Risk prediction model for 24-hour mortality in preterm infants using lactate and blood gas analysis: A machine learning approach and retrospective cohort study

Introduction

Despite the fact that child mortality has decreased significantly in recent decades, neonatal mortality remains the major contributor¹ and prematurity is the largest direct cause of death in this population.² More than half of neonatal deaths occur within the first three days of life.³ In this context, several predictive models use prenatal and immediately post-natal factors to predict death. However, in the neonatal intensive care unit (NICU) the patient’s condition changes over time. Moreover, more than 30% of neonatal deaths occur after three days of life.³ Real-time features, rather than non-modifiable baseline variables, may provide a more customized evaluation of mortality risk.

Predictive models are becoming increasingly popular in clinical research. A number of conditions have been predicted using machine learning techniques, from mortality in intensive care units to morbidities, such as acute kidney injury, septic shock, and heart failure.⁴ The models that are now available for assessing neonatal mortality risk are mostly focused on NICU admission.^5–9 Unfortunately, few models use objective criteria to evaluate mortality risk in real-time.

To address this knowledge gap, we conducted a study to compare the effectiveness of machine learning algorithms for predicting NICU mortality using objective laboratorial features. The objective of this study was to evaluate serum lactate and blood gas analysis as a predictor of mortality in extremely low birth weight infants using machine learning algorithms.

Methods

Results

We identified a total of 257 neonates who had at least one blood gas analysis and a paired serum lactate during neonatal intensive care unit stay from 2012 to 2017. Eleven patients were excluded because of missing data. Baseline characteristics are presented in Table 1. The median gestational age was 27.1 (26 – 29.1) weeks and the median birth weight was 746 (600 – 880) grams. We found 1932 blood gas samples with corresponding serum lactate levels.

All models demonstrated good accuracy (91 – 94%) and AUROC results (0.807 – 0.898) when using blood gas measurements with lactate as features. However, their recall (9-29%) was low. The Extreme Gradient Boosting algorithm obtained the highest AUROC score (0.898), accuracy (94.1%), and precision (87.5%) (Table 2). We then used AutoML Tables to determine the importance of each feature associated with 24-hour mortality, and the top three features were, in order: base excess, lactate, and pH levels (Figure 1).

Figure 1. Feature importance.

BE: Base excess; cGA: Corrected gestational age; BIC: HCO3; DOL: Days of life.

When lactate was removed as a feature in machine learning models, the AUROC score of the Extreme Gradient Boosting model dropped considerably (0.807) (Table 3).

Discussion

Our research found that utilizing blood gas samples and lactate, the Extreme Gradient Boosting algorithm may predict 24-hour mortality in extremely low birth weight infants. Lactate can be used to improve predictive models.

In the NICU, an accurate mortality estimate is a valuable tool that assists healthcare providers. However, most mortality predictive models in preterm infants are limited to NICU admission. For assessing the mortality risk in NICU admission, the Score for Neonatal Acute Physiology Perinatal Extension-II (SNAPPE-II) includes vital signs, laboratory, and baseline characteristics.⁵ The Clinical Risk for Infants and Babies (CRIB-II) score considers sex, birth weight, gestational age, temperature, and base excess to assess the mortality risk upon NICU admission. TRIPS-II,⁷ NMR-2000,⁸ and PISA⁹ are other recent models that predict mortality after admission. However, dynamic events in the critical care unit may have an impact on the initial risk. Combining baseline characteristics and real-time features provides a more individualized assessment of mortality risk that evolves as the patient’s health changes. To overcome this problem, Jaskari J et al.,¹⁶ Lee J et al.,¹⁷ and Feng J et al.¹⁸ created predictive mortality models based on vital sign data collected during the NICU stay. However, vital signs such as respiratory rate, heart rate, and blood pressure may vary amongst neonates and there is no consensus on what constitutes a normal reference range.^19,20 Furthermore, in Lee J et al. study, baseline variables (birth weight and gestational age at birth) were more important than vital sign readings.

Lactate and blood gas values can be acquired quickly and is currently a standard clinical practice in intensive care units. The lack of bias from the examiner which contributes to objectivity is an advantage over clinical examination.²¹ As a result, our research combines the predictive capability of machine learning models with the objectivity of a blood gas analysis, which is a simple readily available, and widely used test. Our findings imply that machine learning models based on lactate and blood gas indicators appears to be superior to logistic regression classifiers in predicting 24-hour mortality in extremely low birth weight infants. To our knowledge, this is the first study to include real-time objective laboratorial features to predict death in preterm infants.

Our research has some limitations, which should be noted. First, this is a single-center retrospective study, and generalizing our findings is challenging. Second, larger datasets assist machine learning predictive models, therefore a larger study is required. Third, it is worth noting that using blood gas analysis to forecast mortality is a highly unbalanced problem; in other words, there are far more blood gas samples than events (death). In unbalanced data, we can acquire a high accuracy and AUROC score by simply predicting that all observations belong to the majority class. Lastly, it is important to note that our models had very high precision with low recall, and it should not be used for screening purposes.

Conclusions

Incorporating lactate and blood gas measurements into mortality predictive models may improve real-time risk stratification in preterm infants. Traditional logistic classification models appear to be outperformed by more robust machine learning algorithms. Extreme Gradient Boosting models could be used as a support tool for clinical risk stratification of extremely low birth weight infants in the neonatal intensive care unit.

Data availability