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Maturational, comorbid, maternal and discharge domain impact on preterm rehospitalizations: a comparison of planned and unplanned rehospitalizations
Objective: To determine the predictive value of (1) maternal, (2) maturational, (3) comorbid and (4) discharge domains associated with preterm infant rehospitalization. Study Design: Retrospective, cohort study of preterm infants discharged home from a level IV neonatal intensive care unit. Rates of...
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| Published in: | Journal of perinatology 2016-04, Vol.36 (4), p.317-324 |
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| Main Authors: | , , , , , , |
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| cited_by | cdi_FETCH-LOGICAL-c480t-e5d851dd2400ea15d42c36fa11ede89847fe451083cd19e14c09b0e983c3cbb93 |
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| cites | cdi_FETCH-LOGICAL-c480t-e5d851dd2400ea15d42c36fa11ede89847fe451083cd19e14c09b0e983c3cbb93 |
| container_end_page | 324 |
| container_issue | 4 |
| container_start_page | 317 |
| container_title | Journal of perinatology |
| container_volume | 36 |
| creator | Schell, S Kase, J S Parvez, B Shah, S I Meng, H Grzybowski, M Brumberg, H L |
| description | Objective:
To determine the predictive value of (1) maternal, (2) maturational, (3) comorbid and (4) discharge domains associated with preterm infant rehospitalization.
Study Design:
Retrospective, cohort study of preterm infants discharged home from a level IV neonatal intensive care unit. Rates of unplanned and planned 6-month readmissions were assessed. The four domains were modeled incrementally and separately to predict relative and combined contributions to the readmission risk.
Result:
Out of 504 infants, 5% had 30-day readmissions (22 unplanned, three planned). By 6 months, 13% were rehospitalized (52 unplanned, 15 planned). Sixty-seven infants had 96 readmission events with 30% of readmission events elective. The four domains together predicted 78% of total 1-month, all 6-month and unplanned 6-month readmissions. Discharge complexity was as predictive as comorbidity in all models.
Conclusion:
These four-domain models were more predictive than single domains. Many total readmission events were planned, suggesting parsing planned and unplanned rehospitalizations may benefit quality-improvement efforts. |
| doi_str_mv | 10.1038/jp.2015.194 |
| format | article |
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To determine the predictive value of (1) maternal, (2) maturational, (3) comorbid and (4) discharge domains associated with preterm infant rehospitalization.
Study Design:
Retrospective, cohort study of preterm infants discharged home from a level IV neonatal intensive care unit. Rates of unplanned and planned 6-month readmissions were assessed. The four domains were modeled incrementally and separately to predict relative and combined contributions to the readmission risk.
Result:
Out of 504 infants, 5% had 30-day readmissions (22 unplanned, three planned). By 6 months, 13% were rehospitalized (52 unplanned, 15 planned). Sixty-seven infants had 96 readmission events with 30% of readmission events elective. The four domains together predicted 78% of total 1-month, all 6-month and unplanned 6-month readmissions. Discharge complexity was as predictive as comorbidity in all models.
Conclusion:
These four-domain models were more predictive than single domains. Many total readmission events were planned, suggesting parsing planned and unplanned rehospitalizations may benefit quality-improvement efforts.</description><identifier>ISSN: 0743-8346</identifier><identifier>EISSN: 1476-5543</identifier><identifier>DOI: 10.1038/jp.2015.194</identifier><identifier>PMID: 26674999</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>692/499 ; 692/700/228 ; Adult ; Analysis ; Comorbidity ; Comparative analysis ; Discharge ; Domains ; Female ; Health aspects ; Hospital admission and discharge ; Humans ; Infant ; Infant, Newborn ; Infant, Premature ; Infants ; Intensive Care Units, Neonatal ; Male ; Medicine ; Medicine & Public Health ; Mothers ; Neonates ; original-article ; Patient Discharge - statistics & numerical data ; Patient Readmission - statistics & numerical data ; Pediatric Surgery ; Pediatrics ; Premature babies ; Premature infants ; Quality Improvement ; Retrospective Studies ; Risk Factors ; Socioeconomic Factors</subject><ispartof>Journal of perinatology, 2016-04, Vol.36 (4), p.317-324</ispartof><rights>Nature America, Inc. 2016</rights><rights>COPYRIGHT 2016 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Apr 2016</rights><rights>Nature America, Inc. 2016.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c480t-e5d851dd2400ea15d42c36fa11ede89847fe451083cd19e14c09b0e983c3cbb93</citedby><cites>FETCH-LOGICAL-c480t-e5d851dd2400ea15d42c36fa11ede89847fe451083cd19e14c09b0e983c3cbb93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26674999$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Schell, S</creatorcontrib><creatorcontrib>Kase, J S</creatorcontrib><creatorcontrib>Parvez, B</creatorcontrib><creatorcontrib>Shah, S I</creatorcontrib><creatorcontrib>Meng, H</creatorcontrib><creatorcontrib>Grzybowski, M</creatorcontrib><creatorcontrib>Brumberg, H L</creatorcontrib><title>Maturational, comorbid, maternal and discharge domain impact on preterm rehospitalizations: a comparison of planned and unplanned rehospitalizations</title><title>Journal of perinatology</title><addtitle>J Perinatol</addtitle><addtitle>J Perinatol</addtitle><description>Objective:
To determine the predictive value of (1) maternal, (2) maturational, (3) comorbid and (4) discharge domains associated with preterm infant rehospitalization.
Study Design:
Retrospective, cohort study of preterm infants discharged home from a level IV neonatal intensive care unit. Rates of unplanned and planned 6-month readmissions were assessed. The four domains were modeled incrementally and separately to predict relative and combined contributions to the readmission risk.
Result:
Out of 504 infants, 5% had 30-day readmissions (22 unplanned, three planned). By 6 months, 13% were rehospitalized (52 unplanned, 15 planned). Sixty-seven infants had 96 readmission events with 30% of readmission events elective. The four domains together predicted 78% of total 1-month, all 6-month and unplanned 6-month readmissions. Discharge complexity was as predictive as comorbidity in all models.
Conclusion:
These four-domain models were more predictive than single domains. Many total readmission events were planned, suggesting parsing planned and unplanned rehospitalizations may benefit quality-improvement efforts.</description><subject>692/499</subject><subject>692/700/228</subject><subject>Adult</subject><subject>Analysis</subject><subject>Comorbidity</subject><subject>Comparative analysis</subject><subject>Discharge</subject><subject>Domains</subject><subject>Female</subject><subject>Health aspects</subject><subject>Hospital admission and discharge</subject><subject>Humans</subject><subject>Infant</subject><subject>Infant, Newborn</subject><subject>Infant, Premature</subject><subject>Infants</subject><subject>Intensive Care Units, Neonatal</subject><subject>Male</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Mothers</subject><subject>Neonates</subject><subject>original-article</subject><subject>Patient Discharge - statistics & numerical data</subject><subject>Patient Readmission - statistics & numerical data</subject><subject>Pediatric Surgery</subject><subject>Pediatrics</subject><subject>Premature babies</subject><subject>Premature infants</subject><subject>Quality Improvement</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>Socioeconomic Factors</subject><issn>0743-8346</issn><issn>1476-5543</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kk1v1DAQhiMEotvCiTuyhIQqsVns2E5sblXFl1TEBc6WY092vUrsYCcH-B38YJzdFlqokA_WzDzz2jN6i-IZwRuCqXi9HzcVJnxDJHtQrAhr6pJzRh8WK9wwWgrK6pPiNKU9xkuxeVycVHXdMCnlqvj5SU9z1JMLXvdrZMIQYuvsGg16gphzSHuLrEtmp-MWkA2Ddh65YdRmQsGjMUIGBxRhF9LoJt27Hwe59AbpRW_U0aUMhg6NvfYe7EFy9jfRv51Piked7hM8vb7Piq_v3n65_FBefX7_8fLiqjRM4KkEbgUn1lYMY9CEW1YZWneaELAgpGBNB4wTLKixRAJhBssWg8wxNW0r6VlxftQdY_g2Q5rUkAeFPn8MwpwUaZoai4ZintEXf6H7MC_7SaqqGeZCUNn8j8pavKZ1hv9QW92Dcr4LU9RmeVpdMI4JFfRAbe6h8rEwOBM8dC7n7zS8vNWwA91PuxT6-bDSu-CrI2hiSClCp8boBh2_K4LVYim1H9ViKZUtlenn1zPN7QD2N3vjoQysj0DKJb-FeGvoe_R-AX3B1RE</recordid><startdate>20160401</startdate><enddate>20160401</enddate><creator>Schell, S</creator><creator>Kase, J S</creator><creator>Parvez, B</creator><creator>Shah, S I</creator><creator>Meng, H</creator><creator>Grzybowski, M</creator><creator>Brumberg, H L</creator><general>Nature Publishing Group US</general><general>Nature Publishing Group</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>7QG</scope><scope>7QL</scope><scope>7RV</scope><scope>7T5</scope><scope>7T7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AN0</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope></search><sort><creationdate>20160401</creationdate><title>Maturational, comorbid, maternal and discharge domain impact on preterm rehospitalizations: a comparison of planned and unplanned rehospitalizations</title><author>Schell, S ; Kase, J S ; Parvez, B ; Shah, S I ; Meng, H ; Grzybowski, M ; Brumberg, H L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c480t-e5d851dd2400ea15d42c36fa11ede89847fe451083cd19e14c09b0e983c3cbb93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>692/499</topic><topic>692/700/228</topic><topic>Adult</topic><topic>Analysis</topic><topic>Comorbidity</topic><topic>Comparative analysis</topic><topic>Discharge</topic><topic>Domains</topic><topic>Female</topic><topic>Health aspects</topic><topic>Hospital admission and discharge</topic><topic>Humans</topic><topic>Infant</topic><topic>Infant, Newborn</topic><topic>Infant, Premature</topic><topic>Infants</topic><topic>Intensive Care Units, Neonatal</topic><topic>Male</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Mothers</topic><topic>Neonates</topic><topic>original-article</topic><topic>Patient Discharge - statistics & numerical data</topic><topic>Patient Readmission - statistics & numerical data</topic><topic>Pediatric Surgery</topic><topic>Pediatrics</topic><topic>Premature babies</topic><topic>Premature infants</topic><topic>Quality Improvement</topic><topic>Retrospective Studies</topic><topic>Risk Factors</topic><topic>Socioeconomic Factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Schell, S</creatorcontrib><creatorcontrib>Kase, J S</creatorcontrib><creatorcontrib>Parvez, B</creatorcontrib><creatorcontrib>Shah, S I</creatorcontrib><creatorcontrib>Meng, H</creatorcontrib><creatorcontrib>Grzybowski, M</creatorcontrib><creatorcontrib>Brumberg, H L</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Nursing & Allied Health Database</collection><collection>Immunology Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>British Nursing Database</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Databases</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Biological Sciences</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</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>MEDLINE - Academic</collection><jtitle>Journal of perinatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schell, S</au><au>Kase, J S</au><au>Parvez, B</au><au>Shah, S I</au><au>Meng, H</au><au>Grzybowski, M</au><au>Brumberg, H L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Maturational, comorbid, maternal and discharge domain impact on preterm rehospitalizations: a comparison of planned and unplanned rehospitalizations</atitle><jtitle>Journal of perinatology</jtitle><stitle>J Perinatol</stitle><addtitle>J Perinatol</addtitle><date>2016-04-01</date><risdate>2016</risdate><volume>36</volume><issue>4</issue><spage>317</spage><epage>324</epage><pages>317-324</pages><issn>0743-8346</issn><eissn>1476-5543</eissn><abstract>Objective:
To determine the predictive value of (1) maternal, (2) maturational, (3) comorbid and (4) discharge domains associated with preterm infant rehospitalization.
Study Design:
Retrospective, cohort study of preterm infants discharged home from a level IV neonatal intensive care unit. Rates of unplanned and planned 6-month readmissions were assessed. The four domains were modeled incrementally and separately to predict relative and combined contributions to the readmission risk.
Result:
Out of 504 infants, 5% had 30-day readmissions (22 unplanned, three planned). By 6 months, 13% were rehospitalized (52 unplanned, 15 planned). Sixty-seven infants had 96 readmission events with 30% of readmission events elective. The four domains together predicted 78% of total 1-month, all 6-month and unplanned 6-month readmissions. Discharge complexity was as predictive as comorbidity in all models.
Conclusion:
These four-domain models were more predictive than single domains. Many total readmission events were planned, suggesting parsing planned and unplanned rehospitalizations may benefit quality-improvement efforts.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>26674999</pmid><doi>10.1038/jp.2015.194</doi><tpages>8</tpages></addata></record> |
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| subjects | 692/499 692/700/228 Adult Analysis Comorbidity Comparative analysis Discharge Domains Female Health aspects Hospital admission and discharge Humans Infant Infant, Newborn Infant, Premature Infants Intensive Care Units, Neonatal Male Medicine Medicine & Public Health Mothers Neonates original-article Patient Discharge - statistics & numerical data Patient Readmission - statistics & numerical data Pediatric Surgery Pediatrics Premature babies Premature infants Quality Improvement Retrospective Studies Risk Factors Socioeconomic Factors |
| title | Maturational, comorbid, maternal and discharge domain impact on preterm rehospitalizations: a comparison of planned and unplanned rehospitalizations |
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