Research - Onkologia i Radioterapia ( 2022) Volume 16, Issue 7

A binary logistic regression approach to identify factors affecting extravasation in chemotherapy treatment

Nihar Ranjan Panda1,2, Jitendra Kumar Pati2 and Ruchi Bhuyan1*
1IMS & SUM hospital Siksha O Anusandhan University (Deemed to be), Bhubaneswar, Odisha, India
2Department of mathematics, CV Raman Global University, Bhubaneswar, Odisha, India
*Corresponding Author:
Ruchi Bhuyan, IMS & SUM hospital Siksha O Anusandhan University (Deemed to be), Bhubaneswar, Odisha, India, Email:

Received: 24-Jun-2022, Manuscript No. OAR-22-67574; Accepted: 18-Jul-2022, Pre QC No. OAR-22-67574 (PQ); Editor assigned: 26-Jun-2022, Pre QC No. OAR-22-67574 (PQ); Reviewed: 10-Jul-2022, QC No. OAR-22-67574 (Q); Revised: 12-Jul-2022, Manuscript No. OAR-22-67574 (R); Published: 19-Jul-2022


Extravasations exert extra pressure on patients in terms of morbidity, mortality, health care expenses, and quality of life. Hence it is the need of the hour to know about the existing prevalence of extravasations to develop the protocol for the administration of chemotherapy. The main spotlight of the study is to determine the factors that affect extravasations and use these factors to reconstitute the existing mathematical model which can predict the status of extravasation for hospitalized patients who are going through chemotherapy.

Based on the data from a multispecialty hospital in Bhubaneswar, Odisha, a binary Logistic Regression model is fitted to deduce the relationship between independent and dependent variables to detect significant parameters.

The mean age of the patients was 33.17 ± 12. Keeping 5% as the level of significance, we find the parameters namely gender (p=0.043) cannula to articulation (p=0.023), flushing after chemotherapy (p<0.000), and history of extravasation (p<0.000) are very significant. The model is adequate up to 79% on a case-to-case basis by establishing these factors in respect of their variation.


Extravasations, logistic regression, chemotherapy


Extravasation may occur due to different kind of reason in the patients who receives chemotherapy [1]. This is happening by the cannula piercing the vessel wall or the leakage caused by increasing venous pressure. As per past data suggest 11% of pediatric patients and up to 70% of neonates getting intravenous treatment can get extravasation.

We may say that the risk of getting extravasation is high with peripheral intravenous catheters [2][3]. We can consider some risk factors for extravasation, increased skin and vein fragility, for example, neonates, multiple cannulas, flexible subcutaneous tissue, and chemotherapy. Also, we may consider some other risk factors for extravasation, for example, the inability to report pain. Inability to visualize insertion sites [4].

Extravasation is the procedure where any fluid unintentionally leaks into the nearby tissue. In cancer therapy, extravasation refers to the inadvertent infiltration of chemotherapy into the subcutaneous or sub-dermal tissues surrounding the intravenous or intra-arterial administration site [5][6][7].


Satisfactory recognition of the probable factors for extravasation is important to reduce the danger in some patients. In case of an increased risk of extravasations. Most extravasations can be prohibited with the methodical execution of careful, consistent, evidence-based management techniques. To reduce the risk of extravasations, the staff concerned in the infusion and running of cytotoxic drugs must be skilled to implement some precautionary protocols [4,6,7]. Different studies had been conducted to compare the risk of extravasation in literature [4].We illustrate the difference between patient-related and procedure-related risk factors for extravasation in table 1.

Tab. 1. Comparison between Patient-related and procedure-related risk for extravasation

Patient-related Procedure-related
There might be the case of Small veins Due to untrained or less experienced employees.
multiple times previous chemotherapy courses or drugs. If numerous attempts at cannula occurred.
important but transportable veins (e.g. old people)  in some cases, the cannulation site is not favorable.
identified diseases or situations linked with a changed or impaired movement It may happen due to Bolus injections.  
 enlarged vascular permeability In some cases, High flow pressure is an issue.  
Fatness in which tangential venous access is harder. In some cases using Equipment is an issue.  
change in sensation at the place of chemotherapy supervision. insufficient dressings or deprived cannula fixation.  
Extended infusion. badly fixed CVAD  

Description of study area and period

This study was carried out at CV Raman global university Bhubaneswar India. The data was collected from a multispecialty hospital in Bhubaneswar. A total of 330 patients were included in this study who received chemotherapy, the demographic variables of the patients were collected and analysed using the spss version 25 program.

Study Design

We have used qualitative as well as quantitative research designs.

Variables Identification

The dependent variable of this study is “extravasation status” which has two binary outcomes if a patient has an extravasation status coded as 1 and if a patient has no extravasation status coded as 0. The predictor variables consider the age of the patient, gender, lifestyle, BMI, BMI category, name of a vein, type of administration, cannula to articulation, after the flush, before the flush, and history of extravasation in table 2&3.

Tab. 2. Description of variables

  Data Types of variable Data Types Values
  Extravasation status Dep Factor 0 No
        1 yes
  Age Ind Continuous  
  Gender Ind Factor 1 Male
        2 Female
  Bmi Ind Continuous  
Lifestyle Ind Factor 1 Sedentary  
      2 Nonsedentary  
Bmi category Ind Factor 0  No obese  
      1  Obese  
Name of vein Ind Factor 1 Major  
      2 Minor  
Type of administration Ind Factor 1 other  
      2 iv infusion  
Cannula to articulation Ind Factor 1 Yes  
      2 No  
After flush Ind Factor 1 Yes  
      2 No  
Before flush Ind Factor 1 Yes  
      2 No  
History of Extravasation Ind Factor   1 yes  
      2 No  

Tab. 3. Descriptive statistics of the variables

Variables N Percent Total
 Male 194 58.8  
 Female 136 41.2 330
           sedentary 205 62.1  
Non sedentary 125 37.9 330
Bmi category      
             No obese 290 87.9  
Obese 40 12.1 330
Name of vein      
Major 305 92.4  
Minor 25 7.6 330
Type of administration      
iv infusion 291 88.2  
other 39 11.8 330
Cannula to articulation      
Yes 67 20.3  
No 263 79.7 330
After flush      
Yes 271 82.1  
No 59 17.9 330
Before flush      
Yes 257 77.9  
No 73 22.1 330
History of Extravasation      
Yes 58 14.6  
No 272 82.4 330

Assumptions of Binary logistic Regression (BLR) model

The characteristic of the BLR model is that the dependent variable only takes on two possible outcomes [8,9,10]. The characteristics of the BLR model are based on independent observations [11]. Multicollinearity is having a bad impact on the logistic regression model, so the characteristic of the BLR model is that a high degree of correlation between the variables is not preferred [12, 13, 14]. Multicollinearity occurs when two or more explanatory variables are highly correlated to each other. In the case of high degree correlation, there is a difficulty for fitting and interpretation of the model. The BLR model is having no extreme outliers, which affects the results of the relevant model. A linear relationship occurs between each variable and the logit of the response variable. [15, 16 17]

Logistic Regression model

The concerned model has a little bit of resemblance with the regression equation. In the case of having one predictor variableX1, the logistic regression equation gives a probability of Y as,


The extension of this equation may include several predictors. In the case of several predictors we have,


Where β012i  are the coefficients of a regression equation? And x0,x1,x2,xiare the independent variables in the given equation.

The above two equations are the same. The linear combination has been extended for any number of predictors.

The Binary Logistic Regression Model

In a logistic regression model, if the dependent variable is categorized into two parts with binary indicator variables 0 and 1 like “yes” or “no”, we apply the logistic regression model.

We can divide the independent variable of the logistic model into 2 types.

• Continuous Variables: it assumes any value within a specified range in the data set.

• Discrete Variable: It assumes only certain values. Introducing a “link function” that links the Dependent variable and independent variable of the predictor variable xi. The link function (x) allows the response variable to be modelled as:


where πi is defined as the probability that the response variable y = 1, β0 is defined as the constant, and β0 is defined the coefficient of the predictor variable xi. The link function allows the response variable to be modelled as:

ImageFor a given binomial response variable, the logistic (logit) a link is defined as the natural logarithm of the odds ratio:


Hence we can easily solve the logistic regression model as:


Using data and with the help of maximum likelihood techniques, we find out the equation with intercept (constant) β0and variable coefficients βi.

To obtain the significance of the coefficient of a logistic regression model, we may use the wald statistic test and the likelihood ratio test.


Here ^ βis defined as the estimated coefficient β and s.(β) is defined as its standard error.

We define the G statistic as the likelihood ratio test for the overall significance of the beta's coefficients for the independent variables as


For fitness of the model we use likelihood statistic L. Using the concept of the null hypothesis with a view that all the regression coefficient of the model is zero, we may say the p-value takes measure roll for checking the variables as significant.

Basically, to check the goodness of fit of the logistic regression model we use the Hosmer and Lemeshow method. The method was mainly based on the value of estimated probabilities.

We also make a test by using Pearson x2 statistic from observed and expected frequencies as,


Where Ni Is defined as the number of observations in the ith group. Oi is defined as the number of event outcomes in the ith group. πi Is defined as the average estimated probability of an event outcome for the ith group.

Odds ratio

The odds ratio plays a vital role in the logistic regression, model [18]. This is the measure of association for the 2×2 contingency table.π1 is the probability of success in row 1 and π2 in row 2[18, 19]. Within row 1, the odds of success are defined to be:


For the binary regression model, the odds ratio is the exponent e(βi) is the ratio of odds for a one-unit change in one variable. When the two groups of odds are identical then the odds ratio is equal to one.

Data and method of analysis

A simple random sampling method was used to collect the data in the hospital, which is located in Bhubaneswar Odisha.

The outcomes are: Y = 1 if extravasation occurs


As per calculation, we obtain:

•From the table 4, the odds ratio for age is 1.006.

Tab. 4. Association of independent variables with the status of extravasation

Variables B S.E Wald df Sig Exp (B)
Age 6 0.008 0.602 1 0.431 1.006
Gender -0.55 0.284 3.742 1 0.043 0.557
 Life style 0.081 0.297 0.074 1 0.785 1.084
Bmi 0.033 0.039 0.696 1 0.404 1.033
Bmicatagory -0.198 0.558 0.125 1 0.723 0.821
Name of vein -0.572 0.485 1.391 1 0.238 0.564
Types of administration 0.434 0.429 1.024 1 0.312 1.543
cannula to articulation 0.775 0.34 5.2 1 0.023 2.171
Past history of Extravasation 2.647 0.421 39.468 1 0 14.149
before flush 0.162 0.352 0.352 1 0.646 1.175
after flush -1.311 0.376 0.376 1 0 0.269
Constant -4.474 -0.474 1.222 1 0.698 0.622

•From the table 4, the odds ratio for gender is 0.557.

•From the table 4, the odds ratio for lifestyle is 1.084

•From the table 4, the odds ratio for bmi is 1.033.

•From the table 4, the odds ratio for bmi category is 0.821 and so on.

•From the table4, the odds ratio for past history of extravasations is 14.149.

Here we can say that the covariates cannula to articulation, history of extravasation, and flushing after chemotherapy are statistically significant, while the covariate's age, BMI, gender, name of the vein, etc. are not significant factors.

The corresponding logit is


In other words, we can write

Y=logit= -4.474+0.006(age).550(gender)+0.081(lifestyle)+0.0 33(Bmi)0.198(bmicat)−0.572(Name of the vein)+0.434(Types of administration)+.775(cannula to articulation)+2.647(History of Extravasation)+0.162(before flush)−1.311 (after flush)


There is a non-linear relationship between outcome and predictor variables From table 5:

Tab. 5. Classification matrix based on logistic regression model

Observed Predicted
Extravasation Status No Yes Percentage
No 194 16 92.4
Yes 54 66 55
Overall Percentage     78.8

a.92.4% of the patients had not extravasation correctly classified and 7.6% were incorrectly classified.

b.55% of the patients having extravasation were correctly classified, 45%of the cases were not classified correctly.

c.The model is quite good and reliable because the total correct percentage was 78.8%.

From Table 6, we can say that the value of Cox & Snell R Square indicates 46.6% of the variation in the model. From the table 6, Nagelkerke R Square indicates a moderately strong relationship of 56.3% between the predictors and the prediction.

Tab. 6. Model summary based on logistic regression model

Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square
1 330.79 .466 .563

From the table 7, the value of the Hosmer Lemeshow goodnessof-fit statistic for the full model was Chi-square=5.694 and the p-value from the chi-square distribution with 8 degrees of freedom is 0.682. This value indicates that the p-value of chisquare is not significant, which is good for our model.

Tab. 7. Hosmer and Lemeshow Test

Step Chi-square value Degrees of freedom significance
1 5.694 8 .682

Discussions of Findings

Based on the observations regarding the model, we herewith conclude that the Model is effective in correlating the parameters with a 5% level of significance. Increasing the level of significance to 10% we recommend there is a possibility of increasing the significant factors. In our future work, the logistic regression curve may be analyzed concerning Points of inflections extremum correlating with the significant factors. so overall the model is recommended for the state of extravasation up to a 5% level of significance.


Awards Nomination

Editors List

  • Prof. Elhadi Miskeen

    Obstetrics and Gynaecology Faculty of Medicine, University of Bisha, Saudi Arabia

  • Ahmed Hussien Alshewered

    University of Basrah College of Medicine, Iraq

  • Sudhakar Tummala

    Department of Electronics and Communication Engineering SRM University – AP, Andhra Pradesh




  • Alphonse Laya

    Supervisor of Biochemistry Lab and PhD. students of Faculty of Science, Department of Chemistry and Department of Chemis


  • Fava Maria Giovanna


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