Development of a Risk Prediction Model for New Episodes of Atrial Fibrillation in Medical-Surgical Critically Ill Patients Using the AmsterdamUMCdb

Abstract

The occurrence of atrial fibrillation (AF) represents clinical deterioration in acutely unwell patients and leads to increased morbidity and mortality. Prediction of the development of AF allows early intervention. Using the AmsterdamUMCdb, clinically relevant variables from patients admitted in sinus rhythm were extracted over the full duration of the ICU stay or until the first recorded AF episode occurred. Multiple logistic regression was performed to identify risk factors for AF. Input variables were automatically selected by a sequential forward search algorithm using cross-validation. We developed three different models: For the overall cohort, for ventilated patients and non-ventilated patients. 16,144 out of 23,106 admissions met the inclusion criteria. 2,374 (12.8%) patients had at least one AF episode during their ICU stay. Univariate analysis revealed that a higher percentage of AF patients were older than 70 years (60% versus 32%) and died in ICU (23.1% versus 7.1%) compared to non-AF patients. Multivariate analysis revealed age to be the dominant risk factor for developing AF with doubling of age leading to a 10-fold increased risk. Our logistic regression models showed excellent performance with AUC.ROC > 0.82 and > 0.91 in ventilated and non-ventilated cohorts, respectively. Increasing age was the dominant risk factor for the development of AF in both ventilated and non-ventilated critically ill patients. In non-ventilated patients, risk for development of AF was significantly higher than in ventilated patients. Further research is warranted to identify the role of ventilatory settings on risk for AF in critical illness and to optimise predictive models.

Sepsis-induced coagulopathy is associated with new episodes of atrial fibrillation in patients admitted to critical care in sinus rhythm

“…Episodes of AF in critical illness can therefore be interpreted as the cardiac manifestation of an acuteon-chronic condition, while the occurrence of SIC is dominated by the severity of sepsis and the acute activation of immune and coagulation systems. This concept is supported by our observation that age is the main risk factor for AF episodes in all models developed (13).…”

“…Missing values were treated as described before ( 13 ), i.e., excluding admissions with more than 35% missing data, and imputing the missing data with the median (numeric variables) or the mode (categorical variables). Variables with dynamic features were converted into tabular representations by extracting their means.…”

Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning

“…It can be associated with conditions like heart block, severe conduction system abnormalities, or drug toxicity, which may contribute to decompensation. High heart rate (tachycardia) can also be associated with decompensation, particularly if it results from underlying heart disease or other medical conditions [32][33][34][35], e.g., atrial fibrillation [36][37][38][39], ventricular tachycardia [40,41], or severe systemic infection [23,42], which may contribute to decompensation.…”

The ESICM datathon and the ESICM and ICMx data science strategy

“…The ESICM datathon and the ESICM and ICMx data science strategy Paul Elbers 1 , Patrick Thoral 1 , Lieuwe D. J. Bos 2 , Massimiliano Greco 3 , Pedro D. Wendel-Garcia 4* and Ari Ercole 5 In this issue of Intensive Care Medicine Experimental we celebrate the success of the ESICM datathons. The fifth consecutive edition was held in the months of May and June, 2023, continuing the tradition of uniting teams of data scientists and intensive care professionals to spend up to 6 weeks to use large intensive care databases to take on clinically relevant challenges and provide new insights to benefit the intensive care community.…”

“…As well as attracting skills from other timezones and continents, the online format allowed the datathon to be held over the course of multiple weeks with review presentations along the way. Working remotely and using agile 'sprint' methodologies, this allowed teams of data scientists and intensive care professionals to make more progress and come up with astonishing achievements: the first two virtual datathons have already resulted in four peerreviewed publications [5][6][7][8].…”