The obesity paradox in critically ill patients : a causal learning approach to a casual finding

Background While obesity confers an increased risk of death in the general population, numerous studies have reported an association between obesity and improved survival among critically ill patients. This contrary finding has been referred to as the obesity paradox. In this retrospective study, two causal inference approaches were used to address whether the survival of non-obese critically ill patients would have been improved if they had been obese. Methods The study cohort comprised 6557 adult critically ill patients hospitalized at the Intensive Care Unit of the Ghent University Hospital between 2015 and 2017. Obesity was defined as a body mass index of >= 30 kg/m(2). Two causal inference approaches were used to estimate the average effect of obesity in the non-obese (AON): a traditional approach that used regression adjustment for confounding and that assumed missingness completely at random and a robust approach that used machine learning within the targeted maximum likelihood estimation framework along with multiple imputation of missing values under the assumption of missingness at random. 1754 (26.8%) patients were discarded in the traditional approach because of at least one missing value for obesity status or confounders. Results Obesity was present in 18.9% of patients. The in-hospital mortality was 14.6% in non-obese patients and 13.5% in obese patients. The raw marginal risk difference for in-hospital mortality between obese and non-obese patients was - 1.06% (95% confidence interval (CI) - 3.23 to 1.11%,P = 0.337). The traditional approach resulted in an AON of - 2.48% (95% CI - 4.80 to - 0.15%,P = 0.037), whereas the robust approach yielded an AON of - 0.59% (95% CI - 2.77 to 1.60%,P = 0.599). Conclusions A causal inference approach that is robust to residual confounding bias due to model misspecification and selection bias due to missing (at random) data mitigates the obesity paradox observed in critically ill patients, whereas a traditional approach results in even more paradoxical findings. The robust approach does not provide evidence that the survival of non-obese critically ill patients would have been improved if they had been obese

Autonomic care platform for optimizing query performance

Background: As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems. Methods: We extend the COSARA architecture, an infection surveillance and antibiotic management service platform for the Intensive Care Unit (ICU), with self-managed components to increase the performance of data retrievals. We used real-life ICU COSARA queries to analyse slow performance and measure the impact of optimizations. Each day more than 2 million COSARA queries are executed. Three control loops, which monitor the executions and take action, have been proposed: reactive, deliberative and reflective control loops. We focus on improvements of the execution time of microbiology queries directly related to the visual displays of patients' data on the bedside screens. Results: The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU. The application of reactive control loop results in a reduction of 8.61% of the average execution time of microbiology results. The combined application of the reactive and deliberative control loop results in an average query time reduction of 10.92% and the combination of reactive, deliberative and reflective control loops provides a reduction of 13.04%. Conclusions: We found that by controlled reduction of queries' executions the performance for the end-user can be improved. The implementation of autonomic control loops in an existing health platform, COSARA, has a positive effect on the timely data visualization for the physician and nurse

Urinary chitinase 3-like protein 1 for early diagnosis of acute kidney injury : a prospective cohort study in adult critically ill patients

Background: Acute kidney injury (AKI) occurs frequently and adversely affects patient and kidney outcomes, especially when its severity increases from stage 1 to stages 2 or 3. Early interventions may counteract such deterioration, but this requires early detection. Our aim was to evaluate whether the novel renal damage biomarker urinary chitinase 3-like protein 1 (UCHI3L1) can detect AKI stage >= 2 more early than serum creatinine and urine output, using the respective Kidney Disease vertical bar Improving Global Outcomes (KDIGO) criteria for definition and classification of AKI, and compare this to urinary neutrophil gelatinase-associated lipocalin (UNGAL). Methods: This was a translational single-center, prospective cohort study at the 22-bed surgical and 14-bed medical intensive care units (ICU) of Ghent University Hospital. We enrolled 181 severely ill adult patients who did not yet have AKI stage >= 2 based on the KDIGO criteria at time of enrollment. The concentration of creatinine (serum, urine) and CHI3L1 (serum, urine) was measured at least daily, and urine output hourly, in the period from enrollment till ICU discharge with a maximum of 7 ICU-days. The concentration of UNGAL was measured at enrollment. The primary endpoint was the development of AKI stage >= 2 within 12 h after enrollment. Results: After enrollment, 21 (12 %) patients developed AKI stage >= 2 within the next 7 days, with 6 (3 %) of them reaching this condition within the first 12 h. The enrollment concentration of UCHI3L1 predicted the occurrence of AKI stage >= 2 within the next 12 h with a good AUC-ROC of 0.792 (95 % CI: 0.726-0.849). This performance was similar to that of UNGAL (AUC-ROC of 0.748 (95 % CI: 0.678-0.810)). Also, the samples collected in the 24-h time frame preceding diagnosis of the 1st episode of AKI stage >= 2 had a 2.0 times higher (95 % CI: 1.3-3.1) estimated marginal mean of UCHI3L1 than controls. We further found that increasing UCHI3L1 concentrations were associated with increasing AKI severity. Conclusions: In this pilot study we found that UCHI3L1 was a good biomarker for prediction of AKI stage >= 2 in adult ICU patients

Antenatal corticosteroids-to-birth interval in preterm birth

Objectives: The purpose of this study was to compare short-term outcomes in children born between 24 and 34 weeks’ gestation, according to observed antenatal corticosteroids (ACS)-to-birth intervals. Research question: ‘Is there a difference in short-term outcomes between observed ACS-to-birth intervals across a range of gestational ages at birth?’ Methods: Cohort study assessing differences in incidence of short-term neonatal outcomes according to the observed interval between the last administration of ACS and birth. Linear, non-weighted GEE models with an independence working correlation structure were fitted to infant level data providing valid point estimates for either incidence or rate differences (binary outcomes) or average differences (continuous outcomes). Results: Of 886 children, 35.9% were born within 2 days after the last administration of ACS, 32.2% within 2 to 7 days, 14.1% within 8 to 14 days, and 17.8% more than 14 days after. Across gestational ages at birth, there were no differences in birth weight between children born at an ACS-to-birth interval of 7 days or less compared to more than 7 days, nor were there differences in respiratory outcomes, cerebral outcomes, or composite outcome. Conclusion: Drawing conclusions on the importance of the ACS-to-birth interval is difficult due to the post-hoc nature of the variable. In the absence of tools to better estimate if and when PTB will occur, it might not have any value in daily practice, regardless of whether there is an optimal ACS-to-birth interval or not

Epidemiology, causes, evolution and outcome in a single-center cohort of 1116 critically ill patients with hypoxic hepatitis

Background: Hypoxic hepatitis (HH) is a type of acute hepatic injury that is histologically characterized by centrilobular liver cell necrosis and that is caused by insufficient oxygen delivery to the hepatocytes. Typical for HH is the sudden and significant increase of aspartate aminotransferase (AST) in response to cardiac, circulatory or respiratory failure. The aim of this study is to investigate its epidemiology, causes, evolution and outcome. Methods: The screened population consisted of all adults admitted to the intensive care unit (ICU) at the Ghent University Hospital between January 1, 2007 and September 21, 2015. HH was defined as peak AST > 5 times the upper limit of normal (ULN) after exclusion of other causes of liver injury. Thirty-five variables were retrospectively collected and used in descriptive analysis, time series plots and Kaplan-Meier survival curves with multi-group log-rank tests. Results: HH was observed in 4.0% of the ICU admissions at our center. The study cohort comprised 1116 patients. Causes of HH were cardiac failure (49.1%), septic shock (29.8%), hypovolemic shock (9.4%), acute respiratory failure (6.4%), acute on chronic respiratory failure (3.3%), pulmonary embolism (1.4%) and hyperthermia (0.5%). The 28-day mortality associated with HH was 45.0%. Mortality rates differed significantly (P = 0.007) among the causes, ranging from 33.3% in the hyperthermia subgroup to 52.9 and 56.2% in the septic shock and pulmonary embolism subgroups, respectively. The magnitude of AST increase was also significantly correlated (P 20x ULN, respectively. Conclusion: This study surpasses by far the largest cohort of critically ill patients with HH. HH is more common than previously thought with an ICU incidence of 4.0%, and it is associated with a high all-cause mortality of 45.0% at 28 days. The main causes of HH are cardiac failure and septic shock, which include more than 3/4 of all episodes. Clinicians should search actively for any underlying hemodynamic or respiratory instability even in patients with moderately increased AST levels