Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements

Zimmerman, Lindsay; Reyfman, Paul A.; Smith, Angela D. R.; Zeng, Zexian; Kho, Abel N.; Sanchez-Pinto, L. Nelson; Luo, Yuan

doi:10.1186/s12911-019-0733-z

Cited by 71 publications

(60 citation statements)

References 37 publications

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“…Clinical prediction tasks such as patient mortality and disease prediction are highly important for early disease prevention and timely intervention [1,2]. Patient mortality prediction in intensive care units (ICUs) is a key application for large-scale health data and plays an important role in selecting interventions, planning care, and allocating resources.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of time series summary statistics as features for clinical prediction tasks

Guo

Chen

2020

BMC Med Inform Decis Mak

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Background: Clinical prediction tasks such as patient mortality, length of hospital stay, and disease diagnosis are highly important in critical care research. The existing studies for clinical prediction mainly used simple summary statistics to summarize information from physiological time series. However, this lack of statistics leads to a lack of information. In addition, using only maximum and minimum statistics to indicate patient features fails to provide an adequate explanation. Few studies have evaluated which summary statistics best represent physiological time series. Methods: In this paper, we summarize 14 statistics describing the characteristics of physiological time series, including the central tendency, dispersion tendency, and distribution shape. Then, we evaluate the use of summary statistics of physiological time series as features for three clinical prediction tasks. To find the combinations of statistics that yield the best performances under different tasks, we use a cross-validation-based genetic algorithm to approximate the optimal statistical combination. Results: By experiments using the EHRs of 6,927 patients, we obtained prediction results based on both single statistics and commonly used combinations of statistics under three clinical prediction tasks. Based on the results of an embedded cross-validation genetic algorithm, we obtained 25 optimal sets of statistical combinations and then tested their prediction results. By comparing the performances of prediction with single statistics and commonly used combinations of statistics with quantitative analyses of the optimal statistical combinations, we found that some statistics play central roles in patient representation and different prediction tasks have certain commonalities. Conclusion: Through an in-depth analysis of the results, we found many practical reference points that can provide guidance for subsequent related research. Statistics that indicate dispersion tendency, such as min, max, and range, are more suitable for length of stay prediction tasks, and they also provide information for short-term mortality prediction. Mean and quantiles that reflect the central tendency of physiological time series are more suitable for mortality and disease prediction. Skewness and kurtosis perform poorly when used separately for prediction but can be used as supplementary statistics to improve the overall prediction effect.

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Section: Introductionmentioning

confidence: 99%

An evaluation of time series summary statistics as features for clinical prediction tasks

Guo

Chen

2020

BMC Med Inform Decis Mak

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“…Zimmerman et al conducted a retrospective cohort of 23,950 adult critical care patients and developed a predictive model by logistic regression for early prediction of AKI in the first 72 h. following ICU admission with an AUROC of 0.783 [118]. Their model included first-day measurements of physiologic variables but not medications and procedures, in order to detect which deterioration of patients' physiologic baselines are predictive of AKI [118]. This was cross-validated with ML algorithms, demonstrating an accurate and early prediction of AKI with their risk prediction score [118].…”

Section: The Era Of Artificial Intelligencementioning

confidence: 99%

“…Their model included first-day measurements of physiologic variables but not medications and procedures, in order to detect which deterioration of patients' physiologic baselines are predictive of AKI [118]. This was cross-validated with ML algorithms, demonstrating an accurate and early prediction of AKI with their risk prediction score [118].…”

Section: The Era Of Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence in Acute Kidney Injury Risk Prediction

Gameiro

Branco

Lopes

2020

JCM

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Acute kidney injury (AKI) is a frequent complication in hospitalized patients, which is associated with worse short and long-term outcomes. It is crucial to develop methods to identify patients at risk for AKI and to diagnose subclinical AKI in order to improve patient outcomes. The advances in clinical informatics and the increasing availability of electronic medical records have allowed for the development of artificial intelligence predictive models of risk estimation in AKI. In this review, we discussed the progress of AKI risk prediction from risk scores to electronic alerts to machine learning methods.

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“…The AUC was 0.96 (0.96-0.96) for receipt of renal replacement therapy in the next 48 hours [19]. Another study demonstrated that machine learning models (multivariate logistic regression, RF and artificial neural networks (ANN)) can predict AKI onset following ICU admission in 23,950 patients with a competitive AUC (mean AUC 0.783) [20] (Table 1). Alerting CKD…”

Section: Alerting Akimentioning

confidence: 99%

Role of Artificial Intelligence in Kidney Disease

Yuan¹,

Zhang²,

Deng³

et al. 2020

Int. J. Med. Sci.

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Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. Challenges remain in its diagnosis and treatment. AI could take individual conditions into account, produce suitable decisions and promise to make great strides in kidney disease management. Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.

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Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements

Cited by 71 publications

References 37 publications

An evaluation of time series summary statistics as features for clinical prediction tasks

An evaluation of time series summary statistics as features for clinical prediction tasks

Artificial Intelligence in Acute Kidney Injury Risk Prediction

Role of Artificial Intelligence in Kidney Disease

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