Dataset Information

Prognostication in Advanced Cancer by Combining Actigraphy-Derived Rest-Activity and Sleep Parameters with Routine Clinical Data: An Exploratory Machine Learning Study.

ABSTRACT: Survival prediction is integral to oncology and palliative care, yet robust prognostic models remain elusive. We assessed the feasibility of combining actigraphy, sleep diary data, and routine clinical parameters to prognosticate. Fifty adult outpatients with advanced cancer and estimated prognosis of <1 year were recruited. Patients were required to wear an Actiwatch® (wrist actigraph) for 8 days, and complete a sleep diary. Univariate and regularised multivariate regression methods were used to identify predictors from 66 variables and construct predictive models of survival. A total of 49 patients completed the study, and 34 patients died within 1 year. Forty-two patients had disrupted rest-activity rhythms (dichotomy index (I < O ≤ 97.5%) but I < O did not have prognostic value in univariate analyses. The Lasso regularised derived algorithm was optimal and able to differentiate participants with shorter/longer survival (log rank p < 0.0001). Predictors associated with increased survival time were: time of awakening sleep efficiency, subjective sleep quality, clinician’s estimate of survival and global health status score, and haemoglobin. A shorter survival time was associated with self-reported sleep disturbance, neutrophil count, serum urea, creatinine, and C-reactive protein. Applying machine learning to actigraphy and sleep data combined with routine clinical data is a promising approach for the development of prognostic tools.

SUBMITTER: Patel SD

PROVIDER: S-EPMC9856985 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Prognostication in Advanced Cancer by Combining Actigraphy-Derived Rest-Activity and Sleep Parameters with Routine Clinical Data: An Exploratory Machine Learning Study.

Patel Shuchita Dhwiren SD Davies Andrew A Laing Emma E Wu Huihai H Mendis Jeewaka J Dijk Derk-Jan DJ

Cancers 20230113 2

Survival prediction is integral to oncology and palliative care, yet robust prognostic models remain elusive. We assessed the feasibility of combining actigraphy, sleep diary data, and routine clinical parameters to prognosticate. Fifty adult outpatients with advanced cancer and estimated prognosis of <1 year were recruited. Patients were required to wear an Actiwatch® (wrist actigraph) for 8 days, and complete a sleep diary. Univariate and regularised multivariate regression methods were used t ...[more]

PMID: 36672452

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Project description:BackgroundThe concept of misperception of sleep refers to the estimated discrepancy between subjective and objective measures of sleep. This has been assessed only in a few prior studies in individuals with Bipolar Disorder (BD) as compared to Healthy Controls (HC) and with mixed results.MethodsWe assessed a sample of 133 euthymic individuals with BD and 63 HC for retrospective subjective (Pittsburgh Sleep Quality Index) and objective (21 days of actigraphy recording) measures of total sleep time, sleep latency and sleep efficiency. We first investigated the correlations between these subjective and objective measures in the two groups. We then compared individuals with BD and HC for the absolute values of the differences between subjective and objective sleep parameters, used as a proxy of the magnitude of misperception of sleep. Finally, we undertook regression analyses to assess associations between clinical groups, core demographics, clinical factors and misperception of sleep.ResultsThe correlation coefficients between subjective and objective measures of sleep did not differ between groups (total sleep time: rho = .539 in BD and rho = .584 in HC; sleep latency: rho = .190 in BD and rho = .125 in HC; sleep efficiency: rho = .166 in BD and rho = .222 in HC). Individuals with BD did not differ from HC in the magnitude of misperception of total sleep time, sleep latency nor sleep efficiency. Individuals with BD type 1 misperceived their sleep efficiency significantly more than individuals with BD type 2, with no further difference between BD type 1 and BD type 2 regarding sleep latency and sleep duration misperceptions. Three factors (age, symptoms of obstructive sleep apnea, and mild depressive symptoms), were the main contributors to the magnitude of misperception of sleep.ConclusionsMisperception of sleep was not associated with a diagnosis of BD. In this sample, mild depressive symptoms, older age, or symptoms of obstructive sleep apnea may be related to greater sleep misperception. In that case, the reliability of subjective measures may decrease as the misperception of sleep increases. This study may help guide clinicians in selecting the best approach for assessing sleep (objective versus subjective measures) in individuals with BD.

Project description:BackgroundIsolated rapid-eye-movement sleep behavior disorder (iRBD) is in most cases a prodrome of neurodegenerative synucleinopathies, affecting 1% to 2% of middle-aged and older adults; however, accurate ambulatory diagnostic methods are not available. Questionnaires lack specificity in nonclinical populations. Wrist actigraphy can detect characteristic features in individuals with RBD; however, high-frequency actigraphy has been rarely used.ObjectiveThe aim was to develop a machine learning classifier using high-frequency (1-second resolution) actigraphy and a short patient survey for detecting iRBD with high accuracy and precision.MethodsThe method involved analysis of home actigraphy data (for seven nights and more) and a nine-item questionnaire (RBD Innsbruck inventory and three synucleinopathy prodromes of subjective hyposmia, constipation, and orthostatic dizziness) in a data set comprising 42 patients with iRBD, 21 sleep clinic patients with other sleep disorders, and 21 community controls.ResultsThe actigraphy classifier achieved 95.2% (95% confidence interval [CI]: 88.3-98.7) sensitivity and 90.9% (95% CI: 82.1-95.8) precision. The questionnaire classifier achieved 90.6% accuracy and 92.7% precision, exceeding the performance of the Innsbruck RBD Inventory and prodromal questionnaire alone. Concordant predictions between actigraphy and questionnaire reached a specificity and precision of 100% (95% CI: 95.7-100.0) with 88.1% sensitivity (95% CI: 79.2-94.1) and outperformed any combination of actigraphy and a single question on RBD or prodromal symptoms.ConclusionsActigraphy detected iRBD with high accuracy in a mixed clinical and community cohort. This cost-effective fully remote procedure can be used to diagnose iRBD in specialty outpatient settings and has potential for large-scale screening of iRBD in the general population. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Dataset Information

Prognostication in Advanced Cancer by Combining Actigraphy-Derived Rest-Activity and Sleep Parameters with Routine Clinical Data: An Exploratory Machine Learning Study.

Publications

Prognostication in Advanced Cancer by Combining Actigraphy-Derived Rest-Activity and Sleep Parameters with Routine Clinical Data: An Exploratory Machine Learning Study.

Similar Datasets

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