Analgesics
Antiandrogens
Antihistamines
Azvudine
Bromhexine
Budesonide
Colchicine
Conv. Plasma
Curcumin
Famotidine
Favipiravir
Fluvoxamine
Hydroxychlor..
Ivermectin
Lifestyle
Melatonin
Metformin
Minerals
Molnupiravir
Monoclonals
Naso/orophar..
Nigella Sativa
Nitazoxanide
PPIs
Paxlovid
Quercetin
Remdesivir
Thermotherapy
Vitamins
More

Other
Feedback
Home
 
next
study
previous
study
c19early.org COVID-19 treatment researchSleepSleep (more..)
Melatonin Meta
Metformin Meta
Antihistamines Meta
Azvudine Meta Molnupiravir Meta
Bromhexine Meta
Budesonide Meta
Colchicine Meta Nigella Sativa Meta
Conv. Plasma Meta Nitazoxanide Meta
Curcumin Meta PPIs Meta
Famotidine Meta Paxlovid Meta
Favipiravir Meta Quercetin Meta
Fluvoxamine Meta Remdesivir Meta
Hydroxychlor.. Meta Thermotherapy Meta
Ivermectin Meta

All Studies   Meta Analysis       

Association of High-Risk Obstructive Sleep Apnea with Artificial Intelligence-Guided, CT-Based Severity Scores in Patients with COVID-19 Pneumonia

Atceken et al., Journal of Clinical Medicine, doi:10.3390/jcm13216415
Oct 2024  
  Post
  Facebook
Share
  Source   PDF   All Studies   Meta AnalysisMeta
Severe case 67% Improvement Relative Risk Sleep for COVID-19  Atceken et al.  Prophylaxis Is better sleep beneficial for COVID-19? Retrospective 221 patients in Germany Lower severe cases with higher quality sleep (p=0.013) c19early.org Atceken et al., J. Clinical Medicine, Oct 2024 Favorsgood sleep Favorscontrol 0 0.5 1 1.5 2+
Sleep for COVID-19
16th treatment shown to reduce risk in March 2021, now with p = 0.00000000084 from 16 studies.
Lower risk for mortality, hospitalization, and cases.
No treatment is 100% effective. Protocols combine treatments.
5,100+ studies for 112 treatments. c19early.org
Retrospective 221 COVID-19 patients showing an association between high-risk obstructive sleep apnea and COVID-19 severity.
risk of severe case, 67.4% lower, OR 0.33, p = 0.01, higher quality sleep 39, lower quality sleep 182, inverted to make OR<1 favor higher quality sleep, RR approximated with OR.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Atceken et al., 26 Oct 2024, retrospective, Germany, peer-reviewed, 4 authors. Contact: yuksel.peker@lungall.gu.se (corresponding author), zatceken@kuh.ku.edu.tr, catasoy@kuh.ku.edu.tr, yecelik@ku.edu.tr.
This PaperSleepAll
Association of High-Risk Obstructive Sleep Apnea with Artificial Intelligence-Guided, CT-Based Severity Scores in Patients with COVID-19 Pneumonia
Zeynep Atceken, Yeliz Celik, Cetin Atasoy, Yüksel Peker
Journal of Clinical Medicine, doi:10.3390/jcm13216415
Background: We have previously demonstrated that high-risk obstructive sleep apnea (HR-OSA), based on a modified Berlin Questionnaire (mBQ), is linked to worse clinical outcomes. Chest computed tomography (CT) imaging with the implementation of an artificial intelligence (AI) analysis program has been a valuable tool for the speedy assessment of huge numbers of patients during the COVID-19 epidemic. In the current study, we addressed how the severity of AI-guided, CT-based total opacity ratio (TOR) scores are associated with high-risk OSA and short-term outcomes in the same cohort. Methods: The ratio of the volume of high opacity areas to that of the total lung volume constituted the TOR. We arbitrarily applied thresholds of <5 (no or mild TOR), ≥5 and <15 (moderate TOR), and ≥15 (severe TOR). Results: In total, 221 patients were included. HR-OSA was observed among 11.0% of the no or mild TOR group, 22.2% of the moderate TOR group, and 38.7% of the severe TOR group (p < 0.001). In a logistic regression analysis, HR-OSA was associated with a severe TOR with an adjusted odds ratio of 3.06 (95% confidence interval [CI] 1.27-7.44; p = 0.01). A moderate TOR predicted clinical worsening with an adjusted hazard ratio (HR) of 1.93 (95% CI 1.00-3.72; p = 0.05) and a severe TOR predicted worsening with an HR of 3.06 (95% CI 1.56-5.99; p = 0.001). Conclusions: Our results offer additional radiological proof of the relationship between HR-OSA and worse outcomes in patients with COVID-19 pneumonia. A TOR may also potentially indicate the individuals that are at higher risk of HR-OSA, enabling early intervention and management strategies. The clinical significance of TOR thresholds needs further evaluation in larger samples.
References
Abadia, Yacoub, Stringer, Snoddy, Kocher et al., Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease: A Noninferiority Study, J. Thorac. Imaging, doi:10.1097/RTI.0000000000000613
Afshar-Oromieh, Prosch, Schaefer-Prokop, Bohn, Alberts et al., A comprehensive review of imaging findings in COVID-19-Status in early 2021, Eur. J. Nucl. Med. Mol. Imaging, doi:10.1007/s00259-021-05375-3
Arentz, Yim, Klaff, Lokhandwala, Riedo et al., Characteristics and Outcomes of 21 Critically Ill Patients With COVID-19 in Washington State, JAMA, doi:10.1001/jama.2020.4326
Arish, Izbicki, Rokach, Jarjou'i, Kalak et al., Association of the risk of obstructive sleep apnoea with the severity of COVID-19, PLoS ONE, doi:10.1371/journal.pone.0284063
Atceken, Celik, Atasoy, Peker, The Diagnostic Utility of Artificial Intelligence-Guided Computed Tomography-Based Severity Scores for Predicting Short-Term Clinical Outcomes in Adults with COVID-19 Pneumonia, J. Clin. Med, doi:10.3390/jcm12227039
Bernal-Ramirez, Chavez-Barba, Cobian-Machuca, Delgado-Figueroa, Martinez-Solano et al., Comparing the diagnostic performance of an artificial intelligence system with human readers in the tomographic evaluation of SARS-CoV-2 pneumonia
Bhatraju, Ghassemieh, Nichols, Kim, Jerome et al., Covid-19 in Critically Ill Patients in the Seattle Region-Case Series, N. Engl. J. Med, doi:10.1056/NEJMoa2004500
Breville, Herrmann, Adler, Deffert, Bommarito et al., Obstructive sleep apnea: A major risk factor for COVID-19 encephalopathy?, BMC Neurol, doi:10.1186/s12883-023-03393-2
Cade, Dashti, Hassan, Redline, Karlson, Sleep Apnea and COVID-19 Mortality and Hospitalization, Am. J. Respir. Crit. Care Med, doi:10.1164/rccm.202006-2252LE
Cariou, Hadjadj, Wargny, Pichelin, Al-Salameh et al., Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: The CORONADO study, Diabetologia, doi:10.1007/s00125-020-05180-x
Celik, Baygül, Peker, Validation of the Modified Berlin Questionnaire for the Diagnosis of Obstructive Sleep Apnea in Patients with a History of COVID-19 Infection, J. Clin. Med, doi:10.3390/jcm12093047
Chamberlin, Kocher, Waltz, Snoddy, Stringer et al., Automated detection of lung nodules and coronary artery calcium using artificial intelligence on low-dose CT scans for lung cancer screening: Accuracy and prognostic value, BMC Med, doi:10.1186/s12916-021-01928-3
Demir, Ardic, Firat, Karadeniz, Aksu et al., Prevalence of sleep disorders in the Turkish adult population epidemiology of sleep study, Sleep. Biol. Rhythm, doi:10.1111/sbr.12118
Gottlieb, Punjabi, Diagnosis and Management of Obstructive Sleep Apnea: A Review, JAMA, doi:10.1001/jama.2020.3514
Grasselli, Zangrillo, Zanella, Antonelli, Cabrini et al., Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy, JAMA, doi:10.1001/jama.2020.5394
Harmon, Sanford, Xu, Turkbey, Roth et al., Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets, Nat. Commun, doi:10.1038/s41467-020-17971-2
Iannella, Vicini, Lechien, Ravaglia, Poletti et al., Association Between Severity of COVID-19 Respiratory Disease and Risk of Obstructive Sleep Apnea, Ear Nose Throat J, doi:10.1177/01455613211029783
Isaacs, Artificial intelligence in health care, J. Paediatr. Child. Health, doi:10.1111/jpc.14828
Javaheri, Barbe, Campos-Rodriguez, Dempsey, Khayat et al., Sleep Apnea: Types, Mechanisms, and Clinical Cardiovascular Consequences, J. Am. Coll. Cardiol, doi:10.1016/j.jacc.2016.11.069
Kardos, Simon, Nardocci, Szabó, Nagy et al., The diagnostic performance of deep-learning-based CT severity score to identify COVID-19 pneumonia, Br. J. Radiol, doi:10.1259/bjr.20210759
Khanna, Maindarkar, Viswanathan, Fernandes, Paul et al., in Healthcare: Diagnosis vs, Economics of Artificial Intelligence
Kimura-Sandoval, Arevalo-Molina, Cristancho-Rojas, Kimura-Sandoval, Rebollo-Hurtado et al., Validation of chest computed tomography artificial intelligence to determine the requirement for mechanical ventilation and risk of mortality in hospitalized coronavirus disease-19 patients in a tertiary care center in Mexico City, Rev. Investig. Clínica, doi:10.24875/RIC.20000451
Li, Qin, Xu, Yin, Wang et al., Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy, Radiology, doi:10.1148/radiol.2020200905
Maas, Kim, Malkani, Abbott, Zee, Obstructive Sleep Apnea and Risk of COVID-19 Infection, Hospitalization and Respiratory Failure, Sleep. Breath, doi:10.1007/s11325-020-02272-1
Mcsharry, Malhotra, Potential influences of obstructive sleep apnea and obesity on COVID-19 severity, J. Clin. Sleep. Med, doi:10.5664/jcsm.8538
Mei, Lee, Diao, Huang, Lin et al., Artificial intelligenceenabled rapid diagnosis of patients with COVID-19, Nat. Med, doi:10.1038/s41591-020-0931-3
Peker, Celik, Arbatli, Isik, Balcan et al., Effect of High-Risk Obstructive Sleep Apnea on Clinical Outcomes in Adults with Coronavirus Disease 2019: A Multicenter, Prospective, Observational Clinical Trial, Ann. Am. Thorac Soc, doi:10.1513/AnnalsATS.202011-1409OC
Seetharam, Min, Artificial Intelligence and Machine Learning in Cardiovascular Imaging, Methodist. Debakey Cardiovasc. J, doi:10.14797/mdcj-16-4-263
Sezer, Esendagli, Erol, Hekimoglu, New challenges for management of COVID-19 patients: Analysis of MDCT based "Automated pneumonia analysis program, Eur. J. Radiol. Open, doi:10.1016/j.ejro.2021.100370
Suleyman, Fadel, Malette, Hammond, Abdulla et al., Clinical Characteristics and Morbidity Associated With Coronavirus Disease 2019 in a Series of Patients in Metropolitan Detroit, JAMA Netw. Open, doi:10.1001/jamanetworkopen.2020.12270
Tasmi, Raihan, Shams, Obstructive sleep apnea (OSA) and COVID-19: Mortality prediction of COVID-19infected patients with OSA using machine learning approaches, COVID, doi:10.3390/covid2070064
Trimarchi, Pizzino, Paradossi, Gueli, Palazzini et al., Charting the Unseen: How Non-Invasive Imaging Could Redefine Cardiovascular Prevention, J. Cardiovasc. Dev. Dis, doi:10.3390/jcdd11080245
Tufik, Gozal, Ishikura, Pires, Andersen, Does obstructive sleep apnea lead to increased risk of COVID-19 infection and severity?, J. Clin. Sleep. Med, doi:10.5664/jcsm.8596
Van Oosten, Hamilton, Petsikas, Payne, Redfearn et al., Effect of preoperative obstructive sleep apnea on the frequency of atrial fibrillation after coronary artery bypass grafting, Am. J. Cardiol, doi:10.1016/j.amjcard.2013.11.047
Zhou, Yu, Du, Fan, Liu et al., Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study, Lancet, doi:10.1016/S0140-6736(20)30566-3
{ 'indexed': { 'date-parts': [[2024, 10, 29]], 'date-time': '2024-10-29T04:11:40Z', 'timestamp': 1730175100637, 'version': '3.28.0'}, 'reference-count': 37, 'publisher': 'MDPI AG', 'issue': '21', 'license': [ { 'start': { 'date-parts': [[2024, 10, 26]], 'date-time': '2024-10-26T00:00:00Z', 'timestamp': 1729900800000}, 'content-version': 'vor', 'delay-in-days': 0, 'URL': 'https://creativecommons.org/licenses/by/4.0/'}], 'funder': [ { 'DOI': '10.13039/100003305', 'name': 'ResMed Foundation', 'doi-asserted-by': 'publisher', 'award': ['N/A'], 'id': [{'id': '10.13039/100003305', 'id-type': 'DOI', 'asserted-by': 'publisher'}]}], 'content-domain': {'domain': [], 'crossmark-restriction': False}, 'abstract': '<jats:p>Background: We have previously demonstrated that high-risk obstructive sleep apnea ' '(HR-OSA), based on a modified Berlin Questionnaire (mBQ), is linked to worse clinical ' 'outcomes. Chest computed tomography (CT) imaging with the implementation of an artificial ' 'intelligence (AI) analysis program has been a valuable tool for the speedy assessment of huge ' 'numbers of patients during the COVID-19 epidemic. In the current study, we addressed how the ' 'severity of AI-guided, CT-based total opacity ratio (TOR) scores are associated with ' 'high-risk OSA and short-term outcomes in the same cohort. Methods: The ratio of the volume of ' 'high opacity areas to that of the total lung volume constituted the TOR. We arbitrarily ' 'applied thresholds of &lt;5 (no or mild TOR), ≥5 and &lt;15 (moderate TOR), and ≥15 (severe ' 'TOR). Results: In total, 221 patients were included. HR-OSA was observed among 11.0% of the ' 'no or mild TOR group, 22.2% of the moderate TOR group, and 38.7% of the severe TOR group (p ' '&lt; 0.001). In a logistic regression analysis, HR-OSA was associated with a severe TOR with ' 'an adjusted odds ratio of 3.06 (95% confidence interval [CI] 1.27–7.44; p = 0.01). A moderate ' 'TOR predicted clinical worsening with an adjusted hazard ratio (HR) of 1.93 (95% CI ' '1.00–3.72; p = 0.05) and a severe TOR predicted worsening with an HR of 3.06 (95% CI ' '1.56–5.99; p = 0.001). Conclusions: Our results offer additional radiological proof of the ' 'relationship between HR-OSA and worse outcomes in patients with COVID-19 pneumonia. A TOR may ' 'also potentially indicate the individuals that are at higher risk of HR-OSA, enabling early ' 'intervention and management strategies. The clinical significance of TOR thresholds needs ' 'further evaluation in larger samples.</jats:p>', 'DOI': '10.3390/jcm13216415', 'type': 'journal-article', 'created': { 'date-parts': [[2024, 10, 28]], 'date-time': '2024-10-28T11:47:18Z', 'timestamp': 1730116038000}, 'page': '6415', 'source': 'Crossref', 'is-referenced-by-count': 0, 'title': 'Association of High-Risk Obstructive Sleep Apnea with Artificial Intelligence-Guided, CT-Based ' 'Severity Scores in Patients with COVID-19 Pneumonia', 'prefix': '10.3390', 'volume': '13', 'author': [ { 'given': 'Zeynep', 'family': 'Atceken', 'sequence': 'first', 'affiliation': [ { 'name': 'Department of Radiology, Koc University School of Medicine, ' 'Istanbul 34010, Türkiye'}]}, { 'ORCID': 'http://orcid.org/0000-0002-4041-4529', 'authenticated-orcid': False, 'given': 'Yeliz', 'family': 'Celik', 'sequence': 'additional', 'affiliation': [ { 'name': 'Department of Pulmonary Medicine, Koc University School of ' 'Medicine, and Koc University Research Center for Translational ' 'Medicine (KUTTAM), Koc University, Istanbul 34010, Türkiye'}]}, { 'given': 'Cetin', 'family': 'Atasoy', 'sequence': 'additional', 'affiliation': [ { 'name': 'Department of Radiology, Koc University School of Medicine, ' 'Istanbul 34010, Türkiye'}]}, { 'ORCID': 'http://orcid.org/0000-0001-9067-6538', 'authenticated-orcid': False, 'given': 'Yüksel', 'family': 'Peker', 'sequence': 'additional', 'affiliation': [ { 'name': 'Department of Pulmonary Medicine, Koc University School of ' 'Medicine, and Koc University Research Center for Translational ' 'Medicine (KUTTAM), Koc University, Istanbul 34010, Türkiye'}, { 'name': 'Department of Molecular and Clinical Medicine, Sahlgrenska ' 'Academy, University of Gothenburg, 40530 Gothenburg, Sweden'}, { 'name': 'Department of Clinical Sciences, Respiratory Medicine and ' 'Allergology, Faculty of Medicine, Lund University, 22185 Lund, ' 'Sweden'}, { 'name': 'Division of Pulmonary, Allergy, and Critical Care Medicine, ' 'University of Pittsburgh School of Medicine, Pittsburgh, PA ' '15213, USA'}]}], 'member': '1968', 'published-online': {'date-parts': [[2024, 10, 26]]}, 'reference': [ { 'key': 'ref_1', 'unstructured': '(2024, May 26). Coronavirus Disease (COVID-19 Pandemic). Available ' 'online: ' 'https://www.who.int/emergencies/diseases/novel-coronavirus-2019.'}, { 'key': 'ref_2', 'doi-asserted-by': 'crossref', 'first-page': '1054', 'DOI': '10.1016/S0140-6736(20)30566-3', 'article-title': 'Clinical course and risk factors for mortality of adult inpatients with ' 'COVID-19 in Wuhan, China: A retrospective cohort study', 'volume': '395', 'author': 'Zhou', 'year': '2020', 'journal-title': 'Lancet'}, { 'key': 'ref_3', 'doi-asserted-by': 'crossref', 'first-page': '1574', 'DOI': '10.1001/jama.2020.5394', 'article-title': 'Baseline Characteristics and Outcomes of 1591 Patients Infected With ' 'SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy', 'volume': '323', 'author': 'Grasselli', 'year': '2020', 'journal-title': 'JAMA'}, { 'key': 'ref_4', 'doi-asserted-by': 'crossref', 'first-page': 'e2012270', 'DOI': '10.1001/jamanetworkopen.2020.12270', 'article-title': 'Clinical Characteristics and Morbidity Associated With Coronavirus ' 'Disease 2019 in a Series of Patients in Metropolitan Detroit', 'volume': '3', 'author': 'Suleyman', 'year': '2020', 'journal-title': 'JAMA Netw. Open'}, { 'key': 'ref_5', 'doi-asserted-by': 'crossref', 'first-page': '1500', 'DOI': '10.1007/s00125-020-05180-x', 'article-title': 'Phenotypic characteristics and prognosis of inpatients with COVID-19 ' 'and diabetes: The CORONADO study', 'volume': '63', 'author': 'Cariou', 'year': '2020', 'journal-title': 'Diabetologia'}, { 'key': 'ref_6', 'doi-asserted-by': 'crossref', 'first-page': '1389', 'DOI': '10.1001/jama.2020.3514', 'article-title': 'Diagnosis and Management of Obstructive Sleep Apnea: A Review', 'volume': '323', 'author': 'Gottlieb', 'year': '2020', 'journal-title': 'JAMA'}, { 'key': 'ref_7', 'doi-asserted-by': 'crossref', 'first-page': '841', 'DOI': '10.1016/j.jacc.2016.11.069', 'article-title': 'Sleep Apnea: Types, Mechanisms, and Clinical Cardiovascular ' 'Consequences', 'volume': '69', 'author': 'Javaheri', 'year': '2017', 'journal-title': 'J. Am. Coll. Cardiol.'}, { 'key': 'ref_8', 'doi-asserted-by': 'crossref', 'first-page': '1645', 'DOI': '10.5664/jcsm.8538', 'article-title': 'Potential influences of obstructive sleep apnea and obesity on COVID-19 ' 'severity', 'volume': '16', 'author': 'McSharry', 'year': '2020', 'journal-title': 'J. Clin. Sleep. Med.'}, { 'key': 'ref_9', 'doi-asserted-by': 'crossref', 'first-page': '1425', 'DOI': '10.5664/jcsm.8596', 'article-title': 'Does obstructive sleep apnea lead to increased risk of COVID-19 ' 'infection and severity?', 'volume': '16', 'author': 'Tufik', 'year': '2020', 'journal-title': 'J. Clin. Sleep. Med.'}, { 'key': 'ref_10', 'doi-asserted-by': 'crossref', 'first-page': '2105', 'DOI': '10.1007/s11325-020-02272-1', 'article-title': 'Obstructive Sleep Apnea and Risk of COVID-19 Infection, Hospitalization ' 'and Respiratory Failure', 'volume': '25', 'author': 'Maas', 'year': '2020', 'journal-title': 'Sleep. Breath.'}, { 'key': 'ref_11', 'doi-asserted-by': 'crossref', 'first-page': '1462', 'DOI': '10.1164/rccm.202006-2252LE', 'article-title': 'Sleep Apnea and COVID-19 Mortality and Hospitalization', 'volume': '202', 'author': 'Cade', 'year': '2020', 'journal-title': 'Am. J. Respir. Crit. Care Med.'}, { 'key': 'ref_12', 'doi-asserted-by': 'crossref', 'first-page': '1548', 'DOI': '10.1513/AnnalsATS.202011-1409OC', 'article-title': 'Effect of High-Risk Obstructive Sleep Apnea on Clinical Outcomes in ' 'Adults with Coronavirus Disease 2019: A Multicenter, Prospective, ' 'Observational Clinical Trial', 'volume': '18', 'author': 'Peker', 'year': '2021', 'journal-title': 'Ann. Am. Thorac Soc.'}, { 'key': 'ref_13', 'doi-asserted-by': 'crossref', 'unstructured': 'Celik, Y., Baygül, A., and Peker, Y. (2023). Validation of the Modified ' 'Berlin Questionnaire for the Diagnosis of Obstructive Sleep Apnea in ' 'Patients with a History of COVID-19 Infection. J. Clin. Med., 12.', 'DOI': '10.3390/jcm12093047'}, { 'key': 'ref_14', 'doi-asserted-by': 'crossref', 'first-page': '263', 'DOI': '10.14797/mdcj-16-4-263', 'article-title': 'Artificial Intelligence and Machine Learning in Cardiovascular Imaging', 'volume': '16', 'author': 'Seetharam', 'year': '2020', 'journal-title': 'Methodist. Debakey Cardiovasc. J.'}, { 'key': 'ref_15', 'doi-asserted-by': 'crossref', 'unstructured': 'Trimarchi, G., Pizzino, F., Paradossi, U., Gueli, I.A., Palazzini, M., ' 'Gentile, P., Di Spigno, F., Ammirati, E., Garascia, A., and Tedeschi, A. ' '(2024). Charting the Unseen: How Non-Invasive Imaging Could Redefine ' 'Cardiovascular Prevention. J. Cardiovasc. Dev. Dis., 11.', 'DOI': '10.3390/jcdd11080245'}, { 'key': 'ref_16', 'doi-asserted-by': 'crossref', 'unstructured': 'Atceken, Z., Celik, Y., Atasoy, C., and Peker, Y. (2023). The Diagnostic ' 'Utility of Artificial Intelligence-Guided Computed Tomography-Based ' 'Severity Scores for Predicting Short-Term Clinical Outcomes in Adults ' 'with COVID-19 Pneumonia. J. Clin. Med., 12.', 'DOI': '10.3390/jcm12227039'}, { 'key': 'ref_17', 'doi-asserted-by': 'crossref', 'first-page': '298', 'DOI': '10.1111/sbr.12118', 'article-title': 'Prevalence of sleep disorders in the Turkish adult population ' 'epidemiology of sleep study', 'volume': '13', 'author': 'Demir', 'year': '2015', 'journal-title': 'Sleep. Biol. Rhythm.'}, { 'key': 'ref_18', 'doi-asserted-by': 'crossref', 'first-page': '919', 'DOI': '10.1016/j.amjcard.2013.11.047', 'article-title': 'Effect of preoperative obstructive sleep apnea on the frequency of ' 'atrial fibrillation after coronary artery bypass grafting', 'volume': '113', 'author': 'Hamilton', 'year': '2014', 'journal-title': 'Am. J. Cardiol.'}, { 'key': 'ref_19', 'unstructured': '(2000). Obesity: Preventing and managing the global epidemic. Report of ' 'a WHO consultation. World Health Organ. Tech. Rep. Ser., 894, 1–253.'}, { 'key': 'ref_20', 'doi-asserted-by': 'crossref', 'unstructured': 'Chamberlin, J., Kocher, M.R., Waltz, J., Snoddy, M., Stringer, N.F.C., ' 'Stephenson, J., Sahbaee, P., Sharma, P., Rapaka, S., and Schoepf, U.J. ' '(2021). Automated detection of lung nodules and coronary artery calcium ' 'using artificial intelligence on low-dose CT scans for lung cancer ' 'screening: Accuracy and prognostic value. BMC Med., 19.', 'DOI': '10.1186/s12916-021-01928-3'}, { 'key': 'ref_21', 'doi-asserted-by': 'crossref', 'first-page': '154', 'DOI': '10.1097/RTI.0000000000000613', 'article-title': 'Diagnostic Accuracy and Performance of Artificial Intelligence in ' 'Detecting Lung Nodules in Patients With Complex Lung Disease: A ' 'Noninferiority Study', 'volume': '37', 'author': 'Abadia', 'year': '2022', 'journal-title': 'J. Thorac. Imaging'}, { 'key': 'ref_22', 'doi-asserted-by': 'crossref', 'unstructured': 'Arish, N., Izbicki, G., Rokach, A., Jarjou’i, A., Kalak, G., and ' 'Goldberg, S. (2023). Association of the risk of obstructive sleep apnoea ' 'with the severity of COVID-19. PLoS ONE, 18.', 'DOI': '10.1371/journal.pone.0284063'}, { 'key': 'ref_23', 'doi-asserted-by': 'crossref', 'unstructured': 'Breville, G., Herrmann, F., Adler, D., Deffert, C., Bommarito, G., ' 'Stancu, P., Accorroni, A., Uginet, M., Assal, F., and Tamisier, R. ' '(2023). Obstructive sleep apnea: A major risk factor for COVID-19 ' 'encephalopathy?. BMC Neurol., 23.', 'DOI': '10.1186/s12883-023-03393-2'}, { 'key': 'ref_24', 'doi-asserted-by': 'crossref', 'first-page': 'Np10', 'DOI': '10.1177/01455613211029783', 'article-title': 'Association Between Severity of COVID-19 Respiratory Disease and Risk ' 'of Obstructive Sleep Apnea', 'volume': '103', 'author': 'Iannella', 'year': '2024', 'journal-title': 'Ear Nose Throat J.'}, { 'key': 'ref_25', 'doi-asserted-by': 'crossref', 'first-page': '2012', 'DOI': '10.1056/NEJMoa2004500', 'article-title': 'Covid-19 in Critically Ill Patients in the Seattle Region—Case Series', 'volume': '382', 'author': 'Bhatraju', 'year': '2020', 'journal-title': 'N. Engl. J. Med.'}, { 'key': 'ref_26', 'doi-asserted-by': 'crossref', 'first-page': '1612', 'DOI': '10.1001/jama.2020.4326', 'article-title': 'Characteristics and Outcomes of 21 Critically Ill Patients With ' 'COVID-19 in Washington State', 'volume': '323', 'author': 'Arentz', 'year': '2020', 'journal-title': 'JAMA'}, { 'key': 'ref_27', 'doi-asserted-by': 'crossref', 'first-page': '4080', 'DOI': '10.1038/s41467-020-17971-2', 'article-title': 'Artificial intelligence for the detection of COVID-19 pneumonia on ' 'chest CT using multinational datasets', 'volume': '11', 'author': 'Harmon', 'year': '2020', 'journal-title': 'Nat. Commun.'}, { 'key': 'ref_28', 'doi-asserted-by': 'crossref', 'first-page': 'E65', 'DOI': '10.1148/radiol.2020200905', 'article-title': 'Using Artificial Intelligence to Detect COVID-19 and Community-acquired ' 'Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy', 'volume': '296', 'author': 'Li', 'year': '2020', 'journal-title': 'Radiology'}, { 'key': 'ref_29', 'doi-asserted-by': 'crossref', 'first-page': '1224', 'DOI': '10.1038/s41591-020-0931-3', 'article-title': 'Artificial intelligence-enabled rapid diagnosis of patients with ' 'COVID-19', 'volume': '26', 'author': 'Mei', 'year': '2020', 'journal-title': 'Nat. Med.'}, { 'key': 'ref_30', 'unstructured': 'Bernal-Ramirez, J.M., Chavez-Barba, O.A., Cobian-Machuca, H., ' 'Delgado-Figueroa, N., Martinez-Solano, L.F., Aguirre-Diaz, S.A., ' 'Gonzalez-Diaz, E., Figueroa-Sanchez, M., and Alanis-Salazar10, R.M. ' 'Comparing the diagnostic performance of an artificial intelligence ' 'system with human readers in the tomographic evaluation of SARS-CoV-2 ' 'pneumonia.'}, { 'key': 'ref_31', 'doi-asserted-by': 'crossref', 'first-page': '20210759', 'DOI': '10.1259/bjr.20210759', 'article-title': 'The diagnostic performance of deep-learning-based CT severity score to ' 'identify COVID-19 pneumonia', 'volume': '95', 'author': 'Kardos', 'year': '2022', 'journal-title': 'Br. J. Radiol.'}, { 'key': 'ref_32', 'doi-asserted-by': 'crossref', 'first-page': '100370', 'DOI': '10.1016/j.ejro.2021.100370', 'article-title': 'New challenges for management of COVID-19 patients: Analysis of MDCT ' 'based “Automated pneumonia analysis program”', 'volume': '8', 'author': 'Sezer', 'year': '2021', 'journal-title': 'Eur. J. Radiol. Open'}, { 'key': 'ref_33', 'doi-asserted-by': 'crossref', 'first-page': '2500', 'DOI': '10.1007/s00259-021-05375-3', 'article-title': 'A comprehensive review of imaging findings in COVID-19—Status in early ' '2021', 'volume': '48', 'author': 'Prosch', 'year': '2021', 'journal-title': 'Eur. J. Nucl. Med. Mol. Imaging'}, { 'key': 'ref_34', 'doi-asserted-by': 'crossref', 'first-page': '877', 'DOI': '10.3390/covid2070064', 'article-title': 'Obstructive sleep apnea (OSA) and COVID-19: Mortality prediction of ' 'COVID-19-infected patients with OSA using machine learning approaches', 'volume': '2', 'author': 'Tasmi', 'year': '2022', 'journal-title': 'COVID'}, { 'key': 'ref_35', 'first-page': '111', 'article-title': 'Validation of chest computed tomography artificial intelligence to ' 'determine the requirement for mechanical ventilation and risk of ' 'mortality in hospitalized coronavirus disease-19 patients in a tertiary ' 'care center in Mexico City', 'volume': '73', 'year': '2021', 'journal-title': 'Rev. Investig. Clínica'}, { 'key': 'ref_36', 'first-page': '1493', 'article-title': 'Artificial intelligence in health care', 'volume': '10', 'author': 'Isaacs', 'year': '2022', 'journal-title': 'J. Paediatr. Child. Health'}, { 'key': 'ref_37', 'doi-asserted-by': 'crossref', 'unstructured': 'Khanna, N.N., Maindarkar, M.A., Viswanathan, V., Fernandes, J.F.E., ' 'Paul, S., Bhagawati, M., Ahluwalia, P., Ruzsa, Z., Sharma, A., and ' 'Kolluri, R. (2022). Economics of Artificial Intelligence in Healthcare: ' 'Diagnosis vs. Treatment. Healthcare, 10.', 'DOI': '10.3390/healthcare10122493'}], 'container-title': 'Journal of Clinical Medicine', 'original-title': [], 'language': 'en', 'link': [ { 'URL': 'https://www.mdpi.com/2077-0383/13/21/6415/pdf', 'content-type': 'unspecified', 'content-version': 'vor', 'intended-application': 'similarity-checking'}], 'deposited': { 'date-parts': [[2024, 10, 28]], 'date-time': '2024-10-28T12:49:06Z', 'timestamp': 1730119746000}, 'score': 1, 'resource': {'primary': {'URL': 'https://www.mdpi.com/2077-0383/13/21/6415'}}, 'subtitle': [], 'short-title': [], 'issued': {'date-parts': [[2024, 10, 26]]}, 'references-count': 37, 'journal-issue': {'issue': '21', 'published-online': {'date-parts': [[2024, 11]]}}, 'alternative-id': ['jcm13216415'], 'URL': 'http://dx.doi.org/10.3390/jcm13216415', 'relation': {}, 'ISSN': ['2077-0383'], 'subject': [], 'container-title-short': 'JCM', 'published': {'date-parts': [[2024, 10, 26]]}}
Loading..
Please send us corrections, updates, or comments. c19early involves the extraction of 100,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. Treatments and other interventions are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
  or use drag and drop   
Submit