Electronic health records accurately predict renal replacement therapy in acute kidney injury

Low, Sanmay; Vathsala, Anantharaman; Murali, T; Pang, Long; MacLaren, Graeme; Ng, Wan-Ying; Haroon, Sabrina; Mukhopadhyay, Amartya; Lim, S.; Tan, Bee-Hong; Lau, Titus; Chua, Horng-Ruey

doi:10.1186/s12882-019-1206-4

Cited by 13 publications

(9 citation statements)

References 36 publications

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“…Taking into account, the negative effects of acute kidney injury (AKI) on patient outcome, it is important to design tools to detect patients at risk [39,40]. Several research groups have used ML algorithms for predicting AKI [41][42][43][44]. For example, Koyner et al [41] were able to predict serum creatinine-based Kidney Disease Improving Global Outcomes (KDIGO) stage 2 AKI with a sensitivity of 84% and specificity of 85% by including demographics, routine laboratory parameters, and other electronic health record (EHR) data from 121,158 patients in a GBM algorithm.…”

Section: Disease and Outcome Prediction From Routine Laboratory Parammentioning

confidence: 99%

“…with an AUROC of 0.90 and 0.94, respectively. Furthermore, a DT model reached a balanced accuracy of 70.4% with a PPV of 97% and NPV of 78% in the detection of AKI (KDIGO stages 1, 2, and 3) [43]. Toma sev et al [44] developed a deep learning model based on a large, longitudinal dataset of EHRs from a diversity of clinical environments (703,782 adult patients across 1062 outpatient and 172 inpatient sites).…”

Section: Disease and Outcome Prediction From Routine Laboratory Parammentioning

confidence: 99%

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Recent evolutions of machine learning applications in clinical laboratory medicine

Bruyne

Speeckaert

Biesen

et al. 2020

Critical Reviews in Clinical Laboratory Sciences

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Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

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Section: Disease and Outcome Prediction From Routine Laboratory Parammentioning

confidence: 99%

Section: Disease and Outcome Prediction From Routine Laboratory Parammentioning

confidence: 99%

Recent evolutions of machine learning applications in clinical laboratory medicine

Bruyne

Speeckaert

Biesen

et al. 2020

Critical Reviews in Clinical Laboratory Sciences

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“…The information contained in the EHRs provides the opportunity to conduct research in different areas such as risk prediction, patient subtyping, estimation of treatment effect and patient similarity analysis. Risk prediction derived from EHR can be calculated and updated automatically during the inpatient time (19,20). The aim of this study is to bring together the researches on prediction models developed with artificial intelligence technologies using the EHRs of patients hospitalized in the ICU by a systematic review method and to evaluate them in terms of risk management in healthcare.…”

Section: Introductionmentioning

confidence: 99%

Risk Management In Intensive Care Units With Artificial Intelligence Technologies: Systematic Review of Prediction Models Using Electronic Health Records

Çayirtepe¹,

Senel

2022

Journal of Basic and Clinical Health Sciences

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Background and aim: Clinical risk assessments should be made to protect patients from negative outcomes, and the definition, frequency and severity of the risk should be determined. The information contained in the electronic health records (EHRs) can use in different areas such as risk prediction, estimation of treatment effect ect. Many prediction models using artificial intelligence (AI) technologies that can be used in risk assessment have been developed. The aim of this study is to bring together the researches on prediction models developed with AI technologies using the EHRs of patients hospitalized in the intensive care unit (ICU) and to evaluate them in terms of risk management in healthcare. Methods: The study restricted the search to the Web of Science, Pubmed, Science Direct, and Medline databases to retrieve research articles published in English in 2010 and after. Studies with a prediction model using data obtained from EHRs in the ICU are included. The study focused solely on research conducted in ICU to predict a health condition that poses a significant risk to patient safety using artificial intellegence (AI) technologies. Results: Recognized prediction subcategories were mortality (n=6), sepsis (n=4), pressure ulcer (n=4), acute kidney injury (n=3), and other areas (n=10). It has been found that EHR-based prediction models are good risk management and decision support tools and adoption of such models in ICUs may reduce the prevalence of adverse conditions. Conclusions: The article results remarks that developed models was found to have higher performance and better selectivity than previously developed risk models, so they are better at predicting risks and serious adverse events in ICU. It is recommended to use AI based prediction models developed using EHRs in risk management studies. Future work is still needed to researches to predict different health conditions risks.

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“…The clinical burden of acute kidney injury (AKI) worsens globally with the increasing complexity of cardiovascular diseases, anticancer therapy, and aging population [1][2][3]. AKI develops in 4% of patients admitted to our institution and involves more than 3000 patients annually [4]. A total of 39% of AKI cases develop during hospitalization following clinical deterioration and multiorgan dysfunction [4,5].…”

Section: Introductionmentioning

confidence: 99%

“…AKI develops in 4% of patients admitted to our institution and involves more than 3000 patients annually [4]. A total of 39% of AKI cases develop during hospitalization following clinical deterioration and multiorgan dysfunction [4,5]. Additionally, 15% of patients who receive antimicrobials or chemotherapy of nephrotoxic potential develop drug-induced AKI [6,7].…”

Section: Introductionmentioning

confidence: 99%

Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study

et al. 2021

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Background Acute kidney injury (AKI) develops in 4% of hospitalized patients and is a marker of clinical deterioration and nephrotoxicity. AKI onset is highly variable in hospitals, which makes it difficult to time biomarker assessment in all patients for preemptive care. Objective The study sought to apply machine learning techniques to electronic health records and predict hospital-acquired AKI by a 48-hour lead time, with the aim to create an AKI surveillance algorithm that is deployable in real time. Methods The data were sourced from 20,732 case admissions in 16,288 patients over 1 year in our institution. We enhanced the bidirectional recurrent neural network model with a novel time-invariant and time-variant aggregated module to capture important clinical features temporal to AKI in every patient. Time-series features included laboratory parameters that preceded a 48-hour prediction window before AKI onset; the latter’s corresponding reference was the final in-hospital serum creatinine performed in case admissions without AKI episodes. Results The cohort was of mean age 53 (SD 25) years, of whom 29%, 12%, 12%, and 53% had diabetes, ischemic heart disease, cancers, and baseline eGFR <90 mL/min/1.73 m2, respectively. There were 911 AKI episodes in 869 patients. We derived and validated an algorithm in the testing dataset with an AUROC of 0.81 (0.78-0.85) for predicting AKI. At a 15% prediction threshold, our model generated 699 AKI alerts with 2 false positives for every true AKI and predicted 26% of AKIs. A lowered 5% prediction threshold improved the recall to 60% but generated 3746 AKI alerts with 6 false positives for every true AKI. Representative interpretation results produced by our model alluded to the top-ranked features that predicted AKI that could be categorized in association with sepsis, acute coronary syndrome, nephrotoxicity, or multiorgan injury, specific to every case at risk. Conclusions We generated an accurate algorithm from electronic health records through machine learning that predicted AKI by a lead time of at least 48 hours. The prediction threshold could be adjusted during deployment to optimize recall and minimize alert fatigue, while its precision could potentially be augmented by targeted AKI biomarker assessment in the high-risk cohort identified.

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Electronic health records accurately predict renal replacement therapy in acute kidney injury

Cited by 13 publications

References 36 publications

Recent evolutions of machine learning applications in clinical laboratory medicine

Recent evolutions of machine learning applications in clinical laboratory medicine

Risk Management In Intensive Care Units With Artificial Intelligence Technologies: Systematic Review of Prediction Models Using Electronic Health Records

Health Care Analytics With Time-Invariant and Time-Variant Feature Importance to Predict Hospital-Acquired Acute Kidney Injury: Observational Longitudinal Study

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