Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data
To develop a physiological data-driven model for early identification of impending cardiac arrest in neonates and infants with cardiac disease hospitalised in the cardiovascular ICU. We performed a single-institution retrospective cohort study (11 January 2013-16 September 2015) of patients ≤1 year...
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| Veröffentlicht in: | Cardiology in the young 2019-11, Vol.29 (11), p.1340-1348 |
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| creator | Bose, Sanjukta N. Verigan, Adam Hanson, Jade Ahumada, Luis M. Ghazarian, Sharon R. Goldenberg, Neil A. Stock, Arabela Jacobs, Jeffrey P. |
| description | To develop a physiological data-driven model for early identification of impending cardiac arrest in neonates and infants with cardiac disease hospitalised in the cardiovascular ICU.
We performed a single-institution retrospective cohort study (11 January 2013-16 September 2015) of patients ≤1 year old with cardiac disease who were hospitalised in the cardiovascular ICU at a tertiary care children's hospital. Demographics and diagnostic codes of cardiac arrest were obtained via the electronic health record. Diagnosis of cardiac arrest was validated by expert clinician review. Minute-to-minute physiological monitoring data were recorded via bedside monitors. A generalized linear model was used to compute a minute by minute risk score. Training and test data sets both included data from patients who did and did not develop cardiac arrest. An optimal risk-score threshold was derived based on the model's discriminatory capacity for impending arrest versus non-arrest. Model performance measures included sensitivity, specificity, accuracy, likelihood ratios, and post-test probability of arrest.
The final model consisting of multiple clinical parameters was able to identify impending cardiac arrest at least 2 hours prior to the event with an overall accuracy of 75% (sensitivity = 61%, specificity = 80%) and observed an increase in probability of detection of cardiac arrest from a pre-test probability of 9.6% to a post-test probability of 21.2%.
Our findings demonstrate that a predictive model using physiologic monitoring data in neonates and infants with cardiac disease hospitalised in the paediatric cardiovascular ICU can identify impending cardiac arrest on average 17 hours prior to arrest. |
| doi_str_mv | 10.1017/S1047951119002002 |
| format | Article |
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We performed a single-institution retrospective cohort study (11 January 2013-16 September 2015) of patients ≤1 year old with cardiac disease who were hospitalised in the cardiovascular ICU at a tertiary care children's hospital. Demographics and diagnostic codes of cardiac arrest were obtained via the electronic health record. Diagnosis of cardiac arrest was validated by expert clinician review. Minute-to-minute physiological monitoring data were recorded via bedside monitors. A generalized linear model was used to compute a minute by minute risk score. Training and test data sets both included data from patients who did and did not develop cardiac arrest. An optimal risk-score threshold was derived based on the model's discriminatory capacity for impending arrest versus non-arrest. Model performance measures included sensitivity, specificity, accuracy, likelihood ratios, and post-test probability of arrest.
The final model consisting of multiple clinical parameters was able to identify impending cardiac arrest at least 2 hours prior to the event with an overall accuracy of 75% (sensitivity = 61%, specificity = 80%) and observed an increase in probability of detection of cardiac arrest from a pre-test probability of 9.6% to a post-test probability of 21.2%.
Our findings demonstrate that a predictive model using physiologic monitoring data in neonates and infants with cardiac disease hospitalised in the paediatric cardiovascular ICU can identify impending cardiac arrest on average 17 hours prior to arrest.</description><identifier>ISSN: 1047-9511</identifier><identifier>EISSN: 1467-1107</identifier><identifier>DOI: 10.1017/S1047951119002002</identifier><identifier>PMID: 31496467</identifier><language>eng</language><publisher>Cambridge, UK: Cambridge University Press</publisher><subject>Cardiac arrest ; Cardiovascular disease ; Coronary artery disease ; Data collection ; Data warehouses ; Defibrillators ; Demographics ; Demography ; Diagnostic systems ; Diagnostic tests ; Electronic health records ; Electronic Health Records - statistics & numerical data ; Electronic medical records ; Emergency communications systems ; Female ; Florida - epidemiology ; Follow-Up Studies ; Generalized linear models ; Heart Arrest - diagnosis ; Heart Arrest - epidemiology ; Heart attacks ; Heart diseases ; Heart failure ; Heart rate ; Hospitals ; Humans ; Identification ; Incidence ; Infant ; Infant Mortality - trends ; Infant, Newborn ; Infants ; Inpatients - statistics & numerical data ; Intensive Care Units, Pediatric ; Male ; Mathematical models ; Model accuracy ; Models, Statistical ; Monitoring ; Monitoring, Physiologic - statistics & numerical data ; Mortality ; Neonates ; Original Article ; Parameter identification ; Patients ; Pediatrics ; Physiology ; Prediction models ; Retrospective Studies ; Risk Assessment - methods ; Sensitivity ; Severity of Illness Index ; Statistical analysis ; Statistical models ; Surgery ; Survival Rate - trends ; Time series</subject><ispartof>Cardiology in the young, 2019-11, Vol.29 (11), p.1340-1348</ispartof><rights>Cambridge University Press 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c373t-1be1f7aabb2b73e0664f43645705f56a7f7651631f6f4e302a6c7a44a0129d2b3</citedby><cites>FETCH-LOGICAL-c373t-1be1f7aabb2b73e0664f43645705f56a7f7651631f6f4e302a6c7a44a0129d2b3</cites><orcidid>0000-0003-1077-4010</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.cambridge.org/core/product/identifier/S1047951119002002/type/journal_article$$EHTML$$P50$$Gcambridge$$H</linktohtml><link.rule.ids>164,314,777,781,27905,27906,55609</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31496467$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bose, Sanjukta N.</creatorcontrib><creatorcontrib>Verigan, Adam</creatorcontrib><creatorcontrib>Hanson, Jade</creatorcontrib><creatorcontrib>Ahumada, Luis M.</creatorcontrib><creatorcontrib>Ghazarian, Sharon R.</creatorcontrib><creatorcontrib>Goldenberg, Neil A.</creatorcontrib><creatorcontrib>Stock, Arabela</creatorcontrib><creatorcontrib>Jacobs, Jeffrey P.</creatorcontrib><title>Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data</title><title>Cardiology in the young</title><addtitle>Cardiol Young</addtitle><description>To develop a physiological data-driven model for early identification of impending cardiac arrest in neonates and infants with cardiac disease hospitalised in the cardiovascular ICU.
We performed a single-institution retrospective cohort study (11 January 2013-16 September 2015) of patients ≤1 year old with cardiac disease who were hospitalised in the cardiovascular ICU at a tertiary care children's hospital. Demographics and diagnostic codes of cardiac arrest were obtained via the electronic health record. Diagnosis of cardiac arrest was validated by expert clinician review. Minute-to-minute physiological monitoring data were recorded via bedside monitors. A generalized linear model was used to compute a minute by minute risk score. Training and test data sets both included data from patients who did and did not develop cardiac arrest. An optimal risk-score threshold was derived based on the model's discriminatory capacity for impending arrest versus non-arrest. Model performance measures included sensitivity, specificity, accuracy, likelihood ratios, and post-test probability of arrest.
The final model consisting of multiple clinical parameters was able to identify impending cardiac arrest at least 2 hours prior to the event with an overall accuracy of 75% (sensitivity = 61%, specificity = 80%) and observed an increase in probability of detection of cardiac arrest from a pre-test probability of 9.6% to a post-test probability of 21.2%.
Our findings demonstrate that a predictive model using physiologic monitoring data in neonates and infants with cardiac disease hospitalised in the paediatric cardiovascular ICU can identify impending cardiac arrest on average 17 hours prior to arrest.</description><subject>Cardiac arrest</subject><subject>Cardiovascular disease</subject><subject>Coronary artery disease</subject><subject>Data collection</subject><subject>Data warehouses</subject><subject>Defibrillators</subject><subject>Demographics</subject><subject>Demography</subject><subject>Diagnostic systems</subject><subject>Diagnostic tests</subject><subject>Electronic health records</subject><subject>Electronic Health Records - statistics & numerical data</subject><subject>Electronic medical records</subject><subject>Emergency communications systems</subject><subject>Female</subject><subject>Florida - epidemiology</subject><subject>Follow-Up Studies</subject><subject>Generalized linear models</subject><subject>Heart Arrest - diagnosis</subject><subject>Heart Arrest - epidemiology</subject><subject>Heart attacks</subject><subject>Heart diseases</subject><subject>Heart failure</subject><subject>Heart rate</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Identification</subject><subject>Incidence</subject><subject>Infant</subject><subject>Infant Mortality - trends</subject><subject>Infant, Newborn</subject><subject>Infants</subject><subject>Inpatients - statistics & numerical data</subject><subject>Intensive Care Units, Pediatric</subject><subject>Male</subject><subject>Mathematical models</subject><subject>Model accuracy</subject><subject>Models, Statistical</subject><subject>Monitoring</subject><subject>Monitoring, Physiologic - statistics & numerical data</subject><subject>Mortality</subject><subject>Neonates</subject><subject>Original Article</subject><subject>Parameter identification</subject><subject>Patients</subject><subject>Pediatrics</subject><subject>Physiology</subject><subject>Prediction models</subject><subject>Retrospective Studies</subject><subject>Risk Assessment - methods</subject><subject>Sensitivity</subject><subject>Severity of Illness Index</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Surgery</subject><subject>Survival Rate - trends</subject><subject>Time series</subject><issn>1047-9511</issn><issn>1467-1107</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><recordid>eNp1kc9q3DAQxk1paf71AXopgl56caORZCnurSxpEwjk0ORsxrK0q2BLriQH9oX6nJXZbQstBYE0M7_5NMNXVW-BfgQK6vIbUKHaBgBaSlk5L6pTEFLVAFS9LO9Srtf6SXWW0hOlwDnQ19UJB9HKAp5WP64xjnviBuOzs05jdsGTYImbZuMH57dEYxwcaoIxmpSJ88Sb4DGbRNAPJbboc1rzeWcOdHjGpJcRI7ndPH4iSFIuwikX_ZFMYTDjuCrjPMeAekeWtIbzbp9cGMPW6QJ5l0Nc0wNmvKheWRyTeXO8z6vHL9cPm5v67v7r7ebzXa254rmG3oBViH3PesUNlVJYwaVoFG1sI1FZJRuQHKy0wnDKUGqFQiAF1g6s5-fVh4NuGez7UtbtJpd0GRfLzkvqGLtSDWeCtQV9_xf6FJboy3Qd41QJJkR7VSg4UDqGlKKx3RzdhHHfAe1WE7t_TCw9747KSz-Z4XfHL9cKwI-iOPXRDVvz5-__y_4E-gWoqA</recordid><startdate>201911</startdate><enddate>201911</enddate><creator>Bose, Sanjukta N.</creator><creator>Verigan, Adam</creator><creator>Hanson, Jade</creator><creator>Ahumada, Luis M.</creator><creator>Ghazarian, Sharon R.</creator><creator>Goldenberg, Neil A.</creator><creator>Stock, Arabela</creator><creator>Jacobs, Jeffrey P.</creator><general>Cambridge University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>M7Z</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1077-4010</orcidid></search><sort><creationdate>201911</creationdate><title>Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data</title><author>Bose, Sanjukta N. ; Verigan, Adam ; Hanson, Jade ; Ahumada, Luis M. ; Ghazarian, Sharon R. ; Goldenberg, Neil A. ; Stock, Arabela ; Jacobs, Jeffrey P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c373t-1be1f7aabb2b73e0664f43645705f56a7f7651631f6f4e302a6c7a44a0129d2b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Cardiac arrest</topic><topic>Cardiovascular disease</topic><topic>Coronary artery disease</topic><topic>Data collection</topic><topic>Data warehouses</topic><topic>Defibrillators</topic><topic>Demographics</topic><topic>Demography</topic><topic>Diagnostic systems</topic><topic>Diagnostic tests</topic><topic>Electronic health records</topic><topic>Electronic Health Records - statistics & numerical data</topic><topic>Electronic medical records</topic><topic>Emergency communications systems</topic><topic>Female</topic><topic>Florida - epidemiology</topic><topic>Follow-Up Studies</topic><topic>Generalized linear models</topic><topic>Heart Arrest - diagnosis</topic><topic>Heart Arrest - epidemiology</topic><topic>Heart attacks</topic><topic>Heart diseases</topic><topic>Heart failure</topic><topic>Heart rate</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Identification</topic><topic>Incidence</topic><topic>Infant</topic><topic>Infant Mortality - trends</topic><topic>Infant, Newborn</topic><topic>Infants</topic><topic>Inpatients - statistics & numerical data</topic><topic>Intensive Care Units, Pediatric</topic><topic>Male</topic><topic>Mathematical models</topic><topic>Model accuracy</topic><topic>Models, Statistical</topic><topic>Monitoring</topic><topic>Monitoring, Physiologic - statistics & numerical data</topic><topic>Mortality</topic><topic>Neonates</topic><topic>Original Article</topic><topic>Parameter identification</topic><topic>Patients</topic><topic>Pediatrics</topic><topic>Physiology</topic><topic>Prediction models</topic><topic>Retrospective Studies</topic><topic>Risk Assessment - 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Academic</collection><jtitle>Cardiology in the young</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bose, Sanjukta N.</au><au>Verigan, Adam</au><au>Hanson, Jade</au><au>Ahumada, Luis M.</au><au>Ghazarian, Sharon R.</au><au>Goldenberg, Neil A.</au><au>Stock, Arabela</au><au>Jacobs, Jeffrey P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data</atitle><jtitle>Cardiology in the young</jtitle><addtitle>Cardiol Young</addtitle><date>2019-11</date><risdate>2019</risdate><volume>29</volume><issue>11</issue><spage>1340</spage><epage>1348</epage><pages>1340-1348</pages><issn>1047-9511</issn><eissn>1467-1107</eissn><abstract>To develop a physiological data-driven model for early identification of impending cardiac arrest in neonates and infants with cardiac disease hospitalised in the cardiovascular ICU.
We performed a single-institution retrospective cohort study (11 January 2013-16 September 2015) of patients ≤1 year old with cardiac disease who were hospitalised in the cardiovascular ICU at a tertiary care children's hospital. Demographics and diagnostic codes of cardiac arrest were obtained via the electronic health record. Diagnosis of cardiac arrest was validated by expert clinician review. Minute-to-minute physiological monitoring data were recorded via bedside monitors. A generalized linear model was used to compute a minute by minute risk score. Training and test data sets both included data from patients who did and did not develop cardiac arrest. An optimal risk-score threshold was derived based on the model's discriminatory capacity for impending arrest versus non-arrest. Model performance measures included sensitivity, specificity, accuracy, likelihood ratios, and post-test probability of arrest.
The final model consisting of multiple clinical parameters was able to identify impending cardiac arrest at least 2 hours prior to the event with an overall accuracy of 75% (sensitivity = 61%, specificity = 80%) and observed an increase in probability of detection of cardiac arrest from a pre-test probability of 9.6% to a post-test probability of 21.2%.
Our findings demonstrate that a predictive model using physiologic monitoring data in neonates and infants with cardiac disease hospitalised in the paediatric cardiovascular ICU can identify impending cardiac arrest on average 17 hours prior to arrest.</abstract><cop>Cambridge, UK</cop><pub>Cambridge University Press</pub><pmid>31496467</pmid><doi>10.1017/S1047951119002002</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0003-1077-4010</orcidid></addata></record> |
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| source | MEDLINE; Cambridge University Press Journals Complete |
| subjects | Cardiac arrest Cardiovascular disease Coronary artery disease Data collection Data warehouses Defibrillators Demographics Demography Diagnostic systems Diagnostic tests Electronic health records Electronic Health Records - statistics & numerical data Electronic medical records Emergency communications systems Female Florida - epidemiology Follow-Up Studies Generalized linear models Heart Arrest - diagnosis Heart Arrest - epidemiology Heart attacks Heart diseases Heart failure Heart rate Hospitals Humans Identification Incidence Infant Infant Mortality - trends Infant, Newborn Infants Inpatients - statistics & numerical data Intensive Care Units, Pediatric Male Mathematical models Model accuracy Models, Statistical Monitoring Monitoring, Physiologic - statistics & numerical data Mortality Neonates Original Article Parameter identification Patients Pediatrics Physiology Prediction models Retrospective Studies Risk Assessment - methods Sensitivity Severity of Illness Index Statistical analysis Statistical models Surgery Survival Rate - trends Time series |
| title | Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data |
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