A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury

By Nenad Tomašev, Xavier Glorot, Jack W. Rae, Michal Zielinski, Harry Askham, Andre Saraiva, A. Mottram, Clemens Meyer, Suman V. Ravuri, Ivan V. Protsyuk and 17 more. 2019

949 citations recorded by the source corpus.

The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients1. To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records2–17 and using acute kidney injury—a common and potentially life-threatening condition18—as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests9. Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment. A deep learning approach that predicts the risk of acute kidney injury may help to identify patients at risk of health deterioration within a time window that enables early treatment.

Subfields: Artificial Intelligence, Epidemiology, Nephrology, Medical & Health AI

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Citations in the corpus

Improving the accuracy of medical diagnosis with causal machine learning (2020, background): “In recent years, arti ﬁ cial intelligence and machine learning have emerged as powerful tools for solving complex problems in diverse domains 5 – 7 .”
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries (2019, background): “…a domain, there may be differences in the performance of models trained with different datasets due to variations in demographics, class prevalence, data collection instrument, and data acquisition settings (Zech et al. , 2018; Tomašev et al. , 2019; AlBadawy et al. , 2018; Ting et al. , 2017).”
From Real‐World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges (2020, methodology): “Data from EHRs have already been successfully leveraged in several machine learning applications, such as modeling disease progression, 10 predicting disease deterioration, 11 and risk factors, 12,13 as well as predicting treatment responses.”
CheXclusion: Fairness gaps in deep chest X-ray classifiers (2020, background): “Identifying and eliminating disparities is particularly important as large datasets begin to be used by high-capacity neural models, but are based on highly skewed population, e.g., kidney injury prediction in a population that is 93.6% male [43].”
Axes of a revolution: challenges and promises of big data in healthcare (2020, background): “One recent example is the prediction of inpatient episodes of acute kidney injury 88 .”
Key challenges for delivering clinical impact with artificial intelligence (2019, background): “…mortality, readmission risk, prolonged length of stay and discharge diagnoses [34], predicting future deterioration, including acute kidney injury [35], improving decision-making strategies, including weaning of mechanical ventilation [36] and management of sepsis [37], and learning treatment…”
AI in health and medicine (2022, background): “Multimodal models have also taken advantage of far more complex inputs, such as electronic health records, which encompass a wide variety of data such as medical diagnoses, vital signs, prescriptions and laboratory results 66,67 .”
Fairness Violations and Mitigation under Covariate Shift (2019, methodology): “A recent study [70] showed good predictive performance for AKI based on patient data provided by the U.”
Multimodal biomedical AI (2022, methodology): “…EHR-based dataset comprising 620,000 dimensions that were projected into a continuous embedding space with only 800 dimensions, capturing a wide array of information in a 6-h time frame for each patient, and built a recurrent neural network to predict acute kidney injury over time 129 .”
Bias in artificial intelligence algorithms and recommendations for mitigation (2023, methodology): “An example of this includes an algorithm developed to predict acute kidney injury (AKI) using clinical data from the US Department of Veteran Affairs [27].”
Surgical data science – from concepts toward clinical translation (2020, background): “Other companies, societies and initiatives, such as Google (Mountain View, CA, USA) DeepMind Health (Graves et al., 2016; Tomašev et al., 2019), Intel (Santa Clara, CA, USA) (Healthcare IT News, 2012) and the American Society of Clinical Oncology (ASCO) CancerLinQ® (Sledge et al.”