Incidence of In-Hospital Mortality and Its Determinants among Intensive Care Unit Patients with Acute Respiratory Distress Syndrome in Ethiopian: A multilevel analysis

Incidence of In-Hospital Mortality and Its Determinants among Intensive Care Unit Patients with Acute Respiratory Distress Syndrome in Ethiopian: A multilevel analysis | 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 Incidence of In-Hospital Mortality and Its Determinants among Intensive Care Unit Patients with Acute Respiratory Distress Syndrome in Ethiopian: A multilevel analysis Semagn Mekonnen Abate, Melkamu Kebede, Seyoum Hailu, Yayeh Adamu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4137280/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Purpose Acute respiratory distress syndrome (ARDS) is a heterogeneous syndrome with substantial morbidity and mortality globally. Body of evidence revealed that the epidemiologic estimates are currently disproportional due to differences in patient populations, risk factors, resources, and practice protocols around the world, and the rate of mortality and its predictors are uncertain in Ethiopia. Method A multi-centre longitudinal study was conducted in Ethiopia from January 2018 to June 2023. After receiving ethical clearance from the Institutional Review Board (IRB) of Dilla University College of Health Science and Medicine, 356 ARDS patients’ records were retrieved with a systematic random sampling technique. A multilevel multivariate analysis was used to control the effect of clustering. A P < 0.05 was taken as statistically significant. Results This study demonstrated that the cumulative mortality rate of patients with ARDS was 59% (95% CI: 53.5 to 63.9). The multilevel multivariable model analysis showed that GCS < 8 (AOR = 7.4; 95% CI: 2.79, 19.75), severe form of ARDS (AOR 4.7 95% CI 1.64, 13.36), invasive ventilation (AOR 3.2, 95% CI 1.56, 6.42), and respiratory comorbidity (AOR 4.9, 95% CI 1.71, 14.32) were independent predictors of in-hospital mortality among patients with ARDS. Conclusion The study revealed that the hospital mortality rate was substantially higher than that of developed nations. The study also highlighted various risk factors that independently predicted in-hospital mortality.The findings of this study call for mitigating strategies to improve ICU care for ARDS patients. mortality intensive care unit ARDS cohort Multicenter Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Acute respiratory distress syndrome (ARDS) is a stereotypical response to various triggering stimuli, evolving through stages of exudative, proliferative, and fibrotic transformations. In its initial phase, ARDS results in heightened leakage, characterized by alveolar oedema due to epithelial and endothelial damage and the infiltration of neutrophils. Subsequently, it progresses to fibro-proliferative changes marked by ventilation/perfusion discrepancies such as dead space and shunt ( 1 ). More than half a century of research and development have been dedicated to diagnosing and treating ARDS( 2 ), with various advancements occurring in different periods, such as before the 1990s, the American-European consensus of 1994( 3 ), the Berlin definition of 2012( 4 ), and the Kigali modification of the Berlin criteria, tailored explicitly for resource-limited environments ( 5 , 6 ). The American European Consensus Conference (AECC) set up a standard definition and criteria for diagnosis, including sudden onset, chest infiltration on both sides and low oxygen levels without signs of left atrial hypertension or capillary wedge pressure over 18 cm H2O( 3 ). This definition was later modified by the American Thoracic Society and the Society of Critical Care Medicine in Berlin, resulting in the Berlin definition in 2012( 4 ). The key components of the Berlin definition included the emergence of acute respiratory failure within one week of a known trigger, categorizing the severity of low oxygen levels as mild (200 mmHg > PaO2 ≤ 300 mmHg), moderate (100 mmHg > PaO2 ≤ 200 mmHg), and severe (PaO2 ≤ 100 mmHg), the need for positive end-expiratory pressure (PEEP) of at least 5 cmH2O, and ruling out a heart-related cause for lung fluid buildup through echocardiography( 4 ). The Kigali modification of the Berlin definition of ARDS was adopted for resource-limited settings where arterial blood gas analysis may not be readily available. It defines ARDS without the need for positive end-expiratory pressure (PEEP) by focusing on bilateral opacities on chest radiographs or lung ultrasounds, along with hypoxemia as indicated by a SpO2/FIO2 ratio of 31 or less. This modification allows for a practical and accessible way to diagnose ARDS in settings where advanced diagnostic tools may not be accessible( 6 ). ARDS is a heterogeneous syndrome associated with respiratory failure due to pulmonary and nonpulmonary causes. Pneumonia is the leading pulmonary risk factor, which accounted for more than 50 percent followed by aspiration of gastric content and pulmonary contusion, whereas as sepsis, noncardiogenic shock and massive blood transfusion are the most common nonpulmonary causes of ARDS( 7 – 9 ). Several strategies have been studied for managing ARDS over the past few decades ( 10 – 12 ). Lung protective ventilation to minimize ventilator-induced lung injury ( 13 – 16 ), while the prone position can improve oxygenation in some patients( 17 , 18 ). Lung recruitment manoeuvres help open up collapsed lung tissue( 19 , 20 ), and early administration of glucocorticoids may reduce inflammation in the lungs( 21 , 22 ). However, the use of lung stem cell transplants( 23 , 24 ) and extracorporeal membrane oxygenation (ECMO)( 25 , 26 ) are more advanced techniques that are being explored for severe lung conditions. Despite advancements in diagnosis and management protocols of ARDS, the burden of ARDS is still substantially high globally. A large observational study incorporating 50 high- and middle-income countries with 459 intensive care unit (ICU) centres demonstrated that the incidence of ARDS was 10.4%, with patient mortality of around 50% in severe cases( 27 ). A systematic review found that the worldwide mortality rate from ARDS was significant, ranging from 30–40%( 28 ). However, there was a noticeable variation in mortality rates across different continents and subcontinents. A systematic review by Buregeya et al. revealed that the mortality rate from ARDS was 41% in Scandinavian countries( 29 ), 34% in Australia( 30 ), 41% in the USA( 31 ), and 55.5% in Brazil( 32 ). The challenges of ARDS in low and middle-income countries varies across different regions. It is influenced by factors such as access to healthcare, prevalence of risk factors and overall healthcare infrastructure. However, limited data is available on the exact prevalence, mortality, and determinants of ARDS in Sub-Saharan Africa, and this multicenter cohort study was designed to investigate the magnitude, mortality and independent predictors of ARDS in Ethiopia. Method and Materials Study design and setting This multicenter retrospective cohort study was conducted in multiple teaching referral hospital ICUs in Southern Ethiopia between January 2018 and June 2023. Four teaching and referral hospital ICUs were chosen randomly from a selection of ten teaching hospitals in Southern Ethiopia, namely Dilla University Referral Hospital (DUGH), Hawassa University Comprehensive Specialized Hospital (HUCSH), Wolaita Sodo University Comprehensive Specialized Hospital (WSUCSH), and Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital (WCUNEMMCSH). These ICUs are overseen by the Federal Ministry of Health and Education of Ethiopia and offer similar levels of care with comparable staff profiles, equipment, medical supplies, and patient admission patterns. Due to a shortage of essential medical supplies like ventilators, monitors, vasopressors, and nutrition support and a lack of adequately trained personnel such as intensivists, nutritionists, and physiotherapists, the quality of care in these ICUs is suboptimal. Eligibility criteria Source population All adult patients admitted to Dilla University Referral Hospital (DUGH), Hawassa University Comprehensive Specialized Hospital (HUCSH), Wolaita Sodo University Comprehensive Specialized Hospital (WSUCSH), and Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital (WCUNEMMCSH) with confirmed diagnosis of ARDS. Study population All adult patients admitted to Dilla University Referral Hospital (DUGH), Hawassa University Comprehensive Specialized Hospital (HUCSH), Wolaita Sodo University Comprehensive Specialized Hospital (WSUCSH), and Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital (WCUNEMMCSH) with confirmed diagnosis of ARDS from January 2018 to June 2023. Inclusion and exclusion criteria All adult ARDS patients with completed relevant data were included, while COVID-19 ARDS patients, incomplete data, patients transferred to another Hospital, and patients who died before 24hrs were excluded. Study variables The magnitude of ARDS, the cumulative incidence of 30-day mortality, complications, Pattern of disease, and length of stay in ICU were dependent variables, while sociodemographic characteristics : Age, Sex, Height, Weight, BMI; Admission characteristics: date of ICU admission, causes of ARDS, severity of ARDS, presences of comorbidity, date of discharge, vital sign, diagnosis, time of admission, disease severity score; Mechanical ventilation parameters: Arterial oxygen saturation, date of initiation, initial mode, date of weaning were independent variables. Sample size and sampling Technique We calculated the sample size (n) in R using the pwr package, assuming a 95% confidence level, a power of 95, and Cohen's h effect size of 0.2 to anticipate a slight difference in effect size. We utilized the command pwr.p.test (h=0.2, power=0.95, sig.level=0.05, alternative="two. sided"), and 10% contingency for potential loss to follow up. The resulting sample size was determined to be 356. Sampling procedure 7395 ICU admissions were registered from January 2018 to June 2023 in the study area ICUs, from which 730 were ARDS cases. The total ARDS admissions to the ICU of DUGH, HUCSH, WSUCSH and WCUNEMMCSH were 143, 221, 195 and 170, respectively. Using a proportionate allocation to size, the study populations were drawn from each ICU by dividing the number of admissions in each ICU by the total number of admissions across the four ICUs, then multiplying the result by the sample size (n =356). Therefore, DUGH = n1/N * n, HUCSH = n2/N* n, WSUCSH = n3/N*n and WCUNEMMCSH= n4/N*n where n1, n2, n3 and n4 are the total admissions in DUGH, HUCSH, WSUCSH and WCUNEMMCSH; that is 70, 108, 95 and 83 respectively. Then, the required number of participants was selected with systematic random sampling with a skip interval of (k =2) for each centre, and the first patient record was selected by lottery method ( Fig1 ). Data collection procedures The data were collected retrospectively from four hospital ICUs in patients with ARDS. The data was collected with a validated questionnaire and tools adopted from previous studies ( 33-37 ). A total of 356 patients' records were selected from each ICU from January 1, 2018, to June 30, 2023, as per proportion allocation to size ( Fig 1). The data collection included baseline information : Age, Sex, Height, Weight, BMI); Admission characteristics : date of ICU admission, vital signs, diagnosis, cause of admission, types of comorbidity, severity of ARDS, time of admission, laboratory indices, disease severity score; Complication: nosocomial infection, ventilator-associated pneumonia, sepsis, ARDS, aspiration, unplanned extubation, endotracheal tube blockage, tracheostomy loose/stenosis/fistula, cardiac arrest, acute kidney injury, bedsore); comorbidities: hypertension, diabetes mellitus, cardiovascular disease, Asthma/COPD, liver failure, renal failure, others, and clinical outcomes, including prevalence of ARDS, incidence of mortality, and incidence of complications were measured for 30 days. Data processing and analysis The data were checked, coded, and entered into Epi-info version 7.0 and imported to STATA version 17 for analysis. Descriptive statistics were summarized with tables and figures. The categorical variables were reported in Frequency and percentage. The numerical data were reported as mean ± SD for symmetric and median (Interquartile range) for asymmetric numeric data. The outlier of the data was checked with standardized residual, while Shapiro-Wilk tests were employed for the normality test. The multicollinearity among independent variables was checked by the variance inflation factor (VIF), and a VIF greater than ten was considered multicollinearity. The association of demographic characteristics, causes of ARDS, severity of ARDS, comorbidity, and complications on 30-day mortality were analyzed using multilevel binary logistic regression as there was clustering as depicted with Intraclass correlation coefficient(ICC=0.18). All Variables showing significance on multilevel bivariate analysis at a p-value less than 0.25 will be taken to multilevel multivariate analysis. In multivariate analysis, a p-value of less than 0.05 was considered for the statistical association. Ethical statement This study was reviewed and approved by Dilla University, College of Health Science and Medicine Research Ethics and review Board(RERB) and a Unique Identification Number (UIN- duirb/001/21-08) was received. The study was conducted in compliance with the Helsinki declaration for human and animal studies(38). All the patient identifiers were kept anonymous at all times. Besides, a formal letter was written to each University hospital ICU director to get permission. Model building and comparison A multivariable multilevel logistic regression analysis was employed to account for this clustering effect due to the hierarchical nature of the hospital ICU care with which the patients are nested within clusters. Thence, four models containing variables of interest were fitted for this study as follows: Model I (Empty model) was fitted without explanatory variables to test random variability in the intercept and to estimate the intra-class correlation coefficient (ICC), Model II assessed the effects of individual-level predictors, Model III assessed the effects of hospital-level predictors, and Model IV ( Full model ) examined the effects of both individual and hospital-level characteristics simultaneously. The Akaike's Information Criterion (AIC) and Bayesian Information Criteria (BIC) were used to select the model, and the model with low AIC and BIC values was considered a best-fitted model. Based on AIC and BIC, the full (model with individual and hospital-related variables) model had the smallest AIC and BIC value among the model considered. Therefore, the full model best fits the data. AOR with a 95% Confidence interval in the multivariable model was used to select variables with a statistically significant association with short birth intervals. Results Sociodemographic and baseline characteristics A total of 340 (95.50%) medical records of ARDS patients were retrieved from the selected hospital ICUs from January 2018 and June 2023, while 16 medical records were excluded due to missing data. The mean (standard deviation) age of the patients was 39.8±17.20 years, and two-thirds of them, 173 (66.68%), were in the age ranges of 18 to 29 and > 50 years. From a total of 340 ARDS patients, 173 (50.9%) were female, and the majority of study participants, 193 (56.76%) had SPO2 <80% (Table 1). Table 1: Baseline and Sociodemographic characteristics of patients with ARDS in Southern Ethiopia University Hospitals ICUs, 2023(n=340) Variable All (n=340 %) Alive (n %) Dead (n%) P value Age group 18-28 102 (30) 52(51.0) 50(49.0) 50 119 (35) 37(31.1) 82(68.9) Gender Male 167 (49.1) 65(39.0) 102(61.0) 0.470 Female 173 (50.9) 75(43.4) 98(56.6) Heart Rate 100 239 (70.3) 95(40.0) 144(60.0) 0.240 Respiratory Rate 35 269 (79.1) 114(42.4) 155(57.6) Oxygen saturation <80 193 (56.8) 60(31.1) 133(68.9) 80 147 (43.2) 80(54.4) 67(45.6) Glasgow Coma Scale <8 150 (44.1) 45(30) 105(70) 12 123 (36.2) 74(60.2) 49(39.8) Admission characteristics The majority of cases 243 (71.47%) were admitted from medical ward, whereas 29 (8.52%) of them were admitted from obstetric department with severe preeclmacia and eclampsia. More than two-thirds of ARDS patients were admitted during the daytime and at night 145 (42.6%) ( Fig 2 ). This study demonstrated that respiratory problems were the commonest cause of ARDS 124 (36.5%), followed by unknown causes 111 (32.6%) and 105 (30.9%) septic shock. Out of 340 ARDS patients, 56 (16.4%), 125 (36.7%), and 159 (46.8%) patients had mild, moderate, and severe ARDS, respectively. The majority of ARDS patients, 276 (81.2%) had comorbidities, with respiratory (asthma, PTB, and COPD) being the most common at 85 (25%), followed by DM at 49 (14.4%), while only 64 (18.8%) of patients had unknown comorbidities (Table 2). Table 2: Admission characteristics of patients with ARDS in Southern Ethiopia University Hospitals ICUs, 2023 (n=340) Variable All (n=340) % Alive (%) Dead (%) P value Admission pattern Medical 243 (71.5) 81(33.3) 162(66.7) <0.001** Surgical 26 (7.6) 18(69.2) 8(30.8) Gyn/Obs 29 (8.5) 24(82.8) 5(17.2) Postoperative 25 (7.4) 6(24.0) 19(76.0) Others 17 (5.0) 10(58.8) 7(41.2) Admission time Weekend 72(21.2) 23(31.9) 49(68.1) <0.001** Night 145(42.6) 40(27.6) 105(72.4) Day 123(36.2) 76(61.8) 47(38.2) ARDS severity Mild 56 (16.4) 41(73.2) 15(26.8) <0.001** Moderate 125 (36.8) 52(41.6) 73(58.4) Severe 159 (46.8) 46(28.9) 113(71.1) Causes Respiratory 124 (36.5) 25(20.1) 99(79.8) <0.001** Sepsis 105 (30.9) 47(44.8) 58(55.2) Others 111 (32.6) 67(60.3) 44(39.7) Comorbidity HTN 30 (8.8) 12(40.0) 18(60.0) 0.841 DM 49 (14.4) 14(28.6) 35(71.4) 0.138 Cardiac 42 (12.4) 20(47.6) 22(52.4) 0.582 COPD/TB/Asthmatic 85 (25.0) 22(25.9) 63(74.1) 0.037* More than one comorbidities 70 (20.6) 44(62.9) 26(37.1) 0.017* Unknown 64 (18.8) 27(42.2) 37(57.8) *significant, ** very significant; HTN : Hypertension; DM : Diabetes Mellitus, COPD : Chronic Obstructive Pulmonary Disease; TB : Tuberculosis; Gyn/Obs : gynaecology and Obstetrics Incidence of clinical outcomes The study demonstrated that the period prevalence of ARDS was 9.87%, while mild, moderate, and severe ARDS was reported in 56 (16.4%), 125 (36.7%), and 159 (46.8%) patients, respectively. This study showed that the incidence of 30-mortality in patients with ARDS was 59% (95% CI: 53.5 to 63.9), where 96.2% of severe cases died compared to 58.4%, and 26.9% for mild and moderate cases. The majority of patients, 240 (70.6%) developed at least one complication during their ICU, including Sepsis, ventilator-associated pneumonia (VAP) and cardiac arrest, where ventilator-associated pneumonia was the commonest107 (31.5%), followed by cardiac arrest 80 (23.5%) and sepsis 53 (15.6%) (Fig 3). Predictive probability of mortality The graph below illustrates the predictive probability of in-hospital mortality among patients with ARDS concerning severity of ARDS. As had been observed, the mortality progressively increased, and a significant change has been observed since the outbreak of the COVID-19 pandemic. There is a significant difference in mortality between mild and moderate to severe cases ( Fig 5 ). The predictive probability of mortality among patients with ARDS increased with age, specifically in the age group of 40-49 years. However, there was no significant difference in mortality observed between those with severe and moderate ARDS but with mild cases of ARDS ( Fig 6A ). The graph also illustrated a steady increase in mortality among patients with invasive mechanical ventilation compared to those with non-invasive oxygen therapy ( Fig 6B ). Besides, the predicted probability of mortality increased with higher disease severity scores ( Fig 6 C and D ). Determinants of mortality This study identified a number of independent predictors of mortality among patients with ARDS. The multilevel multivariate analysis revealed that GCS, severity of ARDS, time of admission, causes of ARDS, ventilation type, comorbidity, and complication were strongly associated with hospital mortality. However, gender, baseline heart rates, and respiratory rates didn’t show significant association with in-hospital mortality among patients with ARDS in ICU. The study demonstrated that patients with ARDS of GCS 12. Regarding to time of admission, patients admitted at weekend and night time had a higher odds of mortality at (AOR= 3.3, 95% CI 1.12, 9.84) and (AOR= 3.9 95% CI 1.79, 8.87) respectively. Those patients who were admitted at night time were approximately 4 times more likely to die compared to those who were admitted at weekend days and day time. In this study, Respiratory disorders and sepsis were the major causes of mortality in patients with ARDS. Patients with respiratory cause were more than 9 times more likely to die (AOR=9.4, 95% CI 3.84, 23.03), similarly, patients with sepsis were more than 7 times more likely to die AOR 7.2, 95% CI 2.82, 18.51) compared to other causes of ARDS. Mode ventilation is the most determinant of mortality in ARDS patients as the lungs of the patients are prone to barotrauma, biotrauma, atlectotrauma, and volutrauma. The current study showed that patients managed with invasive ventilation were more than 3 times more to be expected to die compared to patients managed by non -invasive type (AOR =3.2, 95% CI 1.56, 6.42). This study demonstrated that respiratory comorbidities such as asthma, and COPD caused a higher odds of 30-day mortality (AOR= 4.9, 95% CI 1.71, 14.32). Similarly, patients with Diabetes Mellitus are approximately 4 times more likely to experience death as compared to other comorbidities. A number of complications were identified in this study, and from which ventilator-associated pneumonia contributed a higher odds of in-hospital mortality at about (AOR=4.1, 95% CI 1.7, 9.91) compared to sepsis, cardiac arrest and others ( Table 3 ). Table 3: Multilevel multivariable model of the relationship between risk factors and in-hospital mortality in ARDS patients in Southern Ethiopia University Hospitals ICUs, 2023(n=340) Variable All (n=340) Alive (n=) Dead(n=) AOR(90%CI) P value Age groups 18-28 102 (30) 52(51.0) 50(49.0) Reff 29-39 68 (20) 39(57.4) 29(42.6) 0.8(0.32, 1.96) 0.60 40-49 51 (15) 12(23.5) 39(76.5) 2.3(o.80, 6.48) 0.127 >50 119 (35) 37(31.1) 82(68.9) 2.1(0.95, 4.84) 0.065 Oxygen saturation 80 147 (43.2) 80(54.4) 67(45.6) Reff Glasgow coma scale <8 150 (44.1) 45(30) 105(70) 7.4(2.79, 19.75) 12 123 (36.2) 74(60.2) 49(39.8) Ref Severity of Acute Respiratory Distress Syndrome Mild 56 (16.4) 41(73.2) 15(26.8) Reff Moderate 125 (36.8) 52(41.6) 73(58.4) 4.7(1.64, 13.36) <0.001 Severe 159 (46.8) 46(28.9) 113(71.1) 4.6(1.57, 13.74) <0.001 Admission time Weekend 72(21.2) 23(31.9) 49(68.1) 3.3(1.12, 9.84) <0.03 Night 145(42.6) 40(27.6) 105(72.4) 3.9(1.79, 8.87) <0.001 Day 123(36.2) 76(61.8) 47(38.2) Reff Causes of Acute Respiratory Distress Syndrome Respiratory 124 (36.5) 25(20.1) 99(79.8) 9.4(3.84, 23.03) <0.001** Sepsis 105 (30.9) 47(44.8) 58(55.2) 7.2(2.82, 18.51) <0.001** Others 111 (32.6) 67(60.3) 44(39.7) Reff Ventilation Invasive 181(53.2) 50(27.6) 131(72.4) 3.2(1.56, 6.42) 0.001 Noninvasive 159(46.8) 89(56.0) 70(44.0) Reff Comorbidities Hypertension 30 (8.8) 12(40.0) 18(60.0) 0.9(0.23, 3.18) 0.80 Diabetic Mellitus 49 (14.4) 14(28.6) 35(71.4) 3.9(1.20, 12.53) 0.002 Cardiac 42 (12.4) 20(47.6) 22(52.4) 1.5(0.42, 5.27) 0.53 COPD/TB/Asthmatic 85 (25.0) 22(25.9) 63(74.1) 4.9(1.71, 14.32) 0.001 More than one comorbidities 70 (20.6) 44(62.9) 26(37.1) 0.5(0.17, 1.54) 0.22 Unknown 64 (18.8) 27(42.2) 37(57.8) Reff Complications VAP 107(31.5) 35(32.7) 72(67.3) 4.1(1.72, 9.96) 0.001 Sepsis 53(15.6) 17(32.1) 36(67.9) 2.3(0.80, 6.48) 0.13 Cardiac arrest 80(23.5) 31(38.7) 49(61.3) 0.9(0.37, 2.58) 0.96 Unknown 100(29.4) 56(56.0) 44(44.0) Reff *significant, ** very significant; COPD : Chronic Obstructive Pulmonary Disease; TB : Tuberculosis; VAP : Ventilator associated Pneumonia Discussion This multicenter study was designed to investigate the incidence of in-hospital mortality and its determinants among ARDS patients admitted to ICU in selected southern Ethiopia university hospitals. This research illustrated that the magnitude of ARDS was notably higher at 98.1 per 100,000 person-years compared to previous studies (39-45). Prior studies in three Scandinavian nations, for instance, documented an incidence of 13.5 per 100,000 person-years(29). Similarly, a French national study found a rate of 24.6 per 100,000 person-years(45). Additional investigations in the USA and Brazil reported incidence rates of 58.7 and 6.3 per 100,000 person-years, respectively(31, 32). It has been observed that there are substantial variations in ARDS prevalence globally, which can be attributed to various factors such as differences in healthcare infrastructure and access to medical services, disparities in reporting methods and diagnostic criteria, environmental influences like pollution and infectious diseases, genetic predispositions in specific populations, variations in data collection practices, and different research approaches This study revealed that the cumulative incidence of in-hospital mortality among ARDS patients in ICUs was 59.1 %( 95% CI: 53.8 to 64). The current study demonstrated that the rate of in-hospital mortality among ARDS patients was higher compared to previous studies. For instance, a global observational study by Villar J. et al found that the in-hospital mortality rate for ARDS patients was 40% (43). Similarly, a study in the Netherlands reported a range of in-hospital mortality rates from 32.7% to 57.9% (39, 42). %. Besides, a retrospective cohort study conducted in Spain by Villar J et al revealed an in-hospital mortality rate of 47.8% (37), and a retrospective cohort study in China by Dai Q et al. in 2019 showed a mortality rate of 39% among ARDS patients(40). However, a study conducted in Uganda found a higher rate of mortality at 77% (46). These discrepancies might be attributable to differences in ICU infrastructure, epidemiology of risk factors for ARDS in different populations, differences in ventilation modalities, misclassification during diagnosis, and variation in methodology of studies. The current research identified several factors that independently predict in-hospital mortality in ICU patients with ARDS. These factors include, but are not limited to, low GCs score, ARDS severity, time and cause of admission, ventilation method, comorbidities, complications, and baseline characteristics. Our study revealed that the percentages of patients categorized as having mild, moderate, and severe ARDS were 26.8%, 58.4%, and 71.1% respectively. These rates were higher than those reported in a multinational observational study, which showed hospital mortality rates of 34.9% for mild, 40.3% for moderate, and 46.1% for severe ARDS (27). The study showed that patients admitted during nighttime had a higher risk of mortality, with approximately 4 times more likely (AOR= 3.9 95% CI 1. 79, 8.87) compared to those admitted on weekends or during the day. This finding contradicts a previous study conducted in the same area by Abate SM. et al., where the odds of mortality for nighttime admissions were lower (AOR=2.5; 95%CI: 1.24, 5.05) (47), and the possible discrepancy might be variation in participant characteristics, sample size, Severity of ARDS, variation in treatment protocols, and individual response to treatment, each patient's response to treatment can also vary, influencing mortality rates, and it has of paramount importance for healthcare providers to consider these factors when managing patients with ARDS to improve outcomes. This study found that respiratory and sepsis were detrimental causes of admission and mortality of patients with ARDS, where patients with respiratory disorder were more than 9 times, and patients with sepsis were more than 7 times more likely to die, respectively, compared to other causes of ARDS (AOR 9.4, 95% CI 3.84, 23.03, AOR 7.2, 95% CI 2.82, 18.51). A retrospective cohort study done in Michigan, USA, showed that the most common primary causes of death in ARDS patients were sepsis, pulmonary dysfunction, and neurologic dysfunction which is consistent to our study (48). Similarly, a multicenter prospective study in China also revealed that pneumonia, sepsis out of chest origin, and aspiration (77%, 7.3% and 3.3%, respectively were the main risk factors for ARDS(49). The current study showed that the patients with ventilator associated pneumonia were 4 times more likely to die (AOR 4.1, 95% CI 1.7, 9.91), which is dissimilar with a retrospective cohort study done in Vietnam, where (50%) of the patients developed sepsis, 31 (67.4%) of them experienced multiorgan failure, 14 (30%) had obstructive shock, and 15 (32.6%) developed refractory hypoxemia (36). This calls mitigating strategies to prevent VAP, which includes strict adherence to hand hygiene protocols, providing regular oral care to minimize the chances of aspiration and bacterial spread, conducting frequent suctioning of the endotracheal tube to prevent secretion buildup, and ensuring regular assessment and adjustment of cuff pressure on the endotracheal tube to prevent micro aspirations. Strengths and limitations of the study To the best of our knowledge, this is the first ever multicenter longitudinal study in Sub-Saharan Africa examining the prevalence of ARDS, in-hospital death rates, and the factors independently associated with these outcomes among ARDS patients in intensive care units. The study employed multilevel analysis to address clustering effects arising from the hierarchical structure of the data, given that patients were situated in various hospitals and ICUs offering different levels of care. However, this study is not without limitations. Firstly, the study featured a diverse group of participants in terms of sociodemographic factors, diagnoses, and comorbidities. Secondly, it was a retrospective study, leading to some missing data that could have helped in assessing disease severity. Implication for policymakers The burden of ARDS requires a multi-faceted approach that involves collaboration among policymakers, healthcare professionals, and the broader community to improve outcomes for individuals affected by this serious condition. Policymakers may need to prioritize funding for early detection and treatment strategies, as well as support for hospitals and healthcare providers to ensure timely and effective care for ARDS patients. Additionally, policymakers may also need to address broader public health concerns related to factors that contribute to ARDS, such as air pollution or infectious diseases, to prevent future outbreaks and reduce overall mortality rates. Implication for further research This was a retrospective multicentre longitudinal study with a fairly large group of participants, suggests that more future studies should be done with a homogenous population in terms of sociodemographic characteristics, admission pattern, comorbidity, and similar ICU infrastructure. Conclusion The study revealed that in hospital mortality rate was substantially high compared to the developed nations among patients with ARDS in Ethiopia is a concerning issue for stakeholders that requires urgent attention. The findings suggest that there are challenges within the healthcare system that need to be addressed in order to improve patient outcomes and reduce mortality rates. It is imperative that healthcare facilities prioritize the management of ARDS patients and implement evidence-based protocols to enhance the quality of care provided. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials Data and material can be available where appropriate. Competing interests The authors declare that there are no competing interests Funding This research was funded by Dilla University with a total of 25,000 Ethiopian Birr Authors' contributions Semagn Abate: Conceptualization, Methodology, Software, Reviewing; Melkamu Kebede: writing original draft, supervision; Seyoum Hailu: Reviewing, editing the draft; Yayeh Adamu: Reviewing, editing the draft; Bahru Mantefardo: Reviewing and Editing; and Abinet Meno: Reviewing and Editing; Lakew Lafebo: Reviewing and Editing Acknowledgments The authors would like to acknowledge Dilla University for technical support and encouragement to carry out the project. References Blondonnet R, Constantin J-M, Sapin V, Jabaudon M. A pathophysiologic approach to biomarkers in acute respiratory distress syndrome. 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Annals of intensive care. 2017;7(1):1-13. Wilcox ME, Jaramillo-Rocha V, Hodgson C, Taglione MS, Ferguson ND, Fan E. Long-term quality of life after extracorporeal membrane oxygenation in ARDS survivors: systematic review and meta-analysis. Journal of intensive care medicine. 2020;35(3):233-43. Bellani G, Laffey JG, Pham T, Fan E, Brochard L, Esteban A, et al. Epidemiology, patterns of care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. Jama. 2016;315(8):788-800. Buregeya E, Fowler RA, Talmor DS, Twagirumugabe T, Kiviri W, Riviello ED. Acute respiratory distress syndrome in the global context. Global heart. 2014;9(3):289-95. Luhr OR, Antonsen K, Karlsson M, Aardal S, Thorsteinsson A, FROSTELL CG, et al. Incidence and mortality after acute respiratory failure and acute respiratory distress syndrome in Sweden, Denmark, and Iceland. American journal of respiratory and critical care medicine. 1999;159(6):1849-61. Bersten AD, Edibam C, Hunt T, Moran J, GROUP TA, TRIALS NZICSC. Incidence and mortality of acute lung injury and the acute respiratory distress syndrome in three Australian States. American journal of respiratory and critical care medicine. 2002;165(4):443-8. Rubenfeld GD, Caldwell E, Peabody E, Weaver J, Martin DP, Neff M, et al. Incidence and outcomes of acute lung injury. New England Journal of Medicine. 2005;353(16):1685-93. Caser EB, Zandonade E, Pereira E, Gama AMC, Barbas CS. Impact of distinct definitions of acute lung injury on its incidence and outcomes in Brazilian ICUs: prospective evaluation of 7,133 patients. Critical care medicine. 2014;42(3):574-82. Abate SM, Assen S, Yinges M, Basu B. Survival and predictors of mortality among patients admitted to the intensive care units in southern Ethiopia: A multi-center cohort study. Annals of Medicine and Surgery. 2021;65:102318. Endeshaw AS, Tarekegn F, Bayu HT, Ayalew SB, Gete BC. The magnitude of mortality and its determinants in Ethiopian adult intensive care units: A systematic review and meta-analysis. Annals of Medicine and Surgery. 2022:104810. Rashid M, Ramakrishnan M, Muthu DS, Chandran VP, Thunga G, Kunhikatta V, et al. Factors affecting the outcomes in patients with acute respiratory distress syndrome in a tertiary care setting. Clinical Epidemiology and Global Health. 2022;13:100972. Sharif N, Irfan M, Hussain J, Khan J. Factors associated within 28 days in-hospital mortality of patients with acute respiratory distress syndrome. BioMed Research International. 2013;2013. Villar J, Blanco J, Añón JM, Santos-Bouza A, Blanch L, Ambrós A, et al. The ALIEN study: incidence and outcome of acute respiratory distress syndrome in the era of lung protective ventilation. Intensive care medicine. 2011;37:1932-41. Association WM. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. Jama. 2013;310(20):2191-4. Bos LD, Martin-Loeches I, Schultz MJ. ARDS: challenges in patient care and frontiers in research. European Respiratory Review. 2018;27(147). Dai Q, Wang S, Liu R, Wang H, Zheng J, Yu K. Risk factors for outcomes of acute respiratory distress syndrome patients: a retrospective study. Journal of thoracic disease. 2019;11(3):673. Kwizera A, Dünser MW. A global perspective on acute respiratory distress syndrome and the truth about hypoxia in resource-limited settings. American Thoracic Society; 2016. p. 5-7. Rilinger J, Zotzmann V, Bemtgen X, Schumacher C, Biever PM, Duerschmied D, et al. Prone positioning in severe ARDS requiring extracorporeal membrane oxygenation. Critical Care. 2020;24(1):1-9. Villar J, Blanco J, Kacmarek RM. Current incidence and outcome of the acute respiratory distress syndrome. Current opinion in critical care. 2016;22(1):1-6. Villar J, Mora-Ordoñez JM, Soler JA, Mosteiro F, Vidal A, Ambrós A, et al. The PANDORA study: prevalence and outcome of acute hypoxemic respiratory failure in the Pre-COVID-19 Era. Critical Care Explorations. 2022;4(5). Papazian L, Pauly V, Hamouda I, Daviet F, Orleans V, Forel J-M, et al. National incidence rate and related mortality for acute respiratory distress syndrome in France. Anaesthesia Critical Care & Pain Medicine. 2021;40(1):100795. Kwizera A, Nakibuuka J, Nakiyingi L, Sendagire C, Tumukunde J, Katabira C, et al. Acute hypoxaemic respiratory failure in a low-income country: a prospective observational study of hospital prevalence and mortality. BMJ Open Respiratory Research. 2020;7(1):e000719. Abate SM, Basu B, Jemal B, Ahmed S, Mantefardo B, Taye T. Pattern of disease and determinants of mortality among ICU patients on mechanical ventilator in Sub-Saharan Africa: a multilevel analysis. Critical Care. 2023;27(1):1-13. Ketcham SW, Sedhai YR, Miller HC, Bolig TC, Ludwig A, Co I, et al. Causes and characteristics of death in patients with acute hypoxemic respiratory failure and acute respiratory distress syndrome: a retrospective cohort study. Critical Care. 2020;24:1-9. Huang X, Zhang R, Fan G, Wu D, Lu H, Wang D, et al. Incidence and outcomes of acute respiratory distress syndrome in intensive care units of mainland China: a multicentre prospective longitudinal study. Critical Care. 2020;24:1-11. Additional Declarations No competing interests reported. Supplementary Files STROBEchecklistcohort.doc Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 22 Mar, 2024 Submission checks completed at journal 22 Mar, 2024 First submitted to journal 20 Mar, 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. 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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-4137280","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282551271,"identity":"7a5ae342-fae0-454a-94df-f83583254c52","order_by":0,"name":"Semagn Mekonnen 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chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4137280/v1/3300e013f77e423bbb1d3988.png"},{"id":53669054,"identity":"2f655a54-5024-456d-b94f-40b8568fcc04","added_by":"auto","created_at":"2024-03-28 17:33:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17703,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStatus of the patients with source of admission of patients with ARDS in Southern Ethiopia University Hospitals ICUs, 2023 (n=340)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4137280/v1/859390082f7db07b6d111ac9.png"},{"id":53669058,"identity":"5b2a468b-2a2f-422c-967e-aff09fba1112","added_by":"auto","created_at":"2024-03-28 17:33:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83827,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of complications among ARDS patients Admitted in Southern Ethiopia University hospital ICUs (n=340)\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4137280/v1/0ce4099591f5fce9fca72860.png"},{"id":53669056,"identity":"a195513f-4fe2-4c58-9426-0b18c0ec2e84","added_by":"auto","created_at":"2024-03-28 17:33:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":76857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 5:\u003c/strong\u003e The predictive probability mortality of patients among ARDS in Southern Ethiopia University Hospital ICU over a six-year period, 2018 to 2023 (n=340).\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4137280/v1/34cea87c9dd707696cd72448.png"},{"id":53669055,"identity":"33004aab-81ff-413f-bac1-c1a31b8b6ae4","added_by":"auto","created_at":"2024-03-28 17:33:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":113270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig 6:\u003c/strong\u003e predictive probability of mortality among patients with ARDS in Southern Ethiopia University Hospital. A: Severity of ARDS VS age group; B: types of oxygen therapy VS ARDS severity; C: Disease severity score (APACHE II) VS ARDS severity, D: disease Severity Score (SOFA) VS ARDS severity.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4137280/v1/920a01d82f578a27797f96f0.png"},{"id":53670670,"identity":"3243df3c-f829-44c2-bf11-4bda016bffdb","added_by":"auto","created_at":"2024-03-28 17:49:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1173077,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4137280/v1/e5dc39ee-a85e-4133-9df3-71a94d811db3.pdf"},{"id":53669746,"identity":"cd52f4ba-c223-4aaf-9377-0ef10b775d39","added_by":"auto","created_at":"2024-03-28 17:41:50","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":94208,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistcohort.doc","url":"https://assets-eu.researchsquare.com/files/rs-4137280/v1/fb6bb72be7a5b8cd570bc582.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Incidence of In-Hospital Mortality and Its Determinants among Intensive Care Unit Patients with Acute Respiratory Distress Syndrome in Ethiopian: A multilevel analysis","fulltext":[{"header":"Background","content":"\u003cp\u003eAcute respiratory distress syndrome (ARDS) is a stereotypical response to various triggering stimuli, evolving through stages of exudative, proliferative, and fibrotic transformations. In its initial phase, ARDS results in heightened leakage, characterized by alveolar oedema due to epithelial and endothelial damage and the infiltration of neutrophils. Subsequently, it progresses to fibro-proliferative changes marked by ventilation/perfusion discrepancies such as dead space and shunt (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore than half a century of research and development have been dedicated to diagnosing and treating ARDS(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), with various advancements occurring in different periods, such as before the 1990s, the American-European consensus of 1994(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), the Berlin definition of 2012(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), and the Kigali modification of the Berlin criteria, tailored explicitly for resource-limited environments (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe American European Consensus Conference (AECC) set up a standard definition and criteria for diagnosis, including sudden onset, chest infiltration on both sides and low oxygen levels without signs of left atrial hypertension or capillary wedge pressure over 18 cm H2O(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This definition was later modified by the American Thoracic Society and the Society of Critical Care Medicine in Berlin, resulting in the Berlin definition in 2012(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe key components of the Berlin definition included the emergence of acute respiratory failure within one week of a known trigger, categorizing the severity of low oxygen levels as mild (200 mmHg\u0026thinsp;\u0026gt;\u0026thinsp;PaO2\u0026thinsp;\u0026le;\u0026thinsp;300 mmHg), moderate (100 mmHg\u0026thinsp;\u0026gt;\u0026thinsp;PaO2\u0026thinsp;\u0026le;\u0026thinsp;200 mmHg), and severe (PaO2\u0026thinsp;\u0026le;\u0026thinsp;100 mmHg), the need for positive end-expiratory pressure (PEEP) of at least 5 cmH2O, and ruling out a heart-related cause for lung fluid buildup through echocardiography(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Kigali modification of the Berlin definition of ARDS was adopted for resource-limited settings where arterial blood gas analysis may not be readily available. It defines ARDS without the need for positive end-expiratory pressure (PEEP) by focusing on bilateral opacities on chest radiographs or lung ultrasounds, along with hypoxemia as indicated by a SpO2/FIO2 ratio of 31 or less. This modification allows for a practical and accessible way to diagnose ARDS in settings where advanced diagnostic tools may not be accessible(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eARDS is a heterogeneous syndrome associated with respiratory failure due to pulmonary and nonpulmonary causes. Pneumonia is the leading pulmonary risk factor, which accounted for more than 50 percent followed by aspiration of gastric content and pulmonary contusion, whereas as sepsis, noncardiogenic shock and massive blood transfusion are the most common nonpulmonary causes of ARDS(\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral strategies have been studied for managing ARDS over the past few decades (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Lung protective ventilation to minimize ventilator-induced lung injury (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), while the prone position can improve oxygenation in some patients(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Lung recruitment manoeuvres help open up collapsed lung tissue(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), and early administration of glucocorticoids may reduce inflammation in the lungs(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). However, the use of lung stem cell transplants(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and extracorporeal membrane oxygenation (ECMO)(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) are more advanced techniques that are being explored for severe lung conditions.\u003c/p\u003e \u003cp\u003eDespite advancements in diagnosis and management protocols of ARDS, the burden of ARDS is still substantially high globally. A large observational study incorporating 50 high- and middle-income countries with 459 intensive care unit (ICU) centres demonstrated that the incidence of ARDS was 10.4%, with patient mortality of around 50% in severe cases(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA systematic review found that the worldwide mortality rate from ARDS was significant, ranging from 30\u0026ndash;40%(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). However, there was a noticeable variation in mortality rates across different continents and subcontinents. A systematic review by Buregeya et al. revealed that the mortality rate from ARDS was 41% in Scandinavian countries(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), 34% in Australia(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), 41% in the USA(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), and 55.5% in Brazil(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe challenges of ARDS in low and middle-income countries varies across different regions. It is influenced by factors such as access to healthcare, prevalence of risk factors and overall healthcare infrastructure. However, limited data is available on the exact prevalence, mortality, and determinants of ARDS in Sub-Saharan Africa, and this multicenter cohort study was designed to investigate the magnitude, mortality and independent predictors of ARDS in Ethiopia.\u003c/p\u003e"},{"header":"Method and Materials","content":"\u003ch2\u003eStudy design and setting\u003c/h2\u003e\n\u003cp\u003eThis multicenter retrospective cohort study was conducted in multiple teaching referral hospital ICUs in Southern Ethiopia between January 2018 and June 2023. Four teaching and referral hospital ICUs were chosen randomly from a selection of ten teaching hospitals in Southern Ethiopia, namely Dilla University Referral Hospital (DUGH), Hawassa University Comprehensive Specialized Hospital (HUCSH), Wolaita Sodo University Comprehensive Specialized Hospital (WSUCSH), and Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital (WCUNEMMCSH). These ICUs are overseen by the Federal Ministry of Health and Education of Ethiopia and offer similar levels of care with comparable staff profiles, equipment, medical supplies, and patient admission patterns. Due to a shortage of essential medical supplies like ventilators, monitors, vasopressors, and nutrition support and a lack of adequately trained personnel such as intensivists, nutritionists, and physiotherapists, the quality of care in these ICUs is suboptimal.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEligibility criteria\u0026nbsp;\u003c/h2\u003e\n\u003ch2\u003eSource population\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAll adult patients admitted to Dilla University Referral Hospital (DUGH), Hawassa University Comprehensive Specialized Hospital (HUCSH), Wolaita Sodo University Comprehensive Specialized Hospital (WSUCSH), and Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital (WCUNEMMCSH) with confirmed diagnosis of ARDS.\u003c/p\u003e\n\u003ch2\u003eStudy population\u003c/h2\u003e\n\u003cp\u003eAll adult patients admitted to Dilla University Referral Hospital (DUGH), Hawassa University Comprehensive Specialized Hospital (HUCSH), Wolaita Sodo University Comprehensive Specialized Hospital (WSUCSH), and Wachemo University Nigist Eleni Mohammed Memorial Comprehensive Specialized Hospital (WCUNEMMCSH) with confirmed diagnosis of ARDS from January 2018 to June 2023.\u003c/p\u003e\n\u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e\n\u003cp\u003eAll adult ARDS patients with completed relevant data were included, while COVID-19 ARDS patients, incomplete data, patients transferred to another Hospital, and patients who died before 24hrs were excluded.\u003c/p\u003e\n\u003ch2\u003eStudy variables\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe magnitude of ARDS, the cumulative incidence of 30-day mortality, complications, Pattern of disease, and length of stay in ICU were dependent variables, while \u003cstrong\u003esociodemographic characteristics\u003c/strong\u003e: Age, Sex, Height, Weight, BMI;\u003cstrong\u003e\u0026nbsp;Admission characteristics:\u003c/strong\u003e date of ICU admission, causes of ARDS, severity of ARDS, presences of comorbidity, date of discharge, vital sign, diagnosis, time of admission, disease severity score; \u003cstrong\u003eMechanical ventilation parameters:\u003c/strong\u003e Arterial oxygen saturation, date of initiation, initial mode, date of weaning were independent variables.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSample size and sampling Technique\u003c/h2\u003e\n\u003cp\u003eWe calculated the sample size (n) in R using the pwr package, assuming a 95% confidence level, a power of 95, and Cohen\u0026apos;s h effect size of 0.2 to anticipate a slight difference in effect size. We utilized the command pwr.p.test (h=0.2, power=0.95, sig.level=0.05, alternative=\u0026quot;two. sided\u0026quot;), and 10% contingency for potential loss to follow up. The resulting sample size was determined to be 356.\u003c/p\u003e\n\u003ch2\u003eSampling procedure\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003e7395 ICU admissions were registered from January 2018 to June 2023 in the study area ICUs, from which 730 were ARDS cases. The total ARDS admissions to the ICU of DUGH, HUCSH, WSUCSH and\u0026nbsp;WCUNEMMCSH were 143, 221, 195 and 170, respectively. Using a proportionate allocation to size, the study populations were drawn from each ICU by dividing the number of admissions in each ICU by the total number of admissions across the four ICUs, then multiplying the result by the sample size (n =356). Therefore, DUGH = n1/N * n, HUCSH = n2/N* n, WSUCSH = n3/N*n and WCUNEMMCSH= n4/N*n where n1, n2, n3 and n4 are the total admissions in DUGH, HUCSH, WSUCSH and WCUNEMMCSH; that is 70, 108, 95 and 83 respectively. Then, the required number of participants was selected with systematic random sampling with a skip interval of (k =2) for each centre, and the first patient record was selected by lottery method (\u003cstrong\u003eFig1\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003eData collection procedures\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe data were collected retrospectively from four hospital ICUs in patients with ARDS. The data was collected with a validated questionnaire and tools adopted from previous studies\u0026nbsp;(\u003ca href=\"#_ENREF_33\" title=\"Abate, 2021 #55\"\u003e33-37\u003c/a\u003e). A total of 356 patients\u0026apos; records were selected from each ICU from January 1, 2018, to June 30, 2023, as per proportion allocation to size (\u003cstrong\u003eFig 1).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data collection included \u003cstrong\u003ebaseline information\u003c/strong\u003e: Age, Sex, Height, Weight, BMI); \u003cstrong\u003eAdmission characteristics\u003c/strong\u003e: date of ICU admission, vital signs, diagnosis, cause of admission, types of comorbidity, severity of ARDS, time of admission, laboratory indices, disease severity score; \u003cstrong\u003eComplication:\u0026nbsp;\u003c/strong\u003enosocomial infection, ventilator-associated pneumonia, sepsis, ARDS, aspiration, unplanned extubation, endotracheal tube blockage, tracheostomy loose/stenosis/fistula, cardiac arrest, acute kidney injury, bedsore); comorbidities: hypertension, diabetes mellitus, cardiovascular disease, Asthma/COPD, liver failure, renal failure, others, and \u003cstrong\u003eclinical outcomes, including prevalence of ARDS, incidence of mortality, and incidence of complications were\u0026nbsp;\u003c/strong\u003emeasured for 30 days.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData processing and analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe data were checked, coded, and entered into Epi-info version 7.0 and imported to STATA version 17 for analysis. Descriptive statistics were summarized with tables and figures. The categorical variables were reported in Frequency and percentage. The numerical data were reported as mean \u0026plusmn; SD for symmetric and median (Interquartile range) for asymmetric numeric data. The outlier of the data was checked with standardized residual, while Shapiro-Wilk tests were employed for the normality test. The multicollinearity among independent variables was checked by the variance inflation factor (VIF), and a VIF greater than ten was considered multicollinearity. The association of demographic characteristics, causes of ARDS, severity of ARDS, comorbidity, and complications on 30-day mortality were analyzed using multilevel binary logistic regression as there was clustering as depicted with Intraclass correlation coefficient(ICC=0.18). All Variables showing significance on multilevel bivariate analysis at a p-value less than 0.25 will be taken to multilevel multivariate analysis. In multivariate analysis, a p-value of less than 0.05 was considered for the statistical association. \u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEthical statement\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis study was reviewed and approved by Dilla University, College of Health Science and Medicine Research Ethics and review Board(RERB) and a Unique Identification Number (UIN- duirb/001/21-08) was received. The study was conducted in compliance with the Helsinki declaration for human and animal studies(38). All the patient identifiers were kept anonymous at all times. Besides, a formal letter was written to each University hospital ICU director to get permission.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eModel building and comparison\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eA multivariable multilevel logistic regression analysis was employed to account for this clustering effect due to the hierarchical nature of the hospital ICU care with which the patients are nested within clusters. Thence, four models containing variables of interest were fitted for this study as follows: \u003cstrong\u003eModel I (Empty model)\u0026nbsp;\u003c/strong\u003ewas fitted without explanatory variables to test random variability in the intercept and to estimate the intra-class correlation coefficient (ICC), \u003cstrong\u003eModel II\u0026nbsp;\u003c/strong\u003eassessed the effects of individual-level predictors, \u003cstrong\u003eModel III\u0026nbsp;\u003c/strong\u003eassessed the effects of hospital-level predictors, and \u003cstrong\u003eModel IV\u0026nbsp;\u003c/strong\u003e(\u003cstrong\u003eFull model\u003c/strong\u003e) examined the effects of both individual and hospital-level characteristics simultaneously.\u003c/p\u003e\n\u003cp\u003eThe Akaike\u0026apos;s Information Criterion (AIC) and Bayesian Information Criteria (BIC) were used to select the model, and the model with low AIC and BIC values was considered a best-fitted model. Based on AIC and BIC, the full (model with individual and hospital-related variables) model had the smallest AIC and BIC value among the model considered. Therefore, the full model best fits the data. AOR with a 95% Confidence interval in the multivariable model was used to select variables with a statistically significant association with short birth intervals.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003eSociodemographic and baseline characteristics\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eA total of 340 (95.50%) medical records of ARDS patients were retrieved from the selected hospital \u003cem\u003eICUs\u003c/em\u003e from January 2018 and June 2023, while 16 medical records were excluded due to missing data. The mean (standard deviation) age of the patients was 39.8\u0026plusmn;17.20 years, and two-thirds of them, 173 (66.68%), were in the age ranges of 18 to 29 and \u0026gt; 50 years. From a total of 340 ARDS patients, 173 (50.9%) were female, and the majority of study participants, 193 (56.76%) had SPO2 \u0026lt;80% \u003cstrong\u003e(Table 1).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1: Baseline and Sociodemographic characteristics of patients with ARDS in Southern Ethiopia University Hospitals ICUs, 2023(n=340)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eVariable \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003eAll (n=340 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003eAlive (n %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003eDead (n%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e18-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e102 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e52(51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e50(49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.80952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e29-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e68 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e39(57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e29(42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.80952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e51 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e12(23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e39(76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.80952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e119 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e37(31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e82(68.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eGender\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e167 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e65(39.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e102(61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.80952380952381%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e173 (50.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e75(43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e98(56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eHeart Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e7 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e1(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e6(85.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e60-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e94 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e44(47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e50(53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e239 (70.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e95(40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e144(60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e71 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e25(35.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e46(64.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.80952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e269 (79.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e114(42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e155(57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eOxygen saturation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e193 (56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e60(31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e133(68.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.80952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e147 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e80(54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e67(45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eGlasgow Coma Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.9873417721519%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e150 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e45(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" valign=\"top\"\u003e\n \u003cp\u003e105(70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.253164556962027%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.80952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e8-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e67 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e21(31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e46(68.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.80952380952381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e123 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e74(60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.396825396825395%\" valign=\"top\"\u003e\n \u003cp\u003e49(39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cstrong\u003eAdmission characteristics\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe majority of cases 243 (71.47%) were admitted from medical ward, whereas 29 (8.52%) of them were admitted from obstetric department with severe preeclmacia and eclampsia. More than two-thirds of ARDS patients were admitted during the daytime and at night 145 (42.6%) (\u003cstrong\u003eFig 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThis study demonstrated that respiratory problems were the commonest cause of ARDS 124 (36.5%), followed by unknown causes 111 (32.6%) and 105 (30.9%) septic shock. Out of 340 ARDS patients, 56 (16.4%), 125 (36.7%), and 159 (46.8%) patients had mild, moderate, and severe ARDS, respectively. The majority of ARDS patients, 276 (81.2%) had comorbidities, with respiratory (asthma, PTB, and COPD) being the most common at 85 (25%), followed by DM at 49 (14.4%), while only 64 (18.8%) of patients had unknown comorbidities \u003cstrong\u003e(Table 2).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2: Admission characteristics of patients with ARDS in Southern Ethiopia University Hospitals ICUs, 2023 (n=340)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"639\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.413145539906104%\" valign=\"top\"\u003e\n \u003cp\u003eVariable \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.779342723004696%\" valign=\"top\"\u003e\n \u003cp\u003eAll (n=340) %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003eAlive (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003eDead (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eAdmission pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.413145539906104%\" valign=\"top\"\u003e\n \u003cp\u003eMedical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.779342723004696%\" valign=\"top\"\u003e\n \u003cp\u003e243 (71.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e81(33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e162(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.115830115830114%\" valign=\"top\"\u003e\n \u003cp\u003eSurgical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.166023166023166%\" valign=\"top\"\u003e\n \u003cp\u003e26 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e18(69.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e8(30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.115830115830114%\" valign=\"top\"\u003e\n \u003cp\u003eGyn/Obs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.166023166023166%\" valign=\"top\"\u003e\n \u003cp\u003e29 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e24(82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e5(17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.115830115830114%\" valign=\"top\"\u003e\n \u003cp\u003ePostoperative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.166023166023166%\" valign=\"top\"\u003e\n \u003cp\u003e25 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e6(24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e19(76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.115830115830114%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.166023166023166%\" valign=\"top\"\u003e\n \u003cp\u003e17 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e10(58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e7(41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eAdmission time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.413145539906104%\" valign=\"top\"\u003e\n \u003cp\u003eWeekend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.779342723004696%\" valign=\"top\"\u003e\n \u003cp\u003e72(21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e23(31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e49(68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.115830115830114%\" valign=\"top\"\u003e\n \u003cp\u003eNight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.166023166023166%\" valign=\"top\"\u003e\n \u003cp\u003e145(42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e40(27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e105(72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.115830115830114%\" valign=\"top\"\u003e\n \u003cp\u003eDay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.166023166023166%\" valign=\"top\"\u003e\n \u003cp\u003e123(36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e76(61.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e47(38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eARDS severity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.413145539906104%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.779342723004696%\" valign=\"top\"\u003e\n \u003cp\u003e56 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e41(73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e15(26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.115830115830114%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.166023166023166%\" valign=\"top\"\u003e\n \u003cp\u003e125 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e52(41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e73(58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.115830115830114%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.166023166023166%\" valign=\"top\"\u003e\n \u003cp\u003e159 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e46(28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e113(71.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eCauses\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.413145539906104%\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.779342723004696%\" valign=\"top\"\u003e\n \u003cp\u003e124 (36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e25(20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e99(79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.115830115830114%\" valign=\"top\"\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.166023166023166%\" valign=\"top\"\u003e\n \u003cp\u003e105 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e47(44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e58(55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"30.115830115830114%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.166023166023166%\" valign=\"top\"\u003e\n \u003cp\u003e111 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e67(60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.35907335907336%\" valign=\"top\"\u003e\n \u003cp\u003e44(39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eComorbidity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.413145539906104%\" valign=\"top\"\u003e\n \u003cp\u003eHTN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.779342723004696%\" valign=\"top\"\u003e\n \u003cp\u003e30 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e12(40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e18(60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.413145539906104%\" valign=\"top\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.779342723004696%\" valign=\"top\"\u003e\n \u003cp\u003e49 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e14(28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e35(71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.413145539906104%\" valign=\"top\"\u003e\n \u003cp\u003eCardiac\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.779342723004696%\" valign=\"top\"\u003e\n \u003cp\u003e42 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e20(47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e22(52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.413145539906104%\" valign=\"top\"\u003e\n \u003cp\u003eCOPD/TB/Asthmatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.779342723004696%\" valign=\"top\"\u003e\n \u003cp\u003e85 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e22(25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e63(74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e0.037*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.413145539906104%\" valign=\"top\"\u003e\n \u003cp\u003eMore than one comorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.779342723004696%\" valign=\"top\"\u003e\n \u003cp\u003e70 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e44(62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e26(37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e0.017*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.413145539906104%\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.779342723004696%\" valign=\"top\"\u003e\n \u003cp\u003e64 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e27(42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\n \u003cp\u003e37(57.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.9358372456964%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*significant, ** very significant; \u003cstrong\u003eHTN\u003c/strong\u003e: Hypertension; \u003cstrong\u003eDM\u003c/strong\u003e: Diabetes Mellitus, \u003cstrong\u003eCOPD\u003c/strong\u003e: Chronic Obstructive Pulmonary Disease; \u003cstrong\u003eTB\u003c/strong\u003e: Tuberculosis; \u003cstrong\u003eGyn/Obs\u003c/strong\u003e: gynaecology and Obstetrics\u003c/p\u003e\n\u003ch2\u003eIncidence of clinical outcomes\u003c/h2\u003e\n\u003cp\u003eThe study demonstrated that the period prevalence of ARDS was 9.87%, while mild, moderate, and severe ARDS was reported in 56 (16.4%), 125 (36.7%), and 159 (46.8%) patients, respectively. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study showed that the incidence of 30-mortality in patients with ARDS was 59% (95% CI: 53.5 to 63.9), where 96.2% of severe cases died compared to 58.4%, and 26.9% for mild and moderate cases. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe majority of patients, 240 (70.6%) developed at least one complication during their ICU, including Sepsis, ventilator-associated pneumonia (VAP) and cardiac arrest, where ventilator-associated pneumonia was the commonest107 (31.5%), followed by \u0026nbsp;cardiac arrest 80 (23.5%) and sepsis 53 (15.6%) \u003cstrong\u003e(Fig 3).\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive probability of mortality\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe graph below illustrates the predictive probability of in-hospital mortality among patients with ARDS concerning severity of ARDS. As had been observed, the mortality progressively increased, and a significant change has been observed since the outbreak of the COVID-19 pandemic. There is a significant difference in mortality between mild and moderate to severe cases (\u003cstrong\u003eFig 5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe predictive probability of mortality among patients with ARDS increased with age, specifically in the age group of 40-49 years. However, there was no significant difference in mortality observed between those with severe and moderate ARDS but with mild cases of ARDS (\u003cstrong\u003eFig 6A\u003c/strong\u003e). The graph also illustrated a steady increase in mortality among patients with invasive mechanical ventilation compared to those with non-invasive oxygen therapy (\u003cstrong\u003eFig 6B\u003c/strong\u003e). Besides, the predicted probability of mortality increased with higher disease severity scores (\u003cstrong\u003eFig 6 C and D\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eDeterminants of mortality\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study identified a number of independent predictors of mortality among patients with ARDS. The multilevel multivariate analysis revealed that GCS, severity of ARDS, time of admission, causes of ARDS, ventilation type, comorbidity, and complication were strongly associated with hospital mortality. However,\u0026nbsp;gender, baseline heart rates, and respiratory rates didn\u0026rsquo;t show significant association with in-hospital mortality among patients with ARDS in ICU.\u003c/p\u003e\n\u003cp\u003eThe study demonstrated that patients with ARDS of GCS \u0026lt;8 were more than 7 times more likely to die (AOR=7.4; 95% CI: 2.79, 19.75)\u0026nbsp;compared to patients with GCS of 8-12(AOR =2.6, 95% CI: 1.81, 5.89) and GCS \u0026gt;12.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding to time of admission, patients admitted at weekend and night time had a higher odds of mortality at (AOR= 3.3, 95% CI 1.12, 9.84) and (AOR= 3.9 95% CI 1.79, 8.87) respectively. Those patients who were admitted at night time were approximately 4 times more likely to die compared to those who were admitted at weekend days and day time.\u003c/p\u003e\n\u003cp\u003eIn this study, Respiratory disorders and sepsis were the major causes of mortality in patients with ARDS. Patients with respiratory cause were more than 9 times more likely to die (AOR=9.4, 95% CI 3.84, 23.03), similarly, patients with sepsis were more than 7 times more likely to die AOR 7.2, 95% CI 2.82, 18.51) compared to other causes of ARDS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMode ventilation is the most determinant of mortality in ARDS patients as the lungs of the patients are prone to barotrauma, biotrauma, atlectotrauma, and volutrauma. The current study showed that patients managed with invasive ventilation were more than 3 times more to be expected to die compared to patients managed by non -invasive type (AOR =3.2, 95% CI 1.56, 6.42). This study demonstrated that respiratory comorbidities such as asthma, and COPD caused a higher odds of 30-day mortality (AOR= 4.9, 95% CI 1.71, 14.32). Similarly, patients with Diabetes Mellitus are approximately 4 times more likely to experience death as compared to other comorbidities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA number of complications were identified in this study, and from which ventilator-associated pneumonia contributed a higher odds of in-hospital mortality at about (AOR=4.1, 95% CI 1.7, 9.91) compared to sepsis, cardiac arrest and others (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Multilevel multivariable model of the relationship between risk factors and in-hospital mortality in ARDS patients in Southern Ethiopia University Hospitals ICUs, 2023(n=340)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"647\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eVariable\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eAll (n=340)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eAlive (n=)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eDead(n=)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eAOR(90%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"647\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eAge groups\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e18-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"96\" valign=\"top\"\u003e\n \u003cp\u003e102 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e52(51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e50(49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eReff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e29-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"96\" valign=\"top\"\u003e\n \u003cp\u003e68 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e39(57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e29(42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.8(0.32, 1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e40-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"96\" valign=\"top\"\u003e\n \u003cp\u003e51 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e12(23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e39(76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e2.3(o.80, 6.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e\u0026gt;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"96\" valign=\"top\"\u003e\n \u003cp\u003e119 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e37(31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e82(68.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e2.1(0.95, 4.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eOxygen saturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"96\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"80\" colspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e\u0026lt;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e193 (56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e60(31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e133(68.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e1.4(0.69, 2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e\u0026gt;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e147 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e80(54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e67(45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eReff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"647\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eGlasgow coma scale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e150 (44.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e45(30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e105(70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e7.4(2.79, 19.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003e8-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e67 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e21(31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e46(68.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e2.6(1.64, 13.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e123 (36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e74(60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e49(39.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"647\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eSeverity of Acute Respiratory Distress Syndrome\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e56 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e41(73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e15(26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eReff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e125 (36.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e52(41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e73(58.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e4.7(1.64, 13.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e159 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e46(28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e113(71.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e4.6(1.57, 13.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"647\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eAdmission time\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eWeekend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e72(21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e23(31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e49(68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e3.3(1.12, 9.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e\u0026lt;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eNight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e145(42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e40(27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e105(72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e3.9(1.79, 8.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eDay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e123(36.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e76(61.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e47(38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eReff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"647\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eCauses of Acute Respiratory Distress Syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e124 (36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e25(20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e99(79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e9.4(3.84, 23.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e105 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e47(44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e58(55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e7.2(2.82, 18.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e111 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e67(60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e44(39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eReff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"647\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eVentilation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eInvasive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e181(53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e50(27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e131(72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e3.2(1.56, 6.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eNoninvasive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e159(46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e89(56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e70(44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eReff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"647\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e30 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e12(40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e18(60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.9(0.23, 3.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003eDiabetic Mellitus \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e49 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e14(28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e35(71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e3.9(1.20, 12.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003eCardiac\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e42 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e20(47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e22(52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e1.5(0.42, 5.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003eCOPD/TB/Asthmatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e85 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e22(25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e63(74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e4.9(1.71, 14.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003eMore than one comorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e70 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e44(62.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e26(37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.5(0.17, 1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp\u003eUnknown\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e64 (18.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp\u003e27(42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp\u003e37(57.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eReff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"647\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eComplications\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eVAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e107(31.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e35(32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e72(67.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e4.1(1.72, 9.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e53(15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e17(32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e36(67.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e2.3(0.80, 6.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eCardiac arrest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e80(23.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e31(38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e49(61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.9(0.37, 2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"141\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"97\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e100(29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e56(56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"84\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003e44(44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"146\" valign=\"top\"\u003e\n \u003cp align=\"left\"\u003eReff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"100\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*significant, ** very significant; \u003cstrong\u003eCOPD\u003c/strong\u003e: Chronic Obstructive Pulmonary Disease; \u003cstrong\u003eTB\u003c/strong\u003e: Tuberculosis; \u003cstrong\u003eVAP\u003c/strong\u003e: Ventilator associated Pneumonia\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis multicenter study was designed to investigate the incidence of in-hospital mortality and its determinants among ARDS patients admitted to ICU in selected southern Ethiopia university hospitals.\u003c/p\u003e\n\u003cp\u003eThis research illustrated that the magnitude of ARDS was notably higher at 98.1 per 100,000 person-years compared to previous studies (39-45). Prior studies in three Scandinavian nations, for instance, documented an incidence of 13.5 per 100,000 person-years(29). Similarly, a French national study found a rate of 24.6 per 100,000 person-years(45). Additional investigations in the USA and Brazil reported incidence rates of 58.7 and 6.3 per 100,000 person-years, respectively(31, 32). It has been observed that there are substantial variations in ARDS prevalence globally, which can be attributed to various factors such as differences in healthcare infrastructure and access to medical services, disparities in reporting methods and diagnostic criteria, environmental influences like pollution and infectious diseases, genetic predispositions in specific populations, variations in data collection practices, and different research approaches\u003c/p\u003e\n\u003cp\u003eThis study revealed that the cumulative incidence of in-hospital mortality among ARDS patients in ICUs was 59.1 %( 95% CI: 53.8 to 64). The current study demonstrated that the rate of in-hospital mortality among ARDS patients was higher compared to previous studies. For instance, a global observational study by Villar J. et al found that the in-hospital mortality rate for ARDS patients was 40% (43). Similarly, a study in the Netherlands reported a range of in-hospital mortality rates from 32.7% to 57.9% (39, 42). %. Besides, a retrospective cohort study conducted in Spain by Villar J et al revealed an in-hospital mortality rate of 47.8% (37), and a retrospective cohort study in China by Dai Q et al. in 2019 showed a mortality rate of 39% among ARDS patients(40). However, a study conducted in Uganda found a higher rate of mortality at 77% (46). These discrepancies might be attributable to differences in ICU infrastructure, epidemiology of risk factors for ARDS in different populations, differences in ventilation modalities, misclassification during diagnosis, and variation in methodology of studies.\u003c/p\u003e\n\u003cp\u003eThe current research identified several factors that independently predict in-hospital mortality in ICU patients with ARDS. These factors include, but are not limited to, low GCs score, ARDS severity, time and cause of admission, ventilation method, comorbidities, complications, and baseline characteristics. Our study revealed that the percentages of patients categorized as having mild, moderate, and severe ARDS were 26.8%, 58.4%, and 71.1% respectively. These rates were higher than those reported in a multinational observational study, which showed hospital mortality rates of 34.9% for mild, 40.3% for moderate, and 46.1% for severe ARDS (27). \u003c/p\u003e\n\u003cp\u003eThe study showed that patients admitted during nighttime had a higher risk of mortality, with approximately 4 times more likely (AOR= 3.9 95% CI 1. 79, 8.87) compared to those admitted on weekends or during the day. This finding contradicts a previous study conducted in the same area by Abate SM. et al., where the odds of mortality for nighttime admissions were lower (AOR=2.5; 95%CI: 1.24, 5.05) (47), and the possible discrepancy might be variation in participant characteristics, sample size, Severity of ARDS, variation in treatment protocols, and individual response to treatment, each patient\u0026apos;s response to treatment can also vary, influencing mortality rates, and it has of paramount importance for healthcare providers to consider these factors when managing patients with ARDS to improve outcomes.\u003c/p\u003e\n\u003cp\u003eThis study found that respiratory and sepsis were detrimental causes of admission and mortality of patients with ARDS, where patients with respiratory disorder were more than 9 times, and patients with sepsis were more than 7 times more likely to die, respectively, compared to other causes of ARDS (AOR 9.4, 95% CI 3.84, 23.03, AOR 7.2, 95% CI 2.82, 18.51). A retrospective cohort study done in Michigan, USA, showed that the most common primary causes of death in ARDS patients were sepsis, pulmonary dysfunction, and neurologic dysfunction which is consistent to our study (48). Similarly, a multicenter prospective study in China also revealed that pneumonia, sepsis out of chest origin, and aspiration (77%, 7.3% and 3.3%, respectively were the main risk factors for ARDS(49). \u003c/p\u003e\n\u003cp\u003eThe current study showed that the patients with ventilator associated pneumonia were 4 times more likely to die (AOR 4.1, 95% CI 1.7, 9.91), which is dissimilar with a retrospective cohort study done in Vietnam, where (50%) of the patients developed sepsis, 31 (67.4%) of them experienced multiorgan failure, 14 (30%) had obstructive shock, and 15 (32.6%) developed refractory hypoxemia (36). This calls mitigating strategies to prevent VAP, which includes strict adherence to hand hygiene protocols, providing regular oral care to minimize the chances of aspiration and bacterial spread, conducting frequent suctioning of the endotracheal tube to prevent secretion buildup, and ensuring regular assessment and adjustment of cuff pressure on the endotracheal tube to prevent micro aspirations. \u003c/p\u003e\n\u003cp\u003eStrengths and limitations of the study\u003c/p\u003e\n\u003cp\u003eTo the best of our knowledge, this is the first ever multicenter longitudinal study in Sub-Saharan Africa examining the prevalence of ARDS, in-hospital death rates, and the factors independently associated with these outcomes among ARDS patients in intensive care units. The study employed multilevel analysis to address clustering effects arising from the hierarchical structure of the data, given that patients were situated in various hospitals and ICUs offering different levels of care. However, this study is not without limitations. Firstly, the study featured a diverse group of participants in terms of sociodemographic factors, diagnoses, and comorbidities. Secondly, it was a retrospective study, leading to some missing data that could have helped in assessing disease severity.\u003c/p\u003e\n\u003cp\u003eImplication for policymakers \u003c/p\u003e\n\u003cp\u003eThe burden of ARDS requires a multi-faceted approach that involves collaboration among policymakers, healthcare professionals, and the broader community to improve outcomes for individuals affected by this serious condition. Policymakers may need to prioritize funding for early detection and treatment strategies, as well as support for hospitals and healthcare providers to ensure timely and effective care for ARDS patients. Additionally, policymakers may also need to address broader public health concerns related to factors that contribute to ARDS, such as air pollution or infectious diseases, to prevent future outbreaks and reduce overall mortality rates.\u003c/p\u003e\n\u003cp\u003eImplication for further research\u003c/p\u003e\n\u003cp\u003eThis was a retrospective multicentre longitudinal study with a fairly large group of participants, suggests that more future studies should be done with a homogenous population in terms of sociodemographic characteristics, admission pattern, comorbidity, and similar ICU infrastructure. \u003c/p\u003e"},{"header":"Conclusion ","content":"\u003cp\u003eThe study revealed that in hospital mortality rate was substantially high compared to the developed nations among patients with ARDS in Ethiopia is a concerning issue for stakeholders that requires urgent attention. The findings suggest that there are challenges within the healthcare system that need to be addressed in order to improve patient outcomes and reduce mortality rates. It is imperative that healthcare facilities prioritize the management of ARDS patients and implement evidence-based protocols to enhance the quality of care provided.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eData and material can be available where appropriate.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was funded by Dilla University with a total of 25,000 Ethiopian Birr\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eSemagn Abate: Conceptualization, Methodology, Software, Reviewing; Melkamu Kebede: writing original draft, supervision; Seyoum Hailu: Reviewing, editing the draft; Yayeh Adamu: Reviewing, editing the draft; Bahru Mantefardo: Reviewing and Editing; and Abinet Meno: \u0026nbsp;Reviewing and Editing;\u0026nbsp;Lakew Lafebo: Reviewing and Editing\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge Dilla University for technical support and encouragement to carry out the project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBlondonnet R, Constantin J-M, Sapin V, Jabaudon M. A pathophysiologic approach to biomarkers in acute respiratory distress syndrome. Disease markers. 2016;2016.\u003c/li\u003e\n\u003cli\u003ePham T, Rubenfeld GD. Fifty years of research in ARDS. The epidemiology of acute respiratory distress syndrome. A 50th birthday review. American journal of respiratory and critical care medicine. 2017;195(7):860-70.\u003c/li\u003e\n\u003cli\u003eBernard GR, Artigas A, Brigham KL, Carlet J, Falke K, Hudson L, et al. The American-European Consensus Conference on ARDS. Definitions, mechanisms, relevant outcomes, and clinical trial coordination. American journal of respiratory and critical care medicine. 1994;149(3):818-24.\u003c/li\u003e\n\u003cli\u003eRanieri V, Rubenfeld G, Thompson B, Ferguson N, Caldwell E, Fan E, et al. Acute respiratory distress syndrome: the Berlin Definition. Jama. 2012;307(23):2526-33.\u003c/li\u003e\n\u003cli\u003eM\u0026aacute;ca J, Jor O, Holub M, Sklienka P, Bur\u0026scaron;a F, Burda M, et al. Past and present ARDS mortality rates: a systematic review. Respiratory care. 2017;62(1):113-22.\u003c/li\u003e\n\u003cli\u003eLazzeri C, Peris A. The Kigali modification of the berlin definition: a new epidemiological tool for ARDS? Journal of thoracic disease. 2016;8(6):E443.\u003c/li\u003e\n\u003cli\u003eThompson BT, Chambers RC, Liu KD. Acute respiratory distress syndrome. New England Journal of Medicine. 2017;377(6):562-72.\u003c/li\u003e\n\u003cli\u003eUmbrello M, Formenti P, Bolgiaghi L, Chiumello D. Current concepts of ARDS: a narrative review. International journal of molecular sciences. 2016;18(1):64.\u003c/li\u003e\n\u003cli\u003eBlank R, Napolitano LM. Epidemiology of ARDS and ALI. Critical care clinics. 2011;27(3):439-58.\u003c/li\u003e\n\u003cli\u003eGriffiths MJ, McAuley DF, Perkins GD, Barrett N, Blackwood B, Boyle A, et al. Guidelines on the management of acute respiratory distress syndrome. BMJ open respiratory research. 2019;6(1):e000420.\u003c/li\u003e\n\u003cli\u003ePapazian L, Aubron C, Brochard L, Chiche J-D, Combes A, Dreyfuss D, et al. Formal guidelines: management of acute respiratory distress syndrome. Annals of intensive care. 2019;9(1):1-18.\u003c/li\u003e\n\u003cli\u003eParhar KKS, Stelfox HT, Fiest KM, Rubenfeld GD, Zuege DJ, Knight G, et al. Standardized management for hypoxemic respiratory failure and ARDS: systematic review and meta-analysis. Chest. 2020;158(6):2358-69.\u003c/li\u003e\n\u003cli\u003eLiaqat A, Mason M, Foster BJ, Kulkarni S, Barlas A, Farooq AM, et al. Evidence-based mechanical ventilatory strategies in ARDS. Journal of Clinical Medicine. 2022;11(2):319.\u003c/li\u003e\n\u003cli\u003edo Amaral Pfeilsticker FJ, Neto AS. \u0026lsquo;Lung-protective\u0026rsquo;ventilation in acute respiratory distress syndrome: still a challenge? Journal of Thoracic Disease. 2017;9(8):2238.\u003c/li\u003e\n\u003cli\u003eAoyama H, Uchida K, Aoyama K, Pechlivanoglou P, Englesakis M, Yamada Y, et al. Assessment of therapeutic interventions and lung protective ventilation in patients with moderate to severe acute respiratory distress syndrome: a systematic review and network meta-analysis. JAMA network open. 2019;2(7):e198116-e.\u003c/li\u003e\n\u003cli\u003eBhattacharjee S, Soni KD, Maitra S. Recruitment maneuver does not provide any mortality benefit over lung protective strategy ventilation in adult patients with acute respiratory distress syndrome: a meta-analysis and systematic review of the randomized controlled trials. Journal of Intensive Care. 2018;6(1):1-8.\u003c/li\u003e\n\u003cli\u003eSud S, Friedrich JO, Adhikari NK, Taccone P, Mancebo J, Polli F, et al. Effect of prone positioning during mechanical ventilation on mortality among patients with acute respiratory distress syndrome: a systematic review and meta-analysis. 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Critical Care. 2020;24:1-11.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mortality, intensive care unit, ARDS, cohort, Multicenter","lastPublishedDoi":"10.21203/rs.3.rs-4137280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4137280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eAcute respiratory distress syndrome (ARDS) is a heterogeneous syndrome with substantial morbidity and mortality globally. Body of evidence revealed that the epidemiologic estimates are currently disproportional due to differences in patient populations, risk factors, resources, and practice protocols around the world, and the rate of mortality and its predictors are uncertain in Ethiopia.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eA multi-centre longitudinal study was conducted in Ethiopia from January 2018 to June 2023. After receiving ethical clearance from the Institutional Review Board (IRB) of Dilla University College of Health Science and Medicine, 356 ARDS patients\u0026rsquo; records were retrieved with a systematic random sampling technique. A multilevel multivariate analysis was used to control the effect of clustering. A P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was taken as statistically significant.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis study demonstrated that the cumulative mortality rate of patients with ARDS was 59% (95% CI: 53.5 to 63.9). The multilevel multivariable model analysis showed that GCS\u0026thinsp;\u0026lt;\u0026thinsp;8 (AOR\u0026thinsp;=\u0026thinsp;7.4; 95% CI: 2.79, 19.75), severe form of ARDS (AOR 4.7 95% CI 1.64, 13.36), invasive ventilation (AOR 3.2, 95% CI 1.56, 6.42), and respiratory comorbidity (AOR 4.9, 95% CI 1.71, 14.32) were independent predictors of in-hospital mortality among patients with ARDS.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study revealed that the hospital mortality rate was substantially higher than that of developed nations. The study also highlighted various risk factors that independently predicted in-hospital mortality.The findings of this study call for mitigating strategies to improve ICU care for ARDS patients.\u003c/p\u003e","manuscriptTitle":"Incidence of In-Hospital Mortality and Its Determinants among Intensive Care Unit Patients with Acute Respiratory Distress Syndrome in Ethiopian: A multilevel analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-28 17:33:46","doi":"10.21203/rs.3.rs-4137280/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-03-22T06:58:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-22T06:57:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2024-03-20T12:52:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1f15d222-673b-4bae-b7d0-467ffd653516","owner":[],"postedDate":"March 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-03-28T17:33:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-28 17:33:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4137280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4137280","identity":"rs-4137280","version":["v1"]},"buildId":"WvIrzKhiLBfengagbw6Ux","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}