Big Data and Pediatric Acute Kidney Injury: The Promise of Electronic Health Record Systems

Improving child health through Big Data and data science

Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.

Serum Creatinine Monitoring After Acute Kidney Injury in the PICU

OBJECTIVES

It is unknown whether children with acute kidney injury during PICU admission have kidney function monitored after discharge. Objectives: 1) describe postdischarge serum creatinine monitoring after PICU acute kidney injury and 2) determine factors associated with postdischarge serum creatinine monitoring.

DESIGN

Secondary analysis of longitudinal cohort study data.

SETTING

Two PICUs in Montreal and Edmonton, Canada.

PATIENTS

Children (0-18 yr old) surviving PICU admission greater than or equal to 2 days from 2005 to 2011. Exclusions: postcardiac surgery and prior kidney disease. Exposure: acute kidney injury by Kidney Disease: Improving Global Outcomes serum creatinine definition.

INTERVENTIONS

None.

MEASUREMENTS

Primary outcome: postdischarge serum creatinine measured by 90 days, 1 year, and 5-7 years.

SECONDARY OUTCOMES

Healthcare events and nephrology follow-up.

ANALYSIS

Proportions with outcomes; logistic regression to evaluate factors associated with the primary outcome. Kaplan-Meier analysis of time to serum creatinine measurement and healthcare events.

MAIN RESULTS

Of n = 277, 69 (25%) had acute kidney injury; 29/69 (42%), 34/69 (49%), and 51/69 (74%) had serum creatinine measured by 90 days, 1 year, and 5-7 year postdischarge, respectively. Acute kidney injury survivors were more likely to have serum creatinine measured versus nonacute kidney injury survivors at all time points (p ≤ 0.01). Factors associated with 90-day serum creatinine measurement were inpatient nephrology consultation (unadjusted odds ratio [95% CI], 14.9 [1.7-127.0]), stage 2-3 acute kidney injury (adjusted odds ratio, 3.4 [1.1-10.2]), and oncologic admission diagnosis (adjusted odds ratio, 10.0 [1.1-93.5]). A higher proportion of acute kidney injury versus nonacute kidney injury survivors were readmitted by 90 days (25 [36%] vs 44 [21%]; p = 0.01) and 1 year (33 [38%] vs 70 [34%]; p = 0.04). Of 24 acute kidney injury survivors diagnosed with chronic kidney disease or hypertension at 5-7 year follow-up, 16 (67%) had serum creatinine measurement and three (13%) had nephrology follow-up postdischarge.

CONCLUSIONS

Half of PICU acute kidney injury survivors have serum creatinine measured within 1-year postdischarge and follow-up is suboptimal for children developing long-term kidney sequelae. Knowledge translation strategies should emphasize the importance of serum creatinine monitoring after childhood acute kidney injury.

A Review on the Application and Limitations of Administrative Health Care Data for the Study of Acute Kidney Injury Epidemiology and Outcomes in Children

Administrative health care databases contain valuable patient information generated by health care encounters. These "big data" repositories have been increasingly used in epidemiological health research internationally in recent years as they are easily accessible and cost-efficient and cover large populations for long periods. Despite these beneficial characteristics, it is also important to consider the limitations that administrative health research presents, such as issues related to data incompleteness and the limited sensitivity of the variables. These barriers potentially lead to unwanted biases and pose threats to the validity of the research being conducted. In this review, we discuss the effectiveness of health administrative data in understanding the epidemiology of and outcomes after acute kidney injury (AKI) among adults and children. In addition, we describe various validation studies of AKI diagnostic or procedural codes among adults and children. These studies reveal challenges of AKI research using administrative data and the lack of this type of research in children and other subpopulations. Additional pediatric-specific validation studies of administrative health data are needed to promote higher volume and increased validity of this type of research in pediatric AKI, to elucidate the large-scale epidemiology and patient and health systems impacts of AKI in children, and to devise and monitor programs to improve clinical outcomes and process of care.

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.