Day-to-day progression of vital-sign circadian rhythms in the intensive care unit

Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges

A wealth of data is available from electronic health records (EHR) that are collected as part of routine clinical care in hospitals worldwide. These rich, longitudinal data offer an attractive object of study for the field of circadian medicine, which aims to translate knowledge of circadian rhythms to improve patient health. This narrative review aims to discuss opportunities for EHR in studies of circadian medicine, highlight the methodological challenges, and provide recommendations for using these data to advance the field. In the existing literature, we find that data collected in real-world clinical settings have the potential to shed light on key questions in circadian medicine, including how 24-hour rhythms in clinical features are associated with-or even predictive of-health outcomes, whether the effect of medication or other clinical activities depend on time of day, and how circadian rhythms in physiology may influence clinical reference ranges or sampling protocols. However, optimal use of EHR to advance circadian medicine requires careful consideration of the limitations and sources of bias that are inherent to these data sources. In particular, time of day influences almost every interaction between a patient and the healthcare system, creating operational 24-hour patterns in the data that have little or nothing to do with biology. Addressing these challenges could help to expand the evidence base for the use of EHR in the field of circadian medicine.

Sleep and Circadian Health of Critical Survivors: A 12-Month Follow-Up Study

OBJECTIVES

To investigate the sleep and circadian health of critical survivors 12 months after hospital discharge and to evaluate a possible effect of the severity of the disease within this context.

DESIGN

Observational, prospective study.

SETTING

Single-center study.

PATIENTS

Two hundred sixty patients admitted to the ICU due to severe acute respiratory syndrome coronavirus 2 infection.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The cohort was composed of 260 patients (69.2% males), with a median (quartile 1-quartile 3) age of 61.5 years (52.0-67.0 yr). The median length of ICU stay was 11.0 days (6.00-21.8 d), where 56.2% of the patients required invasive mechanical ventilation (IMV). The Pittsburgh Sleep Quality Index (PSQI) revealed that 43.1% of the cohort presented poor sleep quality 12 months after hospital discharge. Actigraphy data indicated an influence of the disease severity on the fragmentation of the circadian rest-activity rhythm at the 3- and 6-month follow-ups, which was no longer significant in the long term. Still, the length of the ICU stay and the duration of IMV predicted a higher fragmentation of the rhythm at the 12-month follow-up with effect sizes (95% CI) of 0.248 (0.078-0.418) and 0.182 (0.005-0.359), respectively. Relevant associations between the PSQI and the Hospital Anxiety and Depression Scale (rho = 0.55, anxiety; rho = 0.5, depression) as well as between the fragmentation of the rhythm and the diffusing lung capacity for carbon monoxide (rho = -0.35) were observed at this time point.

CONCLUSIONS

Our findings reveal a great prevalence of critical survivors presenting poor sleep quality 12 months after hospital discharge. Actigraphy data indicated the persistence of circadian alterations and a possible impact of the disease severity on the fragmentation of the circadian rest-activity rhythm, which was attenuated at the 12-month follow-up. This altogether highlights the relevance of considering the sleep and circadian health of critical survivors in the long term.

Postoperative circadian patterns in wearable sensor measured heart rate: a prospective observational study

PURPOSE

This study aimed to describe the 24-hour cycle of wearable sensor-obtained heart rate in patients with deterioration-free recovery and to compare it with patients experiencing postoperative deterioration.

METHODS

A prospective observational trial was performed in patients following bariatric or major abdominal cancer surgery. A wireless accelerometer patch (Healthdot) continuously measured postoperative heart rate, both in the hospital and after discharge, for a period of 14 days. The circadian pattern, or diurnal rhythm, in the wearable sensor-obtained heart rate was described using peak, nadir and peak-nadir excursions.

RESULTS

The study population consisted of 137 bariatric and 100 major abdominal cancer surgery patients. In the latter group, 39 experienced postoperative deterioration. Both surgery types showed disrupted diurnal rhythm on the first postoperative days. Thereafter, the bariatric group had significantly lower peak heart rates (days 4, 7-12, 14), lower nadir heart rates (days 3-14) and larger peak-nadir excursions (days 2, 4-14). In cancer surgery patients, significantly higher nadir (days 2-5) and peak heart rates (days 2-3) were observed prior to deterioration.

CONCLUSIONS

The postoperative diurnal rhythm of heart rate is disturbed by different types of surgery. Both groups showed recovery of diurnal rhythm but in patients following cancer surgery, both peak and nadir heart rates were higher than in the bariatric surgery group. Especially nadir heart rate was identified as a potential prognostic marker for deterioration after cancer surgery.

Lessons Learned from Telemonitoring in an Outpatient Bariatric Surgery Pathway-Secondary Outcomes of a Patient Preference Clinical Trial

BACKGROUND

Remote monitoring is increasingly used to support postoperative care. This study aimed to describe the lessons learned from the use of telemonitoring in an outpatient bariatric surgery pathway.

MATERIALS AND METHODS

Patients were assigned based on their preference to an intervention cohort of same-day discharge after bariatric surgery. In total, 102 patients were monitored continuously for 7 days using a wearable monitoring device with a Continuous and Remote Early Warning Score-based notification protocol (CREWS). Outcome measures included missing data, course of postoperative heart and respiration rate, false positive notification and specificity analysis, and vital sign assessment during teleconsultation.

RESULTS

In 14.7% of the patients, data for heart rate was missing for > 8 h. A day-night-rhythm of heart rate and respiration rate reappeared on average on postoperative day 2 with heart rate amplitude increasing after day 3. CREWS notification had a specificity of 98%. Of the 17 notifications, 70% was false positive. Half of them occurred between day 4 and 7 and were accompanied with surrounding reassuring values. Comparable postoperative complaints were encountered between patients with normal and deviated data.

CONCLUSION

Telemonitoring after outpatient bariatric surgery is feasible. It supports clinical decisions, however does not replace nurse or physician care. Although infrequent, the false notification rate was high. We suggested additional contact may not be necessary when notifications occur after restoration of circadian rhythm or when surrounding reassuring vital signs are present. CREWS supports ruling out serious complications, what may reduce in-hospital re-evaluations. Following these lessons learned, increased patients' comfort and decreased clinical workload could be expected.

TRIAL REGISTRATION

ClinicalTrials.gov. Identifier: NCT04754893.

Machine Learning for Postoperative Continuous Recovery Scores of Oncology Patients in Perioperative Care with Data from Wearables

Assessing post-operative recovery is a significant component of perioperative care, since this assessment might facilitate detecting complications and determining an appropriate discharge date. However, recovery is difficult to assess and challenging to predict, as no universally accepted definition exists. Current solutions often contain a high level of subjectivity, measure recovery only at one moment in time, and only investigate recovery until the discharge moment. For these reasons, this research aims to create a model that predicts continuous recovery scores in perioperative care in the hospital and at home for objective decision making. This regression model utilized vital signs and activity metrics measured using wearable sensors and the XGBoost algorithm for training. The proposed model described continuous recovery profiles, obtained a high predictive performance, and provided outcomes that are interpretable due to the low number of features in the final model. Moreover, activity features, the circadian rhythm of the heart, and heart rate recovery showed the highest feature importance in the recovery model. Patients could be identified with fast and slow recovery trajectories by comparing patient-specific predicted profiles to the average fast- and slow-recovering populations. This identification may facilitate determining appropriate discharge dates, detecting complications, preventing readmission, and planning physical therapy. Hence, the model can provide an automatic and objective decision support tool.

Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method

An ICU is a critical care unit that provides advanced medical support and continuous monitoring for patients with severe illnesses or injuries. Predicting the mortality rate of ICU patients can not only improve patient outcomes, but also optimize resource allocation. Many studies have attempted to create scoring systems and models that predict the mortality of ICU patients using large amounts of structured clinical data. However, unstructured clinical data recorded during patient admission, such as notes made by physicians, is often overlooked. This study used the MIMIC-III database to predict mortality in ICU patients. In the first part of the study, only eight structured variables were used, including the six basic vital signs, the GCS, and the patient's age at admission. In the second part, unstructured predictor variables were extracted from the initial diagnosis made by physicians when the patients were admitted to the hospital and analyzed using Latent Dirichlet Allocation techniques. The structured and unstructured data were combined using machine learning methods to create a mortality risk prediction model for ICU patients. The results showed that combining structured and unstructured data improved the accuracy of the prediction of clinical outcomes in ICU patients over time. The model achieved an AUROC of 0.88, indicating accurate prediction of patient vital status. Additionally, the model was able to predict patient clinical outcomes over time, successfully identifying important variables. This study demonstrated that a small number of easily collectible structured variables, combined with unstructured data and analyzed using LDA topic modeling, can significantly improve the predictive performance of a mortality risk prediction model for ICU patients. These results suggest that initial clinical observations and diagnoses of ICU patients contain valuable information that can aid ICU medical and nursing staff in making important clinical decisions.

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

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.

The Role of Circadian Clock Genes in Critical Illness: The Potential Role of Translational Clock Gene Therapies for Targeting Inflammation, Mitochondrial Function, and Muscle Mass in Intensive Care

The Earth's 24-h planetary rotation, with predictable light and heat cycles, has driven profound evolutionary adaptation, with prominent impacts on physiological mechanisms important for surviving critical illness. Pathways of interest include inflammation, mitochondrial function, energy metabolism, hypoxic signaling, apoptosis, and defenses against reactive oxygen species. Regulation of these by the cellular circadian clock (BMAL-1 and its network) has an important influence on pulmonary inflammation; ventilator-associated lung injury; septic shock; brain injury, including vasospasm; and overall mortality in both animals and humans. Whether it is cytokines, the inflammasome, or mitochondrial biogenesis, circadian medicine represents exciting opportunities for translational therapy in intensive care, which is currently lacking. Circadian medicine also represents a link to metabolic determinants of outcome, such as diabetes and cardiovascular disease. More than ever, we are appreciating the problem of circadian desynchrony in intensive care. This review explores the rationale and evidence for the importance of the circadian clock in surviving critical illness.

Intermittent feeding and circadian rhythm in critical illness

PURPOSE OF REVIEW

Circadian rhythms, i.e., periodic oscillations in internal biological processes, modulate metabolic processes such as hormonal signalling, nutrient absorption, and xenobiotic detoxification. Meal timing is a strong entraining cue for peripheral clocks in various organs, and eating out of circadian phases can impair glucose, gastrointestinal, and muscle metabolism. Sleep/wake cycles and circadian rhythms are extremely disrupted during critical illness. Timing of nutritional support may help preserve circadian rhythms and improve post-Intensive Care Unit (ICU) recovery. This review summarises circadian disruptors during ICU admission and evaluates the potential benefits of intermittent feeding on metabolism and circadian rhythms.

RECENT FINDINGS

Rhythmic expression of core clock genes becomes rapidly disturbed during critical illness and remains disturbed for weeks. Intermittent, bolus, and cyclic enteral feeding have been directly compared to routine continuous feeding, yet no benefits on glycaemic control, gastrointestinal tolerance, and muscle mass have been observed and impacts of circadian clocks remain untested.

SUMMARY

Aligning timing of nutritional intake, physical activity, and/or medication with circadian rhythms are potential strategies to reset peripheral circadian rhythms and may enhance ICU recovery but is not proven beneficial yet. Therefore, selecting intermittent feeding over continuous feeding must be balanced against the pros and cons of clinical practice.

Circadian patterns of heart rate, respiratory rate and skin temperature in hospitalized COVID-19 patients

Rationale

Vital signs follow circadian patterns in both healthy volunteers and critically ill patients, which seem to be influenced by disease severity in the latter. In this study we explored the existence of circadian patterns in heart rate, respiratory rate and skin temperature of hospitalized COVID-19 patients, and aimed to explore differences in circadian rhythm amplitude during patient deterioration.

Methods

We performed a retrospective study of COVID-19 patients admitted to the general ward of a tertiary hospital between April 2020 and March 2021. Patients were continuously monitored using a wireless sensor and fingertip pulse oximeter. Data was divided into three cohorts: patients who recovered, patients who developed respiratory insufficiency and patients who died. For each cohort, a population mean cosinor model was fitted to detect rhythmicity. To assess changes in amplitude, a mixed-effect cosinor model was fitted.

Results

A total of 429 patients were monitored. Rhythmicity was observed in heartrate for the recovery cohort (p<0.001), respiratory insufficiency cohort (p<0.001 and mortality cohort (p = 0.002). Respiratory rate showed rhythmicity in the recovery cohort (p<0.001), but not in the other cohorts (p = 0.18 and p = 0.51). Skin temperature also showed rhythmicity in the recovery cohort (p<0.001), but not in the other cohorts (p = 0.22 and p = 0.12). For respiratory insufficiency, only the amplitude of heart rate circadian pattern increased slightly the day before (1.2 (99%CI 0.16–2.2, p = 0.002)). In the mortality cohort, the amplitude of heart rate decreased (-1.5 (99%CI -2.6- -0.42, p<0.001)) and respiratory rate amplitude increased (0.72 (99%CI 0.27–1.3, p = 0.002) the days before death.

Conclusion

A circadian rhythm is present in heart rate of COVID-19 patients admitted to the general ward. For respiratory rate and skin temperature, rhythmicity was only found in patients who recover, but not in patients developing respiratory insufficiency or death. We found no consistent changes in circadian rhythm amplitude accompanying patient deterioration.

Dysregulated light/dark cycle impairs sleep and delays the recovery of patients in intensive care units: A call for action for COVID-19 treatment

Exposure to an adequate light-dark cycle is important for the speedy recovery of hospitalized and institutionalized patients. Light exposure, including natural light, offers several health benefits to both patients and nursing staff. This includes physical (e.g., decreased confusion and disorientation) and mental health benefits (e.g., prevention of depression) and a reduction in the hospital stay. Improved alertness and performance can also be noted among hospital staff. In this commentary, we discuss disrupting factors that include light during the nighttime along with noise and physical procedures on the patient and others. We then address some of the important steps that can be undertaken to restore a more normal environment for patients in the intensive care unit, which can be particularly important for COVID-19 patients.

Circadian rhythm disruption in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Implications for the post-acute sequelae of COVID-19

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a common and disabling disorder primarily characterized by persistent fatigue and exercise intolerance, with associated sleep disturbances, autonomic dysfunction, and cognitive problems. The causes of ME/CFS are not well understood but may coincide with immune and inflammatory responses following viral infections. During the current SARS-CoV2 coronavirus pandemic, ME/CFS has been increasingly reported to overlap with persistent “long COVID” symptoms, also called the post-acute sequelae of COVID-19 (PASC). Given the prominence of activity and sleep problems in ME/CFS, circadian rhythm disruption has been examined as a contributing factor in ME/CFS. While these studies of circadian rhythms have been pursued for decades, evidence linking circadian rhythms to ME/CFS remains inconclusive. A major limitation of older chronobiology studies of ME/CFS was the unavailability of modern molecular methods to study circadian rhythms and incomplete understanding of circadian rhythms outside the brain in peripheral organ systems. Major methodological and conceptual advancements in chronobiology have since been made. Over the same time, biomarker research in ME/CFS has progressed. Together, these new developments may justify renewed interest in circadian rhythm research in ME/CFS. Presently, we review ME/CFS from the perspective of circadian rhythms, covering both older and newer studies that make use of modern molecular methods. We focus on transforming growth factor beta (TGFB), a cytokine that has been previously associated with ME/CFS and has an important role in circadian rhythms, especially in peripheral cells. We propose that disrupted TGFB signaling in ME/CFS may play a role in disrupting physiological rhythms in sleep, activity, and cognition, leading to the insomnia, energy disturbances, cognition problems, depression, and autonomic dysfunction associated with ME/CFS. Since SARS-like coronavirus infections cause persistent changes in TGFB and previous coronavirus outbreaks have caused ME/CFS-like syndromes, chronobiological considerations may have immediate implications for understanding ME/CFS in the context of the COVID-19 pandemic and possibly suggest new avenues for therapeutic interventions.

•

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is characterized by ​disrupted ​sleep ​and activity ​implicating ​circadian clocks.

•

The incidence of ME/CFS is expected to increase as a result of the post-acute sequelae of COVID-19.

•

Biomarker studies in ME/CFS patients implicate Transforming Growth Factor B (TGFB).

•

TGFB has roles in synchronizing circadian rhythms in peripheral cells.

•

Identification of biomarkers and new methodologies may facilitate progress in the chronobiological basis of ME/CFS.

Circadian variation in new-onset atrial fibrillation in patients in ICUs

New-onset atrial fibrillation (NOAF) is common in patients treated on an intensive care unit (ICU). Onset of certain arrhythmias exhibit circadian variation. Whether NOAF follows a circadian rhythm in patients in ICU is unknown. We undertook a retrospective observational study of two ICU databases to explore the timing of NOAF onset. We identified 2017 patients who developed NOAF during their ICU stay. NOAF onset exhibited a bimodal distribution with peaks at 8 am and 8 pm, consistent with the onset of paroxysmal AF in patients in the community. Future studies in ICUs should record time of AF onset, as understanding high risk periods may inform timing of preventative interventions.