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Article ; Online: Development of a Predictive Model for Mortality in Hospitalized Patients With COVID-19.

Niu, Yuanyuan / Zhan, Zan / Li, Jianfeng / Shui, Wei / Wang, Changfeng / Xing, Yanli / Zhang, Changran

Disaster medicine and public health preparedness

2021 Volume 16, Issue 4, Page(s) 1398–1406

Abstract: ... we included all patients with COVID-19 at Huanggang Central Hospital from January 23 to March 5, 2020. Data ... Introduction: Early identification of patients with novel corona virus disease 2019 (COVID-19 ... would facilitate the early identification of patients with COVID-19 who have a high-risk ...

Abstract	Introduction: Early identification of patients with novel corona virus disease 2019 (COVID-19) who may be at high mortality risk is of great importance. Methods: In this retrospective study, we included all patients with COVID-19 at Huanggang Central Hospital from January 23 to March 5, 2020. Data on clinical characteristics and outcomes were compared between survivors and nonsurvivors. Univariable and multivariable logistic regression were used to explore risk factors associated with in-hospital death. A nomogram was established based on the risk factors selected by multivariable analysis. Results: A total of 150 patients were enrolled, including 31 nonsurvivors and 119 survivors. The multivariable logistic analysis indicated that increasing the odds of in-hospital death associated with higher Sequential Organ Failure Assessment score (odds ratio [OR], 3.077; 95% confidence interval [CI]: 1.848-5.122; Conclusions: This finding would facilitate the early identification of patients with COVID-19 who have a high-risk for fatal outcome.
MeSH term(s)	Humans ; COVID-19 ; Retrospective Studies ; Hospital Mortality ; Prognosis ; Risk Factors
Language	English
Publishing date	2021-01-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	2375268-3
ISSN	1938-744X ; 1935-7893
ISSN (online)	1938-744X
ISSN	1935-7893
DOI	10.1017/dmp.2021.8
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges.

Schut, M C / Dongelmans, D A / de Lange, D W / Brinkman, S / de Keizer, N F / Abu-Hanna, A

BMC medical informatics and decision making

2024 Volume 24, Issue 1, Page(s) 7

Abstract: ... for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry ... and variable transformations using splines. Participants included COVID-19 patients from all ICUs ... per survival status.: Results: A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included ...

Abstract	Background: Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data. Methods: Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status. Results: A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71-0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74-0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables. Conclusions: We developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making.
MeSH term(s)	Humans ; COVID-19 ; Retrospective Studies ; Hospital Mortality ; Pandemics ; Intensive Care Units ; Registries
Language	English
Publishing date	2024-01-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2046490-3
ISSN	1472-6947 ; 1472-6947
ISSN (online)	1472-6947
ISSN	1472-6947
DOI	10.1186/s12911-023-02401-2
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Article ; Online: A Model Predicting Mortality of Hospitalized Covid-19 Patients Four Days After Admission: Development, Internal and Temporal-External Validation.

Heber, Stefan / Pereyra, David / Schrottmaier, Waltraud C / Kammerer, Kerstin / Santol, Jonas / Rumpf, Benedikt / Pawelka, Erich / Hanna, Markus / Scholz, Alexander / Liu, Markus / Hell, Agnes / Heiplik, Klara / Lickefett, Benno / Havervall, Sebastian / Traugott, Marianna T / Neuböck, Matthias J / Schörgenhofer, Christian / Seitz, Tamara / Firbas, Christa /

Karolyi, Mario / Weiss, Günter / Jilma, Bernd / Thålin, Charlotte / Bellmann-Weiler, Rosa / Salzer, Helmut J F / Szepannek, Gero / Fischer, Michael J M / Zoufaly, Alexander / Gleiss, Andreas / Assinger, Alice

Frontiers in cellular and infection microbiology

2022 Volume 11, Page(s) 795026

Abstract: ... Covid-19 patients during the first four days after admission.: Methods: Haematological parameters ... Objective: To develop and validate a prognostic model for in-hospital mortality after four days ... measured during the first 4 days after admission were subjected to a linear mixed model to obtain patient ...

Abstract	Objective: To develop and validate a prognostic model for in-hospital mortality after four days based on age, fever at admission and five haematological parameters routinely measured in hospitalized Covid-19 patients during the first four days after admission. Methods: Haematological parameters measured during the first 4 days after admission were subjected to a linear mixed model to obtain patient-specific intercepts and slopes for each parameter. A prediction model was built using logistic regression with variable selection and shrinkage factor estimation supported by bootstrapping. Model development was based on 481 survivors and 97 non-survivors, hospitalized before the occurrence of mutations. Internal validation was done by 10-fold cross-validation. The model was temporally-externally validated in 299 survivors and 42 non-survivors hospitalized when the Alpha variant (B.1.1.7) was prevalent. Results: The final model included age, fever on admission as well as the slope or intercept of lactate dehydrogenase, platelet count, C-reactive protein, and creatinine. Tenfold cross validation resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.92, a mean calibration slope of 1.0023 and a Brier score of 0.076. At temporal-external validation, application of the previously developed model showed an AUROC of 0.88, a calibration slope of 0.95 and a Brier score of 0.073. Regarding the relative importance of the variables, the (apparent) variation in mortality explained by the six variables deduced from the haematological parameters measured during the first four days is higher (explained variation 0.295) than that of age (0.210). Conclusions: The presented model requires only variables routinely acquired in hospitals, which allows immediate and wide-spread use as a decision support for earlier discharge of low-risk patients to reduce the burden on the health care system. Clinical trial registration: Austrian Coronavirus Adaptive Clinical Trial (ACOVACT); ClinicalTrials.gov, identifier NCT04351724.
MeSH term(s)	COVID-19 ; Hospital Mortality ; Hospitalization ; Humans ; Retrospective Studies ; SARS-CoV-2
Language	English
Publishing date	2022-01-24
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2619676-1
ISSN	2235-2988 ; 2235-2988
ISSN (online)	2235-2988
ISSN	2235-2988
DOI	10.3389/fcimb.2021.795026
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Article ; Online: Development and validation of a predictive model of in-hospital mortality in COVID-19 patients.

Velasco-Rodríguez, Diego / Alonso-Dominguez, Juan-Manuel / Vidal Laso, Rosa / Lainez-González, Daniel / García-Raso, Aránzazu / Martín-Herrero, Sara / Herrero, Antonio / Martínez Alfonzo, Inés / Serrano-López, Juana / Jiménez-Barral, Elena / Nistal, Sara / Pérez Márquez, Manuel / Askari, Elham / Castillo Álvarez, Jorge / Núñez, Antonio / Jiménez Rodríguez, Ángel / Heili-Frades, Sarah / Pérez-Calvo, César / Górgolas, Miguel /

Barba, Raquel / Llamas-Sillero, Pilar

PloS one

2021 Volume 16, Issue 3, Page(s) e0247676

Abstract: ... 800). Our predictive model of in-hospital mortality of COVID-19 patients has been developed ... We retrospectively evaluated 2879 hospitalized COVID-19 patients from four hospitals to evaluate ... of 2070 hospitalized COVID-19 patients were finally included in the multivariable analysis. Age 61-70 ...

Abstract	We retrospectively evaluated 2879 hospitalized COVID-19 patients from four hospitals to evaluate the ability of demographic data, medical history, and on-admission laboratory parameters to predict in-hospital mortality. Association of previously published risk factors (age, gender, arterial hypertension, diabetes mellitus, smoking habit, obesity, renal failure, cardiovascular/ pulmonary diseases, serum ferritin, lymphocyte count, APTT, PT, fibrinogen, D-dimer, and platelet count) with death was tested by a multivariate logistic regression, and a predictive model was created, with further validation in an independent sample. A total of 2070 hospitalized COVID-19 patients were finally included in the multivariable analysis. Age 61-70 years (p<0.001; OR: 7.69; 95%CI: 2.93 to 20.14), age 71-80 years (p<0.001; OR: 14.99; 95%CI: 5.88 to 38.22), age >80 years (p<0.001; OR: 36.78; 95%CI: 14.42 to 93.85), male gender (p<0.001; OR: 1.84; 95%CI: 1.31 to 2.58), D-dimer levels >2 ULN (p = 0.003; OR: 1.79; 95%CI: 1.22 to 2.62), and prolonged PT (p<0.001; OR: 2.18; 95%CI: 1.49 to 3.18) were independently associated with increased in-hospital mortality. A predictive model performed with these parameters showed an AUC of 0.81 in the development cohort (n = 1270) [sensitivity of 95.83%, specificity of 41.46%, negative predictive value of 98.01%, and positive predictive value of 24.85%]. These results were then validated in an independent data sample (n = 800). Our predictive model of in-hospital mortality of COVID-19 patients has been developed, calibrated and validated. The model (MRS-COVID) included age, male gender, and on-admission coagulopathy markers as positively correlated factors with fatal outcome.
MeSH term(s)	Aged ; Aged, 80 and over ; Blood Coagulation ; COVID-19/blood ; COVID-19/diagnosis ; COVID-19/mortality ; Female ; Fibrin Fibrinogen Degradation Products/analysis ; Hospital Mortality ; Humans ; Male ; Middle Aged ; Multivariate Analysis ; Prognosis ; Retrospective Studies ; Risk Factors ; SARS-CoV-2/isolation & purification
Chemical Substances	Fibrin Fibrinogen Degradation Products ; fibrin fragment D
Language	English
Publishing date	2021-03-04
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0247676
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Article ; Online: Predictive Modeling of Morbidity and Mortality in Patients Hospitalized With COVID-19 and its Clinical Implications: Algorithm Development and Interpretation.

Wang, Joshua M / Liu, Wenke / Chen, Xiaoshan / McRae, Michael P / McDevitt, John T / Fenyö, David

Journal of medical Internet research

2021 Volume 23, Issue 7, Page(s) e29514

Abstract: ... on health care systems worldwide. There remains a compelling need to analyze factors that are predictive for patients ... at elevated risk of morbidity and mortality.: Objective: The goal of this retrospective study of patients ... excelled at predicting mortality (area under the curve [AUC]=0.92; specificity=86%; and sensitivity=85 ...

Abstract	Background: The COVID-19 pandemic began in early 2021 and placed significant strains on health care systems worldwide. There remains a compelling need to analyze factors that are predictive for patients at elevated risk of morbidity and mortality. Objective: The goal of this retrospective study of patients who tested positive with COVID-19 and were treated at NYU (New York University) Langone Health was to identify clinical markers predictive of disease severity in order to assist in clinical decision triage and to provide additional biological insights into disease progression. Methods: The clinical activity of 3740 patients at NYU Langone Hospital was obtained between January and August 2020; patient data were deidentified. Models were trained on clinical data during different parts of their hospital stay to predict three clinical outcomes: deceased, ventilated, or admitted to the intensive care unit (ICU). Results: The XGBoost (eXtreme Gradient Boosting) model that was trained on clinical data from the final 24 hours excelled at predicting mortality (area under the curve [AUC]=0.92; specificity=86%; and sensitivity=85%). Respiration rate was the most important feature, followed by SpO Conclusions: Together, this work summarizes efforts to systematically examine the importance of a wide range of features across different endpoint outcomes and at different hospitalization time points.
MeSH term(s)	Adolescent ; Adult ; Aged ; Algorithms ; Area Under Curve ; COVID-19/diagnosis ; COVID-19/mortality ; Child ; Child, Preschool ; Diabetes Mellitus ; Female ; Hospitalization ; Hospitals ; Humans ; Infant ; Infant, Newborn ; Intensive Care Units ; Male ; Middle Aged ; Morbidity ; New York City/epidemiology ; Pandemics ; Retrospective Studies ; SARS-CoV-2 ; Triage ; Young Adult
Language	English
Publishing date	2021-07-09
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1439-4456
ISSN (online)	1438-8871
ISSN	1439-4456
DOI	10.2196/29514
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Article ; Online: Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges

M. C. Schut / D. A. Dongelmans / D. W. de Lange / S. Brinkman / Dutch COVID-19 Research Consortium / N. F. de Keizer / A. Abu-Hanna

BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-

2024 Volume 11

Abstract: ... for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry ... and variable transformations using splines. Participants included COVID-19 patients from all ICUs ... cross validation to prevent overfitting. The predictive ability of the model was assessed via ...

Abstract	Abstract Background Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data. Methods Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status. Results A total of 13,369 confirmed ...
Keywords	Decision rules ; Decision and regression trees ; Prediction model ; Intensive care ; COVID-19 ; In-hospital mortality ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	310
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Development and validation of a predictive model of in-hospital mortality in COVID-19 patients.

PLoS ONE, Vol 16, Iss 3, p e

2021 Volume 0247676

Abstract	We retrospectively evaluated 2879 hospitalized COVID-19 patients from four hospitals to evaluate the ability of demographic data, medical history, and on-admission laboratory parameters to predict in-hospital mortality. Association of previously published risk factors (age, gender, arterial hypertension, diabetes mellitus, smoking habit, obesity, renal failure, cardiovascular/ pulmonary diseases, serum ferritin, lymphocyte count, APTT, PT, fibrinogen, D-dimer, and platelet count) with death was tested by a multivariate logistic regression, and a predictive model was created, with further validation in an independent sample. A total of 2070 hospitalized COVID-19 patients were finally included in the multivariable analysis. Age 61-70 years (p<0.001; OR: 7.69; 95%CI: 2.93 to 20.14), age 71-80 years (p<0.001; OR: 14.99; 95%CI: 5.88 to 38.22), age >80 years (p<0.001; OR: 36.78; 95%CI: 14.42 to 93.85), male gender (p<0.001; OR: 1.84; 95%CI: 1.31 to 2.58), D-dimer levels >2 ULN (p = 0.003; OR: 1.79; 95%CI: 1.22 to 2.62), and prolonged PT (p<0.001; OR: 2.18; 95%CI: 1.49 to 3.18) were independently associated with increased in-hospital mortality. A predictive model performed with these parameters showed an AUC of 0.81 in the development cohort (n = 1270) [sensitivity of 95.83%, specificity of 41.46%, negative predictive value of 98.01%, and positive predictive value of 24.85%]. These results were then validated in an independent data sample (n = 800). Our predictive model of in-hospital mortality of COVID-19 patients has been developed, calibrated and validated. The model (MRS-COVID) included age, male gender, and on-admission coagulopathy markers as positively correlated factors with fatal outcome.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Development and external validation of a prediction risk model for short-term mortality among hospitalized U.S. COVID-19 patients

Kaveh Hajifathalian / Reem Z Sharaiha / Sonal Kumar / Tibor Krisko / Daniel Skaf / Bryan Ang / Walker D Redd / Joyce C Zhou / Kelly E Hathorn / Thomas R McCarty / Ahmad Najdat Bazarbashi / Cheikh Njie / Danny Wong / Lin Shen / Evan Sholle / David E Cohen / Robert S Brown / Walter W Chan / Brett E Fortune

PLoS ONE, Vol 15, Iss 9, p e

A proposal for the COVID-AID risk tool.

2020 Volume 0239536

Abstract: ... mortality risk prediction model for patients hospitalized with COVID-19. Methods We performed a multicenter ... among patients hospitalized with COVID-19. This prediction score could assist with resource utilization, patient ... among hospitalized patients with COVID-19. The objective of this study was to develop and validate a 7-day and 14-day ...

Abstract	Background The 2019 novel coronavirus disease (COVID-19) has created unprecedented medical challenges. There remains a need for validated risk prediction models to assess short-term mortality risk among hospitalized patients with COVID-19. The objective of this study was to develop and validate a 7-day and 14-day mortality risk prediction model for patients hospitalized with COVID-19. Methods We performed a multicenter retrospective cohort study with a separate multicenter cohort for external validation using two hospitals in New York, NY, and 9 hospitals in Massachusetts, respectively. A total of 664 patients in NY and 265 patients with COVID-19 in Massachusetts, hospitalized from March to April 2020. Results We developed a risk model consisting of patient age, hypoxia severity, mean arterial pressure and presence of kidney dysfunction at hospital presentation. Multivariable regression model was based on risk factors selected from univariable and Chi-squared automatic interaction detection analyses. Validation was by receiver operating characteristic curve (discrimination) and Hosmer-Lemeshow goodness of fit (GOF) test (calibration). In internal cross-validation, prediction of 7-day mortality had an AUC of 0.86 (95%CI 0.74-0.98; GOF p = 0.744); while 14-day had an AUC of 0.83 (95%CI 0.69-0.97; GOF p = 0.588). External validation was achieved using 265 patients from an outside cohort and confirmed 7- and 14-day mortality prediction performance with an AUC of 0.85 (95%CI 0.78-0.92; GOF p = 0.340) and 0.83 (95%CI 0.76-0.89; GOF p = 0.471) respectively, along with excellent calibration. Retrospective data collection, short follow-up time, and development in COVID-19 epicenter may limit model generalizability. Conclusions The COVID-AID risk tool is a well-calibrated model that demonstrates accuracy in the prediction of both 7-day and 14-day mortality risk among patients hospitalized with COVID-19. This prediction score could assist with resource utilization, patient and caregiver education, and provide a risk ...
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Development and external validation of a prediction risk model for short-term mortality among hospitalized U.S. COVID-19 patients

PLoS ONE, Vol 15, Iss

A proposal for the COVID-AID risk tool

2020 Volume 9

Abstract	Background The 2019 novel coronavirus disease (COVID-19) has created unprecedented medical challenges. There remains a need for validated risk prediction models to assess short-term mortality risk among hospitalized patients with COVID-19. The objective of this study was to develop and validate a 7-day and 14-day mortality risk prediction model for patients hospitalized with COVID-19. Methods We performed a multicenter retrospective cohort study with a separate multicenter cohort for external validation using two hospitals in New York, NY, and 9 hospitals in Massachusetts, respectively. A total of 664 patients in NY and 265 patients with COVID-19 in Massachusetts, hospitalized from March to April 2020. Results We developed a risk model consisting of patient age, hypoxia severity, mean arterial pressure and presence of kidney dysfunction at hospital presentation. Multivariable regression model was based on risk factors selected from univariable and Chi-squared automatic interaction detection analyses. Validation was by receiver operating characteristic curve (discrimination) and Hosmer-Lemeshow goodness of fit (GOF) test (calibration). In internal cross-validation, prediction of 7-day mortality had an AUC of 0.86 (95%CI 0.74–0.98; GOF p = 0.744); while 14-day had an AUC of 0.83 (95%CI 0.69–0.97; GOF p = 0.588). External validation was achieved using 265 patients from an outside cohort and confirmed 7- and 14-day mortality prediction performance with an AUC of 0.85 (95%CI 0.78–0.92; GOF p = 0.340) and 0.83 (95%CI 0.76–0.89; GOF p = 0.471) respectively, along with excellent calibration. Retrospective data collection, short follow-up time, and development in COVID-19 epicenter may limit model generalizability. Conclusions The COVID-AID risk tool is a well-calibrated model that demonstrates accuracy in the prediction of both 7-day and 14-day mortality risk among patients hospitalized with COVID-19. This prediction score could assist with resource utilization, patient and caregiver education, and provide a risk ...
Keywords	Medicine ; R ; Science ; Q ; covid19
Subject code	310
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Development and external validation of a prediction risk model for short-term mortality among hospitalized U.S. COVID-19 patients

Hajifathalian, Kaveh / Sharaiha, Reem Z. / Kumar, Sonal / Krisko, Tibor / Skaf, Daniel / Ang, Bryan / Redd, Walker D. / Zhou, Joyce C. / Hathorn, Kelly E. / McCarty, Thomas R. / Bazarbashi, Ahmad Najdat / Njie, Cheikh / Wong, Danny / Shen, Lin / Sholle, Evan / Cohen, David E. / Brown, Robert S. / Chan, Walter W. / Fortune, Brett E.

PLOS ONE

A proposal for the COVID-AID risk tool

2020 Volume 15, Issue 9, Page(s) e0239536

Keywords	General Biochemistry, Genetics and Molecular Biology ; General Agricultural and Biological Sciences ; General Medicine ; covid19
Language	English
Publisher	Public Library of Science (PLoS)
Publishing country	us
Document type	Article ; Online
ISSN	1932-6203
DOI	10.1371/journal.pone.0239536
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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