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Identification and Predictive Value of Risk Factors for Mortality Due to Listeria monocytogenes Infection: Use of Machine Learning with a Nationwide Administrative Data Set 单核细胞增生李斯特菌感染死亡率危险因素的识别和预测价值:机器学习与全国行政数据集的使用

Lactic Acid Bacteria Pub Date : 2022-01-18 DOI: 10.3390/bacteria1010003

R. García-Carretero, Julia Roncal-Gomez, Pilar Rodriguez-Manzano, Óscar Vázquez-Gómez

{"title":"Identification and Predictive Value of Risk Factors for Mortality Due to Listeria monocytogenes Infection: Use of Machine Learning with a Nationwide Administrative Data Set","authors":"R. García-Carretero, Julia Roncal-Gomez, Pilar Rodriguez-Manzano, Óscar Vázquez-Gómez","doi":"10.3390/bacteria1010003","DOIUrl":"https://doi.org/10.3390/bacteria1010003","url":null,"abstract":"We used machine-learning algorithms to evaluate demographic and clinical data in an administrative data set to identify relevant predictors of mortality due to Listeria monocytogenes infection. We used the Spanish Minimum Basic Data Set at Hospitalization (MBDS-H) to estimate the impacts of several predictors on mortality. The MBDS-H is a mandatory registry of clinical discharge reports. Data were coded with International Classification of Diseases, either Ninth or Tenth Revisions, codes. Diagnoses and clinical conditions were defined using recorded data from these codes or a combination of them. We used two different statistical approaches to produce two predictive models. The first was logistic regression, a classic statistical approach that uses data science to preprocess data and measure performance. The second was a random forest algorithm, a strategy based on machine learning and feature selection. We compared the performance of the two models using predictive accuracy and the area under the curve. Between 2001 and 2016, a total of 5603 hospitalized patients were identified as having any clinical form of listeriosis. Most patients were adults (94.9%). Among all hospitalized individuals, there were 2318 women (41.4%). We recorded 301 pregnant women and 287 newborns with listeriosis. The mortality rate was 0.13 patients per 100,000 population. The performance of the model produced by logistic regression after intense preprocessing was similar to that of the model produced by the random forest algorithm. Predictive accuracy was 0.83, and the area under the receiver operating characteristic curve was 0.74 in both models. Sepsis, age, and malignancy were the most relevant features related to mortality. Our combined use of data science, preprocessing, conventional statistics, and machine learning provides insights into mortality due to Listeria-related infection. These methods are not mutually exclusive. The combined use of several methods would allow researchers to better explain results and understand data related to Listeria monocytogenes infection.","PeriodicalId":18020,"journal":{"name":"Lactic Acid Bacteria","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87476600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Clinical Features and Predictors for Mortality in Neurolisteriosis: An Administrative Data-Based Study 神经李斯特菌病的临床特征和死亡率预测因素:一项基于管理数据的研究

Lactic Acid Bacteria Pub Date : 2021-12-01 DOI: 10.3390/bacteria1010002

R. García-Carretero

{"title":"Clinical Features and Predictors for Mortality in Neurolisteriosis: An Administrative Data-Based Study","authors":"R. García-Carretero","doi":"10.3390/bacteria1010002","DOIUrl":"https://doi.org/10.3390/bacteria1010002","url":null,"abstract":"Listeriosis is an uncommon and potentially severe zoonotic bacterial infection that usually occurs in outbreaks instead of isolated cases. In recent years, there has been an increase in the incidence of this disease. One of the most severe of its complications involves the central nervous system (CNS) in a condition known as neurolisteriosis. Here, we describe the demographic and clinical features of patients presenting with neurolisteriosis between 2001 and 2015 using administrative data and attempt to identify potential predictors for mortality. We used the Spanish Minimum Basic Data Set at Hospitalization, a compulsory registry that collects data from clinical discharge reports. Up to 2015, data were coded based on the International Classification of Diseases, 9th Revision, so we used diagnoses and clinical conditions based on these codes. Age, sex, clinical presentation, mortality, and involvement of the CNS were identified. Using algorithms to aggregate data, variables such as immunosuppression and malignant disease were obtained. We analyzed correlations among clinical features and identified risk factors for morbidity and mortality. Between 2001 and 2015 we identified 5180 individuals, with a hospitalization rate of 0.76 per 100,000 population. Most (94%) were adults, and only 5.4% were pregnant women. The average age was 66 years. Neurological involvement was present in 2313 patients (44.7%), mostly meningitis (90.4%). Global mortality was 17%, but mortality in CNS infections was 19.2%. Age, severe sepsis, chronic liver disease, chronic kidney disease, and malignancy were the main risk factors for mortality in patients with CNS infections by Listeria monocytogenes. Although it is uncommon, neurolisteriosis can be a severe condition, associated with a high rate of mortality. Health care providers should be aware of potential sources of infection so that appropriate measures can be taken to prevent it.","PeriodicalId":18020,"journal":{"name":"Lactic Acid Bacteria","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79011828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Why a New Journal on Bacteria? 为什么要出版一本关于细菌的新杂志?

Lactic Acid Bacteria Pub Date : 2021-11-28 DOI: 10.3390/bacteria1010001

B. Weimer

引用次数: 0

Probiotics, Vitamin D, and Vitamin D Receptor in Health and Disease 健康和疾病中的益生菌、维生素D和维生素D受体

Lactic Acid Bacteria Pub Date : 2020-02-19 DOI: 10.1201/9780429422591-6

C. Battistini, Najib Nassani, Susan M. Saad, Jun Sun

引用次数: 3

Probiotic Effects of Non-viable Lactic Acid Bacteria 非活菌乳酸菌的益生菌效应

Lactic Acid Bacteria Pub Date : 2019-04-08 DOI: 10.1201/9780429057465-37

M. Bernardeau, M. Cretenet