Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units

Lactate Levels in a Replanted Limb as an Early Biomarker for Assessing Post-Surgical Evolution: A Case Report

Background and Clinical Significance: In the clinical management of major pediatric traumatic injuries and other hypoxic conditions, lactate is widely recognized as a key indicator of tissue hypoxia and potential necrosis. However, its prognostic value remains uncertain. Several factors influence post-surgical outcomes, including the time between amputation and replantation, transport conditions, asepsis, the extent of tissue necrosis, hemorrhagic shock, coagulation disorders, and the heightened risk of contamination. Case presentation: We present this case to emphasize the utility of systemic lactate versus lactate levels in the replanted limb for monitoring post-transplantation outcomes in a pediatric patient with traumatic limb amputation. Significant fluctuations in lactate levels within the replanted limb were observed at the onset of unfavorable evolution, specifically on the seventh postoperative day, coinciding with the identification of Aspergillus spp. infection. This necessitated the use of synthetic saphenous vein grafts and Amphotericin B administration. Despite these interventions, disease progression ultimately led to limb amputation. Conclusions: Lactate levels in the replanted limb may serve as an early biomarker for assessing post-surgical evolution. However, further case reports are required to confirm its predictive value.

The premise, promise, and perils of artificial intelligence in critical care cardiology

Artificial intelligence (AI) is an emerging technology with numerous healthcare applications. AI could prove particularly useful in the cardiac intensive care unit (CICU) where its capacity to analyze large datasets in real-time would assist clinicians in making more informed decisions. This systematic review aimed to explore current research on AI as it pertains to the CICU. A PRISMA search strategy was carried out to identify the pertinent literature on topics including vascular access, heart failure care, circulatory support, cardiogenic shock, ultrasound, and mechanical ventilation. Thirty-eight studies were included. Although AI is still in its early stages of development, this review illustrates its potential to yield numerous benefits in the CICU.

Artificial intelligence and machine learning's role in sepsis-associated acute kidney injury

Sepsis-associated acute kidney injury (SA-AKI) is a serious complication in critically ill patients, resulting in higher mortality, morbidity, and cost. The intricate pathophysiology of SA-AKI requires vigilant clinical monitoring and appropriate, prompt intervention. While traditional statistical analyses have identified severe risk factors for SA-AKI, the results have been inconsistent across studies. This has led to growing interest in leveraging artificial intelligence (AI) and machine learning (ML) to predict SA-AKI better. ML can uncover complex patterns beyond human discernment by analyzing vast datasets. Supervised learning models like XGBoost and RNN-LSTM have proven remarkably accurate at predicting SA-AKI onset and subsequent mortality, often surpassing traditional risk scores. Meanwhile, unsupervised learning reveals clinically relevant sub-phenotypes among diverse SA-AKI patients, enabling more tailored care. In addition, it potentially optimizes sepsis treatment to prevent SA-AKI through continual refinement based on patient outcomes. However, utilizing AI/ML presents ethical and practical challenges regarding data privacy, algorithmic biases, and regulatory compliance. AI/ML allows early risk detection, personalized management, optimal treatment strategies, and collaborative learning for SA-AKI management. Future directions include real-time patient monitoring, simulated data generation, and predictive algorithms for timely interventions. However, a smooth transition to clinical practice demands continuous model enhancements and rigorous regulatory oversight. In this article, we outlined the conventional methods used to address SA-AKI and explore how AI and ML can be applied to diagnose and manage SA-AKI, highlighting their potential to revolutionize SA-AKI care.

Identification of distinct clinical phenotypes of cardiogenic shock using machine learning consensus clustering approach

BACKGROUND

Cardiogenic shock (CS) is a complex state with many underlying causes and associated outcomes. It is still difficult to differentiate between various CS phenotypes. We investigated if the CS phenotypes with distinctive clinical profiles and prognoses might be found using the machine learning (ML) consensus clustering approach.

METHODS

The current study included patients who were diagnosed with CS at the time of admission from the electronic ICU (eICU) Collaborative Research Database. Among 21,925 patients with CS, an unsupervised ML consensus clustering analysis was conducted. The optimal number of clusters was identified by means of the consensus matrix (CM) heat map, cumulative distribution function (CDF), cluster-consensus plots, and the proportion of ambiguously clustered pairs (PAC) analysis. We calculated the standardized mean difference (SMD) of each variable and used the cutoff of ± 0.3 to identify each cluster's key features. We examined the relationship between the phenotypes and several clinical endpoints utilizing logistic regression (LR) analysis.

RESULTS

The consensus cluster analysis identified two clusters (Cluster 1: n = 9,848; Cluster 2: n = 12,077). The key features of patients in Cluster 1, compared with Cluster 2, included: lower blood pressure, lower eGFR (estimated glomerular filtration rate), higher BUN (blood urea nitrogen), higher creatinine, lower albumin, higher potassium, lower bicarbonate, lower red blood cell (RBC), higher red blood cell distribution width (RDW), higher SOFA score, higher APS III score, and higher APACHE IV score on admission. The results of LR analysis showed that the Cluster 2 was associated with lower in-hospital mortality (odds ratio [OR]: 0.374; 95% confidence interval [CI]: 0.347-0.402; P < 0.001), ICU mortality (OR: 0.349; 95% CI: 0.318-0.382; P < 0.001), and the incidence of acute kidney injury (AKI) after admission (OR: 0.478; 95% CI: 0.452-0.505; P < 0.001).

CONCLUSIONS

ML consensus clustering analysis synthesized the pattern of clinical and laboratory data to reveal distinct CS phenotypes with different clinical outcomes.

Development of a prehospital lactic acidosis score for early-mortality. A prospective, multicenter, ambulance-based, cohort study

BACKGROUND

Lactic acidosis is a clinical status related to clinical worsening. Actually, higher levels of lactate is a well-established trigger of emergency situations. The aim of this work is to build-up a prehospital early warning score to predict 2-day mortality and intensive care unit (ICU) admission, constructed with other components of the lactic acidosis besides the lactate.

METHODS

Prospective, multicenter, observational, derivation-validation cohort study of adults evacuated by ambulance and admitted to emergency department with acute diseases, between January 1st, 2020 and December 31st, 2021. Including six advanced life support, thirty-eight basic life support units, referring to four hospitals (Spain). The primary and secondary outcome of the study were 2-day all-cause mortality and ICU-admission. The prehospital lactic acidosis (PLA) score was derived from the analysis of prehospital blood parameters associated with the outcome using a logistic regression. The calibration, clinical utility, and discrimination of PLA were determined and compared to the performance of each component of the score alone.

RESULTS

A total of 3334 patients were enrolled. The final PLA score included: lactate, pCO2, and pH. For 2-day mortality, the PLA showed an AUC of 0.941 (95%CI: 0.914-0.967), a better performance in calibration, and a higher net benefit as compared to the other score components alone. For the ICU admission, the PLA only showed a better performance for AUC: 0.75 (95%CI: 0.706-0.794).

CONCLUSIONS

Our results showed that PLA predicts 2-day mortality better than other lactic acidosis components alone. Including PLA score in prehospital setting could improve emergency services decision-making.

Atypical Presentation of Burkitt Lymphoma With Isolated Peritoneal Involvement and Association With Refractory Type B Lactic Acidosis and Hypoglycemia Secondary to the Warburg Effect

Lactic acidosis is considered to be one of the most common causes of high anion gap metabolic acidosis in hospitalized patients. Warburg effect can present with type B lactic acidosis and is considered to be a rare but well-known complication of hematological malignancies. Here, we present the case of a 39-year-old male who had type B lactic acidosis and recurrent hypoglycemia secondary to newly diagnosed Burkitt lymphoma. This case highlights the importance of considering malignancy workup in any case of unexplained type B lactic acidosis with vague clinical presentation, which can aid in early diagnosis and management.

Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering

Background and Objectives: Our study aimed to cluster dual kidney transplant recipients using an unsupervised machine learning approach to characterize donors and recipients better and to compare the survival outcomes across these various clusters. Materials and Methods: We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 2821 dual kidney transplant recipients from 2010 to 2019 in the OPTN/UNOS database. We determined the important characteristics of each assigned cluster and compared the post-transplant outcomes between clusters. Results: Two clinically distinct clusters were identified by consensus cluster analysis. Cluster 1 patients was characterized by younger patients (mean recipient age 49 ± 13 years) who received dual kidney transplant from pediatric (mean donor age 3 ± 8 years) non-expanded criteria deceased donor (100% non-ECD). In contrast, Cluster 2 patients were characterized by older patients (mean recipient age 63 ± 9 years) who received dual kidney transplant from adult (mean donor age 59 ± 11 years) donor with high kidney donor profile index (KDPI) score (59% had KDPI ≥ 85). Cluster 1 had higher patient survival (98.0% vs. 94.6% at 1 year, and 92.1% vs. 76.3% at 5 years), and lower acute rejection (4.2% vs. 6.1% within 1 year), when compared to cluster 2. Death-censored graft survival was comparable between two groups (93.5% vs. 94.9% at 1 year, and 89.2% vs. 84.8% at 5 years). Conclusions: In summary, DKT in the United States remains uncommon. Two clusters, based on specific recipient and donor characteristics, were identified through an unsupervised machine learning approach. Despite varying differences in donor and recipient age between the two clusters, death-censored graft survival was excellent and comparable. Broader utilization of DKT from high KDPI kidneys and pediatric en bloc kidneys should be encouraged to better address the ongoing organ shortage.

An Oncological Emergency: Severe Type B Lactic Acidosis From Warburg Effect in Diffuse Large B-cell Lymphoma

Lactic acidosis is the most common anion gap metabolic acidosis in critically ill patients. Type B lactic acidosis is most commonly seen with hematological malignancies, especially lymphomas. It is considered an oncological emergency and is associated with high mortality and poor outcomes if not treated promptly. Here, we present the case of a 48-year-old male who developed Type B lactic acidosis secondary to newly diagnosed diffuse large B-cell lymphoma. This case highlights the importance of including Type B lactic acidosis in the differential diagnosis in a patient with unexplained lactic acidosis and hypoglycemia with otherwise vague symptoms and the need for a thorough search for quick diagnosis and early management.

ACEI/ARB Medication During ICU Stay Decrease All-Cause In-hospital Mortality in Critically Ill Patients With Hypertension: A Retrospective Cohort Study Based on Machine Learning

Background: Hypertension is a rather common comorbidity among critically ill patients and hospital mortality might be higher among critically ill patients with hypertension (SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg). This study aimed to explore the association between ACEI/ARB medication during ICU stay and all-cause in-hospital mortality in these patients.

Methods: A retrospective cohort study was conducted based on data from Medical Information Mart for Intensive Care IV (MIMIC-IV) database, which consisted of more than 40,000 patients in ICU between 2008 and 2019 at Beth Israel Deaconess Medical Center. Adults diagnosed with hypertension on admission and those had high blood pressure (SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg) during ICU stay were included. The primary outcome was all-cause in-hospital mortality. Patients were divided into ACEI/ARB treated and non-treated group during ICU stay. Propensity score matching (PSM) was used to adjust potential confounders. Nine machine learning models were developed and validated based on 37 clinical and laboratory features of all patients. The model with the best performance was selected based on area under the receiver operating characteristic curve (AUC) followed by 5-fold cross-validation. After hyperparameter optimization using Grid and random hyperparameter search, a final LightGBM model was developed, and Shapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance of each feature. The features closely associated with hospital mortality were presented as significant features.

Results: A total of 15,352 patients were enrolled in this study, among whom 5,193 (33.8%) patients were treated with ACEI/ARB. A significantly lower all-cause in-hospital mortality was observed among patients treated with ACEI/ARB (3.9 vs. 12.7%) as well as a lower 28-day mortality (3.6 vs. 12.2%). The outcome remained consistent after propensity score matching. Among nine machine learning models, the LightGBM model had the highest AUC = 0.9935. The SHAP plot was employed to make the model interpretable based on LightGBM model after hyperparameter optimization, showing that ACEI/ARB use was among the top five significant features, which were associated with hospital mortality.

Conclusions: The use of ACEI/ARB in critically ill patients with hypertension during ICU stay is related to lower all-cause in-hospital mortality, which was independently associated with increased survival in a large and heterogeneous cohort of critically ill hypertensive patients with or without kidney dysfunction.