Retinal vascular impairment matched to the pulmonary damage in early post-COVID 19 patients
Abstract
Background
Endothelium damage is a crucial element in the pathogenesis of SARS-Cov-2 infection. Most casualties in critical COVID-19 cases are due to ARDS, diffuse coagulopathy and cytokine storm. ARDS itself is a consequence of pulmonary endothelial cells damage. Damage to retinal capillary microcirculation in post-infective period has been investigated through Optical Coherence Tomography Angiography (OCTA). The aim of the present study is to find a correlation between signs of retinal vascular damage and pulmonary impairment.
Methods
Patients admitted to hospital and subsequently recovered from COVID-19 infection were summoned 1 month later to undergo coherence tomography (CT) scan and OCTA examination.
Results
The study population included 87 COVID-19 patients with a mean age of 54.28 ± 14.44 years. Oxygen therapy, non-invasive and invasive mechanical ventilation were necessary in 33, 11 and 4 patients respectively to provide respiratory support during the acute course of the disease. Pulmonary involvement interested 54 patients (62.1%). Peripheral (27.6%) or diffuse (29.9%) involvement and ground glass (GG) opacities (47.1%) represented the prevalent radiological finding. A reduced RCPI FI was independently correlated with the presence of reticulation pattern in CT scan (p = .019). Also, RNFL and GCC were thinner in patients who displayed reticulation pattern (respectively p = .025 and p = .015).
Conclusions
A reduction in RPCP-FI and RNFL and GCC thickness were independently correlated to the presence of CT reticulation pattern. This association can reflect cytokine induced remodeling in both organs as a consequence of systemic endothelial damage and inflammation.
Introduction
Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) epidemic spread from Wuhan, China at the end of 2019.1 By January 2020, the WHO declared a pandemic emergency. From then on, the coronavirus has infected millions of people worldwide and it still represents a threat to humankind.2 Althogh most of the patients are asymptomatic or develop mild symptoms, some patients develop signs of pneumonia and respiratory failure.3 From the beginning of the pandemic till now evidence are indicating the endothelium damage as the crucial element in the pathogenesis of the SARS-Cov-2 infection.4 In fact, COVID-19 acts on endothelium of capillaries by promoting a pro-coagulative state and inducing endothelial inflammation.5 The activation of endothelial cells mediates production of cytokines and mediates local inflammation and damage of the integrity of the vessel barrier. The dysfunction and apoptosis of the endothelial cells results in denudation of vascular walls with exposure of endothelial basement membrane and activation of the coagulation cascade that can result in thrombus formation, typically seen in severe COVID-19 cases.4,6 Most of the patient's death in critical COVID-19 cases are due to ARDS, diffuse coagulopathy and cytokine storm.6 ARDS is considered to be a consequence of pulmonary endothelial cells damage.7 The development of pulmonary embolism and DIC is a feature of severe COVID-19 infection as it is also demonstrated by the finding of the increase in fibrin degradation fragments (D-dimer).4,8,9 Protocols and guidelines have been created with the aim of facilitating the diagnosis and classification of patients in different risk classes.10,11 Previous studies have classified the extent of pulmonary involvement through CT scans identifying a score. The risk score based on CT scans can help identifying patients that need a more aggressive treatment.12–14 The involvement of the retinal capillary microcirculation has been investigated through Optical Coherence Tomography Angiography (OCTA).15,16 A lower capillary perfusion around the optic disk has been found in mild post-COVID-19 patients compared to normal controls, thus highlighting that sings of endothelium damage in the eye can be found even in mild cases.16
The aim of the present study is to find a correlation between signs of retinal vascular damage and pulmonary impairment.
Materials and methods
This observational monocentric cohort study was developed by the Gemelli Against COVID Post-Acute Care Study Group17 as part of a series of studies aimed to clarify subacute consequences of COVID-19 disease. Recruitment took place at the Fondazione Policlinico Universitario Agostino Gemelli IRCSS from 10 March 2020 to 20 October 2020 and included COVID-19 patients selected from hospital recordings. In particular, the study population included patients whose primary diagnosis of admission was SARS-CoV-2 infection. Patients were summoned to undergo CT scan and OCT-A examination 1 month after discharge. Exclusion criteria included previous lung surgery, preexisting idiopathic pulmonary fibrosis, previous pulmonary embolism, choroidal atrophy, pathological myopia, documented pachychoroid conditions, acquired or hereditary optic nerve or retinal diseases, demyelinating disorders, neurodegenerative disorders, ongoing chemotherapy and drug abuse. Approval from the Catholic University/Fondazione Policlinico Universitario A. Gemelli IRCCS Institutional Ethics Committee was obtained and each patient received informed consent and a complete explanation in accordance to the declaration of Helsinki.
Ophthalmic examination
Ophthalmic examination was performed on only one eye for each patient. The chosen eye had to meet the inclusion criteria. In case of both eyes evaluated as eligible, one of the two was randomly selected. The examination included best corrected visual acuity (BCVA) assessment, intraocular pressure (IOP) measurement, slit lamp and fundus examination, optical coherence tomography (OCT) evaluation. Infrared OCT B scan and OCTA images were acquired with Spectral Domain Zeiss Cirrus 5000-HD-OCT Angioplex (sw version 10.0, Carl Zeiss, Meditec, Inc., Dublin, USA). OCT protocol included high resolution 5-line HD scan at posterior pole and macular cube (200 × 200). Manual assessment of subfoveal choroidal thickness (SCT) was performed on cross-sectional OCT B-scans by two blinded. Choroidal thickness was measured in the fovea region, from the rear edge of the retinal pigmented epithelium (RPE) to the choroid-sclera junction. OCTA scanning protocol included 3 × 3 mm volume scan centered on the fovea and 4.5 × 4.5 mm volume scan centered on the optic disc. Automated layer segmentation was applied to detect the superficial (SCP) and deep capillary plexus (DCP) and the radial peripapillary capillary plexus (RPCP). The RPCP was defined as the vascular layer extending superficially from the inner limiting membrane to the posterior surface of the retinal nerve fiber layer (RNFL) in the peripapillary region. Results of the automatic segmentation were evaluated by a retina expert and manual readjustments of the segmentation lines were performed as needed. Foveal avascular zone (FAZ) border was manually outlined by two independent trained physicians. Imaging processing was performed using ImageJ software (National Institutes of Health, Maryland, USA). Images were binarized and thresholded so that all pixels with a ratio value between 0.7 and 1.0 were visualized as white while all pixels with a ratio value lower than 0.7 were shown as black. Vessel density (VD) was calculated as the ratio of the total vessel area to the total area of analyzed region (size of the image in pixels) and was expressed in percentage. Vascular density was calculated with MATLAB v7.10 (R2017a; MathWorks, Inc., Natick, MA, USA) regionprops. The avascular zone of the optic nerve head (ONH) was manually selected to establish baseline background noise level for a global threshold, and the ONH was excluded from quantification. Pixels in the noise region showing a decorrelation value over two standard deviations higher than the mean decorrelation value of the region were identified as vessels. The RPCP vessel density was defined as the percentage of the total area of the peripapillary 4.5 × 4.5 mm scan occupied by vessels. The RPCP flow index was defined as the average decorrelation value in the peripapillary region of the en face retinal angiogram.
CT scan protocol
Chest computed tomography (CT) examinations were performed using a 128-section scanner (SOMATOM Definition Flash, Siemens Healthineers, Germany), with the patient in supine position and in breath-holding following deep inspiration, without administration of contrast material, covering the entire thorax. Images were acquired using a high-resolution CT technique (HRCT); images were reconstructed using 1-mm slice thickness/1-mm reconstruction interval with soft-tissue and high spatial frequency reconstruction algorithms for the assessment of the mediastinum and lung parenchyma, respectively. Two experienced chest radiologists (A.R.L, G.C.) reviewed the images in consensus on a Picture Archiving and Communication Systems (PACS) workstation, blinded to all clinical data. All the CT scans were scored on the axial images, and on multiplanar reconstructions (MPRs) if required. Referring to the method previously described by Yuan et al.,12 the semi-quantitative evaluation was performed for each of three lung zones (with a total of six zones in each patient): upper zone (above the carina); middle zone (between the carina and the inferior ipsilateral pulmonary vein); lower zone (below the ipsilateral inferior pulmonary vein). Each zone was assigned a six-point scale according to the extent of anatomic involvement: 0, no involvement; 1, < 5% involvement; 2, 5–25% involvement; 3, 26–50% involvement; 4, 51–75% involvement; 5, > 75% involvement.13 The resulting global CT extent score was the sum of the score per each zone (0 to 30). Considering the prevalent abnormality per zone, CT findings were also classified as follows: 0, no abnormalities; 1, ground glass opacities (GGO); 2, consolidation; 3, mixed GGO and consolidation; 4, parenchymal bands; 5, reticulation and/or fibrotic abnormalities. The main location of lung abnormalities was defined as: peripheral, if located in the outer one-third of the lung; central, if located in the inner two-third; diffuse, if both peripheral and central distribution coexisted in the same patient. Furthermore, regardless of the zone location, if present, the following features were recorded: bands; reticulation; traction bronchiectasis; consolidation; air bronchogram; underlying lung disease; organizing pneumonia pattern (patchy peribroncovascular or subpleural distribution of consolidation; presence of halo sign; presence of reversed halo sign); pleural thickening, lymphadenopathies (lymph node with a short-axis diameter ≥ 10 mm). Definitions of radiological terms were based on the glossary of terms for thoracic imaging by the Fleischner Society.14
Statistical analysis
G*power (3.1.9.7 software) was used for sample size calculation. Desired power of the study was 80% and alpha error was set to 5% for statistical significance. Statistical analysis was conducted using SPSS software (IBM SPSS Statistics 26.0). Shapiro-Wilk test was used to evaluate normality of the distribution. Normally distributed variables were described using mean and standard deviation. Qualitative variables were described as number of cases over total and percentage. The agreement between three graders in radiological interpretation was assessed using Fleiss kappa analysis, while for quantitative OCT measurements intraclass correlation coefficient was used. Univariate analysis of quantitative variables according to radiological elements was performed using T test for independent groups (as concerns dichotomous radiological features) or one way ANOVA (as concerns radiological features with more than 2 modalities). Univariate analysis of the associations between radiological features and qualitative variables was performed using X2 test with Bonferroni post-hoc correction or Fisher exact test when appropriate. Significant results at univariate analysis were further tested and adjusted for covariates using a two-way MANCOVA. A p value < .05 was considered as statistically significant.
Results
The study population included 87 COVID-19 patients with a mean age of 54.28 ± 14.44 years. All patients were of Caucasian ethnicity and 55.2% were males. Thirty-six (41.4%) patients were affected by diabetes, 21.8% of the cohort suffered from systemic arterial hypertension and 7 patients suffered from underlying lung disease. Mean BMI (body mass index) was 25.7 ± 4.3, with 8 patients exceeding a score of 30 (cut off for obesity). Mean duration of hospital stay was 15.1 ± 3.2 days. Azithromycin was provided to 33 patients during hospital admission while iv corticosteroids were used in 4 patients. Oxygen therapy and non-invasive mechanical ventilation were necessary in 33 and 11 patients respectively to provide respiratory support during the acute course of the disease. Hospital stays included intensive care unit (ICU) admission in 9 patients, 4 of which required invasive mechanical ventilation. Twenty-eight patients (32.2%) were treated with heparin. Two cases of pulmonary embolism and 2 cases of venous thrombosis complicated the clinical course of the infection in 4 patients. Further details on anamnestic and hospital admission information are recorded in Table 1.
| GENERAL CHARACTERISTICS | |
|---|---|
| Age | 54.28 ± 14.44 |
| Sex | M = 48/87 (55.2%) |
| Systemic arterial hypertension | 19/87 (21.8%) |
| Diabetes | 36/87 (41.4%) |
| Autoimmune diseases | 21/87 (24.1%) |
| BMI score | 25.7 ± 4.3 |
| BMI>30 | 8/87 (9.2%) |
| Chronic kidney disease | 8/87 (9.2%) |
| Cognitive impairment | 7/87 (8.0%) |
| ADMISSION DATA | |
| Hydroxychloroquine | 57/87 (65.5%) |
| Lopinavir + ritonavir | 27/87 (31.0%) |
| Darunavir + ritonavir | 35/87 (40.2%) |
| Heparin | 28/87 (32.2%) |
| Azithromycin | 33/87 (37.9%) |
| Antiplatelet therapy | 6/87 (6.9%) |
| Corticosteroids | 4/87 (4.6%) |
| ICU admission | 9/87 (10.3%) |
| Duration of hospital stay (days) | 15.1 ± 3.2 |
| Oxygen therapy | 33/87 (37.9%) |
| Not invasive ventilation | 11/87 (12.6%) |
| Invasive ventilation | 4/87 (4.6%) |
| Pulmonary embolism | 2/87 (2.3%) |
| Venous thrombosis | 2/87 (2.3%) |
BMI: body mass index; ICU: intensive care unit.
Mean BCVA in the population was 0.89 ± 0.1 and mean IOP was 16.3 ± 2.1 mmHg. Central foveal thickness and subfoveal choroidal thickness were within normal limits. Intraclass correlation coefficient proved a good agreement between graders in choroidal measurements (ICC = 0.79). Mean RNFL and GCC values were respectively 94.1 ± 10.9 and 81.2 ± 8.82 µm. RPCP FI was suggestive of a reduced flow compared to data in the general population16 while RPCP PD values were borderline to normal limits (see Table 2).
| Ophthalmic variables of interest | |
|---|---|
| BCVA (decimals) | 0.89 ± 0.1 |
| IOP (mmHg) | 16.3 ± 2.1 |
| RNFL (µm) | 94.1 ± 10.9 |
| GCC (µm) | 81.2 ± 8.82 |
| C/D | 0.42 ± 0.18 |
| Disc area | 1.75 ± 0.327 |
| RPCP FI | 0.453 ± 0.02 |
| RPCP PD | 0.437 ± 0.03 |
| Central foveal thickness (µm) | 263.9 ± 24.3 |
| Subfoveal choroidal thickness (µm) | 311.5 ± 82.7 |
BCVA: best corrected visual acuity; C/D: cup disc ratio; GCC: ganglion cell complex; IOP: intraocular pressure; RNFL: retinal nerve fiber layer; RPCP-FI: radial peripapillary capillary plexus flow index; RPCP PD: radial peripapillary capillary plexus perfusion density.
Pulmonary involvement interested 54 patients (62.1%). Peripheral (27.6%) or diffuse (29.9%) distribution were the most typical pattern of lung radiological involvement, with central disease being a rarer condition (9.2%). Moreover, GGO represented the prevalent radiological finding in 47.1% of the patients (Figure 1). Another common characteristic was the presence of bands, which were found in 44.8% of the population. Thirteen patients showed tractional bronchiectasis while nodal enlargement was an anomaly with little prevalence (3,4%). Fleiss kappa analysis proved a good agreement between graders (k = 0.71, CI = 0.75 – 0.68). Reticulation was present in 15 patients (17.2%) and configured as the most representative pattern in 3 patients. Fibrotic changes were significantly correlated to age (p = .02) and use of both invasive and noninvasive mechanical ventilation (p = .015). Results of the MANCOVA revealed a statistically significant difference in RPCP-FI based on the presence of reticulation pattern (F (9, 67) = 3.935, p < .0001; Wilk's Λ = 0.654, partial η2 = 0.346). Specifically, the presence of reticulation resulted in RPCP-FI of 0.413 ± 0.02 compared to 0.456 ± 0.02 in patients without reticulation (p = .019 from post-hoc analysis) (Figure 2). Neither other radiological features, nor systemic conditions or the need for respiratory support during admission influenced this correlation, even though a slight trend towards significance was noted for systemic hypertension (p = .08 from post-hoc analysis). Also RNFL and GCC were thinner in patients who displayed reticulation pattern (respectively p = .025 and p = .015 from post-hoc analysis) (Figure 3 and Figure 4). All other radiological parameters didn't show a correlation with ophthalmic measurements. A full description of radiological finding and their correlation to ophthalmic variables of interest is provided by Table 3 and 4.
| Lung imaging variables | |
|---|---|
| Number of involved zones | 0 = 31/87 (35.6%) 1 = 4/87 (4.6%) 2 = 10/87 (11.5%) 3 = 4/87 (4.6%) 4 = 6/87 (6.9%) 5 = 5/87 (5.7%) 6 = 27/87 (31.0%) |
| Axial distribution | None = 29/87 (33.3%) Peripheral = 24/87 (27.6%) Central = 8/87 (9.2%) Diffuse = 26/87 (29.9%) |
| Prevalent abnormality | None = 29/87 (33.3%) GG = 41/87 (47.1%) Mixed (GG + consolidation) = 4/87 (4.6%) Bands = 10/87 (11.5%) Reticulation/fibrotic abnormalities = 3/87 (3.4%) |
| Bands | No = 48 (55.2%) Yes = 39/87 (44.8%) |
| Reticulation | No = 72/87 (82.8%) Yes = 15/87 (17.2%) |
| Tractional bronchiectiasias | No = 75/87 (85.1%) Yes = 13/87 (14.9%) |
| Consolidation | No = 72/87 (82.8%) Yes = 15/87 (17.2%) |
| Air bronchogram | No = 82/87 (94.3%) Yes = 5/87 (5.7%) |
| Pleural thickening | No = 61/87 (70.1%) Yes = 26/87 (29.9%) |
| Nodal enlargement | No = 84/87 (96.6%) Yes = 3/87 (3.4%) |
| Typical OP pattern | No = 63/87 (72.4%) Yes = 24/87 (27.6%) |
| Underlying lung disease | No = 80/87 (92%) Yes = 7/87 (8%) |
GG: ground glass; OP: organizing pneumonia.
| Pulmonary involvement | Number of involved zones | Reticulation | Consolidation | Typical OP pattern | |
|---|---|---|---|---|---|
| RPCP FI | No = 0.45 ± 0.02 Yes = 0.45 ± 0.02 |
0 = 0.455 ± 0.01 1 = 0.445 ± 0.03 2 = 0.450 ± 0.02 3 = 0.457 ± 0.003 4 = 0.456 ± 0.03 5 = 0.455 ± 0.01 6 = 0.453 ± 0.02 |
No = 0.456 ± 0.02 Yes = 0.413 ± 0.02 |
No = 0.453 ± 0.02 Yes = 0.457 ± 0.01 |
No = 0.452 ± 0.01 Yes = 0.458 ± 0.01 |
| p = .49 | p = .93 | p = .019 | p = .53 | p = .13 | |
| RPCP PD | No = 0.44 ± 0.03 Yes = 0.44 ± 0.03 |
0 = 0.439 ± 0.03 1 = 0.407 ± 0.06 2 = 0.442 ± 0.019 3 = 0.454 ± 0.01 4 = 0.429 ± 0.02 5 = 0.448 ± 0.05 6 = 0.437 ± 0.03 |
No = 0.440 ± 0.03 Yes = 0.427 ± 0.03 |
No = 0.437 ± 0.03 Yes = 0.438 ± 0.03 |
No = 0.436 ± 0.03 Yes = 0.442 ± 0.03 |
| p = .82 | p = .50 | p = .19 | p = .93 | p = .40 | |
| RNFL | No = 94.2 ± 11.6 Yes = 94.6 ± 10.5 |
0 = 94.2 ± 11.6 1 = 89.5 ± 15.5 2 = 91.3 ± 8.6 3 = 95.5 ± 10.6 4 = 95.0 ± 9.2 5 = 100.4 ± 8.7 6 = 95.6 ± 11.2 |
No = 99.1 ± 11.2 Yes = 85.3 ± 8.8 |
No = 93.9 ± 10.8 Yes = 97.6 ± 11.4 |
No = 92.8 ± 10.3 Yes = 98.9 ± 11.4 |
| p = .86 | p = .76 | p = .025 | p = .29 | p = .26 | |
| GCC | No = 81.4 ± 11.1 Yes = 81.2 ± 7.4 |
0 = 81.4 ± 11.1 1 = 81.3 ± 6.3 2 = 78.4 ± 5.2 3 = 83.5 ± 4.9 4 = 83.0 ± 5.8 5 = 83.4 ± 8.3 6 = 81.2 ± 8.9 |
No = 86.3 ± 8.7 Yes = 73.9 ± 8.0 |
No = 81.4 ± 8.9 Yes = 80.4 ± 9.6 |
No = 80.7 ± 8.9 Yes = 82.6 ± 8.7 |
| p = .89 | p = .95 | p = .015 | p = .72 | p = .41 | |
| Central foveal thickness | No = 260.5 ± 18.3 Yes = 264.9 ± 26.4 |
0 = 260.5 ± 18.3 1 = 278.0 ± 18.0 2 = 242.5 ± 18.4 3 = 262.0 ± 5.6 4 = 279.6 ± 30.1 5 = 283.8 ± 22.1 6 = 265.5 ± 26.2 |
No = 264.2 ± 21.9 Yes = 258.7 ± 31.4 |
No = 264.0 ± 23.2 Yes = 259.1 ± 26.7 |
No = 261.0 ± 23.6 Yes = 269.3 ± 23.1 |
| p = .43 | p = .09 | p = .44 | p = .52 | p = .17 | |
| Subfoveal choroidal thickness | No = 313.6 ± 84.3 Yes = 308.7 ± 83.9 |
0 = 313.6 ± 84.3 1 = 337.8 ± 65.3 2 = 287.2 ± 79.3 3 = 357.5 ± 53.0 4 = 322.4 ± 55.1 5 = 312.4 ± 88.9 6 = 304.68 ± 97.7 |
No = 314.7 ± 81.1 Yes = 289.8 ± 95.3 |
No = 314.7 ± 82.2 Yes = 285.6 ± 90.9 |
No = 311.3 ± 75.5 Yes = 308.3 ± 103.9 |
| p = .80 | p = .91 | p = .32 | p = .28 | p = .88 | |
| C/D | No = 0,44 ± 0.19 Yes = 0.41 ± 0.19 |
0 = 0.44 ± 0.19 1 = 0.38 ± 0.10 2 = 0.42 ± 0.18 3 = 0.60 ± 0.07 4 = 0.36 ± 0.17 5 = 0.35 ± 0.25 6 = 0.44 ± 0.20 |
No = 0.42 ± 0.19 Yes = 0.46 ± 0.17 |
No = 0.41 ± 0.19 Yes = 0.52 ± 0.11 |
No = 0.41 ± 0.19 Yes = 0.46 ± 0.18 |
| p = .63P = 0.63 | p = .75 | p = .49 | p = .070 | p = .38 | |
| Disc area | No = 1.76 ± 0.36 Yes = 1,76 ± 0.31 |
0 = 1.76 ± 0.36 1 = 1.58 ± 0.17 2 = 1.82 ± 0.35 3 = 1.82 ± 0.53 4 = 1.77 ± 0.54 5 = 1.69 ± 0.31 6 = 1.78 ± 0.26 |
No = 1.75 ± 0.32 Yes = 1.80 ± 0.38 |
No = 1.63 ± 0.34 Yes = 1.81 ± 0.15 |
No = 1.71 ± 0.34 Yes = 1.88 ± 0.28 |
| p = .97 | p = .93 | p = .58 | p = .077 | p = .42 |
C/D: cup disc ratio; GCC: ganglion cell complex; RNFL: retinal nerve fiber layer; RPCP-FI: radial peripapillary capillary plexus flow index; RPCP PD: radial peripapillary capillary plexus perfusion density.
Discussion
Despite being a systemic disease, lung involvement in COVID 19 often represents the main clinical finding and cause of death. Pulmonary COVID-19 consists in 4 main morphological stages. Initially, (first few days) edema, incipient epithelial damage and capillaritis/endothelialitis are the prevalent abnormalities. Within the first week, exudative diffuse alveolar damage (DAD) intervenes, followed by organizing and fibrotic stage in weeks to months from the beginning depending on the severity and clinical course of the condition.13,18 In exudative phase, intralveolar hemorrhage in association with thrombotic vessel occlusions, platelet–fibrin thrombi in capillaries and small arterial vessels, significant vascular inflammation and diffuse endothelial damage are prominent features of the disease.19 In our cohort, pulmonary involvement was present in 62.1% of the patients. During hospital admission, pulmonary embolism occurred in 2.3% of cases, 37.9% required oxygen supplementation and 12.6% and 4.6% received respiratory support with a non-invasive and invasive method respectively. By the way, CT scans from our analysis were acquired during early recovery period, thus reflecting late stages of pulmonary affection and providing insight into the recovery pattern of the lungs.
Consistently with literature,20 patients showed a prevalence of bilateral involvement with peripheral or diffuse distribution in the majority of cases and pleural thickening in 29.9%. The prevalent pattern of involvement was residual GGO often accompanied by reticular and/or interlobular septal thickening and areas of organizing pneumonia. This finds confirmation in literature, where GGO was reported as the earliest forming, most persistent and prevalent parenchymal abnormality in COVID-19 pneumonia.21,22 Moreover, spatial and temporal disomogeneity of different DAD manifestations has also been described as a typical feature.23 The moderate association with fibrotic changes, including formation of fibrous bands and interstitial reticulation, is consistent with findings at 1 month follow up of studies describing temporal changes in lung impairment.24,25 In particular, mixed and predominantly reticular patterns were noted from the 14th day in 45% of the patients and persisted in 28.57% of them at 22–31 days.25 Han et al.24 recently found fibrotic-like changes to be correlated to age greater than 50 years, duration of hospital stay greater than or equal to 17 days, acute respiratory distress syndrome, noninvasive mechanical ventilation and total CT score of 18 or more at initial CT. Our study confirmed age and both invasive and noninvasive mechanical ventilation as risk factors for fibrotic changes in the early follow up period.
Interestingly, reticulation pattern was associated with a decrease in RPCP-FI at OCTA analysis. RPCP-FI is a functional parameter dependent from decorrelation amplitude in images of peripapillary vessels and thus indicative of microvascular blood flow to that region.26 This association can reflect the extrinsecation of cytokine induced remodeling in both organs as a consequence of systemic endothelial damage and inflammation. As concerns lungs, following alveolar injury, fibroblast migration from alveolar interstitium to the site of the injury is stimulated by fibroblast growth factor (FGF), platelet-derived growth factor (PDGF), transforming growth factor β (TGF-β), and chemokines. Fibroblasts then synthesize collagen, fibronectin, and extracellular matrix ground substance resulting in reticular fibrosis. In addition, under the influence of PDGF, TGF-β, and IL-1, fibroblasts proliferate and differentiate into myofibroblasts, which in turn secrete VEGF and TGF-β as mediators of the repair process and produce denser but more disorganized extracellular matrix.27 As a collateral event, TGF-β is able to induce endothelial-mesenchymal transition, leading to stiffening of capillary walls.28 Moreover, it must be noticed that PDGF has been reported to play a role in atherosclerosis and induce smooth muscle cells contraction and vasoconstriction. Specifically, PDGF has also been demonstrated to enhance retinal pericytes contractility.29,30 Taken together, these elements suggest that systemic release of TGF-β and PDGF from activated platelets and damaged endothelium in COVID-19 disease could represent the common trigger to both longstanding lung fibrosis and retinal capillary reduced blood flow. To note, neither diabetes or other systemic conditions nor respiratory support during admission influenced this correlation, even though a slight trend towards significance was noted for systemic hypertension. Another important finding from our series is the correlation of reticular pattern with a reduced thickness of both retinal macular GCC and RNFL. This is consistent with the RPCP-FI reduction in these patients, since RNFL and GCC in macular region have been shown to depend from RPCP flow for nourishment and homeostasis.26,31 Limits of our study include the lack of OCTA acquisition prior to the infection, the lack of pulmonary CT scan data during the acute phase of the disease and the short follow up period. Nevertheless, the large cohort of patients and the novelty of the finding provide strenght to the investigation, broadening our knowledge on the post-acute course of the disease.
Ethics approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Consent to participate
Informed consent was obtained from all individual participants included in the study.
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship and/or publication of this article.
ORCID iDs
Maria Cristina Savastano https://orcid.org/0000-0003-1397-4333
Emanuele Crincoli https://orcid.org/0000-0001-9996-9871
Gloria Gambini https://orcid.org/0000-0001-9500-049X
Alfonso Savastano https://orcid.org/0000-0003-1575-6567
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Article first published online: February 17, 2022
Issue published: November 2022
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