Dealing with time-dependent exposures and confounding when defining and estimating attributable fractions-Revisiting estimands and estimators.

The population-attributable fraction (PAF) is commonly interpreted as the proportion of events that can be ascribed to a certain exposure in a certain population. Its estimation is sensitive to common forms of time-dependent bias in the face of a time-dependent exposure. Predominant estimation approaches based on multistate modeling fail to fully eliminate such bias and, as a result, do not permit a causal interpretation, even in the absence of confounding. While recently proposed multistate modeling approaches can successfully eliminate residual time-dependent bias, and moreover succeed to adjust for time-dependent confounding by means of inverse probability of censoring weighting, inadequate application, and misinterpretation prevails in the medical literature. In this paper, we therefore revisit recent work on previously proposed PAF estimands and estimators in settings with time-dependent exposures and competing events and extend this work in several ways. First, we critically revisit the interpretation and applied terminology of these estimands. Second, we further formalize the assumptions under which a causally interpretable PAF estimand can be identified and provide analogous weighting-based representations of the identifying functionals of other proposed estimands. This representation aims to enhance the applied statistician's understanding of different sources of bias that may arise when the aim is to obtain a valid estimate of a causally interpretable PAF. To illustrate and compare these representations, we present a real-life application to observational data from the Ghent University Hospital ICUs to estimate the fraction of ICU deaths attributable to hospital-acquired infections.

On optimal timing of antenatal corticosteroids: time to reformulate the question.

Administration of antenatal corticosteroids (ACS) for accelerating foetal lung maturation in threatened preterm birth is one of the cornerstones of prevention of neonatal mortality and morbidity. To identify the optimal timing of ACS administration, most studies have compared subgroups based on treatment-to-delivery intervals. Such subgroup analysis of the first placebo-controlled randomised controlled trial indicated that a one to seven day interval between ACS administration and birth resulted in the lowest rates of neonatal respiratory distress syndrome. This efficacy window was largely confirmed by a series of subgroup analyses of subsequent trials and observational studies and strongly influenced obstetric management. However, these subgroup analyses suffer from a methodological flaw that often seems to be overlooked and potentially has important consequences for drawing valid conclusions. In this commentary, we point out that studies comparing treatment outcomes between subgroups that are retrospectively identified at birth (i.e. after randomisation) may not only be plagued by post-randomisation confounding bias but, more importantly, may not adequately inform decision making before birth, when the projected duration of the interval is still unknown. We suggest two more formal interpretations of these subgroup analyses, using a counterfactual framework for causal inference, and demonstrate that each of these interpretations can be linked to a different hypothetical trial. However, given the infeasibility of these trials, we argue that none of these rescue interpretations are helpful for clinical decision making. As a result, guidelines based on these subgroup analyses may have led to suboptimal clinical practice. As an alternative to these flawed subgroup analyses, we suggest a more principled approach that clearly formulates the question about optimal timing of ACS treatment in terms of the protocol of a future randomised study. Even if this 'target trial' would never be conducted, its protocol may still provide important guidance to avoid repeating common design flaws when conducting observational 'real world' studies using statistical methods for causal inference.

On estimation and cross-validation of dynamic treatment regimes with competing risks.

The optimal moment to start renal replacement therapy in a patient with acute kidney injury (AKI) remains a challenging problem in intensive care nephrology. Multiple randomized controlled trials have tried to answer this question, but these contrast only a limited number of treatment initiation strategies. In view of this, we use routinely collected observational data from the Ghent University Hospital intensive care units (ICUs) to investigate different prespecified timing strategies for renal replacement therapy initiation based on time-updated levels of serum potassium, pH, and fluid balance in critically ill patients with AKI with the aim to minimize 30-day ICU mortality. For this purpose, we apply statistical techniques for evaluating the impact of specific dynamic treatment regimes in the presence of ICU discharge as a competing event. We discuss two approaches, a nonparametric one - using an inverse probability weighted Aalen-Johansen estimator - and a semiparametric one - using dynamic-regime marginal structural models. Furthermore, we suggest an easy to implement cross-validation technique to assess the out-of-sample performance of the optimal dynamic treatment regime. Our work illustrates the potential of data-driven medical decision support based on routinely collected observational data.

Timing of dialysis in acute kidney injury using routinely collected data and dynamic treatment regimes.

BACKGROUND AND OBJECTIVES: Defining the optimal moment to start renal replacement therapy (RRT) in acute kidney injury (AKI) remains challenging. Multiple randomized controlled trials (RCTs) addressed this question whilst using absolute criteria such as pH or serum potassium. However, there is a need for identification of the most optimal cut-offs of these criteria. We conducted a causal analysis on routinely collected data (RCD) to compare the impact of different pre-specified dynamic treatment regimes (DTRs) for RRT initiation based on time-updated levels of potassium, pH, and urinary output on 30-day ICU mortality. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: Patients in the ICU of Ghent University Hospital were included at the time they met KDIGO-AKI-stage ≥ 2. We applied inverse-probability-of-censoring-weighted Aalen-Johansen estimators to evaluate 30-day survival under 81 DTRs prescribing RRT initiation under different thresholds of potassium, pH, or persisting oliguria. RESULTS: Out of 13,403 eligible patients (60.8 ± 16.8 years, SOFA 7.0 ± 4.1), 5622 (63.4 ± 15.3 years, SOFA 8.2 ± 4.2) met KDIGO-AKI-stage ≥ 2. The DTR that delayed RRT until potassium ≥ 7 mmol/l, persisting oliguria for 24-36 h, and/or pH < 7.0 (non-oliguric) or < 7.2 (oliguric) despite maximal conservative treatment resulted in a reduced 30-day ICU mortality (from 12.7% [95% CI 11.9-13.6%] under current standard of care to 10.5% [95% CI 9.5-11.7%]; risk difference 2.2% [95% CI 1.3-3.8%]) with no increase in patients starting RRT (from 471 [95% CI 430-511] to 475 [95% CI 342-572]). The fivefold cross-validation benchmark for the optimal DTR resulted in 30-day ICU mortality of 10.7%. CONCLUSIONS: Our causal analysis of RCD to compare RRT initiation at different thresholds of refractory low pH, high potassium, and persisting oliguria identified a DTR that resulted in a decrease in 30-day ICU mortality without increase in number of RRTs. Our results suggest that the current criteria to start RRT as implemented in most RCTs may be suboptimal. However, as our analysis is hypothesis generating, this optimal DTR should ideally be validated in a multicentric RCT.

Neonatal magnesium levels are safe after maternal MgSO4 administration: a comparison between unexposed preterm neonates and neonates exposed for fetal neuroprotection or maternal eclampsia prevention-a cohort study.

To objective of this study was to compare neonatal magnesemia in the first 15 days of neonatal life between three groups: a control group not exposed to MgSO4, a neuroprotection group, and an eclampsia prevention group, and to explore its associations with child outcomes. A retrospective single-centre cohort study was performed in a tertiary care setting. Infants admitted at the neonatal intensive care unit born between 24 and 32 weeks' gestation, regardless of etiology of preterm birth, were included. The mean outcome measure was neonatal magnesemia (mmol/L). Linear mixed regression of neonatal magnesemia on exposure group and day of life was done. Generalised estimating equation models of child outcomes on neonatal magnesemia according to exposure group and day of life were made. The analyses showed that in neonatal magnesemia is significantly higher in the preeclampsia group compared to the control and neuroprotection groups. On the day of birth, this is irrespective of maternal magnesemia (preeclampsia vs control groups), and the maternal total dose or duration of MgSO4 administration (preeclampsia vs neuroprotection group). No differences were found in short-term composite outcome between the three groups. CONCLUSION: We found mean differences in neonatal magnesemia between children not exposed to MgSO4 antenatally, children exposed for fetal neuroprotection, and children exposed for maternal eclampsia prevention. A 4-g loading and 1-g/h maintenance doses, for fetal neuroprotection and eclampsia prevention, appear to be safe on the short term for the neonate. WHAT IS KNOWN: • Magnesium sulphate is a valuable medicine in obstetrics. The main indications are prevention of eclampsia and fetal neuroprotection. The most used dosage is a 4- or 6-g loading dose and a 1- or 2-g per h maintenance dose. It reduces neuromotor disabilities in extreme-to-moderate preterm born children. WHAT IS NEW: • Maternal concentrations are supraphysiological and the maternal total dose can be high. Concentrations in neonates appear to remain in safe ranges. A dosage of 4-g loading and 1 g/h seems safe for the preterm neonate on the short term.

"Many roads lead to Rome and the Artificial Intelligence only shows me one road": an interview study on physician attitudes regarding the implementation of computerised clinical decision support systems.

Research regarding the drivers of acceptance of clinical decision support systems (CDSS) by physicians is still rather limited. The literature that does exist, however, tends to focus on problems regarding the user-friendliness of CDSS. We have performed a thematic analysis of 24 interviews with physicians concerning specific clinical case vignettes, in order to explore their underlying opinions and attitudes regarding the introduction of CDSS in clinical practice, to allow a more in-depth analysis of factors underlying (non-)acceptance of CDSS. We identified three general themes from the results. First, 'the perceived role of the AI', including items referring to the tasks that may properly be assigned to the CDSS according to the respondents. Second, 'the perceived role of the physician', referring to the aspects of clinical practice that were seen as being fundamentally 'human' or non-automatable. Third, 'concerns regarding AI', including items referring to more general issues that were raised by the respondents regarding the introduction of CDSS in general and/or in clinical medicine in particular. Apart from the overall concerns expressed by the respondents regarding user-friendliness, we will explain how our results indicate that our respondents were primarily occupied by distinguishing between parts of their job that should be automated and aspects that should be kept in human hands. We refer to this distinction as 'the division of clinical labor.' This division is not based on knowledge regarding AI or medicine, but rather on which parts of a physician's job were seen by the respondents as being central to who they are as physicians and as human beings. Often the respondents' view that certain core parts of their job ought to be shielded from automation was closely linked to claims concerning the uniqueness of medicine as a domain. Finally, although almost all respondents claimed that they highly value their final responsibility, a closer investigation of this concept suggests that their view of 'final responsibility' was not that demanding after all.

An exploration of expectations and perceptions of practicing physicians on the implementation of computerized clinical decision support systems using a Qsort approach.

BACKGROUND: There is increasing interest in incorporating clinical decision support (CDS) into electronic healthcare records (EHR). Successful implementation of CDS systems depends on acceptance of them by healthcare workers. We used a mix of quantitative and qualitative methods starting from Qsort methodology to explore expectations and perceptions of practicing physicians on the use of CDS incorporated in EHR. METHODS: The study was performed in a large tertiary care academic hospital. We used a mixed approach with a Q-sort based classification of pre-defined reactions to clinical case vignettes combined with a thinking-aloud approach, taking into account COREQ recommendations The open source software of Ken-Q Analysis version 1.0.6. was used for the quantitative analysis, using principal components and a Varimax rotation. For the qualitative analysis, a thematic analysis based on the four main themes was performed based on the audiotapes and field notes. RESULTS: Thirty physicians were interviewed (7 in training, 8 junior staff and 15 senior staff; 16 females). Nearly all respondents were strongly averse towards interruptive messages, especially when these also were obstructive. Obstructive interruption was considered to be acceptable only when it increases safety, is adjustable to user expertise level and/or allows deviations when the end-user explains why a deviation is desirable in the case at issue. Transparency was deemed an essential feature, which seems to boil down to providing sufficient clarification on the factors underlying the recommendations of the CDS, so that these can be compared against the physicians' existing knowledge, beliefs and convictions. CONCLUSION: Avoidance of disruptive workflows and transparency of the underlying decision processes are important points to consider when developing CDS systems incorporated in EHR.

Relevance of the antenatal corticosteroids-to-delivery interval in the prevention of neonatal respiratory distress syndrome through the eyes of causal inference: a review and target trial.

PURPOSE: To critically analyse the literature on the antenatal corticosteroids (ACS)-to-birth interval from a causal point of view and to present a solution to the problem of bias caused by post hoc analysis. METHODS: Due to the post hoc nature of the ACS-to-birth interval, a randomised controlled trial (RCT) of ACS versus placebo is not able to examine the importance of the interval. When an RCT is not feasible, for whatsoever reason, a target trial can be set up and an attempt can be made to answer the causal question of interest using observational data. An attempt was made to set up a target trial which could enable to examine the causal effect of the ACS-to-birth interval on neonatal outcomes. An analysis of current literature on the ACS-to-birth interval was done. RESULTS: The majority of studies aimed to examine the causal effect of the interval, but their study design only permitted to find associations between the interval and neonatal outcomes. Barriers for setting up a target trial are highlighted. CONCLUSION: Evidence on the superiority of any ACS-to-birth interval is lacking and the question can only be addressed causally and become clinically relevant if baseline randomisation to ACS-to-birth intervals is made possible.

The web-based application "QUiPP v.2" for the prediction of preterm birth in symptomatic women is not yet ready for worldwide clinical use: ten reflections on development, validation and use.

PURPOSE: In this correspondence, we highlight general and domain-specific caveats in the development and validation of prediction models. METHODS: Development and use of the "QUiPP" application, a tool for preterm birth prediction which is supported by the United Kingdom National Health Service, is scrutinised and commented on. RESULTS: We highlight and elaborate ten points which may be perceived to be unclear or potentially misleading. CONCLUSION: While the QUiPP application has high potential, it lacks transparency (on certain aspects related to model development) and proper validation. This precludes transportability to settings with other treatment policies and to other countries where the app has been made publicly available.

The concept of justifiable healthcare and how big data can help us to achieve it.

Over the last decades, the face of health care has changed dramatically, with big improvements in what is technically feasible. However, there are indicators that the current approach to evaluating evidence in health care is not holistic and hence in the long run, health care will not be sustainable. New conceptual and normative frameworks for the evaluation of health care need to be developed and investigated. The current paper presents a novel framework of justifiable health care and explores how the use of artificial intelligence and big data can contribute to achieving the goals of this framework.

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/m2. 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.

Clinical information extraction for preterm birth risk prediction.

This paper contributes to the pursuit of leveraging unstructured medical notes to structured clinical decision making. In particular, we present a pipeline for clinical information extraction from medical notes related to preterm birth, and discuss the main challenges as well as its potential for clinical practice. A large collection of medical notes, created by staff during hospitalizations of patients who were at risk of delivering preterm, was gathered and analyzed. Based on an annotated collection of notes, we trained and evaluated information extraction components to discover clinical entities such as symptoms, events, anatomical sites and procedures, as well as attributes linked to these clinical entities. In a retrospective study, we show that these are highly informative for clinical decision support models that are trained to predict whether delivery is likely to occur within specific time windows, in combination with structured information from electronic health records.

Obstetrical characteristics and neonatal outcome according to aetiology of preterm birth: a cohort study.

PURPOSE: Preterm birth (PTB) can be categorised according to aetiology into: spontaneous preterm labour (SPL), preterm prelabour rupture of membranes (PPROM), and iatrogenic (iatro) PTB. Outcomes could differ between these groups, which could be of interest in counselling. We aimed to explore differences between aetiologic groups of PTB in maternal demographics, obstetrical characteristics and management, and neonatal outcomes. METHODS: This is a cohort study (2012-2018) in Ghent University Hospital, Belgium, of deliveries from 24 + 0 to 33 + 6 weeks. We compared perinatal demographics, management, and outcomes between the aetiologic types of PTB. Point and interval estimates for differences between aetiologic types were estimated using a Generalised Estimating Equations approach to handle clustering due to multiple gestations. RESULTS: 813 mothers and 987 neonates were included. Prevalences of different aetiologic types of PTB were similar. Maternal BMI was higher in the iatrogenic group (iatro-SPL: + 1.92 kg/m2, 95% CI 1.02, 2.83; iatro-PPROM: + 2.06 kg/m2, 95% CI 1.15, 2.96). There was an inversed sex ratio (0.82, 95% CI 0.65, 1.03), more growth restriction (iatro-SPL: + 22.60%, 95% CI 17.08, 28.13; iatro-PPROM: + 24.64%, 95% CI 19.44, 29.83), and a higher caesarean section rate in the iatrogenic group (iatro-SPL: + 57.23%, 95% CI 50.32, 64.13, iatro-PPROM: + 56.79%, 95% CI 50.20, 63.38) and more patients received at least one complete course of antenatal corticosteroids (iatro-SPL: + 17.60%, 95% CI 10.60, 24.60, iatro-PPROM: + 10.73%, 95% CI 4.52, 16.94). In all types of PTB, adverse neonatal outcomes had a low prevalence, except for respiratory distress syndrome. A composite of adverse neonatal outcome was more prevalent in the SPL- compared to the PPROM group, and there was less intraventricular haemorrhage in the iatrogenic group. CONCLUSION: Additional to gestational age at birth, the aetiology of PTB is associated with neonatal outcome. More data are needed to enable individualised management and counselling in case of threatened PTB. TRIAL REGISTRATION NUMBER: NCT03405116.

Effect of Selepressin vs Placebo on Ventilator- and Vasopressor-Free Days in Patients With Septic Shock: The SEPSIS-ACT Randomized Clinical Trial.

IMPORTANCE: Norepinephrine, the first-line vasopressor for septic shock, is not always effective and has important catecholaminergic adverse effects. Selepressin, a selective vasopressin V1a receptor agonist, is a noncatecholaminergic vasopressor that may mitigate sepsis-induced vasodilatation, vascular leakage, and edema, with fewer adverse effects. OBJECTIVE: To test whether selepressin improves outcome in septic shock. DESIGN, SETTING, AND PARTICIPANTS: An adaptive phase 2b/3 randomized clinical trial comprising 2 parts that included adult patients (n = 868) with septic shock requiring more than 5 µg/min of norepinephrine. Part 1 used a Bayesian algorithm to adjust randomization probabilities to alternative selepressin dosing regimens and to trigger transition to part 2, which would compare the best-performing regimen with placebo. The trial was conducted between July 2015 and August 2017 in 63 hospitals in Belgium, Denmark, France, the Netherlands, and the United States, and follow-up was completed by May 2018. INTERVENTIONS: Random assignment to 1 of 3 dosing regimens of selepressin (starting infusion rates of 1.7, 2.5, and 3.5 ng/kg/min; n = 585) or to placebo (n = 283), all administered as continuous infusions titrated according to hemodynamic parameters. MAIN OUTCOMES AND MEASURES: Primary end point was ventilator- and vasopressor-free days within 30 days (deaths assigned zero days) of commencing study drug. Key secondary end points were 90-day mortality, kidney replacement therapy-free days, and ICU-free days. RESULTS: Among 868 randomized patients, 828 received study drug (mean age, 66.3 years; 341 [41.2%] women) and comprised the primary analysis cohort, of whom 562 received 1 of 3 selepressin regimens, 266 received placebo, and 817 (98.7%) completed the trial. The trial was stopped for futility at the end of part 1. Median study drug duration was 37.8 hours (IQR, 17.8-72.4). There were no significant differences in the primary end point (ventilator- and vasopressor-free days: 15.0 vs 14.5 in the selepressin and placebo groups; difference, 0.6 [95% CI, -1.3 to 2.4]; P = .30) or key secondary end points (90-day mortality, 40.6% vs 39.4%; difference, 1.1% [95% CI, -6.5% to 8.8%]; P = .77; kidney replacement therapy-free days: 18.5 vs 18.2; difference, 0.3 [95% CI, -2.1 to 2.6]; P = .85; ICU-free days: 12.6 vs 12.2; difference, 0.5 [95% CI, -1.2 to 2.2]; P = .41). Adverse event rates included cardiac arrhythmias (27.9% vs 25.2% of patients), cardiac ischemia (6.6% vs 5.6%), mesenteric ischemia (3.2% vs 2.6%), and peripheral ischemia (2.3% vs 2.3%). CONCLUSIONS AND RELEVANCE: Among patients with septic shock receiving norepinephrine, administration of selepressin, compared with placebo, did not result in improvement in vasopressor- and ventilator-free days within 30 days. Further research would be needed to evaluate the potential role of selepressin for other patient-centered outcomes in septic shock. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02508649.

Simultaneous Inhibition of Tumor Necrosis Factor Receptor 1 and Matrix Metalloproteinase 8 Completely Protects Against Acute Inflammation and Sepsis.

OBJECTIVES: Sepsis causes very high mortality and morbidity rates and remains one of the biggest medical challenges. This study investigates whether plasma levels of both matrix metalloproteinase 8 and tumor necrosis factor receptor 1 are associated with sepsis severity and also investigates the therapeutic applicability of simultaneous inhibition of the two molecules in sepsis. DESIGN: Observational human pilot study-prospective controlled animal study. SETTING: University hospital and research laboratory. SUBJECTS: Sepsis patients and C57BL/6 mice deficient for matrix metalloproteinase 8 and/or tumor necrosis factor receptor 1. INTERVENTION: Plasma and whole blood RNA were collected from 13 sepsis patients for 7 consecutive days and within 24 hours of admission to ICU. Matrix metalloproteinase 8 and tumor necrosis factor receptor 1 plasma and expression levels were determined in these patients. Mice deficient for both matrix metalloproteinase 8 and tumor necrosis factor receptor 1 were generated and subjected to endotoxemia and cecal ligation and puncture. Additionally, a bispecific Nanobody that simultaneously blocks matrix metalloproteinase 8 and tumor necrosis factor receptor 1 was created. MEASUREMENTS AND MAIN RESULTS: Plasma levels of matrix metalloproteinase 8 and tumor necrosis factor receptor 1 were positively correlated with the Sequential Organ Failure Assessment score (r, 0.51 and 0.58) and interleukin 6 levels (r, 0.59 and 0.52) in 13 sepsis patients. Combined elimination of tumor necrosis factor receptor 1 and matrix metalloproteinase 8 in double knockout mice resulted in superior survival in endotoxemia and CLP compared with single knockouts and wild-type mice. Cotreatment with our bispecific Nanobody in CLP resulted in improved survival rates (28% vs 19%) compared with untreated mice. CONCLUSIONS: Inhibition of matrix metalloproteinase 8 and tumor necrosis factor receptor 1 might have therapeutic potential to treat sepsis and proof-of-principle was provided as therapeutics that inhibit both tumor necrosis factor receptor 1 and matrix metalloproteinase 8 are effective in CLP.

Systematic comparison of routine laboratory measurements with in-hospital mortality: ICU-Labome, a large cohort study of critically ill patients.

BACKGROUND: In intensive care unit (ICU) patients, many laboratory measurements can be deranged when compared with the standard reference interval (RI). The assumption that larger derangements are associated with worse outcome may not always be correct. The ICU-Labome study systematically evaluated the univariate association of routine laboratory measurements with outcome. METHODS: We studied the 35 most frequent blood-based measurements in adults admitted ≥6 h to our ICU between 1992 and 2013. Measurements were from the first 14 ICU days and before ICU admission. Various metrics, including variability, were related with hospital survival. ICU- based RIs were derived from measurements obtained at ICU discharge in patients who were not readmitted to the ICU and survived for >1 year. RESULTS: In 49,464 patients (cardiothoracic surgery 43%), we assessed >20·106 measurements. ICU readmissions, in-hospital and 1-year mortality were 13%, 14% and 19%, respectively. On ICU admission, lactate had the strongest relation with hospital mortality. Variability was independently related with hospital mortality in 30 of 35 measurements, and 16 of 35 measurements displayed a U-shaped outcome-relation. Medians of 14 of 35 ICU-based ranges were outside the standard RI. Remarkably, γ-glutamyltransferase (GGT) had a paradoxical relation with hospital mortality in the second ICU week because more abnormal GGT-levels were observed in hospital survivors. CONCLUSIONS: ICU-based RIs for may be more useful than standard RIs in identifying ICU patients at risk. The association of variability with outcome for most of the measurements suggests this is a consequence and not a cause of a worse ICU outcome. Late elevation of GGT may confer protection to ICU patients.

A complete and multifaceted overview of antibiotic use and infection diagnosis in the intensive care unit: results from a prospective four-year registration.

BACKGROUND: Preparing an antibiotic stewardship program requires detailed information on overall antibiotic use, prescription indication and ecology. However, longitudinal data of this kind are scarce. Computerization of the patient chart has offered the potential to collect complete data of high resolution. To gain insight in our global antibiotic use, we aimed to explore antibiotic prescription in our intensive care unit (ICU) from various angles over a prolonged time period. METHODS: We studied all adult patients admitted to Ghent University Hospital ICU from 1 January 2013 until 31 December 2016. Antibiotic prescription data were prospectively merged with diagnostic (suspected focus, severity and probability of infection at the time of prescription, or prophylaxis) and microbiology data by ICU physicians during daily workflow through dedicated software. Definite focus of infection and probability of infection (classified as high/moderate/low) were reassessed by dedicated ICU physicians at patient discharge. RESULTS: During the study period, 8763 patients were admitted and overall antibiotic consumption amounted to 1232 days of therapy (DOT)/1000 patient days. Antibacterial DOT (84% of total DOT) were linked with infection in 80%; the predominant foci were the respiratory tract (49%) and the abdomen (19%). A microbial cause was identified in 56% (3169/5686). Moderate/low probability infections accounted for 42% of antibacterial DOT prescribed for respiratory tract infections; for abdominal infections, this figure was 15%. The median treatment duration of moderate/low probability respiratory infections was 4 days (IQR 3-7). Antifungal DOT (16% of total DOT) were linked with infection in 47% of total antifungal DOT. Antifungal prophylaxis was primarily administered in the surgical ICU (76%), with a median duration of 4 DOT (IQR 2-9). CONCLUSIONS: By prospectively combining antibiotic, microbiology and clinical data we were able to construct a longitudinal, multifaceted dataset on antibiotic use and infection diagnosis. A complete overview of this kind may allow the identification of antibiotic prescription patterns that require future antibiotic stewardship attention.

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 | 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 1(st) 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.