Journal of Intensive Care Medicine最新文献

IntroductionIn 2024, a Society of Critical Care Medicine task force updated the pediatric sepsis definition from the presence of suspected or confirmed infection, and a systemic inflammatory response (SIRS) with organ dysfunction, to a novel definition. Our objective is to identify how many patients previously identified as having severe sepsis would continue to meet the new definition.Materials and methodsWe performed a secondary analysis of the Phenotyping Sepsis-Induced Multiple Organ Failure cohort of 401 children with suspected or confirmed infection, two of four SIRS criteria and organ dysfunction enrolled between 2015-2017. We calculated a modified Phoenix Sepsis Criteria Score (mPSC) for participants and compared those with mPSC of greater than or equal to 2 or less than 2 according to the 2024 definition.ResultsOf 401 children, 132 (33%) did not meet mPSC definitions. While children meeting mPSC had more organ dysfunction, the total mortality did not differ. One in 4 children requiring extracorporeal membrane oxygenation and 1 in 4 mortalities did not meet the mPSC definition. In logistic regression models, in the complete cohort, hematologic (OR 4.4, 95% CI: 1.8-10.2, P-value = .001), central nervous system (OR 2.3, 95% CI: 1.0-5.1, P-value = .046) and renal failure (OR: 3.2, 95% CI:1.2-7.9, P-value = .017) predicted mortality; in the mPSC subgroup pulmonary (OR: 3.6, 95% CI:1.3-13.3, P-value = .030) and hematologic failure (OR 5.6, 95% CI: 2.2-14.5, P-value = .0003) were significant predictors. In the mPSC excluded subgroup, only renal failure predicted mortality (OR 9.6, 95% CI 1.1-73.0, P-value = .028).ConclusionsFurther study of the impact of the 2024 data-driven organ dysfunction definition on pediatric sepsis research, patient safety, and clinical benchmarking efforts is warranted.

BackgroundThe incidence of acute kidney injury (AKI) is increased in patients with community-acquired pneumonia (CAP), contributing to poor outcomes in ICUs. Early identification of patients at high risk for AKI is essential for timely intervention. This study aimed to develop a machine learning model for predicting AKI in CAP patients.MethodsPatients with CAP were identified from the MIMIC-IV database using ICD codes. AKI was defined according to the KDIGO criteria. Baseline characteristics, vital signs, laboratory data, comorbidities, and clinical scores were extracted. LASSO regression was applied for feature selection, and eight machine learning models, including logistic regression, k-nearest neighbors, decision tree, random forest, support vector machine, neural network, XGBoost, and LightGBM, were developed. Model performance was evaluated using AUC, sensitivity, specificity, accuracy, recall, F1 score, calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used to interpret the final model. A web-based risk calculator was created for clinical application.ResultsA total of 3213 CAP patients were included, with 2723 (84.8%) developing AKI. XGBoost demonstrated the best performance with an AUC of 0.937 (95% CI: 0.922-0.952), sensitivity of 0.875, specificity of 0.855, accuracy of 0.865 (95% CI: 0.841-0.887), recall of 0.875, and F1 score of 0.866. DCA showed the highest net benefit for XGBoost across various risk thresholds. After recursive feature elimination, a simplified model with seven key variables, including urine output, weight, ventilation, first-day minimum PTT, first-day maximum sodium, first-day minimum heart rate, and first-day maximum temperature, maintained high predictive performance (AUC = 0.925, 95% CI: 0.908-0.941).ConclusionsThe XGBoost model accurately predicted AKI risk in CAP patients, demonstrating robust performance and clinical utility. The web-based calculator offers an accessible tool for individualized risk assessment, supporting early detection and management of AKI in ICUs.

PurposeRight ventricular impairment (RVI) can be alleviated by the initiation of veno-venous extracorporeal membrane oxygenation (V-V ECMO), which enhances gas exchange and allows for less invasive mechanical ventilation. However, the progression of RVI during V-V ECMO remains unclear. This study assesses echocardiographic changes in RVI over a five-day period in twenty acute respiratory distress syndrome (ARDS) patients with V-V ECMO support.Material and MethodsOver a five-day period of V-V ECMO support, we examined echocardiographic markers of RVI, including right and left ventricular end-diastolic area ratio (RVEDA/LVEDA), tricuspid annular plane systolic excursion (TAPSE), tricuspid valve lateral anulus peak systolic velocity (S'), right ventricular fractional area change (FAC), and right ventricular myocardial performance index. Secondary objectives included changes in mechanical power transmitted to the respiratory system, hemodynamics and gas-exchange.ResultsRVEDA/LVEDA ratio remained elevated (0.8 [0.7-0.8] vs 0.7 [0.7-0.9]; p = .986), TAPSE decreased (2.0[1.6-2.5] cm vs 1.7 [1.4-2.2] cm; p = .024) while no changes were observed in S' (16 [13-21] cm/s vs 15 [12-18] cm/s; p = .136) and FAC (38 [27-47] % vs 36 [29-43] %; p = .627). The right ventricular myocardial performance index improved (0.74 [0.45-1.00] vs 0.51 [0.42-0.80]; p = .004). Lung mechanical power was significantly reduced due to a decrease in lung elastic and resistive components.ConclusionsDespite preserved longitudinal function and improved global performance, RVI persisted in severe ARDS patients on V-V ECMO, as indicated by the RVEDA/LVEDA ratio. These findings suggest that mechanisms beyond hypoxemia, hypercapnia and the invasiveness of mechanical ventilation contribute to RVI in these patients.Trial registrationThis trial was registered with the German Clinical Trials Register (DRKS00028584) on March 28, 2022. https://drks.de/search/en/trial/DRKS00028584.

BackgroundPulse oximeters sometimes fail to accurately reflect arterial oxygen saturation (SaO₂), particularly in darker-skinned patients resulting in undiagnosed hypoxemia, potentially delaying recognition and appropriate interventions.Research QuestionWe aimed to evaluate the prevalence and predictors of SpO₂-SaO₂ discrepancies, particularly occult hypoxemia, and to assess their association with clinical outcomes in ICU patients.Study Design and MethodsWe conducted a retrospective cohort analysis using the Blood-gas and Oximetry Linked Dataset (BOLD), analyzing critically ill patients from the eICU-CRD database (2014-2015). Patients with paired SpO₂-SaO₂ measurements within five minutes were included. We identified SpO₂-SaO₂ discrepancies as a difference of >2.99% and defined occult hypoxemia as an arterial partial pressure of oxygen (PaO₂) < 60 mm Hg or SaO₂ < 89% with an SpO₂ > 88%. The primary outcomes included ICU length of stay (LOS), Sequential Organ Failure Assessment (SOFA) score, and in-hospital mortality.ResultsAmong 36,280 ICU patients, 23.6% had SpO₂-SaO₂ discrepancies, and 4.7% had occult hypoxemia. Black patients were overrepresented in both groups, with an adjusted odds ratio (aOR) of 1.35 (95% CI: 1.25-1.47) for discrepancy and 1.22 (95% CI: 1.04-1.47) for occult hypoxemia. Higher BMI, lower pH, elevated creatinine, and higher Charlson Comorbidity Index scores were also significant predictors. Patients with discrepancies had worse clinical outcomes, including increased SOFA scores in the following 24 h (β = 0.31; p < .0001) and higher in-hospital mortality (aOR 1.15; p < .0001). Occult hypoxemia was associated with even worse outcomes, including a longer ICU LOS (IRR 1.12; p < .0001) and significantly increased mortality (aOR 1.73; p < .0001).InterpretationOne in four critically ill patient in our cohort experienced SpO₂-SaO₂ discrepancy which is associated with adverse clinical outcomes. Black race, obesity, and higher comorbidity burden were significant predictors of these discrepancies. Our findings emphasize the need for more rigorous clinician oversight in the use of this technology.

BackgroundThere is limited evidence on the epidemiology and prognostic significance of acid-base disorders in the cardiovascular intensive care unit (CICU). This study examines the association of acid-base status at admission with in-hospital mortality among CICU patients.MethodsWe conducted a retrospective analysis of adults admitted to the Mayo Clinic CICU from 2007-2018 with available blood gas data, utilizing values obtained closest to CICU admission. Arterial pH, serum bicarbonate, base excess, and partial pressure of carbon dioxide (PaCO₂) were examined as predictors of in-hospital mortality. Multivariable logistic regression was used to assess associations, with adjustment for demographics, comorbidities, illness severity, and interventions.ResultsAmong 3229 patients included for analysis, acidemia (pH < 7.35) emerged as the strongest predictor of in-hospital mortality (adjusted odds ratio [aOR] 1.60, 95% confidence interval [CI] 1.29-1.98, P < .003). Metabolic acidosis (HCO3 < 20 mEq/L, aOR 1.55, 95% CI 1.24-1.95, P < .001) and respiratory acidosis (PaCO2 > 45 mm Hg, aOR 1.44, 95% CI 1.14-1.81, P = .002) were associated with higher in-hospital mortality, whereas metabolic and respiratory alkalosis were not. After adjustment, lower pH and more negative base excess were associated with higher in-hospital mortality (both P < .001), whereas HCO3 and PaCO2 were not (P = .053 and P = .051, respectively). Patients with combined metabolic and respiratory acidosis had the highest in-hospital mortality (56.3%).ConclusionsShort-term survival in CICU patients decreases progressively with worse acidemia, especially in the context of combined metabolic and respiratory acidosis. Incorporating metabolic acid-base disorders as key therapeutic targets in randomized cardiogenic shock trials may improve outcomes in this complex population by addressing hemometabolic shock.

BackgroundAdherence to evidence-based care processes and patient outcomes in intensive care units (ICUs) can be influenced by staffing and resource availability. We aimed to evaluate if there is a weekend effect on adherence to evidence-based care processes, and hospitalization outcomes and whether a checklist implementation could mitigate potential differences.MethodsPost hoc analysis of the Checklist for Early Recognition and Treatment of Acute Illness and Injury (CERTAIN) study dataset collected before and after checklist implementation in 34 ICUs across 15 countries (2013-2017). Admission days were classified as 'weekend/holidays' or 'weekdays' according to local work schedules and public holidays. The primary outcome was the omission of 10 evidence-based care processes addressed in the checklist. Mortality and length of stay differences between weekend/holiday and weekday admissions were evaluated as secondary outcomes.Results4256 patients contributed 1141 weekend versus 3501 weekday observation days pre-intervention, and 2014 versus 6507 post-intervention. Pre-intervention, peptic ulcer prophylaxis was omitted more frequently on weekends/holidays than weekdays (adjusted rate ratio [aRR], 0.58 [95%-confidence interval [CI] 0.38-0.88), whereas head-of-bed elevation was omitted more often on weekdays than on weekends/holidays (aRR, 3.17 [1.14-8.86]). Post-intervention, peptic ulcer prophylaxis omission rates became similar (aRR, 1.03 [0.68-1.56], but head-of-bed elevation became omitted more often on weekends than on weekdays (aRR, 0.63 [0.45-0.88]). Post-intervention, oral care was omitted more frequently on weekends/holidays than in weekdays (aRR, 0.63 [0.45-0.9]), and central catheter removal was more frequent on weekdays than in weekends/holidays (aRR, 1.11 [1.02-1.21]). No significant differences in mortality or length of stay were found.ConclusionA weekend effect influenced adherence to some care processes. While checklist implementation improved overall adherence, some disparities diminished, while new ones emerged. Organizational, cultural, and temporal factors should be further studied to optimize care delivery across all times and settings.Clinical Trial Registration NumberNCT01973829.

Although the brain itself lacks nociceptors and cannot directly perceive pain, it can generate chronic pain following injuries such as traumatic brain injury (TBI) or ischemic stroke. This phenomenon arises from disruptions in neural connectivity that distort the interpretation of sensory input. According to Bayes' Rule, the brain combines current sensory input with prior experiences to formulate response predictions. When this process is disrupted by TBI, chronic pain may emerge. This review identified 60 relevant studies through systematic keyword searches, with inclusion based on content relevance following abstract screening. The literature underscores the brain's adaptive processes in interpreting sensory stimuli. Disruptions to this adaptability-such as those caused by neuroinflammation, cytokine activation, or cellular injury-may contribute to persistent pain states. TBI-associated chronic pain is often classified as neuropathic and may arise from peripheral or central nerve damage, inflammation-induced injury, or impaired central processing. Pain resulting from central misinterpretation, as described by Bayesian models, frequently falls outside traditional inflammatory or neuropathic patterns and may not correspond with known dermatomal distributions, complicating diagnosis and treatment.

BackgroundSeptic patients with coronary artery disease (CAD) face elevated mortality risks, potentially exacerbated by glycemic variability (GV). This study aimed to investigate the association between GV and in-hospital and 1-year mortality in septic patients with CAD.MethodsWe conducted a retrospective analysis using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database as the discovery cohort and the Tianjin Health and Medical Database Platform (THMDP) as the validation cohort. Patients with sepsis and CAD who had at least three blood glucose measurements during their ICU stay were included. Glycemic variability was defined as the coefficient of variation of blood glucose levels, categorized into quartiles (Q1-Q4). The primary outcome was in-hospital mortality, with 1-year mortality as a secondary outcome. Cox proportional hazards models were used to assess the association between GV and mortality.ResultsHigher GV was significantly associated with increased in-hospital mortality in both cohorts (MIMIC-IV: n = 2599) adjusted Hazard Ratio (HR) 4.06, 95% CI 1.72-9.58, P = 0.001; THMDP: n = 2,797, adjusted HR 1.56, 95% CI 1.25-1.93, P = 0.001). A pooled two-cohort analysis confirmed a significant association with in-hospital mortality (adjusted HR for Q4 vs Q1: 1.65, 95% CI 1.34-2.03, P = 0.001), while the association with 1-year mortality was weaker (adjusted HR 1.24, 95% CI 0.89-1.73, P = 0.204). Restricted cubic spline (RCS) analyses revealed a nonlinear relationship between GV and in-hospital mortality (P for nonlinearity < 0.001). Kaplan-Meier (KM) survival curves showed reduced survival probability in the highest GV group.ConclusionsHigher GV is independently associated with increased in-hospital mortality among septic patients with CAD, but no significant association was found with 1-year mortality. These findings suggest that stabilizing GV may be a critical area for clinical management and warrants further investigation. Monitoring and managing GV may improve outcomes in this patient population.

BackgroundPost-critical illness cognitive dysfunction (PCICD) is a frequent and debilitating outcome among survivors of critical illness. Although sepsis has been associated with poor cognitive outcomes, its independent contribution remains unclear due to overlapping clinical factors. This study sought to characterize cognitive recovery trajectories over 12 months after intensive care.MethodsIn this single-center prospective cohort study, adult ICU survivors were assessed at 1, 3, 6 and 12 months post-discharge using telephone-administered Mini-Mental State Examination (MMSE) or Montreal Cognitive Assessment (MoCA-Blind). Total scores were standardized within instrument (z-scores). Linear mixed-effects models evaluated change in z-scores over time. Domain-specific analyses examined whether any cognitive domain was disproportionately impaired. Logistic regression estimated odds of cognitive impairment adjusting for time, sepsis status, test type, age, Charlson index, peak SOFA, and benzodiazepine exposure; complete-case analyses were used.ResultsOf 185 eligible patients, 84 (45%) completed at least one cognitive assessment. Standardized scores improved from 1 to 3 months (+0.40 SD; 95% CI 0.02-0.78; p = 0.04) and 6 months (+0.54 SD; 95% CI 0.10-0.98; p = 0.02), with a similar but non-significant rise by 12 months (+0.49 SD; 95% CI -0.05 to 0.95; p = 0.10). Adjusted odds of impairment declined at 6 (OR 0.25, 95% CI 0.12-0.55) and 12 months (OR 0.34, 95% CI 0.14-0.85) versus 1 month; the 3-month reduction did not reach significance (OR 0.48, 95% CI 0.23-1.04). Sepsis was not associated with impairment (OR 1.49, 95% CI 0.63-3.56). No single cognitive domain showed a significant longitudinal slope.ConclusionsICU survivors show measurable cognitive recovery over the first year-most prominently by 3-6 months-with reduced odds of impairment by 6 and 12 months. Sepsis did not independently alter recovery. These findings support early post-ICU cognitive follow-up and rehabilitation within the first six months after discharge.