Dataset Information

Nomogram Estimating the Probability of Intraabdominal Abscesses after Gastrectomy in Patients with Gastric Cancer.

ABSTRACT:

Purpose

Intraabdominal abscess is one of the most common reasons for re-hospitalization after gastrectomy. This study aimed to develop a model for estimating the probability of intraabdominal abscesses that can be used during the postoperative period.

Materials and methods

We retrospectively reviewed the clinicopathological data of 1,564 patients who underwent gastrectomy for gastric cancer between 2010 and 2012. Twenty-six related markers were analyzed, and multivariate logistic regression analysis was used to develop the probability estimation model for intraabdominal abscess. Internal validation using a bootstrap approach was employed to correct for bias, and the model was then validated using an independent dataset comprising of patients who underwent gastrectomy between January 2008 and March 2010. Discrimination and calibration abilities were checked in both datasets.

Results

The incidence of intraabdominal abscess in the development set was 7.80% (122/1,564). The surgical approach, operating time, pathologic N classification, body temperature, white blood cell count, C-reactive protein level, glucose level, and change in the hemoglobin level were significant predictors of intraabdominal abscess in the multivariate analysis. The probability estimation model that was developed on the basis of these results showed good discrimination and calibration abilities (concordance index=0.828, Hosmer-Lemeshow chi-statistic P=0.274). Finally, we combined both datasets to produce a nomogram that estimates the probability of intraabdominal abscess.

Conclusions

This nomogram can be useful for identifying patients at a high risk of intraabdominal abscess. Patients at a high risk may benefit from further evaluation or treatment before discharge.

SUBMITTER: Eom BW

PROVIDER: S-EPMC4723303 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Publications

Nomogram Estimating the Probability of Intraabdominal Abscesses after Gastrectomy in Patients with Gastric Cancer.

Eom Bang Wool BW Joo Jungnam J Kim Young-Woo YW Park Boram B Yoon Hong Man HM Ryu Keun Won KW Kim Soo Jin SJ

Journal of gastric cancer 20151231 4

<h4>Purpose</h4>Intraabdominal abscess is one of the most common reasons for re-hospitalization after gastrectomy. This study aimed to develop a model for estimating the probability of intraabdominal abscesses that can be used during the postoperative period.<h4>Materials and methods</h4>We retrospectively reviewed the clinicopathological data of 1,564 patients who underwent gastrectomy for gastric cancer between 2010 and 2012. Twenty-six related markers were analyzed, and multivariate logistic ...[more]

PMID: 26816657

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Project description:BackgroundGastric cancer (GC) is one of the most malignant diseases and threatens the health of individuals across the globe. Hitherto, the identification of prognosis risk stratification on GC has mainly depended on the TNM staging, but owing to its inaccuracy and incompleteness, the prognostic value it offers remains controversial in the current clinical setting. Thus, an effective prognostic model for GC after radical gastrectomy is still needed.MethodsPatients with pathologically confirmed GC who underwent radical gastrectomy from 2 different centers were retrospectively enrolled into a training and the validation cohort, respectively. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to select variables among multiple factors, including clinical characteristics, pathological parameters, and surgery- and treatment-related indicators. The multivariate Cox regression method was used to establish the model to predict 1-, 2-, and 3-year survival. Both internal and external validations of the nomogram were then completed in terms of discrimination, calibration, and clinical utility. Finally, prognostic risk stratification of GC was conducted with X-tile software.ResultsA total of 1,424 patients with GC were eligible in this study, including 1,010 in the training cohort and 414 in the validation cohort. Seven indicators were selected by LASSO to develop the nomogram, including the number of positive lymph nodes, tumor size, adjacent organ invasion, vascular invasion, the level of carbohydrate antigen 125 (CA 125), depth of invasion, and human epidermal growth factor receptor 2 (HER2) status. The nomogram demonstrated a robust predictive capacity with favorable accuracy, discrimination, and clinical utility both in the internal and external validations. Moreover, we divided the population into 3 risk groups of survival according to the cutoff points generated by X-tile, and in this way, the nomogram was further improved into a risk-stratified prognosis model.ConclusionsWe have developed a prognostic risk stratification nomogram for GC patients after radical gastrectomy with 7 available indicators that may guide clinical practice and help facilitate tailored decision-making, thus avoiding overtreatment or undertreatment and improving communication between clinicians and patients.

Project description:BackgroundAfter radical surgery, early detection of recurrence and metastasis is a crucial factor in enhancing the prognosis and survival of patients with gastric cancer (GC). Therefore, assessing the risk of recurrence in gastric cancer patients and determining the timing for postoperative recurrence is crucial.MethodsThe clinicopathological data of 521 patients with recurrent gastric cancer, who underwent radical gastrectomy at Zhejiang Cancer Hospital between January 2010 and January 2017, were retrospectively analyzed. These patients were randomly divided into two groups: a training group (n = 365) and a validation group (n = 156). In the training set, patients were further categorized into early recurrence (n = 263) and late recurrence (n = 102) groups based on a 2-year boundary. Comparative analyses of clinicopathological features and prognoses were conducted between these two groups. Subsequently, a nomogram for predicting early recurrence was developed and validated.ResultsIn this study, the developed nomogram incorporated age, serous infiltration, lymph node metastasis, recurrence mode, and the tumour marker CA19-9. In the training cohort, the area under the curve (AUC value) was 0.739 (95% CI, 0.682-0.798), with a corresponding C-index of 0.739. This nomogram was subsequently validated in an independent validation cohort, yielding an AUC of 0.743 (95% CI, 0.652-0.833) and a C-index of 0.743. Furthermore, independent risk factors for prognosis were identified, including age, absence of postoperative chemotherapy, early recurrence, lymph node metastasis, abdominal metastasis, and vascular cancer embolus.ConclusionIndependent risk factors for gastric cancer recurrence following radical surgery were utilized to construct a nomogram for predicting early relapse. This nomogram effectively assesses the risk of recurrence, aids in treatment decision-making and follow-up planning in clinical settings, and demonstrated strong performance in the validation cohort.

Dataset Information

Nomogram Estimating the Probability of Intraabdominal Abscesses after Gastrectomy in Patients with Gastric Cancer.

Purpose

Materials and methods

Results

Conclusions

Publications

Nomogram Estimating the Probability of Intraabdominal Abscesses after Gastrectomy in Patients with Gastric Cancer.

Similar Datasets

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