Effective diagnosis of sepsis in critically ill children using probabilistic graphical model

Introduction

Sepsis in critically ill children places them at risk of death and long-term morbidity (1,2). Early detection of sepsis allows for prompt treatment, while the early distinction of a child who is not in sepsis enables the option of timely de-escalation of antibiotic administration. However, these tasks remain challenging for clinicians in the pediatric intensive care unit (PICU) due to the complex nature and heterogeneity of pediatric sepsis. Probabilistic graphical models (PGM) provide an inferencing framework that can aid clinicians at the bedside. It uses graphs (nodes and edges) to model conditional dependencies between variables in complex domains and produce robust predictions (3). With graphical representation, PGM is more comprehensive and interpretable than other black-box machine learning methodologies (e.g., Neural Network, Support Vector Machine) (4). Bayesian Network (BN), a subclass of PGM, has been proven effective in disease diagnosis; adult sepsis is one of them (5-8). However, studies applying PGM in pediatric sepsis remain limited.

Our hypothesis for this preliminary study was that PGM is a robust diagnostic tool for pediatric sepsis. We employed Tree Augmented Naive Bayes (TAN), a PGM method, to develop diagnosis models to test our hypothesis and investigate the effectiveness of PGM in pediatric sepsis diagnosis. We present the following article in accordance with the TRIPOD reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-22-510/rc).

Methods

Study design

We performed a retrospective study using the Pediatric Intensive Care Dataset (PICD), a publicly available dataset of patients aged 0–18 years admitted to the intensive care units (ICU) at Children’s Hospital of Zhejiang University School of Medicine, Zhejiang, China, in 2010–2019 (9). This dataset was provided by PhysioNet and has been used for pediatric research recently (10-12). PhysioNet is a platform for freely accessible clinical data established by members of the Computational Physiology Laboratory at the Massachusetts Institute of Technology. PICD was hosted on PostgreSQL, an open-source relational database management system, for data extraction and processing.

Sepsis definition

We utilized two categories of patients to define sepsis. The first category was selected using the International Disease Classification-10 (ICD-10), who had discharge diagnosis of sepsis (ICD-10: A02.x, A22.x, A26.x, A32.x, A40.x, A41.x, A42.x, B37.x, O85.x, P36.x) (13). The second category included patients with systemic inflammatory response syndrome (SIRS, ICD-10: R65.x) and suspected infection (14). We considered patients with suspected infections as those who had microbiological cultures sampled followed by antibiotic administration within 72 hours, or antibiotic administration followed by cultures taken within 24 hours (15). The infection onset was taken either at the time of cultures or antibiotic administration, whichever occurred first. We reviewed patients with ICD-10 diagnosis of sepsis to ensure that timing of sepsis is close to PICU admission. If the patient did not meet the sepsis onset definition, we examined the dataset to ensure that these patients had an admission diagnosis related to sepsis (e.g., bacterial sepsis, unspecified sepsis, pneumonia), microbiological cultures taken, and/or antibiotics taken within 24 hours of PICU admission. Patient was considered to have septic shock when vasoactive agents were administered (Figure S1, Table S1). The non-sepsis cohort were patients that did not satisfy our study’s sepsis definition.

Data extraction and processing

Within this database, an individual patient may have multiple hospitalizations, and each hospitalization may have multiple PICU admissions. As such, we considered each hospitalization as an independent event and only included clinical information from the first PICU admission within each hospitalization. Extracted clinical information included demographic data, clinical symptoms, vital signs, laboratory, and microbiological tests. PICU admission without clinical data or vital signs for the first 24 hours was excluded.

Clinicians reviewed and selected variables as covariates for the models (Table 1). Selected variables were consistent with published literature on sepsis diagnosis (16-18). Demographic data included gender, age, and prematurity status. As Han ethnicity was predominant (98%), we excluded ethnicity from the study. Age was categorized (i.e., newborn, neonate, infant, toddler and preschool, school-aged child, adolescent, and young adult) as recommended by Goldstein et al. (14). Four categories of covariates included in our model were clinical symptoms, vital signs, laboratory, and microbiological tests. Clinical symptoms were extracted from the physician’s notes, considered as a binary variable and grouped into the following categories: overall, gastrointestinal, central nervous, skin, respiratory, cardiovascular, urinary tract, infection, prematurity, and temperature symptoms. Vital signs were categorized into “high”, “low”, and “normal” based on age group (14). Laboratory tests included 34 variables from blood, urine, and cerebrospinal fluid tests. We utilized laboratory results as defined by PICD without further processing (i.e., “high”, “normal”, and “low” using the dataset’s pre-defined thresholds) (9). Microbiological tests were positive when there was organism growth and negative when there was culture. If clinical symptoms were absent, or laboratory and microbiology tests were not conducted, these were considered as “no record”.

Tree augmented Naïve Bayes

TAN relies on probability and graph theory to perform classification, which labels an outcome based on the provided evidence. Along with other Bayesian classifiers such as Naïve Bayes (NB), TAN is commonly used in medical diagnosis, pattern recognition, and natural language processing (7,25,26). Appendix 1 and Figure S2 show the simplified example of TAN and NB for sepsis diagnosis and the algorithm to construct TAN (27). TAN can also capture the correlations between variables while maintaining a simple graph structure (27,28). We built TAN using GeNie Academic software (Bayesfusion, USA) (29) and learned parameters with the Expectation-Maximization algorithm. A total of 15 models were built from the combinations of four categories of variables (Table 2). All models were trained and tested with ten-fold cross-validations (k=10). Data were randomly split into k parts, trained in k-1 parts, and tested in the left-over. Performance was measured by the average sensitivity (SEN), specificity (SPE), accuracy (ACC), and area under the curve (AUC) of the ten-fold cross-validations with 95% confidence interval (CI). We compared our model against logistic regression (LR), a supervised model for predicting the probability of binary outcomes, as the performance benchmark, using the same study settings and variables (30). We also validated the models with different data cut-off points (24 vs. 48 hours), decision thresholds (0.4 vs. 0.5 vs. 0.6), and cohort subgroups (premature vs. term infants, children under 30 days old vs. one month-one year vs. > one-year age, admission from general wards vs. emergency department).

ACC was calculated by: (true positive cases + true negative cases)/cohort samples. AUC was measured by comparing the true positive rate against the false positive rate. The primary outcome of the TAN classifier was sepsis, and we chose the default decision threshold of 0.5. Patients with outcome probability ≥0.5 were labeled as sepsis. The SEN and SPE determine the model accuracy in sepsis and non-sepsis cases, respectively. We also reported the negative predictive value (NPV) and positive predictive value (PPV).

Statistical analysis

We calculated medians [interquartile ranges (IQRs)] for continuous variables and percentages for categorical variables for data analysis. Significance between groups were analyzed by Mann-Whitney U and Chi-square test. All analyses were performed in Microsoft Excel (version 16.55, Microsoft, USA) with a statistical significance taken as P<0.05.

Ethical statement

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the National University of Singapore’s Institutional Review Board (ID: NUS-IRB-2021-673). Written informed consent was not required for our study because of the retrospective nature of the study and the public availability of the dataset.

Results

The PCID dataset had 13,449 hospitalizations with 13,941 PICU admissions with 357 sepsis cases (2.7%) with a PICU mortality rate of 42/357 (11.8%). We excluded 10,927 ineligible admissions, including 492 cases that were not the first PICU admission, 609 cases that did not have PICU clinical data, and 9,826 cases that did not have vital signs within 24 hours of the PICU stay. Thus, a total of 3,014 admissions with an overall median age of 1.13 (0.15–4.30) years old and 1,698 (56.3%) male patients were included in our study (Figure 1). Of these, 52 patients were admitted more than once (with 108 unique admissions). There were 134 (4.4%) patients identified with sepsis, including 55 cases of septic shock (41%). The majority of them were diagnosed as unspecified sepsis (ICD-10: A41.9, n=69, 51.5%) and bacterial sepsis (ICD-10: P36.9, n=60, 44.7%). Of these unspecified sepsis cases, there were 55 cases of viral sepsis and no case of fungal sepsis found (ICD-10: B37.7, Candida sepsis). The most common source of viral sepsis was pneumonia (43, 32%, ICD-10: J18.0, J18.9, P23.5, P23.9). The dominant organism was Gram-positive cocci (16, 11.9%), followed by Klebsiella pneumonia (13, 9.7%). 90 (67.2%) patients with sepsis had negative cultures. Tables 3,4 show the baseline statistics of our cohort shows the distribution of the selected diagnosis variables.

Figure 1 Data extraction flow chart to derive the study cohort. Sepsis cohort (n=134) consisted of 112 cases identified with ICD-10, 8 suspected infections with SIRS, and 14 cases satisfying for both criteria. Of these, 69 and 60 cases were undefined and bacterial sepsis, respectively. 44 out of 134 patients had positive blood culture. ICD-10, international classification of diseases-10; PICU, pediatric intensive care unit; SIRS, systemic inflammatory response syndrome.

Overall, all models yielded high ACC (0.92–0.96), SPE (0.95–0.99), and AUC (0.77–0.87). SEN varied with different combinations of categories (range: 0.10–0.46) (Figure 2). Individual categories reported low SEN (<0.30), especially vital signs and microbiological tests (<0.10). Models performed better when more variables were incorporated. Laboratory tests combination reported the highest SEN (range: 0.34–0.46). The combination of all four categories (clinical symptoms, vital signs, laboratory, and microbiological tests) yielded the best performance [ACC: 0.93 (95% CI: 0.916-0.936); SEN: 0.46 (95% CI: 0.376–0.550), SPE: 0.95 (95% CI: 0.940–0.956), AUC: 0.87 (95% CI: 0.826–0.906)]. The TAN network for all variables is shown in Figure 3. All models reported high NPV (range: 0.96–0.97) and low PPV (range <0.50).

Figure 2 Performance of TAN diagnosis models: (A) clinical symptoms combinations, (B) vital signs combinations, (C) laboratory test combinations, (D) microbiological test combinations. Accuracy (ACC): line with square, Sensitivity (SEN): line with diamond, Specificity (SPE): line with triangle, area under the curve (AUC): simple line. Model names are listed in Table 2. Model names comprise of S, V, L, or M. For example, SL stands for model built with variable from clinical symptoms and laboratory tests. S, clinical symptoms; V, vital signs; L, laboratory tests; M, microbiological tests; TAN, tree augmented Naïve Bayes.

Figure 3 TAN model with full variables from four categories. Variables are listed in Table 1. ASO, anti-streptolysin O; CSF, cerebrospinal fluid; CSF RBC, cerebrospinal fluid red blood cell; CSF WBC, cerebrospinal fluid white blood cell; CRP, c-reactive protein; DDBP, diastolic blood pressure; DSBP, systolic blood pressure; HR, heart rate; TAN, tree augmented Naïve Bayes; Pct, procalcitonin; PCO2, partial pressure of carbon-oxygen; PO₂, partial pressure of oxygen; PT, prothrombin time; PTT, partial thromboplastin time; RR, respiratory rate; SBE, standard base excess; Temp, temperature.

A comparison of our model against the LR model and sensitivity analysis is shown in Table 5. The ACC, SPE, and PPV were higher in LR [ACC: 0.95 (95% CI: 0.941–0.967), SPE: 0.99 (95% CI: 0.982–0.998), PPV: 0.45 (95% CI: 0.250–0.661)]. The NPV was high in both models [TAN:0.97 (95% CI: 0.968–0.980), LR:0.96 (95% CI: 0.949–0.977)]. However, SEN and AUC in TAN [SEN: 0.46 (95% CI:0.376–0.550), AUC: 0.87 (95% CI: 0.826–0.906)] outperformed LR [SEN: 0.13 (95% CI: 0.05–0.321), AUC: 0.56 (95% CI: 0.509–0.611)]. As a result, TAN achieved a better performance than LR. The performance of our models considering data over 48 hours was better than the 24 hours, indicating that the additional data provided more information and improved the performance. In addition, the models yielded higher SEN when the decision threshold was lowered (Figure 4). They also performed better in the premature, and children less than 30 days old. There was no difference in model performance between patients from general wards and emergency departments (Table 5).

Figure 4 Model performance with different decision thresholds. Line with circles, triangles, diamonds, and squares represents SEN at a decision threshold of 0.3, 0.4, 0.5 and 0.6, respectively. The top line with stars represents the best SEN achieved by each model with the decision threshold ranging from 0.02–0.03. S, clinical symptoms; V, vital signs; M, microbiological tests; L, laboratory tests; SEN, sensitivity. Model names are listed in Table 2. Model names comprise of S, V, L, or M. For example, SL stands for model built with variable from clinical symptoms and laboratory tests.

Discussion

In this study, we demonstrated that PGM is a potential diagnosis model-building tool for pediatric sepsis with a high ability to rule out sepsis within 24 hours of PICU admission. We also evaluated the diagnostic performance of commonly used variables in PICU, and it showed, unsurprisingly, better diagnostic performance when more variables were used.

PGM has been used in several applications from medical diagnosis, object recognition to virus evolution modeling (31-33). Despite limited use in pediatric sepsis, PGM has been investigated in various disease diagnoses, such as cancer, heart disease, and adult sepsis (8,34,35). In 2012, a study applied the Dynamic Bayesian Network (DBN) to detect early sepsis in 3,100 adults within 24 hours of admission and reported an AUC of 0.94 (36). In 2016, Jiang et al. proposed a BN-based sepsis monitoring framework for the elderly that can report patients’ conditions periodically without human intervention. It was able to detect sepsis approximately 0.5 hours earlier than the traditional BN-based diagnosis model (6). Recently, BERG (Massachusetts) released bAIcis, a BN-based software for large-scale health applications. It was capable of handling hundreds of thousands of features and reported true positive rates of 0.9 and a precision of 0.8 on the synthetics dataset (37). To the best of our knowledge, there were two studies using PGM in pediatric sepsis in 2014 and 2021. The first study applied Auto-Regressive Hidden Markov Model (HMM) to 24 critically ill and low birth-weight neonates in the neonatal ICU and could detect sepsis accurately with an AUC of 0.8 (38). The second study used Markov Chain to study sepsis transition in 140 patients with suspected sepsis after blood cultures (39). Consistent with these prior studies, our findings suggest that PGM is a potential framework for pediatric sepsis that can aid clinical decision-making.

The TAN model, and PGM in general, offer both prediction and inference capabilities (i.e., it can produce predictions and allow variable investigation at the same time). Clinicians can investigate the association between sepsis and biomarkers and how one variable affects another. It is also possible to determine which biomarker contributes the most to sepsis recognition. In addition, PGM is a better tool to model uncertainty and offers a graphical representation that most machine learning methods do not have (40). Clinicians can get involved in the model building and validation process using this graphical representation. With these advantages, in our opinion, PGM is a robust tool for disease diagnosis modeling and for future studies examining the utility of machine learning in sepsis.

We observed that individual biomarker categories were not sufficient to diagnose sepsis. The SEN of the diagnosis models only improved when more data were incorporated, and inclusion of laboratory tests increased SEN. When combined with two initial important variable groups (i.e., clinical symptoms and vital signs), laboratory tests improved SEN by approximately 20% (Figure 2). This was not surprising as certain laboratory tests provided more reliable information about sepsis (e.g., C-reactive protein, procalcitonin), whilst vital signs and clinical symptoms were non-specific to sepsis alone. Therefore, even in resource-limited settings, laboratory tests should be considered in the identification of sepsis, compared to vital signs, clinical symptoms, and microbiological tests. However, as each laboratory order involves a certain cost and the variables in our study may not be optimal, future studies should investigate the contributing factor of each laboratory variable in sepsis recognition to help determine which tests should be prioritized in the recognition of sepsis in resource-limited settings. We also observed that there was a high incidence of negative microbiology culture results (67.2%) and microbiological tests contributed little to the SEN performance in our results. Considering the fact that the microbiology results are often unavailable at the time of diagnosis, they should only be used to confirm the presence of sepsis at a later stage instead of being an early diagnosis variable. The clinical symptoms, vital signs, and laboratory tests model (SVL), without microbiological tests, can be used as the diagnosis model instead of the full-feature model (SVLM) at the time of sepsis evaluation. Based on our results, vital signs and clinical symptoms were good indicators to exclude sepsis in the first 24 hours of PICU admission. Models built on these categories should be used as a sepsis screening tool at the time of admission. Adding laboratory tests can be used to identify sepsis; whilst the combinations of vital signs and clinical symptoms can be used to rule out sepsis. At any time, clinicians have the flexibility to choose which model to use based on the availability of the variables (and resources).

Our models were not ideal for sepsis detection due to the low SEN. However, they were efficient at excluding sepsis because of the high SPE and NPV. When the laboratory tests and other variables are available, they can be quickly fed into the model to predict the patient’s conditions. If the prediction is negative, clinicians can consider stopping antibiotics early. When the predicted probability is borderline, the clinician may consider keeping the antibiotics and stopping them only when indicated. Clinicians have the flexibility to choose which model to use based on the availability of the variables. We recommend using the SVL model because it yielded the same performance as the SVLM model without the need for microbiological tests. Once the microbiological result is available, the SVLM model can be used to confirm the patient’s condition.

We included only the worst vital sign measure within 24 hours of PICU admission. This approach may not accurately reflect their dynamic nature. To improve the utility of vital signs in model building, investigators can consider taking hourly data and applying the temporal PGM (DBN and HMM) (41-43). This approach offers more data to the diagnosis models, provides insights into data trends, and improves prediction power. The presence of “no record” was another factor contributing to the low SEN of our models by adding biases and uncertainty. To minimize this issue, investigators can consider two options. First, increase the frequency of data to decrease the number of “no record” entries. This, however, would raise costs and create more challenges for resource-limited settings. Second, apply feature selection to filter the low-contributing variables so that the most valuable information will be retained for predictions. Our dataset suffered an imbalance between positive and negative classes, where the negative cases were predominant. Due to this imbalance, the model produced predictions in favor of the predominant class and underestimated the other. As a result, the model calibration performed poorly and required correction (44). This aspect should be addressed in future studies together with the solutions to the limitation of class imbalance.

There are several limitations to this preliminary study. First, by using the ICD-10 code, we may have missed cases of patients with bacteremia (ICD-10: R78.81) and other subgroups of sepsis (e.g., viral and fungal sepsis). This may have led to a small number of sepsis cases, causing the problem of imbalanced dataset. The ICD-10 code was assigned only at the hospital discharges, so it was also challenging to determine the onset of sepsis and assess the model performance in different subgroups (e.g., nosocomial infections vs. community-acquired sepsis). Second, the dataset lacked certain important clinical data (e.g., Glasgow Coma Scale, oxygen support, invasive mechanical ventilation settings), which precluded us from defining organ dysfunction based on the Surviving Sepsis Campaign 2020 (45). In addition, the dataset did not have granular data on fluid resuscitation and administration, which can be used to study sepsis progression. Third, the prematurity could only be identified using the clinician’s notes, which may be prone to biases. The data dates were offset randomly; therefore, we could not investigate the models across different time periods. We also did not consider comorbidities (e.g., oncological disorders, immunodeficiencies) as a variable in this study. Therefore, we could not investigate the effect of this variable in our study. Furthermore, our study only extracted the children in the ICUs and the diagnostic models were trained solely on their clinical characteristics. Therefore, the models may not be applicable to other clinical settings outside of the ICU (e.g., acute care floors or intermediate care units). To apply the model in these settings, additional amendment and re-training would be required. Finally, this study was heavily dependent on the PICD data set, which may include several types of biases, such as changes in patient care or clinical programs. As of the current version, the dataset owners are facing several challenges in integrating and processing the data and are working on releasing better quality data in the next version. There is no information on the inter-rater reliability and third-party audits from them. The patients we removed from the cohorts due to missing data might have introduced additional bias to the study as well. Therefore, future studies should consider applying the PGM in different datasets with different sepsis population sizes to verify the robustness of this method. Alternatively, time-series data should be considered to enhance the predictive diagnosis in real-time. Future studies should consider comparing PGM with other probabilistic machine learning methods (e.g., Random Forests and Probabilistic Neural Networks) to evaluate its advantages and disadvantages.

Conclusions

Our study has shown that PGM is a reliable diagnostic tool for pediatric sepsis. Laboratory tests contributed the most to the predictions, while clinical symptoms and vital signs were highly capable of excluding sepsis. Microbiological tests were unreliable due to the high negative incidence. Further studies using different data sets should be conducted to verify the utility of PGM in pediatric sepsis.

Acknowledgments

Funding: None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-22-510/rc

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-22-510/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-22-510/coif). JHL serves as an unpaid Deputy Editor-in-Chief of Translational Pediatrics from July 2022 to June 2024. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the National University of Singapore’s Institutional Review Board (ID: NUS-IRB-2021-673). Written informed consent was not required for our study because of the retrospective nature of the study and the public availability of the dataset.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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