Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19
BackgroundTimely recognition of respiratory failure and the need for mechanical ventilation is crucial in managing patients with coronavirus disease 2019 (COVID-19) and reducing hospital mortality rate. A risk stratification tool could assist to avoid clinical deterioration of patients with COVID-19...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-07-01
|
| Series: | Frontiers in Public Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1227935/full |
| _version_ | 1797780184033656832 |
|---|---|
| author | Yi Zhang Yi Zhang Yang-Jie Zhu Dao-Jun Zhu Dao-Jun Zhu Bo-Yang Yu Tong-Tong Liu Lu-Yao Wang Lu-Lu Zhang |
| author_facet | Yi Zhang Yi Zhang Yang-Jie Zhu Dao-Jun Zhu Dao-Jun Zhu Bo-Yang Yu Tong-Tong Liu Lu-Yao Wang Lu-Lu Zhang |
| author_sort | Yi Zhang |
| collection | DOAJ |
| description | BackgroundTimely recognition of respiratory failure and the need for mechanical ventilation is crucial in managing patients with coronavirus disease 2019 (COVID-19) and reducing hospital mortality rate. A risk stratification tool could assist to avoid clinical deterioration of patients with COVID-19 and optimize allocation of scarce resources. Therefore, we aimed to develop a prediction model for early identification of patients with COVID-19 who may require mechanical ventilation.MethodsWe included patients with COVID-19 hospitalized in United States. Demographic and clinical data were extracted from the records of the Healthcare Cost and Utilization Project State Inpatient Database in 2020. Model construction involved the use of the least absolute shrinkage and selection operator and multivariable logistic regression. The model’s performance was evaluated based on discrimination, calibration, and clinical utility.ResultsThe training set comprised 73,957 patients (5,971 requiring mechanical ventilation), whereas the validation set included 10,428 (887 requiring mechanical ventilation). The prediction model incorporating age, sex, and 11 other comorbidities (deficiency anemias, congestive heart failure, coagulopathy, dementia, diabetes with chronic complications, complicated hypertension, neurological disorders unaffecting movement, obesity, pulmonary circulation disease, severe renal failure, and weight loss) demonstrated moderate discrimination (area under the curve, 0.715; 95% confidence interval, 0.709–0.722), good calibration (Brier score = 0.070, slope = 1, intercept = 0) and a clinical net benefit with a threshold probability ranged from 2 to 34% in the training set. Similar model’s performances were observed in the validation set.ConclusionA robust prognostic model utilizing readily available predictors at hospital admission was developed for the early identification of patients with COVID-19 who may require mechanical ventilation. Application of this model could support clinical decision-making to optimize patient management and resource allocation. |
| first_indexed | 2024-03-12T23:40:52Z |
| format | Article |
| id | doaj.art-93390e5dce36495bac8987eb55220ca5 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-03-12T23:40:52Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-93390e5dce36495bac8987eb55220ca52023-07-14T19:59:15ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-07-011110.3389/fpubh.2023.12279351227935Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19Yi Zhang0Yi Zhang1Yang-Jie Zhu2Dao-Jun Zhu3Dao-Jun Zhu4Bo-Yang Yu5Tong-Tong Liu6Lu-Yao Wang7Lu-Lu Zhang8Department of Gastroenterology, Changzheng Hospital, Naval Medical University, Shanghai, ChinaDepartment of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, ChinaDepartment of Military Health Management, College of Health Service, Naval Medical University, Shanghai, ChinaOperating Room, West China Hospital, Sichuan University, Chengdu, ChinaWest China School of Nursing, Sichuan University, Chengdu, ChinaDepartment of Military Health Management, College of Health Service, Naval Medical University, Shanghai, ChinaDepartment of Military Health Management, College of Health Service, Naval Medical University, Shanghai, ChinaDepartment of Military Health Management, College of Health Service, Naval Medical University, Shanghai, ChinaDepartment of Military Health Management, College of Health Service, Naval Medical University, Shanghai, ChinaBackgroundTimely recognition of respiratory failure and the need for mechanical ventilation is crucial in managing patients with coronavirus disease 2019 (COVID-19) and reducing hospital mortality rate. A risk stratification tool could assist to avoid clinical deterioration of patients with COVID-19 and optimize allocation of scarce resources. Therefore, we aimed to develop a prediction model for early identification of patients with COVID-19 who may require mechanical ventilation.MethodsWe included patients with COVID-19 hospitalized in United States. Demographic and clinical data were extracted from the records of the Healthcare Cost and Utilization Project State Inpatient Database in 2020. Model construction involved the use of the least absolute shrinkage and selection operator and multivariable logistic regression. The model’s performance was evaluated based on discrimination, calibration, and clinical utility.ResultsThe training set comprised 73,957 patients (5,971 requiring mechanical ventilation), whereas the validation set included 10,428 (887 requiring mechanical ventilation). The prediction model incorporating age, sex, and 11 other comorbidities (deficiency anemias, congestive heart failure, coagulopathy, dementia, diabetes with chronic complications, complicated hypertension, neurological disorders unaffecting movement, obesity, pulmonary circulation disease, severe renal failure, and weight loss) demonstrated moderate discrimination (area under the curve, 0.715; 95% confidence interval, 0.709–0.722), good calibration (Brier score = 0.070, slope = 1, intercept = 0) and a clinical net benefit with a threshold probability ranged from 2 to 34% in the training set. Similar model’s performances were observed in the validation set.ConclusionA robust prognostic model utilizing readily available predictors at hospital admission was developed for the early identification of patients with COVID-19 who may require mechanical ventilation. Application of this model could support clinical decision-making to optimize patient management and resource allocation.https://www.frontiersin.org/articles/10.3389/fpubh.2023.1227935/fullCOVID-19comorbidityhospitalizationmechanical ventilationprediction modelretrospective study |
| spellingShingle | Yi Zhang Yi Zhang Yang-Jie Zhu Dao-Jun Zhu Dao-Jun Zhu Bo-Yang Yu Tong-Tong Liu Lu-Yao Wang Lu-Lu Zhang Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19 Frontiers in Public Health COVID-19 comorbidity hospitalization mechanical ventilation prediction model retrospective study |
| title | Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19 |
| title_full | Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19 |
| title_fullStr | Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19 |
| title_full_unstemmed | Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19 |
| title_short | Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19 |
| title_sort | development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with covid 19 |
| topic | COVID-19 comorbidity hospitalization mechanical ventilation prediction model retrospective study |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1227935/full |
| work_keys_str_mv | AT yizhang developmentandvalidationofapredictionmodelformechanicalventilationbasedoncomorbiditiesinhospitalizedpatientswithcovid19 AT yizhang developmentandvalidationofapredictionmodelformechanicalventilationbasedoncomorbiditiesinhospitalizedpatientswithcovid19 AT yangjiezhu developmentandvalidationofapredictionmodelformechanicalventilationbasedoncomorbiditiesinhospitalizedpatientswithcovid19 AT daojunzhu developmentandvalidationofapredictionmodelformechanicalventilationbasedoncomorbiditiesinhospitalizedpatientswithcovid19 AT daojunzhu developmentandvalidationofapredictionmodelformechanicalventilationbasedoncomorbiditiesinhospitalizedpatientswithcovid19 AT boyangyu developmentandvalidationofapredictionmodelformechanicalventilationbasedoncomorbiditiesinhospitalizedpatientswithcovid19 AT tongtongliu developmentandvalidationofapredictionmodelformechanicalventilationbasedoncomorbiditiesinhospitalizedpatientswithcovid19 AT luyaowang developmentandvalidationofapredictionmodelformechanicalventilationbasedoncomorbiditiesinhospitalizedpatientswithcovid19 AT luluzhang developmentandvalidationofapredictionmodelformechanicalventilationbasedoncomorbiditiesinhospitalizedpatientswithcovid19 |