The magnitude of mortality rate and its predictors among adult patients admitted to the Intensive care unit at two government hospital in Addis Ababa Ethiopia: retrospective cross-sectional study

The magnitude of mortality rate and its predictors among adult patients admitted to the Intensive care unit at two government hospital in Addis Ababa Ethiopia: retrospective cross-sectional study | 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 The magnitude of mortality rate and its predictors among adult patients admitted to the Intensive care unit at two government hospital in Addis Ababa Ethiopia: retrospective cross-sectional study Shimels Getaneh Weldemedhn, Behaylu Tesfamaryam Hago, Alyas Muche Kebede, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7942616/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : The number of life-threatening conditions requiring intensive care units has increased significantly in low-income countries due to the number of hospital expansions. The rate of ICU mortality rate differs by region in Ethiopia. However, the evidence on ICU mortality and its predictors remains uncertain. Objectives to assess the magnitude of the mortality rate and its predictors among hospitalized adult patients Methodology: two center retrospective cross-sectional study was conducted among patients admitted to ICU from December 1, 2023, to May 30, 2024. A pretested and structured questionnaire was used. The completed data was collected via web link after being prepared by kobtoolbox.org, coded, manually checked, and exported to SPSS version 27 for data analysis. to analyze the data descriptive statistics and logistic regression were used. Result: A total of 309 study participants’ charts were reviewed. Five days was the median duration of ICU stay. postoperative, septic shock, stroke, and congestive heart failure were the leading causes of ICU admissions and the common causes of death were septic shock, stroke, head trauma and ARDS. The overall mortality rate of the ICU-admitted patients was 46.3% (95% CI: 40.5, 51.8). having higher Charleson comorbidity index score, mechanical ventilation required at admission and hospital acquired infection were significantly associated with ICU mortality Conclusion : Compared to some developed countries, the observed mortality rate is higher. According to the current study's findings, hospital-acquired infections, the Charlson comorbidity index, and the need for mechanical ventilation all significantly correlated with the mortality predictor of intensive care unit patients. mortality predictor ICU mortality rate Figures Figure 1 Introduction The intensive care unit is the hospital section responsible for monitoring acute patients, and it relies on specialized multidisciplinary staff and advanced technology to provide the best possible care for these patients, who are typically unstable and at high risk of death. These patients require ongoing monitoring of the most diverse clinical and laboratory parameters, which directly impact their medical progress and the staff's decision-making. Worldwide, the per centage of patients admitted to ICUs varied from 1 to 54%( 1 – 4 ). There is variation in the reasons behind ICU admissions worldwide, most of the data that is currently available indicates that in middle-class and wealthy nations, respiratory and cardiovascular diseases account for 27–41% of admissions( 5 ). the study showed that the outcomes of patients in ICU are related to different factors including the pattern of diseases, the severity of the disease, infrastructure, trained medical staff, nursing care, medical supplies, age of the patient, presence of comorbidities and multiorgan failure, pre-hospital and emergency care trauma score, mechanical ventilation, length of ICU stay, complications in ICU, dissemination of antimicrobial-resistant microorganisms( 6 – 9 ). Identifying critically ill patients who will not survive beyond hospital discharge could result in significant cost savings. In critical care, several score methods are available for assessing severity and predicting outcomes. Outcome prediction models are typically categorized as disease-specific or generic. These methods usually assign points according to the severity of illness and claim to predict the outcome, providing the user with a numeric estimate of the probabilities of an outcome for that patient or group of patients( 10 ). The Acute Physiology and Chronic Health Evaluation and APACHE III. the Simplified Acute Physiology Score (SAPS) II, and the Mortality Probability Model (MPM) II are the most often utilized outcome prediction models in adult intensive care. These systems typically use a set of clinical and physiological variables evaluated and registered upon admission or on the first day of intensive care to predict hospital mortality. The quantity and kinds of variables employed and the duration of data collection vary across these models( 11 , 12 ). ICUs have the highest death rate when compared to the hospital's other ward services, even though mortality rates there vary based on the underlying disease( 13 ). The global average rate of mortality in ICUs ranges between 9 to 61%. Multiple studies found that the ICU mortality rate varied around the world. North America (9.3%), Oceania (10.3%), Europe (18.7%), South America (21.7%), and the Middle East (26.2%) have relatively low rates of ICU death. Other data from Sub-Saharan Africa showed that mortality in ICUs ranged between 27% and 61% and The ICU mortality rate in Ethiopia ranges between 38.7 and 50.4% ( 6 , 14 – 18 ). Understanding the magnitude of intensive care unit mortality and its predictors is vital for interventions in two selected government hospitals in Addis Ababa. Therefore, the current study aims was to assess the magnitude of mortality and its predictors among patients admitted to the ICU at TASH and ZMH. Method Study design and area This study uses an institutional-based Cross-sectional study that was conducted ICU admitted patients at Tikur Anbesa specialized hospital and Zewuditu memorial hospital. Source and study population The source population of this study was all patients were admitted to the ICU from June 2024 to August 2024. The study population was Patients Clinically diagnosed and critically ill older than fourteen years and admitted to ICU for at least two days during this study. Sample size and procedure To determine the minimum sample size of this study, we used a single population proportion formula. the sample size for the descriptive cross-sectional study we used the expected prevalence to be 29.6% from a study done in Ethiopian referral hospitals in 2022. (Zα/2) the standard normal deviate 95% level of precision, the expected prevalence set at 29.6%, the acceptable margin of error set at 4% and considering a 10% non-response missing medical chart. Based on this parameter, the total sample size was determined to be 309. Based on allocation formula sample size was 209 tikur anbesa specialized hospital and 100 for zewuditu memorial hospital. Data collection and quality assurance Secondary data was collected between 10/9/24 − 2/1/25 from chart of patients admitted to ICU with structure questionnaire. Data was collected by using a structured questionnaire inserted into the Kobo toolbox. The data collector was train physician to reduce error and increase the overall reliability of the collected data. Periodic supervision was done by principal investigator to oversee the process and maintain data quality. Statistical Analysis Before the analysis, the data were coded, cleaned, and checked for missing values. SPSS version 27 software was used to enter the data. descriptive statistics was used. To evaluate the relationship between the exploratory factors and ICU mortality, bivariate and multivariable logistic regression analyses were used. For variables to be included in the multivariable logistic regression analysis, the bivariate analysis's p-value has to be less than 0.25. In multivariable analysis, variables were deemed statistically significant predictors of intensive care unit mortality if their p-value was less than 0.05 (AOR at 95%CI). Operational definition source of admission patients admitted to ICU from emergency, medical, surgical and gynecology ward or all length of ICU stays the number of days a patient stays in ICU. admission category patients admitted to ICU as surgical, medical and gynecologic/obstetrics Charlson comorbidity index: a score to calculate sum of comorbidity item score. cause of death intermediate cause that cause of death to the final event Result A descriptive study of the participant Over the course of six months, 626 patients were admitted. Of them, the charts of 309 sample patients were obtained. 100 patients’ chart were from ZMH and the remaining 209 were from TASH. The respondents' median age was 39 years old. As Table 1 shown Over one-third of the patients were between the ages of 21 and 40. Of the patients, 49.8% were female and nearly 50.2% were male. Of the patients admitted, 137 (44.3%) were from the emergency department. Table 1 Socio-demographic characteristics of patients admitted to the ICU of TASH and ZMH, 2024. Variable Category Frequency Percent Age in group in year 80 2 0.6% Sex Male 155 50.2% Female 154 49.8% Location TASH 209 67.4% ZMH 100 32.4% As Table 2 shown admission related character About 44.3% of patients were admitted from the emergency department, followed by the operation theatre (23.6%) and medical ward (19.7%). Of this, commonest cause of admission was (postoperative, septic shock, stroke, and congestive heart failure) and the commonest causes of death were (septic shock, stroke, head trauma and ARDS). 141 (45.6%) of patients were mechanical ventilated. Table 2 The ICU admission-related characteristics of patients admitted to TASH and ZMH, 2024. Variable Category Frequency Percent Source of admission Emergency department 137 44.3% Medical ward 61 19.7% Surgical ward 17 5.5% Operation theater 73 23.6% Gynecology/obstetrics 21 6.8% Admission category Medical patient 198 64% Surgical patient 90 29.1% Gynecology/obstetrics 21 6.8% Admission diagnosis Myocardial infarction 8 2.6% Congestive heart failure 19 6.1% Septic shock 65 21% Pneumonia 9 2.9% Acute respiratory distress syndrome (ARDS) 16 5.2% Pulmonary thromboembolism 3 1% Diabetic ketoacidosis 6 1.9% Stroke 24 7.8% Head trauma 16 5.2% General Surgical 80 25.9% CNS infection 22 7.1% GBS 3 1% Relapsing fever 7 2.3% Poisoning 4 1.3% Thyroid storm 3 1% Others 24 7.8% The magnitude of death among patients admitted to ZMH was 46% and TASH was 46.4%. both hospital ICU was 46.3%. Factor associated with ICU mortality To find independent predictors of death among patients admitted to the critical care unit, logistic regression analysis was performed. To determine relationships between dependent and independent factors, five predictor variables with a p-value < 0.25 at bivariate regression analysis were included in a multivariable logistic regression analysis (Table 3 ) and (Table 4 ) respectively. Table 3 summary table bivariate regression analysis mortality predictor patients admitted ICU to TASH and ZMH, 2024. Variable Total N = 309 p-value COR 95%CI Lower upper GCS 9–12 28 < 0.001 6.7 1 3.6 Need for MV 141 5 45 36 < 0.001 < 0.003 11.3 6.426 1.7 1.06 3.5 2.8 HAI 60 < 0.001 115.9 3.5 22 ARDS (admission diagnosis) 16 < 0.004 8.89 1.025 21.552 On the multivariable logistic regression model, three variables, namely hospital acquired infection, charlison comorbidity index score, and mechanical ventilation required at admission, were significantly associated with ICU mortality at a p-value of 0.001. Table 4 summary table multivariable logistic regression analysis mortality predictor patients admitted ICU to TASH and ZMH, 2024. Variable Total N = 309 p-value AOR 95% CI Lower upper Need for MV 141 3 198 < 0.001 2.2 1.577 3.080 HAI 60 < 0.001 2.9 2.442 3.48 For those patients who had hospital acquired infection, the odds of ICU mortality were 2.9 (AOR = 13.44; 95% CI:2.442, 3.480) times higher than those who had no HAI (Table 4 ). Similarly, for patients who required mechanical ventilation at admission, the odds of ICU mortality were about 4.302 (AOR = 3.8; 95% CI: 3.099, 6.275) times higher than those who did not require mechanical ventilation at admission. for those patients charlison comorbidity index > 3 in the ICU, the odds of ICU mortality were about 2.2 (AOR = 1.56; 95%CI: 1.577, 3.080) times higher compared to those CCIS was less than 2 (Table 4 ). Discussion This study determined the extent of mortality and its determinants among patients admitted to two government hospitals in Addis Abeba. The study found that 46.3% (95% CI: 41, 52) of hospitalized patients died in the ICU. This finding was consistent with the indigenous study from Hosanna of 46.42%( 19 ). This finding was higher than the studies done in Nigeria, 32.9%( 6 ), and Singapor, 26.5%( 20 ). However, it was lower compared to the studies previous done in Ethiopia 67.4% ( 21 ), Kenya: 53.6%( 22 ) and Rwanda 47%( 23 ). Several causes could lead to these differences. Many African countries' inadequate resources and infrastructure might make timely and proper care difficult to obtain, resulting in increased mortality. Furthermore, the lack of a distinct ICU for surgical and medical patients in the research area, as well as the fact that ZMH ICU is still a young facility, may contribute to the higher risk of ICU death. Mortality rates can be influenced by patient demographics as well as comorbidities. Differences in sample size, level of ICU care, availability of medical supplies, and trained personnel classification could also explain the variance. However, when compared to other research locations in Ethiopia and other African countries, the death rate is lower. for patients who required mechanical ventilation at admission, the odds of ICU mortality were about 4.302 (AOR = 3.8; 95% CI: 3.099, 6.275) times higher than those who did not require mechanical ventilation at admission. which is analogous to the study finding in Asian countries( 24 ), Brazil ( 25 ), Nigeria( 6 ). Several causes could lead to these differences. Many African countries' inadequate resources and infrastructure might make timely and proper care difficult to obtain, resulting in increased mortality. Furthermore, the lack of a distinct ICU for surgical and medical patients in the research area, as well as the fact that ZMH ICU is still a young facility, may contribute to the higher risk of ICU death. Mortality rates can be influenced by patient demographics as well as comorbidities. Differences in sample size, level of ICU care, availability of medical supplies, and trained personnel classification could also explain the variance. However, when compared to other research locations in Ethiopia and other African countries, the death rate is lower. for those patients charlison comorbidity index > 3 in the ICU, the odds of ICU mortality were about 2.2 (AOR = 1.56; 95%CI: 1.577, 3.080) times higher compared to those CCIS was less than 2. higher CCI is associated with increased ICU mortality is consistent with previous study( 26 ), ( 27 ). One such explanation is Patients with high CCI scores typically have multiple chronic conditions, which complicates their clinical picture and increases their susceptibility to acute infections. Comorbidities, such as diabetes, heart disease, and chronic respiratory conditions, can influence how the body responds to stressors like trauma or infection, making it more difficult for people to recover from serious illnesses( 28 ). For those patients who had hospital acquired infection, the odds of ICU mortality were 2.9 (AOR = 13.44; 95% CI:2.442, 3.480) times higher than those who had no HAI which is analogous to the study finding in England( 29 ). HAIs frequently affect critically ill patients, worsening their overall health. The fact that infections can induce serious side effects like sepsis, which can overwhelm the body's defenses and lead to multiple organ failure, may help to explain these findings. Additionally, by extending hospital stays and complicating treatment strategies, HAIs raise the risk of unfavorable outcomes. Conclusion Compared to some developed countries, the observed mortality rate is higher. According to the current study's findings, hospital-acquired infections, the Charlson comorbidity index, and the need for mechanical ventilation all significantly correlated with the mortality predictor of intensive care unit patients. Abbreviations AKI-Acute kidney injury ARDS- acute respiratory distress syndrome CCIS- charlson comorbidity index score ICU-Intensive care unites IHD- ischemic heart diseases IRB - Institutional Review Board LOS- length of stay MV- mechanical ventilation TASH-Tikur Anbesa Specialized Hospital VAP- ventilator-associated pneumonia ZMH-zewuditu memorial hospital Declarations Ethics approval and Consent to Participate : This study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board (IRB) of Addis Ababa University (Ref. No.MS/80/2016). Approval was granted on June 2024. The requirement for obtaining individual informed consent was waived by the approving IRBs due to the retrospective nature of the research, which involved the analysis of existing, anonymized data with minimal risk to participants. Consent for publication : Not applicable. Funding: Not applicable. Author Contribution S.G, B.T: was involved in the conception, design of the research protocol, drafting the manuscript and reviewing and data interpretation and analysis. S.G., D.G., M.T, W.B., A.M involved in research design, manuscript writing and reviewing, data collection. S.G. and B.T involved in the study conception, design of the research protocol, manuscript writing and review. All the authors have read and approved the final version of the manuscript Acknowledgement The authors would like to acknowledge Zewuditu memorial hospital and Tikur Anbesa specialized hospital for their permission to conduct this research Data Availability The deidentified version of the dataset generated and analyzed during this study is provided within the supplementary documents. References Rosenberg AL, Watts C. Patients readmitted to ICUs*: a systematic review of risk factors and outcomes. Chest. 2000;118(2):492–502. 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12:16:40","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82779,"visible":true,"origin":"","legend":"","description":"","filename":"66e7110b5dd2431f82e6eebce18fa39d1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7942616/v1/96f84770269dfa7a0ffd10c1.xml"},{"id":94856466,"identity":"0ff7ae1a-8498-40be-98f9-74e15f112d68","added_by":"auto","created_at":"2025-10-31 12:16:40","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88946,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7942616/v1/2f1e87a685c5b19914b6b8f5.html"},{"id":94856460,"identity":"f19b54c0-7dee-4435-81f6-06ef7cce93d3","added_by":"auto","created_at":"2025-10-31 12:16:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5875,"visible":true,"origin":"","legend":"\u003cp\u003eThe ICU outcome of patients admitted to TASH and ZMH, 2024.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7942616/v1/78cac544e9373e834f5b6283.png"},{"id":95227359,"identity":"1fdf8260-8f6a-471b-9a10-1744f7ac3549","added_by":"auto","created_at":"2025-11-05 16:32:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":804369,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7942616/v1/3ae5ef94-ca4b-4134-918c-6fa28500316e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe magnitude of mortality rate and its predictors among adult patients admitted to the Intensive care unit at two government hospital in Addis Ababa Ethiopia: retrospective cross-sectional study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe intensive care unit is the hospital section responsible for monitoring acute patients, and it relies on specialized multidisciplinary staff and advanced technology to provide the best possible care for these patients, who are typically unstable and at high risk of death. These patients require ongoing monitoring of the most diverse clinical and laboratory parameters, which directly impact their medical progress and the staff's decision-making.\u003c/p\u003e\u003cp\u003eWorldwide, the per centage of patients admitted to ICUs varied from 1 to 54%(\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). There is variation in the reasons behind ICU admissions worldwide, most of the data that is currently available indicates that in middle-class and wealthy nations, respiratory and cardiovascular diseases account for 27\u0026ndash;41% of admissions(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ethe study showed that the outcomes of patients in ICU are related to different factors including the pattern of diseases, the severity of the disease, infrastructure, trained medical staff, nursing care, medical supplies, age of the patient, presence of comorbidities and multiorgan failure, pre-hospital and emergency care trauma score, mechanical ventilation, length of ICU stay, complications in ICU, dissemination of antimicrobial-resistant microorganisms(\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIdentifying critically ill patients who will not survive beyond hospital discharge could result in significant cost savings. In critical care, several score methods are available for assessing severity and predicting outcomes. Outcome prediction models are typically categorized as disease-specific or generic. These methods usually assign points according to the severity of illness and claim to predict the outcome, providing the user with a numeric estimate of the probabilities of an outcome for that patient or group of patients(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Acute Physiology and Chronic Health Evaluation and APACHE III. the Simplified Acute Physiology Score (SAPS) II, and the Mortality Probability Model (MPM) II are the most often utilized outcome prediction models in adult intensive care. These systems typically use a set of clinical and physiological variables evaluated and registered upon admission or on the first day of intensive care to predict hospital mortality. The quantity and kinds of variables employed and the duration of data collection vary across these models(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eICUs have the highest death rate when compared to the hospital's other ward services, even though mortality rates there vary based on the underlying disease(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The global average rate of mortality in ICUs ranges between 9 to 61%. Multiple studies found that the ICU mortality rate varied around the world. North America (9.3%), Oceania (10.3%), Europe (18.7%), South America (21.7%), and the Middle East (26.2%) have relatively low rates of ICU death. Other data from Sub-Saharan Africa showed that mortality in ICUs ranged between 27% and 61% and The ICU mortality rate in Ethiopia ranges between 38.7 and 50.4% (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnderstanding the magnitude of intensive care unit mortality and its predictors is vital for interventions in two selected government hospitals in Addis Ababa. Therefore, the current study aims was to assess the magnitude of mortality and its predictors among patients admitted to the ICU at TASH and ZMH.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and area\u003c/h2\u003e\u003cp\u003eThis study uses an institutional-based Cross-sectional study that was conducted ICU admitted patients at Tikur Anbesa specialized hospital and Zewuditu memorial hospital.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSource and study population\u003c/h3\u003e\n\u003cp\u003eThe source population of this study was all patients were admitted to the ICU from June 2024 to August 2024. The study population was Patients Clinically diagnosed and critically ill older than fourteen years and admitted to ICU for at least two days during this study.\u003c/p\u003e\n\u003ch3\u003eSample size and procedure\u003c/h3\u003e\n\u003cp\u003eTo determine the minimum sample size of this study, we used a single population proportion formula. the sample size for the descriptive cross-sectional study we used the expected prevalence to be 29.6% from a study done in Ethiopian referral hospitals in 2022. (Zα/2) the standard normal deviate 95% level of precision, the expected prevalence set at 29.6%, the acceptable margin of error set at 4% and considering a 10% non-response missing medical chart. Based on this parameter, the total sample size was determined to be 309. Based on allocation formula sample size was 209 tikur anbesa specialized hospital and 100 for zewuditu memorial hospital.\u003c/p\u003e\n\u003ch3\u003eData collection and quality assurance\u003c/h3\u003e\n\u003cp\u003eSecondary data was collected between 10/9/24\u0026thinsp;\u0026minus;\u0026thinsp;2/1/25 from chart of patients admitted to ICU with structure questionnaire. Data was collected by using a structured questionnaire inserted into the Kobo toolbox. The data collector was train physician to reduce error and increase the overall reliability of the collected data. Periodic supervision was done by principal investigator to oversee the process and maintain data quality.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eBefore the analysis, the data were coded, cleaned, and checked for missing values. SPSS version 27 software was used to enter the data. descriptive statistics was used. To evaluate the relationship between the exploratory factors and ICU mortality, bivariate and multivariable logistic regression analyses were used. For variables to be included in the multivariable logistic regression analysis, the bivariate analysis's p-value has to be less than 0.25. In multivariable analysis, variables were deemed statistically significant predictors of intensive care unit mortality if their p-value was less than 0.05 (AOR at 95%CI).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eOperational definition\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003esource of admission\u003c/strong\u003e\u003cp\u003epatients admitted to ICU from emergency, medical, surgical and gynecology ward or all\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003elength of ICU stays\u003c/strong\u003e\u003cp\u003ethe number of days a patient stays in ICU.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eadmission category\u003c/strong\u003e\u003cp\u003epatients admitted to ICU as surgical, medical and gynecologic/obstetrics\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCharlson comorbidity index: a\u003c/b\u003e score to calculate sum of comorbidity item score.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ecause of death\u003c/strong\u003e\u003cp\u003eintermediate cause that cause of death to the final event\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eA descriptive study of the participant\u003c/h2\u003e\u003cp\u003eOver the course of six months, 626 patients were admitted. Of them, the charts of 309 sample patients were obtained. 100 patients\u0026rsquo; chart were from ZMH and the remaining 209 were from TASH. The respondents' median age was 39 years old. As Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shown Over one-third of the patients were between the ages of 21 and 40. Of the patients, 49.8% were female and nearly 50.2% were male. Of the patients admitted, 137 (44.3%) were from the emergency department.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-demographic characteristics of patients admitted to the ICU of TASH and ZMH, 2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eAge in group in year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41\u0026thinsp;\u0026minus;\u0026thinsp;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61\u0026ndash;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTASH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e67.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZMH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shown admission related character About 44.3% of patients were admitted from the emergency department, followed by the operation theatre (23.6%) and medical ward (19.7%). Of this, commonest cause of admission was (postoperative, septic shock, stroke, and congestive heart failure) and the commonest causes of death were (septic shock, stroke, head trauma and ARDS). 141 (45.6%) of patients were mechanical ventilated.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe ICU admission-related characteristics of patients admitted to TASH and ZMH, 2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercent\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eSource of admission\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmergency department\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical ward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurgical ward\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOperation theater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGynecology/obstetrics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAdmission category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurgical patient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGynecology/obstetrics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e\u003cp\u003eAdmission diagnosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMyocardial infarction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCongestive heart failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSeptic shock\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePneumonia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAcute respiratory distress syndrome (ARDS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePulmonary thromboembolism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiabetic ketoacidosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHead trauma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral Surgical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCNS infection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGBS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRelapsing fever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoisoning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThyroid storm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe magnitude of death among patients admitted to ZMH was 46% and TASH was 46.4%. both hospital ICU was 46.3%.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eFactor associated with ICU mortality\u003c/h2\u003e\u003cp\u003eTo find independent predictors of death among patients admitted to the critical care unit, logistic regression analysis was performed. To determine relationships between dependent and independent factors, five predictor variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 at bivariate regression analysis were included in a multivariable logistic regression analysis (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) respectively.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003esummary table bivariate regression analysis mortality predictor patients admitted ICU to TASH and ZMH, 2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;309\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003cp\u003eLower upper\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS\u003c/p\u003e\u003cp\u003e9\u0026ndash;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeed for MV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCIS\u003c/p\u003e\u003cp\u003e3\u0026ndash;4\u003c/p\u003e\u003cp\u003e\u0026gt;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u003c/p\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003cp\u003e6.426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARDS (admission diagnosis)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.552\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOn the multivariable logistic regression model, three variables, namely hospital acquired infection, charlison comorbidity index score, and mechanical ventilation required at admission, were significantly associated with ICU mortality at a p-value of 0.001.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003esummary table multivariable logistic regression analysis mortality predictor patients admitted ICU to TASH and ZMH, 2024.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;309\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003cp\u003eLower upper\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeed for MV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.275\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCCIS \u0026gt;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor those patients who had hospital acquired infection, the odds of ICU mortality were 2.9 (AOR\u0026thinsp;=\u0026thinsp;13.44; 95% CI:2.442, 3.480) times higher than those who had no HAI (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similarly, for patients who required mechanical ventilation at admission, the odds of ICU mortality were about 4.302 (AOR\u0026thinsp;=\u0026thinsp;3.8; 95% CI: 3.099, 6.275) times higher than those who did not require mechanical ventilation at admission.\u003c/p\u003e\u003cp\u003efor those patients charlison comorbidity index\u0026thinsp;\u0026gt;\u0026thinsp;3 in the ICU, the odds of ICU mortality were about 2.2 (AOR\u0026thinsp;=\u0026thinsp;1.56; 95%CI: 1.577, 3.080) times higher compared to those CCIS was less than 2 (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study determined the extent of mortality and its determinants among patients admitted to two government hospitals in Addis Abeba. The study found that 46.3% (95% CI: 41, 52) of hospitalized patients died in the ICU. This finding was consistent with the indigenous study from Hosanna of 46.42%(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This finding was higher than the studies done in Nigeria, 32.9%(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and Singapor, 26.5%(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, it was lower compared to the studies previous done in Ethiopia 67.4% (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), Kenya: 53.6%(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and Rwanda 47%(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Several causes could lead to these differences. Many African countries' inadequate resources and infrastructure might make timely and proper care difficult to obtain, resulting in increased mortality. Furthermore, the lack of a distinct ICU for surgical and medical patients in the research area, as well as the fact that ZMH ICU is still a young facility, may contribute to the higher risk of ICU death. Mortality rates can be influenced by patient demographics as well as comorbidities. Differences in sample size, level of ICU care, availability of medical supplies, and trained personnel classification could also explain the variance. However, when compared to other research locations in Ethiopia and other African countries, the death rate is lower.\u003c/p\u003e\u003cp\u003efor patients who required mechanical ventilation at admission, the odds of ICU mortality were about 4.302 (AOR\u0026thinsp;=\u0026thinsp;3.8; 95% CI: 3.099, 6.275) times higher than those who did not require mechanical ventilation at admission. which is analogous to the study finding in Asian countries(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), Brazil (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), Nigeria(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Several causes could lead to these differences. Many African countries' inadequate resources and infrastructure might make timely and proper care difficult to obtain, resulting in increased mortality. Furthermore, the lack of a distinct ICU for surgical and medical patients in the research area, as well as the fact that ZMH ICU is still a young facility, may contribute to the higher risk of ICU death. Mortality rates can be influenced by patient demographics as well as comorbidities. Differences in sample size, level of ICU care, availability of medical supplies, and trained personnel classification could also explain the variance. However, when compared to other research locations in Ethiopia and other African countries, the death rate is lower.\u003c/p\u003e\u003cp\u003efor those patients charlison comorbidity index\u0026thinsp;\u0026gt;\u0026thinsp;3 in the ICU, the odds of ICU mortality were about 2.2 (AOR\u0026thinsp;=\u0026thinsp;1.56; 95%CI: 1.577, 3.080) times higher compared to those CCIS was less than 2. higher CCI is associated with increased ICU mortality is consistent with previous study(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). One such explanation is Patients with high CCI scores typically have multiple chronic conditions, which complicates their clinical picture and increases their susceptibility to acute infections. Comorbidities, such as diabetes, heart disease, and chronic respiratory conditions, can influence how the body responds to stressors like trauma or infection, making it more difficult for people to recover from serious illnesses(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor those patients who had hospital acquired infection, the odds of ICU mortality were 2.9 (AOR\u0026thinsp;=\u0026thinsp;13.44; 95% CI:2.442, 3.480) times higher than those who had no HAI which is analogous to the study finding in England(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). HAIs frequently affect critically ill patients, worsening their overall health. The fact that infections can induce serious side effects like sepsis, which can overwhelm the body's defenses and lead to multiple organ failure, may help to explain these findings. Additionally, by extending hospital stays and complicating treatment strategies, HAIs raise the risk of unfavorable outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCompared to some developed countries, the observed mortality rate is higher. According to the current study's findings, hospital-acquired infections, the Charlson comorbidity index, and the need for mechanical ventilation all significantly correlated with the mortality predictor of intensive care unit patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAKI-Acute kidney injury\u003c/p\u003e\u003cp\u003eARDS- acute respiratory distress syndrome\u003c/p\u003e\u003cp\u003eCCIS- charlson comorbidity index score\u003c/p\u003e\u003cp\u003eICU-Intensive care unites\u003c/p\u003e\u003cp\u003eIHD- ischemic heart diseases\u003c/p\u003e\u003cp\u003eIRB - Institutional Review Board\u003c/p\u003e\u003cp\u003eLOS- length of stay\u003c/p\u003e\u003cp\u003eMV- mechanical ventilation\u003c/p\u003e\u003cp\u003eTASH-Tikur Anbesa Specialized Hospital\u003c/p\u003e\u003cp\u003eVAP- ventilator-associated pneumonia\u003c/p\u003e\u003cp\u003eZMH-zewuditu memorial hospital\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cb\u003eEthics approval and Consent to Participate\u003c/b\u003e:\u003c/strong\u003e\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board (IRB) of Addis Ababa University (Ref. No.MS/80/2016). Approval was granted on June 2024. The requirement for obtaining individual informed consent was waived by the approving IRBs due to the retrospective nature of the research, which involved the analysis of existing, anonymized data with minimal risk to participants.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cb\u003eConsent for publication\u003c/b\u003e:\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.G, B.T: was involved in the conception, design of the research protocol, drafting the manuscript and reviewing and data interpretation and analysis. S.G., D.G., M.T, W.B., A.M involved in research design, manuscript writing and reviewing, data collection. S.G. and B.T involved in the study conception, design of the research protocol, manuscript writing and review. All the authors have read and approved the final version of the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to acknowledge Zewuditu memorial hospital and Tikur Anbesa specialized hospital for their permission to conduct this research\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe deidentified version of the dataset generated and analyzed during this study is provided within the supplementary documents.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRosenberg AL, Watts C. Patients readmitted to ICUs*: a systematic review of risk factors and outcomes. 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BMC Int Health Hum Rights. 2014 Sept 23;14:26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHenry O, Amata A. A Two-year Review of Admissions to the Intensive Care Unit of the Georgetown Public Hospital Corporation, Guyana. West Indian Med J. 2016;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eObsa MS, Adem AO, Gete GB. Clinical outcomes of patients admitted in intensive care units of Nigist Eleni Mohammed Memorial Hospital of Hosanna, Southern Ethiopia. Int J Med Med Sci. 2017 June 30;9(6):79\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMukhopadhyay A, Tai BC, See KC, Ng WY, Lim TK, Onsiong S, et al. Risk Factors for Hospital and Long-Term Mortality of Critically Ill Elderly Patients Admitted to an Intensive Care Unit. BioMed Res Int. 2014;2014:e960575.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeid S, Adane H, Mekete G. Patterns of presentation, prevalence and associated factors of mortality in ICU among adult patients during the pandemic of COVID 19: A retrospective cross-sectional study. Ann Med Surg 2012. 2022;77:103618.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLalani HS, Waweru-Siika W, Mwogi T, Kituyi P, Egger JR, Park LP, et al. Intensive Care Outcomes and Mortality Prediction at a National Referral Hospital in Western Kenya. Ann Am Thorac Soc. 2018;15(11):1336\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUwamariya P. Reasons for admission in intensive care unit (ICU) and factors associated with poor outcome in referral public hospitals in Rwanda. 2022 [cited 2024 Jan 12]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dr.ur.ac.rw/handle/123456789/1926\u003c/span\u003e\u003cspan address=\"http://dr.ur.ac.rw/handle/123456789/1926\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosenthal VD, Jin Z, Rodrigues C, Myatra SN, Divatia JV, Biswas SK, et al. Risk factors for mortality over 18 years in 317 ICUs in 9 Asian countries: The impact of healthcare-associated infections. Infect Control Hosp Epidemiol. 2023;44(8):1261\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJunior CT, Franca SA, Okamoto VN, Salge JM, Carvalho CRR. Infection as an independent risk factor for mortality in the surgical intensive care unit. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104192/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8104192/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Zhang J, Du Z, Ren Y, Nie J, Wu Z, et al. Risk Factors for 28-Day Mortality in a Surgical ICU: A Retrospective Analysis of 347 Cases. Risk Manag Healthc Policy. 2021;14:1555\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVincent JL, Sakr Y, Singer M, Martin-Loeches I, Machado FR, Marshall JC, et al. Prevalence and Outcomes of Infection Among Patients in Intensive Care Units in 2017. JAMA. 2020;323(15):1478\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"mortality predictor, ICU, mortality rate","lastPublishedDoi":"10.21203/rs.3.rs-7942616/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7942616/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The number of life-threatening conditions requiring intensive care units has increased significantly in low-income countries due to the number of hospital expansions. The rate of ICU mortality rate differs by region in Ethiopia. However, the evidence on ICU mortality and its predictors remains uncertain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives \u003c/strong\u003eto assess the magnitude of the mortality rate and its predictors among hospitalized adult patients\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology: \u003c/strong\u003etwo center retrospective\u003cstrong\u003e \u003c/strong\u003ecross-sectional study was conducted among patients admitted to ICU from December 1, 2023, to May 30, 2024. A pretested and structured questionnaire was used. The completed data was collected via web link after being prepared by kobtoolbox.org, coded, manually checked, and exported to SPSS version 27 for data analysis. to analyze the data descriptive statistics and logistic regression were used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003eA total of 309 study participants’ charts were reviewed. Five days was the median duration of ICU stay. postoperative, septic shock, stroke, and congestive heart failure were the leading causes of ICU admissions and the common causes of death were septic shock, stroke, head trauma and ARDS. The overall mortality rate of the ICU-admitted patients was 46.3% (95% CI: 40.5, 51.8). having higher Charleson comorbidity index score, mechanical ventilation required at admission and hospital acquired infection were significantly associated with ICU mortality\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Compared to some developed countries, the observed mortality rate is higher. According to the current study's findings, hospital-acquired infections, the Charlson comorbidity index, and the need for mechanical ventilation all significantly correlated with the mortality predictor of intensive care unit patients.\u003c/p\u003e","manuscriptTitle":"The magnitude of mortality rate and its predictors among adult patients admitted to the Intensive care unit at two government hospital in Addis Ababa Ethiopia: retrospective cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 12:16:35","doi":"10.21203/rs.3.rs-7942616/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"087c2514-fd18-4bb4-b43d-c2de1f682dec","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-18T14:38:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 12:16:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7942616","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7942616","identity":"rs-7942616","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}