Circadian rhythms of vital signs are associated with in-hospital mortality in critically ill patients: A retrospective observational study

ABSTRACT

Vital signs have been widely used to assess the disease severity of patients, but there is still a lack of research on their circadian rhythms. The objective is to explore the circadian rhythms of vital signs in critically ill patients and establish an in-hospital mortality prediction model. Study patients from the recorded eICU Collaborative Research Database were included in the present analyses. The circadian rhythms of vital signs are analyzed in critically ill patients using the cosinor method. Logistic regression was used to screen independent predictors and establish a prediction model for in-hospital mortality by multivariate logistic regression analysis and to show in the nomogram. Internal validation is used to evaluate the prediction model by bootstrapping with 1000 resamples. A total of 29,448 patients were included in the current analyses. The Mesor, Amplitude, and Peak time of vital signs, such as heart rate (HR), temperature, respiration rate (RR), pulse oximetry-derived oxygen saturation (SpO2), and blood pressure (BP), were significant differences between survivors and non-survivors . Logistic regression analysis showed that Mesor, Amplitude, and Peak time of HR, RR, and SpO2 were independent predictors for in-hospital mortality in critically ill patients. The area under the curve (AUC) and c-index of the prediction model for the Medical intensive care unit (MICU) and Surgical intensive care unit (SICU) were 0.807 and 0.801, respectively. The Hosmer-Lemeshow test P-values were 0.076 and 0.085, respectively, demonstrating a good fit for the prediction model. The calibration curve and decision curve analysis (DCA) also demonstrated its accuracy and applicability. Internal validation assesses the consistency of the results. There were significant differences in the circadian rhythms of vital signs between survivors and non-survivors in critically ill patients. The prediction model established by the Mesor, Amplitude, and Peak time of HR, RR, and SpO2 combined with the Acute Physiology and Chronic Health Evaluation (APACHE) IV score has good predictive performance for in-hospital mortality and may eventually support clinical decision-making.

Abbreviations: ICU: Intensive care unit; MICU: Medical intensive care unit; SICU: Surgical intensive care unit; HR: Heart rate; RR: Respiration rate; SpO2: Pulse oximetry-derived oxygen saturation; BP: Blood pressure; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; APACHE: Acute Physiology and Chronic Health Evaluation; bpm: beats per min; BMI: Body mass index; OR: Odd ratio; CI: Confidential interval; IQR: Interquartile range; SD: Standard deviation; ROC: Receiver operating characteristic; AUC: area under the curve; DCA: Decision curve analysis; IRB: Institutional review board.

KEYWORDS:

• Circadian rhythms
• vital signs
• prediction model
• ICU
• mortality

Authors’ contributions

J C conceived this study. ZN Y extracted the data. J C and ZL H designed the statistical analyses. XX X, X Z, L L, and RX B performed the statistical analyses. ZN Y, YJ Q, H L, and YN M wrote the first draft of the manuscript. J C reviewed and modified the final manuscript. All authors read, critically reviewed, and approved the final manuscript.

Consent to participate

All the authors agree to participate in this research and are responsible for their works.

Consent for publication

All the authors mutually agree for its submission and publication in Chronobiology International.

Disclosure statement

No potential conflict of interest was reported by the authors.

Ethics approval

Data collection was in accordance with the ethical standards of the institutional review board of the Massachusetts Institute of Technology (no. 0403000206) and with the 1964 Declaration of Helsinki and its later amendments. This study analyzed a publicly available, anonymized database with preexisting institutional review board (IRB) approval.

Availability of data and materials

Data analyzed during the present study are currently stored in the eICU database (https://eicu-crd.mit.edu). After completing the required training course (the Collaborative Institutional Training Initiative) and requesting access to the eICU Collaborative Research Database, researchers can seek to use the database. The author ZN Y obtained the access of the database (certification number: 40608375).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07420528.2022.2163656

Additional information

Funding

This work was supported by The Special Support Scheme for Shaanxi Province, and the Subject Innovation Team of Shaanxi University of Chinese Medicine (#2019-YS01), Shaanxi province administration of traditional Chinese medicine (#2021-ZZ-JC018), and Natural Science Foundation of Shaanxi Province(2023-JC-YB-656).