Modeling Pneumonia-Induced Bloodstream Infection Using Graph Theory to Estimate Hospital Mortality
Hospital-acquired pneumonia (HAP) bloodstream infections comprise a major cause of crude hospital mortality. This is a cross-sectional study that used claims data from the Centers for Medicare and Medicaid Services (<i>N</i> = 565,875). The study objective is to represent the progression...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/8/2/24 |
| _version_ | 1797568194972483584 |
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| author | Dimitrios Zikos Maria Athanasopoulou |
| author_facet | Dimitrios Zikos Maria Athanasopoulou |
| author_sort | Dimitrios Zikos |
| collection | DOAJ |
| description | Hospital-acquired pneumonia (HAP) bloodstream infections comprise a major cause of crude hospital mortality. This is a cross-sectional study that used claims data from the Centers for Medicare and Medicaid Services (<i>N</i> = 565,875). The study objective is to represent the progression of pneumonia-induced bloodstream infections using graph theory principles, where each path of the graph represents a different scenario of bloodstream-infection progression, and aims to further estimate the likelihood if hospital death for each path. To disseminate the results, the study makes available a prototype applet to navigate various paths of the graph interactively. Bayesian probabilities were calculated for each scenario, and multivariate logistic regression was conducted to estimate the adjusted OR for inpatient death after controlling for patient age, sex, and comorbidities. The mortality rate ranged from 4.99% for patients admitted with community pneumonia without bloodstream infection and reached 63.18% for cases admitted with bloodstream infection that progressed to hospital septicemia, sepsis, and septic shock. The prototype applet can be used to unfold bloodstream infection events and their associated risk for mortality and could be used in university curricula to assist educators in helping students understand the progression of pneumonia-induced bloodstream infections in a data-driven way. |
| first_indexed | 2024-03-10T19:52:59Z |
| format | Article |
| id | doaj.art-d94dfc82372b4357b9017d966dc8e2cd |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-10T19:52:59Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-d94dfc82372b4357b9017d966dc8e2cd2023-11-20T00:16:42ZengMDPI AGTechnologies2227-70802020-05-01822410.3390/technologies8020024Modeling Pneumonia-Induced Bloodstream Infection Using Graph Theory to Estimate Hospital MortalityDimitrios Zikos0Maria Athanasopoulou1College of Health Professions, Central Michigan University, Mount Pleasant, MI 48859, USALanguages Department, CUNY Queens College, New York, NY 11367, USAHospital-acquired pneumonia (HAP) bloodstream infections comprise a major cause of crude hospital mortality. This is a cross-sectional study that used claims data from the Centers for Medicare and Medicaid Services (<i>N</i> = 565,875). The study objective is to represent the progression of pneumonia-induced bloodstream infections using graph theory principles, where each path of the graph represents a different scenario of bloodstream-infection progression, and aims to further estimate the likelihood if hospital death for each path. To disseminate the results, the study makes available a prototype applet to navigate various paths of the graph interactively. Bayesian probabilities were calculated for each scenario, and multivariate logistic regression was conducted to estimate the adjusted OR for inpatient death after controlling for patient age, sex, and comorbidities. The mortality rate ranged from 4.99% for patients admitted with community pneumonia without bloodstream infection and reached 63.18% for cases admitted with bloodstream infection that progressed to hospital septicemia, sepsis, and septic shock. The prototype applet can be used to unfold bloodstream infection events and their associated risk for mortality and could be used in university curricula to assist educators in helping students understand the progression of pneumonia-induced bloodstream infections in a data-driven way.https://www.mdpi.com/2227-7080/8/2/24health informaticsbloodstream infectionhospital-acquired pneumoniaevent recognitiongraph theory |
| spellingShingle | Dimitrios Zikos Maria Athanasopoulou Modeling Pneumonia-Induced Bloodstream Infection Using Graph Theory to Estimate Hospital Mortality Technologies health informatics bloodstream infection hospital-acquired pneumonia event recognition graph theory |
| title | Modeling Pneumonia-Induced Bloodstream Infection Using Graph Theory to Estimate Hospital Mortality |
| title_full | Modeling Pneumonia-Induced Bloodstream Infection Using Graph Theory to Estimate Hospital Mortality |
| title_fullStr | Modeling Pneumonia-Induced Bloodstream Infection Using Graph Theory to Estimate Hospital Mortality |
| title_full_unstemmed | Modeling Pneumonia-Induced Bloodstream Infection Using Graph Theory to Estimate Hospital Mortality |
| title_short | Modeling Pneumonia-Induced Bloodstream Infection Using Graph Theory to Estimate Hospital Mortality |
| title_sort | modeling pneumonia induced bloodstream infection using graph theory to estimate hospital mortality |
| topic | health informatics bloodstream infection hospital-acquired pneumonia event recognition graph theory |
| url | https://www.mdpi.com/2227-7080/8/2/24 |
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