Pathophysiologic Signatures of Bloodstream Infection in Critically Ill Adults

Zimmet, Alex; Clark, Matthew; Gadrey, Shrirang M; Bell, Taison D.; Zimmet, Amanda M.; Moorman, J. Randall; Moore, Christopher C.

doi:10.1097/cce.0000000000000191

Cited by 4 publications

(1 citation statement)

References 37 publications

(41 reference statements)

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“…We used multivariable logistic regression adjusted for repeated measures to relate physiologic data to the hypoglycemia outcome on the entire cohort (21). We systematically built the model by: 1) removing, blinded to the outcome, the most predictable features correlated more than R 2 of 0.9 with other features, 2) imputing missing values with median values for the study population, 3) building a model with all remaining features and restricted cubic splines (three knots) on each feature with enough unique values (21, 22), adjusting for repeated measures using the Huber-White method (21), and 4) using ridge regression (23) to penalize model coefficients, shrinking the effective degrees of freedom to maximize the corrected Akaike information criterion (24, 25).…”

Section: Methodsmentioning

confidence: 99%

Pathophysiologic Signature of Impending ICU Hypoglycemia in Bedside Monitoring and Electronic Health Record Data: Model Development and External Validation

Horton

Barros

Andris

et al. 2021

Critical Care Medicine

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We tested the hypothesis that routine monitoring data could describe a detailed and distinct pathophysiologic phenotype of impending hypoglycemia in adult ICU patients. DESIGN:Retrospective analysis leading to model development and validation. SETTING:All ICU admissions wherein patients received insulin therapy during a 4-year period at the University of Virginia Medical Center. Each ICU was equipped with continuous physiologic monitoring systems whose signals were archived in an electronic data warehouse along with the entire medical record. PATIENTS:Eleven thousand eight hundred forty-seven ICU patient admissions. INTERVENTIONS:The primary outcome was hypoglycemia, defined as any episode of blood glucose less than 70 mg/dL where 50% dextrose injection was administered within 1 hour. We used 61 physiologic markers (including vital signs, laboratory values, demographics, and continuous cardiorespiratory monitoring variables) to inform the model. MEASUREMENTS AND MAIN RESULTS:Our dataset consisted of 11,847 ICU patient admissions, 721 (6.1%) of which had one or more hypoglycemic episodes. Multivariable logistic regression analysis revealed a pathophysiologic signature of 41 independent variables that best characterized ICU hypoglycemia. The final model had a cross-validated area under the receiver operating characteristic curve of 0.83 (95% CI, 0.78-0.87) for prediction of impending ICU hypoglycemia. We externally validated the model in the Medical Information Mart for Intensive Care III critical care dataset, where it also demonstrated good performance with an area under the receiver operating characteristic curve of 0.79 (95% CI, 0.77-0.81). CONCLUSIONS:We used data from a large number of critically ill inpatients to develop and externally validate a predictive model of impending ICU hypoglycemia. Future steps include incorporating this model into a clinical decision support system and testing its effects in a multicenter randomized controlled clinical trial.

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Section: Methodsmentioning

confidence: 99%

Pathophysiologic Signature of Impending ICU Hypoglycemia in Bedside Monitoring and Electronic Health Record Data: Model Development and External Validation

Horton

Barros

Andris

et al. 2021

Critical Care Medicine

Self Cite

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Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration

Monfredi

Moore

Sullivan

et al. 2023

Journal of Electrocardiology

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Pathophysiological Responses to Bloodstream Infection in Critically Ill Transplant Recipients Compared With Non-Transplant Recipients

Qiu,

Zimmet,

Bell

et al. 2023

Clinical Infectious Diseases

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Background Identification of bloodstream infection (BSI) in transplant recipients may be difficult due to immunosuppression. Accordingly, we aimed to compare responses to BSI in critically ill transplant and non-transplant recipients and to modify systemic inflammatory response syndrome (SIRS) criteria for transplant recipients. Methods We analyzed univariate risks and developed multivariable models of BSI with 27 clinical variables from adult intensive care unit (ICU) patients at the University of Virginia (UVA) and at the University of Pittsburgh (Pitt). We used Bayesian inference to adjust SIRS criteria for transplant recipients. Results We analyzed 38.7 million hourly measurements from 41,725 patients at UVA, including 1,897 transplant recipients with 193 episodes of BSI, and 53,608 patients at Pitt, including 1,614 transplant recipients with 768 episodes of BSI. The univariate responses to BSI were comparable in transplant and non-transplant recipients. The area under the receiver operating characteristic curve (AUC) was 0.82 (95% confidence interval [CI], 0.80-0.83) for the model using all UVA patient data and 0.80 (95% CI, 0.76-0.83) when using only transplant recipient data. The UVA all-patient model had an AUC of 0.77 (95%CI, 0.76-0.79) in non-transplant recipients and 0.75 (95% CI, 0.71-0.79) in transplant recipients at Pitt. The relative importance of the 27 predictors was similar in transplant and non-transplant models. An upper temperature of 37.5°C in SIRS criteria improved reclassification performance in transplant recipients. Conclusion Critically ill transplant and non-transplant recipients had similar responses to BSI. An upper temperature of 37.5°C in SIRS criteria improved BSI screening in transplant recipients.

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Pathophysiologic Signatures of Bloodstream Infection in Critically Ill Adults

Abstract: Supplemental Digital Content is available in the text.

Cited by 4 publications

References 37 publications

Pathophysiologic Signature of Impending ICU Hypoglycemia in Bedside Monitoring and Electronic Health Record Data: Model Development and External Validation

Pathophysiologic Signature of Impending ICU Hypoglycemia in Bedside Monitoring and Electronic Health Record Data: Model Development and External Validation

Continuous ECG monitoring should be the heart of bedside AI-based predictive analytics monitoring for early detection of clinical deterioration

Pathophysiological Responses to Bloodstream Infection in Critically Ill Transplant Recipients Compared With Non-Transplant Recipients

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