Digital healthcare systems in a federated learning perspective

"Decision Fatigue" (DF) describes the impaired ability to make decisions because of repeated acts of decision-making. 

Decision fatigue in hospital settings: A scoping review

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.

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

Chronic obstructive pulmonary disease (COPD) patients requiring admission to the intensive care unit (ICU) typically have poor prognosis. In this study, the objective was to construct a prognostic nomogram to estimate the 28-day mortality for COPD patients in ICU. Data of 853 patients from the MIMIC-IV database and MIMIC-CXR-JPG database were extracted. To construct the prognostic nomogram, a multivariate Cox regression analysis was conducted using predictive factors identified based on the least absolute shrinkage and selection operator (LASSO) regression analysis. The performance of the nomogram was assessed by evaluating its discrimination and calibration using the area under the receiver operating characteristic curve (AUC), concordance index (C-index), and calibration curves. The prognostic nomogram included age, SpO2, mean blood pressure and creatinine. The C-index was 0.76 (95% CI 0.71 to 0.81) and AUCs for predicting 7-day, 14-day, and 28-day survival were 0.77 (95% CI 0.70 to 0.84), 0.75 (95% CI 0.68 to 0.81) and 0.72 (95% CI 0.62 to 0.82), respectively. The calibration curves demonstrated good concordance between the predicted outcomes and actual observations. A prognostic nomogram for COPD patients in ICU was developed with reasonable performance. 

Development of a prognostic nomogram among chronic obstructive pulmonary disease patients based on laboratory tests and chest radiography

Abstract Circadian rhythms in humans are biological rhythms that regulate various physiological processes within a 24-hour time frame. Critical illness can disrupt the circadian rhythm, as can environmental and clinical factors, including altered light exposure, organ replacement therapies, disrupted sleep–wake cycles, noise, continuous enteral feeding, immobility, and therapeutic interventions. Nonpharmacological interventions, controlling the ICU environment, and pharmacological treatments are among the treatment strategies for circadian disruption. Nutrition establishes biological rhythms in metabolically active peripheral tissues and organs through appropriate synchronization with endocrine signals. Therefore, adhering to a feeding schedule based on the biological clock, a concept known as “chrononutrition,” appears to be vitally important for regulating peripheral clocks. Chrononutritional approaches, such as intermittent enteral feeding that includes overnight fasting and consideration of macronutrient composition in enteral solutions, could potentially restore circadian health by resetting peripheral clocks. However, due to the lack of evidence, further studies on the effect of chrononutrition on clinical outcomes in critical illness are needed. The purpose of this review was to discuss the role of chrononutrition in regulating biological rhythms in critical illness, and its impact on clinical outcomes. 

Chrononutrition in Critical Illness

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.

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

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