Daily Ards Research Analysis
Analyzed 11 papers and selected 3 impactful papers.
Summary
Analyzed 11 papers and selected 3 impactful articles.
Selected Articles
1. High Bicarbonate Dialysis With or Without Extracorporeal Carbon Dioxide Removal for pH Control in a Swine Model of Acute Kidney Injury.
In a randomized swine model of anuric AKI with protocolized hypoventilation, very-high bicarbonate CRRT (60 mEq/L) alone achieved pH control and enabled ~50% tidal volume reduction, comparable to CRRT plus low-flow ECCO2R (40 mEq/L). Hemodynamic tolerance was similar between groups, supporting the feasibility of ultralow-Vt ventilation without ECCO2R in this model.
Impact: This study directly compares two extracorporeal strategies to overcome acidemia that limits lung-protective ventilation in ARDS with AKI, suggesting a simpler approach may suffice. It can inform trial design and resource-limited settings.
Clinical Implications: In ARDS with concurrent AKI, CRRT with very-high bicarbonate dialysate could enable ultralow tidal volume ventilation without ECCO2R, potentially simplifying setups where ECCO2R is unavailable. Careful monitoring for hemodynamic and electrolyte effects remains essential.
Key Findings
- Both strategies corrected hypercapnic acidemia and enabled ~50% Vt reduction from baseline within 12 hours.
- No difference at 12 hours between groups for lowest achievable Vt at pH ≥7.2 (p = 0.756).
- Time to vasopressor initiation was similar (HR 0.76, 95% CI 0.21–2.71), and cardiac output was preserved.
Methodological Strengths
- Randomized controlled preclinical design with protocolized ventilation steps
- Clear physiologic endpoints and head-to-head comparison of clinically relevant strategies
Limitations
- Small sample size (n=12) and short 12-hour observation limit generalizability
- Animal model; no assessment of lung injury histology or long-term outcomes
Future Directions: Prospective human studies should test high-bicarbonate CRRT to enable ultralow-Vt ventilation in ARDS with AKI, evaluating safety (hemodynamics, electrolytes) and clinical outcomes versus ECCO2R.
In acute respiratory distress syndrome (ARDS) complicated by acute kidney injury (AKI), severe acidemia may limit implementation of protective ventilation. Continuous renal replacement therapy (CRRT) may improve acid-base control either by increasing dialysate bicarbonate concentration or by combining CRRT with extracorporeal carbon dioxide removal (ECCO 2 R). We compared these strategies in a randomized experimental model. Twelve anesthetized Landrace pigs underwent surgical induction of anuric AKI followed by protocolized hypoventilation with stepwise tidal volume (Vt) reduction. Animals were assigned to CRRT with very-high bicarbonate dialysate (60 mEq/L) alone or CRRT with high bicarbonate dialysate (40 mEq/L) plus low-flow ECCO 2 R. The primary outcomes were time to vasopressor initiation and the lowest Vt achieved while maintaining arterial pH ≥7.2 during a 12 hour protocol. Both strategies corrected hypercapnic acidemia and enabled substantial Vt reduction, approximately 50% from baseline, without differences between groups at 12 hours ( p = 0.756). Time to vasopressor initiation was likewise similar (hazard ratio [HR] = 0.76, 95% confidence interval [CI] = 0.21-2.71). Cardiac output remained preserved despite increasing vasopressor requirements. In this experimental AKI model, very-high bicarbonate CRRT provided short-term pH control comparable to CRRT plus ECCO 2 R, supporting ultralow-Vt ventilation.
2. The sigma-1 receptor agonist fluvoxamine alleviates endotoxin-induced acute lung injury in mice.
In a murine LPS-induced lung injury model, fluvoxamine preserved respiratory mechanics by counteracting reductions in tidal volume, minute ventilation, and flow parameters observed after endotoxin exposure. The protective effects paralleled dexamethasone and depended on sigma-1 receptor signaling, implicating S1R as a therapeutic target.
Impact: The study provides receptor-specific mechanistic evidence that an approved antidepressant can mitigate inflammatory lung injury via S1R. This opens a repurposing avenue with defined biology.
Clinical Implications: S1R agonism could offer a steroid-sparing, targeted anti-inflammatory option for acute lung injury/ARDS pending dose-finding, safety, and efficacy trials in humans.
Key Findings
- LPS decreased tidal volume, minute ventilation, and multiple flow parameters in mice.
- Fluvoxamine counteracted these impairments in wild-type mice, similar to dexamethasone.
- Protective effects were sigma-1 receptor–dependent, supporting a receptor-specific mechanism.
Methodological Strengths
- Use of sigma-1 receptor knockout mice to establish receptor dependence
- Comprehensive assessment of respiratory mechanics with an active comparator (dexamethasone)
Limitations
- Preclinical mouse LPS model may not fully recapitulate human ARDS pathophysiology
- Sample size and dosing/pharmacokinetics details are not specified in the abstract
Future Directions: Define optimal dosing, timing, and safety of S1R agonism in larger animal models and initiate early-phase clinical trials for acute lung injury/ARDS.
INTRODUCTION: Acute lung inflammation has recently gained increasing attention due to the high acute respiratory distress syndrome complications with subsequent fibrosis during the COVID-19 pandemic. Our group identified that the antifibrotic effect of the antidepressant fluvoxamine (FLU) in various organs is meditated via sigma-1 receptor (S1R) agonism. Since the actions of FLU on the inflammatory components have not been elucidated, this study investigated its effects in a mouse model of interstitial pneumonitis. METHODS: Pneumonitis was induced in wild-type (WT) and S1R knockout ( RESULTS: LPS reduced tidal volume, minute ventilation, peak expiratory, inspiratory and mid-tidal expiratory flows. Similarly to the reference compound dexamethasone FLU counteracted all effects in WT, but not in CONCLUSION: Overall, FLU mitigates LPS-induced pulmonary inflammation and functional deterioration primarily via S1R signaling, highlighting a receptor-specific mechanism underlying its protective effects. Thus, targeting S1R may be an effective and safe alternative to other therapeutic approaches, including glucocorticoids to treat inflammatory lung injury.
3. ARDSML
Using MIMIC-IV data from 3,807 adults with SCAP, the authors selected 8 features via LASSO and built multiple ML models; XGBoost achieved the best internal test AUROC (0.9466). A web calculator facilitates bedside risk estimation of SCAP-associated ARDS.
Impact: Provides a parsimonious, high-performing ARDS risk model tailored to SCAP with transparent feature set and internal validation, supporting early identification workflows.
Clinical Implications: The model can triage high-risk SCAP patients for intensified monitoring and early ARDS-preventive strategies, pending external validation and impact evaluation.
Key Findings
- From 80 initial variables, 8 features (Charlson, Lactate, Stroke, Race, anion gap, albumin, Sepsis, ROX) were selected by LASSO.
- XGBoost achieved the highest internal test AUROC (0.9466), outperforming most other ML models.
- A web-based calculator was developed to predict SCAP-associated ARDS at the bedside.
Methodological Strengths
- Large ICU cohort with systematic internal train–test split
- Rigorous feature selection (LASSO) and multi-model benchmarking with calibration and decision analysis
Limitations
- Retrospective single-database study with internal validation only; no external validation
- Potential confounding, dataset shift, and limited interpretability of some ML models
Future Directions: Prospective external validation across diverse hospitals and evaluation of clinical impact when integrating the model into ARDS prevention bundles.
BACKGROUND: Early identification of acute respiratory distress syndrome (ARDS) in severe community-acquired pneumonia (SCAP) are crucial for reducing morbidity and mortality. This study focuses on developing an optimal prediction model based on clinical data and biomarkers to detect the risk of SCAP-associated ARDS in adult ICU patients. METHODS: This investigation, utilizing the MIMIC-IV database, enrolled 3,807 patients with SCAP and randomly allocated them into a training set (n=2,664) for model development and a testing set (n=1,143) as an internal validation cohort to assess the model's predictive performance. The outcome was defined as the incidence of SCAP-associated ARDS. Baseline clinical and laboratory characteristics of the patients were obtained. Selection of characteristic variables was performed using LASSO regression, followed by the construction of ten ML models: LGBM, KNN, CatBoost, SVM, XGBoost, DesicionTree, NB, RF, KNNC, and MLP. The evaluation of model performance is conducted through various indicators such as ROC curves, calibration curve, DCA, accuracy, specificity, recall, presicion and F1 score. RESULTS: Initially, 80 characteristic variables were collected, and then 67 of them were selected for the next analysis. After conducting a univariate regression analysis, 32 variables with P< 0.1 were gathered. Through a multicollinearity analysis(VIF≥10), 3 variables were deleted. The remaining 29 variables were subjected to LASSO regression analysis, and 8 of the most significant characteristic variables (Charlson, Lactate, Stroke, Race, AG, ALB, Sepsis, ROX) were selected to build 10 prediction models using machine learning methods. In the training set, the AUROC of the prediction models were respectively LGBM(AUROC=0.9770), KNN(AUROC=0.9879), CatBoost(AUROC=0.9827), SVM(AUROC=0.8095), XGBoost(AUROC=0.9999), DesicionTree(AUROC=0.7611), NB(AUROC=0.5759), RF(AUROC=0.8068), KNNC(AUROC=0.9879), and MLP(AUROC=0.9661). While in the test set, the AUROC were LGBM(AUROC=0.9355), KNN(AUROC=0.6343), CatBoost(AUROC=0.8745), SVM(AUROC=0.7912), XGBoost(AUROC=0.9466), DesicionTree(AUROC=0.7791), NB(AUROC=0.5765), RF(AUROC=0.8074), KNNC(AUROC=0.6343), and MLP(AUROC=0.7386). A web calculator utilizing 8 crucial variables to predict SCAP-associated ARDS in adult ICU patients for ARDSML CONCLUSION: The prediction model constructed based on 8 characteristic variables selected by LASSO regression and using the XGBoost algorithm has excellent predictive performance in predicting the occurrence of SCAP-associated ARDS in adult ICU patients. This data-driven predictive model will help clinicians to make quick and accurate diagnosis.