Prediction of blood lactate values in critically ill patients: a retrospective multi-center cohort study

Lactate Monitoring in Intensive Care: A Comprehensive Review of Its Utility and Interpretation

Lactate monitoring is critical in managing critically ill patients in intensive care settings. Elevated lactate levels often signify underlying metabolic disturbances such as tissue hypoxia, anaerobic metabolism, or impaired lactate clearance, which are prevalent in conditions like sepsis, shock, and trauma. Understanding the physiological basis of lactate production and its significance in clinical practice is essential for interpreting its diagnostic and prognostic value. This comprehensive review aims to explore the utility of lactate monitoring across various critical care scenarios. It provides an overview of lactate's metabolic pathways, methods of measurement, and the clinical implications of interpreting lactate levels in different contexts. Additionally, the review discusses current evidence on lactate-guided therapeutic interventions and highlights challenges and limitations to their application. By synthesizing the existing literature and clinical insights, this review aims to enhance the understanding of the role of lactate monitoring in assessing disease severity, guiding treatment strategies, and predicting outcomes in critically ill patients. Ultimately, this review underscores the importance of integrating lactate monitoring into routine clinical practice to optimize patient care and improve clinical outcomes in intensive care settings.

Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study

BACKGROUND

The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD.

OBJECTIVE

The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis.

METHODS

The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers.

RESULTS

The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy).

CONCLUSIONS

The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.

The combination of lactate level, lactate clearance and APACHE II score better predicts short-term outcomes in critically Ill patients: a retrospective cohort study

BACKGROUND

The mortality rate is high in critically ill patients due to the difficulty of diagnosis and treatment. Thus, it is very important to explore the predictive value of different indicators related to prognosis in critically ill patients.

METHODS

This was a retrospective cohort study of patients in the intensive care unit (ICU) of the Sixth People's Hospital in Shanghai, China. A total of 1465 ICU patients had lactate values > 2.1 mmol/L at least once within 24 h of ICU admission, and arterial blood gas was monitored more than twice during the ICU stay.

RESULTS

The predictive value of lactate clearance at 24 h was not high, and the sensitivity and specificity were lower. The predictive value of the lactate level at baseline and the APACHE II score was higher than that of lactate clearance at 24 h in critically ill patients. The predictive value of the lactate level at baseline combined with the APACHE II score was higher than that of the lactate level at baseline or the APACHE II score alone. In addition, the predictive value of lactate clearance at 24 h combined with the APACHE II score was also significantly higher than that of lactate clearance at 24 h or the APACHE II score alone. In particular, the area under the ROC curve reached 0.900, the predictive value was markedly higher than that of the ROC alone, and the sensitivity and specificity were better when these three indicators were combined.

CONCLUSIONS

The combination of lactate level, lactate clearance and APACHE II score better predicts short-term outcomes in critically ill patients.