A novel nomogram for predicting the decision to delayed extubation after thoracoscopic lung cancer surgery

Tong, Chaoyang; Miao, Qing; Zheng, Jijian; Wu, Jingxiang

doi:10.1080/07853890.2022.2160490

Cited by 16 publications

(11 citation statements)

References 32 publications

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“…In recent years, the Nomogram model has emerged as a reliable clinical diagnostic tool, demonstrating exceptional predictive accuracy and offering personalized diagnostic strategies for patients ( Tong et al., 2023 ). Leveraging machine learning algorithms, we have identified PLEK, IRF8, BTK, CCR1, and CD68 as evaluative markers to assist in the diagnosis of atherosclerosis.…”

Section: Discussionmentioning

confidence: 99%

Integrating machine learning algorithms and single-cell analysis to identify gut microbiota-related macrophage biomarkers in atherosclerotic plaques

Ke,

Yue,

et al. 2024

Front. Cell. Infect. Microbiol.

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ObjectiveThe relationship between macrophages and the gut microbiota in patients with atherosclerosis remains poorly defined, and effective biological markers are lacking. This study aims to elucidate the interplay between gut microbial communities and macrophages, and to identify biomarkers associated with the destabilization of atherosclerotic plaques. The goal is to enhance our understanding of the underlying molecular pathways and to pave new avenues for diagnostic approaches and therapeutic strategies in the disease.MethodsThis study employed Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression analysis on atherosclerosis datasets to identify macrophage-associated genes and quantify the correlation between these genes and gut microbiota gene sets. The Random Forest algorithm was utilized to pinpoint PLEK, IRF8, BTK, CCR1, and CD68 as gut microbiota-related macrophage genes, and a nomogram was constructed. Based on the top five genes, a Non-negative Matrix Factorization (NMF) algorithm was applied to construct gut microbiota-related macrophage clusters and analyze their potential biological alterations. Subsequent single-cell analyses were conducted to observe the expression patterns of the top five genes and the interactions between immune cells. Finally, the expression profiles of key molecules were validated using clinical samples from atherosclerosis patients.ResultsUtilizing the Random Forest algorithm, we ultimately identified PLEK, IRF8, CD68, CCR1, and BTK as gut microbiota-associated macrophage genes that are upregulated in atherosclerotic plaques. A nomogram based on the expression of these five genes was constructed for use as an auxiliary tool in clinical diagnosis. Single-cell analysis confirmed the specific expression of gut microbiota-associated macrophage genes in macrophages. Clinical samples substantiated the high expression of PLEK in unstable atherosclerotic plaques.ConclusionGut microbiota-associated macrophage genes (PLEK, IRF8, CD68, CCR1, and BTK) may be implicated in the pathogenesis of atherosclerotic plaques and could serve as diagnostic markers to aid patients with atherosclerosis.

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Section: Discussionmentioning

confidence: 99%

Integrating machine learning algorithms and single-cell analysis to identify gut microbiota-related macrophage biomarkers in atherosclerotic plaques

Ke,

Yue,

et al. 2024

Front. Cell. Infect. Microbiol.

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“…Nomograms are widely recognized as valuable prognostic tools in medicine as they integrate various prognostic and determining variables to generate individual probabilities of clinical events. 21 , 22 However, there is a scarcity of reports on nomogram prediction models for intussusception, with most studies being single-center studies. For instance, Ting et al 18 conducted a study with 368 patients undergoing intussusception surgery and developed a nomogram to predict pathological intussusception in children.…”

Section: Discussionmentioning

confidence: 99%

Developing a Nomogram for Predicting Surgical Intervention in Pediatric Intussusception After Pneumatic Reduction: A Multicenter Study from China

Liu,

Wang,

Jiang

et al. 2024

TCRM

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Purpose The objective of this study was to develop and validate a nomogram for predicting the need for surgical intervention in pediatric intussusception after pneumatic reduction. Patients and Methods This retrospective study analyzed the clinical data of children with acute intussusception admitted to four hospitals in China from January 2019 to January 2022. Based on the results of pneumatic reduction, the patients were divided into two groups: the successful reduction group (control group) and the failed reduction group (operation group). The total sample was randomly divided into a training set and a validation set in a 7:3 ratio. Logistic regression analysis was performed to establish a predictive model for surgical risk. Results A total of 1041 samples were included in this study, with 852 in the control group and 189 in the operation group. Among the total sample, 728 cases were used for training and 313 cases were used for validation. Logistic regression analysis of the training set identified age, time of abdominal pain, presence or absence of hematostoecia, C-reactive protein value from blood test on admission, and nested position indicated by B-ultrasound as independent predictors of intussusception intervention. Based on the five independent risk factors identified through multivariate logistic regression, a nomogram was successfully constructed to predict the failure of resetting by air enema under X-ray. Conclusion A nomogram was developed to predict the need for surgical intervention after intussusception pneumatic reduction in children. The nomogram was based on clinical risk factors including age, time of abdominal pain, presence or absence of blood in stool, value of C-reactive protein in blood test on admission, and nested position indicated by B-ultrasound. Our internal validation demonstrated that this nomogram can serve as a useful tool for identifying risk factors associated with failure of air enema in children with intussusception.

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“…Calibration was evaluated by plotting calibration curves and calculating the Brier score using the equation (Y-p) 2 , where Y represented the actual observed outcome and P represented the predicted probability from the nomogram 30 . In addition to developing quantitative nomograms for predicting the probability of postoperative neurological complications in aortic surgery, a decision curve analysis (DCA) was performed to assess the clinical applicability of the nomograms 31 . The DCA quantified the clinical net benefit of the predictive model at different threshold probabilities, providing valuable insights into the practical use of the model in clinical decision-making.…”

Section: Methodsmentioning

confidence: 99%

Relative band power in assessing temporary neurological dysfunction post- type A aortic dissection surgery: a prospective study

Wang,

Li,

Jin

et al. 2024

Sci Rep

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Temporary neurological dysfunction (TND), a common complication following surgical repair of Type A Aortic Dissection (TAAD), is closely associated with increased mortality and long-term cognitive impairment. Currently, effective treatment options for TND remain elusive. Therefore, we sought to investigate the potential of postoperative relative band power (RBP) in predicting the occurrence of postoperative TND, with the aim of identifying high-risk patients prior to the onset of TND. We conducted a prospective observational study between February and December 2022, involving 165 patients who underwent surgical repair for TAAD at our institution. Bedside Quantitative electroencephalography (QEEG) was utilized to monitor the post-operative brain electrical activity of each participant, recording changes in RBP (RBP Delta, RBP Theta, RBP Beta and RBP Alpha), and analyzing their correlation with TND. Univariate and multivariate analyses were employed to identify independent risk factors for TND. Subsequently, line graphs were generated to estimate the incidence of TND. The primary outcome of interest was the development of TND, while secondary outcomes included intensive care unit (ICU) admission and length of hospital stay. A total of 165 patients were included in the study, among whom 68 (41.2%) experienced TND. To further investigate the independent risk factors for postoperative TND, we conducted both univariate and multivariate logistic regression analyses on all variables. In the univariate regression analysis, we identified age (Odds Ratio [OR], 1.025; 95% CI, 1.002–1.049), age ≥ 60 years (OR, 2.588; 95% CI, 1.250–5.475), hemopericardium (OR, 2.767; 95% CI, 1.150–7.009), cardiopulmonary bypass (CPB) (OR, 1.007; 95% CI, 1.001–1.014), RBP Delta (OR, 1.047; 95% CI, 1.020–1.077), RBP Alpha (OR, 0.853; 95% CI, 0.794–0.907), and Beta (OR, 0.755; 95% CI, 0.649–0.855) as independent risk factors for postoperative TND. Further multivariate regression analyses, we discovered that CPB time ≥ 180 min (OR, 1.021; 95% CI, 1.011–1.032), RBP Delta (OR, 1.168; 95% CI, 1.105–1.245), and RBP Theta (OR, 1.227; 95% CI, 1.135–1.342) emerged as independent risk factors. TND patients had significantly longer ICU stays (p < 0.001), and hospital stays (p = 0.002). We obtained the simplest predictive model for TND, consisting of three variables (CPB time ≥ 180 min, RBP Delta, RBP Theta, upon which we constructed column charts. The areas under the receiver operating characteristic (AUROC) were 0.821 (0.755, 0.887). Our study demonstrates that postoperative RBP monitoring can detect changes in brain function in patients with TAAD during the perioperative period, providing clinicians with an effective predictive method that can help improve postoperative TND in TAAD patients. These findings have important implications for improving clinical care in this population.Trial registration ChiCTR2200055980. Registered 30th Jan. 2022. This trial was registered before the first participant was enrolled.

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A novel nomogram for predicting the decision to delayed extubation after thoracoscopic lung cancer surgery

Cited by 16 publications

References 32 publications

Integrating machine learning algorithms and single-cell analysis to identify gut microbiota-related macrophage biomarkers in atherosclerotic plaques

Integrating machine learning algorithms and single-cell analysis to identify gut microbiota-related macrophage biomarkers in atherosclerotic plaques

Developing a Nomogram for Predicting Surgical Intervention in Pediatric Intussusception After Pneumatic Reduction: A Multicenter Study from China

Relative band power in assessing temporary neurological dysfunction post- type A aortic dissection surgery: a prospective study

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