Max Lactate Research Articles

A Novel Composite Indicator of Predicting Mortality Risk for Heart Failure Patients With Diabetes Admitted to Intensive Care Unit Based on Machine Learning.

BackgroundPatients with heart failure (HF) with diabetes may face a poorer prognosis and higher mortality than patients with either disease alone, especially for those in intensive care unit. So far, there is no precise mortality risk prediction indicator for this kind of patient.MethodTwo high-quality critically ill databases, the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the Telehealth Intensive Care Unit (eICU) Collaborative Research Database (eICU-CRD) Collaborative Research Database, were used for study participants’ screening as well as internal and external validation. Nine machine learning models were compared, and the best one was selected to define indicators associated with hospital mortality for patients with HF with diabetes. Existing attributes most related to hospital mortality were identified using a visualization method developed for machine learning, namely, Shapley Additive Explanations (SHAP) method. A new composite indicator ASL was established using logistics regression for patients with HF with diabetes based on major existing indicators. Then, the new index was compared with existing indicators to confirm its discrimination ability and clinical value using the receiver operating characteristic (ROC) curve, decision curve, and calibration curve.ResultsThe random forest model outperformed among nine models with the area under the ROC curve (AUC) = 0.92 after hyper-parameter optimization. By using this model, the top 20 attributes associated with hospital mortality in these patients were identified among all the attributes based on SHAP method. Acute Physiology Score (APS) III, Sepsis-related Organ Failure Assessment (SOFA), and Max lactate were selected as major attributes related to mortality risk, and a new composite indicator was developed by combining these three indicators, which was named as ASL. Both in the initial and external cohort, the new indicator, ASL, had greater risk discrimination ability with AUC higher than 0.80 in both low- and high-risk groups compared with existing attributes. The decision curve and calibration curve indicated that this indicator also had a respectable clinical value compared with APS III and SOFA. In addition, this indicator had a good risk stratification ability when the patients were divided into three risk levels.ConclusionA new composite indicator for predicting mortality risk in patients with HF with diabetes admitted to intensive care unit was developed on the basis of attributes identified by the random forest model. Compared with existing attributes such as APS III and SOFA, the new indicator had better discrimination ability and clinical value, which had potential value in reducing the mortality risk of these patients.

Altered circulating microRNA profiles after endurance training: an ultramarathon runner cohort study

Abstract Background and aim Characterized by prolonged, highly dynamic exercise that is low-to-high power in nature, endurance training has proven beneficial in cardiovascular disease prevention. Several miRNAs have been reported to be regulated in response to exercise in healthy humans. Our aim was to determine the miRNA expression changes before and after an ultramarathon run in elite runners [1]. Method We performed bioinformatic analyses - tissue-specific/CV process-specific. Each bioinformatic analysis included 55 miRNAs (figure 1). The multiMiR 1.4 R package was used to identify the targets of miRNAs while a screening of the gene ontology terms for the presence of keywords with the biomaRt interface was used to identify genes associated with angiogenesis, cardiac muscle function, muscle hypertrophy, coagulation, inflammation, and fibrosis. Cardiac tissue-specific targets were identified in Tissue 2.0 database using expression confidence score >2. Gene-gene interaction data were retrieved from the STRING app for Cytoscape. Enrichment analyses of biological processes and reactome and KEGG pathways were also conducted on the STRING app. miR-1-3p, miR-126, miR-223, miR-125a-5p, miR-106a, and miR-15a/b were chosen, based on bioinformatic analysis for qRT-PCR validation, among those related to inflammation, fibrosis and cardiac function [2]. Total RNA was extracted from plasma samples using the MirVana PARIS Kit (ABI, California, USA) and quality was assessed using fluorometric assay. qRT-PCR was performed using the TaqMan Advance (ABI, Foster City, California) protocol on a high throughput thermal cycler. The Wilcoxon test was used for paired comparison – before vs after ultramarathon running – and Spearman's Correlation was used for database analysis of miRNA expressions, p<0.05. Results MiR-1-3p, miR-126, miR-223, miR-125a-5p, miR-106a, and miR-15a/b expressions were measured in 22 endurance athletes before and after an ultramarathon wherein athletes ran to completion (100 km) or exhaustion (52–91 km, median 74 km). We found that the expressions of miR-125a-5p, miR-126, miR-223 were significantly increased after the ultramarathon (p=0.018; p=0.001; p=0.014, respectively), whereas miR-15b was significantly decreased (p=0.028). No significant difference was observed for miR-1-3p and miR-15a, while miR-106a was not detectable in the circulating plasma (figure 2). Additionally, miR-1-3p expression post-run showed negative correlation with hsCRP levels post-run (r=−0.631, p=0.005). miR-125a-5p expression post-run showed negative correlation with max lactate levels during run (r=−0.759, p=0.004). Conclusion Extreme physical activity, as exemplified by an ultramarathon, may have an impact on the expression alterations of miRNAs associated with inflammation, fibrosis, and cardiac muscle function. Further studies will clarify the long-term impact of these observations. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): Medical University of Warsaw Figure 1Figure 2. Differences in miRNA expression pre- and post-ultramarathon

Lactate indices as predictors of in-hospital mortality or 90-day survival after admission to an intensive care unit in unselected critically ill patients.

BackgroundWe performed an exclusive study to investigate the associations between a total of 23 lactate-related indices during the first 24h in an intensive care unit (ICU) and in-hospital mortality.MethodsNine static and 14 dynamic lactate indices, including changes in lactate concentrations (Δ Lac) and slope (linear regression coefficient), were calculated from individual critically ill patient data extracted from the Multiparameter Intelligent Monitoring for Intensive Care (MIMIC) III database.ResultsData from a total of 781 ICU patients were extracted, consisted of 523 survivors and 258 non-survivors. The in-hospital mortality rate for this cohort was 33.0%. A multivariate logistic regression model identified maximal lactate concentration at 24h after ICU admission (max lactate at T24) as a significant predictor of in-hospital mortality (odds ratio = 1.431, 95% confidence interval [CI] = 1.278–1.604, p<0.001) after adjusting for predefined confounders (age, gender, sepsis, Elixhauser comorbidity score, mechanical ventilation, renal replacement therapy, vasopressors, ICU severity scores). Area under curve (AUC) for max lactate at T24 was larger (AUC = 0.776, 95% CI = 0.740–0.812) than other indices (p<0.001), comparable to an APACHE III score of 0.771. When combining max lactate at T24 with APACHE III, the AUC was increased to 0.815 (95% CI:0.783–0.847). The sensitivity, specificity, and positive and negative predictive values for the cut-off value of 3.05 mmol/L were 64.3%, 77.4%, 58.5%, and 81.5%, respectively.Kaplan-Myer survival curves of the max lactate at T24 for 90-day survival after admission to ICU demonstrated a significant difference according to the cut-off value (p<0.001).ConclusionsThese data indicate that the maximal arterial lactate concentration at T24 is a robust predictor of in-hospital mortality as well as 90-day survival in unselected ICU patients with predictive ability as comparable with APACHE III score.

Heart rate variability in athletes: relationship with central and peripheral performance.

To investigate relationships between heart rate variability (HRV) and peripheral and central performance measures, 17 cross-country (X-C) skiers and seven canoeists were studied before and after a training period of 7 months. For the skiers and canoeists respectively, leg and arm peak torque (Tq), time to peak torque (TiTq), and total work (Wrk) were measured in an isokinetic dynamometer. Maximal oxygen uptakes (VO(2max)) were obtained from treadmill tests. Power spectral analysis of HRV was performed on electrocardiographic recordings in the resting supine position and after a tilt to yield power in the low-frequency (0.04--0.15 Hz) and high-frequency (0.15--0.45 Hz) components of HRV. The change in normalized LF-variability in standing (Delta LFnT) correlated (P < 0.01) with the changes in TiTq (r = 0.63), max lactate (r = -0.63), and VO(2max) (r = -0.53). The change in absolute LFT was inversely correlated with the change in Tq. Subjects who improved VO(2max) were characterized by consistently higher high-frequency and total HRV than subjects with deteriorated aerobic capacity (P < 0.05). The results suggest that improved measures of both peripheral and central (aerobic) work capacities were associated with a reduction of low-frequency HRV in the tilted position. High-frequency and total HRV did not change in proportion with changes in muscle performance or aerobic capacity, but the ability to further improve VO(2max) with training in these already fit subjects seemed to depend on their average levels of these HRV measures, interpreted to reflect parasympathetic activity.

A simple method for individual anaerobic threshold as predictor of max lactate steady state.

The individual anaerobic threshold (IAT) is defined (18) as the highest metabolic rate where blood lactate (La) concentrations are maintained at a steady-state during prolonged exercise. Stegmann et al.'s (18) method to detect IAT, using La-performance relationship during incremental graded exercise, is based on the assumption that La is in relatively steady state by the end of each 3-min stage of work rate. However, at the end of a 3-min stage, an La steady state (Lass) is not reached (13). The present study was designed to investigate whether the IAT should be determined by attributing La value to the antecedent stage (IATa) or to the same stage of its measurement (IATm), then to verify whether this IAT would be a valid indicator of the max Lass during prolonged exercise. Forty-one athletes (21 male and 20 female), regularly involved in different physical training, performed three exercise tests on treadmill. The first one was a 3-min stage incremental test to detect the IATa and IATm. The other two tests were 30-min prolonged tests at the IATa and IATm workload. Lass were present in IATa intensity (about 4.0 mmol x L(-1)) both in male and female athletes, whereas at IATm intensity a Lass was not present and a premature break-off occurred in some cases. This protocol can be useful for practical use because: 1) the method of choosing the anaerobic threshold is easy to apply; 2) it does not require to reach the maximal effort; and 3) although in some cases the IATa could probably underestimate the workload of max Lass, the IATa can be regarded as guideline to define the intensity of endurance training.

Short-term overtraining: effects on performance, circulatory responses, and heart rate variability.

Nine elite canoeists were investigated concerning changes in performance, heart rate variability (HRV), and blood-chemical parameters over a 6-d training camp. The training regimen consisted of cross-country skiing and strength training, in total 13.0+/-1.6 h, corresponding to a 50% increase in training load. Time to exhaustion (RunT) decreased from 19.1+/-1.0 to 18.0+/-1.2 min (P < 0.05). VO2max and max lactate (La(max)) both decreased significantly (P < 0.05) over the training period (4.99+/-0.97 to 4.74+/-0.98 L x min(-1) and from 10.08+/-1.25 to 8.98+/-1.03 mmol x L(-1) respectively). Heart rates (HR) decreased significantly at all workloads. Plasma volume increased by 7+/-7% (P < 0.05). Resting cortisol, decreased from 677+/-244 to 492+/-222 nmol x L(-1) (P < 0.05), whereas resting levels of adrenaline and noradrenaline remained unchanged. The change between tests in RunT correlated significantly with the change in HRmax (r = 0.79; P = 0.01). There were no group changes in high or low frequency HRV, neither at rest nor following a tilt. The reduced maximal performance indicates a state of fatigue/overreaching and peripheral factors are suggested to limit performance even though HRmax and La(max) both were reduced. The reduced submaximal heart rates are probably a result of increased plasma volume. HRV in this group didn't seem to be affected by short-term overtraining.