Evaluation and analysis of incidence and risk factors of lower extremity venous thrombosis after urologic surgeries: A prospective two-center cohort study using LASSO-logistic regression

Tang, Guyu; Qi, Lin; Sun, Zepeng; Liu, Jing; Lv, Zhengtong; Chen, Lingxiao; Huang, Bin; Zhu, Sipin; Liu, Yao; Li, Yuan

doi:10.1016/j.ijsu.2021.105948

Cited by 46 publications

(20 citation statements)

References 24 publications

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“…The least absolute shrinkage and selection operator (lasso) regression was applied to deal with multicollinearity issues [21]. Lasso regression compresses some coe cients to 0 to generate a re ned model.…”

Section: Lasso Regression and Aicmentioning

confidence: 99%

RETRACTED: A Wnt pathway-associated model and nomogram predict the outcome of GBM patients

Li,

Chai,

Yan

et al. 2024

Preprint

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Background: The Wnt pathway is a highly conserved signalling pathway, and the dysregulation of the Wnt pathway contributes to the progression of many cancers, especially glioblastoma (GBM). Methods: The Wnt pathway-correlated RNAseq was downloaded from the TCGA and CGGA datasets and subjected to bioinformatic analysis to identify potential prognostic genes for GBM. A risk score model was constructed based on eight genes through Lasso regression and Akaike Information Criterion. Results: Eight potential prognostic genes related to the Wnt pathway were identified. IGFBP6, ACHE, LRRC61, PDGFA, and SEZ6L2 were risk genes, while GPR27, HIST3H2A, and OPHN1 were protective genes in GBM. A risk score model was constructed based on these eight genes and had great prognostic classification efficiency. A nomogram was constructed that exhibited powerful prediction capacity in GBM prognosis. Finally, we analyzed the difference of Wnt pathway-correlated score at the single-cell level. The enrichment score of some endothelial cell subpopulations in the central areas of tumour was higher than that in the peripheral areas of tumour. Conclusion: The Wnt pathway-related risk model and nomogram were excellent in predicting GBM patient prognosis.

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Section: Lasso Regression and Aicmentioning

confidence: 99%

RETRACTED: A Wnt pathway-associated model and nomogram predict the outcome of GBM patients

Li,

Chai,

Yan

et al. 2024

Preprint

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“…At the same time, the calibration curves showed good consistency between prediction and actual observation, respectively, and the decision curve analysis indicated this nomogram had good clinical benefit. LASSO regression is a common method for variable selection in fitting high-dimensional generalized linear and has been widely used in clinical research (22,23). The LASSO method selects variables via minimizing the coefficients of relatively irrelevant variables to 0 and subsequently removing these variables by constructing a penalty function, which effectively avoids the overfitting and makes the model more refined (24, 25).…”

Section: Discussionmentioning

confidence: 99%

Nomogram prediction model of postoperative pneumonia in patients with lung cancer: A retrospective cohort study

Jin

Liu²,

Qiao³

et al. 2023

Front. Oncol.

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BackgroundThe prediction model of postoperative pneumonia (POP) after lung cancer surgery is still scarce.MethodsRetrospective analysis of patients with lung cancer who underwent surgery at The Fourth Hospital of Hebei Medical University from September 2019 to March 2020 was performed. All patients were randomly divided into two groups, training cohort and validation cohort at the ratio of 7:3. The nomogram was formulated based on the results of multivariable logistic regression analysis and clinically important factors associated with POP. Concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, Hosmer-Lemeshow goodness-of-fit test and decision curve analysis (DCA) were used to evaluate the predictive performance of the nomogram.ResultsA total of 1252 patients with lung cancer was enrolled, including 877 cases in the training cohort and 375 cases in the validation cohort. POP was found in 201 of 877 patients (22.9%) and 89 of 375 patients (23.7%) in the training and validation cohorts, respectively. The model consisted of six variables, including smoking, diabetes mellitus, history of preoperative chemotherapy, thoracotomy, ASA grade and surgery time. The C-index from AUC was 0.717 (95%CI:0.677-0.758) in the training cohort and 0.726 (95%CI:0.661-0.790) in the validation cohort. The calibration curves showed the model had good agreement. The result of DCA showed that the model had good clinical benefits.ConclusionThis proposed nomogram could predict the risk of POP in patients with lung cancer surgery in advance, which can help clinician make reasonable preventive and treatment measures.

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“…In the third part, the preoperatively available variables with P < 0.1 in the differential comparisons were included in the least absolute shrinkage and selection operator (LASSO) model based on the one standard error rule with 3-fold cross-validation to reduce feature dimensionality ( 22 , 23 ). Subsequently, the selected risk factors and the imaging-PS status were combined and used to develop a nomogram based on the whole cohort.…”

Section: Methodsmentioning

confidence: 99%

Multidimensional characteristics, prognostic role, and preoperative prediction of peritoneal sarcomatosis in retroperitoneal sarcoma

et al. 2022

Front. Oncol.

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BackgroundPeritoneal sarcomatosis (PS) could occur in patients with retroperitoneal sarcomas (RPS). This study aimed to expand the understanding of PS on its characteristics and prognostic role, and develop a nomogram to predict its occurrence preoperatively.MethodsData of 211 consecutive patients with RPS who underwent surgical treatment between 2011 and 2019 was retrospectively reviewed. First, the clinicopathological characteristics of PS were summarized and analyzed. Second, the disease-specific survival (DSS) and recurrence-free survival (RFS) of patients were analyzed to evaluate the prognostic role of PS. Third, preoperative imaging, nearly the only way to detect PS preoperatively, was combined with other screened risk factors to develop a nomogram. The performance of the nomogram was assessed.ResultsAmong the 211 patients, 49 (23.2%) patients had PS with an incidence of 13.0% in the primary patients and 35.4% in the recurrent patients. The highest incidence of PS occurred in dedifferentiated liposarcoma (25.3%) and undifferentiated pleomorphic sarcoma (25.0%). The diagnostic sensitivity of the preoperative imaging was 71.4% and its specificity was 92.6%. The maximum standardized uptake value (SUVmax) was elevated in patients with PS (P<0.001). IHC staining for liposarcoma revealed that the expression of VEGFR-2 was significantly higher in the PS group than that in the non-PS group (P = 0.008). Survival analysis (n =196) showed significantly worse DSS in the PS group than in non-PS group (median: 16.0 months vs. not reached, P < 0.001). In addition, PS was proven as one of the most significant prognostic predictors of both DSS and RFS by random survival forest algorithm. A nomogram to predict PS status was developed based on preoperative imaging combined with four risk factors including the presentation status (primary vs. recurrent), ascites, SUVmax, and tumor size. The nomogram significantly improved the diagnostic sensitivity compared to preoperative imaging alone (44/49, 89.8% vs. 35/49, 71.4%). The C-statistics of the nomogram was 0.932, and similar C-statistics (0.886) was achieved at internal cross-validation.ConclusionPS is a significant prognostic indicator for RPS, and it occurs more often in recurrent RPS and in RPS with higher malignant tendency. The proposed nomogram is effective to predict PS preoperatively.

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Evaluation and analysis of incidence and risk factors of lower extremity venous thrombosis after urologic surgeries: A prospective two-center cohort study using LASSO-logistic regression

Cited by 46 publications

References 24 publications

RETRACTED: A Wnt pathway-associated model and nomogram predict the outcome of GBM patients

RETRACTED: A Wnt pathway-associated model and nomogram predict the outcome of GBM patients

Nomogram prediction model of postoperative pneumonia in patients with lung cancer: A retrospective cohort study

Multidimensional characteristics, prognostic role, and preoperative prediction of peritoneal sarcomatosis in retroperitoneal sarcoma

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