Dataset Information

Prediction of hospital mortality after colorectal perforation surgery from inflammation-based prognostic scores.

ABSTRACT:

Background

Inflammation-based prognostic scores have prognostic value in cancer or cardiovascular disease patients. This study evaluated the prognostic value of inflammation-based prognostic scores in colorectal perforation patients.

Methods

Data of 97 patients who underwent surgery for colorectal perforation were reviewed. We calculated various inflammation-based prognostic scores and analyzed the relationship between inflammation-based prognostic score and hospital mortality due to colorectal perforation.

Results

Multivariate analyses of hospital mortality revealed neutrophil-lymphocyte ratio (P = .0021), C-reactive protein/albumin ratio (P = .0224), and prognostic nutritional index (P = .0078) as independent predictive factors. The Kaplan-Meier analysis showed that patients who met all of the following parameters avoided hospital death: neutrophil-lymphocyte ratio < 30, prognostic nutritional index ≥ 27.2, age < 75 years, and perforation of the left colon.

Conclusion

Neutrophil-lymphocyte ratio, C-reactive protein/albumin ratio, and prognostic nutritional index were superior to other inflammation-based prognostic scores in predicting mortality of colorectal perforation. Neutrophil-lymphocyte ratio, prognostic nutritional index, patient's age, and sidedness of the perforation site may be useful parameters to identify subgroups in which a favorable prognosis can be expected.

SUBMITTER: Kudou K

PROVIDER: S-EPMC8914212 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Prediction of hospital mortality after colorectal perforation surgery from inflammation-based prognostic scores.

Kudou Kensuke K Kusumoto Tetsuya T Ebata Yuho Y Nambara Sho S Tsuda Yasuo Y Kusumoto Eiji E Yoshida Rintaro R Sakaguchi Yoshihisa Y Ikejiri Koji K

Surgery open science 20220126

<h4>Background</h4>Inflammation-based prognostic scores have prognostic value in cancer or cardiovascular disease patients. This study evaluated the prognostic value of inflammation-based prognostic scores in colorectal perforation patients.<h4>Methods</h4>Data of 97 patients who underwent surgery for colorectal perforation were reviewed. We calculated various inflammation-based prognostic scores and analyzed the relationship between inflammation-based prognostic score and hospital mortality due ...[more]

PMID: 35280120

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Project description:BACKGROUND:The Sequential Organ Failure Assessment (SOFA) score is commonly used in ICUs around the world, designed to assess the severity of the patient's clinical state based on function/dysfunction of six major organ systems. The goal of this work is to build a computational model to predict mortality based on a series of SOFA scores. In addition, we examined the possibility of improving the prediction by incorporating a new component designed to measure the performance of the gastrointestinal system, added to the other six components. METHODS:In this retrospective study, we used patients' three latest SOFA scores recorded during an individual ICU stay as input to different machine learning models and ensemble learning models. We added three validated parameters representing gastrointestinal failure. Among others, we used classification models such as Support Vector Machines (SVMs), Neural Networks, Logistic Regression and a penalty function used to increase model robustness in regard to certain extreme cases, which may be found in ICU population. We used the Area under Curve (AUC) performance metric to examine performance. RESULTS:We found an ensemble model of linear and logistic regression achieves a higher AUC compared related works in past years. After incorporating the gastrointestinal failure score along with the penalty function, our best performing ensemble model resulted in an additional improvement in terms of AUC metrics. We implemented and compared 36 different models that were built using both the information from the SOFA score as well as that of the gastrointestinal system. All compared models have approximately similar and relatively large AUC (between 0.8645 and 0.9146) with the best results are achieved by incorporating the gastrointestinal parameters into the prediction models. CONCLUSIONS:Our findings indicate that gastrointestinal parameters carry significant information as a mortality predictor in addition to the conventional SOFA score. This information improves the predictive power of machine learning models by extending the SOFA to include information related to gastrointestinal organ system. The described method improves mortality prediction by considering the dynamics of the extended SOFA score. Although tested on a limited data set, the results' stability across different models suggests robustness in real-time use.

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Dataset Information

Prediction of hospital mortality after colorectal perforation surgery from inflammation-based prognostic scores.

Background

Methods

Results

Conclusion

Publications

Prediction of hospital mortality after colorectal perforation surgery from inflammation-based prognostic scores.

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