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Published on: December 9, 2015
Early prediction of long COVID-19 syndrome persistence at 12 months after hospitalisation: a prospective
Oleksii Honchar1, Tetiana Ashcheulova2, Tetyana Chumachenko3
1Department of Propedeutics of Internal Medicine, Nursing and Bioethics, Kharkiv National Medical University, Kharkiv, Ukraine oleksiygonchar@gmail.com.
Insights
Long COVID syndrome (LCS) persisting at 12 months is linked to initial comorbidities, pulmonary issues, and worse organ function. An AI model accurately predicts LCS persistence using early markers like erythrocyte sedimentation rate and physical symptoms.
Area of Science:
- Pulmonology and Infectious Diseases
- Cardiology
- Nephrology
- Psychiatry
Background:
- Long COVID syndrome (LCS) affects a significant portion of hospitalized COVID-19 patients.
- Understanding predictors of persistent LCS is crucial for patient management and prognosis.
- Early identification of individuals at risk for prolonged symptoms can guide targeted interventions.
Purpose of the Study:
- To identify early predictors of self-reported persistent Long COVID syndrome (LCS) at 12 months post-hospitalization.
- To develop a prognostic model for predicting the development and persistence of LCS.
- To analyze clinical, sonographic, and survey-based data for predictive factors.
Main Methods:
- A combined cross-sectional and prospective observational study involving 166 patients.
- Comprehensive clinical, sonographic, and survey-based evaluations were performed pre-discharge and at 1 and 12 months.
- Logistic regression and machine learning models, including artificial neural networks, were utilized for prediction.
Main Results:
- LCS persisted in 43% of patients at 12 months.
- Predictors included higher comorbidity burden, residual pulmonary consolidations, poorer blood pressure control, impaired renal and endothelial function, elevated liver enzymes, and higher inflammatory markers (ESR).
- Anxiety, depression, and physical symptom scores were significantly higher in patients with persistent LCS.
Conclusions:
- Persistent LCS is associated with more pronounced predischarge abnormalities, which largely resolve by 1 month, except for physical symptoms.
- An artificial neural network model incorporating peak ESR, creatinine, ALT, weight loss, 6-minute walk distance, and symptom scores achieved 92% accuracy in predicting LCS persistence.
- This model offers a valuable tool for early identification of patients at risk for long-term LCS symptoms.
Objective:
To identify the early predictors of a self-reported persistence of long COVID syndrome (LCS) at 12 months after hospitalisation and to propose the prognostic model of its development.
Design:
A combined cross-sectional and prospective observational study.
Setting:
A tertiary care hospital.
Participants:
221 patients hospitalised for COVID-19 who have undergone comprehensive clinical, sonographic and survey-based evaluation predischarge and at 1 month with subsequent 12-month follow-up. The final cohort included 166 patients who had completed the final visit at 12 months.
Main Outcome Measure:
A self-reported persistence of LCS at 12 months after discharge.
Results:
Self-reported LCS was detected in 76% of participants at 3 months and in 43% at 12 months after discharge. Patients who reported incomplete recovery at 1 year were characterised by a higher burden of comorbidities (Charlson index of 0.69±0.96 vs 0.31±0.51, p=0.001) and residual pulmonary consolidations (1.56±1.78 vs 0.98±1.56, p=0.034), worse blood pressure (BP) control (systolic BP of 138.1±16.2 vs 132.2±15.8 mm Hg, p=0.041), renal (estimated glomerular filtration rate of 59.5±14.7 vs 69.8±20.7 mL/min/1.73 m2, p=0.007) and endothelial function (flow-mediated dilation of the brachial artery of 10.4±5.4 vs 12.4±5.6%, p=0.048), higher in-hospital levels of liver enzymes (alanine aminotransferase (ALT) of 76.3±60.8 vs 46.3±25.3 IU/L, p=0.002) and erythrocyte sedimentation rate (ESR) (34.3±12.1 vs 28.3±12.6 mm/h, p=0.008), slightly higher indices of ventricular longitudinal function (left ventricular (LV) global longitudinal strain (GLS) of 18.0±2.4 vs 17.0±2.3%, p=0011) and higher levels of Hospital Anxiety and Depression Scale anxiety (7.3±4.2 vs 5.6±3.8, p=0.011) and depression scores (6.4±3.9 vs 4.9±4.3, p=0.022) and EFTER-COVID study physical symptoms score (12.3±3.8 vs 9.2±4.2, p<0.001). At 1 month postdischarge, the persisting differences included marginally higher LV GLS, mitral E/e' ratio and significantly higher levels of both resting and exertional physical symptoms versus patients who reported complete recovery. Logistic regression and machine learning-based binary classification models have been developed to predict the persistence of LCS symptoms at 12 months after discharge.
Conclusions:
Compared with post-COVID-19 patients who have completely recovered by 12 months after hospital discharge, those who have subsequently developed 'very long' COVID were characterised by a variety of more pronounced residual predischarge abnormalities that had mostly subsided by 1 month, except for steady differences in the physical symptoms levels. A simple artificial neural networks-based binary classification model using peak ESR, creatinine, ALT and weight loss during the acute phase, predischarge 6-minute walk distance and complex survey-based symptoms assessment as inputs has shown a 92% accuracy with an area under receiver-operator characteristic curve 0.931 in prediction of LCS symptoms persistence at 12 months.
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