ObjectiveTo investigate the factors associated with unplanned readmission within 30 days after discharge in adult patients who underwent coronary artery bypass grafting (CABG) and to develop and validate a risk prediction model. MethodsA retrospective analysis was conducted on the clinical data of patients who underwent isolated CABG at the Nanjing First Hospital between January 2020 and June 2024. Data from January 2020 to August 2023 were used as a training set, and data from September 2023 to June 2024 were used as a validation set. In the training set, patients were divided into a readmission group and a non-readmission group based on whether they had unplanned readmission within 30 days post-discharge. Clinical data between the two groups were compared, and logistic regression was performed to identify independent risk factors for unplanned readmission. A risk prediction model and a nomogram were constructed, and internal validation was performed to assess the model’s performance. The validation set was used for validation. ResultsA total of 2 460 patients were included, comprising 1 787 males and 673 females, with a median age of 70 (34, 89) years. The training set included 1 932 patients, and the validation set included 528 patients. In the training set, there were statistically significant differences between the readmission group (79 patients) and the non-readmission group (1 853 patients) in terms of gender, age, carotid artery stenosis, history of myocardial infarction, preoperative anemia, and heart failure classification (P<0.05). The main causes of readmission were poor wound healing, postoperative pulmonary infections, and new-onset atrial fibrillation. Multivariable logistic regression analysis revealed that females [OR=1.659, 95%CI (1.022, 2.692), P=0.041], age [OR=1.042, 95%CI (1.011, 1.075), P=0.008], carotid artery stenosis [OR=1.680, 95%CI (1.130, 2.496), P=0.010], duration of first ICU stay [OR=1.359, 95%CI (1.195, 1.545), P<0.001], and the second ICU admission [OR=4.142, 95%CI (1.507, 11.383), P=0.006] were independent risk factors for unplanned readmission. In the internal validation, the area under the curve (AUC) was 0.806, and the net benefit rate of the clinical decision curve analysis (DCA) was >3%. In the validation set, the AUC was 0.732, and the DCA net benefit rate ranged from 3% to 48%. ConclusionFemales, age, carotid artery stenosis, duration of first ICU stay, and second ICU admission are independent risk factors for unplanned readmission within 30 days after isolated CABG. The constructed nomogram demonstrates good predictive power.
Objective To predict the patients who can benefit from local surgery for bone-only metastatic breast cancer (bMBC). Methods Patients newly diagnosed with bMBC between 2010 and 2019 in SEER database were randomly divided into a training set and a validation set at a ratio of 7∶3. The Cox proportional hazards model was used to analyze the independent prognostic factors of overall survival in the training set, and the variables were screened and the prognostic prediction model was constructed. The concordance index (C-index), time-dependent clinical receiver operating characteristic curve and area under the curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical applicability of the model in the training set and validation set, respectively. The model was used to calculate the patient risk score and classify the patients into low-, medium- and high-risk groups. Survival analysis was used to compare the survival difference between surgical and non-surgical patients in different risk groups. Results A total of 2057 patients were enrolled with a median age of 45 years (interquartile range 47-62 years) and a median follow-up of 32 months (interquartile range 16-53 months). Totally 865 patients (42.1%) died. Multivariate Cox proportional hazards model analysis showed that the overall survival of patients with surgery was better than that of patients without surgery [hazard ratio=0.51, 95% confidence interval (0.43, 0.60), P<0.001]. Chemotherapy, marital status, molecular subtype, age, pathological type and histological grade were independent prognostic factors for overall survival (P<0.05), and a prognostic prediction model was constructed based on the independent prognostic factors. The C-index was 0.702 in the training set and 0.703 in the validation set. The 1-, 3-, and 5-year AUCs of the training set and validation set were 0.734, 0.727, 0.731 and 0.755, 0.737, 0.708, respectively. The calibration curve showed that the predicted survival rates of 1, 3, and 5 years in the training set and the validation set were highly consistent with the actual survival rates. DCA showed that the prediction model had certain clinical applicability in the training set and the validation set. Patients were divided into low-, medium- and high-risk subgroups according to their risk scores. The results of log-rank test showed that local surgery improved overall survival in the low-risk group (training set: P=0.013; validation set: P=0.024), but local surgery did not improve overall survival in the medium-risk group (training set: P=0.45; validation set: P=0.77) or high-risk group (training set: P=0.56; validation set: P=0.94). Conclusions Local surgery can improve the overall survival of some patients with newly diagnosed bMBC. The prognostic stratification model based on clinicopathological features can evaluate the benefit of local surgery in patients with newly diagnosed bMBC.
ObjectiveTo reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. MethodsPublications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. ResultsA marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. ConclusionOver the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.
Objective To construct a risk prediction score model for serious adverse event (SAE) after cardiac catheterization in patients with adult congenital heart disease (ACHD) and pulmonary hypertension (PH) and verify its predictive effect. Methods The patients with PH who underwent cardiac catheterization in Wuhan Asian Heart Hospital Affiliated to Wuhan University of Science and Technology from January 2018 to January 2022 were retrospectively collected. The patients were randomly divided into a model group and a validation group according to the order of admission. The model group was divided into a SAE group and a non-SAE group according to whether SAE occurred after the catheterization. The data of the two groups were compared, and the risk prediction score model was established according to the results of multivariate logistic regression analysis. The discrimination and calibration of the model were evaluated using the area under the receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test, respectively. Results A total of 758 patients were enrolled, including 240 (31.7%) males and 518 (68.3%) females, with a mean age of 43.1 (18.0-81.0) years. There were 530 patients in the model group (47 patients in the SAE group and 483 patients in the non-SAE group) and 228 patients in the validation group. Univariate analysis showed statistical differences in age, smoking history, valvular disease history, heart failure history, N-terminal pro-B-type natriuretic peptide, and other factors between the SAE and non-SAE groups (P<0.05). Multivariate analysis showed that age≥50 years, history of heart failure, moderate to severe congenital heart disease, moderate to severe PH, cardiac catheterization and treatment, surgical general anesthesia, and N-terminal pro-B-type natriuretic peptide≥126.65 pg/mL were risk factors for SAE after cardiac catheterization for ACHD-PH patients (P<0.05). The risk prediction score model had a total score of 0-139 points and patients who had a score>50 points were high-risk patients. Model validation results showed an area under the ROC curve of 0.937 (95%CI 0.897-0.976). Hosmer-Lemeshow goodness-of-fit test: χ2=3.847, P=0.797. Conclusion Age≥50 years, history of heart failure, moderate to severe congenital heart disease, moderate to severe PH, cardiac catheterization and treatment, general anesthesia for surgery, and N-terminal pro-B-type natriuretic peptide≥126.65 pg/mL were risk factors for SAE after cardiac catheterization for ACHD-PH patients. The risk prediction model based on these factors has a high predictive value and can be applied to the risk assessment of SAE after interventional therapy in ACHD-PH patients to help clinicians perform early intervention.
Objective To explore the factors influencing 2-month sputum smear conversion (2m-SSC) in patients with systemic lupus erythematosus (SLE) and tuberculosis, and to establish a prediction model for 2m-SSC. Methods The initial and follow-up medical records of inpatients with SLE and sputum smear-positive tuberculosis in West China Hospital of Sichuan University from December 2013 to September 2019 were retrospectively reviewed. Single factor analyses and multivariable Firth’s logistic regression were used to determine the influencing factors of 2m-SSC, and a prediction model for 2m-SSC was established. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated to evaluate the performance of the model. Results A total of 91 patients with SLE and sputum smear-positive tuberculosis were ultimately included, with 8 cases in the non-conversion group and 83 in the conversion group. There were statistically significant differences in white blood cell count, total bilirubin, indirect bilirubin (IBIL), triglyceride, and rheumatoid factor (RF) between the two groups (P<0.05). Serum RF [odds ratio (OR)=2.271, 95% confidence interval (CI) (1.312, 4.350), P=0.003], IBIL [OR=2.363, 95%CI (1.206, 5.045), P=0.013], and glucose [OR=2.058, 95%CI (1.016, 4.280), P=0.045] were identified as risk factors unfavorable to 2m-SSC outcomes. The constructed model (including three variables: RF, IBIL, and glucose) had a good ability in predicting 2m-SSC [AUC=0.893, 95%CI (0.744, 1.000)], with a sensitivity of 87.5% and a specificity of 84.3%. Conclusion This study established a prediction model for 2m-SSC in patients with SLE and tuberculosis, and found the value of serum RF, IBIL, and glucose in predicting 2m-SSC, providing certain guidance for clinicians in treatment decisions.
ObjectiveTo explore the CT imaging features and independent risk factors for cystic pulmonary nodules and establish a malignant probability prediction model. Methods The patients with cystic pulmonary nodules admitted to the Department of Thoracic Surgery of the First People's Hospital of Neijiang from January 2017 to February 2022 were retrospectively enrolled. They were divided into a malignant group and a benign group according to the pathological results. The clinical data and preoperative chest CT imaging features of the two groups were collected, and the independent risk factors for malignant cystic pulmonary nodules were screened out by logistic regression analysis, so as to establish a prediction model for benign and malignant cystic pulmonary nodules. ResultsA total of 107 patients were enrolled. There were 76 patients in the malignant group, including 36 males and 40 females, with an average age of 59.65±11.74 years. There were 31 patients in the benign group, including 16 males and 15 females, with an average age of 58.96±13.91 years. Multivariate logistic analysis showed that the special CT imaging features such as cystic wall nodules [OR=3.538, 95%CI (1.231, 10.164), P=0.019], short burrs [OR=4.106, 95%CI (1.454, 11.598), P=0.008], cystic wall morphology [OR=6.978, 95%CI (2.374, 20.505), P<0.001], and the number of cysts [OR=4.179, 95%CI (1.438, 12.146), P=0.009] were independent risk factors for cystic lung cancer. A prediction model was established: P=ex/(1+ex), X=–2.453+1.264×cystic wall nodules+1.412×short burrs+1.943×cystic wall morphology+1.430×the number of cysts. The area under the receiver operating charateristic curve was 0.830, the sensitivity was 82.9%, and the specificity was 74.2%. ConclusionCystic wall nodules, short burrs, cystic wall morphology, and the number of cysts are the independent risk factors for cystic lung cancer, and the established prediction model can be used as a screening method for cystic pulmonary nodules.
ObjectiveTo summarise the application research progress of clinical prediction models in postoperative complications of gastric cancer, in order to reduce the risk of complications after gastric cancer surgery. MethodThe literature on the study of postoperative complications of gastric cancer at home and abroad was read and reviewed. ResultsAt present, the main way of treating gastric cancer was still radical resection, and the occurrence of complications after surgical treatment seriously affected the recovery and survival quality of patients. With the deepening of research, the prediction models of postoperative complications in gastric cancer were constantly constructed, and these models provided strong evidence for the early judgement of postoperative complications in gastric cancer, and provided a scientific basis for the improvement of patients’ life quality. ConclusionClinical predictive models are expected to become risk screening tools for predicting the risk of postoperative complications of gastric cancer with clinical utility.
Objective To systematically evaluate the prognostic prediction model for chronic heart failure patients in China, and provide reference for the construction, application, and promotion of related prognostic prediction models. Methods A comprehensive search was conducted on the studies related to prognostic prediction model for Chinese patients with chronic heart failure published in The Cochrane Library, PubMed, EMbase, Web of Science, CNKI, VIP, Wanfang, and the China Biological Medicine databases from inception to March 31, 2023. Two researchers strictly followed the inclusion and exclusion criteria to independently screen literature and extract data, and used the prediction model risk of bias assessment tool (PROBAST) to evaluate the quality of the models. Results A total of 25 studies were enrolled, including 123 prognostic prediction models for chronic heart failure patients. The area under the receiver operating characteristic curve (AUC) of the models ranged from 0.690 to 0.959. Twenty-two studies mostly used random splitting and Bootstrap for internal model validation, with an AUC range of 0.620-0.932. Seven studies conducted external validation of the model, with an AUC range of 0.720-0.874. The overall bias risk of all models was high, and the overall applicability was low. The main predictive factors included in the models were the N-terminal pro-brain natriuretic peptide, age, left ventricular ejection fraction, New York Heart Association heart function grading, and body mass index. Conclusion The quality of modeling methodology for predicting the prognosis of chronic heart failure patients in China is poor, and the predictive performance of different models varies greatly. For developed models, external validation and clinical application research should be vigorously carried out. For model development research, it is necessary to comprehensively consider various predictive factors related to disease prognosis before modeling. During modeling, large sample and prospective studies should be conducted strictly in accordance with the PROBAST standard, and the research results should be comprehensively reported using multivariate prediction model reporting guidelines to develop high-quality predictive models with strong scalability.
ObjectiveTo evaluate existing predictive models for surgical site infection (SSI) following colorectal cancer (CRC) surgery, aiming to provide a scientific basis for refining risk prediction models and developing clinically practical and widely applicable screening tools. MethodA comprehensive review of existing literature on predictive models for SSI following CRC surgery, both domestically and internationally, were conducted. ResultsThe determination of SSI following CRC surgery primarily relied on the Centers for Disease Control and Prevention standard of USA, which presented issues of consistency and accuracy. Various predictive models had been developed, including traditional statistical models and machine learning models, with 0.991 of an area under the operating characteristic curve of predictive model. However, most studies were based on retrospective and single-center data, which limited their applicability and accuracy. ConclusionsAlthough existing models provide strong support for predicting SSI following CRC surgery, there is a need for multi-center, prospective studies to enhance the generalizability and accuracy of these models. Additionally, future research should focus on improving model interpretability to better apply them in clinical practice, providing personalized risk assessments and intervention strategies for patients.
ObjectiveTo explore the risk factors affecting occurrence of arteriosclerosis obliterans (ASO) for patients with type 2 diabetes mellitus (T2DM) and to develop a nomogram predictive model using these risk factors. MethodsA case-control study was conducted. The patients with T2DM accompanied with ASO and those with T2DM alone, admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2017 to December 2022, were retrospectively collected according to the inclusion and exclusion criteria. The basic characteristics, blood, thyroid hormones, and other relevant indicators of the paitents in two groups were compared. The multivariate logistic regression analysis was used to identify the risk factors for the occurrence of ASO in the patients with T2DM, and then a nomogram predictive model was developed. ResultsThere were 119 patients with T2DM alone and 114 patients with T2DM accompanied with lower extremity ASO in this study. The significant differences were observed between the two groups in terms of smoking history, white blood cell count, neutrophil count, lymphocyte count, platelet count, systemic immune-inflammation index, systemic inflammatory response index (SIRI), high-density lipoprotein cholesterol, apolipoprotein A1 (ApoA1), apolipoprotein α (Apoα), serum cystatin C, free-triiodothyronine (FT3), total triiodothyronine, FT3/total triiodothyronine ratio, fibrinogen (Fib), fibrinogen degradation products, and plasma D-dimer (P<0.05). Further the results of the multivariate logistic regression analysis revealed that the history of smoking, increased Fib level and SIRI value increased the probabilities of ASO occurrence in the patients with T2DM [OR (95%CI)=2.921 (1.023, 4.227), P=0.003; OR (95%CI)=2.641 (1.810, 4.327), P<0.001; OR (95%CI)=1.020 (1.004, 1.044), P=0.018], whereas higher levels of ApoA1 and FT3 were associated with reduced probabilities of ASO occurrence in the patients with T2DM [OR (95%CI)=0.231 (0.054, 0.782), P=0.021; OR (95%CI)=0.503 (0.352, 0.809), P=0.002]. The nomogram predictive model based on these factors demonstrated a good discrimination for predicting the ASO occurrence in the T2DM patients [area under the receiver operating characteristic curve (95%CI)=0.788 (0.730, 0.846)]. The predicted curve closely matched the ideal curve (Hosmer-Lemeshow goodness-of-fit test, χ2=5.952, P=0.653). The clinical decision analysis curve showed that the clinical net benefit of intervention based on the nomogram model was higher within a threshold probability range of 0.18 to 0.80 compared to no intervention or universal intervention. ConclusionsThe analysis results indicate that T2DM patients with a smoking history, elevated Fib level and SIRI value, as well as decreased ApoA1 and FT3 levels should be closely monitored for ASO risk. The nomogram predictive model based on these features has a good discriminatory power for ASO occurrence in T2DM patients, though its value warrants further investigation.