Leveraging Big Data and Electronic Health Records to Enhance Novel Approaches to Acute Kidney Injury Research and Care

Machine Learning for Renal Pathologies: An Updated Survey

Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especially regarding the study of renal pathologies, because they are very common and widespread in our society, afflicting a high percentage of the population and leading to various complications, up to death in some cases. For these reasons, the authors have considered it appropriate to collect, using one of the major bibliographic databases available, and analyze the studies carried out until February 2022 on the use of machine learning techniques in the nephrological field, grouping them according to the addressed pathologies: renal masses, acute kidney injury, chronic kidney disease, kidney stone, glomerular disease, kidney transplant, and others less widespread. Of a total of 224 studies, 59 were analyzed according to inclusion and exclusion criteria in this review, considering the method used and the type of data available. Based on the study conducted, it is possible to see a growing trend and interest in the use of machine learning applications in nephrology, becoming an additional tool for physicians, which can enable them to make more accurate and faster diagnoses, although there remains a major limitation given the difficulty in creating public databases that can be used by the scientific community to corroborate and eventually make a positive contribution in this area.

Self-Correcting Recurrent Neural Network for Acute Kidney Injury Prediction in Critical Care

Background. In critical care, intensivists are required to continuously monitor high-dimensional vital signs and lab measurements to detect and diagnose acute patient conditions, which has always been a challenging task. Recently, deep learning models such as recurrent neural networks (RNNs) have demonstrated their strong potential on predicting such events. However, in real deployment, the patient data are continuously coming and there is no effective adaptation mechanism for RNN to incorporate those new data and become more accurate.Methods. In this study, we propose a novel self-correcting mechanism for RNN to fill in this gap. Our mechanism feeds prediction errors from the predictions of previous timestamps into the prediction of the current timestamp, so that the model can "learn" from previous predictions. We also proposed a regularization method that takes into account not only the model's prediction errors on the labels but also its estimation errors on the input data.Results. We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets for the task of acute kidney injury (AKI) prediction and demonstrated that the proposed model achieved an area under ROC curve at 0.893 on the MIMIC-III dataset and 0.871 on the Philips eICU dataset.Conclusions. The proposed self-correcting RNNs demonstrated effectiveness in AKI prediction and have the potential to be applied to clinical applications.

Baseline creatinine determination method impacts association between acute kidney injury and clinical outcomes

BACKGROUND

Current consensus definition for acute kidney injury (AKI) does not specify how baseline serum creatinine should be determined. We assessed how baseline determination impacted AKI incidence and association between AKI and clinical outcomes.

METHODS

We retrospectively applied empirical (measured serum creatinine) and imputed (age/height) baseline estimation methods to pediatric patients discharged between 2014 and 2019 from an academic hospital. Using each method, we estimated AKI incidence and assessed area under ROC curve (AUROC) for AKI as a predictor of three clinical outcomes: application of AKI billing code (proxy for more clinically overt disease), inpatient mortality, and post-hospitalization chronic kidney disease.

RESULTS

Incidence was highly variable across baseline methods (12.2-26.7%). Incidence was highest when lowest pre-admission creatinine was used if available and Schwartz bedside equation was used to impute one otherwise. AKI was more predictive of application of an AKI billing code when baseline was imputed universally, regardless of pre-admission values (AUROC 80.7-84.9%) than with any empirical approach (AUROC 64.5-76.6%). AKI was predictive of post-hospitalization CKD when using universal imputation baseline methods (AUROC 67.0-74.6%); AKI was not strongly predictive of post-hospitalization CKD when using empirical baseline methods (AUROC 46.4-58.5%). Baseline determination method did not affect the association between AKI and inpatient mortality.

CONCLUSIONS

Method of baseline determination influences AKI incidence and association between AKI and clinical outcomes, illustrating the need for standard criteria. Imputing baseline for all patients, even when preadmission creatinine is available, may identify a more clinically relevant subset of the disease.

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.

Big data in nephrology: Are we ready for the change?

Chronic kidney disease (CKD) is a major public health issue worldwide. However, the status of kidney health care needs to be strengthened globally and research evidence in nephrology is relatively limited. The unmet needs in nephrology leave ample space for imagination regarding leveraging big data and artificial intelligence (AI). Big data has potential to drive medical innovation, reduce medical costs and improve health care quality. Compared with other specialties such as cardiology, the scopes of utilizing big data in nephrology need to be enhanced. We reviewed the studies on the application of big data in nephrology, such as disease surveillance, risk prediction and clinical decision support systems (CDSS), and proposed several potential directions of utilizing big data and AI. The efforts including building a CKD surveillance system and collaborative network, implementing a real-world cohort in a cost-effective manner, strengthening the application and transformation of AI and CDSS, and stimulating the activeness of medical imaging in nephrology, could be considered. In the era of big data, a nephrologist would be stronger and smarter if he or she could get intelligent assistance from knowledge or big data-driven CDSS.

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

Over the last decade, our understanding of acute kidney injury (AKI) has evolved considerably. The development of a consensus definition standardized the approach to identifying and investigating AKI in children. As a result, pediatric AKI epidemiology has been refined and the consequences of renal injury are better established. Similarly, "big data" methodologies experienced a dramatic evolution and maturation, leading the critical care community to explore potential AKI/big data synergies. One such concept with tremendous potential is electronic health record (EHR) enabled informatics. Much of the promise surrounding these approaches is due to the unique position of the EHR which sits at the intersection of data accumulation and care delivery. EHR data is generated simply via the provision of routine clinical care and should be considered "big" from the standpoint of volume, variety, and velocity as a myriad of diverse elements accumulate rapidly in real time, spontaneously generating an immense dataset. This massive dataset interfaces directly with providers which creates tremendous opportunity. AKI can be diagnosed more accurately, AKI-related care can be optimized, and subsequent outcomes can be improved. Although applying big data concepts to the EHR has proven more challenging than originally thought, we have seen much success and continue to explore its potential. In this review article, we will discuss the EHR in the context of big data concepts, describe approaches applied to date, examine the challenges surrounding optimal application, and explore future directions.

Acute Kidney Injury Definition and Diagnosis: A Narrative Review

Acute kidney injury (AKI) is a complex syndrome characterized by a decrease in renal function and associated with numerous etiologies and pathophysiological mechanisms. It is a common diagnosis in hospitalized patients, with increasing incidence in recent decades, and associated with poorer short- and long-term outcomes and increased health care costs. Considering its impact on patient prognosis, research has focused on methods to assess patients at risk of developing AKI and diagnose subclinical AKI, as well as prevention and treatment strategies, for which an understanding of the epidemiology of AKI is crucial. In this review, we discuss the evolving definition and classification of AKI, and novel diagnostic methods.