Home Development and validation of a novel model to predict pulmonary embolism in cardiology suspected patients: A 10-year retrospective analysis
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Development and validation of a novel model to predict pulmonary embolism in cardiology suspected patients: A 10-year retrospective analysis

  • Fang Ling , Qiang Jianling and Wang Maofeng EMAIL logo
Published/Copyright: March 8, 2024
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Open Medicine
From the journal Open Medicine Volume 19 Issue 1

Abstract

As there are no predictive models for pulmonary embolism (PE) in patients with suspected PE at cardiology department. This study developed a predictive model for the probability of PE development in these patients. This retrospective analysis evaluated data from 995 patients with suspected PE at the cardiology department from January 2012 to December 2021. Patients were randomly divided into the training and validation cohorts (7:3 ratio). Using least absolute shrinkage and selection operator regression, optimal predictive features were selected, and the model was established using multivariate logistic regression. The features used in the final model included clinical and laboratory factors. A nomogram was developed, and its performance was assessed and validated by discrimination, calibration, and clinical utility. Our predictive model showed that six PE-associated variables (age, pulse, systolic pressure, syncope, D-dimer, and coronary heart disease). The area under the curve – receiver operating characteristic curves of the model were 0.721 and 0.709 (95% confidence interval: 0.676–0.766 and 0.633–0.784), respectively, in both cohorts. We also found good consistency between the predictions and real observations in both cohorts. In decision curve analysis, the numerical model had a good net clinical benefit. This novel model can predict the probability of PE development in patients with suspected PE at cardiology department.

Keywords: prediction model; clinical symptoms; biomarkers; nomogram

1 Introduction

Pulmonary embolism (PE) is a serious and potentially life-threatening medical condition resulting from blood clot formation in one or more arteries in the lungs [1]. Patients with cardiovascular diseases and cancer [2], particularly those with heart failure, atrial fibrillation, and coronary artery disease, are at an increased risk of developing PE because of the pro-thrombotic state of their conditions [3]. Furthermore, patients who undergo cardiac surgeries or interventions [4], such as coronary artery bypass grafting and percutaneous coronary intervention, are at a higher risk of developing PE. PE can have a significant effect on the prognosis and quality of life of patients with cardiovascular diseases [5]; therefore, timely diagnosis and treatment are essential for improving patients’ outcomes [6]. Current diagnostic methods have limitations and may lead to unnecessary testing and delays in treatment [7].

Computed tomography pulmonary angiography (CTPA) is the gold standard diagnostic method for PE [8], but it is associated with high radiation exposure, contrast-induced nephropathy, and allergic reactions. Furthermore, CTPA may not be appropriate for some patients such as pregnant women or those with kidney disease. Therefore, there is a need for noninvasive and accurate methods to identify patients with high risk of PE. Several studies have been conducted to develop and validate models for predicting the risk of PE in various populations including emergency department patients [9], pregnant and postpartum women [10], and patients with underlying medical conditions such as cancer [11] and heart failure [12]. One example of a scoring system is the Wells score, which is widely used to assess the probability of PE in patients with suspected PE [13]. The Wells score is based on clinical variables such as prior deep vein thrombosis symptoms, clinical symptoms, and presence of limb edema. Other scoring systems [14] such as the Geneva score and the PE rule-out criteria have also been developed and validated in patients presenting with a primary complaint of shortness of breath or chest pain, and it is reasonable to use it for either of these symptoms.

In recent years, there has been increasing interest in developing predictive models for PE in specific populations. For example, Jen et al. developed a new model that outperforms existing predictive tools in all patients with PE [15]. Lin et al. developed a new clinical predictive model that can identify patients who are at high risk of venous thromboembolism and help provide medical intervention in patients with diabetes and the general population [16]. While PE predictive models have been developed and validated in various populations [17], there are still some shortcomings and deficiencies that need to be addressed. One limitation of existing PE predictive models is their lack of generalizability across different patient populations. For example, a model developed in a population of emergency department patients may not be applicable to patients in a primary care setting or those with underlying medical conditions such as cancer or heart failure. In addition, some PE predictive models may not fully capture the complex interactions between various risk factors and their contribution to PE development. For example, the Wells score, although widely used, does not include variables such as the presence of a hypercoagulable state, which may increase the risk of thromboembolism [18].

Currently, there is no widely accepted model or guideline for predicting the probability of PE in patients with cardiovascular disease. Therefore, there is a critical need for developing an accurate and reliable predictive model for PE in patients with cardiovascular diseases.

In this study, we bridge this gap by developing and validating a novel numerical model to predict the probability of PE in patients with cardiovascular disease. This model simplifies risk assessment and provides a user-friendly interface for medical practitioners to assess a patient’s risk level.

2 Methods

2.1 Patient enrollment and data collection

This retrospective study enrolled patients with suspected PE at the Department of Cardiology at the Affiliated Dongyang Hospital of Wenzhou Medical University from January 2012 to December 2021. The data of 995 subjects were collected from the hospital’s clinical research data platform, after baseline data clearing and extraction. The patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3.

  1. Ethical approval: This study was approved by the Medical Ethics Committee of the Affiliated Dongyang Hospital of Wenzhou Medical University (No.: 2022-YX-160). The requirement for informed consent was waived. Patient records or information were anonymized and de-identified before our analysis. Our research was conducted in adherence with the Declaration of Helsinki.

2.2 Diagnostic criteria

The diagnosis of PE in our study was based on the criteria outlined in the European Society of Cardiology Guidelines [19], and patients who had undergone CTPA examination were classified as those having suspected PE. The diagnosis was based on the presence of a filling defect in the pulmonary artery system, including the subsegment pulmonary artery, as seen on CTPA. In addition to CTPA results, we collected patients’ past medical history, clinical features, complications, and biomarker data using strictly defined indicators. For instance, we selected the lowest value of blood oxygen saturation, systolic blood pressure, and diastolic pressure from admission to CTPA, while the highest value was chosen for other indicators. A flowchart of the steps involved in PE prediction model is presented in Figure 1.

Figure 1 Flowchart of the steps for predicting PE diagnosis.
Figure 1

Flowchart of the steps for predicting PE diagnosis.

2.3 Statistical analysis

The data were analyzed using R Studio software for Windows. Categorical variables were presented as frequencies with percentages and were compared using either the χ² test or Fisher’s exact test. Continuous variables were expressed as mean values with standard deviations or medians with interquartile ranges and were compared using either Student’s t-test or Mann–Whitney U test. A total of 58 variables were collected for each subject. To ensure data reliability, 13 indicators with missing information in greater than 20% of patients were excluded. Multiple imputation techniques [20] using the “mice” package in R software were applied to impute the remaining missing predictor values. The optimal predictive features were selected using the least absolute shrinkage and selection operator (LASSO) regression analysis [21] with the “glmnet” package, and the numerical model was established using multivariate logistic regression analysis with the “rms” package. A nomogram was constructed using the “regplot” package in R software. The features were presented as odds ratios (ORs) with 95% confidence intervals (CIs). A two-sided p-value of less than 0.05 was considered statistically significant.

2.4 Model development, validation, and evaluation

In the training cohort, we employed LASSO regression to select the optimal predictive features and developed a multivariable logistic regression model to predict the probability of PE. To evaluate the performance of the model, discrimination, calibration, and clinical utility were assessed and validated in both cohorts. Discrimination was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) with the “pROC” package. Calibration was assessed with calibration curve analysis using the “calibrate” package. We performed decision curve analysis (DCA), clinical impact curve, and net reduction curve with the “rmda” package to quantify the net benefit under different threshold probabilities and determined the clinical utility of the model.

3 Results

3.1 Study population characteristics

In this study, we excluded 13 variables with missing information in more than 20% of patients, leaving 45 variables with missing data in less than 20% of patients (as shown in Appendix 1). The multiple imputation technique was used to impute the missing data for these 45 variables, which ranged from 0.00 to 12.46%. A total of 995 subjects with suspected PE were included, and the incidence of PE in our study was 17.98%. The baseline characteristics of patients with suspected PE at the cardiology department are presented in Table 1. We randomly divided the patients into the training cohort (n = 697) and the validation cohort (n = 298). The baseline characteristics of patients in the two cohorts are shown in Table 2. There was no significant difference in each indicator between the two cohorts, except for two indicators (platelet distribution width and thrombin time).

Table 1

Baseline characteristics of the study subjects

Variables Total (N = 995) No PE (N = 816) PE (N = 179) p
Sex, n (%) 0.150
 Female 499 (50.2%) 400 (49.0%) 99 (55.3%)
 Male 496 (49.8%) 416 (51.0%) 80 (44.7%)
Age (years) 76.0 [67.0; 82.0] 75.0 [65.0; 81.2] 78.0 [71.0; 84.0] <0.001
Breathing (breaths/min) 22.0 [20.0; 26.0] 22.0 [20.0; 26.0] 24.0 [22.0; 28.0] <0.001
Pulse (beats/min) 103 [89.0; 124] 102 [88.0; 120] 112 [98.0; 140] <0.001
Systolic pressure (mmHg) 101 [91.0; 113] 102 [92.0; 115] 95.0 [87.5; 105] <0.001
Diastolic pressure (mmHg) 54.0 [47.0; 63.0] 55.0 [48.0; 64.0] 51.0 [45.0; 57.5] <0.001
Headache, n (%) 0.204
 No 968 (97.3%) 791 (96.9%) 177 (98.9%)
 Yes 27 (2.71%) 25 (3.06%) 2 (1.12%)
Dizzy, n (%) 0.474
 No 957 (96.2%) 787 (96.4%) 170 (95.0%)
 Yes 38 (3.82%) 29 (3.55%) 9 (5.03%)
Chest tightness, n (%) 0.198
 No 601 (60.4%) 501 (61.4%) 100 (55.9%)
 Yes 394 (39.6%) 315 (38.6%) 79 (44.1%)
Anhelation, n (%) 0.055
 No 664 (66.7%) 556 (68.1%) 108 (60.3%)
 Yes 331 (33.3%) 260 (31.9%) 71 (39.7%)
Hemoptysis, n (%) 0.548
 No 991 (99.6%) 813 (99.6%) 178 (99.4%)
 Yes 4 (0.40%) 3 (0.37%) 1 (0.56%)
Chest pain, n (%) 0.410
 No 910 (91.5%) 743 (91.1%) 167 (93.3%)
 Yes 85 (8.54%) 73 (8.95%) 12 (6.70%)
Syncope, n (%) 0.227
 No 947 (95.2%) 773 (94.7%) 174 (97.2%)
 Yes 48 (4.82%) 43 (5.27%) 5 (2.79%)
Cough, n (%) 0.163
 No 744 (74.8%) 618 (75.7%) 126 (70.4%)
 Yes 251 (25.2%) 198 (24.3%) 53 (29.6%)
Fever, n (%) 1.000
 No 973 (97.8%) 798 (97.8%) 175 (97.8%)
 Yes 22 (2.21%) 18 (2.21%) 4 (2.23%)
Lower limb edema, n (%) 0.003
 No 842 (84.6%) 704 (86.3%) 138 (77.1%)
 Yes 153 (15.4%) 112 (13.7%) 41 (22.9%)
COPD, n (%) 0.630
 No 794 (79.8%) 654 (80.1%) 140 (78.2%)
 Yes 201 (20.2%) 162 (19.9%) 39 (21.8%)
Hypertension, n (%) 0.706
 No 382 (38.4%) 316 (38.7%) 66 (36.9%)
 Yes 613 (61.6%) 500 (61.3%) 113 (63.1%)
Diabetes, n (%) 0.840
 No 836 (84.0%) 687 (84.2%) 149 (83.2%)
 Yes 159 (16.0%) 129 (15.8%) 30 (16.8%)
Coronary heart disease, n (%) 0.009
 No 330 (33.2%) 286 (35.0%) 44 (24.6%)
 Yes 665 (66.8%) 530 (65.0%) 135 (75.4%)
Hyperlipidemia, n (%) 1.000
 No 969 (97.4%) 794 (97.3%) 175 (97.8%)
 Yes 26 (2.61%) 22 (2.70%) 4 (2.23%)
Atrial fibrillation, n (%) <0.001
 No 763 (76.7%) 645 (79.0%) 118 (65.9%)
 Yes 232 (23.3%) 171 (21.0%) 61 (34.1%)
Operation, n (%) 1.000
 No 988 (99.3%) 810 (99.3%) 178 (99.4%)
 Yes 7 (0.70%) 6 (0.74%) 1 (0.56%)
Tumor, n (%) 0.955
 No 932 (93.7%) 765 (93.8%) 167 (93.3%)
 Yes 63 (6.33%) 51 (6.25%) 12 (6.70%)
Smoking, n (%) 1.000
 No 697 (70.1%) 572 (70.1%) 125 (69.8%)
 Yes 298 (29.9%) 244 (29.9%) 54 (30.2%)
Drinking, n (%) 0.818
 No 666 (66.9%) 548 (67.2%) 118 (65.9%)
 Yes 329 (33.1%) 268 (32.8%) 61 (34.1%)
WBC (109/L) 4.33 [3.93; 4.70] 4.33 [3.92; 4.71] 4.32 [3.97; 4.68] 0.799
RBC (1012/L) 7.28 [5.75; 9.64] 7.18 [5.61; 9.51] 7.61 [6.21; 10.7] 0.009
Mg (mmol/L) 0.90 [0.84; 0.95] 0.90 [0.84; 0.96] 0.88 [0.81; 0.93] <0.001
HGB (g/L) 131 [119; 144] 132 [119; 145] 129 [119; 142] 0.295
Hct 0.40 [0.36; 0.43] 0.40 [0.36; 0.43] 0.40 [0.36; 0.43] 0.874
Neutrophil percent 0.73 [0.65; 0.82] 0.73 [0.64; 0.81] 0.76 [0.67; 0.84] 0.004
Neutrophil count (109/L) 5.13 [3.75; 7.41] 5.03 [3.64; 7.16] 5.43 [4.26; 8.31] 0.003
Lymphocyte percent 0.23 [0.16; 0.31] 0.23 [0.17; 0.31] 0.21 [0.16; 0.29] 0.051
Lymphocyte count (109/L) 1.46 [1.07; 1.96] 1.49 [1.09; 1.96] 1.38 [1.02; 1.89] 0.297
PLT (109/L) 199 [161; 248] 198 [161; 246] 207 [165; 264] 0.193
PDW (%) 16.1 [15.4; 16.4] 16.1 [15.3; 16.4] 16.1 [15.6; 16.4] 0.306
RDW (%) 0.13 [0.13; 0.14] 0.13 [0.13; 0.14] 0.14 [0.13; 0.15] <0.001
Fibrinogen (g/L) 3.53 [2.94; 4.40] 3.53 [2.97; 4.37] 3.67 [2.86; 4.52] 0.772
D-dimer (mg/L) 1.60 [0.87; 5.09] 1.35 [0.80; 3.93] 3.86 [1.63; 8.49] <0.001
PT (s) 13.9 [13.2; 14.8] 13.8 [13.1; 14.6] 14.4 [13.6; 15.5] <0.001
APTT (s) 37.6 [34.6; 41.2] 37.6 [34.6; 41.2] 37.7 [34.8; 41.4] 0.984
TT (s) 16.4 [15.8; 17.1] 16.4 [15.8; 17.1] 16.5 [15.8; 17.2] 0.413

Notes: COPD, chronic obstructive pulmonary disease; WBC, white blood cell count; RBC, red blood cell count; Mg, magnesium; HGB, hemoglobin; Hct, hematocrit; PLT, platelet count; PDW, platelet distribution width; RDW, red blood cell distribution width; PT, prothrombin time; APTT, activated partial prothrombin time; TT, thrombin time; PE, pulmonary embolism.

Table 2

Baseline characteristics of the enrolled patients in the training and validation cohorts

Variables Total (N = 995) Validation (N = 298, 30%) Training (N = 697, 70%) p
PE, n (%) 0.075
 No 816 (82.0%) 234 (78.5%) 582 (83.5%)
 Yes 179 (18.0%) 64 (21.5%) 115 (16.5%)
Sex, n (%) 0.895
 Female 499 (50.2%) 148 (49.7%) 351 (50.4%)
 Male 496 (49.8%) 150 (50.3%) 346 (49.6%)
Age (years) 76.0 [67.0; 82.0] 75.0 [66.2; 82.0] 76.0 [67.0; 82.0] 0.996
Breathing (breaths/min) 22.0 [20.0; 26.0] 22.0 [20.0; 26.0] 22.0 [20.0; 26.0] 0.400
Pulse (beats/min) 103 [89.0; 124] 103 [89.2; 121] 103 [89.0; 125] 0.704
Systolic pressure (mmHg) 101 [91.0; 113] 101 [91.0; 113] 100 [91.0; 113] 0.680
Diastolic pressure (mmHg) 54.0 [47.0; 63.0] 54.0 [48.2; 62.0] 54.0 [47.0; 64.0] 0.979
Headache, n (%) 0.547
 No 968 (97.3%) 288 (96.6%) 680 (97.6%)
 Yes 27 (2.71%) 10 (3.36%) 17 (2.44%)
Dizzy, n (%) 0.444
 No 957 (96.2%) 284 (95.3%) 673 (96.6%)
 Yes 38 (3.82%) 14 (4.70%) 24 (3.44%)
Chest tightness, n (%) 0.620
 No 601 (60.4%) 184 (61.7%) 417 (59.8%)
 Yes 394 (39.6%) 114 (38.3%) 280 (40.2%)
Anhelation, n (%) 0.496
 No 664 (66.7%) 204 (68.5%) 460 (66.0%)
 Yes 331 (33.3%) 94 (31.5%) 237 (34.0%)
Hemoptysis, n (%) 0.587
 No 991 (99.6%) 296 (99.3%) 695 (99.7%)
 Yes 4 (0.40%) 2 (0.67%) 2 (0.29%)
Chest pain, n (%) 0.796
 No 910 (91.5%) 271 (90.9%) 639 (91.7%)
 Yes 85 (8.54%) 27 (9.06%) 58 (8.32%)
Syncope, n (%) 0.968
 No 947 (95.2%) 283 (95.0%) 664 (95.3%)
 Yes 48 (4.82%) 15 (5.03%) 33 (4.73%)
Cough, n (%) 0.221
 No 744 (74.8%) 231 (77.5%) 513 (73.6%)
 Yes 251 (25.2%) 67 (22.5%) 184 (26.4%)
Fever, n (%) 0.325
 No 973 (97.8%) 294 (98.7%) 679 (97.4%)
 Yes 22 (2.21%) 4 (1.34%) 18 (2.58%)
Lower limb edema, n (%) 0.897
 No 842 (84.6%) 251 (84.2%) 591 (84.8%)
 Yes 153 (15.4%) 47 (15.8%) 106 (15.2%)
COPD, n (%) 0.248
 No 794 (79.8%) 245 (82.2%) 549 (78.8%)
 Yes 201 (20.2%) 53 (17.8%) 148 (21.2%)
Hypertension, n (%) 1.000
 No 382 (38.4%) 114 (38.3%) 268 (38.5%)
 Yes 613 (61.6%) 184 (61.7%) 429 (61.5%)
Diabetes, n (%) 0.868
 No 836 (84.0%) 249 (83.6%) 587 (84.2%)
 Yes 159 (16.0%) 49 (16.4%) 110 (15.8%)
Coronary heart disease, n (%) 0.221
 No 330 (33.2%) 90 (30.2%) 240 (34.4%)
 Yes 665 (66.8%) 208 (69.8%) 457 (65.6%)
Hyperlipidemia, n (%) 0.757
 No 969 (97.4%) 289 (97.0%) 680 (97.6%)
 Yes 26 (2.61%) 9 (3.02%) 17 (2.44%)
Atrial fibrillation, n (%) 0.327
 No 763 (76.7%) 235 (78.9%) 528 (75.8%)
 Yes 232 (23.3%) 63 (21.1%) 169 (24.2%)
Operation, n (%) 1.000
 No 988 (99.3%) 296 (99.3%) 692 (99.3%)
 Yes 7 (0.70%) 2 (0.67%) 5 (0.72%)
Tumor, n (%) 0.338
 No 932 (93.7%) 283 (95.0%) 649 (93.1%)
 Yes 63 (6.33%) 15 (5.03%) 48 (6.89%)
Smoking, n (%) 0.734
 No 697 (70.1%) 206 (69.1%) 491 (70.4%)
 Yes 298 (29.9%) 92 (30.9%) 206 (29.6%)
Drinking, n (%) 0.887
 No 666 (66.9%) 198 (66.4%) 468 (67.1%)
 Yes 329 (33.1%) 100 (33.6%) 229 (32.9%)
WBC (109/L) 4.33 [3.93; 4.70] 4.34 [3.97; 4.68] 4.32 [3.92; 4.71] 0.883
RBC (1012/L) 7.28 [5.75; 9.64] 7.04 [5.74; 9.56] 7.41 [5.75; 9.64] 0.454
Mg (mmol/L) 0.90 [0.84; 0.95] 0.91 [0.83; 0.96] 0.90 [0.84; 0.95] 0.400
HGB (g/L) 131 [119; 144] 131 [120; 144] 131 [118; 145] 0.692
Hct 0.40 [0.36; 0.43] 0.39 [0.37;0.43] 0.40 [0.36; 0.43] 0.790
Neutrophil percent 0.73 [0.65; 0.82] 0.73 [0.64;0.83] 0.74 [0.65; 0.81] 0.542
Neutrophil count (109/L) 5.13 [3.75; 7.41] 4.96 [3.64;7.34] 5.25 [3.78; 7.41] 0.250
Lymphocyte percent 0.23 [0.16; 0.31] 0.24 [0.16;0.32] 0.23 [0.16; 0.30] 0.249
Lymphocyte count (109/L) 1.46 [1.07; 1.96] 1.46 [1.08;2.03] 1.46 [1.07; 1.94] 0.669
PLT (109/L) 199 [161; 248] 197 [161;246] 200 [161; 248] 0.699
PDW (%) 16.1 [15.4; 16.4] 16.0 [14.7;16.4] 16.1 [15.6; 16.4] 0.018
RDW (%) 0.13 [0.13; 0.14] 0.13 [0.13;0.14] 0.13 [0.13; 0.14] 0.856
Fibrinogen (g/L) 3.53 [2.94; 4.40] 3.47 [2.89;4.32] 3.57 [2.97; 4.43] 0.236
D-dimer (mg/L) 1.60 [0.87; 5.09] 1.42 [0.83;4.89] 1.69 [0.87; 5.14] 0.257
PT (s) 13.9 [13.2; 14.8] 13.7 [13.1;14.7] 14.0 [13.2; 14.8] 0.092
APTT (s) 37.6 [34.6; 41.2] 37.4 [34.6;40.7] 37.7 [34.7; 41.4] 0.223
TT (s) 16.4 [15.8; 17.1] 16.5 [15.9;17.2] 16.4 [15.7; 17.0] 0.027

Notes: COPD, chronic obstructive pulmonary disease; WBC, white blood cell count; RBC, red blood cell count; Mg, magnesium; HGB, hemoglobin; Hct, hematocrit; PLT, platelet count; PDW, platelet distribution width; RDW, red blood cell distribution width; PT, prothrombin time; APTT, activated partial prothrombin time; TT, thrombin time.

3.2 Selected predictors and construction model

After applying the LASSO regression analysis, we identified six of 45 variables that were potential predictive features (Figure 2a and b). The optimal predictors were age, pulse, systolic pressure, syncope, D-dimer, and coronary heart disease. These six potential predictive features were used to develop the final model based on the multivariable logistic regression analysis in the training cohort (Table 3). In the training cohort, our model had a sensitivity of 69.1%, a specificity of 63.4%, a positive predictive value of 28.8%, and a negative predictive value of 90.5%.

Figure 2 Tuning parameter selection using LASSO regression in the training cohort. (a) LASSO coefficient profiles of the clinical features. (b) Optimal penalization coefficient lambda was generated in LASSO through tenfold cross-validation. The lambda value of the minimum mean square error is shown in the figure.
Figure 2

Tuning parameter selection using LASSO regression in the training cohort. (a) LASSO coefficient profiles of the clinical features. (b) Optimal penalization coefficient lambda was generated in LASSO through tenfold cross-validation. The lambda value of the minimum mean square error is shown in the figure.

Table 3

Final model coefficients

Variables β SE OR 95% CI p
Age (years) 0.022 0.01048 1.022 1.002–1.044 0.034
Pulse (beats/min) 0.007 0.004 1.007 1.000–1.015 0.065
Systolic pressure (mmHg) −0.014 0.00635 0.986 0.974–0.999 0.03
Syncope (yes or no) −1.224 0.75105 0.294 0.067–1.282 0.103
CHD (yes or no) 0.709 0.2476 2.032 1.251–3.302 0.004
D-dimer (mg/L) 0.086 0.01947 1.089 1.049–1.132 <0.001

Notes: CI, confidence interval; OR, odds ratio; SE, standard error; CHD, coronary heart disease.

3.3 Model visualization

Multivariate analysis revealed that age (OR = 1.022, 95% CI, 1.002–1.044), pulse (OR = 1.007, 95% CI, 1.000–1.015), systolic pressure (OR = 0.986, 95% CI, 0.974–0.999), D-dimer (OR = 2.032, 95% CI, 1.251–3.302), and coronary heart disease (OR = 1.089, 95% CI, 1.049–1.132) were independent predictors for PE (Table 3). The nomogram (Figure 3) shows the predictive model for PE based on the six selected variables: age, pulse, systolic pressure, syncope, D-dimer, and coronary heart disease. To use the nomogram, each variable was assigned a score based on its value, and the scores were summed to obtain a total score. A vertical line was then drawn from the total score axis to the probability axis to obtain the estimated probability of PE. For example, if a patient is 80 years old, has a pulse rate of 160 beats per minute, systolic pressure of 82 mmHg, no history of syncope, a D-dimer level of 4.3 mg/L, and no history of coronary heart disease, then the total score would be 250. The vertical line from the total score of 250 intersects the probability axis at approximately 0.21, indicating a 21% estimated probability of PE.

Figure 3 Nomogram based on the combination of the six indicators was developed using logistic regression analysis. If a patient has a total score of 250, then the probability of developing PE is 0.21. DD, D-dimer; CHD, coronary heart disease.
Figure 3

Nomogram based on the combination of the six indicators was developed using logistic regression analysis. If a patient has a total score of 250, then the probability of developing PE is 0.21. DD, D-dimer; CHD, coronary heart disease.

3.4 Model validation and evaluation

The discriminatory ability of the numerical model, as measured by the AUC, was 0.721 (95% CI, 0.676–0.766) in the training cohort and 0.709 (95% CI, 0.633–0.784) in the validation cohort, indicating that the model can effectively differentiate between PE and non-PE cases (as illustrated in Figure 4a and b). The calibration plots, shown in Figure 5a and b, reveal good consistency between predicted probabilities and actual outcomes for both the training and validation cohorts, as evidenced by the proximity of the apparent calibration curve to the ideal line. The DCA curves, presented in Figure 6a and b, demonstrate that the numerical model had a favorable net clinical benefit, with screening strategies based on our nomogram PE risk estimates yielding greater net benefit than both screen-none and screen-all strategies within the threshold probability range of 0.08–0.50. Furthermore, the clinical impact curve and net reduction curve depicted in Figures 7 and 8, respectively, indicate that our nomogram has a significant net clinical benefit.

Figure 4 ROC curves of the model to distinguish PE from non-PE in the training (a) and validation (b) cohorts.
Figure 4

ROC curves of the model to distinguish PE from non-PE in the training (a) and validation (b) cohorts.

Figure 5 Calibration curves of the model in the training (a) and validation (b) cohorts. A perfect accurate predictive model will generate a plot where the probability of the actual observed and prediction completely fall along the ideal line (dashed line). The apparent calibration curve (blue line) represents the calibration of the model, while the bias-corrected curve (red line) is the calibration result after correcting the optimism with fivefold cross-validation.
Figure 5

Calibration curves of the model in the training (a) and validation (b) cohorts. A perfect accurate predictive model will generate a plot where the probability of the actual observed and prediction completely fall along the ideal line (dashed line). The apparent calibration curve (blue line) represents the calibration of the model, while the bias-corrected curve (red line) is the calibration result after correcting the optimism with fivefold cross-validation.

Figure 6 Decision curve of the model in the training (a) and validation (b) cohorts. The red line is a nomogram net clinical benefit of PE, while the cross-validated curve (blue line) is a net clinical benefit of PE after correcting the optimism with fivefold cross-validation. The solid gray line indicates that all patients had PE, while the fine solid black line indicates that no patient had PE. This DCA could provide a larger net benefit, in the range of 8–50%. If the risk threshold is less than 50%, then the nomogram model will obtain more benefit than all treatment (assuming all patients were PE) or no treatment (assuming all patients were non-PE).
Figure 6

Decision curve of the model in the training (a) and validation (b) cohorts. The red line is a nomogram net clinical benefit of PE, while the cross-validated curve (blue line) is a net clinical benefit of PE after correcting the optimism with fivefold cross-validation. The solid gray line indicates that all patients had PE, while the fine solid black line indicates that no patient had PE. This DCA could provide a larger net benefit, in the range of 8–50%. If the risk threshold is less than 50%, then the nomogram model will obtain more benefit than all treatment (assuming all patients were PE) or no treatment (assuming all patients were non-PE).

Figure 7 Clinical impact curve of the model in the training (a) and validation (b) cohorts. The red line indicates the number of subjects who are judged as being at high risk by the model under different probability thresholds. The blue line indicates the number of subjects who are judged by the model to be at high risk and who actually have an outcome event under different probability thresholds.
Figure 7

Clinical impact curve of the model in the training (a) and validation (b) cohorts. The red line indicates the number of subjects who are judged as being at high risk by the model under different probability thresholds. The blue line indicates the number of subjects who are judged by the model to be at high risk and who actually have an outcome event under different probability thresholds.

Figure 8 Net reduction curve of the model in the training (a) and validation (b) cohorts. Using predictive models can reduce the number of interventions by 40% at a risk threshold of 30%.
Figure 8

Net reduction curve of the model in the training (a) and validation (b) cohorts. Using predictive models can reduce the number of interventions by 40% at a risk threshold of 30%.

4 Discussion

In this study, we developed a novel predictive model for the probability of PE in patients with cardiovascular diseases. Our model utilized six variables representing high-risk disease, namely age, pulse, systolic pressure, syncope, D-dimer, and coronary heart disease, all of which are easily obtainable clinical features and biomarkers during routine health assessments. Our findings showed that the model exhibited good discrimination with an area under the ROC curve of 0.721 (95% CI, 0.676–0.766), indicating its ability to distinguish between patients with and without PE. Furthermore, the calibration plots demonstrated that the model had a good consistency between predicted and observed probabilities in both training and validation cohorts. Additionally, the DCA suggested that our model had a favorable net clinical benefit within the threshold probability range of 0.08–0.50.

Currently, there are no predictive models available for predicting the risk of PE specifically in patients with cardiovascular disease. However, previous studies have developed predictive models for venous thromboembolism in other patient populations. For instance, Zhou et al. [22] developed a predictive model for PE in patients with cough or chest pain based on laboratory variables, which had an AUC of 0.692. Li et al. [23] developed a clinical predictive model for lower extremity deep venous thrombosis in patients admitted to the neurointensive care unit, which had an AUC of 0.817. Zhang et al. developed a predictive model for postoperative venous thromboembolism [24].

Our study has several strengths compared to previous studies. First, we utilized the LASSO regression method to select the optimal predictive features, which improved the accuracy and robustness of the predictive models. Second, our model used only six readily available high-risk variables, which makes it simpler and more efficient to use in clinical practice. Finally, our model specifically focuses on the prediction of PE in patients with cardiovascular disease, which makes it highly relevant for clinicians dealing with this population.

D-dimer is an indicator reflecting fibrinolytic function and can be used to diagnose thrombotic diseases. According to our study, D-dimer (OR = 2.032, 95% CI, 1.251–3.302) was identified as an independent predictor for increased risk of PE. This finding is consistent with those of previous research [25] that has linked high D-dimer levels with increased risk of developing PE. Although D-dimer is currently the only biomarker used in routine clinical practice to predict PE, its specificity is limited, leading to high rates of false-positive results. Elderly patients have increased hospitalization rates and the highest inpatient mortality due to PE, as demonstrated in a large-sample study conducted from 2000 to 2015 [26]. Another retrospective study indicated that age is associated with the severity of submassive PE stadium [27], and our model also found age (OR = 1.022, 95% CI, 1.002–1.044) to be a high-risk factor for PE, which is consistent with the findings of previous research. Most of the factors in our model were positively associated with the risk of PE, except for systolic blood pressure, which was negatively associated. Low systolic pressure has been linked to an increased risk of PE-related mortality, as shown in a previous study [28]. The relationship between lower systolic blood pressure and higher PE occurrence is primarily due to the pathophysiology of PE itself. When a blood clot (or embolus) travels through the bloodstream and lodges in the pulmonary arteries, it prevents effective oxygen exchange in the lungs. This can lead to acute right heart failure because the right side of the heart has to pump harder against the increased resistance in these blocked arteries. Our data also revealed that pulse rate was included in the model to predict PE. Consistent with our findings, a previous study [29] identified pulse rate as a good predictor of PE. This happens because when a blood clot obstructs the pulmonary arteries, the right ventricle of the heart has to work harder to pump blood through these vessels. This increased workload can result in a faster heart rate. Coronary heart disease [30] has also been identified as a factor that affects the risk of PE, which is similar to the indicators present in our model. Our study obtained an interesting result that patients with syncope are less likely to develop PE. Syncope is a common clinical symptom of PE [31], but our findings differ from that generally observed. We analyzed the clinical information of 48 patients with syncope and found that the possible explanation for this interesting result is that these patients mainly had vasovagal syncope or orthostatic hypotension syncope. They were sent for CTPA only for exclusion and did not have a high suspicion of PE. Additionally, patients with PE who experienced syncope were sent to the respiratory department for treatment.

The development of an accurate model for predicting the probability of PE in patients with cardiovascular disease has significant implications in clinical practice. This model can aid healthcare professionals in making timely and personalized diagnoses, leading to the formulation of effective and personalized treatment plans. By identifying high-risk variables such as age, pulse, systolic pressure, syncope, D-dimer, and coronary heart disease, this model can assist in identifying patients who require further diagnostic workup or more aggressive treatment, while reducing the need for unnecessary CTPA screening. Furthermore, the use of a nomogram to visualize the model’s output makes it easier for healthcare professionals to interpret the results and communicate them to patients. The high net clinical benefit demonstrated by the clinical decision curve, clinical impact curve, and net reduction curve analyses suggests that this model has the potential to improve patient outcomes and reduce healthcare costs [32].

However, there are some limitations to our study. For example, the sample size of this study was smaller than that in some previous studies, which may limit the generalizability of our findings. Additionally, because our model was developed using retrospective data, there may be a risk of selection bias and confounding. Finally, external validation in other clinical settings is required to assess the generalizability and reliability of our model.

In conclusion, the novel model developed in this study has the potential to become a valuable clinical tool for predicting the probability of PE in patients with cardiovascular diseases, leading to more accurate diagnoses and personalized treatment plans. However, it is important to note that this study is retrospective, and further prospective studies are required to validate the accuracy and clinical usefulness of this model.

Abbreviations

CHD

coronary heart disease

CI

confidence interval

CTPA

computed tomography pulmonary angiography

DCA

decision curve analysis

LASSO

least absolute shrinkage and selection operator

OR

odds ratios

PE

pulmonary embolism

ROC

receiver operating characteristic

# These authors contributed equally to this work.

Acknowledgements

None.

  1. Funding information: This work was supported by the Zhejiang Provincial Natural Science Foundation of China under Grant no. LTGY23H200002.

  2. Author contributions: All the authors contributed to this manuscript and approved the submitted version of the manuscript. F.L. and W.M.F. conceived and designed the research strategy. F.L., Q.J.L., and W.M.F. wrote the manuscript text. F.L. and W.M.F. collected the clinical data. W.M.F. contributed to the analysis and interpretation of the data.

  3. Conflict of interest: The authors declare no conflicts of interest.

  4. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Appendix

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Received: 2023-07-03
Revised: 2024-01-12
Accepted: 2024-01-28
Published Online: 2024-03-08

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  134. Echinacoside inhibits PASMCs calcium overload to prevent hypoxic pulmonary artery remodeling by regulating TRPC1/4/6 and calmodulin
  135. ADAR1 plays a protective role in proximal tubular cells under high glucose conditions by attenuating the PI3K/AKT/mTOR signaling pathway
  136. The risk of cancer among insulin glargine users in Lithuania: A retrospective population-based study
  137. The unusual location of primary hydatid cyst: A case series study
  138. Intraoperative changes in electrophysiological monitoring can be used to predict clinical outcomes in patients with spinal cavernous malformation
  139. Obesity and risk of placenta accreta spectrum: A meta-analysis
  140. Shikonin alleviates asthma phenotypes in mice via an airway epithelial STAT3-dependent mechanism
  141. NSUN6 and HTR7 disturbed the stability of carotid atherosclerotic plaques by regulating the immune responses of macrophages
  142. The effect of COVID-19 lockdown on admission rates in Maternity Hospital
  143. Temporal muscle thickness is not a prognostic predictor in patients with high-grade glioma, an experience at two centers in China
  144. Luteolin alleviates cerebral ischemia/reperfusion injury by regulating cell pyroptosis
  145. Therapeutic role of respiratory exercise in patients with tuberculous pleurisy
  146. Effects of CFTR-ENaC on spinal cord edema after spinal cord injury
  147. Irisin-regulated lncRNAs and their potential regulatory functions in chondrogenic differentiation of human mesenchymal stem cells
  148. DMD mutations in pediatric patients with phenotypes of Duchenne/Becker muscular dystrophy
  149. Combination of C-reactive protein and fibrinogen-to-albumin ratio as a novel predictor of all-cause mortality in heart failure patients
  150. Significant role and the underly mechanism of cullin-1 in chronic obstructive pulmonary disease
  151. Ferroptosis-related prognostic model of mantle cell lymphoma
  152. Observation of choking reaction and other related indexes in elderly painless fiberoptic bronchoscopy with transnasal high-flow humidification oxygen therapy
  153. A bibliometric analysis of Prader-Willi syndrome from 2002 to 2022
  154. The causal effects of childhood sunburn occasions on melanoma: A univariable and multivariable Mendelian randomization study
  155. Oxidative stress regulates glycogen synthase kinase-3 in lymphocytes of diabetes mellitus patients complicated with cerebral infarction
  156. Role of COX6C and NDUFB3 in septic shock and stroke
  157. Trends in disease burden of type 2 diabetes, stroke, and hypertensive heart disease attributable to high BMI in China: 1990–2019
  158. Purinergic P2X7 receptor mediates hyperoxia-induced injury in pulmonary microvascular endothelial cells via NLRP3-mediated pyroptotic pathway
  159. Investigating the role of oviductal mucosa–endometrial co-culture in modulating factors relevant to embryo implantation
  160. Analgesic effect of external oblique intercostal block in laparoscopic cholecystectomy: A retrospective study
  161. Elevated serum miR-142-5p correlates with ischemic lesions and both NSE and S100β in ischemic stroke patients
  162. Correlation between the mechanism of arteriopathy in IgA nephropathy and blood stasis syndrome: A cohort study
  163. Risk factors for progressive kyphosis after percutaneous kyphoplasty in osteoporotic vertebral compression fracture
  164. Predictive role of neuron-specific enolase and S100-β in early neurological deterioration and unfavorable prognosis in patients with ischemic stroke
  165. The potential risk factors of postoperative cognitive dysfunction for endovascular therapy in acute ischemic stroke with general anesthesia
  166. Fluoxetine inhibited RANKL-induced osteoclastic differentiation in vitro
  167. Detection of serum FOXM1 and IGF2 in patients with ARDS and their correlation with disease and prognosis
  168. Rhein promotes skin wound healing by activating the PI3K/AKT signaling pathway
  169. Differences in mortality risk by levels of physical activity among persons with disabilities in South Korea
  170. Review Articles
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  172. XRCC1 and hOGG1 polymorphisms and endometrial carcinoma: A meta-analysis
  173. A narrative review on adverse drug reactions of COVID-19 treatments on the kidney
  174. Emerging role and function of SPDL1 in human health and diseases
  175. Adverse reactions of piperacillin: A literature review of case reports
  176. Molecular mechanism and intervention measures of microvascular complications in diabetes
  177. Regulation of mesenchymal stem cell differentiation by autophagy
  178. Molecular landscape of borderline ovarian tumours: A systematic review
  179. Advances in synthetic lethality modalities for glioblastoma multiforme
  180. Investigating hormesis, aging, and neurodegeneration: From bench to clinics
  181. Frankincense: A neuronutrient to approach Parkinson’s disease treatment
  182. Sox9: A potential regulator of cancer stem cells in osteosarcoma
  183. Early detection of cardiovascular risk markers through non-invasive ultrasound methodologies in periodontitis patients
  184. Advanced neuroimaging and criminal interrogation in lie detection
  185. Maternal factors for neural tube defects in offspring: An umbrella review
  186. The chemoprotective hormetic effects of rosmarinic acid
  187. CBD’s potential impact on Parkinson’s disease: An updated overview
  188. Progress in cytokine research for ARDS: A comprehensive review
  189. Utilizing reactive oxygen species-scavenging nanoparticles for targeting oxidative stress in the treatment of ischemic stroke: A review
  190. NRXN1-related disorders, attempt to better define clinical assessment
  191. Lidocaine infusion for the treatment of complex regional pain syndrome: Case series and literature review
  192. Trends and future directions of autophagy in osteosarcoma: A bibliometric analysis
  193. Iron in ventricular remodeling and aneurysms post-myocardial infarction
  194. Case Reports
  195. Sirolimus potentiated angioedema: A case report and review of the literature
  196. Identification of mixed anaerobic infections after inguinal hernia repair based on metagenomic next-generation sequencing: A case report
  197. Successful treatment with bortezomib in combination with dexamethasone in a middle-aged male with idiopathic multicentric Castleman’s disease: A case report
  198. Complete heart block associated with hepatitis A infection in a female child with fatal outcome
  199. Elevation of D-dimer in eosinophilic gastrointestinal diseases in the absence of venous thrombosis: A case series and literature review
  200. Four years of natural progressive course: A rare case report of juvenile Xp11.2 translocations renal cell carcinoma with TFE3 gene fusion
  201. Advancing prenatal diagnosis: Echocardiographic detection of Scimitar syndrome in China – A case series
  202. Outcomes and complications of hemodialysis in patients with renal cancer following bilateral nephrectomy
  203. Anti-HMGCR myopathy mimicking facioscapulohumeral muscular dystrophy
  204. Recurrent opportunistic infections in a HIV-negative patient with combined C6 and NFKB1 mutations: A case report, pedigree analysis, and literature review
  205. Letter to the Editor
  206. Letter to the Editor: Total parenteral nutrition-induced Wernicke’s encephalopathy after oncologic gastrointestinal surgery
  207. Erratum
  208. Erratum to “Bladder-embedded ectopic intrauterine device with calculus”
  209. Retraction
  210. Retraction of “XRCC1 and hOGG1 polymorphisms and endometrial carcinoma: A meta-analysis”
  211. Corrigendum
  212. Corrigendum to “Investigating hormesis, aging, and neurodegeneration: From bench to clinics”
  213. Corrigendum to “Frankincense: A neuronutrient to approach Parkinson’s disease treatment”
  214. Special Issue The evolving saga of RNAs from bench to bedside - Part II
  215. Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma
  216. Unlocking the future of hepatocellular carcinoma treatment: A comprehensive analysis of disulfidptosis-related lncRNAs for prognosis and drug screening
  217. Elevated mRNA level indicates FSIP1 promotes EMT and gastric cancer progression by regulating fibroblasts in tumor microenvironment
  218. Special Issue Advancements in oncology: bridging clinical and experimental research - Part I
  219. Ultrasound-guided transperineal vs transrectal prostate biopsy: A meta-analysis of diagnostic accuracy and complication rates
  220. Assessment of diagnostic value of unilateral systematic biopsy combined with targeted biopsy in detecting clinically significant prostate cancer
  221. SENP7 inhibits glioblastoma metastasis and invasion by dissociating SUMO2/3 binding to specific target proteins
  222. MARK1 suppress malignant progression of hepatocellular carcinoma and improves sorafenib resistance through negatively regulating POTEE
  223. Analysis of postoperative complications in bladder cancer patients
  224. Carboplatin combined with arsenic trioxide versus carboplatin combined with docetaxel treatment for LACC: A randomized, open-label, phase II clinical study
  225. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part I
  226. Comprehensive pan-cancer investigation of carnosine dipeptidase 1 and its prospective prognostic significance in hepatocellular carcinoma
  227. Identification of signatures associated with microsatellite instability and immune characteristics to predict the prognostic risk of colon cancer
  228. Single-cell analysis identified key macrophage subpopulations associated with atherosclerosis
https://doi.org/10.1515/med-2024-0924
Keywords for this article
prediction model; clinical symptoms; biomarkers; nomogram
Creative Commons
BY 4.0

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  134. Echinacoside inhibits PASMCs calcium overload to prevent hypoxic pulmonary artery remodeling by regulating TRPC1/4/6 and calmodulin
  135. ADAR1 plays a protective role in proximal tubular cells under high glucose conditions by attenuating the PI3K/AKT/mTOR signaling pathway
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  140. Shikonin alleviates asthma phenotypes in mice via an airway epithelial STAT3-dependent mechanism
  141. NSUN6 and HTR7 disturbed the stability of carotid atherosclerotic plaques by regulating the immune responses of macrophages
  142. The effect of COVID-19 lockdown on admission rates in Maternity Hospital
  143. Temporal muscle thickness is not a prognostic predictor in patients with high-grade glioma, an experience at two centers in China
  144. Luteolin alleviates cerebral ischemia/reperfusion injury by regulating cell pyroptosis
  145. Therapeutic role of respiratory exercise in patients with tuberculous pleurisy
  146. Effects of CFTR-ENaC on spinal cord edema after spinal cord injury
  147. Irisin-regulated lncRNAs and their potential regulatory functions in chondrogenic differentiation of human mesenchymal stem cells
  148. DMD mutations in pediatric patients with phenotypes of Duchenne/Becker muscular dystrophy
  149. Combination of C-reactive protein and fibrinogen-to-albumin ratio as a novel predictor of all-cause mortality in heart failure patients
  150. Significant role and the underly mechanism of cullin-1 in chronic obstructive pulmonary disease
  151. Ferroptosis-related prognostic model of mantle cell lymphoma
  152. Observation of choking reaction and other related indexes in elderly painless fiberoptic bronchoscopy with transnasal high-flow humidification oxygen therapy
  153. A bibliometric analysis of Prader-Willi syndrome from 2002 to 2022
  154. The causal effects of childhood sunburn occasions on melanoma: A univariable and multivariable Mendelian randomization study
  155. Oxidative stress regulates glycogen synthase kinase-3 in lymphocytes of diabetes mellitus patients complicated with cerebral infarction
  156. Role of COX6C and NDUFB3 in septic shock and stroke
  157. Trends in disease burden of type 2 diabetes, stroke, and hypertensive heart disease attributable to high BMI in China: 1990–2019
  158. Purinergic P2X7 receptor mediates hyperoxia-induced injury in pulmonary microvascular endothelial cells via NLRP3-mediated pyroptotic pathway
  159. Investigating the role of oviductal mucosa–endometrial co-culture in modulating factors relevant to embryo implantation
  160. Analgesic effect of external oblique intercostal block in laparoscopic cholecystectomy: A retrospective study
  161. Elevated serum miR-142-5p correlates with ischemic lesions and both NSE and S100β in ischemic stroke patients
  162. Correlation between the mechanism of arteriopathy in IgA nephropathy and blood stasis syndrome: A cohort study
  163. Risk factors for progressive kyphosis after percutaneous kyphoplasty in osteoporotic vertebral compression fracture
  164. Predictive role of neuron-specific enolase and S100-β in early neurological deterioration and unfavorable prognosis in patients with ischemic stroke
  165. The potential risk factors of postoperative cognitive dysfunction for endovascular therapy in acute ischemic stroke with general anesthesia
  166. Fluoxetine inhibited RANKL-induced osteoclastic differentiation in vitro
  167. Detection of serum FOXM1 and IGF2 in patients with ARDS and their correlation with disease and prognosis
  168. Rhein promotes skin wound healing by activating the PI3K/AKT signaling pathway
  169. Differences in mortality risk by levels of physical activity among persons with disabilities in South Korea
  170. Review Articles
  171. Cutaneous signs of selected cardiovascular disorders: A narrative review
  172. XRCC1 and hOGG1 polymorphisms and endometrial carcinoma: A meta-analysis
  173. A narrative review on adverse drug reactions of COVID-19 treatments on the kidney
  174. Emerging role and function of SPDL1 in human health and diseases
  175. Adverse reactions of piperacillin: A literature review of case reports
  176. Molecular mechanism and intervention measures of microvascular complications in diabetes
  177. Regulation of mesenchymal stem cell differentiation by autophagy
  178. Molecular landscape of borderline ovarian tumours: A systematic review
  179. Advances in synthetic lethality modalities for glioblastoma multiforme
  180. Investigating hormesis, aging, and neurodegeneration: From bench to clinics
  181. Frankincense: A neuronutrient to approach Parkinson’s disease treatment
  182. Sox9: A potential regulator of cancer stem cells in osteosarcoma
  183. Early detection of cardiovascular risk markers through non-invasive ultrasound methodologies in periodontitis patients
  184. Advanced neuroimaging and criminal interrogation in lie detection
  185. Maternal factors for neural tube defects in offspring: An umbrella review
  186. The chemoprotective hormetic effects of rosmarinic acid
  187. CBD’s potential impact on Parkinson’s disease: An updated overview
  188. Progress in cytokine research for ARDS: A comprehensive review
  189. Utilizing reactive oxygen species-scavenging nanoparticles for targeting oxidative stress in the treatment of ischemic stroke: A review
  190. NRXN1-related disorders, attempt to better define clinical assessment
  191. Lidocaine infusion for the treatment of complex regional pain syndrome: Case series and literature review
  192. Trends and future directions of autophagy in osteosarcoma: A bibliometric analysis
  193. Iron in ventricular remodeling and aneurysms post-myocardial infarction
  194. Case Reports
  195. Sirolimus potentiated angioedema: A case report and review of the literature
  196. Identification of mixed anaerobic infections after inguinal hernia repair based on metagenomic next-generation sequencing: A case report
  197. Successful treatment with bortezomib in combination with dexamethasone in a middle-aged male with idiopathic multicentric Castleman’s disease: A case report
  198. Complete heart block associated with hepatitis A infection in a female child with fatal outcome
  199. Elevation of D-dimer in eosinophilic gastrointestinal diseases in the absence of venous thrombosis: A case series and literature review
  200. Four years of natural progressive course: A rare case report of juvenile Xp11.2 translocations renal cell carcinoma with TFE3 gene fusion
  201. Advancing prenatal diagnosis: Echocardiographic detection of Scimitar syndrome in China – A case series
  202. Outcomes and complications of hemodialysis in patients with renal cancer following bilateral nephrectomy
  203. Anti-HMGCR myopathy mimicking facioscapulohumeral muscular dystrophy
  204. Recurrent opportunistic infections in a HIV-negative patient with combined C6 and NFKB1 mutations: A case report, pedigree analysis, and literature review
  205. Letter to the Editor
  206. Letter to the Editor: Total parenteral nutrition-induced Wernicke’s encephalopathy after oncologic gastrointestinal surgery
  207. Erratum
  208. Erratum to “Bladder-embedded ectopic intrauterine device with calculus”
  209. Retraction
  210. Retraction of “XRCC1 and hOGG1 polymorphisms and endometrial carcinoma: A meta-analysis”
  211. Corrigendum
  212. Corrigendum to “Investigating hormesis, aging, and neurodegeneration: From bench to clinics”
  213. Corrigendum to “Frankincense: A neuronutrient to approach Parkinson’s disease treatment”
  214. Special Issue The evolving saga of RNAs from bench to bedside - Part II
  215. Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma
  216. Unlocking the future of hepatocellular carcinoma treatment: A comprehensive analysis of disulfidptosis-related lncRNAs for prognosis and drug screening
  217. Elevated mRNA level indicates FSIP1 promotes EMT and gastric cancer progression by regulating fibroblasts in tumor microenvironment
  218. Special Issue Advancements in oncology: bridging clinical and experimental research - Part I
  219. Ultrasound-guided transperineal vs transrectal prostate biopsy: A meta-analysis of diagnostic accuracy and complication rates
  220. Assessment of diagnostic value of unilateral systematic biopsy combined with targeted biopsy in detecting clinically significant prostate cancer
  221. SENP7 inhibits glioblastoma metastasis and invasion by dissociating SUMO2/3 binding to specific target proteins
  222. MARK1 suppress malignant progression of hepatocellular carcinoma and improves sorafenib resistance through negatively regulating POTEE
  223. Analysis of postoperative complications in bladder cancer patients
  224. Carboplatin combined with arsenic trioxide versus carboplatin combined with docetaxel treatment for LACC: A randomized, open-label, phase II clinical study
  225. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part I
  226. Comprehensive pan-cancer investigation of carnosine dipeptidase 1 and its prospective prognostic significance in hepatocellular carcinoma
  227. Identification of signatures associated with microsatellite instability and immune characteristics to predict the prognostic risk of colon cancer
  228. Single-cell analysis identified key macrophage subpopulations associated with atherosclerosis
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