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Pezeshkian F, Leo R, McAllister MA, Singh A, Mazzola E, Hooshmand F, Herrera-Zamora J, Silvestri M, Barcelos RR, Bueno R, Figueroa PU, Jaklitsch MT, Swanson SJ. Predictors of prolonged hospital stay after segmentectomy. J Thorac Cardiovasc Surg 2024:S0022-5223(24)00365-9. [PMID: 38688448 DOI: 10.1016/j.jtcvs.2024.04.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/14/2024] [Accepted: 04/15/2024] [Indexed: 05/02/2024]
Abstract
OBJECTIVE Segmentectomy is becoming the standard of care for small, peripheral non-small cell lung cancer. To improve perioperative management in this population, this study aims to identify factors influencing hospital length of stay after segmentectomy. METHODS Patients who underwent segmentectomy for any indication between January 2018 and May 2023 were identified using a prospectively maintained institutional database. Multivariable logistic regression models were used to estimate associations between clinical features and prolonged (≥3 days) hospital stay. A nomogram was designed to understand better and possibly calculate the individual risk of prolonged hospital stays. RESULTS In total, 533 cases were included; 337 (63%) were female. Median age was 66 years (interquartile range [IQR], 63-75). The median size of resected lesions was 1.6 cm (IQR, 1.3-2.1 cm). Median hospital stay was 3 days (IQR, 2-4 days). Major adverse events occurred in 31 (5.8%) cases. The 30-day readmission rate was 5.8% (n = 31). There was no 30-day mortality; 90-day mortality was <1%. Patients older than 75 years (odds ratio [OR], 2.01, 95% confidence interval [CI], 1.15-3.57, P = .02), those with forced expiratory volume in 1 second <88% predicted (OR, 1.99; 95% CI, 1.38-2.89, P < .001), or positive smoking history (OR, 1.72; 95% CI, 1.15-2.60, P = .01) were more likely to have prolonged hospital stays after segmentectomy. A nomogram accounting for age, sex, forced expiratory volume in 1 second, body mass index, smoking history, and comorbidities was created to predict the probability of prolonged hospital stay with an area under the receiver operating characteristic curve of 0.66. CONCLUSIONS Older patients, those with reduced pulmonary function, and current and past smokers have elevated risk for prolonged hospital stays after segmentectomy. Validation of our nomogram could improve perioperative risk stratification in patients who undergo segmentectomy.
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Affiliation(s)
| | - Rachel Leo
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, Mass
| | - Miles A McAllister
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, Mass
| | - Anupama Singh
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, Mass
| | - Emanuele Mazzola
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Mass
| | - Fatemeh Hooshmand
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, Mass
| | | | - Mia Silvestri
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, Mass
| | | | - Raphael Bueno
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, Mass
| | | | | | - Scott J Swanson
- Division of Thoracic Surgery, Brigham and Women's Hospital, Boston, Mass
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Bo R, Chen X, Zheng X, Yang Y, Dai B, Yuan Y. A Nomogram Model to Predict Deep Vein Thrombosis Risk After Surgery in Patients with Hip Fractures. Indian J Orthop 2024; 58:151-161. [PMID: 38312904 PMCID: PMC10830990 DOI: 10.1007/s43465-023-01074-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 11/28/2023] [Indexed: 02/06/2024]
Abstract
Aims This study aimed to establish a nomogram model for predicting the probability of postoperative deep vein thrombosis (DVT) risk in patients with hip fractures. Methods 504 patients were randomly assigned to the training set and validation set, and then divided into a DVT group and a non-DVT group. The study analysed the risk factors for DVT using univariate and multivariate analyses. Based on these parameters, a nomogram model was constructed and validated. The predicting performance of nomogram was evaluated by discrimination, calibration, and clinical usefulness. Results The predictors contained in the nomogram model included age, surgical approach, 1-day postoperative D-dimer value and admission ultrasound diagnosis of the lower limb vein. Furthermore, the area under the ROC curve (AUC) for the specific DVT risk-stratification nomogram model (0.815; 95% CI 0.746-0.884) was significantly higher than the current model (Caprini) (0.659; 95% CI 0.572-0.746, P < 0.05). According to the calibration plots, the prediction and actual observation were in good agreement. In the range of threshold probabilities of 0.2-0.8, the predictive performance of the model on DVT risk could be maximized. Conclusions The current predictive model could serve as a reliable tool to quantify the possibility of postoperative DVT in hip fractures patients.
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Affiliation(s)
- Ruting Bo
- Department of Ultrasound, Tianjin Hospital, Tianjin Hexi District Jiefangnan Road, Tianjin, 300211 China
| | - Xiaoyu Chen
- Department of Ultrasound, Tianjin Hospital, Tianjin Hexi District Jiefangnan Road, Tianjin, 300211 China
| | - Xiuwei Zheng
- Clinical Medical College of Tianjin Medical University, Tianjin, 300276 China
| | - Yang Yang
- Department of Hip Surgery, Tianjin Hospital, Tianjin, 300211 China
| | - Bing Dai
- Department of Vascular Surgery, Tianjin Hospital, Tianjin, 300211 China
| | - Yu Yuan
- Department of Ultrasound, Tianjin Hospital, Tianjin Hexi District Jiefangnan Road, Tianjin, 300211 China
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Huang Z, Li L, Gong Z, Tang L. Construction and Validation of a Nomogram to Predict the Postoperative Venous Thromboembolism Risk in Patients with HGSOC. Clin Appl Thromb Hemost 2024; 30:10760296241255958. [PMID: 38767088 PMCID: PMC11107311 DOI: 10.1177/10760296241255958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/20/2024] [Accepted: 05/02/2024] [Indexed: 05/22/2024] Open
Abstract
Venous thromboembolism (VTE) is a common complication in patients with high-grade serous ovarian cancer (HGSOC) after surgery. This study aims to establish a comprehensive risk assessment model to better identify the potential risk of postoperative VTE in HGSOC. Clinical data from 587 HGSOC patients who underwent surgical treatment were retrospectively collected. Univariate and multivariate logistic regression analyses were performed to identify independent factors influencing the occurrence of postoperative VTE in HGSOC. A nomogram model was constructed in the training set and further validated in the verification set. Logistic regression identified age (odds ratio [OR] = 1.063, P = .002), tumor size (OR = 3.815, P < .001), postoperative transfusion (OR = 5.646, P = .001), and postoperative D-dimer (OR = 1.246, P = .003) as independent risk factors for postoperative VTE in HGSOC patients. A nomogram was constructed using these factors. The receiver operating characteristic curve showed an area under the curve (AUC) of 0.840 (95% confidence interval [CI]: 0.782, 0.898) in the training set and 0.793 (95% CI: 0.704, 0.882) in the validation set. The calibration curve demonstrated a good consistency between model predictions and actual results. The decision curve analysis indicated the model benefits at a threshold probability of less than 70%. A nomogram predicting postoperative VTE in HGSOC was established and validated. This model will assist clinicians in the early identification of high-risk patients, enabling the implementation of appropriate preventive measures.
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Affiliation(s)
- Zhen Huang
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ling Li
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhengxin Gong
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Liangdan Tang
- Department of Gynecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Qin D, Cai H, Liu Q, Lu T, Tang Z, Shang Y, Cui Y, Wang R. Nomogram model combined thrombelastography for venous thromboembolism risk in patients undergoing lung cancer surgery. Front Physiol 2023; 14:1242132. [PMID: 38162832 PMCID: PMC10757630 DOI: 10.3389/fphys.2023.1242132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024] Open
Abstract
Background: The aim of this study was to develop a nomogram model in combination with thromboelastography (TEG) to predict the development of venous thromboembolism (VTE) after lung cancer surgery. Methods: The data of 502 patients who underwent surgical treatment for lung cancer from December 2020 to December 2022 were retrospectively analyzed. Patients were then randomized into training and validation groups. Univariate and multivariate logistic regression analyses were carried out in the training group and independent risk factors were included in the nomogram to construct risk prediction models. The predictive capability of the model was assessed by the consistency index (C-index), receiver operating characteristic curves (ROC), the calibration plot and decision curve analysis (DCA). Results: The nomogram risk prediction model comprised of the following five independent risk factors: age, operation time, forced expiratory volume in one second and postoperative TEG parameters k value(K) and reaction time(R). The nomogram model demonstrated better predictive power than the modified Caprini model, with the C-index being greater. The calibration curve verified the consistency of nomogram between the two groups. Furthermore, DCA demonstrated the clinical value and potential for practical application of the nomogram. Conclusion: This study is the first to combine TEG and clinical risk factors to construct a nomogram to predict the occurrence of VTE in patients after lung cancer surgery. This model provides a simple and user-friendly method to assess the probability of VTE in postoperative lung cancer patients, enabling clinicians to develop individualized preventive anticoagulation strategies to reduce the incidence of such complications.
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Affiliation(s)
- Da Qin
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China
- Organ Transplantation Center, The First Hospital of Jilin University, Changchun, China
| | - Hongfei Cai
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Qing Liu
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Tianyu Lu
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Ze Tang
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Yuhang Shang
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Youbin Cui
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China
- Organ Transplantation Center, The First Hospital of Jilin University, Changchun, China
| | - Rui Wang
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun, China
- Organ Transplantation Center, The First Hospital of Jilin University, Changchun, China
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Wei T, Wei W, Ma Q, Shen Z, Lu K, Zhu X. Development of a Clinical-Radiomics Nomogram That Used Contrast-Enhanced Ultrasound Images to Anticipate the Occurrence of Preoperative Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma Patients. Int J Gen Med 2023; 16:3921-3932. [PMID: 37662506 PMCID: PMC10474867 DOI: 10.2147/ijgm.s424880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/15/2023] [Indexed: 09/05/2023] Open
Abstract
Background and Objectives Papillary thyroid carcinoma (PTC) is a prevalent histological type of thyroid cancer; however, noninvasive assessment of cervical lymph node metastasis (LNM) poses a challenge. This study aims to develop a novel clinical-radiomics nomogram that utilizes ultrasound (US) images to predict the presence of cervical LNM metastasis in patients with PTC. Methods A total of 423 patients with PTC were recruited to participate in this study between January 2020 and December 2022, of which 282 were classified into the training group and 141 patients were classified into the validation set. Contrast-enhanced ultrasound (CEUS) and B-mode ultrasound (BMUS) images were subjected to radiomic analysis, leading to the extraction of 912 radiomic features. Thereafter, a radiomics score (Radscore) was developed to effectively integrate the information derived from BMUS and CEUS modalities. Univariate and multivariate backward stepwise logistic regression analysis techniques were used to construct the clinical and clinical-radiomics models, respectively. Results The findings revealed that the clinical-radiomics nomogram incorporated age, sex, CEUS Radscore, and US-reported LNM as risk factors. The nomogram demonstrated good performance using data from the training (AUC = 0.891) and validation (AUC = 0.870) sets. The decision curve analysis implied that this nomogram exhibited good clinical utility, which was further supported by the results of the calibration curves and Hosmer-Lemeshow test. Conclusion The CEUS Radscore-based clinical radiomics nomogram could serve as a valuable tool for predicting cervical LNM metastasis in patients with PTC, thereby tailoring individualized treatment strategies for them.
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Affiliation(s)
- Tianjun Wei
- School of Continuing Education, Anhui Medical University, Hefei, 230032, People’s Republic of China
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People’s Republic of China
| | - Wei Wei
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People’s Republic of China
| | - Qiang Ma
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People’s Republic of China
| | - Zhongbing Shen
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People’s Republic of China
| | - Kebing Lu
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People’s Republic of China
| | - Xiangming Zhu
- School of Continuing Education, Anhui Medical University, Hefei, 230032, People’s Republic of China
- Department of Ultrasound, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241001, People’s Republic of China
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Nomogram prediction for the risk of venous thromboembolism in patients with lung cancer. Cancer Cell Int 2023; 23:40. [PMID: 36872336 PMCID: PMC9985855 DOI: 10.1186/s12935-023-02882-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/28/2023] [Indexed: 03/07/2023] Open
Abstract
OBJECTIVE The aim of this study was to establish a nomogram graph model to accurately predict the venous thromboembolism (VTE) risk probability in the general population with lung cancer. METHODS Based on data from patients with lung cancer in Chongqing University Cancer Hospital of China, the independent risk factors of VTE were identified by the logistic univariable and multivariable analysis and were integrated to construct a nomogram, which was validated internally. The predictive effectiveness of the nomogram was evaluated by the receiver operating characteristic curve (ROC) and calibration curve. RESULTS A total of 3398 lung cancer patients were included for analysis. The nomogram incorporated eleven independent VTE risk factors including karnofsky performance scale (KPS), stage of cancer, varicosity, chronic obstructive pulmonary disease (COPD), central venous catheter (CVC), albumin, prothrombin time (PT), leukocyte counts, epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), dexamethasone, and bevacizumab. The C-index of the nomogram model was 0.843 and 0.791 in the training and validation cohort, respectively, demonstrating good discriminative power. The calibration plots of the nomogram revealed excellent agreement between the predicted and actual probabilities. CONCLUSIONS We established and validated a novel nomogram for predicting the risk of VTE in patients with lung cancer. The nomogram model could precisely estimate the VTE risk of individual lung cancer patients and identify high-risk patients who are in need of a specific anticoagulation treatment strategy.
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