Conversion is a risk factor for postoperative anastomotic leak in rectal cancer patients - A retrospective cohort study

Liu, Xuan-hui; Wu, Xianrui; Zhou, Chang; Zheng, Xiaobin; Ke, Jia; Liu, Huashan; Hu, Tuo; Chen, Yufeng; He, Xiaowen; He, Xiaosheng; Chen, Yongle; Zou, Yifeng; Wang, Jianping; Wu, Xiaojian; Lan, Ping

doi:10.1016/j.ijsu.2018.01.024

Cited by 13 publications

(16 citation statements)

References 44 publications

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“…It was a hint for surgeons that a temporary stoma could reduce the incidence of reoperation necessitated by AL. Besides, the stoma rate was 66.2% in our study which was higher than several other studies (12,20,25). In contrast, in our study the average distance of tumor from the anal verge was lower than that in most previous studies (around 7.5 cm) (12,20,25,26).…”

contrasting

confidence: 85%

“…Besides, the stoma rate was 66.2% in our study which was higher than several other studies (12,20,25). In contrast, in our study the average distance of tumor from the anal verge was lower than that in most previous studies (around 7.5 cm) (12,20,25,26). In our study, the overall average distance was 6.99±3.18 cm, and that in the stoma group was 5.75±2.52 cm, while in non-stoma group that was 9.41±2.90 cm, which implied that there were more low rectal cancer patients in our study.…”

contrasting

confidence: 78%

“…Thus, the results in our study are likely to be more reliable and convincing. Moreover, compared to other studies, our model also showed a better predicting performance (11,12,19,20,(25)(26)(27) (Table 6).…”

mentioning

confidence: 45%

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Machine learning-based random forest predicts anastomotic leakage after anterior resection for rectal cancer

Wen¹,

Zheng²,

Zhang³

et al. 2021

J Gastrointest Oncol

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Background: Anastomotic leakage (AL) is one of the commonest and most serious complications after rectal cancer surgery. The previous analyses on predictors for AL included small-scale patients, and their prediction models performed unsatisfactorily.Methods: Clinical data of 5,220 patients who underwent anterior resection for rectal cancer were scrutinized to create a prediction model via random forest classifier. Additionally, data of 836 patients served as the test dataset. Patients diagnosed with AL within 6 months' follow-up were recorded. A total of 20 candidate factors were included. Receiver operating characteristic (ROC) curve was conducted to determine the clinical efficacy of our model, and compare the predictive performance of different models.Results: The incidence of AL was 6.2% (326/5,220). A multivariate logistic regression analysis and the random forest classifier indicated that sex, distance of tumor from the anal verge, bowel stenosis or obstruction, preoperative hemoglobin, surgeon volume, diabetes, neoadjuvant chemoradiotherapy, and surgical approach were significantly associated with AL. After propensity score matching, the temporary stoma was not identified as a protective factor for AL (P=0.58). Contrastingly, the first year of performing laparoscopic surgery was a predictor (P=0.009). We created a predictive random forest classifier based on the above predictors that demonstrated satisfactory prediction efficacy. The area under the curve (AUC) showed that the random forest had higher efficiency (AUC =0.87) than the nomogram (AUC =0.724).Conclusions: Our findings suggest that eight factors may affect the incidence of AL. Our random forest classifier is an innovative and practical model to effectively predict AL, and could provide rational advice on whether to perform a temporary stoma, which might reduce the rate of stoma and avoid the ensuing complications.

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contrasting

confidence: 85%

contrasting

confidence: 78%

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Machine learning-based random forest predicts anastomotic leakage after anterior resection for rectal cancer

Wen¹,

Zheng²,

Zhang³

et al. 2021

J Gastrointest Oncol

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“…The nomogram decision curve analysis is shown in Figure 2. The calibration curve of the nomogram for the probability of mortality demonstrated good agreement between prediction and observation in the primary cohort (Figure 2) (24). The decision curve showed that if the threshold probability of a patient or doctor is >0%, using the nomogram to predict the mortality for COVID-19 patients adds more benefit.…”

Section: Development and Validation Of A Nomogrammentioning

confidence: 80%

A Predicting Nomogram for Mortality in Patients With COVID-19

Pan

Cheng

Cao

et al. 2020

Front. Public Health

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Background: The global COVID-19 epidemic remains severe, with the cumulative global death toll reaching more than 207,170 as of May 2, 2020 (1). Purpose: Our research objective is to establish a reliable nomogram to predict mortality in COVID-19 patients. The nomogram can help us distinguish between patients who are at high risk of death and need close attention. Patients and Methods: For the single-center retrospective study, we collected 21 cases of patients who died in the critical illness area of the Optical Valley Branch of Tongji Hospital, Huazhong University of Science and Technology, from February 9 to March 10. Additionally, we selected 99 patients discharged during this period for analysis. The nomogram was constructed to predict the mortality for COVID-19 patients using the primary group of 120 patients and was validated using an independent cohort of 84 patients. We used multivariable logistic regression analysis to construct the prediction model. The nomogram was evaluated for calibration, differentiation, and clinical usefulness. Results: The predictors included in the nomogram were c-reactive protein, PaO 2 /FiO 2 , and cTnI. The areas under the curves of the nomogram were 0.988 (95% CI: 0.972-1.000) and 0.956 (95% CI, 0.874-1.000) in the primary and validation groups, respectively. Decision curve analysis suggests that the nomogram may have clinical usefulness. Conclusion: This study provides a nomogram containing c-reactive protein, PaO 2 /FiO 2 , and cTnI that can be conveniently used to predict individual mortality in COVID-19 patients. Next, we will collect as many cases as possible from multiple centers to build a more reliable nomogram to predict mortality for COVID-19 patients.

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“…Eindeutig führen Konversionen zu einer Verlängerung der Rekonvaleszenz. Es gibt aber auch eine Reihe von Hinweisen darauf, dass Konversionen bei laparoskopischen Operationen des Rektumkarzinoms mit einer Erhöhung der Anastomoseninsuffizienzrate [13,14] und mit einer Verschlechterung des tumorfreien und Gesamtüberlebens einhergehen [13]. Daher ist zu betonen, dass sich in den vorliegenden vergleichenden Untersuchungs-serien für die TaTME eine kumulative Konversionsrate von unter 1 % findet, während in den laparoskopischen Armen Konversionsraten von etwa 10 % beschrieben sind (.…”

Section: In Vielen Studien Zur Minimalinvasiven Tme Finden Sich Hohe unclassified

Transanale totale mesorektale Exzision – eine sinnvolle Operationstechnik zur individualisierten Behandlung von Patienten mit Rektumkarzinom

et al. 2019

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Conversion is a risk factor for postoperative anastomotic leak in rectal cancer patients - A retrospective cohort study

Abstract: Conversion during laparoscopic LAR was found to be associated with an increased risk for the postoperative AL in RC patients. A nomogram model incorporating conversion, gender and patient's clinical N stage seems to offers a useful tool for predicting postoperative AL in these patients.

Cited by 13 publications

References 44 publications

Machine learning-based random forest predicts anastomotic leakage after anterior resection for rectal cancer

Machine learning-based random forest predicts anastomotic leakage after anterior resection for rectal cancer

A Predicting Nomogram for Mortality in Patients With COVID-19

Transanale totale mesorektale Exzision – eine sinnvolle Operationstechnik zur individualisierten Behandlung von Patienten mit Rektumkarzinom

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