Failed/difficult Intubation Comparing between Pre-COVID-19 and COVID-19 Pandemic Period using A National Insurance Claims Database and Information System of a University Hospital

Failed/difficult Intubation Comparing between Pre-COVID-19 and COVID-19 Pandemic Period using A National Insurance Claims Database and Information System of a University Hospital | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Failed/difficult Intubation Comparing between Pre-COVID-19 and COVID-19 Pandemic Period using A National Insurance Claims Database and Information System of a University Hospital Sumidtra Prathep, Alan Geater, Hutcha Sriplung, Ponlagrit Kumwichar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4592086/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Dec, 2024 Read the published version in BMC Anesthesiology → Version 1 posted 4 You are reading this latest preprint version Abstract Introduction Endotracheal intubation can be difficult or even fail under certain patient and intubator conditions. During the COVID-19 pandemic a country-wide lockdown policy was enforced in Thailand which stipulated that intubators wear personal protective equipment, powered air purifying respirator, or goggles and surgical/N95 mask during the intubation procedure. Thus clad, an intubator’s vision is restricted and grip on the equipment less sure. Under these conditions, the incidence of difficult intubation was expected to increase. Methods This time-series study was based on the aggregated age- and sex-standardized monthly incidence of difficult intubation among all intubated patients whose data were recorded in the national insurance claims database and among patients recorded in the records of a university hospital from January 2018 to September 2022. Changes in incidence of difficult intubation following the implementation of a lockdown policy from 26 March 2020 during the COVID-19 pandemic were explored using negative binomial regression and interrupted linear regression time-series analysis. Results Data of 922,274 individuals in the national database and 95,457 individuals in the university database were retrieved. The overall incidence of difficult intubation in both settings dropped by 25% following lockdown, significantly so in the national database (p < 0.001). Slight increasing and decreasing trends pre- and post-lockdown were not significant. Discussion The decreased incidence of difficult intubation during the lockdown period was contrary to expectation but might be related to the deployment solely of anaesthesiologists and more experienced anaesthetic staff using videolaryngoscopes during lockdown following the recommendation for intubation during respiratory disease pandemics. airway airway management difficult airway COVID-19 intubation pandemic time series analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Ideally endotracheal intubation should be performed quickly and without delay in the process of ventilation, laryngoscopy and intubation.[ 1 ] This ideal is likely to be compromised when intubating patients with special airway management or “difficult airway”. There are a number of difficult clinical situations, described as facemask ventilation, laryngoscopy, supraglottic airway ventilation, difficult/failed tracheal intubation, difficult/failed tracheal extubation, difficult/failed invasive airway or inadequate ventilation. During the COVID-19 pandemic, COVID patients may have special requirements, such as oxygen therapy using a high-flow oxygen cannula or mechanical ventilator due to the possible complication of pneumonia or acute respiratory distress syndrome (ARDS) after getting the infection.[ 2 ] Delayed intubation is reported to be associated with increased mortility.[ 3 ] An additional issue possibly adding to the delayed intubation of patients during the pandemic was the need to follow the national lockdown policy stipulating that personal protective equipment (PPE), powered air purifying respirator (PAPR),[ 4 ] or goggles and surgical or N95 mask, must be worn, and this might make the procedure more difficult owing to the somewhat restricted vision and less sure grip on the intubation instruments/equipment. The incidence of difficult intubation might therefore be higher during the COVID-19 lockdown period. Difficult intubation is defined as a normally trained anesthesiologist needing more than 3 attempts or more than 10 minutes for a successful endotracheal intubation.[ 5 ] In different settings, baseline incidence of difficult intubation varies. In the United Kingdom 6 and Germany 7 the incidence was reported to be around 10%, while in Australia the reported incidence was 0.5%, and in Singapore around 5% among caesearean section patients. Both the United Kingdom and Germany have a website of their national audit project of difficult airway databases, which can guide the policy plan for airway management to prevent unfavorable outcomes e.g., hypoxia, hypercarbia, cardiac arrest or death. From 2016 the Ministry of Public Health of Thailand has recorded all intubations of patients at all government hospitals throughout the country as well as indicating whether they were classified as difficult or not. Similarly, the Department of Anaesthesiology of Songklanagarind Hospital, a university hospital in the south of the country, has maintained a database since 2016 of all patients intubated. Both these databases span the period prior to and during the COVID-19 lockdown and might provide insights into the effect of the COVID-19 lockdown policy on the incidence of difficult intubation, and how best to prepare for and manage the difficulties of airway management and endotracheal intubation should similar pandemics occur in the future. The databases of hospital and national level[ 6 ] , [ 7 ] are important to give us the information of policy planning in the future if we face with the pandemics of respiratory infectious diseases again. Songklanagarind Hospital has another information database for the anesthesia work, but it does not use international codes. We can review the data among these data sources and shape them to be of international standard and thereby benchmark our data widely. Methods Study design This is a retrospective time-series study of data on patients who underwent endotracheal intubation, including both difficult and non-difficult intubations, recorded in a Thailand national database and in the information system of a university hospital in the south of Thailand. The data spanned the period from October 2016 to September 2021 covering approximately 27 months before and 30 months during the COVID-19 lockdown period, which started of 26 March 2020. Setting and Data sources The national database was that maintained by the National Health Security Office (NHSO). All governmental heath service units performing the procedure of intubation and difficult intubation can be reimbursed from NHSO. This enabled NHSO to obtain the health service data of all intubated individuals in Thailand. The university database was that recorded in the information system of the Department of Anaesthesiology and the Digital Innovation and Data Analytics (DIDA) unit of the Faculty of Medicine, Prince of Songkla University and Songklanagarind Hospital. This database contains the records of all intubation procedures performed in the operation room as well as those carried out under consultation on the ward. These data were validated against the against the DIDA database to ensure their accuracy. Participants and data management From each database, patients were included in the analysis if they were aged 18 or over at the date of admission prior to intubation. For analysis of patients from the university database, their inpatient (IP) electronic medical records were linked using encrypted identification numbers to protect their personal identity. Using e-claim codes, ICD-9-CM, ICD-10 and IP data were mined to classify the intubation status as difficult or non-difficult. ICD-9-CM code 9604 is defined as insertion of endotracheal tube. ICD-10 code T88.4 is defined as failed/difficult intubation. Difficult intubation is defined as the anesthesiologists making more than 3 attempts or taking more than 15 minutes for intubation in Songklanagarind Hospital. Outcome and Statistical analysis The Shapiro-Wilks normality test was used to assess the normality of continuous variables. Continuous variables were presented as mean and standard deviation (SD) or median and interquartile range (IQR) as appropriate. Categorical variables were presented as number of patients and percentage. Within the national and within the university dataset, data on individual patients’ intubation status were aggregated into monthly periods and the proportions of intubations recorded as difficult standardized by age group [18–30, 31–40, 41–50, 51–60, 61–70, 71–80 and more than 80 years] and sex using the overall patient mix for the whole analysis period within the corresponding dataset (1st January 2018 to 30th September 2022) Based on the age and sex standardized monthly proportions of difficult intubation, negative binomial regression was used to evaluate the change in mean monthly proportions from pre-lockdown to post-lockdown in each dataset. To explore possible trends and changes in trend according to the pre- and post-lockdown implementation periods, interrupted time-series analyses with linear regression were performed marking the point of interruption as the end of March 2020. (The lockdown was implemented form 26 March 2020). The presence of first-order autocorrelation in the residuals from the models was checked using the Durbin-Watson test. All analyses were conducted using R version 4.2.2 and STATA release 14.2. A p-value of < 0.05 was considered statistically significant. Ethics statement Patient data were encrypted and de-identified for personalized anonymization according to the Thai Personal Data Protection Act 2019, Thailand (PDPA). Data were received from NHSO under a project approval granted by the Human Research Ethics Committee, Prince of Songkla University (REC 66-117-18-8) on 8th March 2023. Informed consent was not required because the data obtained could not identify any individual. The clinical trial registration is not applicable. Results Population demographics The national database recorded 681,176 patients aged 18 or over intubated within the study period (316,664 pre-lockdown and 364,512 post-lockdown). Overall, 59.3% were male, and median age was 63 years (range 18–117). Age and sex distributions were similar in the two periods, with a preponderance of 51-80-year age groups among all intubations (Fig. 2 ). A total of 0.61% of intubations were difficult pre-lockdown compared with 0.46% post-lockdown. The proportions of difficult intubation were higher in the younger age groups (18–40 than in older age groups in both periods. The university database recorded 30,275 and 35,848 patients aged 18 or overe in pre- and post-lockdown study periods with overall males accounting for 49.9% and median age 55 (range 18–105). Unlike the national data base, the university data showed no preponderance of older patients among all intubations. The proportions of difficult intubation in the university database were only around one half those at the national level − 0.28% and 0.20% in pre- and post-lockdown periods (Table 1 ). Also, unlike the national database, the university data did not show a higher proportion of difficult intubation in the younger age groups in either period (Fig. 3 ). Table 1 Characteristics of intubated patients in the National and University Hospital databases. National Database (n = 681,176) University Hospital Database (n = 66,123) Pre-lockdown (n = 316,664) Lockdown (n = 364,512) Pre-lockdown (n = 30,275) Lockdown (n = 35,848) Age (years), median (IQR) 65 (53, 7 5) 64 (52, 74) 54 (38, 66) 55 (40, 67) Male, n (%) 187,099 (59.1) 217,170 (59.6) 15,326 (50.6) 17,710 (49.4) Intubations Monthly number, mean ± SD median (IQR) range 11,728.3 ± 729.32 11,701 (11,162, 12197) 10,778 − 13,444 12,150.4 ± 1 528.67 12,343 (11,619, 12,669) 7,176 − 15,926 1121.3 ± 182.4 1101 (976, 1192) 872-1,660 1194.9 ± 290.2 1206 (964, 1416) 645-1,814 SARS-CoV-2 infection, n (%) Monthly number, median (IQR) range 24 (0.07) 0 (0, 0) 0–24 17,739 (4.8) 425.5 (7, 948) 0-2109 0 0 (0, 0) 0 197 (0.5) 3 (0, 8) 0–35 SARS-CoV-2 pneumonia, n (%) Monthly number, median (IQR) range 24 (0.07) 0 (0, 0) 0–24 14,925 (4.1) 343 (4, 738) 0-1930 0 0 (0, 0) 0 70 (0.2) 0.5 (0, 5) 0–10 Difficult intubation, n (%) Monthly number, median (IQR) Monthly percent, median (IQR) range 1,959 (0.62) 75 (60, 83) 0.61 (0.54, 0.71) 45–99 1,698 (0.47) 56 (50, 65) 0.47 (0.39, 0.53) 16–90 84 (0.28) 2 (1, 4) 0.22 (0.09, 0.38) 0–9 73 (0.20) 2.5 (1, 4) 0.22 (0.08, 0.30) 0–8 Using the age- and sex-standardized monthly proportions of difficult intubation, negative binomial regression revealed that the mean monthly proportions per thousand intubations [and 95% confidence interval] of difficult intubations were significantly 25% lower in the post-lockdown period than in the pre-lockdown period in both datasets (pre- and post- respectively 6.17 [5.77, 6.61] and 4.65 [4.35, 4.98] (p < .001) in the national dataset and 2.80 [2.07, 3.78] and 2.10 [1.54, 2.85] (p = 0.188) in the university database. Thus, the mean monthly proportion of difficult intubations dropped by around 25% in both datasets. Investigation of trends and changes in trend of difficult intubation monthly proportions in the two periods using interrupted time-series analysis revealed similar patterns in the national and university data, albeit at lower proportions in the university than in the national data (Figs. 4 and 5 ). In both settings the regression suggested a non-significant increase in proportion of difficult intubation in the pre-lockdown period and a non-significant decreasing trend following implementation of lockdown. At the point of interruption, a significant drop in level was evident in the national data (of 1.682 per thousand per month, P = 0.003) and a non-significant drop at the university level (of 1.118 per thousand per month, P = 0.304). Discussion The patient characteristics differed between the national database and PSU database in terms of age and sex. Older patients got more intubated in national database while younger patients got more intubated in PSU database. Incidence of failed/difficult intubation was higher in younger age group than older age group in national database. We cannot find the pattern of failed/difficult intubation among PSU patients. Even though, the mean monthly proportion of difficult intubations dropped by around 25% in both datasets. When we did the standardization of age and sex to do the linear regression of the incidence of failed/difficult intubation, the trend of slope before and after launching lockdown policy in Thailand are similar. The number of intubation patients decreased after lockdown policy similar to the report of Uansri et al[ 8 ], who surveyed in greater Bangkok. Our study showed a significant drop in level was evident in the national data and a non-significant drop at the university level at the point of interruption. Difficult intubation There were different definitions of “failed/difficult intubation”. Two databases of this study did not use the same definition of failed/difficult intubation. The intubators from ICD code of national database may not be anesthesiologists who are the experts in intubation unlike in PSU database. There are other publications that used yet other definitions, such as Jayaraj et al.[ 9 ] defined difficult intubation on a 5-points scale depending on numbers of intubation or whether other rescue equipment is used. Schroeder et al.[ 10 ] defined more than 3 attempts of intubation as difficult intubation and failed intubation as requiring either surgical or percutaneous tracheotomy, cricothyrotomy, or wake-up of the patient. ICD code from PSU database is under reported so the author used the database from Anesthesiology Department. Intubation is minor procedure, so the clinicians and coders may disregard/overlook the intubation code. They coded the major diseases or procedures that the patients received. Intubation before and during COVID-19 pandemic The incidence of difficult intubation seems increasing over years since 2018 until COVID-19 pandemic in early 2020 in Thailand (pre-interruption slope; β 0.028, p-value 0.275 for national database). It is maybe there were more obese patients and the patients with difficult medical conditions so the clinicians cannot give them the sedation drugs before intubation.[ 11 ] If difficult intubation is anticipated[ 12 ], the equipment and multidisciplinary team (anesthesiologists, otolaryngologist, pulmonologists and intensivists) need to be prepared. After Thai government announced the lockdown policy on 26 March 2020, the incidence of difficult intubation was decreased. Videolaryngoscope[ 13 ] , [ 14 ]is a tool to help the clinicians intubate the patients because of the more curved blade and the screen that can display the larynx outside the oral cavity and show the large pictures of the larynx directly inside the oral cavity. Also, the risk respiratory infection transmission is decreased if the intubators and the patients are farther apart.[ 15 ] There is a survey through the European Airway Management Society’s network showed videolaryngoscope using 24.1% before COVID-19 and 43.1% during COVID-19(P < 0.001).[ 16 ] The anesthesiologists who have the most experience of intubation among all clinicians take the role of intubators during COVID-19 pandemic. They wear protective equipment and also follow the checklist guideline for taking care of COVID-19 patients.[ 15 ] , [ 17 ] These protective equipment was not the factors to increase the incidence of failed/difficult intubation as our hypothesis. Difficult airway database Difficult intubation database is needed in Thailand. It could be web-base as United Kingdom[ 18 ], Germany[ 19 ], Australia[ 20 ]. Airway assessment is needed for the clinicians and nurses to predict difficult intubation especially in emergency cases which there is short time to evaluate. Since difficult intubation is rare, so we need to collect data from many institutions to explore the factors of failed/difficult intubation. It would be great if we have national database. If we have well-recorded database, failed/difficult intubation could be well predicted among Thai population. If they can predict well, they can prepare the equipment and activate the intubation team to avoid the adverse events from the intubation. In the other hand, mis-prediction of easy intubation is also needed. We will not waste the prepared equipment and the experts who we ask for stand by for difficult airway management. Strength National Health Security Office (NHSO) provides national database which are big data to analyze intubation and difficult intubation incidence before and during COVID-19 pandemic. PSU is a university hospital which can provide big data from other database beside ICD-code. Limitation PSU database from DIDA report ICD code is lower the incidence from anesthesiology department. The predisposing factors of increasing difficult intubation before COVID-19 pandemic cannot be described. There was missing data in PSU database. More data exploration or prospective study should be done for further research. Declarations Ethics approval and consent to participate Data were received from NHSO under a project approval granted by the Human Research Ethics Committee, Prince of Songkla University (REC 66-117-18-8) on 8 th March 2023 by Prof. Boonsin Tangtrakulwanich MD, Chairman of Human Research Ethics Commitee Faculty of Medicine, Prince of Songkla University. Consent for publication This is a retrospective time-series study. The consent for publication is not applicable. Availability of data and materials The national database was that maintained by the National Health Security Office (NHSO). Patient data were encrypted and de-identified for personalized anonymization according to the Thai Personal Data Protection Act 2019, Thailand (PDPA). Competing interests The authors declare that they have no competing interests. Funding There was no funding. Authors' contributions SP : acquisition, analysis, drafting the work, final approval of the manuscript AG : acquisition, analysis, review, drafting, final approval of the manuscript HS : acquisition, analysis, review, drafting, final approval of the manuscript PK : acquisition, analysis, review, drafting, final approval of the manuscript VC : acquisition, analysis, review, drafting, final approval of the manuscript Acknowledgements Not applicable References Mouri Mi, Krishnan S, Maani CV. 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Clinical Anesthesia. 8th ed. Wolters Kluwer; 2017. ICD9CM2010.pdf n.d. International Statistical. Classification of Diseases and Related Health Problems 2016. Uansri S, Tuangratananon T, Phaiyarom M, Rajatanavin N, Suphanchaimat R, Jaruwanno W. Predicted Impact of the Lockdown Measure in Response to Coronavirus Disease 2019 (COVID-19) in Greater Bangkok, Thailand, 2021. Int J Environ Res Public Health. 2021;18:12816. https://doi.org/10.3390/ijerph182312816 . Jayaraj AK, Siddiqui N, Abdelghany SMO, Balki M. Management of difficult and failed intubation in the general surgical population: a historical cohort study in a tertiary care centre. Can J Anesth/J Can Anesth. 2022;69:427–37. https://doi.org/10.1007/s12630-021-02161-5 . Schroeder RA, Pollard R, Dhakal I, Cooter M, Aronson S, Grichnik K, et al. Temporal Trends in Difficult and Failed Tracheal Intubation in a Regional Community Anesthetic Practice. Anesthesiology. 2018;128:502–10. https://doi.org/10.1097/ALN.0000000000001974 . Engstrom K, Brown CS, Mattson AE, Lyons N, Rech MA. Pharmacotherapy optimization for rapid sequence intubation in the emergency department. Am J Emerg Med. 2023;70:19–29. https://doi.org/10.1016/j.ajem.2023.05.004 . Kuhar HN, Bliss A, Evans K, Besecker B, Spitzer C, Lyaker M et al. Difficult Airway Response Team (DART) and Airway Emergency Outcomes: A Retrospective Quality Improvement Study. Otolaryngology–Head and Neck Surgery n.d.;n/a. https://doi.org/10.1002/ohn.358 . Lewis SR, Butler AR, Parker J, Cook TM, Smith AF. Videolaryngoscopy versus direct laryngoscopy for adult patients requiring tracheal intubation. Cochrane Database Syst Rev 2016;2016:CD011136. https://doi.org/10.1002/14651858.CD011136.pub2 . de Carvalho CC, da Silva DM, Lemos VM, dos Santos TGB, Agra IC, Pinto GM, et al. Videolaryngoscopy vs. direct Macintosh laryngoscopy in tracheal intubation in adults: a ranking systematic review and network meta-analysis. Anaesthesia. 2022;77:326–38. https://doi.org/10.1111/anae.15626 . Shamim F, Khan MF, Samad K, Latif A. Development of an emergency airway response system for COVID-19 at a tertiary care hospital in resource limited country. Pak J Med Sci. 2023;39:300–3. https://doi.org/10.12669/pjms.39.1.5689 . Saracoglu A, Saracoglu KT, Sorbello M, Çakmak G, Greif R. The influence of the COVID-19 pandemic on videolaryngoscopy: a cross-sectional before-and-after survey. Anaesthesiol Intensive Ther. 2023;55:93–102. https://doi.org/10.5114/ait.2023.129278 . Meléndez-Cervantes A, González-Merino IB, García-Galicia A, Montiel-Jarquín ÁJ, Velasco-Orea JI, Loría-Castellanos J, et al. [Security protocols adapted to COVID-19 in elective surgery thru 2021]. Rev Med Inst Mex Seguro Soc. 2022;60:616–23. Sajayan A, Nair A, McNarry AF, Mir F, Ahmad I, El-Boghdadly K. Analysis of a national difficult airway database. Anaesthesia. 2022;77:1081–8. https://doi.org/10.1111/anae.15820 . Jansen G, Scholz SS, Rehberg SW, Wnent J, Gräsner J-T, Seewald S. Indications and measures of medical emergency teams: a retrospective evaluation of in-hospital emergency operations of the German Resuscitation Register. Minerva Anestesiol. 2023;89:56–65. https://doi.org/10.23736/S0375-9393.22.16665-4 . Endlich Y, Lee J, Culwick MD. Difficult and failed intubation in the first 4000 incidents reported on webAIRS. Anaesth Intensive Care. 2020;48:477–87. https://doi.org/10.1177/0310057X20957657 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Dec, 2024 Read the published version in BMC Anesthesiology → Version 1 posted Editorial decision: Revision requested 05 Jul, 2024 Editor assigned by journal 04 Jul, 2024 Submission checks completed at journal 04 Jul, 2024 First submitted to journal 17 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4592086","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":323071911,"identity":"d0090295-4698-4882-bdcd-9f7ee708b985","order_by":0,"name":"Sumidtra Prathep","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYJACZgjF2CABJOVAzAMP8OtgbIZrOcDAYAzWkkCcFgYGkJbEBhALnxZz6cPPHxdU3LE3uHa48fbHPXbp88MOPwTaYien24Bdi2VfmmHzjDPPEjfcTmy2OPAsOXfj7TQDoJZkY7MD2LUYnGEwbOZtO5xgcDuxTeLAAebcjbMTQFoOJG7DqYX9YzPvv8P2UC316Yaz0z8Q0MIDtKXhMOMGiJbDCfLSOfhtsezhKZzNc+xZ4kyQX84cOG64QTqn4ECCAW6/mPOwb/jMU3PHnu92+sMbFQeq5eVnp2/+8KHCTg6n9yEUkqzBASRx4rTIN+BWPQpGwSgYBSMTAAB6HG1X1mDTvwAAAABJRU5ErkJggg==","orcid":"","institution":"Songklanagarind Hospital, Prince of Songkla University","correspondingAuthor":true,"prefix":"","firstName":"Sumidtra","middleName":"","lastName":"Prathep","suffix":""},{"id":323071912,"identity":"eadbe69e-1052-42e3-a0f1-843528a9a90c","order_by":1,"name":"Alan Geater","email":"","orcid":"","institution":"Songklanagarind Hospital, Prince of Songkla University","correspondingAuthor":false,"prefix":"","firstName":"Alan","middleName":"","lastName":"Geater","suffix":""},{"id":323071913,"identity":"2af5b96f-cd2c-4f21-bf0f-da44256c860b","order_by":2,"name":"Hutcha Sriplung","email":"","orcid":"","institution":"Songklanagarind Hospital, Prince of Songkla University","correspondingAuthor":false,"prefix":"","firstName":"Hutcha","middleName":"","lastName":"Sriplung","suffix":""},{"id":323071914,"identity":"f1d3a067-3034-472c-b44d-fe17212cdf27","order_by":3,"name":"Ponlagrit Kumwichar","email":"","orcid":"","institution":"Songklanagarind Hospital, Prince of Songkla University","correspondingAuthor":false,"prefix":"","firstName":"Ponlagrit","middleName":"","lastName":"Kumwichar","suffix":""},{"id":323071915,"identity":"583eeee1-2519-420c-bc31-4eef510fe9fd","order_by":4,"name":"Virasakdi Chongsuvivatwong","email":"","orcid":"","institution":"Songklanagarind Hospital, Prince of Songkla University","correspondingAuthor":false,"prefix":"","firstName":"Virasakdi","middleName":"","lastName":"Chongsuvivatwong","suffix":""}],"badges":[],"createdAt":"2024-06-17 06:16:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4592086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4592086/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12871-024-02788-z","type":"published","date":"2024-12-06T15:57:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61346257,"identity":"917f4fde-615e-4dd3-b282-f5e48805bdcf","added_by":"auto","created_at":"2024-07-29 18:06:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94725,"visible":true,"origin":"","legend":"\u003cp\u003eCONSORT of intubation patients; PSU = Songklanagarind Hospital\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4592086/v1/cac6e903d0d84bcadee4038a.png"},{"id":61346256,"identity":"1e80271e-0f1a-4552-82f0-1622ac37b8a0","added_by":"auto","created_at":"2024-07-29 18:06:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81937,"visible":true,"origin":"","legend":"\u003cp\u003eNational hospitalized intubation patients and the incidence of failed/difficult intubation of each age group by sex dividing to pre-launching lockdown and post-launching lockdown.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4592086/v1/463f7696cda62afe082c0c59.png"},{"id":61347026,"identity":"6d30933e-1e69-4e34-801e-aaece2a52b24","added_by":"auto","created_at":"2024-07-29 18:14:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76524,"visible":true,"origin":"","legend":"\u003cp\u003ePSU hospitalized intubation patients and the incidence of failed/difficult intubation of each age group by sex dividing to pre-launching lockdown and post-launching lockdown.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4592086/v1/da18f375c0658350440c9c9b.png"},{"id":61346258,"identity":"b0fea123-4dca-4bd2-9d88-288183a33ad5","added_by":"auto","created_at":"2024-07-29 18:06:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":64657,"visible":true,"origin":"","legend":"\u003cp\u003eInterrupted time-series analysis showing age-sex standardized difficult intubation in Thailand\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4592086/v1/3335228c1e66ac74a03e25ae.png"},{"id":61346259,"identity":"bba2c7cc-2e92-46ff-9d18-1a6436d3d428","added_by":"auto","created_at":"2024-07-29 18:06:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":75410,"visible":true,"origin":"","legend":"\u003cp\u003eInterrupted time-series analysis showing age-sex standardized difficult intubation in PSU\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4592086/v1/6915c160552bcdf09bd824c6.png"},{"id":70964763,"identity":"45c35cca-5bbf-414d-8e39-16c797231e30","added_by":"auto","created_at":"2024-12-09 16:15:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":876170,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4592086/v1/f10b8d12-e31a-42ec-8fa0-d355a5384dff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Failed/difficult Intubation Comparing between Pre-COVID-19 and COVID-19 Pandemic Period using A National Insurance Claims Database and Information System of a University Hospital","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIdeally endotracheal intubation should be performed quickly and without delay in the process of ventilation, laryngoscopy and intubation.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] This ideal is likely to be compromised when intubating patients with special airway management or \u0026ldquo;difficult airway\u0026rdquo;. There are a number of difficult clinical situations, described as facemask ventilation, laryngoscopy, supraglottic airway ventilation, difficult/failed tracheal intubation, difficult/failed tracheal extubation, difficult/failed invasive airway or inadequate ventilation. During the COVID-19 pandemic, COVID patients may have special requirements, such as oxygen therapy using a high-flow oxygen cannula or mechanical ventilator due to the possible complication of pneumonia or acute respiratory distress syndrome (ARDS) after getting the infection.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Delayed intubation is reported to be associated with increased mortility.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAn additional issue possibly adding to the delayed intubation of patients during the pandemic was the need to follow the national lockdown policy stipulating that personal protective equipment (PPE), powered air purifying respirator (PAPR),[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] or goggles and surgical or N95 mask, must be worn, and this might make the procedure more difficult owing to the somewhat restricted vision and less sure grip on the intubation instruments/equipment. The incidence of difficult intubation might therefore be higher during the COVID-19 lockdown period.\u003c/p\u003e \u003cp\u003eDifficult intubation is defined as a normally trained anesthesiologist needing more than 3 attempts or more than 10 minutes for a successful endotracheal intubation.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] In different settings, baseline incidence of difficult intubation varies. In the United Kingdom\u003csup\u003e6\u003c/sup\u003e and Germany\u003csup\u003e7\u003c/sup\u003e the incidence was reported to be around 10%, while in Australia the reported incidence was 0.5%, and in Singapore around 5% among caesearean section patients. Both the United Kingdom and Germany have a website of their national audit project of difficult airway databases, which can guide the policy plan for airway management to prevent unfavorable outcomes e.g., hypoxia, hypercarbia, cardiac arrest or death.\u003c/p\u003e \u003cp\u003eFrom 2016 the Ministry of Public Health of Thailand has recorded all intubations of patients at all government hospitals throughout the country as well as indicating whether they were classified as difficult or not. Similarly, the Department of Anaesthesiology of Songklanagarind Hospital, a university hospital in the south of the country, has maintained a database since 2016 of all patients intubated. Both these databases span the period prior to and during the COVID-19 lockdown and might provide insights into the effect of the COVID-19 lockdown policy on the incidence of difficult intubation, and how best to prepare for and manage the difficulties of airway management and endotracheal intubation should similar pandemics occur in the future.\u003c/p\u003e \u003cp\u003eThe databases of hospital and national level[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] are important to give us the information of policy planning in the future if we face with the pandemics of respiratory infectious diseases again. Songklanagarind Hospital has another information database for the anesthesia work, but it does not use international codes. We can review the data among these data sources and shape them to be of international standard and thereby benchmark our data widely.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis is a retrospective time-series study of data on patients who underwent endotracheal intubation, including both difficult and non-difficult intubations, recorded in a Thailand national database and in the information system of a university hospital in the south of Thailand. The data spanned the period from October 2016 to September 2021 covering approximately 27 months before and 30 months during the COVID-19 lockdown period, which started of 26 March 2020.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSetting and Data sources\u003c/h2\u003e \u003cp\u003eThe national database was that maintained by the National Health Security Office (NHSO). All governmental heath service units performing the procedure of intubation and difficult intubation can be reimbursed from NHSO. This enabled NHSO to obtain the health service data of all intubated individuals in Thailand. The university database was that recorded in the information system of the Department of Anaesthesiology and the Digital Innovation and Data Analytics (DIDA) unit of the Faculty of Medicine, Prince of Songkla University and Songklanagarind Hospital. This database contains the records of all intubation procedures performed in the operation room as well as those carried out under consultation on the ward. These data were validated against the against the DIDA database to ensure their accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and data management\u003c/h2\u003e \u003cp\u003eFrom each database, patients were included in the analysis if they were aged 18 or over at the date of admission prior to intubation. For analysis of patients from the university database, their inpatient (IP) electronic medical records were linked using encrypted identification numbers to protect their personal identity. Using e-claim codes, ICD-9-CM, ICD-10 and IP data were mined to classify the intubation status as difficult or non-difficult. ICD-9-CM code 9604 is defined as insertion of endotracheal tube. ICD-10 code T88.4 is defined as failed/difficult intubation. Difficult intubation is defined as the anesthesiologists making more than 3 attempts or taking more than 15 minutes for intubation in Songklanagarind Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcome and Statistical analysis\u003c/h2\u003e \u003cp\u003eThe Shapiro-Wilks normality test was used to assess the normality of continuous variables. Continuous variables were presented as mean and standard deviation (SD) or median and interquartile range (IQR) as appropriate. Categorical variables were presented as number of patients and percentage.\u003c/p\u003e \u003cp\u003eWithin the national and within the university dataset, data on individual patients\u0026rsquo; intubation status were aggregated into monthly periods and the proportions of intubations recorded as difficult standardized by age group [18\u0026ndash;30, 31\u0026ndash;40, 41\u0026ndash;50, 51\u0026ndash;60, 61\u0026ndash;70, 71\u0026ndash;80 and more than 80 years] and sex using the overall patient mix for the whole analysis period within the corresponding dataset (1st January 2018 to 30th September 2022)\u003c/p\u003e \u003cp\u003eBased on the age and sex standardized monthly proportions of difficult intubation, negative binomial regression was used to evaluate the change in mean monthly proportions from pre-lockdown to post-lockdown in each dataset. To explore possible trends and changes in trend according to the pre- and post-lockdown implementation periods, interrupted time-series analyses with linear regression were performed marking the point of interruption as the end of March 2020. (The lockdown was implemented form 26 March 2020). The presence of first-order autocorrelation in the residuals from the models was checked using the Durbin-Watson test.\u003c/p\u003e \u003cp\u003eAll analyses were conducted using R version 4.2.2 and STATA release 14.2. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003ePatient data were encrypted and de-identified for personalized anonymization according to the Thai Personal Data Protection Act 2019, Thailand (PDPA). Data were received from NHSO under a project approval granted by the Human Research Ethics Committee, Prince of Songkla University (REC 66-117-18-8) on 8th March 2023. Informed consent was not required because the data obtained could not identify any individual. The clinical trial registration is not applicable.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePopulation demographics\u003c/h2\u003e \u003cp\u003eThe national database recorded 681,176 patients aged 18 or over intubated within the study period (316,664 pre-lockdown and 364,512 post-lockdown). Overall, 59.3% were male, and median age was 63 years (range 18\u0026ndash;117). Age and sex distributions were similar in the two periods, with a preponderance of 51-80-year age groups among all intubations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). A total of 0.61% of intubations were difficult pre-lockdown compared with 0.46% post-lockdown. The proportions of difficult intubation were higher in the younger age groups (18\u0026ndash;40 than in older age groups in both periods. The university database recorded 30,275 and 35,848 patients aged 18 or overe in pre- and post-lockdown study periods with overall males accounting for 49.9% and median age 55 (range 18\u0026ndash;105). Unlike the national data base, the university data showed no preponderance of older patients among all intubations. The proportions of difficult intubation in the university database were only around one half those at the national level \u0026minus; 0.28% and 0.20% in pre- and post-lockdown periods (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Also, unlike the national database, the university data did not show a higher proportion of difficult intubation in the younger age groups in either period (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of intubated patients in the National and University Hospital databases.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNational Database\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;681,176)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eUniversity Hospital Database\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;66,123)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-lockdown\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;316,664)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLockdown\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;364,512)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePre-lockdown\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30,275)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLockdown\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;35,848)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years), median (IQR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (53, 7 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (52, 74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (38, 66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e55 (40, 67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187,099 (59.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217,170 (59.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,326 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e17,710 (49.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntubations\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMonthly number, mean \u0026plusmn; SD\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003emedian (IQR)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003erange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,728.3 \u003cb\u003e\u0026plusmn;\u003c/b\u003e 729.32\u003c/p\u003e \u003cp\u003e11,701 (11,162, 12197)\u003c/p\u003e \u003cp\u003e10,778\u0026thinsp;\u0026minus;\u0026thinsp;13,444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,150.4 \u003cb\u003e\u0026plusmn;\u003c/b\u003e1 528.67\u003c/p\u003e \u003cp\u003e12,343 (11,619, 12,669)\u003c/p\u003e \u003cp\u003e7,176\u0026thinsp;\u0026minus;\u0026thinsp;15,926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1121.3 \u003cb\u003e\u0026plusmn;\u003c/b\u003e 182.4\u003c/p\u003e \u003cp\u003e1101 (976, 1192)\u003c/p\u003e \u003cp\u003e872-1,660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1194.9 \u003cb\u003e\u0026plusmn;\u003c/b\u003e 290.2\u003c/p\u003e \u003cp\u003e1206 (964, 1416)\u003c/p\u003e \u003cp\u003e645-1,814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSARS-CoV-2 infection, n (%)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMonthly number, median (IQR)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003erange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (0.07)\u003c/p\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003cp\u003e0\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,739 (4.8)\u003c/p\u003e \u003cp\u003e425.5 (7, 948)\u003c/p\u003e \u003cp\u003e0-2109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e197 (0.5)\u003c/p\u003e \u003cp\u003e3 (0, 8)\u003c/p\u003e \u003cp\u003e0\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSARS-CoV-2 pneumonia, n (%)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMonthly number, median (IQR)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003erange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (0.07)\u003c/p\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003cp\u003e0\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,925 (4.1)\u003c/p\u003e \u003cp\u003e343 (4, 738)\u003c/p\u003e \u003cp\u003e0-1930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e70 (0.2)\u003c/p\u003e \u003cp\u003e0.5 (0, 5)\u003c/p\u003e \u003cp\u003e0\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDifficult intubation, n (%)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMonthly number, median (IQR)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eMonthly percent, median (IQR)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003erange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,959 (0.62)\u003c/p\u003e \u003cp\u003e75 (60, 83)\u003c/p\u003e \u003cp\u003e0.61 (0.54, 0.71)\u003c/p\u003e \u003cp\u003e45\u0026ndash;99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,698 (0.47)\u003c/p\u003e \u003cp\u003e56 (50, 65)\u003c/p\u003e \u003cp\u003e0.47 (0.39, 0.53)\u003c/p\u003e \u003cp\u003e16\u0026ndash;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (0.28)\u003c/p\u003e \u003cp\u003e2 (1, 4)\u003c/p\u003e \u003cp\u003e0.22 (0.09, 0.38)\u003c/p\u003e \u003cp\u003e0\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e73 (0.20)\u003c/p\u003e \u003cp\u003e2.5 (1, 4)\u003c/p\u003e \u003cp\u003e0.22 (0.08, 0.30)\u003c/p\u003e \u003cp\u003e0\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing the age- and sex-standardized monthly proportions of difficult intubation, negative binomial regression revealed that the mean monthly proportions per thousand intubations [and 95% confidence interval] of difficult intubations were significantly 25% lower in the post-lockdown period than in the pre-lockdown period in both datasets (pre- and post- respectively 6.17 [5.77, 6.61] and 4.65 [4.35, 4.98] (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) in the national dataset and 2.80 [2.07, 3.78] and 2.10 [1.54, 2.85] (p\u0026thinsp;=\u0026thinsp;0.188) in the university database. Thus, the mean monthly proportion of difficult intubations dropped by around 25% in both datasets.\u003c/p\u003e \u003cp\u003eInvestigation of trends and changes in trend of difficult intubation monthly proportions in the two periods using interrupted time-series analysis revealed similar patterns in the national and university data, albeit at lower proportions in the university than in the national data (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In both settings the regression suggested a non-significant increase in proportion of difficult intubation in the pre-lockdown period and a non-significant decreasing trend following implementation of lockdown. At the point of interruption, a significant drop in level was evident in the national data (of 1.682 per thousand per month, P\u0026thinsp;=\u0026thinsp;0.003) and a non-significant drop at the university level (of 1.118 per thousand per month, P\u0026thinsp;=\u0026thinsp;0.304).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe patient characteristics differed between the national database and PSU database in terms of age and sex. Older patients got more intubated in national database while younger patients got more intubated in PSU database. Incidence of failed/difficult intubation was higher in younger age group than older age group in national database. We cannot find the pattern of failed/difficult intubation among PSU patients. Even though, the mean monthly proportion of difficult intubations dropped by around 25% in both datasets. When we did the standardization of age and sex to do the linear regression of the incidence of failed/difficult intubation, the trend of slope before and after launching lockdown policy in Thailand are similar. The number of intubation patients decreased after lockdown policy similar to the report of Uansri et al[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], who surveyed in greater Bangkok. Our study showed a significant drop in level was evident in the national data and a non-significant drop at the university level at the point of interruption.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDifficult intubation\u003c/h2\u003e \u003cp\u003eThere were different definitions of \u0026ldquo;failed/difficult intubation\u0026rdquo;. Two databases of this study did not use the same definition of failed/difficult intubation. The intubators from ICD code of national database may not be anesthesiologists who are the experts in intubation unlike in PSU database. There are other publications that used yet other definitions, such as Jayaraj et al.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] defined difficult intubation on a 5-points scale depending on numbers of intubation or whether other rescue equipment is used. Schroeder et al.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] defined more than 3 attempts of intubation as difficult intubation and failed intubation as requiring either surgical or percutaneous tracheotomy, cricothyrotomy, or wake-up of the patient.\u003c/p\u003e \u003cp\u003eICD code from PSU database is under reported so the author used the database from Anesthesiology Department. Intubation is minor procedure, so the clinicians and coders may disregard/overlook the intubation code. They coded the major diseases or procedures that the patients received.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIntubation before and during COVID-19 pandemic\u003c/h2\u003e \u003cp\u003eThe incidence of difficult intubation seems increasing over years since 2018 until COVID-19 pandemic in early 2020 in Thailand (pre-interruption slope; β 0.028, p-value 0.275 for national database). It is maybe there were more obese patients and the patients with difficult medical conditions so the clinicians cannot give them the sedation drugs before intubation.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] If difficult intubation is anticipated[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], the equipment and multidisciplinary team (anesthesiologists, otolaryngologist, pulmonologists and intensivists) need to be prepared.\u003c/p\u003e \u003cp\u003eAfter Thai government announced the lockdown policy on 26 March 2020, the incidence of difficult intubation was decreased. Videolaryngoscope[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]is a tool to help the clinicians intubate the patients because of the more curved blade and the screen that can display the larynx outside the oral cavity and show the large pictures of the larynx directly inside the oral cavity. Also, the risk respiratory infection transmission is decreased if the intubators and the patients are farther apart.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] There is a survey through the European Airway Management Society\u0026rsquo;s network showed videolaryngoscope using 24.1% before COVID-19 and 43.1% during COVID-19(P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe anesthesiologists who have the most experience of intubation among all clinicians take the role of intubators during COVID-19 pandemic. They wear protective equipment and also follow the checklist guideline for taking care of COVID-19 patients.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003csup\u003e,\u003c/sup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] These protective equipment was not the factors to increase the incidence of failed/difficult intubation as our hypothesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDifficult airway database\u003c/h2\u003e \u003cp\u003eDifficult intubation database is needed in Thailand. It could be web-base as United Kingdom[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], Germany[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Australia[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Airway assessment is needed for the clinicians and nurses to predict difficult intubation especially in emergency cases which there is short time to evaluate.\u003c/p\u003e \u003cp\u003eSince difficult intubation is rare, so we need to collect data from many institutions to explore the factors of failed/difficult intubation. It would be great if we have national database. If we have well-recorded database, failed/difficult intubation could be well predicted among Thai population. If they can predict well, they can prepare the equipment and activate the intubation team to avoid the adverse events from the intubation. In the other hand, mis-prediction of easy intubation is also needed. We will not waste the prepared equipment and the experts who we ask for stand by for difficult airway management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrength\u003c/h2\u003e \u003cp\u003eNational Health Security Office (NHSO) provides national database which are big data to analyze intubation and difficult intubation incidence before and during COVID-19 pandemic.\u003c/p\u003e \u003cp\u003ePSU is a university hospital which can provide big data from other database beside ICD-code.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003ePSU database from DIDA report ICD code is lower the incidence from anesthesiology department. The predisposing factors of increasing difficult intubation before COVID-19 pandemic cannot be described. There was missing data in PSU database. More data exploration or prospective study should be done for further research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eData were received from NHSO under a project approval granted by the Human Research Ethics Committee, Prince of Songkla University (REC 66-117-18-8) on 8\u003csup\u003eth\u003c/sup\u003e March 2023 by Prof. Boonsin Tangtrakulwanich MD, Chairman of Human Research Ethics Commitee Faculty of Medicine, Prince of Songkla University.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eThis is a retrospective time-series\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003estudy. The consent for publication is not applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe national database was that maintained by the National Health Security Office (NHSO). Patient data were encrypted and de-identified for personalized anonymization according to the Thai Personal Data Protection Act 2019, Thailand (PDPA).\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThere was no funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eSP : acquisition, analysis, drafting the work, final approval of the manuscript\u003c/p\u003e\n\u003cp\u003eAG : acquisition, analysis, review, drafting, final approval of the manuscript\u003c/p\u003e\n\u003cp\u003eHS : acquisition, analysis, review, drafting, final approval of the manuscript\u003c/p\u003e\n\u003cp\u003ePK : acquisition, analysis, review, drafting, final approval of the manuscript\u003c/p\u003e\n\u003cp\u003eVC : acquisition, analysis, review, drafting, final approval of the manuscript\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMouri Mi, Krishnan S, Maani CV. 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Difficult and failed intubation in the first 4000 incidents reported on webAIRS. Anaesth Intensive Care. 2020;48:477\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0310057X20957657\u003c/span\u003e\u003cspan address=\"10.1177/0310057X20957657\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"airway, airway management, difficult airway, COVID-19, intubation, pandemic, time series analysis","lastPublishedDoi":"10.21203/rs.3.rs-4592086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4592086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEndotracheal intubation can be difficult or even fail under certain patient and intubator conditions. During the COVID-19 pandemic a country-wide lockdown policy was enforced in Thailand which stipulated that intubators wear personal protective equipment, powered air purifying respirator, or goggles and surgical/N95 mask during the intubation procedure. Thus clad, an intubator’s vision is restricted and grip on the equipment less sure. Under these conditions, the incidence of difficult intubation was expected to increase.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis time-series study was based on the aggregated age- and sex-standardized monthly incidence of difficult intubation among all intubated patients whose data were recorded in the national insurance claims database and among patients recorded in the records of a university hospital from January 2018 to September 2022. Changes in incidence of difficult intubation following the implementation of a lockdown policy from 26 March 2020 during the COVID-19 pandemic were explored using negative binomial regression and interrupted linear regression time-series analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData of 922,274 individuals in the national database and 95,457 individuals in the university database were retrieved. The overall incidence of difficult intubation in both settings dropped by 25% following lockdown, significantly so in the national database (p \u0026lt; 0.001). Slight increasing and decreasing trends pre- and post-lockdown were not significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe decreased incidence of difficult intubation during the lockdown period was contrary to expectation but might be related to the deployment solely of anaesthesiologists and more experienced anaesthetic staff using videolaryngoscopes during lockdown following the recommendation for intubation during respiratory disease pandemics.\u003c/p\u003e","manuscriptTitle":"Failed/difficult Intubation Comparing between Pre-COVID-19 and COVID-19 Pandemic Period using A National Insurance Claims Database and Information System of a University Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 18:06:50","doi":"10.21203/rs.3.rs-4592086/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-05T09:12:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-04T12:41:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-04T12:41:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Anesthesiology","date":"2024-06-17T06:14:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af07e433-1db2-4a21-888b-32968a404807","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T16:03:21+00:00","versionOfRecord":{"articleIdentity":"rs-4592086","link":"https://doi.org/10.1186/s12871-024-02788-z","journal":{"identity":"bmc-anesthesiology","isVorOnly":false,"title":"BMC Anesthesiology"},"publishedOn":"2024-12-06 15:57:38","publishedOnDateReadable":"December 6th, 2024"},"versionCreatedAt":"2024-07-29 18:06:50","video":"","vorDoi":"10.1186/s12871-024-02788-z","vorDoiUrl":"https://doi.org/10.1186/s12871-024-02788-z","workflowStages":[]},"version":"v1","identity":"rs-4592086","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4592086","identity":"rs-4592086","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}