Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients
Abstract Objective Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate...
Gespeichert in:
| Veröffentlicht in: | Journal of the American Medical Informatics Association : JAMIA Jg. 28; H. 7; S. 1480 - 1488 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
England
Oxford University Press
14.07.2021
|
| Schlagworte: | |
| ISSN: | 1527-974X, 1527-974X |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Abstract
Objective
Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.
Materials and Methods
For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)–positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model’s calibration and evaluated feature importances to interpret model output.
Results
The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve—MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve—MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition.
Discussion
Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients.
Conclusions
We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results. |
|---|---|
| AbstractList | Abstract
Objective
Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.
Materials and Methods
For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)–positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model’s calibration and evaluated feature importances to interpret model output.
Results
The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve—MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve—MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition.
Discussion
Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients.
Conclusions
We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results. Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.OBJECTIVECoronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output.MATERIALS AND METHODSFor each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output.The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition.RESULTSThe predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition.Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients.DISCUSSIONOur models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients.We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.CONCLUSIONSWe develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results. Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output. The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results. |
| Author | Sengupta, Soumitra Elhadad, Noemie Adelman, Jason Chen, Ruijun Pang, Chao Natarajan, Karthik Hripcsak, George Elias, Pierre Groves, Holden Green, Robert Perotte, Adler Rodriguez, Victor Alfonso Metitiri, Katherine Schlosser Mohan, Sumit Bhave, Shreyas |
| Author_xml | – sequence: 1 givenname: Victor Alfonso surname: Rodriguez fullname: Rodriguez, Victor Alfonso email: victor.a.rodriguez@columbia.edu organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 2 givenname: Shreyas surname: Bhave fullname: Bhave, Shreyas organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 3 givenname: Ruijun orcidid: 0000-0001-5281-4143 surname: Chen fullname: Chen, Ruijun organization: Department of Biomedical Informatics, Columbia University – sequence: 4 givenname: Chao surname: Pang fullname: Pang, Chao organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 5 givenname: George surname: Hripcsak fullname: Hripcsak, George organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 6 givenname: Soumitra surname: Sengupta fullname: Sengupta, Soumitra organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 7 givenname: Noemie surname: Elhadad fullname: Elhadad, Noemie organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 8 givenname: Robert surname: Green fullname: Green, Robert organization: Department of Emergency Medicine, Columbia University Irving Medical Center, New York, New York, USA – sequence: 9 givenname: Jason surname: Adelman fullname: Adelman, Jason organization: Division of General Medicine, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA – sequence: 10 givenname: Katherine Schlosser surname: Metitiri fullname: Metitiri, Katherine Schlosser organization: Department of Pediatrics, Columbia University Irving Medical Center, New York, New York, USA – sequence: 11 givenname: Pierre surname: Elias fullname: Elias, Pierre organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 12 givenname: Holden surname: Groves fullname: Groves, Holden organization: Department of Anesthesiology, Columbia University Irving Medical Center, New York, New York, USA – sequence: 13 givenname: Sumit surname: Mohan fullname: Mohan, Sumit organization: Division of Nephrology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA – sequence: 14 givenname: Karthik orcidid: 0000-0002-9066-9431 surname: Natarajan fullname: Natarajan, Karthik organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA – sequence: 15 givenname: Adler surname: Perotte fullname: Perotte, Adler organization: Department of Biomedical Informatics, Columbia University, New York, New York, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33706377$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkU1P3DAQhq1qq7LQHnutfOyBgD-SODlWC5SVVuJSUG_R2J4VRo6d2tmV9jfwpwnptnCar3cejeY9JYsQAxLylbMLzlp5-QS9g8toQDPRfiBLXglVtKr8vXiXn5DTnJ8Y47WQ1SdyIqVitVRqSZ6vcI8-Dj2GkUKwdA_eWRhdDDRu6ZDQOjNXfbToM93GRHs0jxCcAU_3057zs_6cJgxTK-HgweBMHB8xwXA4n9EJwfYu51eaC3R197C-KnhLh2l9EufP5OMWfMYvx3hG7m-uf61ui83dz_Xqx6YwFS_HolFcl6psBK80StAgtTQaGatta1ULaE1bc142reYlE2i5tUJXuubKMI1GnpHvf7lDin92mMduusqg9xAw7nInKsZF3VSimaTfjtKd7tF2Q3I9pEP374FvrLgb_k8561696WZvuqM38gUsFIWN |
| CitedBy_id | crossref_primary_10_1016_j_imu_2022_100908 crossref_primary_10_1093_jamia_ocab256 crossref_primary_10_3389_fpubh_2022_993946 crossref_primary_10_1186_s12916_022_02503_0 crossref_primary_10_3389_fpubh_2022_898254 crossref_primary_10_3390_diagnostics14141511 crossref_primary_10_1186_s12879_023_08326_5 crossref_primary_10_1259_bjr_20220058 crossref_primary_10_1002_for_3246 crossref_primary_10_1093_jamia_ocac062 crossref_primary_10_14423_SMJ_0000000000001472 crossref_primary_10_1016_j_ijmedinf_2023_105286 crossref_primary_10_1007_s11739_022_03101_x crossref_primary_10_3389_fpubh_2023_1227935 crossref_primary_10_1186_s12911_022_01880_z |
| ContentType | Journal Article |
| Copyright | The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. 2021 The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. |
| Copyright_xml | – notice: The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. 2021 – notice: The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. |
| DBID | TOX CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1093/jamia/ocab029 |
| DatabaseName | Oxford Journals Open Access Collection Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: TOX name: Oxford Journals Open Access Collection url: https://academic.oup.com/journals/ sourceTypes: Publisher – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1527-974X |
| EndPage | 1488 |
| ExternalDocumentID | 33706377 10.1093/jamia/ocab029 |
| Genre | Validation Study Research Support, Non-U.S. Gov't Journal Article Research Support, N.I.H., Extramural |
| GrantInformation_xml | – fundername: NLM NIH HHS grantid: T15 LM007079 – fundername: NHLBI NIH HHS grantid: R01 HL148248 – fundername: NLM NIH HHS grantid: R01HL148248 – fundername: NLM NIH HHS grantid: F31 LM012894 – fundername: NIH HHS grantid: F31LM012894 – fundername: NLM NIH HHS grantid: 5T15LM007079 |
| GroupedDBID | --- --K .DC .GJ 0R~ 18M 1B1 1TH 29L 2WC 4.4 48X 53G 5GY 5RE 5WD 6PF 7RV 7X7 7~T 88E 88I 8AF 8AO 8FE 8FG 8FI 8FJ 8FW AABZA AACZT AAEDT AAJQQ AALRI AAMVS AAOGV AAPGJ AAPQZ AAPXW AARHZ AAUAY AAUQX AAVAP AAWDT AAWTL AAXUO ABDFA ABEJV ABEUO ABGNP ABIXL ABJNI ABNHQ ABOCM ABPQP ABPTD ABQLI ABQNK ABSMQ ABUWG ABVGC ABWST ABWVN ABXVV ACFRR ACGFO ACGFS ACGOD ACHQT ACRPL ACUFI ACUTJ ACVCV ACYHN ACZBC ADBBV ADGZP ADHKW ADHZD ADIPN ADJOM ADMTO ADMUD ADNBA ADNMO ADQBN ADRTK ADVEK ADYVW AEGPL AEJOX AEKSI AEMDU AEMQT AENEX AENZO AEPUE AETBJ AEWNT AFFQV AFFZL AFIYH AFKRA AFOFC AFXAL AFYAG AGINJ AGKRT AGMDO AGQXC AGSYK AGUTN AHMBA AHMMS AJDVS AJEEA AJNCP ALIPV ALMA_UNASSIGNED_HOLDINGS ALUQC ALXQX APIBT APJGH AQDSO AQKUS AQUVI ARAPS ATGXG AVNTJ AVWKF AXUDD AYCSE AZQEC BAWUL BAYMD BCRHZ BENPR BEYMZ BGLVJ BHONS BKEYQ BPHCQ BTRTY BVRKM BVXVI BZKNY C1A C45 CCPQU CDBKE CS3 DAKXR DIK DILTD DU5 DWQXO E3Z EBD EBS EIHJH EJD EMOBN ENERS EO8 EX3 F5P FDB FECEO FLUFQ FOEOM FOTVD FQBLK FYUFA G-Q GAUVT GJXCC GNUQQ GX1 H13 HAR HCIFZ HMCUK IH2 IHE J21 JXSIZ K6V K7- KBUDW KOP KSI KSN LSO M0T M1P M2P M2Q M41 MBLQV MHKGH NAPCQ NOMLY NOYVH NQ- NU- NVLIB O9- OAUYM OAWHX OBFPC OCZFY ODMLO OJQWA OJZSN OK1 OPAEJ OVD OWPYF P2P P62 PAFKI PCD PEELM PHGZT PQQKQ PROAC PSQYO Q5Y R53 RIG ROL ROX ROZ RPM RPZ RUSNO RWL RXO S0X SSZ SV3 TAE TEORI TJX TMA TOX UKHRP WOQ WOW YAYTL YHZ YKOAZ YXANX ZGI ~S- AGORE AHGBF AJBYB CGR CUY CVF ECM EIF NPM 77I 7X8 |
| ID | FETCH-LOGICAL-c514t-871b4748215be3aba3b3cbe006d9d79aedc9611489b1402ed1dd2b5b617c0bec3 |
| IEDL.DBID | TOX |
| ISICitedReferencesCount | 19 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000685209200014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1527-974X |
| IngestDate | Thu Oct 02 08:38:28 EDT 2025 Mon Jul 21 06:00:25 EDT 2025 Wed Apr 02 07:06:05 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Keywords | COVID-19 supervised machine learning patient readmission renal replacement therapy artificial respiration |
| Language | English |
| License | This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c514t-871b4748215be3aba3b3cbe006d9d79aedc9611489b1402ed1dd2b5b617c0bec3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3 |
| ORCID | 0000-0002-9066-9431 0000-0001-5281-4143 |
| OpenAccessLink | https://dx.doi.org/10.1093/jamia/ocab029 |
| PMID | 33706377 |
| PQID | 2501268528 |
| PQPubID | 23479 |
| PageCount | 9 |
| ParticipantIDs | proquest_miscellaneous_2501268528 pubmed_primary_33706377 oup_primary_10_1093_jamia_ocab029 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-07-14 |
| PublicationDateYYYYMMDD | 2021-07-14 |
| PublicationDate_xml | – month: 07 year: 2021 text: 2021-07-14 day: 14 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England |
| PublicationTitle | Journal of the American Medical Informatics Association : JAMIA |
| PublicationTitleAlternate | J Am Med Inform Assoc |
| PublicationYear | 2021 |
| Publisher | Oxford University Press |
| Publisher_xml | – name: Oxford University Press |
| SSID | ssj0016235 |
| Score | 2.4634757 |
| Snippet | Abstract
Objective
Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal... Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy... |
| SourceID | proquest pubmed oup |
| SourceType | Aggregation Database Index Database Publisher |
| StartPage | 1480 |
| SubjectTerms | Adolescent Adult Aged Aged, 80 and over Area Under Curve COVID-19 - complications COVID-19 - therapy Electronic Health Records Female Humans Logistic Models Male Middle Aged Models, Statistical Patient Readmission Prognosis Renal Replacement Therapy Respiration, Artificial Retrospective Studies ROC Curve Statistics, Nonparametric Young Adult |
| Title | Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/33706377 https://www.proquest.com/docview/2501268528 |
| Volume | 28 |
| WOSCitedRecordID | wos000685209200014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1bS8MwFA46RHzxfpmXEcHHFdemXZpHmQ4Fd3mY0reSyxn0wW60m-Bv8E97ktYhzgdfCoU0afOdnvMl5xJCbowGAUwYj6H690IRStSDQnodBWaqDEeT4JB-5sNhnCRiXO93lH-48AWzBYIyeYuaXXUCm6nnR7EV6MkoWbkL0IZHdQHNtSd-Za6tEUhnSPp7_3-FfbJbk0V6V6F7QDYgPyTbg9odfkQ-f4T8UJkbimKTVYck0dmUzgvb0t25A29KigyVvoFN9rXYUBfrWAXDtWkBdqwCXJSW67HKzfpou66RXBqUCbu5RrOc9kavT_eeL2hdmLU8Ji_9h0nv0atPV_A0kqQFqkFfhTyM0eYrYFJJpphWgH-hEYYLCUaLrl0tCYWLsACMb0ygIoWURyOSmp2QRj7L4YzQrsRFEzcdo6YC6VUglbbeT4hBMG2M3yTXCEE6r-pnpJXfm6VuVtN6VrHNN0Apfo11W8gcZssyRZLmB904CuImOa2QW3XFGEeOxfn5P0a4IDuBDUixVTHDS9JYFEu4Ilv6fZGVRYts8iTG63A8aDnh-gKnk8_V |
| linkProvider | Oxford University Press |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Development+and+validation+of+prediction+models+for+mechanical+ventilation%2C+renal+replacement+therapy%2C+and+readmission+in+COVID-19+patients&rft.jtitle=Journal+of+the+American+Medical+Informatics+Association+%3A+JAMIA&rft.au=Rodriguez%2C+Victor+Alfonso&rft.au=Bhave%2C+Shreyas&rft.au=Chen%2C+Ruijun&rft.au=Pang%2C+Chao&rft.date=2021-07-14&rft.pub=Oxford+University+Press&rft.eissn=1527-974X&rft.volume=28&rft.issue=7&rft.spage=1480&rft.epage=1488&rft_id=info:doi/10.1093%2Fjamia%2Focab029&rft.externalDocID=10.1093%2Fjamia%2Focab029 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1527-974X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1527-974X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1527-974X&client=summon |