Impact of atrial fibrillation in critically ill patients admitted to a stepdown unit

Falsetti, Lorenzo; Proietti, Marco; Zaccone, Vincenzo; Guerra, Federico; Nitti, Cinzia; Salvi, Aldo; Viticchi, Giovanna; Riccomi, Francesca; Sampaolesi, Mattia; Silvestrini, Mauro; Moroncini, Gianluca; Lip, Gregory Y.H.; Capucci, Alessandro

doi:10.1111/eci.13317

Cited by 8 publications

(15 citation statements)

References 32 publications

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“…AFICILL (Atrial Fibrillation In Critically ILL) is a retrospective cohort study enrolling medical critically ill patients affected by pre-existing AF admitted to the SDU of the internal and sub-intensive medicine department of the Azienda Ospedaliero-Universitaria “Ospedali Riuniti”, Ancona, Italy. Full details regarding the data collection procedure are reported in a previous paper 5 : we retrospectively considered a cohort of critically ill patients with pre-existing AF admitted to the internal medicine department of the Azienda Ospedaliero-Universitaria “Ospedali Riuniti”, Ancona, Italy. This department implemented, since January 01st 2002, an electronic medical record system (eMRS) for inpatients’ management.…”

Section: Methodsmentioning

confidence: 99%

“…While both CHA 2 DS 2 -VASc and HAS-BLED scores are almost universally recognized as the mainstay for the baseline evaluation of the “usual” AF patients, observational data suggest that the predictive ability of such scores is extremely limited in critically ill patients, due to the complex clinical status and to the overall clinical severity 5 . Despite this, and in absence of specifically-designed risk scores, stratification with CHA 2 DS 2 -VASc and HAS-BLED is still recommended by experts also in critical illness 6 .…”

Section: Introductionmentioning

confidence: 99%

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Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation

Falsetti

Rucco²,

Proietti

et al. 2021

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Critically ill patients affected by atrial fibrillation are at high risk of adverse events: however, the actual risk stratification models for haemorrhagic and thrombotic events are not validated in a critical care setting. With this paper we aimed to identify, adopting topological data analysis, the risk factors for therapeutic failure (in-hospital death or intensive care unit transfer), the in-hospital occurrence of stroke/TIA and major bleeding in a cohort of critically ill patients with pre-existing atrial fibrillation admitted to a stepdown unit; to engineer newer prediction models based on machine learning in the same cohort. We selected all medical patients admitted for critical illness and a history of pre-existing atrial fibrillation in the timeframe 01/01/2002–03/08/2007. All data regarding patients’ medical history, comorbidities, drugs adopted, vital parameters and outcomes (therapeutic failure, stroke/TIA and major bleeding) were acquired from electronic medical records. Risk factors for each outcome were analyzed adopting topological data analysis. Machine learning was used to generate three different predictive models. We were able to identify specific risk factors and to engineer dedicated clinical prediction models for therapeutic failure (AUC: 0.974, 95%CI: 0.934–0.975), stroke/TIA (AUC: 0.931, 95%CI: 0.896–0.940; Brier score: 0.13) and major bleeding (AUC: 0.930:0.911–0.939; Brier score: 0.09) in critically-ill patients, which were able to predict accurately their respective clinical outcomes. Topological data analysis and machine learning techniques represent a concrete viewpoint for the physician to predict the risk at the patients’ level, aiding the selection of the best therapeutic strategy in critically ill patients affected by pre-existing atrial fibrillation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation

Falsetti

Rucco²,

Proietti

et al. 2021

Sci Rep

Self Cite

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“…A recent study reported AF outcomes for critically ill patients admitted to step down units. CHA 2 DS 2 -VAS c scores were not associated with the occurrence of stroke or TIA and failed to predict these thromboembolic events 14 .…”

Section: Accepted Manuscriptmentioning

confidence: 93%

“…13 A recent retrospective study showed that CHA 2 DS 2 -VASc and HAS-BLED scores were not associated with stroke or major bleeding and were unable to predict these events. 14 Therefore, decisions around anticoagulation strategies in critical care populations are complex and challenging. There are currently no recommendations relating specifically to the rational therapy of AF in the critical care setting.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Anticoagulation Strategies for Patients with Atrial Fibrillation in Critical Care

et al. 2021

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Background. Atrial fibrillation (AF) is the most common cardiac arrhythmia in critically ill patients. There is a paucity of data assessing the impact of anticoagulation strategies on clinical outcomes for general critical care patients with AF. Our aim was to assess the existing literature to evaluate the effectiveness of anticoagulation strategies used in critical care for AF. Methodology. A systematic literature search was conducted using MEDLINE, EMBASE, CENTRAL and PubMed databases. Studies reporting anticoagulation strategies for AF in adults admitted to a general critical care setting were assessed for inclusion. Results. Four studies were selected for data extraction. A total of 44087 patients were identified with AF, of which 17.8-49.4% received anticoagulation. The reported incidence of thromboembolic events was 0-1.4% for anticoagulated patients, and 0-1.3% in non-anticoagulated patients. Major bleeding events were reported in three studies and occurred in 7.2-8.6% of the anticoagulated patients and up to 7.1% of the non-anticoagulated patients. Conclusions. There was an increased incidence of major bleeding events in anticoagulated patients with AF in critical care compared to non-anticoagulated patients. There was no significant difference in the incidence of reported thromboembolic events within studies, between patients who did and did not receive anticoagulation. However, the outcomes reported within studies were not standardised, therefore, the generalisability of our results to the general critical care population remains unclear. Further data is required to facilitate an evidence-based assessment of the risks and benefits of anticoagulation for critically ill patients with AF.

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“…1 Even in the context of acutely ill patients in ICUs or sub-intensive/stepdown units, the presence or the occurrence of AF very often complicates clinical management and has an impact on the burden of adverse clinical outcomes. [2][3][4] Despite the relevant impact of AF in the management of acutely ill patients, looking at the major clinical guidelines about both AF and sepsis, we can find a general lack of consideration about this clinical issue and an overall absence of specific recommendations. 1,5,6 Among the various aspects related to the treatment of patients with AF, the choice of and management of rate control drugs play important roles in the optimization and integrated care of these patients.…”

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confidence: 99%

Management of Ventricular Heart Rate in Atrial Fibrillation Patients With Sepsis

Proietti

Romiti

2021

Chest

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Impact of atrial fibrillation in critically ill patients admitted to a stepdown unit

Cited by 8 publications

References 32 publications

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation

Risk prediction of clinical adverse outcomes with machine learning in a cohort of critically ill patients with atrial fibrillation

A Systematic Review of Anticoagulation Strategies for Patients with Atrial Fibrillation in Critical Care

Management of Ventricular Heart Rate in Atrial Fibrillation Patients With Sepsis

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