R. García-Carretero, Julia Roncal-Gomez, Pilar Rodriguez-Manzano, Óscar Vázquez-Gómez
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.
{"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}
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.
{"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}
As the inaugural editor-in-chief of the journal Bacteria (ISSN: 2674-1334) [...]
作为《细菌》杂志(ISSN: 2674-1334)的首任主编[…]
{"title":"Why a New Journal on Bacteria?","authors":"B. Weimer","doi":"10.3390/bacteria1010001","DOIUrl":"https://doi.org/10.3390/bacteria1010001","url":null,"abstract":"As the inaugural editor-in-chief of the journal Bacteria (ISSN: 2674-1334) [...]","PeriodicalId":18020,"journal":{"name":"Lactic Acid Bacteria","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88215285","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}
C. Battistini, Najib Nassani, Susan M. Saad, Jun Sun
{"title":"Probiotics, Vitamin D, and Vitamin D Receptor in Health and Disease","authors":"C. Battistini, Najib Nassani, Susan M. Saad, Jun Sun","doi":"10.1201/9780429422591-6","DOIUrl":"https://doi.org/10.1201/9780429422591-6","url":null,"abstract":"","PeriodicalId":18020,"journal":{"name":"Lactic Acid Bacteria","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90146062","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}
Pub Date : 2019-04-08DOI: 10.1201/9780429057465-31
J. Villena, A. Suvorov, H. Kitazawa, S. Alvarez
{"title":"Lactic Acid Bacteria and Respiratory Health","authors":"J. Villena, A. Suvorov, H. Kitazawa, S. Alvarez","doi":"10.1201/9780429057465-31","DOIUrl":"https://doi.org/10.1201/9780429057465-31","url":null,"abstract":"","PeriodicalId":18020,"journal":{"name":"Lactic Acid Bacteria","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73413681","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}
Pub Date : 2019-04-08DOI: 10.1201/9780429057465-41
J. Powers
{"title":"Regulation of Probiotics in Canada","authors":"J. Powers","doi":"10.1201/9780429057465-41","DOIUrl":"https://doi.org/10.1201/9780429057465-41","url":null,"abstract":"","PeriodicalId":18020,"journal":{"name":"Lactic Acid Bacteria","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89289104","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}
{"title":"Introduction to the Genera Pediococcus, Leuconostoc, Weissella, and Carnobacterium","authors":"E. Säde, J. Björkroth","doi":"10.1201/9780429057465-6","DOIUrl":"https://doi.org/10.1201/9780429057465-6","url":null,"abstract":"","PeriodicalId":18020,"journal":{"name":"Lactic Acid Bacteria","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83191428","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：