International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study.
| العنوان: | International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study. |
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| المؤلفون: | Weber GM; Harvard Medical School, Department of Biomedical Informatics., Hong C; Harvard Medical School, Department of Biomedical Informatics., Palmer NP; Harvard Medical School, Department of Biomedical Informatics., Avillach P; Harvard Medical School, Department of Biomedical Informatics., Murphy SN; Massachusetts General Hospital, Neurology., Gutiérrez-Sacristán A; Harvard Medical School, Department of Biomedical Informatics., Xia Z; University of Pittsburgh, Neurology., Serret-Larmande A; Ho pital Européen Georges Pompidou, Assistance Publique - Ho pitaux de Paris, Department of biomedical informatics., Neuraz A; Necker-Enfants Malades Hospitals., Omenn GS; University of Michigan, Dept of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health., Visweswaran S; University of Pittsburgh., Klann JG; Massachusetts General Hospital Department of Medicine., South AM; Wake Forest School of Medicine., Loh NHW; National University Health System., Cannataro M; Magna Graecia University of Catanzaro., Beaulieu-Jones BK; Harvard Medical School, Department of Biomedical Informatics., Bellazzi R; University of Pavia., Agapito G; Magna Graecia University of Catanzaro., Alessiani M; ASST di Pavia., Aronow BJ; Cincinnati Children's Hospital Medical Center., Bell DS; David Geffen School of Medicine at UCLA, Medicine., Bellasi A; ASST Papa Giovanni XXIII., Benoit V; APHP., Beraghi M; ASST di Pavia., Boeker M; Medical Center-University of Freiburg., Booth J; Great Ormond Street Hospital for Children., Bosari S; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico., Bourgeois FT; Boston Children's Hospital, Computational Health Informatics Program., Brown NW; Harvard Medical School, Department of Biomedical Informatics., Bucalo M; BIOMERIS (BIOMedical Research Informatics Solutions)., Chiovato L; Istituti Clinici Scientifici Maugeri SpA SB IRCCS., Chiudinelli L; ASST Papa Giovanni XXIII., Dagliati A; University of Pavia., Devkota B; The Children's Hospital of Philadelphia., DuVall SL; VA Salt Lake City Health Care System., Follett RW; David Geffen School of Medicine at UCLA, Medicine., Ganslandt T; Ruprecht Karls University Heidelberg Faculty of Medicine Mannheim., García Barrio N; Hospital Universitario 12 de Octubre., Gradinger T; Ruprecht Karls University Heidelberg Faculty of Medicine Mannheim., Griffier R; University Hospital Centre Bordeaux., Hanauer DA; University of Michigan Institute for Healthcare Policy & Innovation., Holmes JH; University of Pennsylvania Perelman School of Medicine., Horki P; Medical Center-University of Freiburg., Huling KM; Harvard Medical School, Department of Biomedical Informatics., Issitt RW; Great Ormond Street Hospital for Children., Jouhet V; University Hospital Centre Bordeaux., Keller MS; Harvard Medical School, Department of Biomedical Informatics., Kraska D; Erlangen University Hospital., Liu M; Harvard University T H Chan School of Public Health., Luo Y; Northwestern University., Lynch KE; Tennessee Valley Healthcare System., Malovini A; Istituti Clinici Scientifici Maugeri SpA SB IRCCS., Mandl KD; Boston Children's Hospital, Computational Health Informatics Program., Mao C; Northwestern University., Maram A; Tennessee Valley Healthcare System., Matheny ME; Harvard Medical School, Harvard Catalyst., Maulhardt T; Medical Center-University of Freiburg., Mazzitelli M; Magna Graecia University of Catanzaro., Milano M; Magna Graecia University of Catanzaro., Moore JH; University of Pennsylvania Perelman School of Medicine., Morris JS; University of Pennsylvania Perelman School of Medicine., Morris M; University of Pittsburgh., Mowery DL; University of Pennsylvania Perelman School of Medicine., Naughton TP; Tennessee Valley Healthcare System., Ngiam KY; National University Health System., Norman JB; Harvard Medical School, Department of Biomedical Informatics., Patel LP; University of Kansas Medical Center., Pedrera Jimenez M; Hospital Universitario 12 de Octubre., Ramoni RB; Veterans Affairs Medical Center., Schriver ER; University of Pennsylvania Health System., Scudeller L; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico., Sebire NJ; Great Ormond Street Hospital for Children., Serrano Balazote P; Hospital Universitario 12 de Octubre., Spiridou A; Great Ormond Street Hospital for Children., Tan AL; Harvard Medical School, Department of Biomedical Informatics., Tan BW; National University Health System., Tibollo V; Istituti Clinici Scientifici Maugeri SpA SB IRCCS., Torti C; Magna Graecia University of Catanzaro., Trecarichi EM; Magna Graecia University of Catanzaro., Vitacca M; Istituti Clinici Scientifici Maugeri SpA SB IRCCS., Zambelli A; ASST Papa Giovanni XXIII., Zucco C; Magna Graecia University of Catanzaro., Kohane IS; Harvard Medical School, Department of Biomedical Informatics., Cai T; Harvard Medical School, Department of Biomedical Informatics., Brat GA; Beth Israel Deaconess Medical Center, Surgery.; Harvard Medical School, Department of Biomedical Informatics. |
| مؤلفون مشاركون: | Consortium for Clinical Characterization of COVID-19 by EHR (4CE) |
| المصدر: | MedRxiv : the preprint server for health sciences [medRxiv] 2021 Feb 05. Date of Electronic Publication: 2021 Feb 05. |
| نوع المنشور: | Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101767986 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: medRxiv Subsets: PubMed not MEDLINE |
| مستخلص: | Objectives: To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. Design: Retrospective cohort study. Setting: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. Participants: Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and Secondary Outcome Measures: Patients were categorized as "ever-severe" or "never-severe" using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. Results: Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. Conclusions: Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models. Competing Interests: COMPETING INTEREST STATEMENT There are no competing interests to report. |
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| معلومات مُعتمدة: | UL1 TR000005 United States TR NCATS NIH HHS; UL1 TR001857 United States TR NCATS NIH HHS; R01 NS098023 United States NS NINDS NIH HHS; T32 HG002295 United States HG NHGRI NIH HHS; UL1 TR001422 United States TR NCATS NIH HHS; UL1 TR001420 United States TR NCATS NIH HHS; P30 ES017885 United States ES NIEHS NIH HHS; K23 HL148394 United States HL NHLBI NIH HHS; R01 LM012095 United States LM NLM NIH HHS; UL1 TR001881 United States TR NCATS NIH HHS; UL1 TR002366 United States TR NCATS NIH HHS; UL1 TR001878 United States TR NCATS NIH HHS; FS/19/52/34563 United Kingdom BHF_ British Heart Foundation; U24 HL148865 United States HL NHLBI NIH HHS; R01 HG009174 United States HG NHGRI NIH HHS; U24 CA210967 United States CA NCI NIH HHS; R01 LM013345 United States LM NLM NIH HHS; UL1 TR002541 United States TR NCATS NIH HHS; UL1 TR002240 United States TR NCATS NIH HHS; L40 HL148910 United States HL NHLBI NIH HHS |
| تواريخ الأحداث: | Date Created: 20210210 Latest Revision: 20240216 |
| رمز التحديث: | 20240216 |
| مُعرف محوري في PubMed: | PMC7872369 |
| DOI: | 10.1101/2020.12.16.20247684 |
| PMID: | 33564777 |
| قاعدة البيانات: | MEDLINE |
| DOI: | 10.1101/2020.12.16.20247684 |
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