Derivation and Validation of a Risk Score to Predict Mortality of Early Neonates at Neonatal Intensive Care Unit: The END in NICU Score
Background: Early neonatal death is death of infants in the first week of life. And 34% to 92% of neonatal deaths happen within 7 days of postnatal period. Thus, the early neonatal period is the most critical time for an infant, requiring different strategies to prevent mortality. Among strategies,...
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| Veröffentlicht in: | International journal of general medicine 2021-01, Vol.14, p.8121-8134 |
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| creator | Belsti, Yitayeh Nigussie, Zelalem Mehari Tsegaye, Gebeyaw Wudie |
| description | Background: Early neonatal death is death of infants in the first week of life. And 34% to 92% of neonatal deaths happen within 7 days of postnatal period. Thus, the early neonatal period is the most critical time for an infant, requiring different strategies to prevent mortality. Among strategies, deriving and implementing early warning scores is crucial to predict early neonatal mortality earlier upon hospital admission.
Objective: To derive and validate a risk score to predict mortality of early neonates at Felege Hiwot Specialized Hospital neonatal intensive care unit, Bahir Dar, 2021.
Methods: The document review was conducted from February 24, to April 08, 2021, on all early neonates admitted to neonatal intensive care unit from January 1, 2018 to December 31, 2020. The total number of early neonates included in the derivation study was 1100. Data were collected by using checklists prepared on EpiCollect5 software. After exporting the data to R version 4.0.5 software, variables with (p < 0.25) from the simple binary regression were entered into a multiple logistic regression model, and significant variables (p < 0.05) were kept in the model. The discrimination and calibration were assessed. The model was internally validated using bootstrapping technique.
Results: Admission weight, birth Apgar score, perinatal asphyxia, respiratory distress syndrome, mode of delivery, sepsis, and gestational age at birth remained in the final multiple logistic regression prediction model. The area under curve of receiver operating characteristic curve for early neonatal mortality score was 90.7%. The model retained excellent discrimination under internal validation. The sensitivity, specificity, and positive predictive value, negative predictive value of the model was 89.4%, 82.5%, 55.5%, and 96.9%, respectively.
Conclusion: The derived score has an excellent discriminative ability and good prediction performance. This is an important tool for predicting early neonatal mortality in neonatal intensive care units at admission. |
| doi_str_mv | 10.2147/IJGM.S336888 |
| format | Article |
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Objective: To derive and validate a risk score to predict mortality of early neonates at Felege Hiwot Specialized Hospital neonatal intensive care unit, Bahir Dar, 2021.
Methods: The document review was conducted from February 24, to April 08, 2021, on all early neonates admitted to neonatal intensive care unit from January 1, 2018 to December 31, 2020. The total number of early neonates included in the derivation study was 1100. Data were collected by using checklists prepared on EpiCollect5 software. After exporting the data to R version 4.0.5 software, variables with (p < 0.25) from the simple binary regression were entered into a multiple logistic regression model, and significant variables (p < 0.05) were kept in the model. The discrimination and calibration were assessed. The model was internally validated using bootstrapping technique.
Results: Admission weight, birth Apgar score, perinatal asphyxia, respiratory distress syndrome, mode of delivery, sepsis, and gestational age at birth remained in the final multiple logistic regression prediction model. The area under curve of receiver operating characteristic curve for early neonatal mortality score was 90.7%. The model retained excellent discrimination under internal validation. The sensitivity, specificity, and positive predictive value, negative predictive value of the model was 89.4%, 82.5%, 55.5%, and 96.9%, respectively.
Conclusion: The derived score has an excellent discriminative ability and good prediction performance. This is an important tool for predicting early neonatal mortality in neonatal intensive care units at admission.</description><identifier>ISSN: 1178-7074</identifier><identifier>EISSN: 1178-7074</identifier><identifier>DOI: 10.2147/IJGM.S336888</identifier><identifier>PMID: 34795517</identifier><language>eng</language><publisher>ALBANY: Dove Medical Press Ltd</publisher><subject>Apgar score ; Asphyxia neonatorum ; Birth weight ; Births ; Congenital diseases ; derivation ; early neonatal mortality ; ethiopia ; Evidence-based medicine ; General & Internal Medicine ; Gestational age ; Health aspects ; Illnesses ; Infant mortality ; Infants ; Infants (Newborn) ; Intensive care ; Life Sciences & Biomedicine ; Low income groups ; Maternal & child health ; Medical prognosis ; Medicine, General & Internal ; Neonatal care ; Neonatal intensive care ; nicu ; Obstetrics ; Original Research ; Patient outcomes ; Pediatrics ; Physiology ; Prognosis ; Public health ; Risk factors ; risk score ; Sample size ; Sampling techniques ; Science & Technology ; validation ; Variables</subject><ispartof>International journal of general medicine, 2021-01, Vol.14, p.8121-8134</ispartof><rights>2021 Belsti et al.</rights><rights>COPYRIGHT 2021 Dove Medical Press Limited</rights><rights>2021. This work is licensed under https://creativecommons.org/licenses/by-nc/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 Belsti et al. 2021 Belsti et al.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>3</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000719584700006</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c576t-c34f094ca2214655d73227334594d616c80fc528ea1cd161a70f0bfda47068093</citedby><cites>FETCH-LOGICAL-c576t-c34f094ca2214655d73227334594d616c80fc528ea1cd161a70f0bfda47068093</cites><orcidid>0000-0002-8522-6193 ; 0000-0001-8984-1495 ; 0000-0003-1015-7047</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594787/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594787/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,2103,2115,3863,27929,27930,39263,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34795517$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Belsti, Yitayeh</creatorcontrib><creatorcontrib>Nigussie, Zelalem Mehari</creatorcontrib><creatorcontrib>Tsegaye, Gebeyaw Wudie</creatorcontrib><title>Derivation and Validation of a Risk Score to Predict Mortality of Early Neonates at Neonatal Intensive Care Unit: The END in NICU Score</title><title>International journal of general medicine</title><addtitle>INT J GEN MED</addtitle><addtitle>Int J Gen Med</addtitle><description>Background: Early neonatal death is death of infants in the first week of life. And 34% to 92% of neonatal deaths happen within 7 days of postnatal period. Thus, the early neonatal period is the most critical time for an infant, requiring different strategies to prevent mortality. Among strategies, deriving and implementing early warning scores is crucial to predict early neonatal mortality earlier upon hospital admission.
Objective: To derive and validate a risk score to predict mortality of early neonates at Felege Hiwot Specialized Hospital neonatal intensive care unit, Bahir Dar, 2021.
Methods: The document review was conducted from February 24, to April 08, 2021, on all early neonates admitted to neonatal intensive care unit from January 1, 2018 to December 31, 2020. The total number of early neonates included in the derivation study was 1100. Data were collected by using checklists prepared on EpiCollect5 software. After exporting the data to R version 4.0.5 software, variables with (p < 0.25) from the simple binary regression were entered into a multiple logistic regression model, and significant variables (p < 0.05) were kept in the model. The discrimination and calibration were assessed. The model was internally validated using bootstrapping technique.
Results: Admission weight, birth Apgar score, perinatal asphyxia, respiratory distress syndrome, mode of delivery, sepsis, and gestational age at birth remained in the final multiple logistic regression prediction model. The area under curve of receiver operating characteristic curve for early neonatal mortality score was 90.7%. The model retained excellent discrimination under internal validation. The sensitivity, specificity, and positive predictive value, negative predictive value of the model was 89.4%, 82.5%, 55.5%, and 96.9%, respectively.
Conclusion: The derived score has an excellent discriminative ability and good prediction performance. This is an important tool for predicting early neonatal mortality in neonatal intensive care units at admission.</description><subject>Apgar score</subject><subject>Asphyxia neonatorum</subject><subject>Birth weight</subject><subject>Births</subject><subject>Congenital diseases</subject><subject>derivation</subject><subject>early neonatal mortality</subject><subject>ethiopia</subject><subject>Evidence-based medicine</subject><subject>General & Internal Medicine</subject><subject>Gestational age</subject><subject>Health aspects</subject><subject>Illnesses</subject><subject>Infant mortality</subject><subject>Infants</subject><subject>Infants (Newborn)</subject><subject>Intensive care</subject><subject>Life Sciences & Biomedicine</subject><subject>Low income groups</subject><subject>Maternal & child health</subject><subject>Medical prognosis</subject><subject>Medicine, General & Internal</subject><subject>Neonatal care</subject><subject>Neonatal intensive care</subject><subject>nicu</subject><subject>Obstetrics</subject><subject>Original Research</subject><subject>Patient outcomes</subject><subject>Pediatrics</subject><subject>Physiology</subject><subject>Prognosis</subject><subject>Public health</subject><subject>Risk factors</subject><subject>risk score</subject><subject>Sample size</subject><subject>Sampling techniques</subject><subject>Science & Technology</subject><subject>validation</subject><subject>Variables</subject><issn>1178-7074</issn><issn>1178-7074</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><sourceid>DOA</sourceid><recordid>eNqNkk9v0zAYhyMEYmNw44wsISEkaLHzx3Y4IE1ZGUVbQWzlarnOm9UltYftFPUT8LVx1lJaxIHkENt53sfxm1-SPCV4mJKcvRl_PL8cXmUZ5ZzfS44JYXzAMMvv742PkkfeLzCmlJLsYXKU5awsCsKOk59n4PRKBm0NkqZGX2Wr683UNkiiL9p_Q1fKOkDBos8Oaq0CurQuRDCse2gkXbtGE7BGBvBIhu1YtmhsAhivV4AqGQ1To8NbdD0HNJqcIW3QZFxNN_bHyYNGth6ebJ8nyfT96Lr6MLj4dD6uTi8GqmA0DFSWN7jMlUzj2WlR1CxLU5ZleVHmNSVUcdyoIuUgiaoJJZLhBs-aWuYMU47L7CQZb7y1lQtx6_RSurWwUou7BetuhHRBqxYEgRwXKiWUz-pcFVw2QADnFGOSNbTMouvdxnXbzZZQKzDByfZAevjG6Lm4sSvB49cyzqLg5Vbg7PcOfBBL7RW0rTRgOy_SoiwJKznOI_r8L3RhO2diq3qKlYxF8A91I-MBtGls3Ff1UnFK44Y8ZWnvGv6DincNS62sgUbH9YOCF3sFc5BtmHvbdn1M_CH4egMqZ7130OyaQbDo0yr6tIptWiP-bL-BO_h3PCPAN8APmNnGKw1GwQ7DGDNSFjz-23jRSoe74Fa2MyGWvvr_0uwXSzIBpQ</recordid><startdate>20210101</startdate><enddate>20210101</enddate><creator>Belsti, Yitayeh</creator><creator>Nigussie, Zelalem Mehari</creator><creator>Tsegaye, Gebeyaw Wudie</creator><general>Dove Medical Press Ltd</general><general>Dove Medical Press Limited</general><general>Taylor & Francis Ltd</general><general>Dove</general><general>Dove Medical Press</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>K9.</scope><scope>M0S</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-8522-6193</orcidid><orcidid>https://orcid.org/0000-0001-8984-1495</orcidid><orcidid>https://orcid.org/0000-0003-1015-7047</orcidid></search><sort><creationdate>20210101</creationdate><title>Derivation and Validation of a Risk Score to Predict Mortality of Early Neonates at Neonatal Intensive Care Unit: The END in NICU Score</title><author>Belsti, Yitayeh ; Nigussie, Zelalem Mehari ; Tsegaye, Gebeyaw Wudie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c576t-c34f094ca2214655d73227334594d616c80fc528ea1cd161a70f0bfda47068093</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Apgar score</topic><topic>Asphyxia neonatorum</topic><topic>Birth weight</topic><topic>Births</topic><topic>Congenital diseases</topic><topic>derivation</topic><topic>early neonatal mortality</topic><topic>ethiopia</topic><topic>Evidence-based medicine</topic><topic>General & Internal Medicine</topic><topic>Gestational age</topic><topic>Health aspects</topic><topic>Illnesses</topic><topic>Infant mortality</topic><topic>Infants</topic><topic>Infants (Newborn)</topic><topic>Intensive care</topic><topic>Life Sciences & Biomedicine</topic><topic>Low income groups</topic><topic>Maternal & child health</topic><topic>Medical prognosis</topic><topic>Medicine, General & Internal</topic><topic>Neonatal care</topic><topic>Neonatal intensive care</topic><topic>nicu</topic><topic>Obstetrics</topic><topic>Original Research</topic><topic>Patient outcomes</topic><topic>Pediatrics</topic><topic>Physiology</topic><topic>Prognosis</topic><topic>Public health</topic><topic>Risk factors</topic><topic>risk score</topic><topic>Sample size</topic><topic>Sampling techniques</topic><topic>Science & Technology</topic><topic>validation</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Belsti, Yitayeh</creatorcontrib><creatorcontrib>Nigussie, Zelalem Mehari</creatorcontrib><creatorcontrib>Tsegaye, Gebeyaw Wudie</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>International journal of general medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Belsti, Yitayeh</au><au>Nigussie, Zelalem Mehari</au><au>Tsegaye, Gebeyaw Wudie</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Derivation and Validation of a Risk Score to Predict Mortality of Early Neonates at Neonatal Intensive Care Unit: The END in NICU Score</atitle><jtitle>International journal of general medicine</jtitle><stitle>INT J GEN MED</stitle><addtitle>Int J Gen Med</addtitle><date>2021-01-01</date><risdate>2021</risdate><volume>14</volume><spage>8121</spage><epage>8134</epage><pages>8121-8134</pages><issn>1178-7074</issn><eissn>1178-7074</eissn><abstract>Background: Early neonatal death is death of infants in the first week of life. And 34% to 92% of neonatal deaths happen within 7 days of postnatal period. Thus, the early neonatal period is the most critical time for an infant, requiring different strategies to prevent mortality. Among strategies, deriving and implementing early warning scores is crucial to predict early neonatal mortality earlier upon hospital admission.
Objective: To derive and validate a risk score to predict mortality of early neonates at Felege Hiwot Specialized Hospital neonatal intensive care unit, Bahir Dar, 2021.
Methods: The document review was conducted from February 24, to April 08, 2021, on all early neonates admitted to neonatal intensive care unit from January 1, 2018 to December 31, 2020. The total number of early neonates included in the derivation study was 1100. Data were collected by using checklists prepared on EpiCollect5 software. After exporting the data to R version 4.0.5 software, variables with (p < 0.25) from the simple binary regression were entered into a multiple logistic regression model, and significant variables (p < 0.05) were kept in the model. The discrimination and calibration were assessed. The model was internally validated using bootstrapping technique.
Results: Admission weight, birth Apgar score, perinatal asphyxia, respiratory distress syndrome, mode of delivery, sepsis, and gestational age at birth remained in the final multiple logistic regression prediction model. The area under curve of receiver operating characteristic curve for early neonatal mortality score was 90.7%. The model retained excellent discrimination under internal validation. The sensitivity, specificity, and positive predictive value, negative predictive value of the model was 89.4%, 82.5%, 55.5%, and 96.9%, respectively.
Conclusion: The derived score has an excellent discriminative ability and good prediction performance. This is an important tool for predicting early neonatal mortality in neonatal intensive care units at admission.</abstract><cop>ALBANY</cop><pub>Dove Medical Press Ltd</pub><pmid>34795517</pmid><doi>10.2147/IJGM.S336888</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-8522-6193</orcidid><orcidid>https://orcid.org/0000-0001-8984-1495</orcidid><orcidid>https://orcid.org/0000-0003-1015-7047</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Apgar score Asphyxia neonatorum Birth weight Births Congenital diseases derivation early neonatal mortality ethiopia Evidence-based medicine General & Internal Medicine Gestational age Health aspects Illnesses Infant mortality Infants Infants (Newborn) Intensive care Life Sciences & Biomedicine Low income groups Maternal & child health Medical prognosis Medicine, General & Internal Neonatal care Neonatal intensive care nicu Obstetrics Original Research Patient outcomes Pediatrics Physiology Prognosis Public health Risk factors risk score Sample size Sampling techniques Science & Technology validation Variables |
| title | Derivation and Validation of a Risk Score to Predict Mortality of Early Neonates at Neonatal Intensive Care Unit: The END in NICU Score |
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