Use of latent class analysis and patient reported outcome measures to identify distinct long COVID phenotypes: A longitudinal cohort study.

OBJECTIVES: We sought to 1) identify long COVID phenotypes based on patient reported outcome measures (PROMs) and 2) determine whether the phenotypes were associated with quality of life (QoL) and/or lung function. METHODS: This was a longitudinal cohort study of hospitalized and non-hospitalized patients from March 2020 to January 2022 that was conducted across 4 Post-COVID Recovery Clinics in British Columbia, Canada. Latent class analysis was used to identify long COVID phenotypes using baseline PROMs (fatigue, dyspnea, cough, anxiety, depression, and post-traumatic stress disorder). We then explored the association between the phenotypes and QoL (using the EuroQoL 5 dimensions visual analogue scale [EQ5D VAS]) and lung function (using the diffusing capacity of the lung for carbon monoxide [DLCO]). RESULTS: There were 1,344 patients enrolled in the study (mean age 51 ±15 years; 780 [58%] were females; 769 (57%) were of a non-White race). Three distinct long COVID phenotypes were identified: Class 1) fatigue and dyspnea, Class 2) anxiety and depression, and Class 3) fatigue, dyspnea, anxiety, and depression. Class 3 had a significantly lower EQ5D VAS at 3 (50±19) and 6 months (54 ± 22) compared to Classes 1 and 2 (p<0.001). The EQ5D VAS significantly improved between 3 and 6 months for Class 1 (median difference of 6.0 [95% CI, 4.0 to 8.0]) and Class 3 (median difference of 5.0 [95% CI, 0 to 8.5]). There were no differences in DLCO between the classes. CONCLUSIONS: There were 3 distinct long COVID phenotypes with different outcomes in QoL between 3 and 6 months after symptom onset. These phenotypes suggest that long COVID is a heterogeneous condition with distinct subpopulations who may have different outcomes and warrant tailored therapeutic approaches.

Post-COVID dyspnea: prevalence, predictors, and outcomes in a longitudinal, prospective cohort.

BACKGROUND: The pathophysiology, evolution, and associated outcomes of post-COVID dyspnea remain unknown. The aim of this study was to determine the prevalence, severity, and predictors of dyspnea 12 months following hospitalization for COVID-19, and to describe the respiratory, cardiac, and patient-reported outcomes in patients with post-COVID dyspnea. METHODS: We enrolled a prospective cohort of all adult patients admitted to 2 academic hospitals in Vancouver, Canada with PCR-confirmed SARS-CoV-2 during the first wave of COVID between March and June 2020. Dyspnea was measured 3, 6, and 12 months after initial symptom onset using the University of California San Diego Shortness of Breath Questionnaire. RESULTS: A total of 76 patients were included. Clinically meaningful dyspnea (baseline score > 10 points) was present in 49% of patients at 3 months and 46% at 12 months following COVID-19. Between 3 and 12 months post-COVID-19, 24% patients had a clinically meaningful worsening in their dyspnea, 49% had no meaningful change, and 28% had a clinically meaningful improvement in their dyspnea. There was worse sleep, mood, quality of life, and frailty in patients with clinically meaningful dyspnea at 12 months post-COVID infection compared to patients without dyspnea. There was no difference in PFT findings, troponin, or BNP comparing patients with and without clinically meaningful dyspnea at 12 months. Severity of dyspnea and depressive symptoms at 3 months predicted severity of dyspnea at 12 months. CONCLUSIONS: Post-COVID dyspnea is common, persistent, and negatively impacts quality of life. Mood abnormalities may play a causative role in post-COVID dyspnea in addition to potential cardiorespiratory abnormalities. Dyspnea and depression at initial follow-up predict longer-term post-COVID dyspnea, emphasizing that standardized dyspnea and mood assessment following COVID-19 may identify patients at high risk of post-COVID dyspnea and facilitating early and effective management.

Evaluating fatigue in patients recovering from COVID-19: validation of the fatigue severity scale and single item screening questions.

BACKGROUND: Fatigue is a common symptom in hospitalized and non-hospitalized patients recovering from COVID-19, but no fatigue measurement scales or questions have been validated in these populations. The objective of this study was to perform validity assessments of the fatigue severity scale (FSS) and two single-item screening questions (SISQs) for fatigue in patients recovering from COVID-19. METHODS: We examined patients ≥ 28 days after their first SARS-CoV-2 infection who were hospitalized for their acute illness, as well as non-hospitalized patients referred for persistent symptoms. Patients completed questionnaires through 1 of 4 Post COVID-19 Recovery Clinics in British Columbia, Canada. Construct validity was assessed by comparing FSS scores to quality of life and depression measures. Two SISQs were evaluated based on the ability to classify fatigue (FSS score ≥ 4). RESULTS: Questionnaires were returned in 548 hospitalized and 546 non-hospitalized patients, with scores computable in 96.4% and 98.2% of patients respectively. Cronbach's alpha was 0.96 in both groups. The mean ± SD FSS score was 4.4 ± 1.8 in the hospitalized and 5.2 ± 1.6 in the non-hospitalized group, with 62.5% hospitalized and 78.9% non-hospitalized patients classified as fatigued. Ceiling effects were 7.6% in the hospitalized and 16.1% in non-hospitalized patients. FSS scores negatively correlated with EQ-5D scores in both groups (Spearman's rho - 0.6 in both hospitalized and non-hospitalized; p < 0.001) and were higher among patients with a positive PHQ-2 depression screen (5.4 vs. 4.0 in hospitalized and 5.9 vs. 4.9 in non-hospitalized; p < 0.001). An SISQ asking whether there was "fatigue present" had a sensitivity of 70.6% in hospitalized and 83.2% in non-hospitalized patients; the "always feeling tired" SISQ, had a sensitivity of 70.5% and 89.6% respectively. CONCLUSIONS: Fatigue was common and severe in patients referred for post COVID-19 assessment. Overall, the FSS is suitable for measuring fatigue in these patients, as there was excellent data quality, strong internal consistency, and construct validity. However, ceiling effects may be a limitation in the non-hospitalized group. SISQs had good sensitivity for identifying clinically relevant fatigue in non-hospitalized patients but only moderate sensitivity in the hospitalized group, indicating that there were more false negatives.

An Elastic Net Regression Model for Identifying Long COVID Patients Using Health Administrative Data: A Population-Based Study.

Background: Long coronavirus disease (COVID) patients experience persistent symptoms after acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Healthcare utilization data could provide critical information on the disease burden of long COVID for service planning; however, not all patients are diagnosed or assigned long COVID diagnostic codes. We developed an algorithm to identify individuals with long COVID using population-level health administrative data from British Columbia (BC), Canada. Methods: An elastic net penalized logistic regression model was developed to identify long COVID patients based on demographic characteristics, pre-existing conditions, COVID-19-related data, and all symptoms/conditions recorded >28-183 days after the COVID-19 symptom onset/reported (index) date of known long COVID patients (n = 2430) and a control group (n = 24 300), selected from all adult COVID-19 cases in BC with an index date on/before October 31, 2021 (n = 168 111). Known long COVID cases were diagnosed in a clinic and/or had the International Classification of Diseases, Tenth Revision, Canada (ICD-10-CA) code for "post COVID-19 condition" in their records. Results: The algorithm retained known symptoms/conditions associated with long COVID, demonstrating high sensitivity (86%), specificity (86%), and area under the receiver operator curve (93%). It identified 25 220 (18%) long COVID patients among the remaining 141 381 adult COVID-19 cases, >10 times the number of known cases. Known and predicted long COVID patients had comparable demographic and health-related characteristics. Conclusions: Our algorithm identified long COVID patients with a high level of accuracy. This large cohort of long COVID patients will serve as a platform for robust assessments on the clinical course of long COVID, and provide much needed concrete information for decision-making.

Changes in pulmonary function and patient-reported outcomes during COVID-19 recovery: a longitudinal, prospective cohort study.

OBJECTIVES: The aim of this study was to compare respiratory and patient-reported outcome measures (PROMs) between 3 and 6 months after symptom onset and to identify features that predict these changes. METHODS: This was a consecutive prospective cohort of 73 patients who were hospitalised with coronavirus disease 2019 (COVID-19). We evaluated the changes in pulmonary function tests and PROMs between 3 and 6 months and then investigated the associations between outcomes (change in diffusing capacity of the lung for carbon monoxide (D LCO), dyspnoea and quality of life (QoL)) and clinical and radiological features. RESULTS: There was improvement in forced vital capacity, total lung capacity and D LCO between 3 and 6 months by 3.25%, 3.82% and 5.69%, respectively; however, there was no difference in PROMs. Reticulation and total computed tomography (CT) scores were associated with lower D LCO % predicted at 6 months (coefficients; -8.7 and -5.3, respectively). The association between radiological scores and D LCO were modified by time, with the degree of association between ground glass and D LCO having decreased markedly over time. There was no association between other predictors and change in dyspnoea or QoL over time. CONCLUSIONS: There is improvement in pulmonary function measurements between 3 and 6 months after COVID-19 symptom onset; however, PROMs did not improve. A higher reticulation and total CT score are negatively associated with D LCO, but this association is attenuated over time. Lastly, there is a considerable proportion of patients with unexplained dyspnoea at 6 months, motivating further research to identify the underlying mechanisms.

Pulmonary function and functional capacity in COVID-19 survivors with persistent dyspnoea.

The purpose of this study was to examine the physiological mechanisms of persistent dyspnoea in COVID-19 survivors. Non-critical patients (n = 186) with varying degrees of COVID-19 severity reported persistent symptoms using a standardized questionnaire and underwent pulmonary function and 6-minute walk testing between 30 and 90 days following the onset of acute COVID-19 symptoms. Patients were divided into those with (n = 70) and without (n = 116) persistent dyspnoea. Patients with persistent dyspnoea had significantly lower FVC (p = 0.03), FEV1 (p = 0.04), DLCO (p = 0.01), 6-minute walk distance (% predicted, p = 0.03), and end-exercise oxygen saturation (p < 0.001), and higher Borg 0-10 ratings of dyspnoea and fatigue (both p < 0.001) compared to patients without persistent dyspnoea. We have shown that dyspnoea is a common persistent symptom across varying degrees of initial COVID-19 severity. Patients with persistent dyspnoea had greater restriction on spirometry, lower DLCO, reduced functional capacity, and increased exertional desaturation and symptoms. This suggests that there is a true physiological mechanism that may explain persistent dyspnoea after COVID-19.

A prospective study of 12-week respiratory outcomes in COVID-19-related hospitalisations.

The long-term respiratory morbidity of COVID-19 remains unclear. We describe the clinical, radiological and pulmonary function abnormalities that persist in previously hospitalised patients assessed 12 weeks after COVID-19 symptom onset, and identify clinical predictors of respiratory outcomes. At least one pulmonary function variable was abnormal in 58% of patients and 88% had abnormal imaging on chest CT. There was strong association between days on oxygen supplementation during the acute phase of COVID-19 and both DLCO-% (diffusion capacity of the lung for carbon monoxide) predicted and total CT score. These findings highlight the need to develop treatment strategies and the importance of long-term respiratory follow-up after hospitalisation for COVID-19.

Practical Considerations for the Diagnosis and Treatment of Fibrotic Interstitial Lung Disease During the Coronavirus Disease 2019 Pandemic.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2, has affected virtually all aspects of patient care. Health-care systems around the world are trying simultaneously to treat patients with COVID-19, prepare for its long-term impacts, and treat patients with other acute and chronic diseases. There are multiple ways that the COVID-19 pandemic will directly affect patients with fibrotic interstitial lung disease (ILD), particularly given their common risk factors for poor outcomes. Major issues for patients with ILD will include restricted access to key components of the diagnostic process, new uncertainties in the use of common ILD pharmacotherapies, limited ability to monitor both disease severity and the presence of medication adverse effects, and significantly curtailed research activities. The purpose of this review is to summarize how COVID-19 has impacted key components of the diagnosis and management of fibrotic ILD as well as to provide strategies to mitigate these challenges. We further review major obstacles for researchers and identify priority areas for future ILD research related to COVID-19. Our goals are to provide practical considerations to support the care of patients with ILD during the COVID-19 pandemic and to provide a road map for clinicians caring for these patients during future infectious disease outbreaks.