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Respiratory parameters and acute kidney injury in acute respiratory distress syndrome: a causal inference study.

ABSTRACT: Background:Assess the respiratory-related parameters associated with subsequent severe acute kidney injury in mechanically ventilated patients with acute respiratory distress syndrome (ARDS). Methods:Retrospective cohort, analyzing a large public database-Multiparameter Intelligent Monitoring in Intensive Care-III. Adult patients with at least 48 h of mechanical ventilation (MV), under volume controlled ventilation and an oxygenation index less than 300 mmHg were included. Results:A total of 1,142 patients had complete data and were included in the final analyses. According to a causal directed acyclic graph (DAG) that included respiratory system compliance (Crs), tidal volume (Vt), driving pressure (?P), plateau pressure (PPlat), PEEP, PaO2 and PaCO2 as possible exposures related to severe AKI, only Crs and PEEP levels had significant causal association with severe acute kidney injury (AKI) (OR 0.90, 95% CI: 0.84-0.94 for each 5-mL/cmH2O reduction in Crs; OR, 1.05 95% CI: 1.03-1.10 for each 1-cmH2O increase of PEEP). Using mediation analysis, we examined whether any mechanical ventilation, blood gas or hemodynamic parameters could explain the effects of Csr on AKI. Only PEEP mediated the significant but small effect (less than 5%) of Csr on severe AKI. The effects of PEEP, in turn, were not mediated by any other evaluated parameter. Several sensitivity analyses with (I) need of renal replacement therapy (RRT) as an alternative outcome and (II) only patients with Vt <8 mL/kg, confirmed our main findings. In trying to validate our DAG assumptions, we confirmed that only ?P was associated with mortality but not with severe AKI. Conclusions:Crs and PEEP are the only respiratory-related variables with a direct causal association in severe AKI. No mechanical ventilator or blood gas parameter mediated the effects of Crs. Approaches reducing Vt and/or ?P in ARDS can have limited effect on renal protection.

SUBMITTER: Leite TT

PROVIDER: S-EPMC6990003 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Respiratory parameters and acute kidney injury in acute respiratory distress syndrome: a causal inference study.

Leite Tacyano Tavares TT Gomes Cícero Abdon Malheiro CAM Valdivia Juan Miguel Cosquillo JMC Libório Alexandre Braga AB

Annals of translational medicine 20191201 23

<h4>Background</h4>Assess the respiratory-related parameters associated with subsequent severe acute kidney injury in mechanically ventilated patients with acute respiratory distress syndrome (ARDS).<h4>Methods</h4>Retrospective cohort, analyzing a large public database-Multiparameter Intelligent Monitoring in Intensive Care-III. Adult patients with at least 48 h of mechanical ventilation (MV), under volume controlled ventilation and an oxygenation index less than 300 mmHg were included.<h4>Resu ...[more]

PMID: 32042758

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Project description:BACKGROUND:Acute kidney injury (AKI) is the most frequent extra-pulmonary organ failure in acute respiratory distress syndrome (ARDS). The objective of this study was to assess the factors associated with the development and severity of AKI in patients with ARDS. METHODS:This is a retrospective cohort study of ARDS patients without acute or chronic kidney disease prior to the onset of ARDS over a 7-year period (2010-2017). AKI and severity of AKI were defined according to the Kidney Disease Improving Global Outcomes 2012 guidelines. RESULTS:Of the 634 ARDS patients, 357 patients met study criteria. A total of 244 (68.3%) patients developed AKI after ARDS onset: 60 (24.6%) had stage I AKI, 66 (27%) had stage II AKI, and 118 (48.4%) had stage III AKI. The median time of AKI onset for stage I AKI was 2 days (interquartile range, 1.5-5.5) while stage II and III AKI was 4 days. On multivariable analysis, factors associated with development of AKI were age [subdistribution hazard ratio (SHR) 1.01, 95% confidence interval (CI) 1.00-1.02], SOFA score (SHR 1.16, 95%CI 1.12-1.21), a history of diabetes mellitus (DM) (SHR 1.42, 95%CI 1.07-1.89), and arterial pH on day 1 of ARDS (SHR per 0.1 units decrease was 1.18, 95%CI 1.05-1.32). In severity of AKI, stage I AKI was associated with age (SHR 1.03, 95%CI 1.01-1.05) and serum bicarbonate on day 1 of ARDS (SHR 1.07, 95%CI 1.02-1.13). Stage II AKI was associated with age (SHR 1.03, 95%CI 1.01-1.05), serum bicarbonate on day 1 (SHR 1.12, 95%CI 1.06-1.18), SOFA score (SHR 1.19, 95%CI 1.10-1.30), history of heart failure (SHR 3.71, 95%CI 1.63-8.46), and peak airway pressure (SHR 1.04, 95%CI 1.00-1.07). Stage III AKI was associated with a higher BMI (SHR 1.02, 95%CI 1.00-1.03), a history of DM (SHR 1.79, 95%CI 1.18-2.72), SOFA score (SHR 1.29, 95%CI 1.22-1.36), and arterial pH on day 1 (SHR per 0.1 units decrease was 1.25, 95%CI 1.05-1.49). CONCLUSIONS:Age, a higher severity of illness, a history of diabetes, and acidosis were associated with development of AKI in ARDS patients. Severity of AKI was further associated with BMI, history of heart failure, and peak airway pressure.

Project description:ObjectivesGiven the complex interrelatedness of fluid overload (FO), creatinine, acute kidney injury (AKI), and clinical outcomes, the association of AKI with poor outcomes in critically ill children may be underestimated due to definitions used. We aimed to disentangle these temporal relationships in a large cohort of children with acute respiratory distress syndrome (ARDS).DesignRetrospective cohort study.SettingQuaternary care PICU.PatientsSeven hundred twenty intubated children with ARDS between 2011 and 2019.InterventionsNone.Measurements and main resultsDaily fluid balance, urine output (UOP), and creatinine for days 1-7 of ARDS were retrospectively abstracted. A subset of patients had angiopoietin 2 (ANGPT2) quantified on days 1, 3, and 7. Patients were classified as AKI by Kidney Disease Improving Global Outcomes (KDIGO) stage 2/3 then grouped by timing of AKI onset (early if days 1-3 of ARDS, late if days 4-7 of ARDS, persistent if both) for comparison of PICU mortality and ventilator-free days (VFDs). A final category of "Cryptic AKI" was used to identify subjects who met KDIGO stage 2/3 criteria only when creatinine was adjusted for FO. Outcomes were compared between those who had Cryptic AKI identified by FO-adjusted creatinine versus those who had no AKI. Conventionally defined AKI occurred in 26% of patients (early 10%, late 3%, persistent 13%). AKI was associated with higher mortality and fewer VFDs, with no differences according to timing of onset. The Cryptic AKI group (6% of those labeled no AKI) had higher mortality and fewer VFDs than patients who did not meet AKI with FO-adjusted creatinine. FO, FO-adjusted creatinine, and ANGPT2 increased 1 day prior to meeting AKI criteria in the late AKI group.ConclusionsAKI was associated with higher mortality and fewer VFDs in pediatric ARDS, irrespective of timing. FO-adjusted creatinine captures a group of patients with Cryptic AKI with outcomes approaching those who meet AKI by traditional criteria. Increases in FO, FO-adjusted creatinine, and ANGPT2 occur prior to meeting conventional AKI criteria.

Project description:BackgroundAcute kidney injury (AKI) can make cases of acute respiratory distress syndrome (ARDS) more complex, and the combination of the two can significantly worsen the prognosis. Our objective is to utilize machine learning (ML) techniques to construct models that can promptly identify the risk of AKI in ARDS patients.MethodWe obtained data regarding ARDS patients from the Medical Information Mart for Intensive Care III (MIMIC-III) and MIMIC-IV databases. Within the MIMIC-III dataset, we developed 11 ML prediction models. By evaluating various metrics, we visualized the importance of its features using Shapley additive explanations (SHAP). We then created a more concise model using fewer variables, and optimized it using hyperparameter optimization (HPO). The model was validated using the MIMIC-IV dataset.ResultA total of 928 ARDS patients without AKI were included in the analysis from the MIMIC-III dataset, and among them, 179 (19.3%) developed AKI after admission to the intensive care unit (ICU). In the MIMIC-IV dataset, there were 653 ARDS patients included in the analysis, and among them, 237 (36.3%) developed AKI. A total of 43 features were used to build the model. Among all models, eXtreme gradient boosting (XGBoost) performed the best. We used the top 10 features to build a compact model with an area under the curve (AUC) of 0.850, which improved to an AUC of 0.865 after the HPO. In extra validation set, XGBoost_HPO achieved an AUC of 0.854. The accuracy, sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV), and F1 score of the XGBoost_HPO model on the test set are 0.865, 0.813, 0.877, 0.578, 0.957 and 0.675, respectively. On extra validation set, they are 0.724, 0.789, 0.688, 0.590, 0.851, and 0.675, respectively.ConclusionML algorithms, especially XGBoost, are reliable for predicting AKI in ARDS patients. The compact model maintains excellent predictive ability, and the web-based calculator improves clinical convenience. This provides valuable guidance in identifying AKI in ARDS, leading to improved patient outcomes.

Project description:UnlabelledPET with (18)F-FDG allows for noninvasive assessment of regional lung metabolism reflective of neutrophilic inflammation. This study aimed at determining during early acute lung injury whether local (18)F-FDG phosphorylation rate and volume of distribution were sensitive to the initial regional inflammatory response and whether they depended on the mechanism of injury: endotoxemia and surfactant depletion.MethodsTwelve sheep underwent homogeneous unilateral surfactant depletion (alveolar lavage) and were mechanically ventilated for 4 h (positive end-expiratory pressure, 10 cm H2O; plateau pressure, 30 cm H2O) while receiving intravenous endotoxin (lipopolysaccharide-positive [LPS+] group; n = 6) or not (lipopolysaccharide-negative group; n = 6). (18)F-FDG PET emission scans were then acquired. (18)F-FDG phosphorylation rate and distribution volume were calculated with a 4-compartment model. Lung tissue expression of inflammatory cytokines was measured using real-time quantitative reverse transcription polymerase chain reaction.Results(18)F-FDG uptake increased in LPS+ (P = 0.012) and in surfactant-depleted sheep (P < 0.001). These increases were topographically heterogeneous, predominantly in dependent lung regions, and without interaction between alveolar lavage and LPS. The increase of (18)F-FDG uptake in the LPS+ group was related both to increases in the (18)F-FDG phosphorylation rate (P < 0.05) and to distribution volume (P < 0.01). (18)F-FDG distribution volume increased with infiltrating neutrophils (P < 0.001) and phosphorylation rate with the regional expression of IL-1β (P = 0.026), IL-8 (P = 0.011), and IL-10 (P = 0.023).ConclusionNoninvasive (18)F-FDG PET-derived parameters represent histologic and gene expression markers of early lung injury. Pulmonary metabolism assessed with (18)F-FDG PET depends on the mechanism of injury and appears to be additive for endotoxemia and surfactant depletion. (18)F-FDG PET may be a valuable imaging biomarker of early lung injury.

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Respiratory parameters and acute kidney injury in acute respiratory distress syndrome: a causal inference study.

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Respiratory parameters and acute kidney injury in acute respiratory distress syndrome: a causal inference study.

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