{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:15:01Z","timestamp":1716423301234},"reference-count":38,"publisher":"Termedia Sp. z.o.o.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pol J Radiol"],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>The post-pandemic era calls for appropriate literature on chest X-ray score cut-offs, enabling swift categorization and faster radiological reporting of patients with acute respiratory distress syndrome (ARDS)-like illnesses, hence prompting healthcare equity in low-resource centres where extortionate modalities of imaging such as computed tomography (CT) are unavailable. In this study, we aim to bridge the literature gap using the versatile zonal scoring system.<\/jats:p><\/jats:sec><jats:sec><jats:title>Material and methods<\/jats:title><jats:p>This retrospective cohort study uses data from 751 COVID-19 RT-PCR+ patients. Concordant chest radiograph (CXR) scores were reported, and inter-rater reliability was measured using kappa indices. receiver operating characteristic curves were used to establish cut-off scores for the outcomes of interest: mild or severe disease, admission to an intensive care unit (ICU), and intubation. Categorical data were expressed using means and percentages, and \uf063\uf032 or t-tests were used for comparison at an \u03c7&lt;sup&gt;2&lt;\/sup&gt; level of 0.05. Unadjusted odds ratios for each outcome of interest vs. CXR score and comorbidity were then calculated using binary logistic regression.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>CXR findings included infiltrates (46.07%), pleural effusions (7.05%), consolidation and fibrosis (4.43%), pneumothoraces (2.71%), and cardiomegaly (2.26%). Most patients had an index CXR score of 0 (54.19%). The index cut-off score of \u2264 1 (82.95, 81.68) was established for mild disease, \u2265 4 for severe disease (85.71, 83.99), \u2265 3 for ICU admission (86.90, 71.91), and \u2265 4 for intubation (87.61, 72.90). Hypertension, type 2 diabetes mellitus, hypothyroidism, history of ischaemic heart disease, and history of tuberculosis were independent risk factors for a high CXR index score, intubation, and ICU admission.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>CXR scores can be effectively used in low-resource settings for triaging patients, maintaining records, and disease prognostication.<\/jats:p><\/jats:sec>","DOI":"10.5114\/pjr.2023.125981","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T14:20:55Z","timestamp":1679408455000},"page":"124-134","source":"Crossref","is-referenced-by-count":0,"title":["\u201cKeep it simple \u2013 a\u00a0lesson from COVID-19\u201d: highlighting the utility of chest X-rays in ARDS-associated illnesses through the Zonal Scoring System"],"prefix":"10.5114","volume":"88","author":[{"given":"Anna Rachel","family":"Menezes","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arun","family":"George","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linda Ann","family":"Joseph","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bimal","family":"Saju","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Athul Varghese","family":"Kurian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3061","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"ref-1160085","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaut.2020.102433"},{"key":"ref-1160086","doi-asserted-by":"crossref","unstructured":"Callaway E. 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