Dyspnea Correlations in Patients With Post-Acute Sequelae of COVID-19 Infection
Bibliographic record
Abstract
Background: COVID-19 is a respiratory disease that has caused a modern global pandemic. A subset of patients continues to complain of dyspnea several weeks and months after the initial acute COVID-19 infection. This project was a retrospective analysis of patients diagnosed with Post-Acute Sequelae of COVID-19 infection (PASC) within an Outpatient COVID Care Center in Central PA. This study focused on identifying any correlations between the breathlessness of long COVID-19 patients and multiple other factors. Methods: After IRB approval, a retrospective study using electronic data from Wellspan Health (York PA) Outpatient COVID Care Center was conducted. 97 patients with pulmonary function testing recorded in the electronic medical record from June 2021 to March 2022 were included in the study. The retrospective study analyzed factors including age, prior to COVID smoking history, rest and/or exercise hypoxemia, history of prior pulmonary diseases, post-bronchodilator spirometry, total lung capacity, diffusing capacity, intubation history due to acute COVID-19, current symptoms of depression as shown by patient health care questionnaire (PHQ6), current symptoms of anxiety as shown by the general anxiety disorder score (GAD7), and cognitive function as shown by Montreal cognitive assessment (MoCA0). Results: 78/97 patients identified dyspnea as a complaint 4-6 weeks after acute COVID-19 infection. Statistical analysis with Chi-square, independent T-test and Mann-Whitney U test demonstrated a P-value greater than 0.05 in all analyses indicating no statistical predictor of post-COVID-19 shortness of breath among the tested considerations. Although they were not statistically significant in this study, factors such as abnormal DLCO, TLC, and elevated scores of PHQ6, and GAD7 showed an interesting trend towards a correlation with post-COVID-19 shortness of breath Conclusions: COVID-19 continues to affect patients, even after the initial infection. This study on long COVID-19 patients showed no statistically significant factors that predicted or correlated with ongoing dyspnea. Limitations to our study include small sample size and only the use of single-center patients. Further research should be conducted with larger sample size and continued factors such as DLCO, TLC, anxiety, depression, and other factors that could predict or influence dyspnea in long COVID-19 patients.Covid PASCTest variableDyspnea post Covid (Y/N?)MeanSDp-valueAge YearsNo56.5019.6720.593Yes54.4513.250FVC (% pred)No92.4416.4180.729Yes94.0417.748FEV1 (% pred)No89.2214.9960.768Yes90.6318.781DLCO (% pred)No86.3319.8670.338Yes91.0518.423TLC (% pred)No94.061.165870.285Yes98.961.22312PHQ6Patient HealthNo9.8113.3180.290Yes9.636.958GAD7AnxietyNo6.947.980.355Yes7.796.063MoCACognitive AssessmentNo19.752.8640.749Yes19.374.552
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".