A novel heart rate variability based risk prediction model for septic patients presenting to the emergency department

Mi, Samsudin; Liu, Nan; Sm, Prabhakar; Chong, Si Jack; W, Kit Lye; Koh, Zhi Xiong; Guo, Dan; Rajesh, Radhakrishnan; Ho, Adrienne K.; Ong, Meh

doi:10.1097/md.0000000000010866

Cited by 38 publications

(68 citation statements)

References 84 publications

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“…We previously described a 5-variable Singapore ED Sepsis (SEDS) model to predict the risk of 30-day IHM among septic patients in the ED. [17] The SEDS model was the first risk stratification tool to incorporate HRV parameters with other traditional prognosticators such as patient demographics and vital signs. It was developed via stepwise logistic regression and had improved predictive performance over existing tools that only utilize vital signs in their scoring criteria.…”

Section: Introductionmentioning

confidence: 99%

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Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department

et al. 2019

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Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratification tools, namely the Quick Sequential Organ Failure Assessment (qSOFA), National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), and our previously described Singapore ED Sepsis (SEDS) model, in the prediction of 30-day in-hospital mortality (IHM) among suspected sepsis patients in the ED. Adult patients who presented to Singapore General Hospital (SGH) ED between September 2014 and April 2016, and who met ≥2 of the 4 Systemic Inflammatory Response Syndrome (SIRS) criteria were included. Patient demographics, vital signs and heart rate variability (HRV) measures obtained at triage were used as predictors. Baseline models were created using qSOFA, NEWS, MEWS, and SEDS scores. Candidate models were trained using k-nearest neighbors, random forest, adaptive boosting, gradient boosting and support vector machine. Models were evaluated on F1 score and area under the precision-recall curve (AUPRC). A total of 214 patients were included, of whom 40 (18.7%) met the outcome. Gradient boosting was the best model with a F1 score of 0.50 and AUPRC of 0.35, and performed better than all the baseline comparators (SEDS, F1 0.40, AUPRC 0.22; qSOFA, F1 0.32, AUPRC 0.21; NEWS, F1 0.38, AUPRC 0.28; MEWS, F1 0.30, AUPRC 0.25). A machine learning model can be used to improve prediction of 30-day IHM among suspected sepsis patients in the ED compared to traditional risk stratification tools.

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Section: Introductionmentioning

confidence: 99%

“…It was developed via stepwise logistic regression and had improved predictive performance over existing tools that only utilize vital signs in their scoring criteria. [17]…”

Section: Introductionmentioning

confidence: 99%

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department

et al. 2019

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“…The scores differ in their intended population of use (septic patients versus the general population), intended location of use (ED versus intensive care unit versus general use) and in how much information, and hence time, is required for their calculation. Studies that have utilized these scoring systems in the population of septic patients presenting at the ED have been promising, with the purpose-built MEDS score and the more elaborate scoring systems such as SOFA and APACHE II exhibiting superior predictive abilities [13,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

Combining Heart Rate Variability with Disease Severity Score Variables for Mortality Risk Stratification in Septic Patients Presenting at the Emergency Department

Pong

Fook‐Chong

Koh

et al. 2019

IJERPH

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The emergency department (ED) serves as the first point of hospital contact for many septic patients, where risk-stratification would be invaluable. We devised a combination model incorporating demographic, clinical, and heart rate variability (HRV) parameters, alongside individual variables of the Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation II (APACHE II), and Mortality in Emergency Department Sepsis (MEDS) scores for mortality risk-stratification. ED patients fulfilling systemic inflammatory response syndrome criteria were recruited. National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), quick SOFA (qSOFA), SOFA, APACHE II, and MEDS scores were calculated. For the prediction of 30-day in-hospital mortality, combination model performed with an area under the receiver operating characteristic curve of 0.91 (95% confidence interval (CI): 0.88–0.95), outperforming NEWS (0.70, 95% CI: 0.63–0.77), MEWS (0.61, 95% CI 0.53–0.69), qSOFA (0.70, 95% CI 0.63–0.77), SOFA (0.74, 95% CI: 0.67–0.80), APACHE II (0.76, 95% CI: 0.69–0.82), and MEDS scores (0.86, 95% CI: 0.81–0.90). The combination model had an optimal sensitivity and specificity of 91.4% (95% CI: 81.6–96.5%) and 77.9% (95% CI: 72.6–82.4%), respectively. A combination model incorporating clinical, HRV, and disease severity score variables showed superior predictive ability for the mortality risk-stratification of septic patients presenting at the ED.

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“…In one study, after adjusting for SOFA and acute physiology and chronic health evaluation II (APACHE II) scores, an SDNN of ≤17 ms was associated with a hazard ratio of 6.3 for increased mortality (de Castilho et al, 2017). Measurement of mean RR interval and detrended fluctuation analysis α2 (DFA-α2) alongside age, respiratory rate, and systolic blood pressure created a predictive model for severe sepsis (Samsudin et al, 2018). Combining HRV with other laboratory tests generated a predictive model that could improve the current scores (Barnaby et al, 2019).…”

Section: Variability Characterizes the Biological Systems And Heart mentioning

confidence: 99%

A Personalized Signature and Chronotherapy-Based Platform for Improving the Efficacy of Sepsis Treatment

Kenig

Ilan

2019

Front. Physiol.

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Sepsis remains a major therapeutic challenge and is associated with a high rate of morbidity and mortality. It is a dynamic condition in which multiple parameters change over time, rendering it difficult to overcome the various injurious responses, which worsen the prognosis in these patients. The prognosis of sepsis is associated with a disbalance of compensatory responses to infectious triggers, part of which can be deleterious. Marked inter-and intra-patient variability characterizes the mechanisms that underlie sepsis progression and determine the response to therapy. In this paper, we review some of the data on the use of chronopharmacological approaches for the treatment of patients with sepsis and discuss the role of the autonomic nervous system in the mechanisms associated with immune response and chronotherapy in these patients. We describe the implementation of an individualized platform that is based on the personalized autonomic nervous system, immune, and chronobiologyderived parameters for generating a patient-tailored therapeutic regimen. The notion of overcoming the deleterious compensatory response in a highly dynamic system in sepsis is presented to ensure an improved response to current therapies.

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A novel heart rate variability based risk prediction model for septic patients presenting to the emergency department

Cited by 38 publications

References 84 publications

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department

Combining Heart Rate Variability with Disease Severity Score Variables for Mortality Risk Stratification in Septic Patients Presenting at the Emergency Department

A Personalized Signature and Chronotherapy-Based Platform for Improving the Efficacy of Sepsis Treatment

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