Cardiorespiratory signature of neonatal sepsis: development and validation of prediction models in 3 NICUs.
Cardiorespiratory signature of neonatal sepsis: development and validation of prediction models in 3 NICUs.
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DOI：
10.1038/s41390-022-02444-7
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发表时间：
2023-06
影响因子：
3.6
通讯作者：
Sullivan, Brynne A.
中科院分区：
文献类型：
--
作者：
Kausch, Sherry L.;Brandberg, Jackson G.;Qiu, Jiaxing;Panda, Aneesha;Binai, Alexandra;Isler, Joseph;Sahni, Rakesh;Vesoulis, Zachary A.;Moorman, J. Randall;Fairchild, Karen D.;Lake, Douglas E.;Sullivan, Brynne A.
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 cardiorespiratory data may improve early sepsis detection. We hypothesized that heart rate (HR) and oxygenation (SpO2) data contain signatures that improve sepsis risk prediction over HR or demographics alone. We analyzed cardiorespiratory data from very low birth weight (VLBW, <1500g) infants admitted to three NICUs. We developed and externally validated four machine learning models to predict LOS using features calculated every 10m: mean, standard deviation, skewness, kurtosis of HR and SpO2, and cross-correlation. We compared feature importance, discrimination, calibration, and dynamic prediction across models and cohorts. We built models of demographics and HR or SpO2 features alone for comparison with HR-SpO2 models. Performance, feature importance, and calibration were similar among modeling methods. 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 dynamic performance. 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.
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影响因子：
3.6
作者：
Griffin, MP;O'Shea, TM;Moorman, JR
通讯作者：
Moorman, JR
影响因子：
8
作者：
Griffin, MP;Lake, DE;Moorman, JR
通讯作者：
Moorman, JR
影响因子：
8
作者：
Griffin, MP;Lake, DE;Moorman, JR
通讯作者：
Moorman, JR
影响因子：
8
作者：
Griffin, MP;Moorman, JR
通讯作者：
Moorman, JR
影响因子：
2.9
作者：
Di Fiore, J. M.;Poets, C. F.;MacFarlane, P.
通讯作者：
MacFarlane, P.