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.

The population-attributable fraction for time-to-event data.

BACKGROUND: Even though the population-attributable fraction (PAF) is a well-established metric, it is often incorrectly estimated or interpreted not only in clinical application, but also in statistical research articles. The risk of bias is especially high in more complex time-to-event data settings. METHODS: We explain how the PAF can be defined, identified and estimated in time-to-event settings with competing risks and time-dependent exposures. By using multi-state methodology and inverse probability weighting, we demonstrate how to reduce or completely avoid severe types of biases including competing risks bias, immortal time bias and confounding due to both baseline and time-varying patient characteristics. RESULTS: The method is exemplarily applied to a real data set. Moreover, we estimate the number of deaths that were attributable to ventilator-associated pneumonia in France in the year 2016. The example demonstrates how, under certain simplifying assumptions, PAF estimates can be extrapolated to a target population of interest. CONCLUSIONS: Defining and estimating the PAF in advanced time-to-event settings within a framework that unifies causal and multi-state modelling enables to tackle common sources of bias and allows straightforward implementation with standard software packages.

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.

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.

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 importance of the urinary output criterion for the detection and prognostic meaning of AKI.

Most reports on AKI claim to use KDIGO guidelines but fail to include the urinary output (UO) criterion in their definition of AKI. We postulated that ignoring UO alters the incidence of AKI, may delay diagnosis of AKI, and leads to underestimation of the association between AKI and ICU mortality. Using routinely collected data of adult patients admitted to an intensive care unit (ICU), we retrospectively classified patients according to whether and when they would be diagnosed with KDIGO AKI stage ≥ 2 based on baseline serum creatinine (Screa) and/or urinary output (UO) criterion. As outcomes, we assessed incidence of AKI and association with ICU mortality. In 13,403 ICU admissions (62.2% male, 60.8 ± 16.8 years, SOFA 7.0 ± 4.1), incidence of KDIGO AKI stage ≥ 2 was 13.2% when based only the SCrea criterion, 34.3% when based only the UO criterion, and 38.7% when based on both criteria. By ignoring the UO criterion, 66% of AKI cases were missed and 13% had a delayed diagnosis. The cause-specific hazard ratios of ICU mortality associated with KDIGO AKI stage ≥ 2 diagnosis based on only the SCrea criterion, only the UO criterion and based on both criteria were 2.11 (95% CI 1.85-2.42), 3.21 (2.79-3.69) and 2.85 (95% CI 2.43-3.34), respectively. Ignoring UO in the diagnosis of KDIGO AKI stage ≥ 2 decreases sensitivity, may lead to delayed diagnosis and results in underestimation of KDIGO AKI stage ≥ 2 associated mortality.

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.

Attributable Mortality of Ventilator-associated Pneumonia. Replicating Findings, Revisiting Methods.

Rationale: Estimating the impact of ventilator-associated pneumonia (VAP) from routinely collected intensive care unit (ICU) data is methodologically challenging.Objectives: We aim to replicate earlier findings of limited VAP-attributable ICU mortality in an independent cohort. By refining statistical analyses, we gradually tackle different sources of bias.Methods: Records of 2,720 adult patients admitted to Ghent University Hospital ICUs (2013-2017) and receiving mechanical ventilation within 48 hours after admission were extracted from linked Intensive Care Information System and Computer-based Surveillance and Alerting of Nosocomial Infections, Antimicrobial Resistance, and Antibiotic Consumption in the ICU databases. The VAP-attributable fraction of ICU mortality was estimated using a competing risk analysis that is restricted to VAP-free patients (approach 1), accounts for VAP onset by treating it as either a competing (approach 2) or censoring event (approach 3), or additionally adjusts for time-dependent confounding via inverse probability weighting (approach 4).Results: A total of 210 patients (7.7%) acquired VAP. Based on benchmark approach 4, we estimated that (compared with current preventive measures) hypothetical eradication of VAP would lead to a relative ICU mortality reduction of 1.7% (95% confidence interval, -1.3 to 4.6) by Day 10 and of 3.6% (95% confidence interval, 0.7 to 6.5) by Day 60. Approaches 1-3 produced estimates ranging from -0.7% to 2.5% by Day 10 and from 5.2% to 5.5% by Day 60.Conclusions: In line with previous studies using appropriate methodology, we found limited VAP-attributable ICU mortality given current state-of-the-art VAP prevention measures. Our study illustrates that inappropriate accounting of the time dependency of exposure and confounding of its effects may misleadingly suggest protective effects of early-onset VAP and systematically overestimate attributable mortality.

Impact of perceived inappropiate cardiopulmonary resuscitation on emergency clinicians' intention to leave the job: Results from a cross-sectional survey in 288 centres across 24 countries.

INTRODUCTION: Cardiopulmonary resuscitation (CPR) in patients with a poor prognosis increases the risk of perception of inappropriate care leading to moral distress in clinicians. We evaluated whether perception of inappropriate CPR is associated with intention to leave the job among emergency clinicians. METHODS: A cross-sectional multi-centre survey was conducted in 24 countries. Factors associated with intention to leave the job were analysed by conditional logistic regression models. Results are expressed as odds ratios with 95% confidence intervals. RESULTS: Of 5099 surveyed emergency clinicians, 1836 (36.0%) were physicians, 1313 (25.7%) nurses, 1950 (38.2%) emergency medical technicians. Intention to leave the job was expressed by 1721 (33.8%) clinicians, 3403 (66.7%) often wondered about the appropriateness of a resuscitation attempt, 2955 (58.0%) reported moral distress caused by inappropriate CPR. After adjustment for other covariates, the risk of intention to leave the job was higher in clinicians often wondering about the appropriateness of a resuscitation attempt (1.43 [1.23-1.67]), experiencing associated moral distress (1.44 [1.24-1.66]) and who were between 30-44 years old (1.53 [1.21-1.92] compared to <30 years). The risk was lower when the clinician felt valued by the team (0.53 [0.42-0.66]), when the team leader acknowledged the efforts delivered by the team (0.61 [0.49-0.75]) and in teams that took time for debriefing (0.70 [0.60-0.80]). CONCLUSION: Resuscitation attempts perceived as inappropriate by clinicians, and the accompanying moral distress, were associated with an increased likelihood of intention to leave the job. Interprofessional collaboration, teamwork, and regular interdisciplinary debriefing were associated with a lower risk of intention to leave the job. ClinicalTrials.gov; No.: NCT02356029.

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.

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.

Antimicrobial de-escalation in the critically ill patient and assessment of clinical cure: the DIANA study.

PURPOSE: The DIANA study aimed to evaluate how often antimicrobial de-escalation (ADE) of empirical treatment is performed in the intensive care unit (ICU) and to estimate the effect of ADE on clinical cure on day 7 following treatment initiation. METHODS: Adult ICU patients receiving empirical antimicrobial therapy for bacterial infection were studied in a prospective observational study from October 2016 until May 2018. ADE was defined as (1) discontinuation of an antimicrobial in case of empirical combination therapy or (2) replacement of an antimicrobial with the intention to narrow the antimicrobial spectrum, within the first 3 days of therapy. Inverse probability (IP) weighting was used to account for time-varying confounding when estimating the effect of ADE on clinical cure. RESULTS: Overall, 1495 patients from 152 ICUs in 28 countries were studied. Combination therapy was prescribed in 50%, and carbapenems were prescribed in 26% of patients. Empirical therapy underwent ADE, no change and change other than ADE within the first 3 days in 16%, 63% and 22%, respectively. Unadjusted mortality at day 28 was 15.8% in the ADE cohort and 19.4% in patients with no change [p = 0.27; RR 0.83 (95% CI 0.60-1.14)]. The IP-weighted relative risk estimate for clinical cure comparing ADE with no-ADE patients (no change or change other than ADE) was 1.37 (95% CI 1.14-1.64). CONCLUSION: ADE was infrequently applied in critically ill-infected patients. The observational effect estimate on clinical cure suggested no deleterious impact of ADE compared to no-ADE. However, residual confounding is likely.

Mechanisms of life-course socioeconomic inequalities in adult systemic inflammation: Findings from two cohort studies.

Disadvantaged socioeconomic conditions in childhood heighten systemic inflammatory levels in adulthood; however, life-course mechanisms underlying this association are largely unknown. In the present observational study, we investigated the roles of adulthood socioeconomic and lifestyle factors in mediating this association. Participants were from two prospective Swiss population-based cohorts (N = 5,152, mean age 60 years). We estimated the total effect of paternal occupational position on adult heightened systemic inflammatory levels (C-reactive protein>3 mg/L), and the indirect effects via adulthood socioeconomic positions (SEPs: education and occupational position), financial hardship, and lifestyle factors (body mass index, smoking status, physical inactivity, and alcohol consumption). We estimated odds ratio (OR) and proportion mediated using counterfactual-based mediation models. Individuals whose father had a low occupational position had an OR of 1.51 [95% confidence interval (CI): 1.25, 1.84] for heightened inflammation compared to their more advantaged counterparts. This was jointly mediated (33 [95% CI: 14, 69]%) by adulthood SEPs, whereby the pathway through education followed by occupational position mediated 30 [95% CI: 11, 64]%, while the pathway via occupational position only mediated 3 [95% CI: 4, 13]%. Individuals with the lowest life-course SEPs had an OR of 2.27 [95% CI: 1.71, 2.98] for heightened inflammation compared to having the highest life-course SEPs. This was jointly mediated (63 [95% CI: 44, 97]%) by financial hardship and lifestyle factors. Our study supports a cumulative effect of life-course SEPs on adult heightened systemic inflammation along the pathway paternal occupational position -> education -> adult occupational position. Financial hardship and lifestyle factors in adulthood mediate half of that effect.

Mediation analysis of time-to-event endpoints accounting for repeatedly measured mediators subject to time-varying confounding.

In this article, we will present statistical methods to assess to what extent the effect of a randomised treatment (versus control) on a time-to-event endpoint might be explained by the effect of treatment on a mediator of interest, a variable that is measured longitudinally at planned visits throughout the trial. In particular, we will show how to identify and infer the path-specific effect of treatment on the event time via the repeatedly measured mediator levels. The considered proposal addresses complications due to patients dying before the mediator is assessed, due to the mediator being repeatedly measured, and due to posttreatment confounding of the effect of the mediator by other mediators. We illustrate the method by an application to data from the LEADER cardiovascular outcomes trial.

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.

Perception of inappropriate cardiopulmonary resuscitation by clinicians working in emergency departments and ambulance services: The REAPPROPRIATE international, multi-centre, cross sectional survey.

INTRODUCTION: Cardiopulmonary resuscitation (CPR) is often started irrespective of comorbidity or cause of arrest. We aimed to determine the prevalence of perception of inappropriate CPR of the last cardiac arrest encountered by clinicians working in emergency departments and out-of-hospital, factors associated with perception, and its relation to patient outcome. METHODS: A cross-sectional survey was conducted in 288 centres in 24 countries. Factors associated with perception of CPR and outcome were analyzed by Cochran-Mantel-Haenszel tests and conditional logistic models. RESULTS: Of the 4018 participating clinicians, 3150 (78.4%) perceived their last CPR attempt as appropriate, 548 (13.6%) were uncertain about its appropriateness and 320 (8.0%) perceived inappropriateness; survival to hospital discharge was 370/2412 (15.3%), 8/481 (1.7%) and 8/294 (2.7%) respectively. After adjusting for country, team and clinician's characteristics, the prevalence of perception of inappropriate CPR was higher for a non-shockable initial rhythm (OR 3.76 [2.13-6.64]; P < .0001), a non-witnessed arrest (2.68 [1.89-3.79]; P < .0001), in older patients (2.94 [2.18-3.96]; P < .0001, for patients >79 years) and in case of a "poor" first physical impression of the patient (3.45 [2.36-5.05]; P < .0001). In accordance, non-shockable and non-witnessed arrests were both associated with lower survival to hospital discharge (0.33 [0.26-0.41]; P < 0.0001 and 0.25 [0.15-0.41]; P < 0.0001, respectively), as were older patient age (0.25 [0.14-0.44]; P < 0.0001 for patients >79 years) and a "poor" first physical impression (0.26 [0.19-0.35]; P < 0.0001). CONCLUSIONS: The perception of inappropriate CPR increased when objective indicators of poor prognosis were present and was associated with a low survival to hospital discharge. Factoring clinical judgment into the decision to (not) attempt CPR may reduce harm inflicted by excessive resuscitation attempts.