Prediction of Sepsis in COVID-19 Using Laboratory Indicators

Abstract

BackgroundThe outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients’ condition. We aim to diagnose Viral Sepsis Caused by SARS-CoV-2 by analyzing laboratory test data of patients with COVID-19 and establish an early predictive model for sepsis risk among patients with COVID-19.MethodsThis study retrospectively investigated laboratory test data of 2,453 patients with COVID-19 from electronic health records. Extreme gradient boosting (XGBoost) was employed to build four models with different feature subsets of a total of 69 collected indicators. Meanwhile, the explainable Shapley Additive ePlanation (SHAP) method was adopted to interpret predictive results and to analyze the feature importance of risk factors.FindingsThe model for classifying COVID-19 viral sepsis with seven coagulation function indicators achieved the area under the receiver operating characteristic curve (AUC) 0.9213 (95% CI, 89.94–94.31%), sensitivity 97.17% (95% CI, 94.97–98.46%), and specificity 82.05% (95% CI, 77.24–86.06%). The model for identifying COVID-19 coagulation disorders with eight features provided an average of 3.68 (±) 4.60 days in advance for early warning prediction with 0.9298 AUC (95% CI, 86.91–99.04%), 82.22% sensitivity (95% CI, 67.41–91.49%), and 84.00% specificity (95% CI, 63.08–94.75%).InterpretationWe found that an abnormality of the coagulation function was related to the occurrence of sepsis and the other routine laboratory test represented by inflammatory factors had a moderate predictive value on coagulopathy, which indicated that early warning of sepsis in COVID-19 patients could be achieved by our established model to improve the patient’s prognosis and to reduce mortality.

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

“…Morphology-based Maximum entropy threshold [92]- [94] Context-based Mult-scale attention [91] Saliency Analysis [52] Network dissection [95] [136], [147]- [149]. In addition to web-based applications, visualizing sample clusters [61], [100] and feature importance metrics [31], [65], [71], [98], [117], [121], [122], [134], [135], [150] can offer users without expertise in data analysis an option of understanding the decision-making process of otherwise obscure models.…”

“…Although accuracy is understood by model developers and end-users alike, it should be avoided when significant data imbalance is present. Examples of works using appropriate performance metrics include [98], [103], [117], [118], [121]. Common metrics include Area Under the Receiver Operating Curve (AUROC) and Matthews Correlation Coefficient (MCC), the later being appropriate even in imbalanced binary classification tasks [153].…”

“…Incorporate clinical insights [120] Small sample size Data imputation [30], [53] Interactive user interface [90], [94], [101], [119], [120], [136] Bad data quality Artifact correction [7] Clinical feedback [90] Imbalanced classes Data augmentation [56], [59] Visualization of feature importance [65], [71], [98], [117], [121], [122], [134] Complex disease phenotype Multi-modality data [51] Clustering analysis [61], [87], [100] Data heterogenity Data normalization [53] Decision tree [31], [96], [98], [100]- [102], [115]- [122] Lack of expert annotation Weakly supervised learning [45], [65], [87] Use multiple feature importance approaches [15], [38], [69], [101], [102], [122] Unkown sources of signal Key feature extraction [34], [52], [63] Cross validation when comparing models [41],…”

Case Report on Long COVID-19

“…Reverse Time Polymerase Chain Reaction (RT-PCR) of nasopharyngeal swabs using the Quant Inova Probe RT-PCR kit was performed. 15 Complete blood count, MP Kit (Malarial Parasite), RBS (Random Blood Sugar), Dengue, Blood urea, Serum creatinine tests were done (Table 1). There were increased levels of granulocytes and C-reactive protein.…”

Clinical prediction system of complications among patients with COVID-19: A development and validation retrospective multicentre study during first wave of the pandemic

“…Similarly, for AKI prediction amongst patients with COVID-19, a multivariate logistic regression was developed using findings of CT imaging, laboratory-test results, vital-sign measurements, and patient demographics [ 25 ]. While recent work on SBI mainly focused on its clinical manifestations and occurrence [ 16 , 26 , 27 ], one study investigated sepsis risk prediction among patients with COVID-19 using hematological parameters and other biomarkers [ 28 ]. To summarize, existing work tends to predict a single complication at a time, which is less informative than predicting multiple complications known to be common among patients with COVID-19, use costly input features that may not be readily available, or rely on training deep neural networks that require high computational resources and large training datasets.…”