ArticleScientific reports2022
Prospective multicenter study of heart rate variability with ANI monitor as predictor of mortality in critically ill patients with COVID-19.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07771101 (Association Between Early Autonomic Dysfunction Measured by the Analgesia Nociception Index), which is not on this map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Association Between Early Autonomic Dysfunction Measured by the Analgesia Nociception Index (ANI) and Prolonged Mechanical Ventilation in Postoperative ICU Patients
Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Wearable Devices to Diagnose and Monitor the Progression of COVID-19 Through Heart Rate Variability Measurement: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Heart rate variability as a marker of multiple organ dysfunction syndrome in deeply sedated, prepubescent patients: a secondary analysis.Journal of clinical monitoring and computing · 2026Observational
- The ANI monitor's "Energy" variable detects autonomic state modification during general anesthesia, sedation and spinal anesthesia: a retrospective cohort study.Journal of clinical monitoring and computing · 2026Article
- Electrochemical skin conductance is associated with peripheral tissue hypoperfusion in septic patients.Intensive care medicine experimental · 2025Article
- Classification of Individuals With COVID-19 and Post-COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near-Real-Time Monitoring Component.Journal of medical Internet research · 2025Article
- Pain assessment using the Analgesia Nociception Index in patients undergoing sedation: a systematic review and meta-analysis.The Korean journal of pain · 2025Article
- Article
Corrections and comments
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Authors and funding
9 authors at 7 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study is to demonstrate that the most critically ill patients with COVID-19 have greater autonomic nervous system dysregulation and assessing the heart rate variability, allows us to predict severity and 30-day mortality. This was a multicentre, prospective, cohort study. Patients were divided into two groups depending on the 30-day mortality. The heart rate variability and more specifically the relative parasympathetic activity (ANIm), and the SDNN (Energy), were measured. To predict severity and mortality multivariate analyses of ANIm, Energy, SOFA score, and RASS scales were conducted. 112 patients were collected, the survival group (n = 55) and the deceased group (n = 57). The ANIm value was higher (p = 0.013) and the Energy was lower in the deceased group (p = 0.001); Higher Energy was correlated with higher survival days (p = 0.009), and a limit value of 0.31 s predicted mortalities with a sensitivity of 71.9% and a specificity of 74.5%. Autonomic nervous system and heart rate variability monitoring in critically ill patients with COVID-19 allows for predicting survival days and 30-day mortality through the Energy value. Those patients with greater severity and mortality showed higher sympathetic depletion with a predominance of relative parasympathetic activity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.