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Gao L, Shi Q, Zhang X, Bu X, Zheng P, Zhou L, Yan J, Li H, Tian G. The search for optimal nocturnal diurnal heart rate Index targets in ICU patients: a retrospective observational study from large ICU database. Front Cardiovasc Med 2024; 11:1415467. [PMID: 39041000 PMCID: PMC11260684 DOI: 10.3389/fcvm.2024.1415467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 06/24/2024] [Indexed: 07/24/2024] Open
Abstract
Background Circadian rhythms play a crucial role in cardiovascular health, with the nocturnal diurnal heart rate index (NDHRI) reflecting significant circadian variations. However, the optimal NDHRI target in Intensive Care Unit (ICU) patients remains undefined. This study aims to establish an evidence-based NDHRI target range and assess its association with mortality. Methods Data from the eICU Collaborative Research Database (n = 32,412) were analyzed. NDHRI was calculated by dividing cumulative nighttime heart rate area by daytime area. Generalized additive models (GAMs) explored the non-linear relationship between mean NDHRI and mortality, adjusting for confounders. Subgroup analyses were conducted based on ethnicity, ICU type, and comorbidities. Results A U-shaped association was observed between hospital mortality and mean NDHRI (P < 0.001). The optimal NDHRI range (40.0%-45.0%) demonstrated the lowest mortality rates. The duration spent within this range correlated inversely with mortality (P < 0.001). Subgroup analyses consistently supported these findings across diverse patient profiles. Conclusions Our findings suggest an association between maintaining NDHRI within the 40.0%-45.0% range and lower mortality rates in critically ill patients, highlighting the potential utility of monitoring circadian heart rate variations in the ICU. Further research and future randomized controlled trials are essential to confirm causality and should consider this NDHRI range as a pivotal reference target.
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Affiliation(s)
- Lan Gao
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Shaanxi Provincial Key Laboratory of Sepsis in Critical Care Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - QinDong Shi
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Shaanxi Provincial Key Laboratory of Sepsis in Critical Care Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - XiaoYu Zhang
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Shaanxi Provincial Key Laboratory of Sepsis in Critical Care Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Xiang Bu
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Shaanxi Provincial Key Laboratory of Sepsis in Critical Care Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - PeiYing Zheng
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Shaanxi Provincial Key Laboratory of Sepsis in Critical Care Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - LinJing Zhou
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Shaanxi Provincial Key Laboratory of Sepsis in Critical Care Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - JinQi Yan
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Shaanxi Provincial Key Laboratory of Sepsis in Critical Care Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Hao Li
- Department of Critical Care Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
- Shaanxi Provincial Key Laboratory of Sepsis in Critical Care Medicine, Xi’an Jiaotong University, Xi’an, Shaanxi, China
| | - Gang Tian
- Department of Cardiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
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Zhang TY, Du YJ, Hou YZ, Du Q, Dou HR, Gao XM. Heart/breathing rate ratio (HBR) as a predictor of mortality in critically ill patients. Heliyon 2024; 10:e31187. [PMID: 38803872 PMCID: PMC11128922 DOI: 10.1016/j.heliyon.2024.e31187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 05/08/2024] [Accepted: 05/12/2024] [Indexed: 05/29/2024] Open
Abstract
Objectives The early prediction of death is a challenge for medical staff. We evaluated the ability of the heart/breathing rate ratio (HBR) to predict mortality. Methods This was a single-center retrospective observational study of adult patients who had fever with or without respiratory symptoms, who survived at least 2 h after visiting the hospital, and whose lactate levels and vital signs were tested. We evaluated the distribution of mortality at different HBR levels and compared HBR with lactate. Results A total of 18,872 fever clinic visits were screened, and 183 patients whose lactate levels were tested were recruited. Patients who had HBR values lower than 4·5 or higher than 5·5 had greater mortality than patients who had HBR values between 4·5 and 5·5 (21·3 % vs. 3·4 %, p = 0·003; 28·9 % vs. 3·4 %, p < 0·001, respectively). In patients whose HBR was <5, the AUROC for HBR for mortality was 0·762 (95 % CI: 0.643-0·880), and that for lactate was 0·701 (95 % CI: 0·564-0·837). In patients whose HBR was ≥5, the AUROC for HBR for mortality was 0·721 (95 % CI: 0·584-0·857), and that for lactate was 0·742 (95 % CI: 0·607-0·848). Conclusions HBR is helpful for stratifying mortality risk among critically ill patients in acute care clinics for infectious diseases.
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Affiliation(s)
- Tong Yan Zhang
- Infectious Diseases Department, Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Ya Jun Du
- TEDA International Cardiovascular Hospital, Tianjin, China
| | - Ya Zhu Hou
- Department of Cardiology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Qian Du
- Department of Pharmacy, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hai Rong Dou
- Infectious Diseases Department, Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xiu Mei Gao
- Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
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Li M, Liu F, Yang Y, Lao J, Yin C, Wu Y, Yuan Z, Wei Y, Tang F. Identifying vital sign trajectories to predict 28-day mortality of critically ill elderly patients with acute respiratory distress syndrome. Respir Res 2024; 25:8. [PMID: 38178157 PMCID: PMC10765902 DOI: 10.1186/s12931-023-02643-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 12/18/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND The mortality rate of acute respiratory distress syndrome (ARDS) increases with age (≥ 65 years old) in critically ill patients, and it is necessary to prevent mortality in elderly patients with ARDS in the intensive care unit (ICU). Among the potential risk factors, dynamic subphenotypes of respiratory rate (RR), heart rate (HR), and respiratory rate-oxygenation (ROX) and their associations with 28-day mortality have not been clearly explored. METHODS Based on the eICU Collaborative Research Database (eICU-CRD), this study used a group-based trajectory model to identify longitudinal subphenotypes of RR, HR, and ROX during the first 72 h of ICU stays. A logistic model was used to evaluate the associations of trajectories with 28-day mortality considering the group with the lowest rate of mortality as a reference. Restricted cubic spline was used to quantify linear and nonlinear effects of static RR-related factors during the first 72 h of ICU stays on 28-day mortality. Receiver operating characteristic (ROC) curves were used to assess the prediction models with the Delong test. RESULTS A total of 938 critically ill elderly patients with ARDS were involved with five and 5 trajectories of RR and HR, respectively. A total of 204 patients fit 4 ROX trajectories. In the subphenotypes of RR, when compared with group 4, the odds ratios (ORs) and 95% confidence intervals (CIs) of group 3 were 2.74 (1.48-5.07) (P = 0.001). Regarding the HR subphenotypes, in comparison to group 1, the ORs and 95% CIs were 2.20 (1.19-4.08) (P = 0.012) for group 2, 2.70 (1.40-5.23) (P = 0.003) for group 3, 2.16 (1.04-4.49) (P = 0.040) for group 5. Low last ROX had a higher mortality risk (P linear = 0.023, P nonlinear = 0.010). Trajectories of RR and HR improved the predictive ability for 28-day mortality (AUC increased by 2.5%, P = 0.020). CONCLUSIONS For RR and HR, longitudinal subphenotypes are risk factors for 28-day mortality and have additional predictive enrichment, whereas the last ROX during the first 72 h of ICU stays is associated with 28-day mortality. These findings indicate that maintaining the health dynamic subphenotypes of RR and HR in the ICU and elevating static ROX after initial critical care may have potentially beneficial effects on prognosis in critically ill elderly patients with ARDS.
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Affiliation(s)
- Mingzhuo Li
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Data Open Innovative Application Laboratory, Jinan, China
| | - Fen Liu
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
| | - Yang Yang
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Data Open Innovative Application Laboratory, Jinan, China
| | - Jiahui Lao
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Data Open Innovative Application Laboratory, Jinan, China
| | - Chaonan Yin
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China
- Shandong Data Open Innovative Application Laboratory, Jinan, China
| | - Yafei Wu
- Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
- Institute for Medical Dataology, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yongyue Wei
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Fang Tang
- Department of Critical Care Medicine, Shandong Medicine and Health Key Laboratory of Emergency Medicine, Shandong Institute of Anesthesia and Respiratory Critical Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jingshi Road 16766, Jinan, China.
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
- Shandong Data Open Innovative Application Laboratory, Jinan, China.
- Shandong Provincial Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
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