Predictive value of laboratory indicators for in-hospital death in children with community-onset sepsis: a prospective observational study of 266 patients

Introduction

Sepsis, a syndrome of physiological, pathological and biochemical abnormalities induced by infection, is a major public health concern.1 Sepsis incidence and the percentage of all global deaths (from any cause) which were related to sepsis peaked in early childhood.2–4 The most recent estimates of the global burden of sepsis indicate that there were 20.3 million incident sepsis cases resulting in more than 2.9 million deaths among children younger than 5 years, and 4.9 million sepsis cases caused 45 400 deaths among children and adolescents aged 5–19 years globally in 2017.3 Sepsis remains a leading cause of death in the intensive care unit (ICU) of hospitals, with the case fatality rate (CFR) from paediatric sepsis of 25%.5–7 However, the prospective differentiation of patients with sepsis at low risk of death remains a major clinical challenge for improving overall survival. Thus, predictors to identify patients who are at higher risk of sepsis-specific mortality are of great clinical relevance.

A traditional sepsis outcome prediction approach, the Sepsis-related Organ Failure Assessment (SOFA), has a good correlation with mortality.8 Furthermore, there is a body of literature exploring the application of SOFA to critically ill paediatric patients in both the paediatric ICU (PICU) and emergency department.9 10 Other scoring systems for assessing prognosis in paediatric patients with clinically critical condition, such as Paediatric Risk of Mortality III score,11 Paediatric Index of Mortality 312 and the Paediatric Early Warning Score are recognised for their ability to generate accurate probabilistic survival estimates.13 However, it is essential to acknowledge that these scoring systems, while providing valuable prognostic insights, may exhibit a trade-off between sensitivity and specificity. In particular, there may be instances where optimising sensitivity takes precedence over specificity, considering the critical nature of predicting adverse outcomes in paediatric patients. More recently, more attention has been directed towards identification of metabolite biomarkers such as metabolites or proteins.14 15 Novel biomarkers such as interleukin 8 and the neutrophil CD64 index have been reported to be stratification tools for diagnosing sepsis and predicting outcomes,16 17 but they are not yet readily available at the bedside. Search for biomarkers that could provide a reliable and early estimate of the risk stratification and mortality risk estimate still continues, and currently, there are few useful predictive markers in clinical practice to identify which patients are at the highest risk of death.

The aim of this paper is to assess the association between laboratory biomarkers readily available for clinical use and the in-hospital death of patients with sepsis.

Methods

Results

Clinical characteristics of patients with sepsis

There were a total of 569 children with sepsis admitted to the PICU from November 2016 and January 2020. 290 children were excluded based on our predetermined inclusion criteria. Of the 279 children initially enrolled, 266 (95%) were included in this study. The flow chart of the study is shown in figure 1. The median age of the patients was 13.5 (4.0, 39.0) months, and 181 (68.5%) were male.

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Figure 1

The flow chart of study screening selection process. PICU, paediatric intensive care unit.

The patients were divided into two groups: sepsis non-survivors (n=44, 16.5%) and sepsis survivors (n=222, 83.5%). There was no statistical difference in age and gender between groups (table 1). Length of PICU stay was significantly lower in the sepsis non-survivors group compared with the sepsis survivors group (median values, 11 and 16 days, respectively; p=0.012). Compared with the survivors group, the non-survivors group exhibited higher rates of mechanical ventilation (93.18% vs 54.50%, p<0.001), bloodstream infections (68.18% vs 40.54%, p<0.001) and gastrointestinal infection (43.18% vs 16.67%, p<0.001).

Table 1

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Clinical characteristics of patients in the sepsis group who were survivors and non-survivors

There were significant differences between groups in the frequency of individual complications, including ARDS, MODS, DIC and septic shock, and in the organ failure aspect, including respiratory, heart, nervous system, liver and renal failure (all p<0.001).

Risk factors of in-hospital death in patients with sepsis

LASSO regression was employed to identify characteristic variables associated with in-hospital death from sepsis. Figure 2A illustrates the distribution of LASSO coefficients for clinical features among 39 clinical parameters, while figure 2B displays the tuning parameter (λ) selection in the LASSO model using 10-fold cross-validation based on the minimum criterion. Ultimately, 13 parameters were found to be related to prognosis: PT, APTT, AST, platelet, CRP, BE, Ca²⁺, HBDB, TG, HDL, BUN, fibrinogen degradation products(FDP) and pH.

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Figure 2

Feature selection using the Least Absolute Shrinkage and Selection Operator regression model. (A) the distribution of LASSO coefficients. (B) LASSO model with the parameter (λ) adjusted by 10-fold cross-validation.

In the final multivariate Cox regression model (figure 3), we found that elevated TG remained the most significant risk factor of death (HR=1.469, 95% CI 1.010 to 2.136, p=0.044), followed by BE (HR=1.131, 95% CI 1.046 to 1.223, p=0.002). For every 0.01 increase in pH, we observed a decrease in the risk of death by about 5% (HR=0.95, 95% CI 0.93 to 0.97, p<0.001) in the final model.

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Figure 3

Cox regression hazard analysis of laboratory indicators screened by Least Absolute Shrinkage and Selection Operator analysis. ^aPer 0.1 mmol/L increase; ^bPer 0.1 mmol/L increase; ^cPer 0.01 increase. APTT, activated partial thromboplastin time; AST, aspartate transaminase; BE, base excess; BUN, blood urea nitrogen; CRP, C reactive protein; FDP, fibrinogen degradation products;HBDB, hydroxybutyrate; HDL, high-density lipoprotein; PLT, platelet; PT, prothrombin time; TG, triglyceride. Statistically significant variables have been bolded.

The predictive value of factors for differentiating non-survivors and survivors in patients with sepsis

In order to estimate the relative accuracy and generalisation properties of the multivariate Cox model, a ROC curve analysis was performed (figure 4), obtaining an AUC of pH (0.71, 0.62 to 0.81, p<0.001), TG (0.61, 0.51 to 0.70, p=0.013) and BE (0.64, 0.54 to 0.74, p=0.002). The combined diagnosis of the three indicators of TG+BE+pH has the best AUC (AUC=0.77, 95% CI 0.69 to 0.85, p<0.001), with a 68% sensitivity and 80% specificity (table 2).

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Figure 4

Receiver operating characteristic curve of factors in predicting in-hospital mortality of children with sepsis. BE, base excess; TG, triglyceride.

Table 2

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Accuracy of multivariate Cox model in differentiating non-survivors and survivors among patients with sepsis

Discussion

Despite all efforts to improve sepsis mortality, determining reliable mortality risk is still challenging and a very difficult process early in the clinical setting. To the best of our knowledge, this is one of the few prospective multicentre studies to address this clinical dilemma, describing clinical characteristics associated with 266 patients with sepsis.

Our data suggest that the CFR from paediatric sepsis was 16.5% (44 of 266). This finding is consistent with a meta-analysis conducted among 94 studies and 7561 children that showed that pooled CFRs were 31.7% and 19.3% in developing and developed countries, respectively.20 In addition, a study revealed that the mortality rate of paediatric patients with severe sepsis managed in Japanese PICUs was 19%.21

Our results showed that most of the sepsis cases were children younger than 1.5 years, with a higher proportion of males. About one-sixth of our patients with sepsis died in hospital and half developed severe complications such as MODS, ARDS, DIC and shock. On the other hand, the most common complications were MODS (90.9%) and shock (86.4%) in the non-survivors group. Septic shock, the most severe form of sepsis, is commonly associated with MODS.22 Respiratory failure (86.4%) was the most common organ failure whether in the total population or in the non-survivors group. Risks of respiratory infections (72.8%) and mechanical ventilation (93.2%) were highest for the non-survivors group. Unlike in adult studies, only 40% of patients with severe sepsis received mechanical ventilation, and the association between case volume and mortality was more modest among severe sepsis cases requiring mechanical ventilation.23

In this study, we show that platelets were markedly reduced in the non-survivors group. Although not significant in the multivariate analysis, low-level platelets were a significant risk factor in the univariate model. The thrombocytopenia that occurs in sepsis is believed to reflect accelerated platelet consumption due to increased blood coagulation.24 Alterations in platelet number and function are common in both clinical and experimental sepsis. Previous research has implicated that both thrombocytopenia and platelet hyper-reactivity have been associated with increased sepsis-related morbidity and mortality.25 Also, a recent study has revealed sepsis alters the transcriptional and translational landscape of human and murine platelets, and sepsis-induced upregulation of ITGA2B predicts mortality.26

The mechanism of poor outcomes in patients with sepsis may be multiple and cannot be ascertained by our data. An excessive inflammatory activation response by pathogens, massive activation of coagulation and various types of cell death seem to be the main mechanisms of the high mortality from sepsis.27 This is consistent with the increase in inflammatory markers, such as CRP, procalcitonin and coagulation indices including PT, APTT and INR in our study. Prolonged APTT and PT indicate a consumption or inhibition of clotting factors. Coagulation activation may have a prominent role in the patients with sepsis, and this may explain their poorer clinical course. Although our studies have observed a possible relationship between sepsis mortality and coagulation, the mechanism is unclear. It is possible that blood clotting is the major cause of host death following inflammasome activation,28 and inflammasomes, such as NLRP3, NALP1 and caspase-11, bridge inflammation with thrombosis.29 30

Organ dysfunction occurs frequently in and represents a significant comorbidity of sepsis, but the mechanistic basis for sepsis-induced organ dysfunction is controversial. Our data suggest that sepsis altered biochemical markers, (eg, LDH, HBDB, CK, CK-MB, ALT, AST, ALB, Cr, BUN) in the heart, liver and kidney. Sepsis causes profound myocardial depression. There is also evidence that bedside echocardiography frequently reveals severe biventricular dysfunction.31 We did not find that CK-MB was significantly associated with sepsis mortality in multivariate model. Other studies have shown that no significant association was found between left ventricular ejection fraction and mortality.32 More research is needed to explore the other possible predictions driving the relationship between sepsis-related cardiac function and death. Sepsis-associated acute kidney injury is also a common, life-threatening complication, increasing in-hospital mortality sixfold to eightfold, up to a quarter of whom will require renal replacement therapy.33 In our study, we observed that half of patients had renal failure, and BUN and Cr were significantly increased in the non-survivor group indicating kidney dysfunction as the probable cause of death. BUN was a risk factor of in-hospital death in univariate analysis. Other studies have shown similar findings. A higher BUN level is independently linked with the presence and severity of neonatal sepsis.34 A retrospective cohort study presented that BUN with 41.1 mg/dL as a cut-off level was associated with 28-day mortality in adult patients with sepsis.35

We found that ALT and AST were significantly elevated, and ALB decreased slightly in the non-survivors group. Basic research shows liver dysfunction is an early and commonplace event in the rat model of sepsis and detected changes significantly precede conventional markers and are reflected by early alterations in plasma bile acids.27 A deeper understanding of sepsis-induced changes at the organ level through multiomics analysis of several tissues (including liver, heart, kidney, etc) would provide an essential guide for clinical practice.

Alterations in lipid metabolism and the activation of lipid signalling pathways have important physiological and pathological meanings in the progression of sepsis. In the non-survivors group, we demonstrate elevated plasma levels of TG and decreased levels of HDL and LDL. We found that elevated TGs were the most significant risk factor of sepsis in-hospital death. In addition, similar adult results have been reported that baseline lipid values, particularly TG concentrations, were also associated with hospital mortality in patients with sepsis.36 Barber et al also found that low HDL is associated with poor outcome in sepsis.37 We recommend the utility of plasma TG as a prognostic marker. Lipid mediators play an important role in the proinflammatory and counter-regulatory anti-inflammatory changes in the microvasculature in sepsis. Future research focusing on production and metabolism of various lipid species in sepsis has the potential to identify novel biomarkers and therapeutic targets.

Our results revealed that pH and BE were important variables for in-hospital death in patients with sepsis. pH was different and was maintained within the normal range between non-survivors and survivors. It seems that tissue hypoperfusion and metabolic disturbances may not be evident in the early stages. However, very small pH and BE changes were very important in predicting sepsis outcomes. Notably, meta-analyses provide pooled estimates of pH (7.21 vs 7.31) and base deficit (4.6 vs 2.7) between non-survivors and survivors.38 Although lactate for sepsis diagnosis has been well identified, it was not found to be a determinant of sepsis mortality in our multivariate model. But some investigations pointed out that lactate normalisation (<2 mmol/L) within 4 hours was associated with decreased persistent organ dysfunction.39 Calcium levels were significantly decreased, while potassium, sodium and chloride ions exhibited no difference in non-survivors on admission. Previous research has shown that movement of calcium and phosphorus is part of the process in which vitamin D, parathyroid hormone, fibroblast growth factor and klotho interact with sepsis defence mechanisms.40

Despite our promising findings, our study has limitations. First, data on patients’ sepsis status serially during hospitalisation were not available for patients included in the present study. Second, we included only patients presenting with community-acquired sepsis; those with complex underlying medical conditions were excluded. Thus, patients included in our study may be slightly healthier than the general population. The limited cohort size may impact the external validity of our findings, particularly in terms of generalisability to broader sepsis populations. Lastly, our study only explored objective laboratory tests as predictors of sepsis mortality. Future studies may benefit from incorporating scoring systems or other clinical parameters to improve the identification of death from sepsis.

Conclusion

In conclusion, we have shown that the mortality rate of community-acquired sepsis in Chinese PICUs was 16.5%. Laboratory factors of TG, BE and pH during the first 24 hours after ICU admission were associated with in-hospital mortality in PICU patients with sepsis. The combination of the three indices had high diagnostic value. These results indicate that these readily available laboratory biomarkers have the potential to effectively identify previously well paediatric patients with community-acquired sepsis at high risk of in-hospital mortality. Potential benefits from timely risk stratification could allow for closer monitoring and more timely interventions.