Dataset Information

Circadian rhythm of COPD symptoms in clinically based phenotypes. Results from the STORICO Italian observational study.

ABSTRACT: BACKGROUND:Chronic Obstructive Pulmonary Disease (COPD) encompasses various phenotypes that severely limit the applicability of precision respiratory medicine. The present investigation is aimed to assess the circadian rhythm of symptoms in pre-defined clinical COPD phenotypes and its association with health-related quality of life (HR-QoL), the quality of sleep and the level of depression/anxiety in each clinical phenotype. METHODS:The STORICO (NCT03105999) Italian observational prospective cohort study enrolled COPD subjects. A clinical diagnosis of either chronic bronchitis (CB), emphysema (EM) or mixed COPD-asthma (MCA) phenotype was made by clinicians at enrollment. Baseline early-morning, day-time and nocturnal symptoms (gathered via the Night-time, Morning and Day-time Symptoms of COPD questionnaire), HR-QoL (via the St. George's Respiratory Questionnaire), anxiety and depression levels (via the Hospital Anxiety and Depression Scale), quality of sleep (via COPD and Asthma Sleep Impact Scale), physical activity (via the International Physical Activity Questionnaire) as well as lung function were recorded. RESULTS:606 COPD subjects (age 71.4?±?8.2?years, male 75.1%) were studied. 57.9, 35.5 5.3 and 1.3% of the sample belonged to the CB, EM, MCA and EM?+?CB phenotypes respectively. The vast majority of subjects reported early-morning and day-time symptoms (79.5 and 79.2% in the CB and 75.8 and 77.7% in the EM groups); the proportion suffering from night-time symptoms was higher in the CB than in the EM group (53.6% vs. 39.5%, p?=?0.0016). In both CB and EM, indiscriminately, the presence of symptoms during the 24-h?day was associated with poorer HR-QoL, worse quality of sleep and higher levels of anxiety/depression. CONCLUSIONS:The findings highlight the primary classificatory role of nocturnal symptoms in COPD. TRIAL REGISTRATION:Trial registration number: NCT03105999 , date of registration: 10th April 2017.

SUBMITTER: Scichilone N

PROVIDER: S-EPMC6734215 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Circadian rhythm of COPD symptoms in clinically based phenotypes. Results from the STORICO Italian observational study.

Scichilone Nicola N Antonelli Incalzi Raffaele R Blasi Francesco F Schino Pietro P Cuttitta Giuseppina G Zullo Alessandro A Ori Alessandra A Canonica Giorgio Walter GW

BMC pulmonary medicine 20190909 1

<h4>Background</h4>Chronic Obstructive Pulmonary Disease (COPD) encompasses various phenotypes that severely limit the applicability of precision respiratory medicine. The present investigation is aimed to assess the circadian rhythm of symptoms in pre-defined clinical COPD phenotypes and its association with health-related quality of life (HR-QoL), the quality of sleep and the level of depression/anxiety in each clinical phenotype.<h4>Methods</h4>The STORICO (NCT03105999) Italian observational ...[more]

PMID: 31500607

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Project description:BackgroundVirtually, all organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders, and disturbance of the circadian rhythm is known to be very closely related. Attempts have also been made to derive clinical implications associated with mood disorders using the vast amounts of digital log that is acquired by digital technologies develop and using computational analysis techniques.ObjectiveThis study was conducted to evaluate the mood state or episode, activity, sleep, light exposure, and heart rate during a period of about 2 years by acquiring various digital log data through wearable devices and smartphone apps as well as conventional clinical assessments. We investigated a mood prediction algorithm developed with machine learning using passive data phenotypes based on circadian rhythms.MethodsWe performed a prospective observational cohort study on 55 patients with mood disorders (major depressive disorder [MDD] and bipolar disorder type 1 [BD I] and 2 [BD II]) for 2 years. A smartphone app for self-recording daily mood scores and detecting light exposure (using the installed sensor) were provided. From daily worn activity trackers, digital log data of activity, sleep, and heart rate were collected. Passive digital phenotypes were processed into 130 features based on circadian rhythms, and a mood prediction algorithm was developed by random forest.ResultsThe mood state prediction accuracies for the next 3 days in all patients, MDD patients, BD I patients, and BD II patients were 65%, 65%, 64%, and 65% with 0.7, 0.69, 0.67, and 0.67 area under the curve (AUC) values, respectively. The accuracies of all patients for no episode (NE), depressive episode (DE), manic episode (ME), and hypomanic episode (HME) were 85.3%, 87%, 94%, and 91.2% with 0.87, 0.87, 0.958, and 0.912 AUC values, respectively. The prediction accuracy in BD II patients was distinctively balanced as high showing 82.6%, 74.4%, and 87.5% of accuracy (with generally good sensitivity and specificity) with 0.919, 0.868, and 0.949 AUC values for NE, DE, and HME, respectively.ConclusionsOn the basis of the theoretical basis of chronobiology, this study proposed a good model for future research by developing a mood prediction algorithm using machine learning by processing and reclassifying digital log data. In addition to academic value, it is expected that this study will be of practical help to improve the prognosis of patients with mood disorders by making it possible to apply actual clinical application owing to the rapid expansion of digital technology.

Dataset Information

Circadian rhythm of COPD symptoms in clinically based phenotypes. Results from the STORICO Italian observational study.

Publications

Circadian rhythm of COPD symptoms in clinically based phenotypes. Results from the STORICO Italian observational study.

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