Sepsis and Mortality Prediction in Very Low Birth Weight Infants: Analysis of HeRO and nSOFA.

OBJECTIVE: Scores to predict sepsis or define sepsis severity could improve care for very low birth weight (VLBW) infants. The heart rate characteristics (HRC) index (HeRO score) was developed as an early warning system for late-onset sepsis (LOS), and also rises before necrotizing enterocolitis (NEC). The neonatal sequential organ failure assessment (nSOFA) was developed to predict sepsis-associated mortality using respiratory, hemodynamic, and hematologic data. The aim of this study was to analyze the HRC index and nSOFA near blood cultures in VLBW infants relative to diagnosis and sepsis-associated mortality. STUDY DESIGN: Retrospective, single-center study of VLBW infants from 2011 to 2019. We analyzed HRC index and nSOFA around blood cultures diagnosed as LOS/NEC. In a subgroup of the cohort, we analyzed HRC and nSOFA near the first sepsis-like illness (SLI) or sepsis ruled-out (SRO) compared with LOS/NEC. We compared scores by diagnosis and mortality during treatment. RESULTS: We analyzed 179 LOS/NEC, 93 SLI, and 96 SRO blood culture events. In LOS/NEC, the HRC index increased before the blood culture, while nSOFA increased at the time of culture. Both scores were higher in nonsurvivors compared with survivors and in LOS/NEC compared with SRO. The nSOFA 12 hours after the time of blood culture predicted mortality during treatment better than any other time point analyzed (area under the curve 0.91). CONCLUSION: The HRC index provides earlier warning of imminent sepsis, whereas nSOFA after blood culture provides better prediction of mortality. KEY POINTS: · The HRC index and nSOFA provide complementary information on sepsis risk and sepsis-related mortality risk.. · This study adds to existing literature evaluating these risk scores independently by analyzing them together and in cases of not only proven but also suspected infections.. · The impact of combining risk models could be improved outcomes for premature infants..

Predictive Models of Fever, ICU Transfer, and Mortality in Hospitalized Patients With Neutropenia.

Neutropenia is a common side effect of myelosuppressive chemotherapy and is associated with adverse outcomes. Early Warning Scores are used to identify at-risk patients and facilitate rapid clinical interventions. Since few Early Warning Scores have been validated in patients with neutropenia, we aimed to create predictive models and nomograms of fever, ICU transfer, and mortality in hospitalized neutropenic patients. DESIGN: Development of statistical prediction models and nomograms using data from a retrospective cohort study of hospitalized patients with neutropenia. SETTING: University of Virginia Medical Center, a tertiary-care academic medical center in Charlottesville, VA. PATIENTS: The derivation and validation cohorts included hospitalized adult patients with neutropenia who were admitted to the inpatient wards between October 2010 and January 2015, and April 2017 and April 2020, respectively. We defined neutropenia as an absolute neutrophil count of less than 500 cells/mm3. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The derivation cohort included 1,531 hospital admissions in patients with neutropenia. Fever, ICU transfer, and in-hospital mortality occurred in 955 admissions (62%), 297 admissions (19%), and 147 admissions (10%), respectively. In the derivation cohort, the internally validated area under the curves with 95% CI for the fever, ICU transfer, and mortality models were HYPERLINK "callto:0.74%20(0.67-0.84),%200.77"0.74 (0.67-0.84), 0.77 (0.67-0.86), and HYPERLINK "callto:0.95%20(0.0.87-1.0"0.95 (0.0.87-1.0), respectively. The validation cohort included 1,250 admissions in patients with neutropenia. In the validation cohort, the area under the curve (95% CI) for the fever, ICU transfer, and mortality models were HYPERLINK "callto:0.70%20(0.67-0.73),%200.78"0.70 (0.67-0.73), 0.78 (0.72-0.84), and HYPERLINK "callto:0.91%20(0.88-0.94"0.91 (0.88-0.94), respectively. Using these models, we developed clinically applicable nomograms which detected adverse events a median of 4.0-11.4 hours prior to onset. CONCLUSIONS: We created predictive models and nomograms for fever, ICU transfer, and mortality in patients with neutropenia. These models could be prospectively validated to detect high-risk patients and facilitate early clinical intervention to improve patient outcomes.