Reduced oxygen saturation entropy is associated with poor prognosis in critically ill patients with sepsis

Gheorghita, Margaret; Wikner, Matthew; Cawthorn, Anika; Oyelade, Tope; Nemeth, Kristof; Rockenschaub, Patrick; Hernandez, Ferran Gonzalez; Swanepoel, Nel; Lilaonitkul, Watjana; Mani, Ali R.

doi:10.14814/phy2.15546

Cited by 8 publications

(5 citation statements)

References 39 publications

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“…This is further supported by physiological network disconnections along pH-associated axes, which predict mortality independent of SOFA score and KCC. Indeed, the strength of network analysis is the ability to assess the interactions of multiple organ systems in critically ill patients where the risk of multiple organ failure is high and similar findings continue to show significant promise [80,81]. Future studies could benefit from using a multicentre, multinational cohort of ALF patients to validate the findings herein.…”

Section: Discussionmentioning

confidence: 80%

Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure

Oyelade,

Moore,

Mani

2024

Preprint

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Reduced functional connectivity of physiological systems is associated with poor prognosis is critically ill patients. However, physiological network analysis is not commonly used in clinical practice and awaits quantitative evidence. Acute liver failure (ALF) is associated with multiorgan failure and mortality. Prognostication in ALF is highly important for clinical management but is currently dependent on models that do not consider the interaction between organ systems. This study is aimed to examine the impact of physiological network analysis, in prognostication of patients with ALF. Data from 640 adult patients admitted to the ICU for paracetamol-induced ALF were extracted from the MIMIC-III database. Parenclitic network analysis was performed on the routine biomarkers and network clusters were identified using the k-clique percolation method. Network analysis showed that the liver function biomarkers were more clustered in survivors than in non-survivors. Arterial pH was also found to cluster with serum creatinine and bicarbonate in survivors compared with non-survivors, where it clustered with respiratory nodes indicating physiologically distinctive compensatory mechanism. Deviation along the pH-bicarbonate and pH-creatinine axes could significantly predict mortality independent of current prognostic indicators. These results demonstrate that network analysis can provide pathophysiologic insight and predict survival in critically ill patients with ALF.

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Section: Discussionmentioning

confidence: 80%

Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure

Oyelade,

Moore,

Mani

2024

Preprint

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“…Further, variability (entropy) in oxygen saturation (SpO 2 ) was recently assessed by Gheorghita et al. ( 2022 ) in critically ill patients with sepsis showing that SpO 2 entropy can predict mortality independent of Age, SOFA score, and mean SpO 2 . This work corroborated a previous work by Bhogal and Mani ( 2017 ) which showed that variability in oxygen saturation carries information about organ systems uncoupling that possibly drives aging.…”

Section: Network Physiology In Other Diseasesmentioning

confidence: 99%

“…This failure to adapt or respond appropriately can be transient and mild, or it can be devastating, as with sepsis and multiple organ failure (Buchman, 2006 ; Ivanov, 2021 ). Reduced complexity in physiological variables such as cardiac rhythm (Satti et al., 2019 ), blood oxygen saturation (Al Rajeh et al., 2021 ; Gheorghita et al., 2022 ), and skin or core body temperature (Bottaro et al., 2020 ; Mowery et al., 2011 ) is associated with increased mortality. Further, reduced functional connectivity between organ systems significantly and independently predicts survival in cirrhosis (Oyelade et al., 2023 ; Zhang et al., 2022 ) or sepsis (Asada et al., 2016 , 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Physiological network approach to prognosis in cirrhosis: A shifting paradigm

Oyelade,

Moore,

Mani

2024

Physiological Reports

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Decompensated liver disease is complicated by multi‐organ failure and poor prognosis. The prognosis of patients with liver failure often dictates clinical management. Current prognostic models have focused on biomarkers considered as individual isolated units. Network physiology assesses the interactions among multiple physiological systems in health and disease irrespective of anatomical connectivity and defines the influence or dependence of one organ system on another. Indeed, recent applications of network mapping methods to patient data have shown improved prediction of response to therapy or prognosis in cirrhosis. Initially, different physical markers have been used to assess physiological coupling in cirrhosis including heart rate variability, heart rate turbulence, and skin temperature variability measures. Further, the parenclitic network analysis was recently applied showing that organ systems connectivity is impaired in patients with decompensated cirrhosis and can predict mortality in cirrhosis independent of current prognostic models while also providing valuable insights into the associated pathological pathways. Moreover, network mapping also predicts response to intravenous albumin in patients hospitalized with decompensated cirrhosis. Thus, this review highlights the importance of evaluating decompensated cirrhosis through the network physiologic prism. It emphasizes the limitations of current prognostic models and the values of network physiologic techniques in cirrhosis.

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“…entropy) describes the level of apparent disorder within a system. Low signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular feedback/regulating system leading to (or reflecting) autoregulation failure [9][10][11][12] This, in turn, leaves the brain susceptible to secondary injury. Physiological systems are regulated by multiple, interacting, mechanisms leading to dynamically changing biosignals across different temporal scales.…”

Section: Introductionmentioning

confidence: 99%

Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study

Bögli,

Olakorede,

Veldeman

et al. 2024

Crit Care

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Background Signal complexity (i.e. entropy) describes the level of order within a system. Low physiological signal complexity predicts unfavorable outcome in a variety of diseases and is assumed to reflect increased rigidity of the cardio/cerebrovascular system leading to (or reflecting) autoregulation failure. Aneurysmal subarachnoid hemorrhage (aSAH) is followed by a cascade of complex systemic and cerebral sequelae. In aSAH, the value of entropy has not been established yet. Methods aSAH patients from 2 prospective cohorts (Zurich—derivation cohort, Aachen—validation cohort) were included. Multiscale Entropy (MSE) was estimated for arterial blood pressure, intracranial pressure, heart rate, and their derivatives, and compared to dichotomized (1–4 vs. 5–8) or ordinal outcome (GOSE—extended Glasgow Outcome Scale) at 12 months using uni- and multivariable (adjusted for age, World Federation of Neurological Surgeons grade, modified Fisher (mFisher) grade, delayed cerebral infarction), and ordinal methods (proportional odds logistic regression/sliding dichotomy). The multivariable logistic regression models were validated internally using bootstrapping and externally by assessing the calibration and discrimination. Results A total of 330 (derivation: 241, validation: 89) aSAH patients were analyzed. Decreasing MSE was associated with a higher likelihood of unfavorable outcome independent of covariates and analysis method. The multivariable adjusted logistic regression models were well calibrated and only showed a slight decrease in discrimination when assessed in the validation cohort. The ordinal analysis revealed its effect to be linear. MSE remained valid when adjusting the outcome definition against the initial severity. Conclusions MSE metrics and thereby complexity of physiological signals are independent, internally and externally valid predictors of 12-month outcome. Incorporating high-frequency physiological data as part of clinical outcome prediction may enable precise, individualized outcome prediction. The results of this study warrant further investigation into the cause of the resulting complexity as well as its association to important and potentially preventable complications including vasospasm and delayed cerebral ischemia.

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Reduced oxygen saturation entropy is associated with poor prognosis in critically ill patients with sepsis

Cited by 8 publications

References 39 publications

Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure

Application of physiological network mapping in the prediction of survival in critically ill patients with acute liver failure

Physiological network approach to prognosis in cirrhosis: A shifting paradigm

Predicting outcome after aneurysmal subarachnoid hemorrhage by exploitation of signal complexity: a prospective two-center cohort study

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