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Development and validation of a prognostic model for assessing long COVID risk following Omicron wave-a large
Lu-Cheng Fang1,2, Xiao-Ping Ming1,2, Wan-Yue Cai1,2
1Department of Otorhinolaryngology, Head and Neck Surgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Insights
A new nomogram model can predict Long COVID risk in hospitalized patients. This tool aids early identification and clinical management of Long COVID, improving patient outcomes after COVID-19 infection.
Area of Science:
- Infectious Diseases
- Clinical Medicine
- Epidemiology
Background:
- Long coronavirus disease (COVID) poses a global health threat.
- Effective prediction models for Long COVID risk in hospitalized patients are lacking.
- Early risk identification is crucial for managing COVID-19 patients.
Purpose of the Study:
- To develop and validate a reliable prediction model for Long COVID risk.
- To identify key risk factors for Long COVID in hospitalized COVID-19 patients.
- To aid clinical decision-making for Long COVID management.
Main Methods:
- 1905 hospitalized COVID-19 patients were analyzed.
- Long COVID status was assessed 4-8 weeks post-discharge.
- Lasso regression, logistic regression, and nomogram visualization were employed.
- Model performance was evaluated using AUC, calibration curves, and DCA.
Main Results:
- 34.5% of patients developed Long COVID symptoms, primarily fatigue, sleep difficulties, and cough.
- A nomogram incorporating age, diabetes, CKD, vaccination, procalcitonin, leukocytes, lymphocytes, IL-6, and D-dimer was developed.
- The model demonstrated good predictive performance with AUCs of 0.762 (training) and 0.713 (validation).
- Calibration curves and DCA confirmed the model's accuracy and clinical utility.
Conclusions:
- A validated nomogram model for predicting Long COVID risk in hospitalized patients was established.
- The model shows relatively good predictive performance and aids early identification of high-risk individuals.
- This tool can significantly assist in the clinical management of Long COVID.
Background:
Long coronavirus disease (COVID) after COVID-19 infection is continuously threatening the health of people all over the world. Early prediction of the risk of Long COVID in hospitalized patients will help clinical management of COVID-19, but there is still no reliable and effective prediction model.
Methods:
A total of 1905 hospitalized patients with COVID-19 infection were included in this study, and their Long COVID status was followed up 4-8 weeks after discharge. Univariable and multivariable logistic regression analysis were used to determine the risk factors for Long COVID. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and factors for constructing the model were screened using Lasso regression in the training cohort. Visualize the Long COVID risk prediction model using nomogram. Evaluate the performance of the model in the training and validation cohort using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).
Results:
A total of 657 patients (34.5%) reported that they had symptoms of long COVID. The most common symptoms were fatigue or muscle weakness (16.8%), followed by sleep difficulties (11.1%) and cough (9.5%). The risk prediction nomogram of age, diabetes, chronic kidney disease, vaccination status, procalcitonin, leukocytes, lymphocytes, interleukin-6 and D-dimer were included for early identification of high-risk patients with Long COVID. AUCs of the model in the training cohort and validation cohort are 0.762 and 0.713, respectively, demonstrating relatively high discrimination of the model. The calibration curve further substantiated the proximity of the nomogram's predicted outcomes to the ideal curve, the consistency between the predicted outcomes and the actual outcomes, and the potential benefits for all patients as indicated by DCA. This observation was further validated in the validation cohort.
Conclusions:
We established a nomogram model to predict the long COVID risk of hospitalized patients with COVID-19, and proved its relatively good predictive performance. This model is helpful for the clinical management of long COVID.
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