained multiple regression models using the Medical Information Mart for Intensive Care III (MIMIC III) database recorded in the period between 2001 and 2012. The training and validation dataset included pneumonia, sepsis, congestive heart failure, hypotension, chest pain, coronary artery disease, fever, respiratory failure, acute coronary syndrome, shortness of breath, seizure and transient ischemic attack, and aortic stenosis patients’ recorded data. Then we tested the models on the unseen data of patients diagnosed with coronary artery disease, congestive heart failure or acute coronary syndrome. We included the admission characteristics, clinical prescriptions, physiological measurements, and discharge characteristics of those patients. We assessed the models’ performance using mean residuals and running times as metrics. We ran multiple experiments to study the data partition’s impact on the learning phase. The total running time of our best-evaluated model is 123,450.9 mS. The best model gives an average accuracy of 98%, highlighting the location of discharge, initial diagnosis, location of admission, drug therapy, length of stay and internal transfers as the most influencing patterns to decide a patient’s readiness for discharge.

The Impact of Health Information Technology for Early Detection of Patient Deterioration on Mortality and Length of Stay in the Hospital Acute Care Setting: Systematic Review and Meta-Analysis

OBJECTIVE

To evaluate the impact of health information technology (HIT) for early detection of patient deterioration on patient mortality and length of stay (LOS) in acute care hospital settings.

DATA SOURCES

We searched MEDLINE and Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Scopus from 1990 to January 19, 2021.

STUDY SELECTION

We included studies that enrolled patients hospitalized on the floor, in the ICU, or admitted through the emergency department. Eligible studies compared HIT for early detection of patient deterioration with usual care and reported at least one end point of interest: hospital or ICU LOS or mortality at any time point.

DATA EXTRACTION

Study data were abstracted by two independent reviewers using a standardized data extraction form.

DATA SYNTHESIS

Random-effects meta-analysis was used to pool data. Among the 30 eligible studies, seven were randomized controlled trials (RCTs) and 23 were pre-post studies. Compared with usual care, HIT for early detection of patient deterioration was not associated with a reduction in hospital mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT interventions demonstrated a significant association with improved hospital mortality for the entire study cohort (odds ratio, 0.78 [95% CI, 0.70-0.87]) and reduced hospital LOS overall.

CONCLUSIONS

HIT for early detection of patient deterioration in acute care settings was not significantly associated with improved mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT was associated with improved hospital mortality and LOS; however, these results should be interpreted with caution. The differences in patient outcomes between the findings of the RCTs and pre-post studies may be secondary to confounding caused by unmeasured improvements in practice and workflow over time.

Health system perspectives in acute kidney injury: commitment to kidney health and planning implementation interventions

PURPOSE OF REVIEW

To discuss a deliberate commitment by health systems to optimize kidney health and outcomes of patients who are at risk for or develop acute kidney injury (AKI) during hospitalization.

RECENT FINDINGS

In 2019, the US Department of Health and Human Services set national goals targeted at improving the care and outcomes of patients with kidney disease including prevention by reducing the number of Americans who develop end-stage kidney disease (ESKD) by 25% by 2030. In response to this call to action, there is a need to focus on education to identify patients at high-risk for kidney disease development and progression, active surveillance methods to ensure timely identification, offering better follow-up care after an episode of AKI. A strategic approach to determining the health systems level of commitment and developing a plan for organizational change is discussed in this commentary. Items for consideration are structuring sound implementation projects and selecting implementation interventions. Clinical interventions to consider for implementation include the use of clinical decision support for detecting patients with AKI, novel biomarkers to determine patients at high-risk for AKI and nephrotoxin stewardship to prevent medication safety complications and drug-associated AKI.

SUMMARY

There is a national call to action to improve the care and outcomes of patients with kidney disease. Health systems have an opportunity to respond by providing a high level of commitment towards ensuring the best kidney health for all patients equally. Deliberate change that is sustainable and scalable should be considered by all health systems.

Mortality Prediction in Patients with Severe Acute Kidney Injury Requiring Renal Replacement Therapy

Background and Objective: Acute kidney injury (AKI) remains a serious health condition around the world, and is related to high morbidity, mortality, longer hospitalization duration and worse long-term outcomes. The aim of our study was to estimate the significant related factors for poor outcomes of patients with severe AKI requiring renal replacement therapy (RRT). Materials and Methods: We retrospectively analyzed data from patients (n = 573) with severe AKI requiring RRT within a 5-year period and analyzed the outcomes on discharge from the hospital. We also compared the clinical data of the surviving and non-surviving patients and examined possible related factors for poor patient outcomes. Logistic regression was used to analyze the odds ratio for patient mortality and its related factors. Results: In 32.5% (n = 186) of the patients, the renal function improved and RRT was stopped, 51.7% (n = 296) of the patients died, and 15.9% (n = 91) of the patients remained dialysis-dependent on the day of discharge from the hospital. During the period of 5 years, the outcomes of the investigated patients did not change statistically significantly. Administration of vasopressors, aminoglycosides, sepsis, pulmonary edema, oliguria, artificial pulmonary ventilation (APV), patient age ≥ 65 y, renal cause of AKI, AKI after cardiac surgery, a combination of two or more RRT methods, dysfunction of three or more organs, systolic blood pressure (BP) ≤ 120 mmHg, diastolic BP ≤ 65 mmHg, and Sequential Organ Failure Assessment (SOFA) score on the day of the first RRT procedure ≥ 7.5 were related factors for lethal patient outcome. Conclusions: The mortality rate among patients with severe AKI requiring RRT is very high—52%. Patient death was significantly predicted by the causes of AKI (sepsis, cardiac surgery), clinical course (oliguria, pulmonary edema, hypotension, acidosis, lesion of other organs) and the need for a continuous renal replacement therapy.

Impact of AKI care bundles on kidney and patient outcomes in hospitalized patients: a systematic review and meta-analysis

Background

A bundle of preventive measures can be taken to avoid acute kidney injury (AKI) or progression of AKI. We performed a systematic review and meta-analysis to evaluate the compliance to AKI care bundles in hospitalized patients and its impact on kidney and patient outcomes.

Methods

Randomized controlled trials, observational and interventional studies were included. Studied outcomes were care bundle compliance, occurrence of AKI and moderate-severe AKI, use of kidney replacement therapy (KRT), kidney recovery, mortality (ICU, in-hospital and 30-day) and length-of-stay (ICU, hospital). The search engines PubMed, Embase and Google Scholar were used (January 1, 2012 - June 30, 2021). Meta-analysis was performed with the Mantel Haenszel test (risk ratio) and inverse variance (mean difference). Bias was assessed by the Cochrane risk of bias tool (RCT) and the NIH study quality tool (non-RCT).

Results

We included 23 papers of which 13 were used for quantitative analysis (4 RCT and 9 non-randomized studies with 25,776 patients and 30,276 AKI episodes). Six were performed in ICU setting. The number of trials pooled per outcome was low. There was a high variability in care bundle compliance (8 to 100%). Moderate-severe AKI was less frequent after bundle implementation [RR 0.78, 95%CI 0.62–0.97]. AKI occurrence and KRT use did not differ between the groups [resp RR 0.90, 95%CI 0.76–1.05; RR 0.67, 95%CI 0.38–1.19]. In-hospital and 30-day mortality was lower in AKI patients exposed to a care bundle [resp RR 0.81, 95%CI 0.73–0.90, RR 0.95 95%CI 0.90–0.99]; this could not be confirmed by randomized trials. Hospital length-of-stay was similar in both groups [MD -0.65, 95%CI -1.40,0.09].

Conclusion

This systematic review and meta-analysis shows that implementation of AKI care bundles in hospitalized patients reduces moderate-severe AKI. This result is mainly driven by studies performed in ICU setting. Lack of data and heterogeneity in study design impede drawing firm conclusions about patient outcomes. Moreover, compliance to AKI care bundles in hospitalized patients is highly variable. Additional research in targeted patient groups at risk for moderate-severe AKI with correct and complete implementation of a feasible, well-tailored AKI care bundle is warranted. (CRD42020207523).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-021-02534-4.

Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care

BACKGROUND

Acute kidney injury (AKI) in pediatric critical care patients is diagnosed using elevated serum creatinine, which occurs only after kidney impairment. There are no treatments other than supportive care for AKI once it has developed, so it is important to identify patients at risk to prevent injury. This study develops a machine learning model to learn pre-disease patterns of physiological measurements and predict pediatric AKI up to 48 h earlier than the currently established diagnostic guidelines.

METHODS

EHR data from 16,863 pediatric critical care patients between 1 month to 21 years of age from three independent institutions were used to develop a single machine learning model for early prediction of creatinine-based AKI using intelligently engineered predictors, such as creatinine rate of change, to automatically assess real-time AKI risk. The primary outcome is prediction of moderate to severe AKI (Stage 2/3), and secondary outcomes are prediction of any AKI (Stage 1/2/3) and requirement of renal replacement therapy (RRT). Predictions generate alerts allowing fast assessment and reduction of AKI risk, such as: "patient has 90% risk of developing AKI in the next 48 h" along with contextual information and suggested response such as "patient on aminoglycosides, suggest check level and review dose and indication".

RESULTS

The model was successful in predicting Stage 2/3 AKI prior to detection by conventional criteria with a median lead-time of 30 h at AUROC of 0.89. The model predicted 70% of subsequent RRT episodes, 58% of Stage 2/3 episodes, and 41% of any AKI episodes. The ratio of false to true alerts of any AKI episodes was approximately one-to-one (PPV 47%). Among patients predicted, 79% received potentially nephrotoxic medication after being identified by the model but before development of AKI.

CONCLUSIONS

As the first multi-center validated AKI prediction model for all pediatric critical care patients, the machine learning model described in this study accurately predicts moderate to severe AKI up to 48 h in advance of AKI onset. The model may improve outcome of pediatric AKI by providing early alerting and actionable feedback, potentially preventing or reducing AKI by implementing early measures such as medication adjustment.