Natural language processing diagnosed behavioral disturbance vs confusion assessment method for the intensive care unit: prevalence, patient characteristics, overlap, and association with treatment and outcome
Purpose To compare the prevalence, characteristics, drug treatment for delirium, and outcomes of patients with Natural Language Processing (NLP) diagnosed behavioral disturbance (NLP-Dx-BD) vs Confusion Assessment Method for intensive care unit (CAM-ICU) positivity. Methods In three combined medical...
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| Veröffentlicht in: | Intensive care medicine 2022-05, Vol.48 (5), p.559-569 |
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| creator | Young, Marcus Holmes, Natasha Kishore, Kartik Marhoon, Nada Amjad, Sobia Serpa-Neto, Ary Bellomo, Rinaldo |
| description | Purpose
To compare the prevalence, characteristics, drug treatment for delirium, and outcomes of patients with Natural Language Processing (NLP) diagnosed behavioral disturbance (NLP-Dx-BD) vs Confusion Assessment Method for intensive care unit (CAM-ICU) positivity.
Methods
In three combined medical-surgical ICUs, we obtained data on demographics, treatment with antipsychotic medications, and outcomes. We applied NLP to caregiver progress notes to diagnose behavioral disturbance and analyzed simultaneous CAM-ICU.
Results
We assessed 2313 patients with a median lowest Richmond Agitation-Sedation Scale (RASS) score of − 2 (− 4.0 to − 1.0) and median highest RASS score of 1 (0 to 1). Overall, 1246 (53.9%) patients were NLP-Dx-BD positive (NLP-Dx-BD
pos
) and 578 (25%) were CAM-ICU positive (CAM-ICU
pos
). Among NLP-Dx-BD
pos
patients, 539 (43.3%) were also CAM-ICU
pos
. In contrast, among CAM-ICU
pos
patients, 539 (93.3%) were also NLP-Dx-BD
pos
. The use of antipsychotic medications was highest in patients in the CAM-ICU
pos
and NLP-Dx-BD
pos
group (24.3%) followed by the CAM-ICU
neg
and NLP-Dx-BD
pos
group (10.5%). In NLP-Dx-BD
neg
patients, antipsychotic medication use was lower at 5.1% for CAM-ICU
pos
and NLP-Dx-BD
neg
patients and 2.3% for CAM-ICU
neg
and NLP-Dx-BD
neg
patients (overall
P
|
| doi_str_mv | 10.1007/s00134-022-06650-z |
| format | Article |
| fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9050783</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A723892231</galeid><sourcerecordid>A723892231</sourcerecordid><originalsourceid>FETCH-LOGICAL-c509t-b92afc1250bd520c25eeacb1519ba162c07dbc091a44478dc16b6088f4bc233c3</originalsourceid><addsrcrecordid>eNp9kktv1DAUhSMEokPhD7BAltiwmBQ_8mSBVFW8pAo2sLYc5yZxldiD7QTRf9l_1JtOaSkaoSwi5X7nHOf6JMlLRk8YpeXbQCkTWUo5T2lR5DS9fJRsWCZ4yrioHicbKjKeZkXGj5JnIVwgXhY5e5ociVxwzqtqk1x9VXH2aiSjsv2seiA77zSEYGxPWqN66wK0pIFBLcatYGsCKhplNZAlEO1sNwfjLFEhoG4CG8kEcXAt6ZwncQBibAQbzAJEKw9ktia-wxxY1AhosyU7Fc2q04PySkfwmGF02BK3gB_VbkuUbdcApw2iGPbLxIFEDyreBK5jN0ftJniePOnUGODF7fs4-fHxw_ezz-n5t09fzk7PU53TOqZNzVWnGc9p0-acap4DKN2wnNWNYgXXtGwbTWumsiwrq1azoiloVXVZo7kQWhwn7_e-u7mZoNV4DFyP3HkzKf9bOmXkw4k1g-zdImua07ISaPDm1sC7nzOEKCcTNIx4E-DmIDleXFXVmI7o63_QCzd7i7-HVF5kGS9qfk_1uFdpbOcwV6-m8rTERtScC4ZUeoDqwQIe0lnoDH5-wJ8c4PFpYTL6oIDvBdq7EDx0dzthVK7FlfviSiyuvCmuvETRq7-3eSf501QExB4IOLI9-PsV_Mf2Gqdm_zI</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2656442692</pqid></control><display><type>article</type><title>Natural language processing diagnosed behavioral disturbance vs confusion assessment method for the intensive care unit: prevalence, patient characteristics, overlap, and association with treatment and outcome</title><source>MEDLINE</source><source>SpringerLink Journals - AutoHoldings</source><creator>Young, Marcus ; Holmes, Natasha ; Kishore, Kartik ; Marhoon, Nada ; Amjad, Sobia ; Serpa-Neto, Ary ; Bellomo, Rinaldo</creator><creatorcontrib>Young, Marcus ; Holmes, Natasha ; Kishore, Kartik ; Marhoon, Nada ; Amjad, Sobia ; Serpa-Neto, Ary ; Bellomo, Rinaldo</creatorcontrib><description>Purpose
To compare the prevalence, characteristics, drug treatment for delirium, and outcomes of patients with Natural Language Processing (NLP) diagnosed behavioral disturbance (NLP-Dx-BD) vs Confusion Assessment Method for intensive care unit (CAM-ICU) positivity.
Methods
In three combined medical-surgical ICUs, we obtained data on demographics, treatment with antipsychotic medications, and outcomes. We applied NLP to caregiver progress notes to diagnose behavioral disturbance and analyzed simultaneous CAM-ICU.
Results
We assessed 2313 patients with a median lowest Richmond Agitation-Sedation Scale (RASS) score of − 2 (− 4.0 to − 1.0) and median highest RASS score of 1 (0 to 1). Overall, 1246 (53.9%) patients were NLP-Dx-BD positive (NLP-Dx-BD
pos
) and 578 (25%) were CAM-ICU positive (CAM-ICU
pos
). Among NLP-Dx-BD
pos
patients, 539 (43.3%) were also CAM-ICU
pos
. In contrast, among CAM-ICU
pos
patients, 539 (93.3%) were also NLP-Dx-BD
pos
. The use of antipsychotic medications was highest in patients in the CAM-ICU
pos
and NLP-Dx-BD
pos
group (24.3%) followed by the CAM-ICU
neg
and NLP-Dx-BD
pos
group (10.5%). In NLP-Dx-BD
neg
patients, antipsychotic medication use was lower at 5.1% for CAM-ICU
pos
and NLP-Dx-BD
neg
patients and 2.3% for CAM-ICU
neg
and NLP-Dx-BD
neg
patients (overall
P
< 0.001). Regardless of CAM-ICU status, after adjustment and on time-dependent Cox modelling, NLP-Dx-BD was associated with greater antipsychotic medication use. Finally, regardless of CAM-ICU status, NLP-Dx-BD
pos
patients had longer duration of ICU and hospital stay and greater hospital mortality (all
P
< 0.001).
Conclusion
More patients were NLP-Dx-BD positive than CAM-ICU positive. NLP-Dx-BD and CAM-ICU assessment describe partly overlapping populations. However, NLP-Dx-BD identifies more patients likely to receive antipsychotic medications. In the absence of NLP-Dx-BD, treatment with antipsychotic medications is rare.</description><identifier>ISSN: 0342-4642</identifier><identifier>ISSN: 1432-1238</identifier><identifier>EISSN: 1432-1238</identifier><identifier>DOI: 10.1007/s00134-022-06650-z</identifier><identifier>PMID: 35322288</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Anesthesiology ; Antipsychotic Agents - therapeutic use ; Antipsychotic drugs ; Antipsychotics ; Care and treatment ; Computational linguistics ; Confusion ; Critical Care Medicine ; Delirium ; Delirium - diagnosis ; Delirium - drug therapy ; Delirium - epidemiology ; Demographics ; Emergency Medicine ; Hospital patients ; Humans ; Intensive ; Intensive care ; Intensive Care Units ; Language ; Language processing ; Medical colleges ; Medical diagnosis ; Medical research ; Medicine ; Medicine & Public Health ; Medicine, Experimental ; Mental disorders ; Methods ; Natural language interfaces ; Natural Language Processing ; Original ; Pain Medicine ; Patient outcomes ; Patients ; Pediatrics ; Pneumology/Respiratory System ; Prevalence ; Psychotropic drugs ; Treatment Outcome</subject><ispartof>Intensive care medicine, 2022-05, Vol.48 (5), p.559-569</ispartof><rights>The Author(s) 2022</rights><rights>2022. The Author(s).</rights><rights>COPYRIGHT 2022 Springer</rights><rights>The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c509t-b92afc1250bd520c25eeacb1519ba162c07dbc091a44478dc16b6088f4bc233c3</citedby><cites>FETCH-LOGICAL-c509t-b92afc1250bd520c25eeacb1519ba162c07dbc091a44478dc16b6088f4bc233c3</cites><orcidid>0000-0002-1650-8939</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00134-022-06650-z$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00134-022-06650-z$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35322288$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Young, Marcus</creatorcontrib><creatorcontrib>Holmes, Natasha</creatorcontrib><creatorcontrib>Kishore, Kartik</creatorcontrib><creatorcontrib>Marhoon, Nada</creatorcontrib><creatorcontrib>Amjad, Sobia</creatorcontrib><creatorcontrib>Serpa-Neto, Ary</creatorcontrib><creatorcontrib>Bellomo, Rinaldo</creatorcontrib><title>Natural language processing diagnosed behavioral disturbance vs confusion assessment method for the intensive care unit: prevalence, patient characteristics, overlap, and association with treatment and outcome</title><title>Intensive care medicine</title><addtitle>Intensive Care Med</addtitle><addtitle>Intensive Care Med</addtitle><description>Purpose
To compare the prevalence, characteristics, drug treatment for delirium, and outcomes of patients with Natural Language Processing (NLP) diagnosed behavioral disturbance (NLP-Dx-BD) vs Confusion Assessment Method for intensive care unit (CAM-ICU) positivity.
Methods
In three combined medical-surgical ICUs, we obtained data on demographics, treatment with antipsychotic medications, and outcomes. We applied NLP to caregiver progress notes to diagnose behavioral disturbance and analyzed simultaneous CAM-ICU.
Results
We assessed 2313 patients with a median lowest Richmond Agitation-Sedation Scale (RASS) score of − 2 (− 4.0 to − 1.0) and median highest RASS score of 1 (0 to 1). Overall, 1246 (53.9%) patients were NLP-Dx-BD positive (NLP-Dx-BD
pos
) and 578 (25%) were CAM-ICU positive (CAM-ICU
pos
). Among NLP-Dx-BD
pos
patients, 539 (43.3%) were also CAM-ICU
pos
. In contrast, among CAM-ICU
pos
patients, 539 (93.3%) were also NLP-Dx-BD
pos
. The use of antipsychotic medications was highest in patients in the CAM-ICU
pos
and NLP-Dx-BD
pos
group (24.3%) followed by the CAM-ICU
neg
and NLP-Dx-BD
pos
group (10.5%). In NLP-Dx-BD
neg
patients, antipsychotic medication use was lower at 5.1% for CAM-ICU
pos
and NLP-Dx-BD
neg
patients and 2.3% for CAM-ICU
neg
and NLP-Dx-BD
neg
patients (overall
P
< 0.001). Regardless of CAM-ICU status, after adjustment and on time-dependent Cox modelling, NLP-Dx-BD was associated with greater antipsychotic medication use. Finally, regardless of CAM-ICU status, NLP-Dx-BD
pos
patients had longer duration of ICU and hospital stay and greater hospital mortality (all
P
< 0.001).
Conclusion
More patients were NLP-Dx-BD positive than CAM-ICU positive. NLP-Dx-BD and CAM-ICU assessment describe partly overlapping populations. However, NLP-Dx-BD identifies more patients likely to receive antipsychotic medications. In the absence of NLP-Dx-BD, treatment with antipsychotic medications is rare.</description><subject>Anesthesiology</subject><subject>Antipsychotic Agents - therapeutic use</subject><subject>Antipsychotic drugs</subject><subject>Antipsychotics</subject><subject>Care and treatment</subject><subject>Computational linguistics</subject><subject>Confusion</subject><subject>Critical Care Medicine</subject><subject>Delirium</subject><subject>Delirium - diagnosis</subject><subject>Delirium - drug therapy</subject><subject>Delirium - epidemiology</subject><subject>Demographics</subject><subject>Emergency Medicine</subject><subject>Hospital patients</subject><subject>Humans</subject><subject>Intensive</subject><subject>Intensive care</subject><subject>Intensive Care Units</subject><subject>Language</subject><subject>Language processing</subject><subject>Medical colleges</subject><subject>Medical diagnosis</subject><subject>Medical research</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Medicine, Experimental</subject><subject>Mental disorders</subject><subject>Methods</subject><subject>Natural language interfaces</subject><subject>Natural Language Processing</subject><subject>Original</subject><subject>Pain Medicine</subject><subject>Patient outcomes</subject><subject>Patients</subject><subject>Pediatrics</subject><subject>Pneumology/Respiratory System</subject><subject>Prevalence</subject><subject>Psychotropic drugs</subject><subject>Treatment Outcome</subject><issn>0342-4642</issn><issn>1432-1238</issn><issn>1432-1238</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kktv1DAUhSMEokPhD7BAltiwmBQ_8mSBVFW8pAo2sLYc5yZxldiD7QTRf9l_1JtOaSkaoSwi5X7nHOf6JMlLRk8YpeXbQCkTWUo5T2lR5DS9fJRsWCZ4yrioHicbKjKeZkXGj5JnIVwgXhY5e5ociVxwzqtqk1x9VXH2aiSjsv2seiA77zSEYGxPWqN66wK0pIFBLcatYGsCKhplNZAlEO1sNwfjLFEhoG4CG8kEcXAt6ZwncQBibAQbzAJEKw9ktia-wxxY1AhosyU7Fc2q04PySkfwmGF02BK3gB_VbkuUbdcApw2iGPbLxIFEDyreBK5jN0ftJniePOnUGODF7fs4-fHxw_ezz-n5t09fzk7PU53TOqZNzVWnGc9p0-acap4DKN2wnNWNYgXXtGwbTWumsiwrq1azoiloVXVZo7kQWhwn7_e-u7mZoNV4DFyP3HkzKf9bOmXkw4k1g-zdImua07ISaPDm1sC7nzOEKCcTNIx4E-DmIDleXFXVmI7o63_QCzd7i7-HVF5kGS9qfk_1uFdpbOcwV6-m8rTERtScC4ZUeoDqwQIe0lnoDH5-wJ8c4PFpYTL6oIDvBdq7EDx0dzthVK7FlfviSiyuvCmuvETRq7-3eSf501QExB4IOLI9-PsV_Mf2Gqdm_zI</recordid><startdate>20220501</startdate><enddate>20220501</enddate><creator>Young, Marcus</creator><creator>Holmes, Natasha</creator><creator>Kishore, Kartik</creator><creator>Marhoon, Nada</creator><creator>Amjad, Sobia</creator><creator>Serpa-Neto, Ary</creator><creator>Bellomo, Rinaldo</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><general>Springer Nature B.V</general><scope>C6C</scope><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>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</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>NAPCQ</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-1650-8939</orcidid></search><sort><creationdate>20220501</creationdate><title>Natural language processing diagnosed behavioral disturbance vs confusion assessment method for the intensive care unit: prevalence, patient characteristics, overlap, and association with treatment and outcome</title><author>Young, Marcus ; Holmes, Natasha ; Kishore, Kartik ; Marhoon, Nada ; Amjad, Sobia ; Serpa-Neto, Ary ; Bellomo, Rinaldo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c509t-b92afc1250bd520c25eeacb1519ba162c07dbc091a44478dc16b6088f4bc233c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Anesthesiology</topic><topic>Antipsychotic Agents - therapeutic use</topic><topic>Antipsychotic drugs</topic><topic>Antipsychotics</topic><topic>Care and treatment</topic><topic>Computational linguistics</topic><topic>Confusion</topic><topic>Critical Care Medicine</topic><topic>Delirium</topic><topic>Delirium - diagnosis</topic><topic>Delirium - drug therapy</topic><topic>Delirium - epidemiology</topic><topic>Demographics</topic><topic>Emergency Medicine</topic><topic>Hospital patients</topic><topic>Humans</topic><topic>Intensive</topic><topic>Intensive care</topic><topic>Intensive Care Units</topic><topic>Language</topic><topic>Language processing</topic><topic>Medical colleges</topic><topic>Medical diagnosis</topic><topic>Medical research</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Medicine, Experimental</topic><topic>Mental disorders</topic><topic>Methods</topic><topic>Natural language interfaces</topic><topic>Natural Language Processing</topic><topic>Original</topic><topic>Pain Medicine</topic><topic>Patient outcomes</topic><topic>Patients</topic><topic>Pediatrics</topic><topic>Pneumology/Respiratory System</topic><topic>Prevalence</topic><topic>Psychotropic drugs</topic><topic>Treatment Outcome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Young, Marcus</creatorcontrib><creatorcontrib>Holmes, Natasha</creatorcontrib><creatorcontrib>Kishore, Kartik</creatorcontrib><creatorcontrib>Marhoon, Nada</creatorcontrib><creatorcontrib>Amjad, Sobia</creatorcontrib><creatorcontrib>Serpa-Neto, Ary</creatorcontrib><creatorcontrib>Bellomo, Rinaldo</creatorcontrib><collection>Springer Nature OA Free Journals</collection><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>Nursing & Allied Health Database</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>Technology Research Database</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 Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biochemistry Abstracts 1</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><collection>PubMed Central (Full Participant titles)</collection><jtitle>Intensive care medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Young, Marcus</au><au>Holmes, Natasha</au><au>Kishore, Kartik</au><au>Marhoon, Nada</au><au>Amjad, Sobia</au><au>Serpa-Neto, Ary</au><au>Bellomo, Rinaldo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Natural language processing diagnosed behavioral disturbance vs confusion assessment method for the intensive care unit: prevalence, patient characteristics, overlap, and association with treatment and outcome</atitle><jtitle>Intensive care medicine</jtitle><stitle>Intensive Care Med</stitle><addtitle>Intensive Care Med</addtitle><date>2022-05-01</date><risdate>2022</risdate><volume>48</volume><issue>5</issue><spage>559</spage><epage>569</epage><pages>559-569</pages><issn>0342-4642</issn><issn>1432-1238</issn><eissn>1432-1238</eissn><abstract>Purpose
To compare the prevalence, characteristics, drug treatment for delirium, and outcomes of patients with Natural Language Processing (NLP) diagnosed behavioral disturbance (NLP-Dx-BD) vs Confusion Assessment Method for intensive care unit (CAM-ICU) positivity.
Methods
In three combined medical-surgical ICUs, we obtained data on demographics, treatment with antipsychotic medications, and outcomes. We applied NLP to caregiver progress notes to diagnose behavioral disturbance and analyzed simultaneous CAM-ICU.
Results
We assessed 2313 patients with a median lowest Richmond Agitation-Sedation Scale (RASS) score of − 2 (− 4.0 to − 1.0) and median highest RASS score of 1 (0 to 1). Overall, 1246 (53.9%) patients were NLP-Dx-BD positive (NLP-Dx-BD
pos
) and 578 (25%) were CAM-ICU positive (CAM-ICU
pos
). Among NLP-Dx-BD
pos
patients, 539 (43.3%) were also CAM-ICU
pos
. In contrast, among CAM-ICU
pos
patients, 539 (93.3%) were also NLP-Dx-BD
pos
. The use of antipsychotic medications was highest in patients in the CAM-ICU
pos
and NLP-Dx-BD
pos
group (24.3%) followed by the CAM-ICU
neg
and NLP-Dx-BD
pos
group (10.5%). In NLP-Dx-BD
neg
patients, antipsychotic medication use was lower at 5.1% for CAM-ICU
pos
and NLP-Dx-BD
neg
patients and 2.3% for CAM-ICU
neg
and NLP-Dx-BD
neg
patients (overall
P
< 0.001). Regardless of CAM-ICU status, after adjustment and on time-dependent Cox modelling, NLP-Dx-BD was associated with greater antipsychotic medication use. Finally, regardless of CAM-ICU status, NLP-Dx-BD
pos
patients had longer duration of ICU and hospital stay and greater hospital mortality (all
P
< 0.001).
Conclusion
More patients were NLP-Dx-BD positive than CAM-ICU positive. NLP-Dx-BD and CAM-ICU assessment describe partly overlapping populations. However, NLP-Dx-BD identifies more patients likely to receive antipsychotic medications. In the absence of NLP-Dx-BD, treatment with antipsychotic medications is rare.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>35322288</pmid><doi>10.1007/s00134-022-06650-z</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-1650-8939</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0342-4642 |
| ispartof | Intensive care medicine, 2022-05, Vol.48 (5), p.559-569 |
| issn | 0342-4642 1432-1238 1432-1238 |
| language | eng |
| recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9050783 |
| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Anesthesiology Antipsychotic Agents - therapeutic use Antipsychotic drugs Antipsychotics Care and treatment Computational linguistics Confusion Critical Care Medicine Delirium Delirium - diagnosis Delirium - drug therapy Delirium - epidemiology Demographics Emergency Medicine Hospital patients Humans Intensive Intensive care Intensive Care Units Language Language processing Medical colleges Medical diagnosis Medical research Medicine Medicine & Public Health Medicine, Experimental Mental disorders Methods Natural language interfaces Natural Language Processing Original Pain Medicine Patient outcomes Patients Pediatrics Pneumology/Respiratory System Prevalence Psychotropic drugs Treatment Outcome |
| title | Natural language processing diagnosed behavioral disturbance vs confusion assessment method for the intensive care unit: prevalence, patient characteristics, overlap, and association with treatment and outcome |
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