Prognostic Value of the Lactate–Albumin Difference in Predicting 30-Day Mortality in Critically Ill Patients

Arslantaş, Mustafa Kemal; Arslantaş, Reyhan; Dinçer, Pelin Çorman; Altun, Gülbin Töre; Kararmaz, Alper

doi:10.1097/shk.0000000000001613

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…Early identi cation and prediction of patients at risk of adverse outcomes related to sepsis and septic shock are critical to timely intervention. Today, there many studies on the early predictions of sepsis that rely on determining the presence of infection and organ dysfunction by screening laboratory tests (such as procalcitonin, C-reactive protein, white cells, platelets, and lactic acid) [32][33][34][35]. Even though they are accurately predicted by laboratory testing, these take time, and treatment is further delayed [33,36].…”

Section: Discussionmentioning

confidence: 99%

Using machine learning methods to predict the lactate trend of sepsis patients in the ICU

Arslantaş

Asuroglu

Arslantaş

et al. 2022

Preprint

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Purpose Serum lactate levels are considered a biomarker of tissue hypoxia. In sepsis or septic shock patients as suggested by The Surviving Sepsis Campaign serum lactate levels should be assessed and early lactate clearance-directed therapy is associated with decreased mortality. Monitoring a patient's vital parameters and repeatedly done blood analysis may have deleterious effects on the patient and brings an economical burden. Machine learning algorithms and trend analysis are gaining importance to overcome these unwanted facts. In this context, we aimed to investigate if an artificial intelligence approach can predict lactate trends from non-invasive clinical variables of patients with sepsis. MethodsIn this retrospective study, adult patients with sepsis from the MIMIC-IV dataset who had at least two serum lactate measurements recorded within the first 6 hours of sepsis diagnosis and who also has an ICU length of stay ≥ 24 hours are evaluated and ≥1mmol/l change is considered as a trend indicator. For prediction of lactate trend Naïve Bayes, J48 Decision Tree, Logistic Regression, Random Forest, and Logistic Model Tree (LMT) classifiers are evaluated. ResultsLMT algorithm outperformed other classifiers (AUC= 0.832). J48 decision tree performed worse when predicting constant lactate trend. LMT algorithm with 4 features (heart rate, oxygen saturation, lactate value before sepsis diagnosis, and time interval variables) achieved 0.821 in terms of AUC. ConclusionWe can say that machine learning models that employ logistic regression architectures, i.e. LMT algorithm achieved good results in lactate trend prediction tasks can be effectively used to assess the state of the patient whether it is stable or improving.

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Section: Discussionmentioning

confidence: 99%

Using machine learning methods to predict the lactate trend of sepsis patients in the ICU

Arslantaş

Asuroglu

Arslantaş

et al. 2022

Preprint

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“…The utility of physiologic biomarkers in critical care is debated in this letter to the editor by Arslantas et al (17). The authors comment on a previous publication in Shock by Kong et al (18) heralding lactate:albumin ratio as an index of 30-day mortality risk in critically ill patients.…”

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confidence: 99%

“…The authors comment on a previous publication in Shock by Kong et al (18) heralding lactate:albumin ratio as an index of 30-day mortality risk in critically ill patients. Arslantas et al (17) postulate that the difference between lactate and albumin is a superior representative of disease severity. This group from Turkey test their hypothesis using a large critical care data set and report a superior area under the curve for their proposed model.…”

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confidence: 99%

What's New in Shock, July 2021?

Krocker

Cardenas²

2021

Shock

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Using Machine Learning Methods to Predict the Lactate Trend of Sepsis Patients in the ICU

Arslantas,

Asuroglu,

Arslantas

et al. 2024

Communications in Computer and Information Science

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Serum lactate levels are considered a biomarker of tissue hypoxia. In sepsis or septic shock patients, as suggested by The Surviving Sepsis Campaign, early lactate clearance-directed therapy is associated with decreased mortality; thus, serum lactate levels should be assessed. Monitoring a patient’s vital parameters and repetitive blood analysis may have deleterious effects on the patient and also bring an economic burden. Machine learning and trend analysis are gaining importance to overcome these issues. In this context, we aimed to investigate if a machine learning approach can predict lactate trends from non-invasive parameters of patients with sepsis. This retrospective study analyzed adult sepsis patients in the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset. Inclusion criteria were two or more lactate tests within 6 h of diagnosis, an ICU stay of at least 24 h, and a change of ≥1 mmol/liter in lactate level. Naïve Bayes, J48 Decision Tree, Logistic Regression, Random Forest, and Logistic Model Tree (LMT) classifiers were evaluated for lactate trend prediction. LMT algorithm outperformed other classifiers (AUC = 0.803; AUPRC = 0.921). J48 decision tree performed worse than the other methods when predicting constant trend. LMT algorithm with four features (heart rate, oxygen saturation, initial lactate, and time interval variables) achieved 0.80 in terms of AUC (AUPRC = 0.921). We can say that machine learning models that employ logistic regression architectures, i.e., LMT algorithm achieved good results in lactate trend prediction tasks, and it can be effectively used to assess the state of the patient, whether it is stable or improving.

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Prognostic Value of the Lactate–Albumin Difference in Predicting 30-Day Mortality in Critically Ill Patients

Cited by 5 publications

References 2 publications

Using machine learning methods to predict the lactate trend of sepsis patients in the ICU

Using machine learning methods to predict the lactate trend of sepsis patients in the ICU

What's New in Shock, July 2021?

Using Machine Learning Methods to Predict the Lactate Trend of Sepsis Patients in the ICU

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