Risk prediction models for acute kidney injury in adults: An overview of systematic reviews

Acker, Paulien Van; Biesen, Wim Van; Nagler, Evi; Koobasi, Muguet; Veys, Nic; Vanmassenhove, Jill

doi:10.1371/journal.pone.0248899

Cited by 11 publications

(4 citation statements)

References 26 publications

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“…The correct evaluation and implementation of such algorithms, however, is hampered by an absence of semantic transparency in the use of many different definitions of AKI. A review of algorithm-based prediction models for AKI by Van Acker et al (2021) found that 44 different definitions were used for AKI. Most of these prediction models claim to predict AKI as defined by the widely accepted Kidney Disease Improving Global Outcomes (KDIGO) initiative ( Fliser et al, 2012 ).…”

Section: “Explainability” and “Transparency”: The Importance Of Seman...mentioning

confidence: 99%

“…However, in reality they use different interpretations of this definition, which may even substantially differ from the original KDIGO definition. For example, most interpretations neglect the criterion of urinary output, although this is the most powerful prognosticator in the KDIGO definition ( Van Acker et al, 2021 ). As a consequence, the end user cannot truly know exactly what is understood by the algorithm-predicted condition labelled as AKI, and how that label should be coupled to possible interventions.…”

Section: “Explainability” and “Transparency”: The Importance Of Seman...mentioning

confidence: 99%

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Explainability in medicine in an era of AI-based clinical decision support systems

et al. 2022

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The combination of “Big Data” and Artificial Intelligence (AI) is frequently promoted as having the potential to deliver valuable health benefits when applied to medical decision-making. However, the responsible adoption of AI-based clinical decision support systems faces several challenges at both the individual and societal level. One of the features that has given rise to particular concern is the issue of explainability, since, if the way an algorithm arrived at a particular output is not known (or knowable) to a physician, this may lead to multiple challenges, including an inability to evaluate the merits of the output. This “opacity” problem has led to questions about whether physicians are justified in relying on the algorithmic output, with some scholars insisting on the centrality of explainability, while others see no reason to require of AI that which is not required of physicians. We consider that there is merit in both views but find that greater nuance is necessary in order to elucidate the underlying function of explainability in clinical practice and, therefore, its relevance in the context of AI for clinical use. In this paper, we explore explainability by examining what it requires in clinical medicine and draw a distinction between the function of explainability for the current patient versus the future patient. This distinction has implications for what explainability requires in the short and long term. We highlight the role of transparency in explainability, and identify semantic transparency as fundamental to the issue of explainability itself. We argue that, in day-to-day clinical practice, accuracy is sufficient as an “epistemic warrant” for clinical decision-making, and that the most compelling reason for requiring explainability in the sense of scientific or causal explanation is the potential for improving future care by building a more robust model of the world. We identify the goal of clinical decision-making as being to deliver the best possible outcome as often as possible, and find—that accuracy is sufficient justification for intervention for today’s patient, as long as efforts to uncover scientific explanations continue to improve healthcare for future patients.

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Section: “Explainability” and “Transparency”: The Importance Of Seman...mentioning

confidence: 99%

Section: “Explainability” and “Transparency”: The Importance Of Seman...mentioning

confidence: 99%

Explainability in medicine in an era of AI-based clinical decision support systems

et al. 2022

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“…Using different concepts for the same disease, or different definitions for the same concept results in confusion e.g. in what exactly is predicted by prediction models [ 46 ]. Remarkably, merely providing an explanation was sufficient for most respondents, irrespective of the epistemic correctness of such explanation, as it provided a means of control.…”

Section: Discussionmentioning

confidence: 99%

An exploration of expectations and perceptions of practicing physicians on the implementation of computerized clinical decision support systems using a Qsort approach

Biesen

Cauwenberge

Decruyenaere

et al. 2022

BMC Med Inform Decis Mak

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Background There is increasing interest in incorporating clinical decision support (CDS) into electronic healthcare records (EHR). Successful implementation of CDS systems depends on acceptance of them by healthcare workers. We used a mix of quantitative and qualitative methods starting from Qsort methodology to explore expectations and perceptions of practicing physicians on the use of CDS incorporated in EHR. Methods The study was performed in a large tertiary care academic hospital. We used a mixed approach with a Q-sort based classification of pre-defined reactions to clinical case vignettes combined with a thinking-aloud approach, taking into account COREQ recommendations The open source software of Ken-Q Analysis version 1.0.6. was used for the quantitative analysis, using principal components and a Varimax rotation. For the qualitative analysis, a thematic analysis based on the four main themes was performed based on the audiotapes and field notes. Results Thirty physicians were interviewed (7 in training, 8 junior staff and 15 senior staff; 16 females). Nearly all respondents were strongly averse towards interruptive messages, especially when these also were obstructive. Obstructive interruption was considered to be acceptable only when it increases safety, is adjustable to user expertise level and/or allows deviations when the end-user explains why a deviation is desirable in the case at issue. Transparency was deemed an essential feature, which seems to boil down to providing sufficient clarification on the factors underlying the recommendations of the CDS, so that these can be compared against the physicians’ existing knowledge, beliefs and convictions. Conclusion Avoidance of disruptive workflows and transparency of the underlying decision processes are important points to consider when developing CDS systems incorporated in EHR.

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“…Although early detection of AKI and prompt intervention could improve the prognosis of critically ill patients, none of the procedures, including close monitoring of vital signs, blood tests, and urine analysis, are promising solutions. Serum creatinine, used to diagnose AKI, has a reduced reliability for the early detection of the pathology [5][6][7][8][9].…”

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confidence: 99%

Machine learning model for predicting oliguria in critically ill patients

Yamao

Oami

Yamabe

et al. 2023

Preprint

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Background: Oliguria is an important indicator for the early detection of acute kidney injury (AKI) and prediction of poor outcomes in critically ill patients; however, the accuracy of a prediction model using machine learning has rarely been investigated. This study aimed to develop and evaluate a machine learning algorithm for predicting oliguria in patients admitted to the intensive care unit (ICU). Methods: This retrospective cohort study used electronic health record data of consecutive patients admitted to the ICU between 2010 and 2019. Oliguria was defined as urine output of less than 0.5 mL/kg/h. We developed a machine learning model using a light-gradient boosting machine to predict oliguria between 6 to 72 h. The accuracy of the model was evaluated using receiver operating characteristic curves. We calculated the Shapley additive explanations (SHAP) value to identify important variables in the prediction model. Subgroup analyses were conducted to compare the accuracy of the models in predicting oliguria based on sex, age, and furosemide administration. Results: Among 9,241 patients in the study, the proportions of patients with urine output < 0.5 mL/kg/h for 6 h and those with AKI during the ICU stay were 27.4% and 30.2%, respectively. The area under the curve (AUC) of the prediction algorithm for the onset of oliguria at 6 h and 72 h using 50 clinically relevant variables was 0.966 (95% confidence interval [CI] 0.965–0.968) and 0.923 (95% CI 0.921–0.926), respectively. The SHAP analysis for predicting oliguria at 6 h identified urine-related values, severity scores, serum creatinine, interleukin-6, fibrinogen/fibrin degradation products, and vital signs as important variables. Subgroup analyses revealed that males had a higher AUC than did females (0.969 and 0.952, respectively), and the non-furosemide group had a higher AUC than did the furosemide group (0.971 and 0.957, respectively). Conclusions: The present study demonstrated that a machine learning algorithm could accurately predict oliguria onset in ICU patients, suggesting a potential role for oliguria in the early diagnosis and optimal management of AKI.

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Risk prediction models for acute kidney injury in adults: An overview of systematic reviews

Cited by 11 publications

References 26 publications

Explainability in medicine in an era of AI-based clinical decision support systems

Explainability in medicine in an era of AI-based clinical decision support systems

An exploration of expectations and perceptions of practicing physicians on the implementation of computerized clinical decision support systems using a Qsort approach

Machine learning model for predicting oliguria in critically ill patients

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