7 research outputs found
Corrigendum: Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model
Post-COVID-19 interstitial lung disease: Insights from a machine learning radiographic model
IntroductionPost-acute sequelae of COVID-19 seem to be an emerging global crisis. Machine learning radiographic models have great potential for meticulous evaluation of post-COVID-19 interstitial lung disease (ILD).MethodsIn this multicenter, retrospective study, we included consecutive patients that had been evaluated 3 months following severe acute respiratory syndrome coronavirus 2 infection between 01/02/2021 and 12/5/2022. High-resolution computed tomography was evaluated through Imbio Lung Texture Analysis 2.1.ResultsTwo hundred thirty-two (n = 232) patients were analyzed. FVC% predicted was ≥80, between 60 and 79 and <60 in 74.2% (n = 172), 21.1% (n = 49), and 4.7% (n = 11) of the cohort, respectively. DLCO% predicted was ≥80, between 60 and 79 and <60 in 69.4% (n = 161), 15.5% (n = 36), and 15.1% (n = 35), respectively. Extent of ground glass opacities was ≥30% in 4.3% of patients (n = 10), between 5 and 29% in 48.7% of patients (n = 113) and <5% in 47.0% of patients (n = 109). The extent of reticulation was ≥30%, 5–29% and <5% in 1.3% (n = 3), 24.1% (n = 56), and 74.6% (n = 173) of the cohort, respectively. Patients (n = 13, 5.6%) with fibrotic lung disease and persistent functional impairment at the 6-month follow-up received antifibrotics and presented with an absolute change of +10.3 (p = 0.01) and +14.6 (p = 0.01) in FVC% predicted at 3 and 6 months after the initiation of antifibrotic.ConclusionPost-COVID-19-ILD represents an emerging entity. A substantial minority of patients presents with fibrotic lung disease and might experience benefit from antifibrotic initiation at the time point that fibrotic-like changes are “immature.” Machine learning radiographic models could be of major significance for accurate radiographic evaluation and subsequently for the guidance of therapeutic approaches
Pulmonary embolism and abdominal pain in a young patient: A case report of a rare clinical entity
A 20-year-old man with reduced mobility, from a car accident, presented to
the Emergency Department of our hospital due to progressive dyspnea and
heart palpitations the lasted three days. A bedside cardiac ultrasound and
a Computed Tomography Pulmonary Angiogram (CTPA) were immediately
performed, revealing strain of the right ventricle and pulmonary embolism (PE).
The patient subsequently complained about abdominal pain and a Computed
Tomography of the Abdominal Aorta (CTAO) revealed arterial embolism in the
renal and splenic circulations, along with the right common femoral artery.
Phlebography of inferior limbs exhibited deep vein thrombosis in the left
popliteal vein. A percutaneous suction thrombectomy had been performed
successfully.
The patient underwent a transesophageal echocardiography with agitated
saline test that revealed a patent foramen ovale (PFO), a diagnosis which
explained the paradoxical embolism in both arterial and venous circulations.
Paradoxical embolism is quite uncommon and should not be ignored in cases
with indications of arterial embolism after PE
Reduced immunogenicity of the mRNA vaccine BNT162b2 in patients with idiopathic pulmonary fibrosis
Clinical features and outcomes of patients with myositis associated-interstitial lung disease
Safety and Effectiveness of Mycophenolate Mofetil in Interstitial Lung Diseases: Insights from a Machine Learning Radiographic Model
Introduction: Treatment of interstitial lung diseases (ILDs) other than
idiopathic pulmonary fibrosis (IPF) often includes systemic
corticosteroids. Use of steroid-sparing agents is amenable to avoid
potential side effects.
Methods: Functional indices and high-resolution computed tomography
(HRCT) patterns of patients with non-IPF ILDs receiving mycophenolate
mofetil (MMF) with a minimum follow-up of 1 year were analyzed. Two
independent radiologists and a machine learning software system (Imbio
1.4.2.) evaluated HRCT patterns.
Results: Fifty-five (n = 55) patients were included in the analysis
(male: 30 [55%], median age: 65.0 [95% CI: 59.7-70.0], mean forced
vital capacity %predicted [FVC %pred.] +/- standard deviation
[SD]: 69.4 +/- 18.3, mean diffusing capacity of lung for carbon
monoxide %pred. +/- SD: 40.8 +/- 14.3, hypersensitivity pneumonitis:
26, connective tissue disease-ILDs [CTD-ILDs]: 22, other ILDs: 7).
There was no significant difference in mean FVC %pred. post-6 months
(1.59 +/- 2.04) and 1 year (-0.39 +/- 2.49) of treatment compared to
baseline. Radiographic evaluation showed no significant difference
between baseline and post-1 year %ground glass opacities (20.0 [95%
CI: 14.4-30.0] vs. 20.0 [95% CI: 14.4-25.6]) and %reticulation (5.0
[95% CI: 2.0-15.6] vs. 7.5 [95% CI: 2.0-17.5]). A similar
performance between expert radiologists and Imbio software analysis was
observed in assessing ground glass opacities (intraclass correlation
coefficient [ICC] = 0.73) and reticulation (ICC = 0.88). Fourteen
patients (25.5%) reported at least one side effect and 8 patients
(14.5%) switched to antifibrotics due to disease progression.
Conclusion: Our data suggest that MMF is a safe and effective
steroid-sparing agent leading to disease stabilization in a proportion
of patients with non-IPF ILDs. Machine learning software systems may
exhibit similar performance to specialist radiologists and represent
fruitful diagnostic and prognostic tools