Cardiorespiratory signature of neonatal sepsis: Development and validation of prediction models in 3 NICUs

Abstract

Background: Heart rate characteristics aid early detection of late-onset sepsis (LOS), but respiratory data contain additional signatures of illness due to infection. Predictive models using machine learning and cardiorespiratory data may improve early sepsis detection. Objective: Test the hypothesis that heart rate (HR) and oxygenation (SpO2) data contain signatures that improve continuous sepsis risk prediction over HR or demographics alone. Methods: We analyzed cardiorespiratory data from very low birth weight (VLBW, <1500g) infants admitted to three NICUs from 2012-2021 with annotated blood cultures. We developed and externally validated four machine learning models to predict imminent LOS using features calculated every 10m: mean, standard deviation, skewness, kurtosis of HR and SpO2, and their cross-correlation. We compared feature importance, discrimination, calibration, and dynamic risk prediction across modeling methods and cohorts. We built logistic regression models of demographics and HR or SpO2 features alone for comparison with HR-SpO2 models. Results: Data were available for 2,494 VLBW infants who had 302 LOS events. Performance, feature importance, and calibration were similar among modeling methods. SD of HR, skewness of HR, and kurtosis of SpO2 ranked as important features most consistently across models. All models had favorable external validation performance. The HR-SpO2 model performed better than models using either HR or SpO2 alone. Demographics improved the discrimination of all physiologic data models but dampened their dynamic performance. Conclusion: Cardiorespiratory signatures detect LOS in VLBW infants at 3 NICUs. Demographics risk-stratify, but predictive modeling with both HR and SpO2 features provides the best dynamic risk prediction.

Competing Interest Statement

JRM and DEL own stock in Medical Prediction Sciences Corporation. JRM is a consultant for Nihon Kohden Digital Health Solutions. ZAV is a consultant for Medtronic. All other authors have no financial conflicts to disclose. No authors have any non-financial conflicts of interest to disclose.

Funding Statement

We acknowledge the following grants for funding the work presented in this manuscript: K23 HD097254 [PI: B Sullivan]; R01 HD092071 [Co-PIs KD Fairchild & JR Moorman, Co-I DE Lake] K23NS111086 [PI: Z Vesoulis]

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The IRB of University of Virginia gave waiver of consent approval for this work. The IRB of Columbia University gave waiver of consent approval for this work. The IRB of Washington University in St. Louis gave waiver of consent approval for this work.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

All data produced in the present study are available upon reasonable request to the authors

留言 (0)

沒有登入
gif