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| Name | Class |
|---|---|
| Biofourmis Inc. | INDUSTRY |
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This is a retrospective study drawing on data from the Brigham and Women's Hospital Home Hospital Program's Database. Sociodemographic and clinical data from a training cohort were used to train a machine learning algorithm to predict blood potassium throughout a patient's admission. This algorithm was then validated in a validation cohort.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Training | A subset of patients that are used to train the machine learning algorithm. |
| |
| Validation | A subset of patients that are "held back" and used to validate the algorithm's accuracy. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Potassium estimation algorithm | Other | Apply a machine learning algorithm to estimate a patient's potassium. |
|
| Measure | Description | Time Frame |
|---|---|---|
| Serum potassium concentration | Serum potassium, measured in millimol per liter | From date of admission to date of discharge, through study completion on average 7 days. |
| Measure | Description | Time Frame |
|---|---|---|
| Hyperkalemia | Serum potassium greater than 5.1 millimol per liter | From date of admission to date of discharge, through study completion on average 7 days. |
| Hypokalemia | Serum potassium less than 3.4 millimol per liter |
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Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.
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Subjects admitted at Brigham and Women's Hospital and Brigham and Women's Faulkner Hospital who meet primary diagnosis, age, and residence within 5 mile requirements and are enrolled in home hospital.
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| Name | Affiliation | Role |
|---|---|---|
| David Levine, MD MPH MA | Associate Physician | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Brigham and Women's Hospital | Boston | Massachusetts | 02115 | United States | ||
| Brigham and Women's Faulkner Hospital |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 28641860 | Background | Yasin OZ, Attia Z, Dillon JJ, DeSimone CV, Sapir Y, Dugan J, Somers VK, Ackerman MJ, Asirvatham SJ, Scott CG, Bennet KE, Ladewig DJ, Sadot D, Geva AB, Friedman PA. Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone. J Electrocardiol. 2017 Sep-Oct;50(5):620-625. doi: 10.1016/j.jelectrocard.2017.06.008. Epub 2017 Jun 8. | |
| 25453193 |
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| From date of admission to date of discharge, through study completion on average 7 days. |
| Normokalemia | Serum potassium between 3.4 and 5.1 millimol per liter | From date of admission to date of discharge, through study completion on average 7 days. |
| Serum potassium less than versus greater than or equal to 4 millimol per liter | Serum potassium falling either less than versus greater than or equal to 4 millimol per liter | From date of admission to date of discharge, through study completion on average 7 days. |
| Boston |
| Massachusetts |
| 02130 |
| United States |
| Background |
| Dillon JJ, DeSimone CV, Sapir Y, Somers VK, Dugan JL, Bruce CJ, Ackerman MJ, Asirvatham SJ, Striemer BL, Bukartyk J, Scott CG, Bennet KE, Mikell SB, Ladewig DJ, Gilles EJ, Geva A, Sadot D, Friedman PA. Noninvasive potassium determination using a mathematically processed ECG: proof of concept for a novel "blood-less, blood test". J Electrocardiol. 2015 Jan-Feb;48(1):12-8. doi: 10.1016/j.jelectrocard.2014.10.002. Epub 2014 Oct 18. |
| 31629993 | Background | Krogager ML, Kragholm K, Skals RK, Mortensen RN, Polcwiartek C, Graff C, Nielsen JB, Kanters JK, Holst AG, Sogaard P, Pietersen A, Torp-Pedersen C, Hansen SM. The relationship between serum potassium concentrations and electrocardiographic characteristics in 163,547 individuals from primary care. J Electrocardiol. 2019 Nov-Dec;57:104-111. doi: 10.1016/j.jelectrocard.2019.09.005. Epub 2019 Sep 4. |
| 28198403 | Background | Corsi C, Cortesi M, Callisesi G, De Bie J, Napolitano C, Santoro A, Mortara D, Severi S. Noninvasive quantification of blood potassium concentration from ECG in hemodialysis patients. Sci Rep. 2017 Feb 15;7:42492. doi: 10.1038/srep42492. |
| 31047740 | Background | Rafique Z, Aceves J, Espina I, Peacock F, Sheikh-Hamad D, Kuo D. Can physicians detect hyperkalemia based on the electrocardiogram? Am J Emerg Med. 2020 Jan;38(1):105-108. doi: 10.1016/j.ajem.2019.04.036. Epub 2019 Apr 22. |
| 26811164 | Background | Attia ZI, DeSimone CV, Dillon JJ, Sapir Y, Somers VK, Dugan JL, Bruce CJ, Ackerman MJ, Asirvatham SJ, Striemer BL, Bukartyk J, Scott CG, Bennet KE, Ladewig DJ, Gilles EJ, Sadot D, Geva AB, Friedman PA. Novel Bloodless Potassium Determination Using a Signal-Processed Single-Lead ECG. J Am Heart Assoc. 2016 Jan 25;5(1):e002746. doi: 10.1161/JAHA.115.002746. |
| 30942845 | Background | Galloway CD, Valys AV, Shreibati JB, Treiman DL, Petterson FL, Gundotra VP, Albert DE, Attia ZI, Carter RE, Asirvatham SJ, Ackerman MJ, Noseworthy PA, Dillon JJ, Friedman PA. Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram. JAMA Cardiol. 2019 May 1;4(5):428-436. doi: 10.1001/jamacardio.2019.0640. |
| 32134388 | Background | Lin CS, Lin C, Fang WH, Hsu CJ, Chen SJ, Huang KH, Lin WS, Tsai CS, Kuo CC, Chau T, Yang SJ, Lin SH. A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development. JMIR Med Inform. 2020 Mar 5;8(3):e15931. doi: 10.2196/15931. |
| ID | Term |
|---|---|
| D007239 | Infections |
| D006333 | Heart Failure |
| D029424 | Pulmonary Disease, Chronic Obstructive |
| D001249 | Asthma |
| D051436 | Renal Insufficiency, Chronic |
| D000096003 | Hypertensive Crisis |
| ID | Term |
|---|---|
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D008173 | Lung Diseases, Obstructive |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D002908 | Chronic Disease |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D001982 | Bronchial Diseases |
| D012130 | Respiratory Hypersensitivity |
| D006969 | Hypersensitivity, Immediate |
| D006967 | Hypersensitivity |
| D007154 | Immune System Diseases |
| D051437 | Renal Insufficiency |
| D007674 | Kidney Diseases |
| D014570 | Urologic Diseases |
| D052776 | Female Urogenital Diseases |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D052801 | Male Urogenital Diseases |
| D006973 | Hypertension |
| D014652 | Vascular Diseases |
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