Prediction of ICU Delirium: Validation of Current Delirium Predictive Models in Routine Clinical Practice*

Prediction of ICU Delirium: Validation of Current Delirium Predictive Models in Routine Clinical Practice*

by Green, Cameron; Bonavia, William; Toh, Candice; Tiruvoipati, Ravindranath  

Critical Care Medicine: March 2019 - Volume 47 - Issue 3 - p 428–435

Objectives: To investigate the ability of available delirium risk assessment tools to identify patients at risk of delirium in an Australian tertiary ICU.

Design: Prospective observational study.

Setting: An Australian tertiary ICU. Patients: All patients admitted to the study ICU between May 8, 2017, and December 31, 2017, were assessed bid for delirium throughout their ICU stay using the Confusion Assessment Method for ICU. Patients were included in this study if they remained in ICU for over 24 hours and were excluded if they were delirious on ICU admission, or if they were unable to be assessed using the Confusion Assessment Method for ICU during their ICU stay. Delirium risk was calculated for each patient using the prediction of delirium in ICU patients, early prediction of delirium in ICU patients, and Lanzhou models. Data required for delirium predictor models were obtained retrospectively from patients medical records. Interventions: None.

Measurements and Main Results: There were 803 ICU admissions during the study period, of which 455 met inclusion criteria. 35.2% (n = 160) were Confusion Assessment Method for ICU positive during their ICU admission. Delirious patients had significantly higher Acute Physiology and Chronic Health Evaluation III scores (median, 72 vs 54; p < 0.001), longer ICU (median, 4.8 vs 1.8 d; p < 0.001) and hospital stay (16.0 vs 8.16 d; p < 0.001), greater requirement of invasive mechanical ventilation (70% vs 21.4%; p < 0.001), and increased ICU mortality (6.3% vs 2.4%; p = 0.037). All models included in this study displayed moderate to good discriminative ability. Area under the receiver operating curve for the prediction of delirium in ICU patients was 0.79 (95% CI, 0.75–0.83); recalibrated prediction of delirium in ICU patients was 0.79 (95% CI, 0.75–0.83); early prediction of delirium in ICU patients was 0.72 (95% CI, 0.67–0.77); and the Lanzhou model was 0.77 (95% CI, 0.72–0.81). Conclusions: The predictive models evaluated in this study demonstrated moderate to good discriminative ability to predict ICU patients’ risk of developing delirium. Models calculated at 24-hours post-ICU admission appear to be more accurate but may have limited utility in practice.

ICU Survivors Have a Substantial Higher Risk of Developing New Chronic Conditions Compared to a Population-Based Control Group

ICU Survivors Have a Substantial Higher Risk of Developing New Chronic Conditions Compared to a Population-Based Control Group

 by van Beusekom, Ilse; Bakhshi-Raiez, Ferishta; van der Schaaf, Marike; Busschers, Wim B.; de Keizer, Nicolette F.; Dongelmans, Dave A.  

Critical Care Medicine: March 2019 - Volume 47 - Issue 3 - p 324–330

Objectives: To describe the types and prevalence of chronic conditions in an ICU population and a population-based control group during the year before ICU admission and to quantify the risk of developing new chronic conditions in ICU patients compared with the control group.

Design: We conducted a retrospective cohort study, combining a national health insurance claims database and a national quality registry for ICUs. Claims data in the timeframe 2012–2014 were combined with clinical data of patients who had been admitted to an ICU during 2013. To assess the differences in risk of developing new chronic conditions, ICU patients were compared with a population-based control group using logistic regression modeling.

Setting: Eighty-one Dutch ICUs.

Patients: All patients admitted to an ICU during 2013. A population-based control group was created, and weighted on the age, gender, and socio-economic status of the ICU population. Interventions: None.

Measurements and Main Results: ICU patients (n = 56,760) have more chronic conditions compared with the control group (n = 75,232) during the year before ICU admission (p < 0.0001). After case-mix adjustment ICU patients had a higher risk of developing chronic conditions, with odds ratios ranging from 1.67 (CI, 1.29–2.17) for asthma to 24.35 (CI, 14.00–42.34) for epilepsy, compared with the control group.

Conclusions: Due to the high prevalence of chronic conditions and the increased risk of developing new chronic conditions, ICU follow-up care is advised and may focus on the identification and treatment of the new developed chronic conditions.

Hospital Mechanical Ventilation Volume and Patient Outcomes: Too Much of a Good Thing?

Hospital Mechanical Ventilation Volume and Patient Outcomes: Too Much of a Good Thing?

 by Mehta, Anuj B.; Walkey, Allan J.; Curran-Everett, Douglas; Matlock, Daniel; Douglas, Ivor S.  

Critical Care Medicine: March 2019 - Volume 47 - Issue 3 - p 360–368

Objectives: Prior studies investigating hospital mechanical ventilation volume-outcome associations have had conflicting findings. Volume-outcome relationships within contemporary mechanical ventilation practices are unclear. We sought to determine associations between hospital mechanical ventilation volume and patient outcomes.

Design: Retrospective cohort study.

Setting: The California Patient Discharge Database 2016.

Patients: Adult nonsurgical patients receiving mechanical ventilation. Interventions: The primary outcome was hospital death with secondary outcomes of tracheostomy and 30-day readmission. We used multivariable generalized estimating equations to determine the association between patient outcomes and hospital mechanical ventilation volume quartile.

Measurements and Main Results: We identified 51,689 patients across 274 hospitals who required mechanical ventilation in California in 2016. 38.2% of patients died in the hospital with 4.4% receiving a tracheostomy. Among survivors, 29.5% required readmission within 30 days of discharge. Patients admitted to high versus low volume hospitals had higher odds of death (quartile 4 vs quartile 1 adjusted odds ratio, 1.40; 95% CI, 1.17–1.68) and tracheostomy (quartile 4 vs quartile 1 adjusted odds ratio, 1.58; 95% CI, 1.21–2.06). However, odds of 30-day readmission among survivors was lower at high versus low volume hospitals (quartile 4 vs quartile 1 adjusted odds ratio, 0.77; 95% CI, 0.67–0.89). Higher hospital mechanical ventilation volume was weakly correlated with higher hospital risk-adjusted mortality rates (ρ = 0.16; p = 0.008). These moderately strong observations were supported by multiple sensitivity analyses.

Conclusions: Contrary to previous studies, we observed worse patient outcomes at higher mechanical ventilation volume hospitals. In the setting of increasing use of mechanical ventilation and changes in mechanical ventilation practices, multiple mechanisms of worse outcomes including resource strain are possible. Future studies investigating differences in processes of care between high and low volume hospitals are necessary.

The Fragility and Reliability of Conclusions of Anesthesia and Critical Care Randomized Trials With Statistically Significant Findings: A Systematic Review*

The Fragility and Reliability of Conclusions of Anesthesia and Critical Care Randomized Trials With Statistically Significant Findings: A Systematic Review*

 by Grolleau, François; Collins, Gary S.; Smarandache, Andrei; Pirracchio, Romain; Gakuba, Clément; Boutron, Isabelle; Busse, Jason W.; Devereaux, P. J.; Le Manach, Yannick  

Critical Care Medicine: March 2019 - Volume 47 - Issue 3 - p 456–462

Objectives: The Fragility Index, which represents the number of patients responsible for a statistically significant finding, has been suggested as an aid for interpreting the robustness of results from clinical trials. A small Fragility Index indicates that the statistical significance of a trial depends on only a few events. Our objectives were to calculate the Fragility Index of statistically significant results from randomized controlled trials of anesthesia and critical care interventions and to determine the frequency of distorted presentation of results or “spin”.

Data Sources: We systematically searched MEDLINE from January 01, 2007, to February 22, 2017, to identify randomized controlled trials exploring the effect of critical care medicine or anesthesia interventions.

Study Selection: Studies were included if they randomized patients 1:1 into two parallel arms and reported at least one statistically significant (p < 0.05) binary outcome (primary or secondary). Data Extraction: Two reviewers independently assessed eligibility and extracted data. The Fragility Index was determined for the chosen outcome. We assessed the level of spin in negative trials and the presence of recommendations for clinical practice in positive trials.

Data Synthesis: We identified 166 eligible randomized controlled trials with a median sample size of 207 patients (interquartile range, 109–497). The median Fragility Index was 3 (interquartile range, 1–7), which means that adding three events to one of the trials treatment arms eliminated its statistical significance. High spin was identified in 42% (n = 30) of negative randomized controlled trials, whereas 21% (n = 20) of positive randomized controlled trials provided recommendations. Lower levels of spin and recommendations were associated with publication in journals with high impact factors (p < 0.001 for both).

Conclusions: Statistically significant results in anesthesia and critical care randomized controlled trials are often fragile, and study conclusions are frequently affected by spin. Routine calculation of the Fragility Index in medical literature may allow for better understanding of trials and therefore enhance the quality of reporting.

A Comparison of the Mortality Risk Associated With Ventilator-Acquired Bacterial Pneumonia and Nonventilator ICU-Acquired Bacterial Pneumonia*

A Comparison of the Mortality Risk Associated With Ventilator-Acquired Bacterial Pneumonia and Nonventilator ICU-Acquired Bacterial Pneumonia*

 by Ibn Saied, Wafa; Mourvillier, Bruno; Cohen, Yves; Ruckly, Stephane; Reignier, Jean; Marcotte, Guillaume; Siami, Shidasp; Bouadma, Lila; Darmon, Michael; de Montmollin, Etienne; Argaud, Laurent; Kallel, Hatem; Garrouste-Orgeas, Maité; Soufir, Lilia; Schwebel, Carole; Souweine, Bertrand; Glodgran-Toledano, Dany; Papazian, Laurent; Timsit, Jean-François; on behalf of the OUTCOMEREA Study Group  

Critical Care Medicine: March 2019 - Volume 47 - Issue 3 - p 345–352

Objectives: To investigate the respective impact of ventilator-associated pneumonia and ICU–hospital-acquired pneumonia on the 30-day mortality of ICU patients.

Design: Longitudinal prospective studies.

Setting: French ICUs.

Patients: Patients at risk of ventilator-associated pneumonia and ICU–hospital-acquired pneumonia. Interventions: The first three episodes of ventilator-associated pneumonia or ICU–hospital-acquired pneumonia were handled as time-dependent covariates in Cox models. We adjusted using the case-mix, illness severity, Simplified Acute Physiology Score II score at admission, and procedures and therapeutics used during the first 48 hours before the risk period. Baseline characteristics of patients with regard to the adequacy of antibiotic treatment were analyzed, as well as the Sequential Organ Failure Assessment score variation in the 2 days before the occurrence of ventilator-associated pneumonia or ICU–hospital-acquired pneumonia. Mortality was also analyzed for Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species(ESKAPE) and P. aeruginosa pathogens.

Measurements and Main Results: Of 14,212 patients who were admitted to the ICUs and who stayed for more than 48 hours, 7,735 were at risk of ventilator-associated pneumonia and 9,747 were at risk of ICU–hospital-acquired pneumonia. Ventilator-associated pneumonia and ICU–hospital-acquired pneumonia occurred in 1,161 at-risk patients (15%) and 176 at-risk patients (2%), respectively. When adjusted on prognostic variables, ventilator-associated pneumonia (hazard ratio, 1.38 (1.24–1.52); p < 0.0001) and even more ICU–hospital-acquired pneumonia (hazard ratio, 1.82 [1.35–2.45]; p < 0.0001) were associated with increased 30-day mortality. The early antibiotic therapy adequacy was not associated with an improved prognosis, particularly for ICU–hospital-acquired pneumonia. The impact was similar for ventilator-associated pneumonia and ICU–hospital-acquired pneumonia mortality due to P. aeruginosa and the ESKAPE group.

Conclusions: In a large cohort of patients, we found that both ICU–hospital-acquired pneumonia and ventilator-associated pneumonia were associated with an 82% and a 38% increase in the risk of 30-day mortality, respectively. This study emphasized the importance of preventing ICU–hospital-acquired pneumonia in nonventilated patients.

Sepsis Surveillance Using Adult Sepsis Events Simplified eSOFA Criteria Versus Sepsis-3 Sequential Organ Failure Assessment Criteria*

Sepsis Surveillance Using Adult Sepsis Events Simplified eSOFA Criteria Versus Sepsis-3 Sequential Organ Failure Assessment Criteria*

 by Rhee, Chanu; Zhang, Zilu; Kadri, Sameer S.; Murphy, David J.; Martin, Greg S.; Overton, Elizabeth; Seymour, Christopher W.; Angus, Derek C.; Dantes, Raymund; Epstein, Lauren; Fram, David; Schaaf, Richard; Wang, Rui; Klompas, Michael; for the CDC Prevention Epicenters Program  

Critical Care Medicine: March 2019 - Volume 47 - Issue 3 - p 307–314

Objectives: Sepsis-3 defines organ dysfunction as an increase in the Sequential Organ Failure Assessment score by greater than or equal to 2 points. However, some Sequential Organ Failure Assessment score components are not routinely recorded in all hospitals’ electronic health record systems, limiting its utility for wide-scale sepsis surveillance. The Centers for Disease Control and Prevention recently released the Adult Sepsis Event surveillance definition that includes simplified organ dysfunction criteria optimized for electronic health records (eSOFA). We compared eSOFA versus Sequential Organ Failure Assessment with regard to sepsis prevalence, overlap, and outcomes.

Design: Retrospective cohort study. Setting: One hundred eleven U.S. hospitals in the Cerner HealthFacts dataset.

Patients: Adults hospitalized in 2013-2015.

Interventions: None. Measurements and Main Results: We identified clinical indicators of presumed infection (blood cultures and antibiotics) concurrent with either: 1) an increase in Sequential Organ Failure Assessment score by 2 or more points (Sepsis-3) or 2) 1 or more eSOFA criteria: vasopressor initiation, mechanical ventilation initiation, lactate greater than or equal to 2.0 mmol/L, doubling in creatinine, doubling in bilirubin to greater than or equal to 2.0 mg/dL, or greater than or equal to 50% decrease in platelet count to less than 100 cells/μL (Centers for Disease Control and Prevention Adult Sepsis Event). We compared area under the receiver operating characteristic curves for discriminating in-hospital mortality, adjusting for baseline characteristics. Of 942,360 patients in the cohort, 57,242 (6.1%) had sepsis by Sequential Organ Failure Assessment versus 41,618 (4.4%) by eSOFA. Agreement between sepsis by Sequential Organ Failure Assessment and eSOFA was good (Cronbach’s alpha 0.81). Baseline characteristics and infectious diagnoses were similar, but mortality was higher with eSOFA (17.1%) versus Sequential Organ Failure Assessment (14.4%; p < 0.001) as was discrimination for mortality (area under the receiver operating characteristic curve, 0.774 vs 0.759; p < 0.001). Comparisons were consistent across subgroups of age, infectious diagnoses, and comorbidities.

Conclusions: The Adult Sepsis Event’s eSOFA organ dysfunction criteria identify a smaller, more severely ill sepsis cohort compared with the Sequential Organ Failure Assessment score, but with good overlap and similar clinical characteristics. Adult Sepsis Events may facilitate wide-scale automated sepsis surveillance that tracks closely with the more complex Sepsis-3 criteria.

Improved Guideline Adherence and Reduced Brain Dysfunction After a Multicenter Multifaceted Implementation of ICU Delirium Guidelines in 3,930 Patients

Improved Guideline Adherence and Reduced Brain Dysfunction After a Multicenter Multifaceted Implementation of ICU Delirium Guidelines in 3,930 Patients

by Trogrlic, Zoran; van der Jagt, Mathieu; Lingsma, Hester; Gommers, Diederik; Ponssen, Huibert H.; Schoonderbeek, Jeannette F. J.; Schreiner, Frodo; Verbrugge, Serge J.; Duran, Servet; Bakker, Jan; Ista, Erwin  

Critical Care Medicine: March 2019 - Volume 47 - Issue 3 - p 419–427

Objectives: Implementation of delirium guidelines at ICUs is suboptimal. The aim was to evaluate the impact of a tailored multifaceted implementation program of ICU delirium guidelines on processes of care and clinical outcomes and draw lessons regarding guideline implementation. Design: A prospective multicenter, pre-post, intervention study.

Setting: ICUs in one university hospital and five community hospitals.

Patients: Consecutive medical and surgical critically ill patients were enrolled between April 1, 2012, and February 1, 2015. Interventions: Multifaceted, three-phase (baseline, delirium screening, and guideline) implementation program of delirium guidelines in adult ICUs. Measurements and Main Results: The primary outcome was adherence changes to delirium guidelines recommendations, based on the Pain, Agitation and Delirium guidelines. Secondary outcomes were brain dysfunction (delirium or coma), length of ICU stay, and hospital mortality. A total of 3,930 patients were included. Improvements after the implementation pertained to delirium screening (from 35% to 96%; p < 0.001), use of benzodiazepines for continuous sedation (from 36% to 17%; p < 0.001), light sedation of ventilated patients (from 55% to 61%; p < 0.001), physiotherapy (from 21% to 48%; p < 0.001), and early mobilization (from 10% to 19%; p < 0.001). Brain dysfunction improved: the mean delirium duration decreased from 5.6 to 3.3 days (–2.2 d; 95% CI, –3.2 to –1.3; p < 0.001), and coma days decreased from 14% to 9% (risk ratio, 0.5; 95% CI, 0.4–0.6; p < 0.001). Other clinical outcome measures, such as length of mechanical ventilation, length of ICU stay, and hospital mortality, did not change.

Conclusions: This large pre-post implementation study of delirium-oriented measures based on the 2013 Pain, Agitation, and Delirium guidelines showed improved health professionals’ adherence to delirium guidelines and reduced brain dysfunction. Our findings provide empirical support for the differential efficacy of the guideline bundle elements in a real-life setting and provide lessons for optimization of guideline implementation programs.

Trends Over Time in Drug Administration During Adult In-Hospital Cardiac Arrest*

Trends Over Time in Drug Administration During Adult In-Hospital Cardiac Arrest*

by Moskowitz, Ari; Ross, Catherine E.; Andersen, Lars W.; Grossestreuer, Anne V.; Berg, Katherine M.; Donnino, Michael W.; for the American Heart Association’s Get With The Guidelines – Resuscitation Investigators

Critical Care Medicine: February 2019 - Volume 47 - Issue 2 - p 194–200

Objectives: Clinical providers have access to a number of pharmacologic agents during in-hospital cardiac arrest. Few studies have explored medication administration patterns during in-hospital cardiac arrest. Herein, we examine trends in use of pharmacologic interventions during in-hospital cardiac arrest both over time and with respect to the American Heart Association Advanced Cardiac Life Support guideline updates.

Design: Observational cohort study. Setting: Hospitals contributing data to the American Heart Association Get With The Guidelines–Resuscitation database between 2001 and 2016. Patients: Adult in-hospital cardiac arrest patients.

Interventions: The percentage of patients receiving epinephrine, vasopressin, amiodarone, lidocaine, atropine, bicarbonate, calcium, magnesium, and dextrose each year were calculated in patients with shockable and nonshockable initial rhythms. Hierarchical multivariable logistic regression was used to determine the annual adjusted odds of medication administration. An interrupted time series analysis was performed to assess change in atropine use after the 2010 American Heart Association guideline update.

Measurements and Main Results: A total of 268,031 index in-hospital cardiac arrests were included. As compared to 2001, the adjusted odds ratio of receiving each medication in 2016 were epinephrine (adjusted odds ratio, 1.5; 95% CI, 1.3–1.8), vasopressin (adjusted odds ratio, 1.5; 95% CI, 1.1–2.1), amiodarone (adjusted odds ratio, 3.4; 95% CI, 2.9–4.0), lidocaine (adjusted odds ratio, 0.2; 95% CI, 0.2–0.2), atropine (adjusted odds ratio, 0.07; 95% CI, 0.06–0.08), bicarbonate (adjusted odds ratio, 2.0; 95% CI, 1.8–2.3), calcium (adjusted odds ratio, 2.0; 95% CI, 1.7–2.3), magnesium (adjusted odds ratio, 2.2; 95% CI, 1.9–2.7; p < 0.0001), and dextrose (adjusted odds ratio, 2.8; 95% CI, 2.3–3.4). Following the 2010 American Heart Association guideline update, there was a downward step change in the intercept and slope change in atropine use (p < 0.0001). Conclusions: Prescribing patterns during in-hospital cardiac arrest have changed significantly over time. Changes to American Heart Association Advanced Cardiac Life Support guidelines have had a rapid and substantial effect on the use of a number of commonly used in-hospital cardiac arrest medications.

The Effect of ICU Diaries on Psychological Outcomes and Quality of Life of Survivors of Critical Illness and Their Relatives: A Systematic Review and Meta-Analysis

The Effect of ICU Diaries on Psychological Outcomes and Quality of Life of Survivors of Critical Illness and Their Relatives: A Systematic Review and Meta-Analysis

by McIlroy, Philippa A.; King, Rebecca S.; Garrouste-Orgeas, Maité; Tabah, Alexis; Ramanan, Mahesh

Critical Care Medicine: February 2019 - Volume 47 - Issue 2 - p 273–279

Objectives: To evaluate the effect of ICU diaries on posttraumatic stress disorder symptoms in ICU survivors and their relatives. Secondary objectives were to determine the effect on anxiety, depression, and health-related quality of life in patients and their relatives.

Data Sources: We searched online databases, trial registries, and references of relevant articles. Study Selection: Studies were included if there was an ICU diary intervention group which was compared with a group without a diary.

Data Extraction: Titles, abstracts, and full-text articles were reviewed independently by two authors. Data was abstracted using a structured template.

Data Synthesis: Our search identified 1,790 articles and retained eight studies for inclusion in the analysis. Pooled results found no significant reduction in patients’ posttraumatic stress disorder symptoms with ICU diaries (risk ratio, 0.75 [0.3–1.73]; p = 0.5; n = 3 studies); however, there was a significant improvement in patients’ anxiety (risk ratio, 0.32 [0.12, 0.86]; p = 0.02; n = 2 studies) and depression (risk ratio, 0.39 [0.17–0.87]; p = 0.02; n = 2 studies) symptoms. Two studies reported significant improvement in posttraumatic stress disorder symptoms of relatives of ICU survivors; however, these results could not be pooled due to reporting differences. One study reported no significant improvement in either anxiety (risk ratio, 0.94; 95% [0.66–1.33]; p = 0.72) or depression (risk ratio, 0.98; 95% [0.5–1.9]; p = 0.95) in relatives. There was a significant improvement in health-related quality of life of patients with a mean increase in the Short Form-36 general health score by 11.46 (95% CI, 5.87–17.05; p ≤ 0.0001; n = 2 studies). No studies addressed health-related quality of life of relatives.

Conclusions: ICU diaries decrease anxiety and depression and improve health-related quality of life, but not posttraumatic stress disorder among ICU survivors and may result in less posttraumatic stress disorder among relatives of ICU patients. Multicenter trials with larger sample sizes are necessary to confirm these findings.

Risk Factors for New-Onset Atrial Fibrillation in Patients With Sepsis: A Systematic Review and Meta-Analysis

Risk Factors for New-Onset Atrial Fibrillation in Patients With Sepsis: A Systematic Review and Meta-Analysis

 by Bosch, Nicholas A.; Cohen, David M.; Walkey, Allan J.  

Critical Care Medicine: February 2019 - Volume 47 - Issue 2 - p 280–287

Objective: Atrial fibrillation frequently develops in patients with sepsis and is associated with increased morbidity and mortality. Unfortunately, risk factors for new-onset atrial fibrillation in sepsis have not been clearly elucidated. Clarification of the risk factors for atrial fibrillation during sepsis may improve our understanding of the mechanisms of arrhythmia development and help guide clinical practice.

Data Sources: Medline, Embase, Web of Science, and Cochrane CENTRAL.

Study Selection: We conducted a systematic review and meta-analysis to identify risk factors for new-onset atrial fibrillation during sepsis.

Data Extraction: We extracted the adjusted odds ratio for each risk factor associated with new-onset atrial fibrillation during sepsis. For risk factors present in more than one study, we calculated pooled odds ratios (meta-analysis). We classified risk factors according to type and quantified the factor effect sizes. We then compared sepsis-associated atrial fibrillation risk factors with risk factors for community-associated atrial fibrillation.

Data Synthesis: Forty-four factors were examined as possible risk factors for new-onset atrial fibrillation in sepsis, 18 of which were included in meta-analyses. Risk factors for new-onset atrial fibrillation included demographic factors, comorbid conditions, and most strongly, sepsis-related factors. Sepsis-related factors with a greater than 50% change in odds of new-onset atrial fibrillation included corticosteroid use, right heart catheterization, fungal infection, vasopressor use, and a mean arterial pressure target of 80–85 mm Hg. Several cardiovascular conditions that are known risk factors for community-associated atrial fibrillation were not identified as risk factors for new-onset atrial fibrillation in sepsis.

Conclusions: Our study shows that risk factors for new-onset atrial fibrillation during sepsis are mainly factors that are associated with the acute sepsis event and are not synonymous with risk factors for community-associated atrial fibrillation. Our results provide targets for future studies focused on atrial fibrillation prevention and have implications for several key areas in the management of patients with sepsis such as glucocorticoid administration, vasopressor selection, and blood pressure targets.