Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines
Objective: Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI * ), among pediatric patients with Central Venous Lines (CVLs). Design: Retrospective cohort study. Setting: Single academic children's hospita...
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| Veröffentlicht in: | Frontiers in pediatrics Jg. 9; S. 726870 |
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15.09.2021
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| Abstract | Objective:
Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI
*
), among pediatric patients with Central Venous Lines (CVLs).
Design:
Retrospective cohort study.
Setting:
Single academic children's hospital.
Patients:
All hospital encounters from January 2013 to December 2018, excluding the ones without a CVL or with a length-of-stay shorter than 24 h.
Measurements and Main Results:
Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train a deep learning model to predict the occurrence of PSI
*
during the next 48 h of hospitalization. The proposed model prediction was compared to prediction of PSI
*
by a marker of illness severity (PELOD-2). The baseline prevalence of line infections was 0.34% over all segmented 48-h time windows. Events were identified among cases using onset time. All data from admission till the onset was used for cases and among controls we used all data from admission till discharge. The benchmarks were aggregated over all 48 h time windows [N=748,380 associated with 27,137 patient encounters]. The model achieved an area under the receiver operating characteristic curve of 0.993 (95% CI = [0.990, 0.996]), the enriched positive predictive value (PPV) was 23 times greater than the base prevalence. Conversely, prediction by PELOD-2 achieved a lower PPV of 1.5% [0.9%, 2.1%] which was 5 times the baseline prevalence.
Conclusion:
A deep learning model that employs common clinical features in the electronic health record can help predict the onset of CLABSI in hospitalized children with central venous line 48 hours prior to the time of specimen collection. |
|---|---|
| AbstractList | Objective: Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI*), among pediatric patients with Central Venous Lines (CVLs). Design: Retrospective cohort study. Setting: Single academic children's hospital. Patients: All hospital encounters from January 2013 to December 2018, excluding the ones without a CVL or with a length-of-stay shorter than 24 h. Measurements and Main Results: Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train a deep learning model to predict the occurrence of PSI* during the next 48 h of hospitalization. The proposed model prediction was compared to prediction of PSI* by a marker of illness severity (PELOD-2). The baseline prevalence of line infections was 0.34% over all segmented 48-h time windows. Events were identified among cases using onset time. All data from admission till the onset was used for cases and among controls we used all data from admission till discharge. The benchmarks were aggregated over all 48 h time windows [N=748,380 associated with 27,137 patient encounters]. The model achieved an area under the receiver operating characteristic curve of 0.993 (95% CI = [0.990, 0.996]), the enriched positive predictive value (PPV) was 23 times greater than the base prevalence. Conversely, prediction by PELOD-2 achieved a lower PPV of 1.5% [0.9%, 2.1%] which was 5 times the baseline prevalence. Conclusion: A deep learning model that employs common clinical features in the electronic health record can help predict the onset of CLABSI in hospitalized children with central venous line 48 hours prior to the time of specimen collection. Objective: Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI*), among pediatric patients with Central Venous Lines (CVLs). Design: Retrospective cohort study. Setting: Single academic children's hospital. Patients: All hospital encounters from January 2013 to December 2018, excluding the ones without a CVL or with a length-of-stay shorter than 24 h. Measurements and Main Results: Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train a deep learning model to predict the occurrence of PSI* during the next 48 h of hospitalization. The proposed model prediction was compared to prediction of PSI* by a marker of illness severity (PELOD-2). The baseline prevalence of line infections was 0.34% over all segmented 48-h time windows. Events were identified among cases using onset time. All data from admission till the onset was used for cases and among controls we used all data from admission till discharge. The benchmarks were aggregated over all 48 h time windows [N=748,380 associated with 27,137 patient encounters]. The model achieved an area under the receiver operating characteristic curve of 0.993 (95% CI = [0.990, 0.996]), the enriched positive predictive value (PPV) was 23 times greater than the base prevalence. Conversely, prediction by PELOD-2 achieved a lower PPV of 1.5% [0.9%, 2.1%] which was 5 times the baseline prevalence. Conclusion: A deep learning model that employs common clinical features in the electronic health record can help predict the onset of CLABSI in hospitalized children with central venous line 48 hours prior to the time of specimen collection.Objective: Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI*), among pediatric patients with Central Venous Lines (CVLs). Design: Retrospective cohort study. Setting: Single academic children's hospital. Patients: All hospital encounters from January 2013 to December 2018, excluding the ones without a CVL or with a length-of-stay shorter than 24 h. Measurements and Main Results: Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train a deep learning model to predict the occurrence of PSI* during the next 48 h of hospitalization. The proposed model prediction was compared to prediction of PSI* by a marker of illness severity (PELOD-2). The baseline prevalence of line infections was 0.34% over all segmented 48-h time windows. Events were identified among cases using onset time. All data from admission till the onset was used for cases and among controls we used all data from admission till discharge. The benchmarks were aggregated over all 48 h time windows [N=748,380 associated with 27,137 patient encounters]. The model achieved an area under the receiver operating characteristic curve of 0.993 (95% CI = [0.990, 0.996]), the enriched positive predictive value (PPV) was 23 times greater than the base prevalence. Conversely, prediction by PELOD-2 achieved a lower PPV of 1.5% [0.9%, 2.1%] which was 5 times the baseline prevalence. Conclusion: A deep learning model that employs common clinical features in the electronic health record can help predict the onset of CLABSI in hospitalized children with central venous line 48 hours prior to the time of specimen collection. Objective: Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI * ), among pediatric patients with Central Venous Lines (CVLs). Design: Retrospective cohort study. Setting: Single academic children's hospital. Patients: All hospital encounters from January 2013 to December 2018, excluding the ones without a CVL or with a length-of-stay shorter than 24 h. Measurements and Main Results: Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train a deep learning model to predict the occurrence of PSI * during the next 48 h of hospitalization. The proposed model prediction was compared to prediction of PSI * by a marker of illness severity (PELOD-2). The baseline prevalence of line infections was 0.34% over all segmented 48-h time windows. Events were identified among cases using onset time. All data from admission till the onset was used for cases and among controls we used all data from admission till discharge. The benchmarks were aggregated over all 48 h time windows [N=748,380 associated with 27,137 patient encounters]. The model achieved an area under the receiver operating characteristic curve of 0.993 (95% CI = [0.990, 0.996]), the enriched positive predictive value (PPV) was 23 times greater than the base prevalence. Conversely, prediction by PELOD-2 achieved a lower PPV of 1.5% [0.9%, 2.1%] which was 5 times the baseline prevalence. Conclusion: A deep learning model that employs common clinical features in the electronic health record can help predict the onset of CLABSI in hospitalized children with central venous line 48 hours prior to the time of specimen collection. Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI ), among pediatric patients with Central Venous Lines (CVLs). Retrospective cohort study. Single academic children's hospital. All hospital encounters from January 2013 to December 2018, excluding the ones without a CVL or with a length-of-stay shorter than 24 h. Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train a deep learning model to predict the occurrence of PSI during the next 48 h of hospitalization. The proposed model prediction was compared to prediction of PSI by a marker of illness severity (PELOD-2). The baseline prevalence of line infections was 0.34% over all segmented 48-h time windows. Events were identified among cases using onset time. All data from admission till the onset was used for cases and among controls we used all data from admission till discharge. The benchmarks were aggregated over all 48 h time windows [N=748,380 associated with 27,137 patient encounters]. The model achieved an area under the receiver operating characteristic curve of 0.993 (95% CI = [0.990, 0.996]), the enriched positive predictive value (PPV) was 23 times greater than the base prevalence. Conversely, prediction by PELOD-2 achieved a lower PPV of 1.5% [0.9%, 2.1%] which was 5 times the baseline prevalence. A deep learning model that employs common clinical features in the electronic health record can help predict the onset of CLABSI in hospitalized children with central venous line 48 hours prior to the time of specimen collection. |
| Author | Clifford, Gari D. Kamaleswaran, Rishikesan Orenstein, Evan W. Nemati, Shamim Tabaie, Azade Basu, Rajit K. |
| AuthorAffiliation | 2 Department of Pediatrics, Emory University School of Medicine , Atlanta, GA , United States 1 Department of Biomedical Informatics, Emory School of Medicine , Atlanta, GA , United States 4 Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine , Atlanta, GA , United States 3 Department of Biomedical Informatics, University of California, San Diego , San Diego, CA , United States |
| AuthorAffiliation_xml | – name: 1 Department of Biomedical Informatics, Emory School of Medicine , Atlanta, GA , United States – name: 2 Department of Pediatrics, Emory University School of Medicine , Atlanta, GA , United States – name: 4 Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine , Atlanta, GA , United States – name: 3 Department of Biomedical Informatics, University of California, San Diego , San Diego, CA , United States |
| Author_xml | – sequence: 1 givenname: Azade surname: Tabaie fullname: Tabaie, Azade – sequence: 2 givenname: Evan W. surname: Orenstein fullname: Orenstein, Evan W. – sequence: 3 givenname: Shamim surname: Nemati fullname: Nemati, Shamim – sequence: 4 givenname: Rajit K. surname: Basu fullname: Basu, Rajit K. – sequence: 5 givenname: Gari D. surname: Clifford fullname: Clifford, Gari D. – sequence: 6 givenname: Rishikesan surname: Kamaleswaran fullname: Kamaleswaran, Rishikesan |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34604142$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1080_14787210_2024_2386669 crossref_primary_10_1186_s12879_023_08409_3 crossref_primary_10_1016_j_artmed_2023_102715 crossref_primary_10_1017_ice_2025_1 crossref_primary_10_17049_jnursology_1524051 crossref_primary_10_1038_s41390_022_02116_6 crossref_primary_10_1038_s41598_024_74585_0 |
| Cites_doi | 10.1016/j.jcrc.2018.02.010 10.1016/j.annemergmed.2011.07.035 10.1109/JBHI.2019.2894570 10.3389/fped.2019.00413 10.1038/s41746-020-00318-y 10.1093/cid/cir257 10.1017/ice.2019.205 10.1542/peds.2019-1790 10.1542/peds.2009-1382 10.1097/00003246-199605000-00004 10.1097/CCM.0000000000003020 10.1097/CCM.0000000000004246 10.1038/s41598-020-67629-8 10.1097/CCM.0000000000002936 10.1097/INF.0000000000001884 10.1097/PEC.0000000000001835 10.1016/j.idc.2016.07.001 10.4103/0974-2700.66528 10.1164/ajrccm.163.7.9912080 10.1016/j.ajic.2018.04.233 10.1162/neco.1997.9.8.1735 10.1136/bmjqs-2018-008331 10.1017/ice.2015.264 10.22489/CinC.2019.412 10.1164/rccm.201412-2323OC 10.1016/j.compbiomed.2021.104289 10.1186/s13023-020-01424-6 10.1097/CCM.0b013e31828a2bbd 10.1097/PCC.0000000000002106 10.2196/medinform.5909 10.1109/78.650093 |
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| Copyright | Copyright © 2021 Tabaie, Orenstein, Nemati, Basu, Clifford and Kamaleswaran. Copyright © 2021 Tabaie, Orenstein, Nemati, Basu, Clifford and Kamaleswaran. 2021 Tabaie, Orenstein, Nemati, Basu, Clifford and Kamaleswaran |
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| Keywords | infection predictive model explainable machine learning sepsis PSI |
| Language | English |
| License | Copyright © 2021 Tabaie, Orenstein, Nemati, Basu, Clifford and Kamaleswaran. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| Snippet | Objective:
Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI
*
),... Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI ), among pediatric... Objective: Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI*),... |
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| SubjectTerms | explainable machine learning infection Pediatrics predictive model PSI sepsis |
| Title | Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/34604142 https://www.proquest.com/docview/2579090538 https://pubmed.ncbi.nlm.nih.gov/PMC8480258 https://doaj.org/article/4a4c113b5aa54116a7baefe6701f5629 |
| Volume | 9 |
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