Prince Sultan Military Medical College Pulmonary Edema Article Analysis

FIND A SOLUTION AT Academic Writers Bay

(2021) 21:40 Wang et al. BMC Pulm Med https://doi.org/10.1186/s12890-021-01394-w RESEARCH ARTICLE Open Access Lung ultrasound score assessing the pulmonary edema in pediatric acute respiratory distress syndrome received continuous hemofiltration therapy: a prospective observational study Fei Wang1†, Chunxia Wang1,2†, Jingyi Shi1, Yijun Shan1, Huijie Miao1, Ting Sun1, Yiping Zhou1 and Yucai Zhang1,2* Abstract Background: Lung ultrasound score is a potential method for determining pulmonary edema in acute respiratory distress syndrome (ARDS). Continuous renal replacement therapy (CRRT) has become the preferred modality to manage fluid overload during ARDS. The aim of this study was to evaluate the value of lung ultrasound (LUS) score on assessing the effects of CRRT on pulmonary edema and pulmonary function in pediatric ARDS. Methods: We conducted a prospective cohort study in 70 children with moderate to severe ARDS in a tertiary university pediatric intensive care unit from January 2016 to December 2019. 37 patients received CRRT (CRRT group) and 33 patients treated by conventional therapy (Non-CRRT group). LUS score was measured within 2 h identified ARDS as the value of 1st, and the following three days as the 2nd, 3rd, and 4th. We used Spearman correlation analysis to develop the relationship between LUS score and parameters related to respiratory dynamics, clinical outcomes as well as daily fluid balance during the first four days after ARDS diagnosed. Results: The 1st LUS score in CRRT group were significantly higher than Non-CRRT group (P 10%). The performance and management for CRRT were described as our previous study [16]. The modality of mechanical ventilation was intermittent mandatory ventilation (IMV) with PEEP levels 8–15cmH2O and positive inspiration pressure (PIP) based on target tidal volume (Vt) of 4–8 ml/kg [11]. Parameters were aligned with lung protective ventilation strategy when patients met the diagnosis of moderate to severe pediatric ARDS. Data collection Demographic data such as age, sex, and body mass index (BMI), the pediatric risk mortality III (PRISM III) score [17] and co-morbidity were collected on PICU admission. Clinical parameters including fractional concentration of oxygen in inspired gas ­(FiO2), ­PaO2/FiO2, ­PaCO2, OI, dynamic lung compliance (Cdyn) which was continuously displayed using ventilators (MAQUET company, Servo-i serious) [18, 19]. MV settings including PIP, PEEP, and F ­ iO2 were collected while measuring LUS score from identified ARDS to the following three days. Wang et al. BMC Pulm Med (2021) 21:40 Daily fluid balance information and hospital mortality were collected. LUS score was determined within 2 h after moderate to severe ARDS diagnosed as the value of 1st, then measured every morning in following three days as the values of 2nd, 3rd, and 4th. The schematic diagram of LUS score determination was shown in Fig. 1. In addition, duration of mechanical ventilation, duration of CRRT, length of PICU or hospital stay were recorded. Statistical analysis The data were performed with SPSS 17.0 statistics (SPSS Inc, Chicago). The characteristics of the patients were reported as percentages for categorical variables and compared the differences between groups by chi-square test. The continuous data with abnormal distribution were expressed as median (interquartile range, IQR) and compared using the Mann–Whitney U test. The correlation between LUS score and mechanical ventilation (MV) duration, length of PICU stay, Cdyn, P ­ aCO2, OI and the correlation between the change in LUS scores and the change in daily fluid balance volume during the four days after ARDS diagnosed were all performed using Spearman correlation analysis. Friedman test was used to compare mean of more than 2 sets of data. P value 0.05, Table 2). Correlation of LUS score to OI, ­PaCO2, dynamic lung compliance and fluid balance Except for patients who were forced to wean from mechanical ventilation because of death, only LUS score based on 3rd and 4th values were positively correlated with duration of mechanical ventilation [1st: r = 0.167, P = 0.325, 2nd: r = 0.299, P = 0.072, 3rd: r = 0.579, P 7) in 31 warfarin users and a recent intercurrent illness.11 A recent retrospective cohort study estimated the risk of excessive anticoagulation in people with community acquired infection who received antibiotic treatment (antibiotic group) and those who did not receive antibiotic treatment (unwell controls), as well as controls without infection (stable controls).12 The proportion of people with a follow-up international normalised ratio of 5.0 or more were 3.2%, 2.6%, and 1.2% for the antibiotic group, unwell controls, and stable controls, respectively. Risk of an international normalised ratio of 5.0 or more was greater among the antibiotic and unwell control groups than among the stable control group. Hospital admission for any bleed was infrequent (67 bleeds in a cohort of 12 006 people) and similar across the three groups. Overall, the findings suggested bleeding risk was similar in people with community acquired infection irrespective of whether antibiotics were prescribed. However, the study might have been underpowered to detect a difference between the antibiotic group (n=5857) and unwell control group (n=570), and unmeasured differences between the two groups could have biased the findings towards the null. For example, a greater proportion of people in the unwell control group might have used over-the-counter treatments for symptom relief that elevated their international normalised ratios or increased the risk of bleeding, such as paracetamol13 or non-steroidal anti-inflammatory drugs (NSAIDs).14 Evidence of an association between community acquired infections and major bleeding could lead to targeted monitoring of international normalised ratios (for people prescribed warfarin) and preemptive dose change or other guidance for intercurrent illness (for users of any oral anticoagulant) to prevent some of these events. There are also implications for antibiotic prescribing, because currently the bleeding risk is thought to arise from antibiotic-anticoagulant interactions, but some of this risk could be attributable to the underlying infection. Therefore, this study aimed to estimate the association between community acquired respiratory tract infections (RTI) without immediate antibiotic prescription and a range of bleeding events. We used a self-controlled study design to reduce the impact of time invariant confounding between people. RESEARCH Population and follow-up The source population were 4 553 515 people who contributed at least one day of data to CPRD GOLD between 1 January 2011 and 31 December 2019, and whose data were deemed acceptable for research, and eligible for linkage to hospital admission data. From the source population, we identified people who had their first ever prescription of warfarin or a direct oral anticoagulant within the study period of 1 January 2011 to 31 December 2019. For inclusion, the date of the first prescription needed to be after the year of their 18th birthday, and after the date when their practice’s data were regarded as up to standard. The observation period began on the date of the first new prescription of warfarin or a direct oral anticoagulant, and ended on the earliest of three dates: end of the treatment period (of warfarin or a direct oral anticoagulant), death, end of CPRD data collection, or end of the study period (31 December 2019). The end of the treatment period was defined as the earliest of two dates: 90 days after the date of the last prescription of the drug that was initiated, or the date of the first prescription for a different oral anticoagulant. Thus, the observation period only included the treatment period of a person’s first ever oral anticoagulant, akin to a new user incident design in a cohort study.27 Outcomes For inclusion in the self-controlled case series analysis, an individual needed to have experienced an outcome and exposure of interest within their observation period. The primary outcome was informed by the definition of major bleeding by the subcommittee on control of anticoagulation of the Scientific and Standardization Committee (SSC) of the International Society on Thrombosis and Haemostasis (ISTH). The committee defines major bleeds as those that result in death, are life threatening, cause chronic sequelae, or consume major healthcare resources.28 For this study, we defined major bleeding as a hospital admission for intracerebral or gastrointestinal bleeding. These events are commonly encountered major bleeds and have been ascertained from UK health records by many studies, increasing confidence in the reliability and completeness of their recording in routine health data.4 21 29 These bleeds reflected both a pragmatic approach to ascertainment of major bleeding and acknowledgment of the ISTH criteria for major bleeding. Major bleeding was ascertained from ICD-10 codes recorded in linked hospital admission data and included codes recorded at any point during a hospital stay and in any position within the hierarchy of diagnoses for a hospital admission. The secondary outcomes were events indicating a less severe bleed. the bmj | BMJ 2021;375:e068037 | doi: 10.1136/bmj-2021-068037 The outcome definition was adapted from the ISTH SSC’s criteria for clinically relevant non-major bleeding (CRNMB)30 of a bleed that did not fit the criteria of major bleeding but that required medical intervention, hospital admission, or face-to-face evaluation. For this study, we defined CRNMB as a general practice consultation or hospital admission for haemoptysis, epistaxis, or haematuria, which we ascertained from general practice and hospital admission data using a combination of Read and ICD-10 codes. Code lists are available in eAppendix 2. Exposure and risk periods The exposure in this study was a combination of a general practice consultation for an RTI without immediate antibiotic prescription, which we refer to as an untreated RTI but acknowledge that some people could have used prescribed or over-the-counter nonantibiotic treatments such as cold remedies or NSAIDs. Exposure was ascertained from Read codes that represented possible symptoms or diagnoses of upper RTIs (including inner ear, nose, and throat infections), lower RTIs, and influenza. We did not include Read codes with a high likelihood of representing a clinical presentation that would require immediate antibiotics (eg, bacterial pneumonia). The frequency of recorded Read codes for the RTIs included in the final selfcontrolled case series are available in eFigures 1 and 2. An RTI might start several days before a general practice consultation and serious bleeding during this time could be misclassified as occurring during the unexposed period. Therefore, we included a prerisk period that started seven days before the date of the RTI consultation and ended on the date of the RTI consultation. The risk period started on the date of the RTI consultation and ended on the earliest of five dates: 90 days after the date of the RTI consultation; date of an antibiotic prescription (because we were interested in the risk during the untreated period only); death; end of CPRD data collection; and the end of study period. Multiple exposures and outcomes For exposures and outcomes, we regarded Read codes occurring within 28 days of each other as relating to the same event. A 28 day clear period was required before a code was regarded as relating to a new exposure or outcome. We included multiple exposures and multiple outcomes. Time outside of the period lasting from the start of a seven day pre-risk period to the end of the 90 day risk period was regarded as unexposed time. Figure 1 shows a graphical representation of the selfcontrolled case series with examples of the possible combinations of study exposures and outcomes. Covariates The self-controlled case series design implicitly controls for confounders that remain constant over time, such as sex. We adjusted for three time varying confounders: age with 40 age bands, using quantiles of age at first outcome to define each band; year to 3 BMJ: first published as 10.1136/bmj-2021-068037 on 21 December 2021. Downloaded from http://www.bmj.com/ on 5 April 2022 by guest. Protected by copyright. investigate common infective exposures,25 26 and are particularly useful when an appropriately comparable control group would be difficult to identify. Further details about the study design and its assumptions are described in eAppendix 1. RESEARCH One exposure No subsequent antibiotic prescription allowing full 90 day risk period One outcome Risk period extended to 90 days in secondary analysis with smaller risk windows of 15-30, 31-60, and 61-90 days Exposure period starts on date of general practice consultation for RTI if immediate antibiotics were not prescribed One exposure An antibiotic prescription during risk period shortens 90 day risk period One outcome Bleeding event Antibiotic prescription shortens risk period Mulitple exposures, some with full 90 day risk period, and some shorter risk periods Mulitple outcomes Start of observation Unexposed periods 7 day pre-risk period End of observation Fig 1 | Study design of self-controlled case series, with examples of possible combinations of exposures and outcomes. RTI=respiratory tract infection adjust for changes in health behaviour and clinical management over the study period that could influence how exposures and outcomes were recorded; and season, defined as winter (December-February), spring (March-May), summer (June-August), and autumn (September-November) to reflect the seasonal incidence of RTIs. Statistical analyses We used descriptive statistics to characterise the sample of patients included in the self-controlled case series analysis. We calculated the number of events and person time for the pre-risk, risk, and unexposed periods. We used conditional Poisson regression to estimate incidence rate ratios and 95% confidence intervals for the relative incidence of bleeding events during exposed versus unexposed periods, adjusted for age, calendar time, and season. We also fitted a spline based age effect, where the relative age effect was represented by a smooth function obtained by splicing together polynomials of low dimension, to ensure that we had adjusted flexibly for age effects.31 In prespecified sensitivity analyses, we considered alternative pre-risk periods of three days, and five days. We also excluded people who died within four weeks of an event to explore bias arising from a bleeding event affecting the length and timing of the observation period (violation of the self-controlled study assumptions).32 We did several post hoc sensitivity analyses. We explored the impact of subdividing the 14 day risk window into smaller periods. We explored whether the association between RTI and bleeding differed if we restricted to the first exposure or outcome, and if we included all RTIs irrespective of whether they received immediate antibiotic treatment or not. The effect modification by sex or type of oral anticoagulant was also explored. The main analysis was repeated 4 using ocular and external ear infections as a negative control exposure, because these infections induce a mild and more localised inflammatory response than RTIs. For people using warfarin, we looked for codes suggestive of monitoring or dose change during the 14 day risk window to explore whether that might affect our findings. Finally, in response to peer review, we explored whether NSAIDs prescribed on the day of the RTI might confound the association between RTI and bleeding. Data management and analyses were carried out in RStudio. Self-controlled case series models were fitted using the self-controlled case series package.33 Patient and public involvement Patients from the lead author’s (HA) practice who used warfarin or direct oral anticoagulants were involved from the design phase of this study and helped shape the question and objectives. Three formal patient representatives were recruited from Health and Care Research Wales and Anticoagulation UK during the design phase and provided valuable support with interpretation of study findings, particularly around how oral anticoagulant users self-manage their drug treatment during intercurrent illness. Results Of 61 790 eligible incident users of oral anticoagulants, 1109 warfarin users and 772 users of direct oral anticoagulants had at least one major bleed, and 2538 warfarin users and 1426 users of direct oral anticoagulants had at least one CRNMB, within their observation period. Of the 5845 oral anticoagulant users who had at least one bleed, 1208 had at least one untreated RTI within their observation period and comprised the sample for the self-controlled case series analysis. This sample included 350 people who doi: 10.1136/bmj-2021-068037 | BMJ 2021;375:e068037 | the bmj BMJ: first published as 10.1136/bmj-2021-068037 on 21 December 2021. Downloaded from http://www.bmj.com/ on 5 April 2022 by guest. Protected by copyright. 14 day risk period in main analysis RESEARCH gastrointestinal bleeding (336/395, 85%), and the most common CRNMB was haematuria (587/1272, 46%; table 1). The 1208 people included in the selfcontrolled case series had similar characteristics to those 60 582 people who were not included in terms of age, deprivation, smoking, alcohol intake, and choice of anticoagulant, but had longer treatment periods (eTable 1). 4 553 515 Patients in CPRD GOLD with linked data and at least one day of registration during study period of 1 January 2011 to 31 December 2019 4 419 402 Never prescribed an oral anticoagulant drug 134 113 Patients had at least one prescription for an oral anticoagulant drug 71 724 Prevalent users 62 389 Patients had first ever prescription for an oral anticoagulant drug aer start of practices up to standard period and within study period of 1 January 2011 to 31 December 2019 90 Patients

• WE OFFER THE BEST CUSTOM PAPER WRITING SERVICES. WE HAVE DONE THIS QUESTION BEFORE, WE CAN ALSO DO IT FOR YOU.
• Assignment status: Already Solved By Our Experts
• (USA, AUS, UK & CA PhD. Writers)
• CLICK HERE TO GET A PROFESSIONAL WRITER TO WORK ON THIS PAPER AND OTHER SIMILAR PAPERS, GET A NON PLAGIARIZED PAPER FROM OUR EXPERTS

QUALITY: 100% ORIGINAL PAPER – NO PLAGIARISM – CUSTOM PAPER

Why Choose Us?

How It Works

• Click on the “Place Your Order” tab at the top menu or “Order Now” icon at the bottom and a new page will appear with an order form to be filled.
• Fill in your paper’s requirements in the “PAPER DETAILS” section.
• Fill in your paper’s academic level, deadline, and the required number of pages from the drop-down menus.
• Click “CREATE ACCOUNT & SIGN IN” to enter your registration details and get an account with us for record-keeping and then, click on “PROCEED TO CHECKOUT” at the bottom of the page.
• From there, the payment sections will show, follow the guided payment process and your order will be available for our writing team to work on it.

About AcademicWritersBay.com

AcademicWritersBay.com is an easy-to-use and reliable service that is ready to assist you with your papers 24/7/ 365days a year. 99% of our customers are happy with their papers. Our team is efficient and will always tackle your essay needs comprehensively assuring you of excellent results. Feel free to ask them anything concerning your essay demands or Order.

AcademicWritersBay.com is a private company that offers academic support and assistance to students at all levels. Our mission is to provide proficient and high quality academic services to our highly esteemed clients. AcademicWritersBay.com is equipped with competent and proficient writers to tackle all types of your academic needs, and provide you with excellent results. Most of our writers are holders of master’s degrees or PhDs, which is an surety of excellent results to our clients. We provide assistance to students all over the world.
We provide high quality term papers, research papers, essays, proposals, theses and many others. At AcademicWritersBay.com, you can be sure of excellent grades in your assignments and final exams.