Accepted on 26 Mar 2022
Submitted on 31 Jan 2022
Intensive care, also called critical care, constitute a multidisciplinary and inter-professional specialty dedicated to the full management of patients with or at risk of development, dysfunction for organ development, acute, threatening [1]. It is important to be capable to predict the outcome of patients admitted to an intensive care unit (ICU) so that scarce sources can be more optimally utilized [2].
Critical care medicine has grown considerably over the past decades and now consumes a substantial share of the income of many countries around the world (nearly 1% of gross domestic product [GDP] in the United States [3]. The worldwide occurrence of mortality in ICU has more or fewer levels from nine to 61%. An international study recruiting 13, 796 individuals from Africa, Asia, America, Europe, and Oceania found out that the prevalence of mortality in ICU become 18.2% [4]. Other evidence from Sub-Saharan Africa showed that mortality in ICU was ranged from 27% to 61% [4, 5, 6] The mortality is very high in the low-income country which is as high as 61% as compared to developed countries which varied from 9 to 18% [5, 7, 8]. The rate of ICU mortality in Ethiopia varied from region to region ranging from 27 to 38% [9, 10].
Abnormal vital signs, elderly patients, patients on mechanical ventilation, acute respiratory failure, readmission in intensive care, length of stay in intensive care and sepsis are risk factors for mortality in intensive care unit [4, 11, 12, 13, 14, 15, 16, 17] In addition to this patient aspiration, Glasgow coma scale (GCS), patients on antibiotic for prolonged time and cardiac arrest are independent risk factors for intensive care unit (ICU) mortality [18, 19].
The case is much worse in poor resource settings. Thus knowing the incidence and associated factors of ICU mortality rate is vital, focusing on evaluation of requirements and monetary planning. To our knowledge; so far there is no previous study carried out dealing on morbidity and mortality in ICU at study area. Therefore; the goal of this study look at possible contributing factors to ICU mortality and to perceive regain for development in care.
This retrospective cohort study was conducted at Ambo university referral hospital, Ethiopia from September 2019 – May 2021. Ambo university referral hospital is a teaching hospital located in Oromia National Regional State, West Shewa, about 112 km from Addis Ababa, Western Ethiopia. Based on 2019 report of West Shewa District Health Office there were 2,448,272 inhabitants [20] and Ambo Referral University Hospital Intensive Care Unit (AURH ICU) is the only ICU started in September 2019 as a six bed intensive care capacity with six mechanical ventilators, one defibrillator, four monitors non-invasive hemodynamics, an ultrasound machine and a portable X. -ray machine. The ICU is an open intensive care system managed by the internal medicine department. Currently, the ICU has a team consisting of a specialist in critical care medicine, internal medicine, senior anesthesiologists, 2 critical care and 8 clinical nurses, anesthesiologists and other health professionals depending on the needs of individual cases. The intensive care unit provides intensive care service not only for internal medicine cases but also for surgical and obstetrics cases. This study registered in research registry at www.researchregistry.com with Research Registry UIN: researchregistry7604 and the methodology is reported according to SROSS guidelines 2021 [21].
Institutional based retrospective cohort study
All patients who were admitted to intensive care unit
All intensive care unit admitted patient who will be candidate for inclusion criteria
Clinical Outcome (survived or not survived)
Socio-demographic variables
Variable during patient admission
Management of patient during intensive care unit stay
All patients who were admitted in critical care unit from September 2019 to April 2021, who had completed data were included (Figure I).
Diagram illustrating records series technique amongst patients admitted to intensive care unit from September 2019 to April 2021.
Twenty-six patients had incomplete recording data and their recordings could not be located. A total of 550/390 (70.9%) of all ICU admissions over 17 years of age were included in the study. records of all patients observed consecutively during the study period were included in the study. Daily ICU patient records were used to collect data in a form prepared for this purpose. The registry included the number of patient records, date of admission, age, gender, admission address and diagnosis, length of hospital stay, retroviral status, and treatment outcomes. In cases where register data was incomplete or deemed ambiguous, patient records were obtained from the graphics room to supplement the data to be collected. All registry data and graphic data have been collected by nurses.
To assure the quality of the data, data collectors were trained, and the investigator was made a regular supervision and follow up. In addition, regular checkup for completeness and consistency of the data were made on daily basis. Once the data was collected and checked for completeness, consistency and accuracy, it was sorted, categorized and summarized
Data was cleaned, edited and entered into Epi info. Version 7 and analyzed using SPSS software. Characteristics of study participants were analyzed using descriptive statistics. After multicollinearity was checked using IF < 10 and Tolerance test > 0.1, variables having p value ≤ 0.2 at bivariate logistic regression analysis were fitted into multivariable logistic regression. Bivariate and multivariate logistic regression was used to identify the association between dependent and independent variables. Multiple logistic regressions with a calculation of adjusted odds ratios were used to determine the influence of covariant on mortality. Statistical significance was considered at a p value of < 0.05. Odds ratio with 95% confidence interval was used to show the strength of association between independent and dependent variables.
Of the 550 consecutive patients admitted during the study period, 390 patients were analyzed. Of the total number of patients admitted to the adult intensive cares unit, 203 (52.1%) were men and 187 (47.9%) were women. The average age of the participants was 48.39 years. ± 21.57 years. Of the study participants, 116 (29.7%) were geriatric patients. The majority of them hospitalized 155/390 patients from the medical service (39.7%) followed by emergencies 128/390 patients (32.8%). Of all participants 215 of them are from Rular part west shawa zone and the rest were from urban area (Table I).
Table I
Patient demographic characteristics of patients admitted in intensive care unit.
| VARIABLES | CATEGORIES | FREQUENCY | PERCENTAGES |
|---|---|---|---|
| Age | <65 | 214 | 70.3% |
| 65–96 | 116 | 29.7% | |
| Gender | Male | 203 | 52.1% |
| Female | 187 | 47.9% | |
| Residency | Rular | 215 | 55.1% |
| Urban | 175 | 44.9% | |
Of the 390 patients, 290 (74.4%) had mechanical ventilation and the remaining 100 (25.6%) were not on mechanical ventilation. The mean length of stay in intensive care was 4.37 ± 3.37 with a minimum length of stay 1 and maximum 23 (Figure II). Out of 390 patient 177(45.6%) needs vasopressor and 255 (65.4%) were sedated during their stay.
Length of ICU stay between survivor and non-survivor among patients admitted to intensive care unit between September 2019 to April 2021, (N = 390).
Bar graph showing Supply of intensive care unit admission in intensive care unit from September 2019 to April 2021.
Vital Sign on Admission of Patients Admitted to the ICU: In this study, most patients had unstable vital signs during hospitalization, 198 (50.8%) patients had an elevated heart rate. Majority of patients had altered blood pressure (high or low). In addition, 70.3% of patients had less than 90% saturation. Regarding the level of consciousness of the patients upon arrival at the ICU, 107 (27.4%) patients were GCS above 13, the others presented with a disturbed mental state (Table II).
Table II
Vital sign of patients during admission in intensive care unit.
| VARIABLES | CATEGORIES | FREQUENCY | PERCENTAGE |
|---|---|---|---|
| Heart rate | <60bpm | 30 | 7.7% |
| 60–100bpm | 162 | 4.5% | |
| >100bpm | 198 | 50.8% | |
| Spo2 | <90 | 275 | 70.3% |
| >90 | 116 | 29.5% | |
| SBP | <90 | 193 | 49.5% |
| 90–140 | 128 | 32.8% | |
| >140 | 69 | 17.7% | |
| DBP | <60 | 216 | 54.1% |
| 60–90 | 125 | 32.1% | |
| >90 | 54 | 13.8% | |
| Temperature | <35.5 | 166 | 42.6% |
| 35.5–38 | 145 | 37.2% | |
| 38 | 79 | 20.3% | |
| GCS | <9 | 193 | 49.5% |
| 9–13 | 90 | 23.1% | |
| >13 | 107 | 27.4% | |
The highest mortality rate (20.5%) was observed in 2020; the lowest mortality rate (13.6%) was observed in 2019. (Figure IV)
Distribution of intensive care unit mortality rate in different year.
Our primary outcome incidence of intensive care unit mortality, 183 patients died considering the mortality rate of 46.9% and the immediate cause of death in the majority of patients was a patient with coexisting disease 109/390 (27.9%).
Bivariate logistic evaluation proven that oxygen saturation, Glasco coma scale during admission readmission, patient with coexisting diseases, use of vasopressor, want for mechanical ventilation and period of ICU stay have been appreciably related to the outcome of in ICU. However, in a multivariate evaluation spo2 and readmission have not been associated with patient death in ICU.(Table III)
Table III
Multivariable logistic regression showing the factors associated with mortality among patients admitted to intensive care units from September 2019 to April 2021, (N = 390).
| VARIABLES | COR | AOR | 95% CONFIDENCE INTERVAL | P VALUE | |
|---|---|---|---|---|---|
| Spo2 at admission | <90% | 1.3 | 0.76–2.23 | 0.34 | |
| ≥90% | 1 | 1 | |||
| GCS | <9 | 3.7 | 2.12–6.53 | <0.0001* | |
| 9–13 | 1 | 1 | |||
| >13 | 1 | 1 | |||
| Readmission | Yes | 2.3 | 0.99–5.19 | 0.052 | |
| No | 1 | 1 | |||
| Coexisting | Yes | 2.2 | 1.413–3.523 | 0.001* | |
| No | 1 | 1 | |||
| Use of vasopressor | Yes | 1.9 | 1.156–3.075 | 0.011* | |
| No | 1 | 1 | |||
| Mechanical ventilation | Yes | 2.6 | 1.511–4.456 | 0.001* | |
| No | 1 | 1 | |||
| Length of stay in ICU | ≤4 | 2.4 | 1.471–3.82 | <0.0001* | |
| >4 | 1 | 1 | |||
Key: *Significantly associated at P < 0.05, 1.00: reference.
The ICU death rates at this Ethiopian teaching hospital reflect the challenges ICU delivery faces in the developing world. Late presentation in hospital secondary to poor access to health care plays a predominant role. This is confused by inadequate staffing, training, diagnostics and intervention limitations. Despite limited resources, simple and cost-effective measures can improve morbidity and mortality [8].
The mortality rate in low-income countries is very high, up to 61% compared to developed countries, this rate ranges from 9 to 18% [4, 5, 8] The overall mortality rate in Ambo university referral hospital intensive care unit was 46.9% which is similar in study done in southern part of Ethiopia mortality rate 46.8 [18] but in contrast to our study another study done in Ethiopia Ayder comprehensive specialized hospital the overall mortality rate was 27%, this variation could be explained as Care of critically ill patients is challenging in resource-limited settings, where the burden of illness and mortality from potentially treatable conditions is higher than in resource-rich settings. Barriers to providing quality critical care in these settings include a lack of epidemiological data and contextual evidence for medical decision-making, gaps in health systems organization and resources, and institutional barriers to implementation. implementing life-saving interventions [6].
In this study patient with GCS < 9 are risk factor for intensive care unit mortality like other studies [11, 22]. This could be level of disturbance of consciousness is related to severe decompensated disease, cerebral hypoperfusion due to sepsis, blood loss, intoxication and neurological disorders. In addition to this, patients with abnormal mental status may not protect their airway, which further increases the risk of respiratory and infectious diseases [7].
In our study use of vasopressors were associated with patient mortality in intensive care unit similar to other studies [5, 23] this might be explained as due to vasopressors, serious adverse effects of vasopressors are digital and muscle ischemia, tachyarrhythmias, and atrial fibrillation (increased risk of cerebrovascular accident) [24].
This study found that the need for mechanical ventilation is an independent risk factor for ICU mortality (AOR: 2.6, P = 0.001) that was similar to studies in Hindawi, Gondor and Jimma in which the mechanical ventilator was an independent risk factor for death [7, 19, 25]. The possible explanation for this association could be related to the fact that mechanical ventilation is started for patients with respiratory failure, unable to protect the airway and hemodynamics instability. Additionally, patients requiring intubation and a mechanical ventilator are more vulnerable to ventilator-associated pneumonia and other hospital-acquired infections, further compromising clinical outcomes in critically ill patients [7, 26, 27]. In addition, these patients who were on mechanical ventilator were presented with unstable vital sign and comorbid conditions which might be increased the odds of in ICU mortality.
In this study, the average length of stay in the intensive care unit was 4 days, similar to some studies [5, 27]. In our study, Out of 390 patients, 248(63.6%) where stayed in ICU 4 days or less. Of these, 98 (39.5%) patients died during the indicated period, accounting for 53.6% of ICU overall mortality. Additionally, patients who stayed in the ICU for less than 4 days were 2.4 times more likely to die than patients who stayed in the ICU for more than 4 days (AOR = 2.4, p < 0.001). Study in Gondar and Uganda. [27, 28]. However, our end result changed into distinct from the examine carried out in Jimma, this imply period of ICU live changed into four days, which changed into ICU live greater than four days strongly related to ICU mortality [8]. Differences in the average length of stay in the intensive care unit include delayed admission to the intensive care unit and delayed intervention, lack of essential medicines for emergency treatment such as anticoagulants, antiarrhythmic drugs, and antibiotics, and lack of airway equipment. In addition, the vital signs and overall clinical condition of patients admitted to the adult intensive care unit were very poor, so it is possible that the continuity of care from the emergency room, internal medicine, and surgery to the intensive care unit was cut off. The cause of premature death may also be due to the limited number of ICU beds, as the World Federation of Intensive Care Societies and Critical Care Medicine recommends that ICUs make up at least 5% of all beds. In addition, there is a shortage of functional ventilators that delay hospitalization of critically ill patients and prevent them from entering the intensive care unit [27, 29].
In our study patient admitted with coexisting disease other than their acute illness during admission was risk factor for intensive care unit mortality (AOR= 2.2, P value 0.0001) this is comparable in study done by RI Docking et al and H. Haftu et al [30, 31]. this could be due to Comorbidities have been shown to have significant impact on the clinical course, complications and outcomes in the ICU [32].
The overall mortality rate of (AURH ICU) was high (46.9%), low GCS, need of mechanical ventilation, use of vasopressor, less than four days intensive care unit stay and patient with coexisting diseases are associated factors for ICU mortality. Improvement of acute intensive care through the expansion of care, the provision of emergency equipment, drugs and human resources from different disciplines.
Although this study provided information on clinical outcomes and associated factors in critically ill patients admitted to AURH ICU, some limitations could be addressed in another study. Due to the nature of the study design, the retrospective study based on ICU chart and registry, only limited data were retrieved. Therefore, the necessary variables, which help to identify risk factors independent of the clinical results of patients admitted to intensive care, were not collected. In addition, data on physiological and laboratory variables needed to calculate severity and prognostic score such as Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score (SAPS), and Acute Physiology and Chronic Diseases classification system (APACHE); to predict ICU mortality were not collected due to the impossibility of locating them in the available written chart and ICU registers.
AICU: Adult intensive care unit, AOR: Adjusted odd ratio, AURH: Ambo university referral hospital, AURH ICU: Ambo university referral hospital intensive care unit, GCS: Glasgow coma scale, GDP: Gross domestic product, ICU: Intensive care unit, NIBP: Noninvasive blood pressure, SPSS: Satirical package for social science.
Zenebe Bekele.
Data is available on request.
The study was commenced after ethical clearance is obtained from Ambo university College of Medicine and Health Science. Permission was obtained from Ambo university referral hospital to conduct the research. Patient identification number was recoded. Confidentiality of information obtained was fully secured or assured.
First of all, I would like to thank God my provider. I also wish to thank all my friends and colleagues in the department of Anesthesia, for their helping and valuable suggestion during writing of this proposal. Lastly I would like to thank, those authors and researchers of articles for the valuable works I have read and cited.
This research was funded by Ambo University.
The authors have no competing interests to declare.
ZB, HJ and MH have made substantial contributions to conception, design, analysis, and interpretation of data, participated in the critical review, and editing of the manuscript drafts for scientific merit and depth.
Not commissioned, externally peer-reviewed.
Marshall JC, Bosco L, Adhikari NK, Connolly B, Diaz J V, Dorman T, et al. What is an intensive care unit? A report of the task force of the World Federation of Societies of Intensive and Critical Care Medicine. J Crit Care [Internet]. 2017; 37: 270–6. DOI: https://doi.org/10.1016/j.jcrc.2016.07.015
Eapen CE, Thomas K, Cherian AM, Jeyaseelan L, Mathai D, John G. Predictors of mortality in a medical intensive care unit. Natl Med J India. 1997; 10(6): 270–2.
Vincent JL, Marshall JC, Ñamendys-Silva SA, François B, Martin-Loeches I, Lipman J, et al. Assessment of the worldwide burden of critical illness: The Intensive Care Over Nations (ICON) audit. Lancet Respir Med. 2014; 2(5): 380–6. DOI: https://doi.org/10.1016/S2213-2600(14)70061-X
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. Ann Med Surg. 2021; 65. DOI: https://doi.org/10.1016/j.amsu.2021.102318
Lalani 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): 1336–43. DOI: https://doi.org/10.1513/AnnalsATS.201801-051OC
Diaz J V., Riviello ED, Papali A, Adhikari NKJ, Ferreira JC. Global critical care: Moving forward in resource-limited settings. Ann Glob Heal. 2019; 85(1): 1–11. DOI: https://doi.org/10.5334/aogh.2413
Tessema HG. Admission pattern, Clinical outcomes and associated factors among patients admitted in medical intensive care unit at University of Gondar Comprehensive and specialized hospital, Northwest Ethiopia, 2019. A retrospective cross-sectional study. 2019; 1–20. DOI: https://doi.org/10.21203/rs.2.12415/v1
Smith ZA, Ayele Y, Mcdonald P. Outcomes in critical care delivery at Jimma University Specialised Hospital. Ethiopia. 2020; (May 2013). DOI: https://doi.org/10.1177/0310057X1304100314
Quah P, Li A, Phua J. Mortality rates of patients with COVID-19 in the intensive care unit: a systematic review of the emerging literature. 2020; 1–4. DOI: https://doi.org/10.1186/s13054-020-03006-1
Osinaike B, Olusanya T. Factors Associated with Intensive Care Unit Admission Refusal. 2017; 14(2): 92–5. DOI: https://doi.org/10.4314/aas.v14i2.8
Barfod C, Lauritzen MMP, Danker JK, Sölétormos G, Forberg JL, Berlac PA, et al. Abnormal vital signs are strong predictors for intensive care unit admission and in-hospital mortality in adults triaged in the emergency department – a prospective cohort study. Scand J Trauma Resusc Emerg Med. 2012; 20(April). DOI: https://doi.org/10.1186/1757-7241-20-28
Vosylius S, Sipylaite J, Ivaskevicius J. Determinants of outcome in elderly patients admitted to the intensive care unit. Age Ageing. 2005; 34(2): 157–62. DOI: https://doi.org/10.1093/ageing/afi037
Liang J, Li Z, Dong H, Xu C. Prognostic factors associated with mortality in mechanically ventilated patients in the intensive care unit. Medicine (Baltimore). 2019; 98(42): e17592. DOI: https://doi.org/10.1097/MD.0000000000017592
Khilnani GC, Banga A, Sharma SK. Predictors of mortality of patients with acute respiratory failure secondary to chronic obstructive pulmonary disease admitted to an intensive care unit: A one year study. BMC Pulm Med. 2004; 4: 1–8. DOI: https://doi.org/10.1186/1471-2466-4-12
Renton J, Pilcher D V., Santamaria JD, Stow P, Bailey M, Hart G, et al. Factors associated with increased risk of readmission to intensive care in Australia. Intensive Care Med. 2011; 37(11): 1800–8. DOI: https://doi.org/10.1007/s00134-011-2318-x
Zhang 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: 1555–62. DOI: https://doi.org/10.2147/RMHP.S303514
Zanon F, Caovilla JJ, Michel RS, Cabeda EV, Ceretta DF, Luckemeyer GD, et al. Sepsis in the intensive care unit: etiologies, prognostic factors and mortality. Rev Bras Ter intensiva [Internet]. 2008; 20(2): 128–34. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25306999. DOI: https://doi.org/10.1590/S0103-507X2008000200003
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. Ann Med Surg [Internet]. 2021; 65(April): 102318. DOI: https://doi.org/10.1016/j.amsu.2021.102318
Asrat A, Mirkuzie W, Yemane A, Worku B. Reasons for admission and mortalities following admissions in the intensive care unit of a specialized hospital, in Ethiopia. Int J Med Med Sci. 2014; 6(9): 195–200. DOI: https://doi.org/10.5897/IJMMS2013.0883
Birhanu A, Tiki T, Mekuria M, Yilma D, Melese G, Seifu B. Covid-19-induced anxiety and associated factors among urban residents in west shewa zone, central ethiopia, 2020. Psychol Res Behav Manag. 2021; 14: 99–108. DOI: https://doi.org/10.2147/PRBM.S298781
Mathew G and Agha R, for the STROCSS Group. STROCSS 2021: Strengthening the Reporting of cohort, cross-sectional and case-control studies in Surgery. International Journal of Surgery. 2021; 96: 106165. 2021; 6. DOI: https://doi.org/10.1016/j.ijsu.2021.106185
Ting HW, Chen MS, Hsieh YC, Chan CL. Good Mortality Prediction by Glasgow Coma Scale for Neurosurgical Patients. J Chinese Med Assoc [Internet]. 2010; 73(3): 139–43. DOI: https://doi.org/10.1016/S1726-4901(10)70028-9
Owojuyigbe AM, Adenekan AT, Babalola RN, Adetoye AO, Olateju SOA, Akonoghrere UO. Pattern and outcome of elderly admissions into the Intensive Care Unit (ICU) of a low resource tertiary hospital. East Cent African J Surg. 2016; 21(2): 40. DOI: https://doi.org/10.4314/ecajs.v21i2.6
Beck V, Chateau D, Bryson GL, Pisipati A, Zanotti S, Parrillo JE, et al. Timing of vasopressor initiation and mortality in septic shock: A cohort study. Crit Care. 2014; 18(3). DOI: https://doi.org/10.1186/cc13868
Soares Pinheiro FGDM, Santana Santos E, Barreto ÍDDC, Weiss C, Vaez AC, Oliveira JC, et al. Mortality Predictors and Associated Factors in Patients in the Intensive Care Unit: A Cross-Sectional Study. Crit Care Res Pract. 2020; 2020: 5–10. DOI: https://doi.org/10.1155/2020/1483827
Abebe K, Negasa T, Argaw F. Surgical Admissions and Treatment Outcomes at a Tertiary Hospital Intensive Care Unit in Ethiopia: A Two-Year Review. Ethiop J Health Sci. 2020; 30(5): 725–32. DOI: https://doi.org/10.4314/ejhs.v30i5.11
Teshager NW, Amare AT, Tamirat KS. Incidence and predictors of mortality among children admitted to the pediatric intensive care unit at the University of Gondar comprehensive specialised hospital, northwest Ethiopia: a prospective observational cohort study. 2020; 1–7. DOI: https://doi.org/10.21203/rs.2.19878/v1
Kwizera A, Dünser M, Nakibuuka J. National intensive care unit bed capacity and ICU patient characteristics in a low income country. BMC Res Notes. 2012; 5(August 2014). DOI: https://doi.org/10.1186/1756-0500-5-475
Strand K, Walther SM, Reinikainen M, Ala-Kokko T, Nolin T, Martner J, et al. Variations in the length of stay of intensive care unit nonsurvivors in three scandinavian countries. Crit Care. 2010; 14(5): 1–8. DOI: https://doi.org/10.1186/cc9279
Mackay A, Williams C, Lewsey J, Kinsella J. Comorbidity and intensive care outcome – a multivariate analysis. 2014;October 2008): 205–12. DOI: https://doi.org/10.1177/175114371401500306
Haftu H, Hailu T, Medhaniye A, Gtsadik T. Assessment of pattern and treatment outcome of patients admitted to pediatric intensive care unit, Ayder Referral Hospital, Tigray, Ethiopia, 2015. BMC Res Notes [Internet]. 2018; 11(1): 11–6. DOI: https://doi.org/10.1186/s13104-018-3432-4
Simpson A, Puxty K, McLoone P, Quasim T, Sloan B, Morrison DS. Comorbidity and survival after admission to the intensive care unit: A population-based study of 41,230 patients. J Intensive Care Soc. 2021; 22(2): 143–51. DOI: https://doi.org/10.1177/1751143720914229