Clinical Predictors of Lung Function in Patients Recovering From Mild COVID-19

Arturo Cortes-Telles; Esperanza Figueroa-Hurtado; Diana Lizbeth Ortiz-Farias; Gerald Stanley Zavorsky

Disclosures

BMC Pulm Med. 2022;22(294) 

In This Article

Materials and Methods

Study Design

This is an observational cross-sectional study from the Long-term follow-up COVID-19 clinic at the High Specialty Regional Hospital of Yucatan, Mexico, from March 2021 to August 2021. We consecutively enrolled one hundred and fifty patients during the period. Inclusion criteria were adults over 18 years old recovering from mild COVID-19, defined as symptomatic patients meeting the case definition for COVID-19 without evidence of pneumonia or hypoxia (current WHO diagnostic criteria).[29] All patients were scheduled after 4–6 weeks (34.4 ± 3.8 days) of baseline symptoms to perform pulmonary function tests and were evaluated for persistent symptoms at the clinic. The Ethics Committee of the High Specialty Regional Hospital in Yucatan, Mexico, approved this study (Protocol number 2020–024). All patients provided written informed consent to participate in this study, in compliance with the Helsinki declaration. This study followed the Guidelines for Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).

Methods

Patients received a comprehensive medical assessment with a detailed medical history. Data including demographics, persistent symptoms from surveys, and pulmonary function test results were collected during the follow-up visit. Demographic data included age, sex, body mass index (BMI), previous cardiovascular disease risk factors for which regular pharmacological treatment was incorporated (including systemic hypertension, cardiac disease, diabetes mellitus), tobacco use (current or former smoker vs never smoker), obesity (defined as body mass index > 30 kg/m2). Patients were asked if they received oral corticosteroids and/or anticoagulants during the disease and to recount the presence or absence of symptoms at the time of the visit, including fatigue, shortness of breath on effort, cough, chest tightness, chest pain, sore throat, blocked and/or runny nose, loss of smell, loss of taste, diarrhoea, abdominal pain, muscle or joint pain, headache, tachycardia, sore or red eyes, excessive sweating (over a 24 h period, including night sweats), hair loss, and weight loss.[30,31]

Spirometry and measurements of DLCO were performed by a well-trained respiratory therapist who is also a Registered Pulmonary Function Technologist certified by The National Board for Respiratory Care. All tests were performed under physician supervision. The equipment used for lung function measurements was the Easy One Pro®, NDD Medical Technologies, Switzerland. Spirometry reference equations were obtained from Hankinson (1999).[32] The technical quality of spirometry was adhered to per 2019 spirometry standards.[33] The reference equations for pulmonary diffusing capacity for carbon monoxide (DLCO) were obtained from Vazquez-Garcia and colleagues.[32] The 2017 Technical standards for DLCO were followed for technical quality.[34]

Analysis

Continuous variables are expressed ad mean (S.D.), and categorical variables are expressed as absolute values and percentages. A Fisher's exact test compared the number of males versus females with an impaired DLCO defined as below the lower limit of normal (< LLN, < 5th percentile). A Fisher's exact test also compared the number of males versus females with a restrictive spirometric pattern as defined by the ratio of the pre-bronchodilator forced expiratory volume in 1 s (FEV1) to forced vital capacity (FVC) being ≥ LLN with the FVC being less than the LLN.[35] The Fisher's Exact Test would allow an examination of sex differences in the proportion of males versus females with abnormally low lung function. Several N-1 Chi-Squared Tests were used to determine whether the percentage of several signs and symptoms present post-COVID-19 were different between those with a DLCO < LLN and those with a DLCO ≥ LLN.[36] A Benjamini–Hochberg procedure was used to control the false discovery rate,[37] which we set to 0.05.

Binary logistic regression was performed using the backward: likelihood ratio method. This stepwise method enters all independent variables at once and then removes each variable one at a time according to the probability of the likelihood-ratio statistic until only the significant variables remain in the model. Binary logistic regression was used to determine the factors associated with an impaired DLCO (DLCO < LLN, less than a z-score of -1.645, or < 5th percentile) in patients with previous mild COVID-19. Cardiovascular disease (CVD) risk factors [1 = yes, 0 = no for smoking status, high blood pressure, cardiac arrhythmia, and obesity], true cardiac disease (yes = 1, no = 0), fatigue (yes = 1, no = 0), dyspnoea (yes = 1, no = 0), cough (yes = 1, no = 0), headache (yes = 1, no = 0), chest tightness (yes = 1, no = 0), sore throat (yes = 1, no = 0), persistent loss of smell (yes = 1, no = 0), dysfunction in the sense of taste (yes = 1, no = 0), conjunctivitis (yes = 1, no = 0), blocked and/or runny nose (yes = 1, no = 0), use of oral corticosteroids during the disease (yes = 1, no = 0), use of anticoagulant medications (yes = 1, no = 0), sex (male = 1, female = 0), age (years old), height (cm), weight (kg), body mass index (BMI, kg/m2), a restrictive spirometric pattern (forced vital capacity or FVC below the lower limit of normal, LLN, and FEV1/FVC ≥ LLN, yes = 1, no = 0), forced expiratory volume in 1 s (FEV1, L), forced expiratory flow rate over the middle half of expiration (FEF25–75, L/s) and peak expiratory flow (PEF, L/s).

Using mean data from 22 previous studies (including the current study), multiple linear regression analysis using forward selection was used to identify which of the five following factors would predict the proportion of patients who had previous COVID-19 and impaired DLCO at follow-up. The mean age (years old), mean body mass index (kg/m2), the mean number of days between receiving the COVID-19 diagnosis and follow-up, and history of mild vs severe COVID-19 disease (i.e. patients that were either hospitalised, intubated, presented with fibrotic C.T. changes in the lung were labelled as 1 or severe, compared to those that were not, and labelled as 0 or mild), and the criteria used to define impaired DLCO (DLCO < 80% predicted labelled as 1, vs DLCO < LLN labelled as 0) were predictors used in the model.

All data were analysed by a statistical software package (IBM SPSS Statistics, Version 27, Chicago IL). A p-value of < 0.05 was used to signify statistical significance. Any case with a standardised residual ≥ 3.0 was removed from any model.

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