Endobronchial biopsy (EBB)-induced bleeding is fairly common; however, it can be potentially life-threatening due to difficult hemostasis following EBB. The aim of this study was to develop a predictive model of difficult hemostasis post-EBB. A total of 620 consecutive patients with primary lung cancer who had undergone EBB between 2014 and 2018 in a large tertiary hospital were enrolled in this retrospective single-center cohort study. Patients were classified into the difficult hemostasis group and the nondifficult hemostasis group according to hemostatic measures used following EBB. The LASSO regression method was used to select predictors and multivariate logistic regression was applied to develop the predictive model. The area under the curve (AUC) of the model was calculated. Bootstrapping method was applied for internal validation. Calibration curve analysis and decision curve analysis (DCA) were also performed. A nomogram was constructed to display the model. The incidence of difficult hemostasis post-EBB was 11.9% (74/620). Eight variables were selected by the LASSO regression analysis and seven (histological type of cancer, lesion location, neutrophil percentage, activated partial thromboplastin time, low density lipoprotein cholesterol, apolipoprotein-E, and pulmonary infection) of them were finally included in the predictive model. The AUC of the model was 0.822 (95% CI, 0.777-0.868), and it was 0.808 (95% CI, 0.761-0.856) in the internal validation. The predictive model was well calibrated and DCA indicated its potential clinical usefulness, which suggests that the model has great potential to predict lung cancer patients with a more difficult post-EBB hemostasis.
Bleeding is a very common complication during endobronchial biopsy (EBB), and biopsy-induced difficult hemostasis not only affects further bronchoscopic procedures but also can be life-threatening [
Malignant tissue is more likely to bleed compared to benign tissue during bronchoscopy [
Since uncontrolled endobronchial bleeding is still the main cause of death during bronchoscopy [
This study was based on a single-center retrospective cohort study. A total of 620 lung cancer patients who had consecutively undergone EBB between January 2014 and February 2018 were enrolled at a 2600-bed tertiary hospital in this study. The study was approved by the institutional ethics committee of the hospital (No. 2018001007). Because all patient information used in this study was anonymous, patient informed consent was waived.
In this cohort study, difficult hemostasis was defined as the requirement of APC or electrocoagulation for hemostasis following EBB; the remaining patients with either no bleeding, bleeding stopped on its own, or bleeding stopped with intrabronchial instillation of hemostatic drugs (4°C physiological saline or diluted adrenalin) were placed in the nondifficult hemostasis group. The following variables were collected from this study: patient’s gender, age, brachial artery systolic pressure and diastolic pressure, weight, smoking history (yes or no), coexisting diabetes, COPD (chronic obstructive pulmonary disease) or CHD (coronary heart disease), pulmonary infection (yes or no); tumor features: lesions location, cancer histological type and stage (based on the TNM staging, the stage I-II was classified as early and stage III-IV, as advanced); laboratory tests on admission: white blood cell count, neutrophil percentage, neutrophil counts, hemoglobin, platelets, prothrombin time, activated partial thromboplastin time (APTT), aspartate aminotransferase, alanine aminotransferase, total cholesterol level, low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), triglycerides, apolipoprotein-E, apolipoprotein-B and C-reactive protein (CRP).
The procedure (fiberoptic bronchoscopy, BF-1T60) was performed through a laryngeal mask airway under general anesthesia with the patients in the supine position. At the same location of the lesion 3-5 biopsies were usually performed using rigid endoscopic biopsy forceps. Generally, for obvious bleeding following EBB, intrabronchial instillation of 4°C physiological saline and/or diluted adrenalin (1:10000) was the first choice for hemostasis with repeats several times as needed. Electrocoagulation or APC was required when the bleeding failed to reach hemostasis by the means of the aforementioned drugs. All biopsies were performed by two experienced bronchoscopists.
In this study, multiple imputation method was employed to account for missing data, and the baseline characteristics of the participants were summarized. Categorical variables are expressed as the number (percentage) and continuous variables as median (interquartile). Between two groups comparison, unpaired t-test or Kruskal-Wallis rank sum test, Pearson chi-squared test or the Fisher’s exact test was performed as appropriate. The least absolute shrinkage and selection operator (LASSO) regression method was used for predictor selection and regularization. Multivariable logistic regression analysis using backward stepwise procedure and the likelihood ratio test on the basis of Akaike’s information criterion (AIC) [
Among the 620 patients, 74 (11.9%, 95% confidence interval [CI], 9.4%-14.5%) experienced post-EBB difficult hemostasis, and no patient died of severe bleeding after electrocoagulation and APC hemostasis. Smoking, pulmonary infection, histological types of cancer, lesion location, neutrophil percentage, CRP, APTT, HDL-C, alanine aminotransferase, and apolipoprotein-E were statistically different as assessed by univariate analysis. Patient’s baseline characteristics, tumor features, and laboratory tests are shown in Table
Baseline characteristics and blood tests of the study participants.
Variables | Difficult hemostasis | P value | |
---|---|---|---|
No (n = 546) | Yes (n = 74) | ||
Gender, n (%) | 0.038 | ||
Female | 124 (22.71) | 9 (12.16) | |
Man | 422 (77.29) | 65 (87.84) | |
Age (years) | 65 (59-70) | 65 (59-70) | 0.757 |
Smoking, n (%) | 0.003 | ||
No | 214 (39.19) | 16 (21.62) | |
Yes | 332 (60.81) | 58 (78.38) | |
SBP (mmHg) | 131 (119-145) | 128 (111-143) | 0.184 |
DBP (mmHg) | 78 (70-86) | 70 (70-88) | 0.326 |
Weight (kg) | 60 (53-66) | 60 (53-70) | 0.353 |
COPD, n (%) | 0.738 | ||
No | 511 (93.59) | 70 (94.59) | |
Yes | 35 (6.41) | 4 (5.41) | |
Diabetes, n (%) | 0.410 | ||
No | 516 (94.51) | 72 (97.30) | |
Yes | 30 (5.49) | 2 (2.70) | |
CHD, n (%) | 0.722 | ||
No | 529 (96.89) | 71 (95.95) | |
Yes | 17 (3.11) | 3 (4.05) | |
Pulmonary infection, n (%) | <0.001 | ||
No | 340 (62.27) | 28 (37.84) | |
Yes | 206 (37.73) | 46 (62.16) | |
Cancer stage, n (%) | 0.824 | ||
Early | 295 (54.03) | 41 (55.41) | |
Advanced | 251 (45.97) | 33 (44.59) | |
Histological types, n (%) | <0.001 | ||
Adenocarcinoma | 161 (29.49) | 5 (6.76) | |
Squamous cell carcinoma | 254 (46.52) | 59 (79.73) | |
SCLC | 101 (18.50) | 8 (10.81) | |
Others | 30 (5.49) | 2 (2.70) | |
Lesion location, n (%) | <0.001 | ||
Left main bronchus | 29 (5.31) | 8 (10.81) | |
Left upper lobar bronchi | 129 (23.63) | 13 (17.57) | |
Left lower lobar bronchi | 98 (17.95) | 10 (13.51) | |
Right main bronchus | 16 (2.93) | 8 (10.81) | |
Right upper lobar bronchi | 137 (25.09) | 14 (18.92) | |
Right middle bronchus | 21 (3.85) | 8 (10.81) | |
Right middle lobar bronchi | 27 (4.95) | 2 (2.70) | |
Right lower lobar bronchi | 84 (15.38) | 7 (9.46) | |
The trachea | 5 (0.92) | 4 (5.41) | |
Hospitalization, (days) | 11 (7-15) | 10 (7-14) | 0.360 |
WBC (×109/L) | 6.80 (5.45-8.50) | 6.89 (5.43-8.88) | 0.914 |
Neutrophil (%) | 69.2 (61.8-75.6) | 72.6 (65.8-80.9) | 0.004 |
Neutrophils (×109/L) | 4.54 (3.50-6.35) | 5.10 (3.82-6.70) | 0.787 |
Hemoglobin (g/dl) | 128 (116-140) | 126 (112-137) | 0.256 |
Platelets (×109/L) | 222 (173-280) | 245 (171-317) | 0.086 |
CRP (mg/L) | 5.9 (1.1-31.8) | 18.66 (3.8-44.5) | <0.001 |
PT (s) | 13.0 (12.1-13.6) | 13.3 (12.2-13.9) | 0.663 |
APTT (s) | 34.8 (31.5-38.2) | 36.2 (33.0-41.2) | 0.034 |
ALT (IU/L) | 17.0 (12.0-26.0) | 15.1 (12.0-21.4) | 0.184 |
AST (IU/L) | 23.0 (19.0-29.0) | 23.0 (18.0-27.1) | 0.233 |
Homocysteine ( | 13.3 (10.7-16.6) | 12.7 (10.1-15.7) | 0.627 |
Triglyceride (mmol/L) | 1.08 (0.79-1.46) | 0.96 (0.71-1.15) | 0.006 |
TC (mmol/L) | 4.10 (3.46-4.77) | 4.06 (3.53-4.77) | 0.720 |
HDL-C (mmol/L) | 1.14 (0.94-1.37) | 1.07 (0.92-1.22) | 0.072 |
LDL-C (mmol/L) | 2.75 (2.26-3.30) | 2.86 (2.42-3.40) | 0.074 |
Apolipoprotein-B (g/L) | 0.96 (0.77-1.18) | 0.95 (0.80-1.15) | 0.425 |
Apolipoprotein-E (mg/dL) | 3.60 (2.90-4.80) | 2.95 (2.40-3.98) | 0.006 |
SBP, systolic blood pressure; DBP, diastolic blood pressure; COPD, chronic obstructive pulmonary disease; CHD, coronary heart disease; SCLC, small-cell lung carcinoma; WBC, white blood cell; CRP, C-reactive protein; PT, prothrombin time; APTT, activated partial thromboplastin time; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TC, total cholesterol; HDL-C, high density lipoprotein cholesterol; LDL-C, low density lipoprotein cholesterol.
Of the 31 variables, 8 variables were filtered based on nonzero coefficients calculated by the LASSO regression analysis using the minimum criteria (Figure
Variables selection using the LASSO regression analysis with 10-fold cross-validation. Coefficients were produced against the log lambda sequence. Dotted vertical line was drawn according to the minimum criteria (left dotted line), and a total of 8 nonzero coefficients were filtered and used to construct predictive model.
The aforementioned 8 predictors were included in multivariable logistic regression analysis. Backward stepwise selection was applied to develop a predictive model and 7 predictors (excluding HDL-C) were eventually incorporated into the model according to the likelihood ratio test with AIC.
The AUC for the predictive model reached was 0.822 (95% CI, 0.777-0.868), and it was 0.808 (95% CI, 0.761-0.856) in the internal validation using bootstrapping (resampling times = 1000) (Figure
The AUC represents the discriminatory ability of the model. (a) shows AUC of the predictive model and (b) shows AUC of the internal validation with the bootstrap method (resampling times = 1000). The dotted vertical lines represent 95% confidence interval. AUC, area under the curve.
Nomogram for difficult hemostasis following endobronchial biopsy in lung cancer patients. Firstly, find point for each predictor of an individual on the uppermost rule. Secondly, add all points together and find the “total points” on rule. At last, the corresponding predicted probability of difficult hemostasis following endobronchial biopsy could be found on the lowest rule. Codes annotation: histological type of lung cancer: 0, adenocarcinoma; 1, squamous cell carcinoma; 2, small-cell lung carcinoma; 3, other types. Lesion location: 1, left main bronchi; 2, left upper lobar bronchi; 3, left lower lobar bronchi; 4, right main bronchus; 5, right upper lobar bronchi; 6, right middle bronchus; 7, right middle lobar bronchi; 8, right lower lobar bronchi; 9, the trachea. Pulmonary infection: 0, no; 1, yes. APTT, activated partial thromboplastin time; LDL-C, low density lipoprotein cholesterol; ApoE, apolipoprotein-E.
As is shown in Figure
Calibration curve of the predictive model. It shows a good fit between the predicted risks of difficult hemostasis following endobronchial biopsy and observed outcomes in patients with lung cancer. The red solid line represents an ideal predictive model, and the solid black line shows the actual performance of the predictive model. The yellow shadow represents 95% confidence interval. The Hosmer-Lemeshow test yielded a P value of 0.985, an Emax of 0.027, and an Eavg of 0.006.
The decision curve (Figure
Decision curve of the predictive model. Net benefit was produced against the high risk threshold. The red solid line represents the predictive model. The decision curve indicates that when the threshold probability is less than 90%, application of this predictive model would add net benefit compared with either the treat-all or the treat-none strategies.
In the present study, a predictive model of difficult hemostasis following EBB was developed. This model incorporated 7 predictors, including histological type of cancer, lesion location, pulmonary infection, neutrophil percentage, APTT, LDL-C, and apolipoprotein-E. The model showed good discriminatory ability in the derivation cohort and in the internal validation. The model was well-calibrated and showed potential clinical usefulness.
EBB-induced bleeding has always been a common concern for bronchoscopists [
In previous studies, several risk factors for bleeding during bronchoscopy had been proposed, such as immunosuppression, thrombocytopenia, anticoagulant drug use, or coagulation dysfunction [
For bronchoscopists, to predict intraoperative EBB-induced bleeding risk can help guide their preoperative clinical decision making and select appropriate hemostasis measures during EBB. Currently, commonly used hemostasis measures during EBB include intrabronchial instillation of 4°C physiological saline and/or diluted adrenalin, which is suitable for hemostasis with microbleeding or mild-bleeding [
The predictive model for post-EBB difficult hemostasis in the present study was developed based on 7 predictors, including histological type of cancer, lesion location, pulmonary infection, neutrophil percentage, APTT, LDL-C, and apolipoprotein-E. These predictors were filtered by LASSO regression analysis, which was considered to surpass the technique of choosing predictors based on their univariable association strength with outcome [
Some limitations of this predictive model are worth noting. Firstly, this model was constructed based on a single-center retrospective study, which inevitably suffered from confounding bias; for example, indication of the specific tools (APC, electrocoagulation, or endobronchial balloon tamponade) used for the stop bleeding is related to the decision of the physician, which is difficult to be reconciled among bronchoscopists. Secondly, an independent validation is very important for determining the clinical usefulness of a predictive model; therefore, whether the proposed model is applicable to other endoscopic centers needs further validation. Thirdly, the mechanism underlying some predictors in bleeding is still unclear, such as LDL-C. In addition, during hemostasis, the time and frequency of use of electrocoagulation and/or APC may further distinguish the degree of difficulty in hemostasis; however, these variables were not available in the original data. Despite these limitations, the present study is the first to develop a predictive model for difficult hemostasis following EBB.
A difficult hemostasis risk prediction model for EBB-induced bleeding in lung cancer patients was developed, which incorporated 7 readily available clinical variables. This model showed good discriminatory ability and potential clinical usefulness, and thus it may be of great value to facilitate the prediction and management of EBB-induced difficult hemostasis.
The data used to support the findings of this study are available from the corresponding author upon request.
The author declares that he has no conflicts of interest.
Saibin Wang was responsible for conception, design, data collection, data analysis, and manuscript preparation.
I appreciate the help and support of all the participants involved in the study. This study was supported by the Medical and Health Science and Technology Plan Project of Zhejiang Province (2018RC079), the Youth Research Fund of Jinhua Hospital of Zhejiang University (JY2017205), the Science and Technology Key Project of Jinhua City (20163011), and the Chinese Medicine Science and Technology project of Jinhua City (2017jzk05).