Automated Prediction of Cardiorespiratory Deterioration in Patients With Single Ventricle
Patients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first and second stage palliation surgeries.BACKGROUNDPatients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first and second...
Saved in:
| Published in: | Journal of the American College of Cardiology Vol. 77; no. 25; p. 3184 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
29.06.2021
|
| ISSN: | 1558-3597, 1558-3597 |
| Online Access: | Get more information |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Patients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first and second stage palliation surgeries.BACKGROUNDPatients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first and second stage palliation surgeries.The objective of this study is to develop and validate a real-time computer algorithm that can automatically recognize physiological precursors of cardiorespiratory deterioration in children with single-ventricle physiology during their interstage hospitalization.OBJECTIVESThe objective of this study is to develop and validate a real-time computer algorithm that can automatically recognize physiological precursors of cardiorespiratory deterioration in children with single-ventricle physiology during their interstage hospitalization.A retrospective study was conducted from prospectively collected physiological data of subjects with single-ventricle physiology. Deterioration events were defined as a cardiac arrest requiring cardiopulmonary resuscitation or an unplanned intubation. Physiological metrics were derived from the electrocardiogram (heart rate, heart rate variability, ST-segment elevation, and ST-segment variability) and the photoplethysmogram (peripheral oxygen saturation and pleth variability index). A logistic regression model was trained to separate the physiological dynamics of the pre-deterioration phase from all other data generated by study subjects. Data were split 50/50 into model training and validation sets to enable independent model validation.METHODSA retrospective study was conducted from prospectively collected physiological data of subjects with single-ventricle physiology. Deterioration events were defined as a cardiac arrest requiring cardiopulmonary resuscitation or an unplanned intubation. Physiological metrics were derived from the electrocardiogram (heart rate, heart rate variability, ST-segment elevation, and ST-segment variability) and the photoplethysmogram (peripheral oxygen saturation and pleth variability index). A logistic regression model was trained to separate the physiological dynamics of the pre-deterioration phase from all other data generated by study subjects. Data were split 50/50 into model training and validation sets to enable independent model validation.Our cohort consisted of 238 subjects admitted to the cardiac intensive care unit and stepdown units of Texas Children's Hospital over a period of 6 years. Approximately 300,000 h of high-resolution physiological waveform and vital sign data were collected using the Sickbay software platform (Medical Informatics Corp., Houston, Texas). A total of 112 cardiorespiratory deterioration events were observed. Seventy-two of the subjects experienced at least 1 deterioration event. The risk index metric generated by our optimized algorithm was found to be both sensitive and specific for detecting impending events 1 to 2 h in advance of overt extremis (receiver-operating characteristic curve area: 0.958; 95% confidence interval: 0.950 to 0.965).RESULTSOur cohort consisted of 238 subjects admitted to the cardiac intensive care unit and stepdown units of Texas Children's Hospital over a period of 6 years. Approximately 300,000 h of high-resolution physiological waveform and vital sign data were collected using the Sickbay software platform (Medical Informatics Corp., Houston, Texas). A total of 112 cardiorespiratory deterioration events were observed. Seventy-two of the subjects experienced at least 1 deterioration event. The risk index metric generated by our optimized algorithm was found to be both sensitive and specific for detecting impending events 1 to 2 h in advance of overt extremis (receiver-operating characteristic curve area: 0.958; 95% confidence interval: 0.950 to 0.965).Our algorithm can provide 1 to 2 h of advanced warning for 62% of all cardiorespiratory deterioration events in children with single-ventricle physiology during their interstage period, with only 1 alarm being generated at the bedside per patient per day.CONCLUSIONSOur algorithm can provide 1 to 2 h of advanced warning for 62% of all cardiorespiratory deterioration events in children with single-ventricle physiology during their interstage period, with only 1 alarm being generated at the bedside per patient per day. |
|---|---|
| AbstractList | Patients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first and second stage palliation surgeries.BACKGROUNDPatients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first and second stage palliation surgeries.The objective of this study is to develop and validate a real-time computer algorithm that can automatically recognize physiological precursors of cardiorespiratory deterioration in children with single-ventricle physiology during their interstage hospitalization.OBJECTIVESThe objective of this study is to develop and validate a real-time computer algorithm that can automatically recognize physiological precursors of cardiorespiratory deterioration in children with single-ventricle physiology during their interstage hospitalization.A retrospective study was conducted from prospectively collected physiological data of subjects with single-ventricle physiology. Deterioration events were defined as a cardiac arrest requiring cardiopulmonary resuscitation or an unplanned intubation. Physiological metrics were derived from the electrocardiogram (heart rate, heart rate variability, ST-segment elevation, and ST-segment variability) and the photoplethysmogram (peripheral oxygen saturation and pleth variability index). A logistic regression model was trained to separate the physiological dynamics of the pre-deterioration phase from all other data generated by study subjects. Data were split 50/50 into model training and validation sets to enable independent model validation.METHODSA retrospective study was conducted from prospectively collected physiological data of subjects with single-ventricle physiology. Deterioration events were defined as a cardiac arrest requiring cardiopulmonary resuscitation or an unplanned intubation. Physiological metrics were derived from the electrocardiogram (heart rate, heart rate variability, ST-segment elevation, and ST-segment variability) and the photoplethysmogram (peripheral oxygen saturation and pleth variability index). A logistic regression model was trained to separate the physiological dynamics of the pre-deterioration phase from all other data generated by study subjects. Data were split 50/50 into model training and validation sets to enable independent model validation.Our cohort consisted of 238 subjects admitted to the cardiac intensive care unit and stepdown units of Texas Children's Hospital over a period of 6 years. Approximately 300,000 h of high-resolution physiological waveform and vital sign data were collected using the Sickbay software platform (Medical Informatics Corp., Houston, Texas). A total of 112 cardiorespiratory deterioration events were observed. Seventy-two of the subjects experienced at least 1 deterioration event. The risk index metric generated by our optimized algorithm was found to be both sensitive and specific for detecting impending events 1 to 2 h in advance of overt extremis (receiver-operating characteristic curve area: 0.958; 95% confidence interval: 0.950 to 0.965).RESULTSOur cohort consisted of 238 subjects admitted to the cardiac intensive care unit and stepdown units of Texas Children's Hospital over a period of 6 years. Approximately 300,000 h of high-resolution physiological waveform and vital sign data were collected using the Sickbay software platform (Medical Informatics Corp., Houston, Texas). A total of 112 cardiorespiratory deterioration events were observed. Seventy-two of the subjects experienced at least 1 deterioration event. The risk index metric generated by our optimized algorithm was found to be both sensitive and specific for detecting impending events 1 to 2 h in advance of overt extremis (receiver-operating characteristic curve area: 0.958; 95% confidence interval: 0.950 to 0.965).Our algorithm can provide 1 to 2 h of advanced warning for 62% of all cardiorespiratory deterioration events in children with single-ventricle physiology during their interstage period, with only 1 alarm being generated at the bedside per patient per day.CONCLUSIONSOur algorithm can provide 1 to 2 h of advanced warning for 62% of all cardiorespiratory deterioration events in children with single-ventricle physiology during their interstage period, with only 1 alarm being generated at the bedside per patient per day. |
| Author | Ahmed, Mubbasheer Penny, Daniel J Acosta, Sebastian I Vu, Eric L Brady, Kennith M Rusin, Craig G |
| Author_xml | – sequence: 1 givenname: Craig G surname: Rusin fullname: Rusin, Craig G – sequence: 2 givenname: Sebastian I surname: Acosta fullname: Acosta, Sebastian I – sequence: 3 givenname: Eric L surname: Vu fullname: Vu, Eric L – sequence: 4 givenname: Mubbasheer surname: Ahmed fullname: Ahmed, Mubbasheer – sequence: 5 givenname: Kennith M surname: Brady fullname: Brady, Kennith M – sequence: 6 givenname: Daniel J surname: Penny fullname: Penny, Daniel J |
| BookMark | eNpNjMtKAzEYRoNUsK2-gKss3cyYZCa3ZalXKFjwhquSyfzRlGlSk8zCt7eoC1ff4XD4ZmgSYgCEzimpKaHicltvjbU1I4zWpK2JZEdoSjlXVcO1nPzjEzTLeUsIEYrqKXpbjCXuTIEerxP03hYfA44OL03qfUyQ9z6ZEtMXvoIC6aDMT-IDXh8IQsn41ZcP_OjD-wD45WCStwOcomNnhgxnfztHzzfXT8u7avVwe79crCrbKFEq2TlCHWMUZG_aDoTulHSUy05r1VpiGm1azgUoRxstnQVQrLcEaC8bSRybo4vf332KnyPkstn5bGEYTIA45g3jLRdECtKyb9o2Wlw |
| CitedBy_id | crossref_primary_10_1016_j_jacc_2021_04_074 crossref_primary_10_1002_btm2_10679 crossref_primary_10_1016_j_siny_2024_101544 crossref_primary_10_1017_S1047951125109219 crossref_primary_10_1038_s41390_022_02359_3 crossref_primary_10_3389_fcvm_2021_798215 crossref_primary_10_1007_s40746_023_00272_3 crossref_primary_10_1186_s12872_024_04336_6 crossref_primary_10_1097_HCO_0000000000000927 crossref_primary_10_1016_j_jelectrocard_2023_05_011 crossref_primary_10_1093_jamiaopen_ooaf035 crossref_primary_10_3390_children12010025 crossref_primary_10_1097_CCE_0000000000000563 crossref_primary_10_1080_01942638_2023_2253894 crossref_primary_10_1007_s00246_023_03191_0 crossref_primary_10_3389_fmed_2023_1174429 crossref_primary_10_1007_s00246_021_02729_4 crossref_primary_10_1007_s00246_023_03279_7 crossref_primary_10_1161_CIR_0000000000001225 crossref_primary_10_5409_wjcp_v14_i3_105926 crossref_primary_10_1016_j_jtcvs_2021_11_061 crossref_primary_10_1016_j_eclinm_2025_103255 crossref_primary_10_1016_j_jelectrocard_2022_05_001 crossref_primary_10_1016_j_bja_2021_12_013 crossref_primary_10_1016_j_earlhumdev_2024_106084 crossref_primary_10_1038_s41440_023_01469_7 crossref_primary_10_1097_PCC_0000000000002978 crossref_primary_10_1007_s00246_023_03224_8 crossref_primary_10_1016_j_jacadv_2023_100267 crossref_primary_10_3389_fped_2024_1483940 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved. |
| Copyright_xml | – notice: Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved. |
| DBID | 7X8 |
| DOI | 10.1016/j.jacc.2021.04.072 |
| DatabaseName | MEDLINE - Academic |
| DatabaseTitle | MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1558-3597 |
| GroupedDBID | --- --K --M .1- .FO .~1 0R~ 18M 1B1 1P~ 1~. 1~5 2WC 4.4 457 4G. 53G 5GY 5RE 5VS 6PF 7-5 71M 7X8 8P~ AABNK AABVL AAEDT AAEDW AAIKJ AALRI AAOAW AAQFI AAQQT AAXUO ABBQC ABFNM ABFRF ABLJU ABMAC ABMZM ABOCM ACGFO ACGFS ACIUM ACJTP ACPRK ACVFH ADBBV ADCNI ADEZE ADVLN AEFWE AEKER AENEX AEUPX AEVXI AEXQZ AFPUW AFRAH AFRHN AFTJW AGYEJ AHMBA AIGII AITUG AJRQY AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ BAWUL BLXMC CS3 DIK DU5 E3Z EBS EFKBS EFLBG EO8 EO9 EP2 EP3 F5P FDB FEDTE FNPLU G-Q GBLVA GX1 HVGLF IHE IXB J1W K-O KQ8 L7B MO0 N9A O-L O9- OA. OAUVE OK1 OL~ OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SCC SDF SDG SDP SES SSZ TR2 UNMZH UV1 W8F WH7 WOQ WOW YYM YZZ Z5R ~HD |
| ID | FETCH-LOGICAL-c386t-7bf01f221e7da4be69b87f157b9984c0a39a4556e8f1397fcee82dc0e1d7370f2 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 28 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000664887400007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1558-3597 |
| IngestDate | Sun Sep 28 15:40:09 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 25 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c386t-7bf01f221e7da4be69b87f157b9984c0a39a4556e8f1397fcee82dc0e1d7370f2 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3 |
| PQID | 2545607604 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_2545607604 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-06-29 |
| PublicationDateYYYYMMDD | 2021-06-29 |
| PublicationDate_xml | – month: 06 year: 2021 text: 2021-06-29 day: 29 |
| PublicationDecade | 2020 |
| PublicationTitle | Journal of the American College of Cardiology |
| PublicationYear | 2021 |
| SSID | ssj0006819 |
| Score | 2.5186925 |
| Snippet | Patients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first and second stage palliation... |
| SourceID | proquest |
| SourceType | Aggregation Database |
| StartPage | 3184 |
| Title | Automated Prediction of Cardiorespiratory Deterioration in Patients With Single Ventricle |
| URI | https://www.proquest.com/docview/2545607604 |
| Volume | 77 |
| WOSCitedRecordID | wos000664887400007&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LSwMxEA5qRbz4Ft9E8BrcZ5I9SakWL5aCr3oq2WwGt8iudrf-fjNpSvEmeF3CbsjMzuv7MkPIFRcamYmGhQCaJTotWB5IYAoyoTjEaawKN2xCDAZyNMqGvuDWeFrlwiY6Q13UGmvk1xG6eoSRkpvPL4ZToxBd9SM0VkkntqEMUrrEaNktnEs32MO6TMliGzn7SzNzftdEaWxhGIWu1anrEPzbGDsP09_-7952yJaPLWl3rgy7ZMVUe2TjwaPn--StO2trG6Gagg6n-BSFQmugPUdKnS5Rd3qLLJnSqwctKzqc919t6GvZvtNH6_A-DH3B0jB-64A89--eevfMj1ZgOpa8ZSKHIIQoCo0oVJIbnuVSQJiK3KZfiQ5UnKkkTbmRgCEiWFcqo0IHJixELAKIDslaVVfmiFArTuA2aeLKpiaJyq1SCBC5fXFq0oSrY3K5OLSxVV3EI1Rl6lkzXh7byR_WnJJNlBbStKLsjHTA_p7mnKzr77ZsphdO8j9_ALoW |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Automated+Prediction+of+Cardiorespiratory+Deterioration+in+Patients+With+Single+Ventricle&rft.jtitle=Journal+of+the+American+College+of+Cardiology&rft.au=Rusin%2C+Craig+G&rft.au=Acosta%2C+Sebastian+I&rft.au=Vu%2C+Eric+L&rft.au=Ahmed%2C+Mubbasheer&rft.date=2021-06-29&rft.issn=1558-3597&rft.eissn=1558-3597&rft.volume=77&rft.issue=25&rft.spage=3184&rft_id=info:doi/10.1016%2Fj.jacc.2021.04.072&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1558-3597&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1558-3597&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1558-3597&client=summon |