Neonatal risk mortality scores as predictors for health-related quality of life of infants treated in NICU: a prospective cross-sectional study

Predictive Accuracy of Infant Clinical Sign Algorithms for Mortality in Young Infants Aged 0 to 59 Days: A Systematic Review

CONTEXT

Clinical sign algorithms are a key strategy to identify young infants at risk of mortality.

OBJECTIVE

Synthesize the evidence on the accuracy of clinical sign algorithms to predict all-cause mortality in young infants 0-59 days.

DATA SOURCES

MEDLINE, Embase, CINAHL, Global Index Medicus, and Cochrane CENTRAL Registry of Trials.

STUDY SELECTION

Studies evaluating the accuracy of infant clinical sign algorithms to predict mortality.

DATA EXTRACTION

We used Cochrane methods for study screening, data extraction, and risk of bias assessment. We determined certainty of evidence using Grading of Recommendations Assessment Development and Evaluation.

RESULTS

We included 11 studies examining 26 algorithms. Three studies from non-hospital/community settings examined sign-based checklists (n = 13). Eight hospital-based studies validated regression models (n = 13), which were administered as weighted scores (n = 8), regression formulas (n = 4), and a nomogram (n = 1). One checklist from India had a sensitivity of 98% (95% CI: 88%-100%) and specificity of 94% (93%-95%) for predicting sepsis-related deaths. However, external validation in Bangladesh showed very low sensitivity of 3% (0%-10%) with specificity of 99% (99%-99%) for all-cause mortality (ages 0-9 days). For hospital-based prediction models, area under the curve (AUC) ranged from 0.76-0.93 (n = 13). The Score for Essential Neonatal Symptoms and Signs had an AUC of 0.89 (0.84-0.93) in the derivation cohort for mortality, and external validation showed an AUC of 0.83 (0.83-0.84).

LIMITATIONS

Heterogeneity of algorithms and lack of external validation limited the evidence.

CONCLUSIONS

Clinical sign algorithms may help identify at-risk young infants, particularly in hospital settings; however, overall certainty of evidence is low with limited external validation.

Robust health-score based survival prediction for a neonatal mouse model of polymicrobial sepsis

Infectious disease and sepsis represent a serious problem for all, but especially in early life. Much of the increase in morbidity and mortality due to infection in early life is presumed to relate to fundamental differences between neonatal and adult immunity. Mechanistic insight into the way newborns' immune systems handle infectious threats is lacking; as a result, there has only been limited success in providing effective immunomodulatory interventions to reduce infectious mortality. Given the complexity of the host-pathogen interactions, neonatal mouse models can offer potential avenues providing valuable data. However, the small size of neonatal mice hampers the ability to collect biological samples without sacrificing the animals. Further, the lack of a standardized metric to quantify newborn mouse health increases reliance on correlative biomarkers without a known relationship to 'clinical' outcome. To address this bottleneck, we developed a system that allows assessment of neonatal mouse health in a readily standardized and quantifiable manner. The resulting health scores require no special equipment or sample collection and can be assigned in less than 20 seconds. Importantly, the health scores are highly predictive of survival. A classifier built on our health score revealed a positive relationship between reduced bacterial load and survival, demonstrating how this scoring system can be used to bridge the gap between assumed relevance of biomarkers and the clinical outcome of interest. Adoption of this scoring system will not only provide a robust metric to assess health of newborn mice but will also allow for objective, prospective studies of infectious disease and possible interventions in early life.

Predicting deaths in a resource-limited neonatal intensive care unit in Nepal

Background

This study aimed to determine whether the Neonatal Acute Physiology (SNAP) scoring system (SNAP II) and with perinatal extension (SNAP II PE) can be used to predict neonatal deaths in a resource-limited neonatal intensive care unit in Nepal.

Methods

A prospective observational study was conducted in a neonatal intensive care unit (NICU) of Kanti Children's Hospital in Kathmandu, Nepal. Data required for the SNAP II and SNAP II PE scores were collected. The relationships between the SNAP II and SNAP II PE scores and neonatal mortality were analyzed.

Results

There were 135 neonates admitted during the 6 month study period, of whom 126 met the inclusion criteria. Of these 126 neonates, 29 (23.0%) died. Mortality was 83% (5/6) when SNAP II was >40, and 66.7% (6/9) when SNAP II PE was >50. A SNAP II score of ≥12 had a sensitivity of 75.9%, and specificity of 73.2% for predicting mortality, and a SNAP II PE score of ≥14 had a sensitivity of 82.8% and specificity of 67.0% for it.

Conclusions

SNAP II and SNAP II PE scoring of neonates can be used to predict prognosis of neonates in resource-limited NICUs in Nepal.