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Bradycardia and Heart Rate Fluctuation Are Associated with a Prolonged Intensive Care Unit Stay in Patients with Severe COVID-19.

ABSTRACT: Background and Objective: Bradycardia has been observed among patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is suspected to be associated with poorer outcomes. Heart rate (HR) fluctuation has been found to be correlated with a greater mortality rate in critically ill patients. The association of bradycardia and HR fluctuation with the outcome of severe coronavirus disease 2019 (COVID-19) patients has not been clarified. Therefore, we aimed to examine whether bradycardia and HR fluctuation correlated with poor outcomes in patients with severe COVID-19. Materials and Methods: We conducted a secondary analysis from a prospective data collection of patients admitted to the intensive care unit, between April and June 2021, at Chiang Mai University Hospital. Results: The results showed that 62 of 86 patients (72.1%) had bradycardia, defined by HR < 60 beats per minute (bpm). The number of patients with high HR fluctuation, defined as the difference in HR during admission ≥ 40 bpm, was greater among the bradycardia group than in the non-bradycardia group (70.9% vs. 14.7%, p = 0.015, respectively). The patients with bradycardia had greater levels of erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP). In addition, a greater proportion of patients with bradycardia received interleukin-6 inhibitors and hemoperfusion as a rescue therapy than those with non-bradycardia. After adjusting for age, gender, body mass index, CRP, and mechanical ventilator; bradycardia and the high HR fluctuation were significantly associated with a longer length of stay in the intensive care unit (ICU-LOS), with adjusted risk ratios of 2.67, 95% CI; 1.02, 6.94, p = 0.045 and 2.88, 95% CI; 1.22, 6.78, p = 0.016, respectively. Conclusion: We found that bradycardia and a high heart rate fluctuation were associated with a poorer ICU outcome in terms of longer ICU-LOS among the patients with severe COVID-19.

SUBMITTER: Tajarernmuang P

PROVIDER: S-EPMC9319932 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPost-operative heart transplantation patients often require admission to an intensive care unit (ICU). Early prediction of the ICU length of stay (ICU-LOS) of these patients is of great significance and can guide treatment while reducing the mortality rate among patients. However, conventional linear models have tended to perform worse than non-linear models.Materials and methodsWe collected the clinical data of 365 patients from Wuhan Union Hospital who underwent heart transplantation surgery between April 2017 and August 2020. The patients were randomly divided into training data (N = 256) and test data (N = 109) groups. 84 clinical features were collected for each patient. Features were validated using the Least Absolute Shrinkage and Selection Operator (LASSO) regression's fivefold cross-validation method. We obtained Shapley Additive explanations (SHAP) values by executing package "shap" to interpret model predictions. Four machine learning models and logistic regression algorithms were developed. The area under the receiver operating characteristic curve (AUC-ROC) was used to compare the prediction performance of different models. Finally, for the convenience of clinicians, an online web-server was established and can be freely accessed via the website https://wuhanunion.shinyapps.io/PredictICUStay/.ResultsIn this study, 365 consecutive patients undergoing heart transplantation surgery for moderate (NYHA grade 3) or severe (NYHA grade 4) heart failure were collected in Wuhan Union Hospital from 2017 to 2020. The median age of the recipient patients was 47.2 years, while the median age of the donors was 35.58 years. 330 (90.4%) of the donor patients were men, and the average surgery duration was 260.06 min. Among this cohort, 47 (12.9%) had renal complications, 25 (6.8%) had hepatic complications, 11 (3%) had undergone chest re-exploration and 19 (5.2%) had undergone extracorporeal membrane oxygenation (ECMO). The following six important clinical features were selected using LASSO regression, and according to the result of SHAP, the rank of importance was (1) the use of extracorporeal membrane oxygenation (ECMO); (2) donor age; (3) the use of an intra-aortic balloon pump (IABP); (4) length of surgery; (5) high creatinine (Cr); and (6) the use of continuous renal replacement therapy (CRRT). The eXtreme Gradient Boosting (XGBoost) algorithm presented significantly better predictive performance (AUC-ROC = 0.88) than other models [Accuracy: 0.87; sensitivity: 0.98; specificity: 0.51; positive predictive value (PPV): 0.86; negative predictive value (NPV): 0.93].ConclusionUsing the XGBoost classifier with heart transplantation patients can provide an accurate prediction of ICU-LOS, which will not only improve the accuracy of clinical decision-making but also contribute to the allocation and management of medical resources; it is also a real-world example of precision medicine in hospitals.

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Bradycardia and Heart Rate Fluctuation Are Associated with a Prolonged Intensive Care Unit Stay in Patients with Severe COVID-19.

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