Outpatient Nephrotoxic Medication Prescription after Pediatric Intensive Care Acute Kidney Injury.
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ABSTRACT: Background
Nephrotoxic medication (NTM) avoidance may prevent further kidney damage in children with acute kidney injury (AKI). We compared outpatient NTM prescriptions in children with or without AKI during pediatric intensive care (PICU) hospitalization. We hypothesize that children with AKI are prescribed NTMs at the same rate as those without it.Methods
This was a retrospective administrative data study of children <18 years, admitted to two PICUs in Montreal, Canada, from 2003 to 2005, with ≥30 days of provincial drug coverage. We evaluated the presence of ≥3 outpatient NTM prescriptions during the first year and 5 years after PICU discharge.Results
Of 970 children, 23% had PICU AKI. In the 1st-5th years after discharge, 18% AKI vs. 10% non-AKI and 13% AKI vs. 4% non-AKI patients received ≥3 NTM prescriptions, respectively. There was no association between PICU AKI and prescription of ≥3 NTMs during the first year (adjusted RR 1.02 [95% CI 0.95-1.10]) nor in the first 5 years post-discharge (adjusted RR 1.04 [95%CI 0.96-1.12]).Conclusions
By offering a better understanding of the current state of outpatient NTM prescription to children with AKI, our study is a step toward considering strategies such as knowledge translation interventions for decreasing NTM exposure and improving outcomes in children with AKI.
SUBMITTER: Lefebvre C
PROVIDER: S-EPMC8618325 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and objectivesLittle is known about the long-term burden of AKI in the pediatric intensive care unit. We aim to evaluate if pediatric AKI is associated with higher health service use post-hospital discharge.Design, setting, participants, & measurementsThis is a retrospective cohort study of children (?18 years old) admitted to two tertiary centers in Montreal, Canada. Only the first admission per patient was included. AKI was defined in two ways: serum creatinine alone or serum creatinine and/or urine output. The outcomes were 30-day, 1-year, and 5-year hospitalizations, emergency room visits, and physician visits per person-time using provincial administrative data. Univariable and multivariable Poisson regression were used to evaluate AKI associations with outcomes.ResultsA total of 2041 children were included (56% male, mean admission age 6.5±5.8 years); 299 of 1575 (19%) developed AKI defined using serum creatinine alone, and when urine output was included in the AKI definition 355 of 1622 (22%) children developed AKI. AKI defined using serum creatinine alone and AKI defined using serum creatinine and urine output were both associated with higher 1- and 5-year hospitalization risk (AKI by serum creatinine alone adjusted relative risk, 1.42; 95% confidence interval, 1.12 to 1.82; and 1.80; 1.54 to 2.11, respectively [similar when urine output was included]) and higher 5-year physician visits (adjusted relative risk, 1.26; 95% confidence interval, 1.14 to 1.39). AKI was not associated with emergency room use after adjustments.ConclusionsAKI is independently associated with higher hospitalizations and physician visits postdischarge.
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Project description:Background and objectivesCOVID-19 is responsible for the 2019 novel coronavirus disease pandemic. Despite the vast research about the adult population, there has been little data collected on acute kidney injury (AKI) epidemiology, associated risk factors, treatments, and mortality in pediatric COVID-19 patients admitted to the ICU. AKI is a severe complication of COVID-19 among children and adolescents.MethodsA comprehensive literature search was conducted in PubMed/MEDLINE and Cochrane Center Trials to find all published literature related to AKI in COVID-19 patients, including incidence and outcomes.ResultsTwenty-four studies reporting the outcomes of interest were included. Across all studies, the overall sample size of COVID positive children was 1,247 and the median age of this population was 9.1 years old. Among COVID positive pediatric patients, there was an AKI incidence of 30.51%, with only 0.56% of these patients receiving KRT. The mortality was 2.55% among all COVID positive pediatric patients. The incidence of multisystem inflammatory syndrome in children (MIS-C) among COVID positive patients was 74.29%.ConclusionAKI has shown to be a negative prognostic factor in adult patients with COVID-19 and now also in the pediatric cohort with high incidence and mortality rates. Additionally, our findings show a strong comparison in epidemiology between adult and pediatric COVID-19 patients; however, they need to be confirmed with additional data and studies.
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Project description:BackgroundLarge studies evaluating pediatric acute kidney injury (AKI) epidemiology and outcomes are lacking, partially due to underuse of large administrative health care data.ObjectiveTo assess the diagnostic accuracy of administrative health care data-defined AKI in children admitted to the pediatric intensive care unit (PICU).DesignRetrospective cohort study utilizing chart and administrative data.SettingChildren admitted to the PICU at 2 centers in Montreal, QC.PatientsPatients between 0 and 18 years old with a provincial health insurance number, without end-stage renal disease and admitted to the PICU between January 1, 2003, and March 31, 2005, were included.MeasurementsThe AKI was defined from chart data using the Kidney Disease: Improving Global Outcomes (KDIGO) definition (Chart-AKI). The AKI defined using administrative health data (Admin-AKI) was based on International Classification of Disease, Ninth Revision (ICD-9) AKI codes.MethodsData available from retrospective chart review, including baseline and PICU patient characteristics, and serum creatinine (SCr) and urine output (UO) values during PICU admission, were merged with provincial administrative health care data containing diagnostic and procedure codes used for ascertaining Admin-AKI. Sensitivity, specificity, positive, and negative predictive value of Admin-AKI compared with Chart-AKI (reference standard) were calculated. Univariable associations between Admin-AKI and hospital mortality were evaluated.ResultsA total of 2051 patients (55% male, mean age at admission 6.1 ± 5.8 years, 355 cardiac surgery, 1696 noncardiac surgery) were included. The AKI defined by SCr or UO criteria occurred in 52% of cardiac surgery patients and 24% of noncardiac surgery patients. Overall, Admin-AKI detected Chart-AKI with low sensitivity, but high specificity in cardiac and noncardiac surgery patients. Sensitivity increased by 1.5 to 2 fold with each increase in AKI severity stage. Admin-AKI was associated with hospital mortality (13% in Admin-AKI vs 2% in non-AKI, P < .001).LimitationsThese data were generated in a PICU population; future research should study non-PICU populations.ConclusionsUse of administrative health care data to define AKI in children leads to AKI incidence underestimation. However, for detecting more severe AKI, sensitivity is higher, while maintaining high specificity.
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Project description:IntroductionAcute kidney injury (AKI) is common among hospitalized patients and has a significant impact on morbidity and mortality. Although early prediction of AKI has the potential to reduce adverse patient outcomes, it remains a difficult condition to predict and diagnose. The purpose of this study was to evaluate the ability of a machine learning algorithm to predict for AKI as defined by Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 up to 48 hours in advance of onset using convolutional neural networks (CNNs) and patient electronic health record (EHR) data.MethodsA CNN prediction system was developed to use EHR data gathered during patients' stays to predict AKI up to 48 hours before onset. A total of 12,347 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) database. An XGBoost AKI prediction model and the sequential organ failure assessment (SOFA) scoring system were used as comparators. The outcome was AKI onset. The model was trained on routinely collected patient EHR data. Measurements included area under the receiver operating characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advance prediction of AKI onset.ResultsOn a hold-out test set, the algorithm attained an AUROC of 0.86 and PPV of 0.24, relative to a cohort AKI prevalence of 7.62%, for long-horizon AKI prediction at a 48-hour window before onset.ConclusionA CNN machine learning-based AKI prediction model outperforms XGBoost and the SOFA scoring system, revealing superior performance in predicting AKI 48 hours before onset, without reliance on serum creatinine (SCr) measurements.
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Project description:BackgroundAcute 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.ObjectiveThe 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.MethodsWe 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.ResultsThe 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.ConclusionsNAVOY 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.
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