Eintrag weiter verarbeiten
Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population
Gespeichert in:
| Zeitschriftentitel: | Therapeutic Advances in Infectious Disease |
|---|---|
| Personen und Körperschaften: | , , , , , |
| In: | Therapeutic Advances in Infectious Disease, 4, 2017, 4, S. 95-103 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
SAGE Publications
|
| Schlagwörter: |
| author_facet |
Sonti, Rajiv Conroy, Megan E. Welt, Elena M. Hu, Yi Luta, George Jamieson, Daniel B. Sonti, Rajiv Conroy, Megan E. Welt, Elena M. Hu, Yi Luta, George Jamieson, Daniel B. |
|---|---|
| author |
Sonti, Rajiv Conroy, Megan E. Welt, Elena M. Hu, Yi Luta, George Jamieson, Daniel B. |
| spellingShingle |
Sonti, Rajiv Conroy, Megan E. Welt, Elena M. Hu, Yi Luta, George Jamieson, Daniel B. Therapeutic Advances in Infectious Disease Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population Pharmacology (medical) Infectious Diseases |
| author_sort |
sonti, rajiv |
| spelling |
Sonti, Rajiv Conroy, Megan E. Welt, Elena M. Hu, Yi Luta, George Jamieson, Daniel B. 2049-9361 2049-937X SAGE Publications Pharmacology (medical) Infectious Diseases http://dx.doi.org/10.1177/2049936117715403 <jats:sec><jats:title>Purpose:</jats:title><jats:p>To create a model predictive of an individual’s risk of developing a de novo multidrug-resistant (MDR) infection while in the intensive care unit (ICU).</jats:p></jats:sec><jats:sec><jats:title>Methods:</jats:title><jats:p>This is a case-control study in which 189 ICU patients diagnosed with their first infection with an MDR organism were compared on the basis of demographic, past medical and clinical variables to randomly selected ICU patients without such an infection, era-matched in a 2:1 ratio. A prediction tool was derived using multivariate logistic regression.</jats:p></jats:sec><jats:sec><jats:title>Results:</jats:title><jats:p>Five features remained predictive of developing an infection with a drug-resistant pathogen: hospitalization within a year [adjusted odds ratio (OR) 2.14], chronic hemodialysis (3.86), underlying oxygen-dependent pulmonary disease (1.86), endotracheal intubation within 24 h (2.46) and reason for ICU admission (respiratory failure 2.89, non-respiratory failure, non-shock presentation 1.85). Using a scoring system (0–7 points) based on the adjusted OR, risk categories were derived (low: 0–2 points, intermediate: 3–4 points and high risk: 5–7 points). The negative predictive value at a score cutoff of 2 is excellent (88.9%).</jats:p></jats:sec><jats:sec><jats:title>Conclusions:</jats:title><jats:p>A clinical prediction rule comprised of five easily measured ICU variables reasonably discriminates between patients who will develop their first MDR infection versus those who will not.</jats:p></jats:sec> Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population Therapeutic Advances in Infectious Disease |
| doi_str_mv |
10.1177/2049936117715403 |
| facet_avail |
Online Free |
| finc_class_facet |
Chemie und Pharmazie Medizin |
| format |
ElectronicArticle |
| fullrecord |
blob:ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTE3Ny8yMDQ5OTM2MTE3NzE1NDAz |
| id |
ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTE3Ny8yMDQ5OTM2MTE3NzE1NDAz |
| institution |
DE-Gla1 DE-Zi4 DE-15 DE-Pl11 DE-Rs1 DE-105 DE-14 DE-Ch1 DE-L229 DE-D275 DE-Bn3 DE-Brt1 DE-Zwi2 DE-D161 |
| imprint |
SAGE Publications, 2017 |
| imprint_str_mv |
SAGE Publications, 2017 |
| issn |
2049-9361 2049-937X |
| issn_str_mv |
2049-9361 2049-937X |
| language |
English |
| mega_collection |
SAGE Publications (CrossRef) |
| match_str |
sonti2017modelingriskfordevelopingdrugresistantbacterialinfectionsinanmdrnaivecriticallyillpopulation |
| publishDateSort |
2017 |
| publisher |
SAGE Publications |
| recordtype |
ai |
| record_format |
ai |
| series |
Therapeutic Advances in Infectious Disease |
| source_id |
49 |
| title |
Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population |
| title_unstemmed |
Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population |
| title_full |
Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population |
| title_fullStr |
Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population |
| title_full_unstemmed |
Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population |
| title_short |
Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population |
| title_sort |
modeling risk for developing drug resistant bacterial infections in an mdr-naive critically ill population |
| topic |
Pharmacology (medical) Infectious Diseases |
| url |
http://dx.doi.org/10.1177/2049936117715403 |
| publishDate |
2017 |
| physical |
95-103 |
| description |
<jats:sec><jats:title>Purpose:</jats:title><jats:p>To create a model predictive of an individual’s risk of developing a de novo multidrug-resistant (MDR) infection while in the intensive care unit (ICU).</jats:p></jats:sec><jats:sec><jats:title>Methods:</jats:title><jats:p>This is a case-control study in which 189 ICU patients diagnosed with their first infection with an MDR organism were compared on the basis of demographic, past medical and clinical variables to randomly selected ICU patients without such an infection, era-matched in a 2:1 ratio. A prediction tool was derived using multivariate logistic regression.</jats:p></jats:sec><jats:sec><jats:title>Results:</jats:title><jats:p>Five features remained predictive of developing an infection with a drug-resistant pathogen: hospitalization within a year [adjusted odds ratio (OR) 2.14], chronic hemodialysis (3.86), underlying oxygen-dependent pulmonary disease (1.86), endotracheal intubation within 24 h (2.46) and reason for ICU admission (respiratory failure 2.89, non-respiratory failure, non-shock presentation 1.85). Using a scoring system (0–7 points) based on the adjusted OR, risk categories were derived (low: 0–2 points, intermediate: 3–4 points and high risk: 5–7 points). The negative predictive value at a score cutoff of 2 is excellent (88.9%).</jats:p></jats:sec><jats:sec><jats:title>Conclusions:</jats:title><jats:p>A clinical prediction rule comprised of five easily measured ICU variables reasonably discriminates between patients who will develop their first MDR infection versus those who will not.</jats:p></jats:sec> |
| container_issue |
4 |
| container_start_page |
95 |
| container_title |
Therapeutic Advances in Infectious Disease |
| container_volume |
4 |
| format_de105 |
Article, E-Article |
| format_de14 |
Article, E-Article |
| format_de15 |
Article, E-Article |
| format_de520 |
Article, E-Article |
| format_de540 |
Article, E-Article |
| format_dech1 |
Article, E-Article |
| format_ded117 |
Article, E-Article |
| format_degla1 |
E-Article |
| format_del152 |
Buch |
| format_del189 |
Article, E-Article |
| format_dezi4 |
Article |
| format_dezwi2 |
Article, E-Article |
| format_finc |
Article, E-Article |
| format_nrw |
Article, E-Article |
| _version_ |
1792332928071499789 |
| geogr_code |
not assigned |
| last_indexed |
2024-03-01T14:04:37.967Z |
| geogr_code_person |
not assigned |
| openURL |
url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fvufind.svn.sourceforge.net%3Agenerator&rft.title=Modeling+risk+for+developing+drug+resistant+bacterial+infections+in+an+MDR-naive+critically+ill+population&rft.date=2017-07-01&genre=article&issn=2049-937X&volume=4&issue=4&spage=95&epage=103&pages=95-103&jtitle=Therapeutic+Advances+in+Infectious+Disease&atitle=Modeling+risk+for+developing+drug+resistant+bacterial+infections+in+an+MDR-naive+critically+ill+population&aulast=Jamieson&aufirst=Daniel+B.&rft_id=info%3Adoi%2F10.1177%2F2049936117715403&rft.language%5B0%5D=eng |
| SOLR | |
| _version_ | 1792332928071499789 |
| author | Sonti, Rajiv, Conroy, Megan E., Welt, Elena M., Hu, Yi, Luta, George, Jamieson, Daniel B. |
| author_facet | Sonti, Rajiv, Conroy, Megan E., Welt, Elena M., Hu, Yi, Luta, George, Jamieson, Daniel B., Sonti, Rajiv, Conroy, Megan E., Welt, Elena M., Hu, Yi, Luta, George, Jamieson, Daniel B. |
| author_sort | sonti, rajiv |
| container_issue | 4 |
| container_start_page | 95 |
| container_title | Therapeutic Advances in Infectious Disease |
| container_volume | 4 |
| description | <jats:sec><jats:title>Purpose:</jats:title><jats:p>To create a model predictive of an individual’s risk of developing a de novo multidrug-resistant (MDR) infection while in the intensive care unit (ICU).</jats:p></jats:sec><jats:sec><jats:title>Methods:</jats:title><jats:p>This is a case-control study in which 189 ICU patients diagnosed with their first infection with an MDR organism were compared on the basis of demographic, past medical and clinical variables to randomly selected ICU patients without such an infection, era-matched in a 2:1 ratio. A prediction tool was derived using multivariate logistic regression.</jats:p></jats:sec><jats:sec><jats:title>Results:</jats:title><jats:p>Five features remained predictive of developing an infection with a drug-resistant pathogen: hospitalization within a year [adjusted odds ratio (OR) 2.14], chronic hemodialysis (3.86), underlying oxygen-dependent pulmonary disease (1.86), endotracheal intubation within 24 h (2.46) and reason for ICU admission (respiratory failure 2.89, non-respiratory failure, non-shock presentation 1.85). Using a scoring system (0–7 points) based on the adjusted OR, risk categories were derived (low: 0–2 points, intermediate: 3–4 points and high risk: 5–7 points). The negative predictive value at a score cutoff of 2 is excellent (88.9%).</jats:p></jats:sec><jats:sec><jats:title>Conclusions:</jats:title><jats:p>A clinical prediction rule comprised of five easily measured ICU variables reasonably discriminates between patients who will develop their first MDR infection versus those who will not.</jats:p></jats:sec> |
| doi_str_mv | 10.1177/2049936117715403 |
| facet_avail | Online, Free |
| finc_class_facet | Chemie und Pharmazie, Medizin |
| format | ElectronicArticle |
| format_de105 | Article, E-Article |
| format_de14 | Article, E-Article |
| format_de15 | Article, E-Article |
| format_de520 | Article, E-Article |
| format_de540 | Article, E-Article |
| format_dech1 | Article, E-Article |
| format_ded117 | Article, E-Article |
| format_degla1 | E-Article |
| format_del152 | Buch |
| format_del189 | Article, E-Article |
| format_dezi4 | Article |
| format_dezwi2 | Article, E-Article |
| format_finc | Article, E-Article |
| format_nrw | Article, E-Article |
| geogr_code | not assigned |
| geogr_code_person | not assigned |
| id | ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTE3Ny8yMDQ5OTM2MTE3NzE1NDAz |
| imprint | SAGE Publications, 2017 |
| imprint_str_mv | SAGE Publications, 2017 |
| institution | DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161 |
| issn | 2049-9361, 2049-937X |
| issn_str_mv | 2049-9361, 2049-937X |
| language | English |
| last_indexed | 2024-03-01T14:04:37.967Z |
| match_str | sonti2017modelingriskfordevelopingdrugresistantbacterialinfectionsinanmdrnaivecriticallyillpopulation |
| mega_collection | SAGE Publications (CrossRef) |
| physical | 95-103 |
| publishDate | 2017 |
| publishDateSort | 2017 |
| publisher | SAGE Publications |
| record_format | ai |
| recordtype | ai |
| series | Therapeutic Advances in Infectious Disease |
| source_id | 49 |
| spelling | Sonti, Rajiv Conroy, Megan E. Welt, Elena M. Hu, Yi Luta, George Jamieson, Daniel B. 2049-9361 2049-937X SAGE Publications Pharmacology (medical) Infectious Diseases http://dx.doi.org/10.1177/2049936117715403 <jats:sec><jats:title>Purpose:</jats:title><jats:p>To create a model predictive of an individual’s risk of developing a de novo multidrug-resistant (MDR) infection while in the intensive care unit (ICU).</jats:p></jats:sec><jats:sec><jats:title>Methods:</jats:title><jats:p>This is a case-control study in which 189 ICU patients diagnosed with their first infection with an MDR organism were compared on the basis of demographic, past medical and clinical variables to randomly selected ICU patients without such an infection, era-matched in a 2:1 ratio. A prediction tool was derived using multivariate logistic regression.</jats:p></jats:sec><jats:sec><jats:title>Results:</jats:title><jats:p>Five features remained predictive of developing an infection with a drug-resistant pathogen: hospitalization within a year [adjusted odds ratio (OR) 2.14], chronic hemodialysis (3.86), underlying oxygen-dependent pulmonary disease (1.86), endotracheal intubation within 24 h (2.46) and reason for ICU admission (respiratory failure 2.89, non-respiratory failure, non-shock presentation 1.85). Using a scoring system (0–7 points) based on the adjusted OR, risk categories were derived (low: 0–2 points, intermediate: 3–4 points and high risk: 5–7 points). The negative predictive value at a score cutoff of 2 is excellent (88.9%).</jats:p></jats:sec><jats:sec><jats:title>Conclusions:</jats:title><jats:p>A clinical prediction rule comprised of five easily measured ICU variables reasonably discriminates between patients who will develop their first MDR infection versus those who will not.</jats:p></jats:sec> Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population Therapeutic Advances in Infectious Disease |
| spellingShingle | Sonti, Rajiv, Conroy, Megan E., Welt, Elena M., Hu, Yi, Luta, George, Jamieson, Daniel B., Therapeutic Advances in Infectious Disease, Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population, Pharmacology (medical), Infectious Diseases |
| title | Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population |
| title_full | Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population |
| title_fullStr | Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population |
| title_full_unstemmed | Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population |
| title_short | Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population |
| title_sort | modeling risk for developing drug resistant bacterial infections in an mdr-naive critically ill population |
| title_unstemmed | Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population |
| topic | Pharmacology (medical), Infectious Diseases |
| url | http://dx.doi.org/10.1177/2049936117715403 |