Clinical studies demonstrate that acute renal failure (ARF) is associated with increased mortality, which may be due to pulmonary complications. ARF may affect the lung via increased renal production or impaired clearance of mediators of lung injury, such as proinflammatory cytokines. Bilateral nephrectomy is a method to examine directly the deleterious systemic effects of absent renal clearance in ARF without the confounding effects that are associated with ischemia-reperfusion injury (e.g., ischemic ARF) or systemic toxicity (e.g., cisplatin-induced ARF). This study contrasts the effects of ischemic ARF and bilateral nephrectomy on serum cytokines and lung injury. It demonstrates that the acute absence of kidney function after both ischemic ARF and bilateral nephrectomy is associated with an increase in multiple serum cytokines, including IL-6 and IL-1beta, and that the cytokine profiles were distinct. Lung injury after ischemic ARF and bilateral nephrectomy was similar and was characterized by pulmonary vascular congestion and neutrophil infiltration. For investigation of the role of proinflammatory cytokines in pulmonary injury after ARF, the anti-inflammatory cytokine IL-10 was administered before bilateral nephrectomy. IL-10 treatment improved pulmonary architecture and was associated with a reduction in inflammatory markers, including bronchoalveolar lavage fluid total protein, pulmonary myeloperoxidase activity (a biochemical marker of neutrophils), and the chemokine macrophage inflammatory protein 2. These data demonstrate for the first time that the acute absence of kidney function results in pulmonary injury independent of renal ischemia and highlight the critical role of the kidney in the maintenance of serum cytokine balance and pulmonary homeostasis.

Objectives To assess the compliance of Asian intensive care units and hospitals to the Surviving Sepsis Campaign’s resuscitation and management bundles. Secondary objectives were to evaluate the impact of compliance on mortality and the organisational characteristics of hospitals that were associated with higher compliance. Design Prospective cohort study. Setting 150 intensive care units in 16 Asian countries. Participants 1285 adult patients with severe sepsis admitted to these intensive care units in July 2009. The organisational characteristics of participating centres, the patients’ baseline characteristics, the achievement of targets within the resuscitation and management bundles, and outcome data were recorded. Main outcome measure Compliance with the Surviving Sepsis Campaign’s resuscitation (six hours) and management (24 hours) bundles. Results Hospital mortality was 44.5% (572/1285). Compliance rates for the resuscitation and management bundles were 7.6% (98/1285) and 3.5% (45/1285), respectively. On logistic regression analysis, compliance with the following bundle targets independently predicted decreased mortality: blood cultures (achieved in 803/1285; 62.5%, 95% confidence interval 59.8% to 65.1%), broad spectrum antibiotics (achieved in 821/1285; 63.9%, 61.3% to 66.5%), and central venous pressure (achieved in 345/870; 39.7%, 36.4% to 42.9%). High income countries, university hospitals, intensive care units with an accredited fellowship programme, and surgical intensive care units were more likely to be compliant with the resuscitation bundle. Conclusions While mortality from severe sepsis is high, compliance with resuscitation and management bundles is generally poor in much of Asia. As the centres included in this study might not be fully representative, achievement rates reported might overestimate the true degree of compliance with recommended care and should be interpreted with caution. Achievement of targets for blood cultures, antibiotics, and central venous pressure was independently associated with improved survival.

Objectives Current mortality prediction models used in the intensive care unit (ICU) have a limited role for specific diseases such as influenza, and we aimed to establish an explainable machine learning (ML) model for predicting mortality in critically ill influenza patients using a real-world severe influenza data set. Study design A cross-sectional retrospective multicentre study in Taiwan Setting Eight medical centres in Taiwan. Participants A total of 336 patients requiring ICU-admission for virology-proven influenza at eight hospitals during an influenza epidemic between October 2015 and March 2016. Primary and secondary outcome measures We employed extreme gradient boosting (XGBoost) to establish the prediction model, compared the performance with logistic regression (LR) and random forest (RF), demonstrated the feature importance categorised by clinical domains, and used SHapley Additive exPlanations (SHAP) for visualised interpretation. Results The data set contained 76 features of the 336 patients with severe influenza. The severity was apparently high, as shown by the high Acute Physiology and Chronic Health Evaluation II score (22, 17 to 29) and pneumonia severity index score (118, 88 to 151). XGBoost model (area under the curve (AUC): 0.842; 95% CI 0.749 to 0.928) outperformed RF (AUC: 0.809; 95% CI 0.629 to 0.891) and LR (AUC: 0.701; 95% CI 0.573 to 0.825) for predicting 30-day mortality. To give clinicians an intuitive understanding of feature exploitation, we stratified features by the clinical domain. The cumulative feature importance in the fluid balance domain, ventilation domain, laboratory data domain, demographic and symptom domain, management domain and severity score domain was 0.253, 0.113, 0.177, 0.140, 0.152 and 0.165, respectively. We further used SHAP plots to illustrate associations between features and 30-day mortality in critically ill influenza patients. Conclusions We used a real-world data set and applied an ML approach, mainly XGBoost, to establish a practical and explainable mortality prediction model in critically ill influenza patients.

Rationale: Directly comparative data on sepsis epidemiology and sepsis bundle implementation in countries of differing national wealth remain sparse. Objectives: To evaluate across countries/regions of differing income status in Asia 1) the prevalence, causes, and outcomes of sepsis as a reason for ICU admission and 2) sepsis bundle (antibiotic administration, blood culture, and lactate measurement) compliance and its association with hospital mortality. Methods: A prospective point prevalence study was conducted among 386 adult ICUs from 22 Asian countries/regions. Adult ICU participants admitted for sepsis on four separate days (representing the seasons of 2019) were recruited. Measurements and Main Results: The overall prevalence of sepsis in ICUs was 22.4% (20.9%, 24.5%, and 21.3% in low-income countries/regions [LICs]/lower middle-income countries/regions [LMICs], upper middle-income countries/regions, and high-income countries/regions [HICs], respectively; P < 0.001). Patients were younger and had lower severity of illness in LICs/LMICs. Hospital mortality was 32.6% and marginally significantly higher in LICs/LMICs than HICs on multivariable generalized mixed model analysis (adjusted odds ratio, 1.84; 95% confidence interval, 1.00–3.37; P = 0.049). Sepsis bundle compliance was 21.5% at 1 hour (26.0%, 22.1%, and 16.2% in LICs/LMICs, upper middle-income countries/regions, and HICs, respectively; P < 0.001) and 36.6% at 3 hours (39.3%, 32.8%, and 38.5%, respectively; P = 0.001). Delaying antibiotic administration beyond 3 hours was the only element independently associated with increased mortality (adjusted odds ratio, 2.53; 95% confidence interval, 2.07–3.08; P < 0.001). Conclusions: Sepsis is a common cause of admission to Asian ICUs. Mortality remains high and is higher in LICs/LMICs after controlling for confounders. Sepsis bundle compliance remains low. Delaying antibiotic administration beyond 3 hours from diagnosis is associated with increased mortality. Clinical trial registered with www.ctri.nic.in (CTRI/2019/01/016898).