Dataset Information

Background factors of idiopathic peptic ulcers and optimal treatment methods: a multicenter retrospective Japanese study.

ABSTRACT: This study investigated the trends in idiopathic peptic ulcers, examined the characteristics of refractory idiopathic peptic ulcer, and identified the optimal treatment. The characteristics of 309 patients with idiopathic peptic ulcer were examined. We allocated idiopathic peptic ulcers that did not heal after 8 weeks' treatment (6 weeks for duodenal ulcers) to the refractory group and those that healed within this period to the healed group. The typical risk factors for idiopathic peptic ulcer (atherosclerosis-related underlying disease or liver cirrhosis complications) were absent in 46.6% of patients. Absence of gastric mucosal atrophy (refractory group: 51.4%, healed group: 28.4%; p = 0.016), and gastric fundic gland polyps (refractory group: 17.6%, healed group: 5.9%; p = 0.045) were significantly more common in the refractory group compared to the healed group. A history of H. pylori eradication (refractory group: 85.3%, healed group: 66.0%; p = 0.016), previous H. pylori infection (i.e., gastric mucosal atrophy or history of H. pylori eradication) (refractory group: 48.5%, healed group: 80.0%; p = 0.001), and potassium-competitive acid blocker treatment (refractory group: 28.6%, healed group, 64.1%; p = 0.001) were significantly more frequent in the healed group compared to the refractory group. Thus, acid hypersecretion may be a major factor underlying the refractoriness of idiopathic peptic ulcer.

SUBMITTER: Nakajima N

PROVIDER: S-EPMC10822756 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Background factors of idiopathic peptic ulcers and optimal treatment methods: a multicenter retrospective Japanese study.

Nakajima Noriyuki N Takeuchi Toshihisa T Hokari Ryota R Narimatsu Kazuyuki K Iijima Katsunori K Koizumi Shigeto S Kasugai Kunio K Ebi Masahide M Nagahara Akihito A Takeda Tsutomu T Tomita Toshihiko T Shinzaki Shinichiro S Mizukami Kazuhiro K Murakami Kazunari K Yagi Nobuaki N Mukai Rieko R Okumura Toshikatsu T Tanabe Hiroki H Tanaka Kazuyuki K Iwamoto Junichi J Irisawa Atsushi A Fukushi Koh K Kataoka Hiromi H Nishie Hirotada H Fujiwara Yasuhiro Y Otani Koji K Handa Osamu O Maruyama Yasuhiko Y Uraoka Toshio T Hosaka Hiroko H Furuta Takahisa T Takagi Tomohisa T Nakamura Masakatsu M Nyumura Yuya Y Hakoda Akitoshi A Sugawara Noriaki N Iwatubo Taro T Ota Kazuhiro K Kawaguchi Shinpei S Higuchi Kazuhide K Nishikawa Hiroki H

Journal of clinical biochemistry and nutrition 20231024 1

This study investigated the trends in idiopathic peptic ulcers, examined the characteristics of refractory idiopathic peptic ulcer, and identified the optimal treatment. The characteristics of 309 patients with idiopathic peptic ulcer were examined. We allocated idiopathic peptic ulcers that did not heal after 8 weeks' treatment (6 weeks for duodenal ulcers) to the refractory group and those that healed within this period to the healed group. The typical risk factors for idiopathic peptic ulcer ...[more]

PMID: 38292123

Similar Datasets

Project description:BACKGROUND:Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. OBJECTIVE:The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. METHODS:Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e-Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. RESULTS:Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. CONCLUSIONS:The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning-based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

Dataset Information

Background factors of idiopathic peptic ulcers and optimal treatment methods: a multicenter retrospective Japanese study.

Publications

Background factors of idiopathic peptic ulcers and optimal treatment methods: a multicenter retrospective Japanese study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets