Electrolyte and Metabolic Disturbances in Ebola Patients during a Clinical Trial, Guinea, 2015
By using data from a 2015 clinical trial on Ebola convalescent-phase plasma in Guinea, we assessed the prevalence of electrolyte and metabolic abnormalities at admission and their predictive value to stratify patients into risk groups. Patients underwent testing with a point-of-care device. We used...
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Centers for Disease Control and Prevention
2016-12-01
|
| Series: | Emerging Infectious Diseases |
| Subjects: | |
| Online Access: | https://wwwnc.cdc.gov/eid/article/22/12/16-1136_article |
| _version_ | 1818408695463149568 |
|---|---|
| author | Johan van Griensven Elhadj Ibrahima Bah Nyankoye Haba Alexandre Delamou Bienvenu Salim Camara Kadio Jean-Jacques Olivier Hilde De Clerck Helena Nordenstedt Malcolm G. Semple Michel Van Herp Jozefien Buyze Maaike De Crop Steven Van Den Broucke Lutgarde Lynen Anja De Weggheleire |
| author_facet | Johan van Griensven Elhadj Ibrahima Bah Nyankoye Haba Alexandre Delamou Bienvenu Salim Camara Kadio Jean-Jacques Olivier Hilde De Clerck Helena Nordenstedt Malcolm G. Semple Michel Van Herp Jozefien Buyze Maaike De Crop Steven Van Den Broucke Lutgarde Lynen Anja De Weggheleire |
| author_sort | Johan van Griensven |
| collection | DOAJ |
| description | By using data from a 2015 clinical trial on Ebola convalescent-phase plasma in Guinea, we assessed the prevalence of electrolyte and metabolic abnormalities at admission and their predictive value to stratify patients into risk groups. Patients underwent testing with a point-of-care device. We used logistic regression to construct a prognostic model and summarized the predictive value with the area under the receiver operating curve. Abnormalities were common among patients, particularly hypokalemia, hypocalcemia, hyponatremia, raised creatinine, high anion gap, and anemia. Besides age and PCR cycle threshold value, renal dysfunction, low calcium levels, and low hemoglobin levels were independently associated with increased risk for death. A prognostic model using all 5 factors was highly discriminatory (area under the receiver operating curve 0.95; 95% CI 0.90–0.99) and enabled the definition of risk criteria to guide targeted care. Most patients had a very low (<5%) or very high (>80%) risk for death. |
| first_indexed | 2024-12-14T09:47:49Z |
| format | Article |
| id | doaj.art-19a04b0ee321488996377de5890e3248 |
| institution | Directory Open Access Journal |
| issn | 1080-6040 1080-6059 |
| language | English |
| last_indexed | 2024-12-14T09:47:49Z |
| publishDate | 2016-12-01 |
| publisher | Centers for Disease Control and Prevention |
| record_format | Article |
| series | Emerging Infectious Diseases |
| spelling | doaj.art-19a04b0ee321488996377de5890e32482022-12-21T23:07:36ZengCenters for Disease Control and PreventionEmerging Infectious Diseases1080-60401080-60592016-12-0122122120212710.3201/eid2212.161136Electrolyte and Metabolic Disturbances in Ebola Patients during a Clinical Trial, Guinea, 2015Johan van GriensvenElhadj Ibrahima BahNyankoye HabaAlexandre DelamouBienvenu Salim CamaraKadio Jean-Jacques OlivierHilde De ClerckHelena NordenstedtMalcolm G. SempleMichel Van HerpJozefien BuyzeMaaike De CropSteven Van Den BrouckeLutgarde LynenAnja De WeggheleireBy using data from a 2015 clinical trial on Ebola convalescent-phase plasma in Guinea, we assessed the prevalence of electrolyte and metabolic abnormalities at admission and their predictive value to stratify patients into risk groups. Patients underwent testing with a point-of-care device. We used logistic regression to construct a prognostic model and summarized the predictive value with the area under the receiver operating curve. Abnormalities were common among patients, particularly hypokalemia, hypocalcemia, hyponatremia, raised creatinine, high anion gap, and anemia. Besides age and PCR cycle threshold value, renal dysfunction, low calcium levels, and low hemoglobin levels were independently associated with increased risk for death. A prognostic model using all 5 factors was highly discriminatory (area under the receiver operating curve 0.95; 95% CI 0.90–0.99) and enabled the definition of risk criteria to guide targeted care. Most patients had a very low (<5%) or very high (>80%) risk for death.https://wwwnc.cdc.gov/eid/article/22/12/16-1136_articleEbolaEVDelectrolytemetabolicdeathmortality |
| spellingShingle | Johan van Griensven Elhadj Ibrahima Bah Nyankoye Haba Alexandre Delamou Bienvenu Salim Camara Kadio Jean-Jacques Olivier Hilde De Clerck Helena Nordenstedt Malcolm G. Semple Michel Van Herp Jozefien Buyze Maaike De Crop Steven Van Den Broucke Lutgarde Lynen Anja De Weggheleire Electrolyte and Metabolic Disturbances in Ebola Patients during a Clinical Trial, Guinea, 2015 Emerging Infectious Diseases Ebola EVD electrolyte metabolic death mortality |
| title | Electrolyte and Metabolic Disturbances in Ebola Patients during a Clinical Trial, Guinea, 2015 |
| title_full | Electrolyte and Metabolic Disturbances in Ebola Patients during a Clinical Trial, Guinea, 2015 |
| title_fullStr | Electrolyte and Metabolic Disturbances in Ebola Patients during a Clinical Trial, Guinea, 2015 |
| title_full_unstemmed | Electrolyte and Metabolic Disturbances in Ebola Patients during a Clinical Trial, Guinea, 2015 |
| title_short | Electrolyte and Metabolic Disturbances in Ebola Patients during a Clinical Trial, Guinea, 2015 |
| title_sort | electrolyte and metabolic disturbances in ebola patients during a clinical trial guinea 2015 |
| topic | Ebola EVD electrolyte metabolic death mortality |
| url | https://wwwnc.cdc.gov/eid/article/22/12/16-1136_article |
| work_keys_str_mv | AT johanvangriensven electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT elhadjibrahimabah electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT nyankoyehaba electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT alexandredelamou electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT bienvenusalimcamara electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT kadiojeanjacquesolivier electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT hildedeclerck electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT helenanordenstedt electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT malcolmgsemple electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT michelvanherp electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT jozefienbuyze electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT maaikedecrop electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT stevenvandenbroucke electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT lutgardelynen electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 AT anjadeweggheleire electrolyteandmetabolicdisturbancesinebolapatientsduringaclinicaltrialguinea2015 |