Image_1_The mode and timing of administrating nutritional treatment of critically ill elderly patients in intensive care units: a multicenter prospective study.TIF

IntroductionCritically ill patients are more susceptible to malnutrition due to their severe illness. Moreover, elderly patients who are critically ill lack specific nutrition recommendations, with nutritional care in the intensive care units (ICUs) deplorable for the elderly. This study aims to investigate nutrition treatment and its correlation to mortality in elderly patients who are critically ill in intensive care units.MethodA multiple-center prospective cohort study was conducted in China from 128 intensive care units (ICUs). A total of 1,238 elderly patients were included in the study from 26 April 2017. We analyzed the nutrition characteristics of elderly patients who are critically ill, including initiated timing, route, ways of enteral nutrition (EN), and feeding complications, including the adverse aspects of feeding, acute gastrointestinal injury (AGI), and feeding interruption. Multivariate logistic regression analysis was used to screen out the impact of nutrition treatment on a 28-day survival prognosis of elderly patients in the ICU.ResultA total of 1,238 patients with a median age of 76 (IQR 70–83) were enrolled in the study. The Sequential Organ Failure (SOFA) median score was 7 (interquartile range: IQR 5–10) and the median Acute Physiology and Chronic Health Evaluation (APACHE) II was 21 (IQR 16–25). The all-cause mortality score was 11.6%. The percentage of nutritional treatment initiated 24 h after ICU admission was 58%, with an EN of 34.2% and a parenteral nutrition (PN) of 16.0% in elderly patients who are critically ill. Patients who had gastrointestinal dysfunction with AGI stage from 2 to 4 were 25.2%. Compared to the survivors’ group, the non-survivors group had a lower ratio of EN delivery (57% vs. 71%; p = 0.015), a higher ratio of post-pyloric feeding (9% vs. 2%; p = 0.027), and higher frequency of feeding interrupt (24% vs. 17%, p = 0.048). Multivariable logistics regression analysis showed that patients above 76 years old with OR (odds ratio) 2.576 (95% CI, 1.127–5.889), respiratory rate > 22 beats/min, and ICU admission for 24 h were independent risk predictors of the 28-day mortality study in elderly patients who are critically ill. Similarly, other independent risk predictors of the 28-day mortality study were those with an OR of 2.385 (95%CI, 1.101–5.168), lactate >1.5 mmol/L, and ICU admission for 24 h, those with an OR of 7.004 (95%CI, 2.395–20.717) and early PN delivery within 24 h of ICU admission, and finally those with an OR of 5.401 (95%CI, 1.175–24.821) with EN delivery as reference.ConclusionThis multi-center prospective study describes clinical characteristics, the mode and timing of nutrition treatment, frequency of AGI, and adverse effects of nutrition in elderly ICU patients. According to this survey, ICU patients with early PN delivery, older age, faster respiratory rate, and higher lactate level may experience poor prognosis.</p

Table_1_Machine learning for early prediction of sepsis-associated acute brain injury.DOCX

BackgroundSepsis-associated encephalopathy (SAE) is defined as diffuse brain dysfunction associated with sepsis and leads to a high mortality rate. We aimed to develop and validate an optimal machine-learning model based on clinical features for early predicting sepsis-associated acute brain injury.MethodsWe analyzed adult patients with sepsis from the Medical Information Mart for Intensive Care (MIMIC III) clinical database. Candidate models were trained using random forest, support vector machine (SVM), decision tree classifier, gradients boosting machine (GBM), multiple layer perception (MLP), extreme gradient boosting (XGBoost), light gradients boosting machine (LGBM) and a conventional logistic regression model. These methods were applied to develop and validate the optimal model based on its accuracy and area under curve (AUC).ResultsIn total, 12,460 patients with sepsis met inclusion criteria, and 6,284 (50.4%) patients suffered from sepsis-associated acute brain injury. Compared other models, the LGBM model achieved the best performance. The AUC for both train set and test set indicated excellent validity (Trainset AUC 0.91, Testset AUC 0.87). Feature importance analysis showed that glucose, age, mean arterial pressure, heart rate, hemoglobin, and length of ICU stay were the top 6 important clinical factors to predict occurrence of sepsis-associated acute brain injury.ConclusionAlmost half of patients admitted to ICU with sepsis had sepsis-associated acute brain injury. The LGBM model better identify patients with sepsis-associated acute brain injury than did other machine-learning models. Glucose, age, and mean arterial pressure were the three most important clinical factors to predict occurrence of sepsis-associated acute brain injury.</p

Epidemiology and Outcome of Severe Sepsis and Septic Shock in Intensive Care Units in Mainland China

<div><p>Introduction</p><p>Information about sepsis in mainland China remains scarce and incomplete. The purpose of this study was to describe the epidemiology and outcome of severe sepsis and septic shock in mixed ICU in mainland China, as well as the independent predictors of mortality.</p><p>Methods</p><p>We performed a 2-month prospective, observational cohort study in 22 closed multi-disciplinary intensive care units (ICUs). All admissions into those ICUs during the study period were screened and patients with severe sepsis or septic shock were included.</p><p>Results</p><p>A total of 484 patients, 37.3 per 100 ICU admissions were diagnosed with severe sepsis (n = 365) or septic shock (n = 119) according to clinical criteria and included into this study. The most frequent sites of infection were the lung and abdomen. The overall ICU and hospital mortality rates were 28.7% (n = 139) and 33.5% (n = 162), respectively. In multivariate analyses, APACHE II score (odds ratio[OR], 1.068; 95% confidential interval[CI], 1.027–1.109), presence of ARDS (OR, 2.676; 95%CI, 1.691–4.235), bloodstream infection (OR, 2.520; 95%CI, 1.142–5.564) and comorbidity of cancer (OR, 2.246; 95%CI, 1.141–4.420) were significantly associated with mortality.</p><p>Conclusions</p><p>Our results indicated that severe sepsis and septic shock were common complications in ICU patients and with high mortality in China, and can be of help to know more about severe sepsis and septic shock in China and to improve characterization and risk stratification in these patients.</p></div

Distribution of microorganisms isolated from 148 patients.

<p>*including Burkholderia cepacia, Chryseobacter iumindologenes, Enterobacter cloacae, Enterobacteraerogenes, and Serratialiquefaciens.</p><p>**fungal infection here refers to the invasive fungal infection and fungemia.</p><p>Distribution of microorganisms isolated from 148 patients.</p

Flow diagram of enrolled patients and their outcome.

<p>ICU, intensive care unit; LOS, length of stay.</p

Characteristics of participating ICUs.

<p>ICU, intensive care unit; IQR, interquartile range.</p><p>Characteristics of participating ICUs.</p

The source of infection of patients with severe sepsis (total >100% because 167 patients had more than one infection sites).

<p>The source of infection of patients with severe sepsis (total >100% because 167 patients had more than one infection sites).</p

Characteristics and outcome of patients with severe sepsis.

<p>APACHE II, Acute Physiology and Chronic Health Evaluation II; ARDS, acute respiratory distress syndrome; COPD, Chronic obstructive pulmonary disease; ICU, Intensive care unit; IQR, interquartile range; SOFA, Sequential Organ Failure Assessment.</p><p>Characteristics and outcome of patients with severe sepsis.</p

Multivariate logistic regression analysis of independent predictors of hospital mortality in patients with severe sepsis and septic shock.

<p>APACHE II, Acute Physiology and Chronic Health Evaluation II; ARDS, Acute respiratory distress syndrome; CI, confidence interval; OR, odds ratio.</p><p>Multivariate logistic regression analysis of independent predictors of hospital mortality in patients with severe sepsis and septic shock.</p