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Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children
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Journal of Infection and Public Health journal homepage: http://www.elsevier.com/locate/jiph
Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children Jing Yang ∗ , Yujie Ma, Min Mao, Pingli Zhang, Huixiang Gao Department of Pediatric, Qilu Hospital of Shandong University (Qingdao), Qingdao, 266035, Shandong, China
a r t i c l e
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Article history: Received 10 October 2019 Received in revised form 14 November 2019 Accepted 25 November 2019 Keywords: Logistic regression analysis Computer technology Early warning model for children with sepsis
a b s t r a c t This paper uses computer technology combined with regression model to analyze the risk factors of children with sepsis, determine the relevant factors and establish a corresponding early warning model of sepsis, and then verify the clinical application value of the regression model. The paper collected severe infections and sepsis in children who came to our hospital from 2014 to 2018, including 129 patients with infection and 86 patients with sepsis. The general conditions, clinical symptoms, laboratory tests and other factors were used. Analysis, to identify the risk of infection development into sepsis, and use Logistic regression model combined with computer technology to construct an early warning model of sepsis. The experimental results show that early warning of sepsis is closely related to skin spots, platelets, procalcitonin, creatinine and international normalized ratio. The experiment demonstrates that the early warning model has higher sensitivity and specificity, and has higher accuracy for predicting whether infection develops into sepsis in advance, and has certain clinical value. © 2019 The Authors. Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).
Introduction Sepsis is an acute systemic inflammatory response syndrome caused by infection and is one of the leading causes of death in critically ill patients. It involves multiple organs and systems in the body and is a common complication of severe trauma, shock, infection, and severe cases. Further development can lead to septic shock and multiple organ dysfunction syndrome (MODS) [1]. Sepsis is caused by various factors, such as pathogens and hosts. Besides, its pathogenesis is very complicated. When the pathogenic microorganisms invade the body, they can stimulate the immune function of the body; meanwhile, numerous lymphocytes, such as T cells and B cells, begin to undergo apoptosis. Therefore, the immune function of the body is damaged, resulting in immunosuppression. The two processes of hyperimmune and immunosuppression may exist simultaneously in the occurrence and development of sepsis and may change with the progression of the disease. In such a process, numerous inflammatory factors are generated by the host and involved in the reaction, which causes certain damages to the func-
∗ Corresponding author at: Department of Pediatric, Qilu Hospital of Shandong University (Qingdao), Hefei Road 758#, Qingdao, Shandong, 266035, China. E-mail address: [email protected] (J. Yang).
tion of the body when the inflammatory response is unbalanced. Sepsis currently relies mainly on clinical diagnosis to judge, but the early clinical manifestations of sepsis are not specific, and there is a lack of timely and reliable warning indicators. At the time of diagnosis, the “inflammation waterfall” has been triggered and amplified by cascade, and the patient’s condition is often rapid. Changes and exacerbations, and current interventions for sepsis have limited effectiveness. These factors are one of the important causes of high mortality in patients with sepsis. Therefore, finding a variety of key factors can quickly and accurately diagnose sepsis and explore the early warning method of sepsis is one of the important problems that need to be solved in the field of critical medicine. Diagnosis of sepsis mainly includes: (1) changes in some indicators such as general conditions, inflammatory indicators, hemodynamic indicators, organ dysfunction indicators and tissues; (2) confirmed or suspected infection. Moreover, the meeting focused on the lack of specific diagnostic indicators for sepsis, and only when the disease has no other explanation for the diagnosis of sepsis. Septicemia refers to an acute systemic infection in which pathogenic bacteria or conditional pathogens invade the blood circulation and grow in the blood to produce toxins. If the bacteria that invade the bloodstream are removed by the defense function of the body, and there is no obvious symptom of toxemia, it is called the bacteriemia. If the sepsis is associated with multiple abscesses
https://doi.org/10.1016/j.jiph.2019.11.012 1876-0341/© 2019 The Authors. Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article in press as: Yang J, et al. Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.11.012
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2 Table 1 Logistic regression analysis of sepsis warning.
Hypoxia Hypotension Disorder of consciousness Skin defect Platelet Procalcitonin Total bilirubin Urea Creatinine blood sugar International standardization ratio

SE
Wald
OR
OR95%CI
P
0.472 0.64 0.04 −3.788 −0.106 −0.333 0.131 −0.171 0.073 0.108 2.287
0.808 0.871 1.236 1.527 0.02 0.148 0.088 0.157 0.023 0.237 0.878
1.603 1.897 1.04 0.023 0.9 0.717 1.14 0.843 1.076 1.114 9.842
1.603 1.897 1.04 0.023 0.9 0.717 1.14 0.843 1.076 1.114 9.842
0.329–7.811 0.344–10.461 0.092–11.728 0.001–0.451 0.865–0.936 0.537–0.957 0.959–1.354 0.619–1.147 1.029–1.124 0.700–1.774 1.762–54.972
0.559 0.462 0.974 0.013 <0.001 0.024 0.137 0.276 0.001 0.649 0.009
and longer duration, it is called pyemia. If the sepsis is not timely controlled, it can be developed from the original infected site to other parts of the body, causing a metastatic abscess. The abscess can occur on the surface of the brain, causing meningitis. It can also occur on the envelope around the heart, causing pericarditis. If it occurs on the intima of the heart, it will cause endocarditis; if in the bone marrow, causing osteomyelitis; if in the large joints, causing joint pain or arthritis. Eventually, abscesses can form anywhere in the body due to the accumulation of pus. In severe cases, septic shock and migratory lesions occur. Although many studies have confirmed that sepsis is related to certain factors such as a biomarker, the clinical manifestations of sepsis are complicated, and it is not enough to judge whether a patient develops sepsis by biomarkers alone [1]. With the development of computer technology, through the production and manual simulation software, the relevant data can be input into the computer to enable the computer to comprehensively discriminate the patient’s condition, and the patient’s data can be completely stored in the computer. The use of computer technology saves the workload of medical staff, and the analysis of the disease will be more convenient and comprehensive. Therefore, it is of great clinical value to find a suitable computer-assisted sepsis warning system based on multi-biological indicators to find appropriate clinical symptoms and biomarkers related factors of sepsis. Materials and methods Research object A retrospective analysis of 215 patients admitted to Qilu Hospital of Shandong University (Qingdao) from January 2014 to October 2018 was conducted. Sepsis (test group) inclusion criteria: (1) Any suspected or confirmed (positive culture, tissue staining or PCR) infection caused by bacteria; or a clinical syndrome highly associated with infection. Evidence of infection includes clinical examination, X-ray or laboratory positive results; (2) 2 of the following clinical manifestations: 1. Body temperature >38 ◦ C or <36 ◦ C; 2. Heart rate >90 beats/min; 3 Respiratory rate >20 beats/min or PaCO2 < 32 mmHg; 4. Peripheral blood leukocytes >12 × 109 /L or <4 × 109 /L, or immature (rod-like nucleus) neutrophils >10%; 3. Survival time after admission was greater than 24 h; 4. Age > 18 years old. Inclusion criteria for infection (control group): Any infection caused by suspicious or confirmed (positive culture, tissue staining or PCR) caused by bacteria; or clinical syndrome highly associated with infection. Evidence of infection includes clinical examination, X-ray film or laboratory positive results; (2) Survival time after admission is greater than 24 h; (3) Age ≥18 years old. Exclusion criteria: child patients with immunodeficiency diseases or administered with pre-hospital immunosuppressants; child patients with congenital cardiovascular disease; child patients whose important information, such as age, gender, and diagnosis, was missing.
Research methods Record the clinical data of the selected cases: (1) General conditions, such as age, gender, height, weight, length of hospital stay, diagnosis and outcome (survival or death); (2) Clinical symptoms: chief complaint, current medical history, vital signs; (3) Past history: history of tobacco and alcohol, basic diseases; (4) Laboratory tests: blood gas analysis, blood routine, biochemistry, blood coagulation, blood sugar and infection indicators (PCT, CRP, IL.6, etc.). According to the 2001 international diagnostic criteria for sepsis, when collecting data, (1) The oxygenation index of the selected cases <350 is considered hypoxia: (2) Systolic blood pressure <90 mmHg, mean arterial pressure <70 mmHg or adult systolic blood pressure drop >40 mmHg, or less than 2 standard deviations below the normal value of the age, considered hypotension; (3) Urine volume <0.5 ml/kg/h or 45 mmol/L for at least 2 h considered oliguria; (4) Patients around the things Unresponsive, unconscious or completely unresponsive, loss of perception as a disturbance of consciousness. The clinical data of all the selected cases were within 24 h after hospitalization. If the same examination occurred multiple times within 24 h after admission, it was calculated according to the most serious clinical data of the day [2]. Statistical methods Statistical analysis was performed using SPSS 19.0 statistical software, and the data was measured and obeyed by the normal distribution, and the skewed distribution was expressed by the median and interquartile range (IQR). Counting data is expressed in terms of frequency and percentage, using a test or Fisher’s exact test. Univariate measurement data between the normal and heterogeneous infection group and the sepsis group were tested by two independent samples. Binary logistic regression analysis was used to screen out multiple related factors for the development of infection to sepsis, with P < 0.05 was considered statistically significant. Results 11 variables initially screened by univariate analysis: hypoxia, hypotension, disturbance of consciousness, skin stagnation, platelet count, procalcitonin, total bilirubin, urea, creatinine, blood glucose and international normalized ratio binary Logistic regression analysis. The results showed that the five factors of skin pitting, PLT reduction, PCT elevation, Cr elevation and INR increase were the risk factors for infection to develop sepsis, and the difference was statistically significant, as shown in Table 1. As shown in Tables 2a and 2b, of the 215 laboratory examinations, the differences of platelet count, procalcitonin, total bilirubin, urease, creatinine, blood glucose, and international normalized ratios between the two groups were statistically significant. In the sepsis group, the platelet count was lower than that in the infec-
Please cite this article in press as: Yang J, et al. Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.11.012
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Then the multiple linear regression model can be expressed as Y = Xˇ + ε
Indicators
The control group
The experiment group
P
Hemoglobin Red blood cell count White blood cell count Platelet count C-reactive protein Procalcitonin Alanine aminotransferase Albumin Total bilirubin urease Creatinine Blood sugar Sodium Potassium
124.52 ± 11.55 4.17 ± 0.73 11.48 ± 3.61 138.88 ± 26.04 39.44 ± 13.44 3.26 ± 2.44 23.15 ± 5.99 32.64 ± 3.14 10.72 ± 2.95 7.92 ± 2.09 77.23 ± 18.05 5.95 ± 1.09 138.81 ± 4.18 4.04 ± 0.52
124.51 ± 9.61 3.91 ± 0.80 14.85 ± 4.24 89.71 ± 20.16 40.13 ± 14.28 4.58 ± 2.83 22.85 ± 6.91 30.86 ± 3.43 11.99 ± 4.04 9.53 ± 3.26 105.35 ± 22.62 7.10 ± 1.67 137.16 ± 5.09 3.97 ± 0.61
0.077 0.396 0.164 0.015 0.908 0.009 0.278 0.226 0.021 <0.001 0.044 <0.001 0.162 0.091
Factor
Assignment
Hypoxia Hypotension Disorder of consciousness Skin defect
No = 0 No = 0 No = 0 No = 0
Have = 1 Have = 1 Have = 1 Have = 1
tion group, while other indicators were all higher than those in the infection group (P < 0.05). Establishment of a computerized early warning diagnosis model for sepsis Logistic regression model establishment In the previous study, 11 variables including hypoxia, hypotension, disturbance of consciousness, skin stasis, PLT, PCT, STB, BUN, Cr, GLU, and INR were selected by univariate analysis and infection developed into sepsis. Closely related (P < 0.05). Then 11 factors with P < 0.05 in univariate analysis were included in multivariate logistic regression analysis. Logistic regression analysis used a backward stepwise method to exclude the relevant factors of P < 0.05, until all the factors were left into the equation. Finally, five related factors such as skin spots, PLT, PCT, Cr and INR were left [3]. The regression coefficients, standard errors, P values, and OR values of relevant factors were recorded to construct a logistic regression equation. Let the linear relationship between the variable Y and the variables X1, X2, . . ., XP Y = ˇ0 + ˇ1 X1 + ˇ2 X2 + ... + ˇP XP + ε
(1)
2
where ε∼N 0, , ˇ0 , ˇ1 , ..., ˇP and 2 are unknown parameters, p≥2, said the above model is a multiple linear regression model, then the model can be expressed as: yi = ˇ0 + ˇ1 xi1 + ... + ˇp xip + εi , i = 1, 2, ..., n
where εi ∈ N 0, ate
⎡
y1
⎤
⎡
2
ˇ0
ˇp
where Y is an n-dimensional vector composed of response variables, X is an n × (p + 1)-order design matrix, ˇ is a p + 1dimensional vector, and satisfies E (ε) = 0, V ar (ε) = 2 In
Q ˇ = y − Xˇ
T
y − Xˇ
(5) (6)
The minimum value of ˇ is reached. Least squares estimate of ˇ
ˆ = XT X ˇ
−1
XT y
(7)
Obtain empirical regression equation (8)
The early warning evaluation process of medical staff for sepsis in infected patients is introduced as follows. The first step is to obtain the parameters and establish an electronic medical file for the patient, as well as inputting the basic information, hospital number, and inspection time of the patient into the sepsis warning management system. Then, the clinician asks the patient a detailed medical history and completes the relevant examinations. The second step is to enter the data and input the basic information of the patient into the sepsis warning management system. The system generates the medical file of the patient and records various examination parameters such as clinical manifestation and auxiliary examination, providing an electronic platform and data foundation for future computer-aided diagnostic decisions. The final step is to analyze and predict the disease. According to the results of single-factor analysis and Logistic regression analysis, the corresponding examination indicators are selected and input into the sepsis warning model. The model predicts the possibility of developing sepsis with infected patients and provides an objective basis for subsequent treatment. Logistic regression model test (1) Using Hosmer–Lemeshow goodness of fit detection; (2) Using artificial neural network model (radial basis function method) to reconstruct the sepsis warning system and detecting the accuracy and stability of the early warning system based on ROC curve and AUC value. To evaluate the effectiveness and accuracy of the sepsis warning system, this study selected another statistical model for classification and prediction commonly used in the medical field, which is the artificial neural network (ANN) model. The ANN model consists of three layers, i.e., the input layer, the output layer, and the hidden layer. The output layer contains two nodes. In this study, sepsis and non-sepsis are the two nodes of the output layer. This study used a radial basis function that included a hidden layer. Each node of the input layer interacts with each node of the hidden layer and overlays the weight of ownership.
(2) Modelling results
is and is independently distributed. Immedi-
⎤
⎡
1 x11
x12
...
x1p
⎤
⎡
ε1
⎤
⎢y ⎥ ⎢ˇ ⎥ ⎢1 x ⎥ ⎢ε ⎥ 21 x22 ... x2p ⎥ ⎢ 2⎥ ⎢ 1⎥ ⎢ ⎢ 2⎥ ⎥,ˇ = ⎢ ⎥,X = ⎢ ⎥,ε = ⎢ ⎥ y=⎢ ⎢ . ⎥ ⎢ . ⎥ ⎢. . ⎥ ⎢ . ⎥ .. .. ⎣ .. ⎦ ⎣ .. ⎦ ⎣ .. .. ⎣ .. ⎦ . . ⎦ yn
(4)
ˆ P XP ˆ +ˇ ˆ 1 X1 + . . . + ˇ Yˆ = ˇ
Table 2b 215 cases of logistic regression analysis variable assignment table.
3
1
xn1
xn2
...
xnp
εn (3)
Logistic regression model calculation According to the previous binary logistic regression analysis, the constant term of this model is C = 1.893. For the two-category variables, the variable assignments for the regression analysis (Table 1) are as follows (Table 2a,2b). The Hosmer–Lemeshow goodness-of-fit test P = 0.203 showed that the model fits well. According to the regression equation: Z = C + ˇ1 X1 + ˇ2 X2 + ˇ3 X3 + ...ˇm Xm
(9)
Please cite this article in press as: Yang J, et al. Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.11.012
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Fig. 1. Forecast probability distribution map.
The early warning expression for sepsis can be: Z = 1.893 − 3.788 − 0.106(PLT ) − 0.333(PCT ) + 0.073(Cr) + 2.287(INR)
(10)
Bringing Z into the probability, an early warning probability can be drawn:
P = 1/ 1 + e−z
Fig. 2. ROC curve of Logistic regression model.
(11)
The relevant parameters of 215 cases are input into the early warning probability P, and the distribution of the statistical probability P is obtained (Fig. 1). The average value of the predicted probability P is 0.400. The predicted probability of most cases tends to both ends, 109 (50.7%) predictive probability is less than 0.2, 17 cases (7.9%) are 0.2–0.4, 9 cases (4.2%) are 0.4–0.6, and 14 cases (6.5%) For 0.6–0.8, 66 cases (30.7%) were 0.8–1. Overall, it can be considered that the higher the predicted probability value obtained by a patient, the higher the probability that the infection develops into sepsis [4]. On this basis, the study further considers how to choose an optimal diagnosis point as the standard value for early warning of sepsis. The predicted probability P of 215 cases was taken as the ROC curve, and the area under the curve (AUC) was 97.2%, and the 95% CI was 95.3.99.1%. From the AUC curve, the model can be considered to better predict sepsis. For each numerical analysis of the curve, when the sensitivity is 98.8% and the specificity is 74.8%, the Yoden index is the largest, and the prediction probability is P = 7.6%. It can be considered that this has the highest diagnostic value (Fig. 2) [5]. Logistic regression model test In this study sepsis and not sepsis were the two nodes of the output layer. This study used a radial basis function that included a hidden layer. Each node of the input layer interacts with each node of the hidden layer and overlays the weight of ownership. The establishment of an ANN requires three independent sample groups, a training group, a test group, and a reserved group. In this study, 155 (72.1%) were selected as the training group to construct the ANN, and the rest were used as the test group to construct the ANN. First, the ANN is used separately, and 215 cases of all the detection parameters are entered the input layer to construct the ANN, and the AUC of the ANN is 96.0%. Secondly, the entry range is narrowed down, and the factor of single factor analysis P < 0.05 is entered the ANN, and the AUC is obtained. = 92.8%. Finally, combined with the results of Logistic regression analysis, the entry range was narrowed again, and the final factors (skin spot, platelet count, procalcitonin, creatinine, and international normal-
Fig. 3. Artificial neural network model ROC curves.
ized ratio) established in the logistic regression model were entered the input layer, and AUC = 96.4% (Figs. 3, 5, 6 ). Establish a computer sepsis warning model Establish a computerized warning model for sepsis Improve the comprehensive prevention and treatment system of sepsis, input the logistic regression model specific results into the computer, and construct a computer-based sepsis warning model. The overall process of computer-based sepsis warning model assessment is shown in Fig. 4. Computer-based sepsis warning model Combining the logistic regression model discriminant with computer technology, a computer model for sepsis warning was established. The patient’s basic information, clinical manifestations, physiological parameters and test results are input into the computer model, and the computer is used in the background calcu-
Please cite this article in press as: Yang J, et al. Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.11.012
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Fig. 4. Total flow charts for evaluation of sepsis warning model.
Fig. 5. Sepsis warning computer model.
Fig. 6. Early warning formula of computer model.
lation to determine the possibility of the patient developing sepsis. The website login interface is as shown. Create a new Excel document and fill in the basic information, clinical features, and auxiliary examinations of the patient in order. Import the Excel file containing a large amount of patient information into the computer model, and click on the patient’s basic information or hospitalization information to view the clinical data of the case. Click on the warning formula to create a regression equation. Z = 1.893–3.788 (skin spot) −0.16 (PLT) −0.333 (PCT) +0.073 (Cr) + 2.287 (INR) Enter the system to create an expression that meets the requirements. The selected cases are then imported
into the model. The computer model is based on the P = 1/ (1 + e−z ) background calculation, and finally the results of the selected cases. Children’s sepsis computer early warning model test Sepsis warning risk factors There were 11 cases of sepsis developed in 30 cases. In the first part of the 19 cases of infected patients who did not develop sepsis and 11 patients with sepsis, the five related factors of skin lesions, platelet count, procalcitonin, creatinine, and international normal-
Please cite this article in press as: Yang J, et al. Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.11.012
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Table 3 Analysis of five related factors at 24 h after admission.
Skin defect Platelet count (10*109 /L) Procalcitonin (ng/ml) Creatinine (mol/L) International standardization ratio
19 cases of infection group
11 cases of sepsis group
P
1(5.3%) 151.95 ± 35.36 3.09 ± 1.61 71.21 ± 28.48 1.26 ± 0.30
2(18.2%) 113.45 ± 42.87 3.41 ± 1.60 96.45 ± 38.88 1.49 ± 0.36
0.335 0.019 0.604 0.05 0.068
Table 4 Analysis of five related factors 24 h before diagnosis of sepsis.
Skin defect Platelet count (10*109 /L) Procalcitonin (ng/ml) Creatinine (mol/L) International standardization ratio
19 cases of infection group
11 cases of sepsis group
P
1(5.3%) 171.48 ± 42.95 2.15 ± 0.34 69.30 ± 24.59 1.22 ± 0.28
2(18.2%) 117.05 ± 38.74 3.13 ± 1.04 90.87 ± 29.61 1.57 ± 0.32
0.335 0.04 0.152 0.026 0.013
Table 5 Four-dimensional table for early warning model diagnosis within 24 h of admission.
Early warning model (+) Early warning model (−) Total
Sepsis (+)
Sepsis (−)
Total
5 6 11
3 16 19
8 22 30
Table 6 Diagnosis of the 24 h pre-warning model diagnosis four-table.
Early warning model (+) Early warning model (−) Total
Sepsis (+)
Sepsis (−)
Total
8 3 11
2 17 19
8 22 30
ized ratio were compared. Data analysis of 30 patients within 24 h after admission, the difference between the two factors in the infection group and sepsis group was statistically significant (platelets, creatinine, P < 0.05, Table 3). Data analysis of the 24 h before diagnosis of sepsis in 30 patients, the difference between the three factors in the infection group and sepsis group was statistically significant (platelet, creatinine, international standardized ratio, P < 0.05, Table 4). Analysis of sepsis warning model Thirty patients with early warning factors within 24 h after admission were enrolled in the early warning model, and 8 cases were diagnosed as sepsis. The sensitivity of the early warning model was 45.5%, specificity = 84.2%, positive predictive value = 62.5%, negative predictive value = 72.7%, and diagnostic coincidence rate = 70.0% (Table 5). The early warning factors related to the diagnosis of sepsis within 24 h before the diagnosis of sepsis were entered into the early warning model, and 8 cases were developed as sepsis. The sensitivity of the early warning model was 72.7%, specificity = 89.5%, positive predictive value = 80.0%, negative predictive value = 85.0%, and diagnostic coincidence rate = 83.3% (Table 6). Discussion This study preliminarily explores the clinical application of early warning models. In general, early warning models are not only theoretically feasible, but also have high accuracy in practical applications. Eleven of the 30 patients were enrolled in sepsis. Comparing 11 cases of sepsis with the remaining 19 cases of infection,
2 of the 5 related factors were statistically significant. In addition to platelet count, the rest factors were higher than the infected group. This is inconsistent with the results of Logistic regression analysis in the third section of the first part [6]. The difference is mainly since the total number of clinical applications is small, and the number of patients with skin spots is less in 30 cases, resulting in errors. The early warning model judge’s sepsis based on whether the predicted probability P is greater than 7.6%. After analysis of the sepsis warning model, the sensitivity of the model was 45.5%, the specificity was 84.2%, and the diagnostic coincidence rate was 70.0% [7]. In this study, the model misjudged 6 cases of sepsis into non-sepsis, and 3 cases of non-sepsis were misjudged into sepsis, so the positive predictive value was 62.5%, and the negative predictive value was 72.7%. In general, the model has the certain predictive ability in determining whether the infection develops into sepsis. Since the presence or absence of sepsis is mainly determined by the clinician based on the conditions of patients. When a clinician diagnoses sepsis, the case can be defined as sepsis in this study. If the condition of a patient is relatively mild compared with sepsis or septic shock, and the clinician has diagnosed sepsis at this time, the value of the relevant factors in the case will be mostly low. Also, the predictive probability P calculated by the early warning model is low, which makes the model more inclined to determine the condition as non-sepsis. Therefore, it ultimately leads to an increased possibility of misjudgement of non-sepsis in patients with sepsis. Therefore, the sensitivity of the early warning model in this study is lower than the specificity. In the results of this study, white blood cells, calcitonin, and reactive proteins did not enter the final evaluation model. The reason might be that the research objects are all children with sepsis who have developed sepsis infection. Although the above indicators are better in the diagnosis of infection, the ability to assess the severity of infection remains to be further explored. At present, some scoring systems are often used to evaluate the severity of infection, such as APACHE II, SOFA, MODS, and other scoring systems. Although these scoring systems are more practical, they have certain shortcomings. Through this study, the skin pitting, thrombocytopenia, elevated PCT, elevated Cr, and increased INR can be combined with sepsis to provide a good scoring system for clinical sepsis diagnosis, which is valuable in clinical practices. Overall, the model has a certain predictive power for judging whether the infection develops into sepsis. Because of whether it is sepsis or not, it is mainly judged by the clinician based on the patient’s condition [8]. When a clinician diagnoses sepsis, the case can be defined as sepsis. When the patient’s condition is relatively mild with sepsis or septic shock, if the clinician has diagnosed sepsis at this time, the value of the relevant factors in the case will be mostly low. The predictive probability P calculated by the early warning model is also low, which makes the model more inclined to be judged as non-sepsis, which ultimately leads to an increased possibility of misjudgement of non-sepsis in patients with sepsis. Therefore, the sensitivity of the early warning model in this experiment is lower than the specificity [9].
Conclusion (1) This study found that early warning factors for sepsis may include skin spots, platelets, procalcitonin, creatinine, and international normalized ratios. (2) This study established an early warning diagnosis model for sepsis. The predictive probability P of the early warning model can determine whether the patient develops sepsis. (3) After verification and clinical preliminary application, the early warning model has high sensitivity and specificity, and has high accuracy for predicting whether infection develops into sepsis in advance, and has certain clinical value.
Please cite this article in press as: Yang J, et al. Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.11.012
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Funding No funding sources. Competing interests None declared. Ethical approval Not required. References [1] Chen M, Lu X, Hu L, Liu P, Zhao W, Yan H, et al. Development and validation of a mortality risk model for pediatric sepsis. Medicine 2017;96(20):e6923. [2] Wong HR, Cvijanovich NZ, Anas N, Allen GL, Thomas NJ, Bigham MT, et al. Pediatric sepsis biomarker risk model-ii: redefining the pediatric sepsis biomarker risk model with septic shock phenotype. Crit Care Med 2016;44(11):2010.
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Journal of Infection and Public Health journal homepage: http://www.elsevier.com/locate/jiph
Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children Jing Yang ∗ , Yujie Ma, Min Mao, Pingli Zhang, Huixiang Gao Department of Pediatric, Qilu Hospital of Shandong University (Qingdao), Qingdao, 266035, Shandong, China
a r t i c l e
i n f o
Article history: Received 10 October 2019 Received in revised form 14 November 2019 Accepted 25 November 2019 Keywords: Logistic regression analysis Computer technology Early warning model for children with sepsis
a b s t r a c t This paper uses computer technology combined with regression model to analyze the risk factors of children with sepsis, determine the relevant factors and establish a corresponding early warning model of sepsis, and then verify the clinical application value of the regression model. The paper collected severe infections and sepsis in children who came to our hospital from 2014 to 2018, including 129 patients with infection and 86 patients with sepsis. The general conditions, clinical symptoms, laboratory tests and other factors were used. Analysis, to identify the risk of infection development into sepsis, and use Logistic regression model combined with computer technology to construct an early warning model of sepsis. The experimental results show that early warning of sepsis is closely related to skin spots, platelets, procalcitonin, creatinine and international normalized ratio. The experiment demonstrates that the early warning model has higher sensitivity and specificity, and has higher accuracy for predicting whether infection develops into sepsis in advance, and has certain clinical value. © 2019 The Authors. Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons. org/licenses/by-nc-nd/4.0/).
Introduction Sepsis is an acute systemic inflammatory response syndrome caused by infection and is one of the leading causes of death in critically ill patients. It involves multiple organs and systems in the body and is a common complication of severe trauma, shock, infection, and severe cases. Further development can lead to septic shock and multiple organ dysfunction syndrome (MODS) [1]. Sepsis is caused by various factors, such as pathogens and hosts. Besides, its pathogenesis is very complicated. When the pathogenic microorganisms invade the body, they can stimulate the immune function of the body; meanwhile, numerous lymphocytes, such as T cells and B cells, begin to undergo apoptosis. Therefore, the immune function of the body is damaged, resulting in immunosuppression. The two processes of hyperimmune and immunosuppression may exist simultaneously in the occurrence and development of sepsis and may change with the progression of the disease. In such a process, numerous inflammatory factors are generated by the host and involved in the reaction, which causes certain damages to the func-
∗ Corresponding author at: Department of Pediatric, Qilu Hospital of Shandong University (Qingdao), Hefei Road 758#, Qingdao, Shandong, 266035, China. E-mail address: [email protected] (J. Yang).
tion of the body when the inflammatory response is unbalanced. Sepsis currently relies mainly on clinical diagnosis to judge, but the early clinical manifestations of sepsis are not specific, and there is a lack of timely and reliable warning indicators. At the time of diagnosis, the “inflammation waterfall” has been triggered and amplified by cascade, and the patient’s condition is often rapid. Changes and exacerbations, and current interventions for sepsis have limited effectiveness. These factors are one of the important causes of high mortality in patients with sepsis. Therefore, finding a variety of key factors can quickly and accurately diagnose sepsis and explore the early warning method of sepsis is one of the important problems that need to be solved in the field of critical medicine. Diagnosis of sepsis mainly includes: (1) changes in some indicators such as general conditions, inflammatory indicators, hemodynamic indicators, organ dysfunction indicators and tissues; (2) confirmed or suspected infection. Moreover, the meeting focused on the lack of specific diagnostic indicators for sepsis, and only when the disease has no other explanation for the diagnosis of sepsis. Septicemia refers to an acute systemic infection in which pathogenic bacteria or conditional pathogens invade the blood circulation and grow in the blood to produce toxins. If the bacteria that invade the bloodstream are removed by the defense function of the body, and there is no obvious symptom of toxemia, it is called the bacteriemia. If the sepsis is associated with multiple abscesses
https://doi.org/10.1016/j.jiph.2019.11.012 1876-0341/© 2019 The Authors. Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article in press as: Yang J, et al. Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.11.012
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2 Table 1 Logistic regression analysis of sepsis warning.
Hypoxia Hypotension Disorder of consciousness Skin defect Platelet Procalcitonin Total bilirubin Urea Creatinine blood sugar International standardization ratio

SE
Wald
OR
OR95%CI
P
0.472 0.64 0.04 −3.788 −0.106 −0.333 0.131 −0.171 0.073 0.108 2.287
0.808 0.871 1.236 1.527 0.02 0.148 0.088 0.157 0.023 0.237 0.878
1.603 1.897 1.04 0.023 0.9 0.717 1.14 0.843 1.076 1.114 9.842
1.603 1.897 1.04 0.023 0.9 0.717 1.14 0.843 1.076 1.114 9.842
0.329–7.811 0.344–10.461 0.092–11.728 0.001–0.451 0.865–0.936 0.537–0.957 0.959–1.354 0.619–1.147 1.029–1.124 0.700–1.774 1.762–54.972
0.559 0.462 0.974 0.013 <0.001 0.024 0.137 0.276 0.001 0.649 0.009
and longer duration, it is called pyemia. If the sepsis is not timely controlled, it can be developed from the original infected site to other parts of the body, causing a metastatic abscess. The abscess can occur on the surface of the brain, causing meningitis. It can also occur on the envelope around the heart, causing pericarditis. If it occurs on the intima of the heart, it will cause endocarditis; if in the bone marrow, causing osteomyelitis; if in the large joints, causing joint pain or arthritis. Eventually, abscesses can form anywhere in the body due to the accumulation of pus. In severe cases, septic shock and migratory lesions occur. Although many studies have confirmed that sepsis is related to certain factors such as a biomarker, the clinical manifestations of sepsis are complicated, and it is not enough to judge whether a patient develops sepsis by biomarkers alone [1]. With the development of computer technology, through the production and manual simulation software, the relevant data can be input into the computer to enable the computer to comprehensively discriminate the patient’s condition, and the patient’s data can be completely stored in the computer. The use of computer technology saves the workload of medical staff, and the analysis of the disease will be more convenient and comprehensive. Therefore, it is of great clinical value to find a suitable computer-assisted sepsis warning system based on multi-biological indicators to find appropriate clinical symptoms and biomarkers related factors of sepsis. Materials and methods Research object A retrospective analysis of 215 patients admitted to Qilu Hospital of Shandong University (Qingdao) from January 2014 to October 2018 was conducted. Sepsis (test group) inclusion criteria: (1) Any suspected or confirmed (positive culture, tissue staining or PCR) infection caused by bacteria; or a clinical syndrome highly associated with infection. Evidence of infection includes clinical examination, X-ray or laboratory positive results; (2) 2 of the following clinical manifestations: 1. Body temperature >38 ◦ C or <36 ◦ C; 2. Heart rate >90 beats/min; 3 Respiratory rate >20 beats/min or PaCO2 < 32 mmHg; 4. Peripheral blood leukocytes >12 × 109 /L or <4 × 109 /L, or immature (rod-like nucleus) neutrophils >10%; 3. Survival time after admission was greater than 24 h; 4. Age > 18 years old. Inclusion criteria for infection (control group): Any infection caused by suspicious or confirmed (positive culture, tissue staining or PCR) caused by bacteria; or clinical syndrome highly associated with infection. Evidence of infection includes clinical examination, X-ray film or laboratory positive results; (2) Survival time after admission is greater than 24 h; (3) Age ≥18 years old. Exclusion criteria: child patients with immunodeficiency diseases or administered with pre-hospital immunosuppressants; child patients with congenital cardiovascular disease; child patients whose important information, such as age, gender, and diagnosis, was missing.
Research methods Record the clinical data of the selected cases: (1) General conditions, such as age, gender, height, weight, length of hospital stay, diagnosis and outcome (survival or death); (2) Clinical symptoms: chief complaint, current medical history, vital signs; (3) Past history: history of tobacco and alcohol, basic diseases; (4) Laboratory tests: blood gas analysis, blood routine, biochemistry, blood coagulation, blood sugar and infection indicators (PCT, CRP, IL.6, etc.). According to the 2001 international diagnostic criteria for sepsis, when collecting data, (1) The oxygenation index of the selected cases <350 is considered hypoxia: (2) Systolic blood pressure <90 mmHg, mean arterial pressure <70 mmHg or adult systolic blood pressure drop >40 mmHg, or less than 2 standard deviations below the normal value of the age, considered hypotension; (3) Urine volume <0.5 ml/kg/h or 45 mmol/L for at least 2 h considered oliguria; (4) Patients around the things Unresponsive, unconscious or completely unresponsive, loss of perception as a disturbance of consciousness. The clinical data of all the selected cases were within 24 h after hospitalization. If the same examination occurred multiple times within 24 h after admission, it was calculated according to the most serious clinical data of the day [2]. Statistical methods Statistical analysis was performed using SPSS 19.0 statistical software, and the data was measured and obeyed by the normal distribution, and the skewed distribution was expressed by the median and interquartile range (IQR). Counting data is expressed in terms of frequency and percentage, using a test or Fisher’s exact test. Univariate measurement data between the normal and heterogeneous infection group and the sepsis group were tested by two independent samples. Binary logistic regression analysis was used to screen out multiple related factors for the development of infection to sepsis, with P < 0.05 was considered statistically significant. Results 11 variables initially screened by univariate analysis: hypoxia, hypotension, disturbance of consciousness, skin stagnation, platelet count, procalcitonin, total bilirubin, urea, creatinine, blood glucose and international normalized ratio binary Logistic regression analysis. The results showed that the five factors of skin pitting, PLT reduction, PCT elevation, Cr elevation and INR increase were the risk factors for infection to develop sepsis, and the difference was statistically significant, as shown in Table 1. As shown in Tables 2a and 2b, of the 215 laboratory examinations, the differences of platelet count, procalcitonin, total bilirubin, urease, creatinine, blood glucose, and international normalized ratios between the two groups were statistically significant. In the sepsis group, the platelet count was lower than that in the infec-
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Then the multiple linear regression model can be expressed as Y = Xˇ + ε
Indicators
The control group
The experiment group
P
Hemoglobin Red blood cell count White blood cell count Platelet count C-reactive protein Procalcitonin Alanine aminotransferase Albumin Total bilirubin urease Creatinine Blood sugar Sodium Potassium
124.52 ± 11.55 4.17 ± 0.73 11.48 ± 3.61 138.88 ± 26.04 39.44 ± 13.44 3.26 ± 2.44 23.15 ± 5.99 32.64 ± 3.14 10.72 ± 2.95 7.92 ± 2.09 77.23 ± 18.05 5.95 ± 1.09 138.81 ± 4.18 4.04 ± 0.52
124.51 ± 9.61 3.91 ± 0.80 14.85 ± 4.24 89.71 ± 20.16 40.13 ± 14.28 4.58 ± 2.83 22.85 ± 6.91 30.86 ± 3.43 11.99 ± 4.04 9.53 ± 3.26 105.35 ± 22.62 7.10 ± 1.67 137.16 ± 5.09 3.97 ± 0.61
0.077 0.396 0.164 0.015 0.908 0.009 0.278 0.226 0.021 <0.001 0.044 <0.001 0.162 0.091
Factor
Assignment
Hypoxia Hypotension Disorder of consciousness Skin defect
No = 0 No = 0 No = 0 No = 0
Have = 1 Have = 1 Have = 1 Have = 1
tion group, while other indicators were all higher than those in the infection group (P < 0.05). Establishment of a computerized early warning diagnosis model for sepsis Logistic regression model establishment In the previous study, 11 variables including hypoxia, hypotension, disturbance of consciousness, skin stasis, PLT, PCT, STB, BUN, Cr, GLU, and INR were selected by univariate analysis and infection developed into sepsis. Closely related (P < 0.05). Then 11 factors with P < 0.05 in univariate analysis were included in multivariate logistic regression analysis. Logistic regression analysis used a backward stepwise method to exclude the relevant factors of P < 0.05, until all the factors were left into the equation. Finally, five related factors such as skin spots, PLT, PCT, Cr and INR were left [3]. The regression coefficients, standard errors, P values, and OR values of relevant factors were recorded to construct a logistic regression equation. Let the linear relationship between the variable Y and the variables X1, X2, . . ., XP Y = ˇ0 + ˇ1 X1 + ˇ2 X2 + ... + ˇP XP + ε
(1)
2
where ε∼N 0, , ˇ0 , ˇ1 , ..., ˇP and 2 are unknown parameters, p≥2, said the above model is a multiple linear regression model, then the model can be expressed as: yi = ˇ0 + ˇ1 xi1 + ... + ˇp xip + εi , i = 1, 2, ..., n
where εi ∈ N 0, ate
⎡
y1
⎤
⎡
2
ˇ0
ˇp
where Y is an n-dimensional vector composed of response variables, X is an n × (p + 1)-order design matrix, ˇ is a p + 1dimensional vector, and satisfies E (ε) = 0, V ar (ε) = 2 In
Q ˇ = y − Xˇ
T
y − Xˇ
(5) (6)
The minimum value of ˇ is reached. Least squares estimate of ˇ
ˆ = XT X ˇ
−1
XT y
(7)
Obtain empirical regression equation (8)
The early warning evaluation process of medical staff for sepsis in infected patients is introduced as follows. The first step is to obtain the parameters and establish an electronic medical file for the patient, as well as inputting the basic information, hospital number, and inspection time of the patient into the sepsis warning management system. Then, the clinician asks the patient a detailed medical history and completes the relevant examinations. The second step is to enter the data and input the basic information of the patient into the sepsis warning management system. The system generates the medical file of the patient and records various examination parameters such as clinical manifestation and auxiliary examination, providing an electronic platform and data foundation for future computer-aided diagnostic decisions. The final step is to analyze and predict the disease. According to the results of single-factor analysis and Logistic regression analysis, the corresponding examination indicators are selected and input into the sepsis warning model. The model predicts the possibility of developing sepsis with infected patients and provides an objective basis for subsequent treatment. Logistic regression model test (1) Using Hosmer–Lemeshow goodness of fit detection; (2) Using artificial neural network model (radial basis function method) to reconstruct the sepsis warning system and detecting the accuracy and stability of the early warning system based on ROC curve and AUC value. To evaluate the effectiveness and accuracy of the sepsis warning system, this study selected another statistical model for classification and prediction commonly used in the medical field, which is the artificial neural network (ANN) model. The ANN model consists of three layers, i.e., the input layer, the output layer, and the hidden layer. The output layer contains two nodes. In this study, sepsis and non-sepsis are the two nodes of the output layer. This study used a radial basis function that included a hidden layer. Each node of the input layer interacts with each node of the hidden layer and overlays the weight of ownership.
(2) Modelling results
is and is independently distributed. Immedi-
⎤
⎡
1 x11
x12
...
x1p
⎤
⎡
ε1
⎤
⎢y ⎥ ⎢ˇ ⎥ ⎢1 x ⎥ ⎢ε ⎥ 21 x22 ... x2p ⎥ ⎢ 2⎥ ⎢ 1⎥ ⎢ ⎢ 2⎥ ⎥,ˇ = ⎢ ⎥,X = ⎢ ⎥,ε = ⎢ ⎥ y=⎢ ⎢ . ⎥ ⎢ . ⎥ ⎢. . ⎥ ⎢ . ⎥ .. .. ⎣ .. ⎦ ⎣ .. ⎦ ⎣ .. .. ⎣ .. ⎦ . . ⎦ yn
(4)
ˆ P XP ˆ +ˇ ˆ 1 X1 + . . . + ˇ Yˆ = ˇ
Table 2b 215 cases of logistic regression analysis variable assignment table.
3
1
xn1
xn2
...
xnp
εn (3)
Logistic regression model calculation According to the previous binary logistic regression analysis, the constant term of this model is C = 1.893. For the two-category variables, the variable assignments for the regression analysis (Table 1) are as follows (Table 2a,2b). The Hosmer–Lemeshow goodness-of-fit test P = 0.203 showed that the model fits well. According to the regression equation: Z = C + ˇ1 X1 + ˇ2 X2 + ˇ3 X3 + ...ˇm Xm
(9)
Please cite this article in press as: Yang J, et al. Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.11.012
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Fig. 1. Forecast probability distribution map.
The early warning expression for sepsis can be: Z = 1.893 − 3.788 − 0.106(PLT ) − 0.333(PCT ) + 0.073(Cr) + 2.287(INR)
(10)
Bringing Z into the probability, an early warning probability can be drawn:
P = 1/ 1 + e−z
Fig. 2. ROC curve of Logistic regression model.
(11)
The relevant parameters of 215 cases are input into the early warning probability P, and the distribution of the statistical probability P is obtained (Fig. 1). The average value of the predicted probability P is 0.400. The predicted probability of most cases tends to both ends, 109 (50.7%) predictive probability is less than 0.2, 17 cases (7.9%) are 0.2–0.4, 9 cases (4.2%) are 0.4–0.6, and 14 cases (6.5%) For 0.6–0.8, 66 cases (30.7%) were 0.8–1. Overall, it can be considered that the higher the predicted probability value obtained by a patient, the higher the probability that the infection develops into sepsis [4]. On this basis, the study further considers how to choose an optimal diagnosis point as the standard value for early warning of sepsis. The predicted probability P of 215 cases was taken as the ROC curve, and the area under the curve (AUC) was 97.2%, and the 95% CI was 95.3.99.1%. From the AUC curve, the model can be considered to better predict sepsis. For each numerical analysis of the curve, when the sensitivity is 98.8% and the specificity is 74.8%, the Yoden index is the largest, and the prediction probability is P = 7.6%. It can be considered that this has the highest diagnostic value (Fig. 2) [5]. Logistic regression model test In this study sepsis and not sepsis were the two nodes of the output layer. This study used a radial basis function that included a hidden layer. Each node of the input layer interacts with each node of the hidden layer and overlays the weight of ownership. The establishment of an ANN requires three independent sample groups, a training group, a test group, and a reserved group. In this study, 155 (72.1%) were selected as the training group to construct the ANN, and the rest were used as the test group to construct the ANN. First, the ANN is used separately, and 215 cases of all the detection parameters are entered the input layer to construct the ANN, and the AUC of the ANN is 96.0%. Secondly, the entry range is narrowed down, and the factor of single factor analysis P < 0.05 is entered the ANN, and the AUC is obtained. = 92.8%. Finally, combined with the results of Logistic regression analysis, the entry range was narrowed again, and the final factors (skin spot, platelet count, procalcitonin, creatinine, and international normal-
Fig. 3. Artificial neural network model ROC curves.
ized ratio) established in the logistic regression model were entered the input layer, and AUC = 96.4% (Figs. 3, 5, 6 ). Establish a computer sepsis warning model Establish a computerized warning model for sepsis Improve the comprehensive prevention and treatment system of sepsis, input the logistic regression model specific results into the computer, and construct a computer-based sepsis warning model. The overall process of computer-based sepsis warning model assessment is shown in Fig. 4. Computer-based sepsis warning model Combining the logistic regression model discriminant with computer technology, a computer model for sepsis warning was established. The patient’s basic information, clinical manifestations, physiological parameters and test results are input into the computer model, and the computer is used in the background calcu-
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Fig. 4. Total flow charts for evaluation of sepsis warning model.
Fig. 5. Sepsis warning computer model.
Fig. 6. Early warning formula of computer model.
lation to determine the possibility of the patient developing sepsis. The website login interface is as shown. Create a new Excel document and fill in the basic information, clinical features, and auxiliary examinations of the patient in order. Import the Excel file containing a large amount of patient information into the computer model, and click on the patient’s basic information or hospitalization information to view the clinical data of the case. Click on the warning formula to create a regression equation. Z = 1.893–3.788 (skin spot) −0.16 (PLT) −0.333 (PCT) +0.073 (Cr) + 2.287 (INR) Enter the system to create an expression that meets the requirements. The selected cases are then imported
into the model. The computer model is based on the P = 1/ (1 + e−z ) background calculation, and finally the results of the selected cases. Children’s sepsis computer early warning model test Sepsis warning risk factors There were 11 cases of sepsis developed in 30 cases. In the first part of the 19 cases of infected patients who did not develop sepsis and 11 patients with sepsis, the five related factors of skin lesions, platelet count, procalcitonin, creatinine, and international normal-
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Table 3 Analysis of five related factors at 24 h after admission.
Skin defect Platelet count (10*109 /L) Procalcitonin (ng/ml) Creatinine (mol/L) International standardization ratio
19 cases of infection group
11 cases of sepsis group
P
1(5.3%) 151.95 ± 35.36 3.09 ± 1.61 71.21 ± 28.48 1.26 ± 0.30
2(18.2%) 113.45 ± 42.87 3.41 ± 1.60 96.45 ± 38.88 1.49 ± 0.36
0.335 0.019 0.604 0.05 0.068
Table 4 Analysis of five related factors 24 h before diagnosis of sepsis.
Skin defect Platelet count (10*109 /L) Procalcitonin (ng/ml) Creatinine (mol/L) International standardization ratio
19 cases of infection group
11 cases of sepsis group
P
1(5.3%) 171.48 ± 42.95 2.15 ± 0.34 69.30 ± 24.59 1.22 ± 0.28
2(18.2%) 117.05 ± 38.74 3.13 ± 1.04 90.87 ± 29.61 1.57 ± 0.32
0.335 0.04 0.152 0.026 0.013
Table 5 Four-dimensional table for early warning model diagnosis within 24 h of admission.
Early warning model (+) Early warning model (−) Total
Sepsis (+)
Sepsis (−)
Total
5 6 11
3 16 19
8 22 30
Table 6 Diagnosis of the 24 h pre-warning model diagnosis four-table.
Early warning model (+) Early warning model (−) Total
Sepsis (+)
Sepsis (−)
Total
8 3 11
2 17 19
8 22 30
ized ratio were compared. Data analysis of 30 patients within 24 h after admission, the difference between the two factors in the infection group and sepsis group was statistically significant (platelets, creatinine, P < 0.05, Table 3). Data analysis of the 24 h before diagnosis of sepsis in 30 patients, the difference between the three factors in the infection group and sepsis group was statistically significant (platelet, creatinine, international standardized ratio, P < 0.05, Table 4). Analysis of sepsis warning model Thirty patients with early warning factors within 24 h after admission were enrolled in the early warning model, and 8 cases were diagnosed as sepsis. The sensitivity of the early warning model was 45.5%, specificity = 84.2%, positive predictive value = 62.5%, negative predictive value = 72.7%, and diagnostic coincidence rate = 70.0% (Table 5). The early warning factors related to the diagnosis of sepsis within 24 h before the diagnosis of sepsis were entered into the early warning model, and 8 cases were developed as sepsis. The sensitivity of the early warning model was 72.7%, specificity = 89.5%, positive predictive value = 80.0%, negative predictive value = 85.0%, and diagnostic coincidence rate = 83.3% (Table 6). Discussion This study preliminarily explores the clinical application of early warning models. In general, early warning models are not only theoretically feasible, but also have high accuracy in practical applications. Eleven of the 30 patients were enrolled in sepsis. Comparing 11 cases of sepsis with the remaining 19 cases of infection,
2 of the 5 related factors were statistically significant. In addition to platelet count, the rest factors were higher than the infected group. This is inconsistent with the results of Logistic regression analysis in the third section of the first part [6]. The difference is mainly since the total number of clinical applications is small, and the number of patients with skin spots is less in 30 cases, resulting in errors. The early warning model judge’s sepsis based on whether the predicted probability P is greater than 7.6%. After analysis of the sepsis warning model, the sensitivity of the model was 45.5%, the specificity was 84.2%, and the diagnostic coincidence rate was 70.0% [7]. In this study, the model misjudged 6 cases of sepsis into non-sepsis, and 3 cases of non-sepsis were misjudged into sepsis, so the positive predictive value was 62.5%, and the negative predictive value was 72.7%. In general, the model has the certain predictive ability in determining whether the infection develops into sepsis. Since the presence or absence of sepsis is mainly determined by the clinician based on the conditions of patients. When a clinician diagnoses sepsis, the case can be defined as sepsis in this study. If the condition of a patient is relatively mild compared with sepsis or septic shock, and the clinician has diagnosed sepsis at this time, the value of the relevant factors in the case will be mostly low. Also, the predictive probability P calculated by the early warning model is low, which makes the model more inclined to determine the condition as non-sepsis. Therefore, it ultimately leads to an increased possibility of misjudgement of non-sepsis in patients with sepsis. Therefore, the sensitivity of the early warning model in this study is lower than the specificity. In the results of this study, white blood cells, calcitonin, and reactive proteins did not enter the final evaluation model. The reason might be that the research objects are all children with sepsis who have developed sepsis infection. Although the above indicators are better in the diagnosis of infection, the ability to assess the severity of infection remains to be further explored. At present, some scoring systems are often used to evaluate the severity of infection, such as APACHE II, SOFA, MODS, and other scoring systems. Although these scoring systems are more practical, they have certain shortcomings. Through this study, the skin pitting, thrombocytopenia, elevated PCT, elevated Cr, and increased INR can be combined with sepsis to provide a good scoring system for clinical sepsis diagnosis, which is valuable in clinical practices. Overall, the model has a certain predictive power for judging whether the infection develops into sepsis. Because of whether it is sepsis or not, it is mainly judged by the clinician based on the patient’s condition [8]. When a clinician diagnoses sepsis, the case can be defined as sepsis. When the patient’s condition is relatively mild with sepsis or septic shock, if the clinician has diagnosed sepsis at this time, the value of the relevant factors in the case will be mostly low. The predictive probability P calculated by the early warning model is also low, which makes the model more inclined to be judged as non-sepsis, which ultimately leads to an increased possibility of misjudgement of non-sepsis in patients with sepsis. Therefore, the sensitivity of the early warning model in this experiment is lower than the specificity [9].
Conclusion (1) This study found that early warning factors for sepsis may include skin spots, platelets, procalcitonin, creatinine, and international normalized ratios. (2) This study established an early warning diagnosis model for sepsis. The predictive probability P of the early warning model can determine whether the patient develops sepsis. (3) After verification and clinical preliminary application, the early warning model has high sensitivity and specificity, and has high accuracy for predicting whether infection develops into sepsis in advance, and has certain clinical value.
Please cite this article in press as: Yang J, et al. Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.11.012
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ARTICLE IN PRESS J. Yang et al. / Journal of Infection and Public Health xxx (2019) xxx–xxx
Funding No funding sources. Competing interests None declared. Ethical approval Not required. References [1] Chen M, Lu X, Hu L, Liu P, Zhao W, Yan H, et al. Development and validation of a mortality risk model for pediatric sepsis. Medicine 2017;96(20):e6923. [2] Wong HR, Cvijanovich NZ, Anas N, Allen GL, Thomas NJ, Bigham MT, et al. Pediatric sepsis biomarker risk model-ii: redefining the pediatric sepsis biomarker risk model with septic shock phenotype. Crit Care Med 2016;44(11):2010.
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Please cite this article in press as: Yang J, et al. Application of regression model combined with computer technology in the construction of early warning model of sepsis infection in children. J Infect Public Health (2019), https://doi.org/10.1016/j.jiph.2019.11.012