tetano
Editor, Senior Moderator
BMC Pulm Med
. 2022 May 12;22(1):188.
doi: 10.1186/s12890-022-01986-0.
Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model
Hongbing Peng[SUP] 1 1 [/SUP], Chao Hu[SUP] 2 [/SUP], Wusheng Deng[SUP] 3 [/SUP], Lingmei Huang[SUP] 4 [/SUP], Yushan Zhang[SUP] 5 [/SUP], Baowei Luo[SUP] 6 [/SUP], Xingxing Wang[SUP] 7 [/SUP], Xiaodan Long[SUP] 8 [/SUP], Xiaoying Huang[SUP] 9 [/SUP]
Affiliations
Abstract
Background: Most severe, critical, or mortal COVID-19 cases often had a relatively stable period before their status worsened. We developed a deterioration risk model of COVID-19 (DRM-COVID-19) to predict exacerbation risk and optimize disease management on admission.
Method: We conducted a multicenter retrospective cohort study with 239 confirmed symptomatic COVID-19 patients. A combination of the least absolute shrinkage and selection operator (LASSO), change-in-estimate (CIE) screened out independent risk factors for the multivariate logistic regression model (DRM-COVID-19) from 44 variables, including epidemiological, demographic, clinical, and lung CT features. The compound study endpoint was progression to severe, critical, or mortal status. Additionally, the model's performance was evaluated for discrimination, accuracy, calibration, and clinical utility, through internal validation using bootstrap resampling (1000 times). We used a nomogram and a network platform for model visualization.
Results: In the cohort study, 62 cases reached the compound endpoint, including 42 severe, 18 critical, and two mortal cases. DRM-COVID-19 included six factors: dyspnea [odds ratio (OR) 4.89;confidence interval (95% CI) 1.53-15.80], incubation period (OR 0.83; 95% CI 0.68-0.99), number of comorbidities (OR 1.76; 95% CI 1.03-3.05), D-dimer (OR 7.05; 95% CI, 1.35-45.7), C-reactive protein (OR 1.06; 95% CI 1.02-1.1), and semi-quantitative CT score (OR 1.50; 95% CI 1.27-1.82). The model showed good fitting (Hosmer-Lemeshow goodness, X[SUP]2[/SUP](8) = 7.0194, P = 0.53), high discrimination (the area under the receiver operating characteristic curve, AUROC, 0.971; 95% CI, 0.949-0.992), precision (Brier score = 0.051) as well as excellent calibration and clinical benefits. The precision-recall (PR) curve showed excellent classification performance of the model (AUC[SUB]PR[/SUB] = 0.934). We prepared a nomogram and a freely available online prediction platform ( https://deterioration-risk-model-of-covid-19.shinyapps.io/DRMapp/ ).
Conclusion: We developed a predictive model, which includes the including incubation period along with clinical and lung CT features. The model presented satisfactory prediction and discrimination performance for COVID-19 patients who might progress from mild or moderate to severe or critical on admission, improving the clinical prognosis and optimizing the medical resources.
Keywords: COVID-19; Change-in-estimate (CIE); Deterioration; Incubation period; Prediction model; Semi-quantitative CT score.
. 2022 May 12;22(1):188.
doi: 10.1186/s12890-022-01986-0.
Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model
Hongbing Peng[SUP] 1 1 [/SUP], Chao Hu[SUP] 2 [/SUP], Wusheng Deng[SUP] 3 [/SUP], Lingmei Huang[SUP] 4 [/SUP], Yushan Zhang[SUP] 5 [/SUP], Baowei Luo[SUP] 6 [/SUP], Xingxing Wang[SUP] 7 [/SUP], Xiaodan Long[SUP] 8 [/SUP], Xiaoying Huang[SUP] 9 [/SUP]
Affiliations
- PMID: 35549897
- DOI: 10.1186/s12890-022-01986-0
Abstract
Background: Most severe, critical, or mortal COVID-19 cases often had a relatively stable period before their status worsened. We developed a deterioration risk model of COVID-19 (DRM-COVID-19) to predict exacerbation risk and optimize disease management on admission.
Method: We conducted a multicenter retrospective cohort study with 239 confirmed symptomatic COVID-19 patients. A combination of the least absolute shrinkage and selection operator (LASSO), change-in-estimate (CIE) screened out independent risk factors for the multivariate logistic regression model (DRM-COVID-19) from 44 variables, including epidemiological, demographic, clinical, and lung CT features. The compound study endpoint was progression to severe, critical, or mortal status. Additionally, the model's performance was evaluated for discrimination, accuracy, calibration, and clinical utility, through internal validation using bootstrap resampling (1000 times). We used a nomogram and a network platform for model visualization.
Results: In the cohort study, 62 cases reached the compound endpoint, including 42 severe, 18 critical, and two mortal cases. DRM-COVID-19 included six factors: dyspnea [odds ratio (OR) 4.89;confidence interval (95% CI) 1.53-15.80], incubation period (OR 0.83; 95% CI 0.68-0.99), number of comorbidities (OR 1.76; 95% CI 1.03-3.05), D-dimer (OR 7.05; 95% CI, 1.35-45.7), C-reactive protein (OR 1.06; 95% CI 1.02-1.1), and semi-quantitative CT score (OR 1.50; 95% CI 1.27-1.82). The model showed good fitting (Hosmer-Lemeshow goodness, X[SUP]2[/SUP](8) = 7.0194, P = 0.53), high discrimination (the area under the receiver operating characteristic curve, AUROC, 0.971; 95% CI, 0.949-0.992), precision (Brier score = 0.051) as well as excellent calibration and clinical benefits. The precision-recall (PR) curve showed excellent classification performance of the model (AUC[SUB]PR[/SUB] = 0.934). We prepared a nomogram and a freely available online prediction platform ( https://deterioration-risk-model-of-covid-19.shinyapps.io/DRMapp/ ).
Conclusion: We developed a predictive model, which includes the including incubation period along with clinical and lung CT features. The model presented satisfactory prediction and discrimination performance for COVID-19 patients who might progress from mild or moderate to severe or critical on admission, improving the clinical prognosis and optimizing the medical resources.
Keywords: COVID-19; Change-in-estimate (CIE); Deterioration; Incubation period; Prediction model; Semi-quantitative CT score.