Assessment of pattern and treatment outcome of patients admitted to pediatric intensive care unit, Ayder Referral Hospital, Tigray, Ethiopia, 2015
Haftu et al. BMC Res Notes
Assessment of pattern and treatment outcome of patients admitted to pediatric intensive care unit, Ayder Referral Hospital, Tigray, Ethiopia, 2015
Hansa Haftu 0
Tedrose Hailu 1
Araya Medhaniye 1
Teklit G/tsadik 1
0 Department of Pediatrics and Child Health, College of Health Sciences, Mekelle University , Mekelle , Ethiopia
1 Mekelle University , Mekelle , Ethiopia
Objective: To describe admission pattern and outcome with its predictor variable on the mortality of children admitted to pediatric intensive care unit (PICU), Ayder Referral Hospital, Northern Ethiopia, from September 2012 to August 2014. Result: From 680 admitted patients, 400 patients were analyzed. Average age at admission was 62.99 ± 60.94 months, with F:M ratio of 1:1.2. Overall (from infectious and non-infectious) the most commonly affected systems were respiratory (90/400 pts., 22.5%) and central nervous system (83/400 pts., 20.75%). Most were admitted due to meningitis (44/400 pts., 11%), post-operative (43/400 pts., 10.8%) and acute glomerulonephritis (41/400 pts., 10.3%). The overall mortality rate was 8.5%. Multivariable logistic regression shows, use of inotropes (p = 0.000), need for mechanical ventilator (p = 0.007) and presence of comorbid illness (p = 0.002), infectious cause (p = 0.015) and low level of Glasgow coma scale less than eight (p = 0.04) were independent predictors of mortality. From this study, common cause of PICU admission and death was meningitis. This highlights the importance of focusing on the preventable methods in the public such as vaccine, creating awareness about hygiene, and expanding ICU for early detection and for treatment acutely ill children.
PICU; Mortality; Pattern; Inotropes
An intensive care unit (ICU) is a specially staffed and
equipped, separated area in a hospital, dedicated to the
management of patients with life-threatening illnesses
]. According to World Health Organization (WHO),
the major causes of death in under-five children in
developing countries are preventable and curable diseases, if
the care is optimized [
]. The majority (99%) of
childhood deaths occurring in developing countries, especially
under-five mortality, is highest in sub-Saharan Africa
(> tenfold) [
]. Intensive care could reduce mortality
rates by 15–60%, when it is a well-equipped, and staffed
with intensivists [
There was a study conducted in Jimma, Ethiopia,
mortality was 40% with the most common cause of admission
and death being trauma [
]. A similar study of critical
care unit of all age patients in Tanzania revealed
mortality rate of 41.4% [
]. In a study of severe head injury
patients in the ICU of National Hospital Abuja in
Nigeria, the mortality rate was 68.4%, [
In sub-Saharan Africa, ICUs have varying qualities and
quantities of infrastructure necessary for the provision
of proper critical cares services. The reported disease
characteristics and mortality rates of patients
admitted to ICUs in sub-Saharan Africa vary widely from one
population to another. The regional hospitals send their
critical patients to these referral hospitals for ICU care.
Demographic profile and outcome of PICU patients can
vary widely in different studies while there is a scarcity
of data in Ethiopia’s critically ill children. The aim of the
present study was to describe the demographic profile
and the outcome of our PICU patients, to evaluate the
relationship of the outcome to diagnostic categories and
treatment characteristics, and to investigate mortality
risk with possible outcome predictors.
Ayder Referral Hospital (ARH) is the largest referral in
Northern Ethiopia. It started as a referral and specialized
medical center in 2008, providing service to about 8
million people. A study was conducted in nine bedded PICU
from 1st to 25th July 2015. This data was obtained from
ICU logbook and patient charts. A retrospective
crosssectional study design was used.
Sample size and sampling method
All consecutively admitted patients to PICU from
September 2012 to August 2014 were included, based on
the criteria. Inclusion and exclusion criteria: all patients
admitted to PICU whose age is 14 days–18 years were
included in the study. The cutoff age was determined
to be 14 days because after 1 year of study, those
critically ill patients in the age of 7–14 days were admitted
to neonatal intensive care unit, so that the exact pattern
of diagnosis and age distribution of these patients would
be incomplete. Patients with incomplete or missed data
were excluded from the study. Other patients excluded
from the study were, those who died on arrival (within
2 h of admission); this is not sufficient time to give
optimal care in the ICU, and because the outcome of these
patients is related to the emergency or other ward care.
Age, sex, length of ICU stay, diagnosis at admission,
need for a mechanical ventilator, length of mechanical
ventilation stay, inotropes use, comorbid illness, disease
character (medical vs. surgical and infectious vs.
noninfectious), admission sources, Glasgow Coma Scale
(GCS) at admission.
The following data was collected retrospectively: age,
gender, admission diagnosis, presence of comorbidities,
admission sources, treatment characteristics; the need
for mechanical ventilation (MV) and MV days, the need
for inotropes, length of stay (LOS), outcome and cause of
death. Neurologic status was evaluated using the
pediatric version of GCS. The questionnaire was adapted by
reviewing different literature. Two medical interns were
employed in the data collection process and one general
practitioner as a supervisor. A 2 days training was given
to them on the objectives of the study, the contents of the
questionnaire and on issues related to the confidentiality.
Five days prior to the data collection, a pre-test was
conducted in Mekelle Hospital in 2.5% of the sample size for
completeness of the data collection format. Based on the
findings of the pre-test, some questions were modified
and some others were added for estimating wealth index.
The principal investigator was continuously supervising
the data collectors for completeness and consistency and
the records were cross-checked.
Data was cleaned, edited and entered into Epi data
version 3.1 and analyzed using SPSS software (version 16.0).
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 ≤ 0.25 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
Among 680 consecutive patients admitted in the study
period, 400 patients were analyzed. Of the total
admitted patients, 215 (53.8%) were male and 185 (46.2%) were
female, giving a male: female ratio of 1.2:1. The average
age of admission was 62.99 ± 60.94 months. From the
study participants, 250 (62.5%) were under five (Table 1).
A majority were admitted from EOPD (288/400 pts.,
72%), a transfer from ward (65/400 pts., 16.3%).
The mean ICU stay was 4.9± 5.8 days (range 1–30 days)
with a majority (61%) of them staying for 2–7 days. The
vast majority of patients admitted to ICU were due to
medical problems (85.2%) and non-infectious disease
(266/400 pts., 66.5%) (Table 2). Most were admitted due
to meningitis (44/400 pts., 11%), post-operative (43/400
pts., 10.8%) (Fig. 1).
Thirty-four patients died, given a mortality rate of 8.5%
and the immediate cause of death in most patients was
1. Age (years)
Less than 1
multi-organ failure (MOF) (42.9%) (Additional file 1:
Mortality analysis in relation to different diseases
(Additional file 1: Table S2) shows, meningitis (8/34 pts.,
23.6%), cardiogenic shock (7/34 pts., 20.6%) followed by
pneumonia (3/34, 8.8%) were the major causes of death
in this study. Most patients admitted with pneumonia
(18/36 pts., 90%) and croup (25/28 pts., 89.35%) were
under five (Additional file 1: Table S3). Study participants
with comorbid illness account 45.8% (183/400 pts.) with a
majority of them having one comorbid (41%, 75/183 pts.).
Mortality was higher in those patients with comorbid
illness, 14.8% vs. 3.2%, which is statistically significant.
From study participants, 16 (4%) of patients were a
candidate for MV, and 3.5% of them were intubated with 35.7%
died. Even though the length of MV was not associated
with increased rate of mortality, length of ICU stay was
an independent predictor of mortality. Thirty patients
(7.5%) were on inotropes, 56.7% (17/30) of patients
survived and 43.3% (13/30 pts.) died. Age and gender were
not statistically significant risk factors for mortality.
Multivariate logistic regression was used to calculate the
association between variables. Patients who had
comorbid illnesses were 10 times more likely to die than those
without [AOR = 10.2 (2.4–44), CI = 95%]. There was
strong association between the level of consciousness
and mortality. As a result of severe impairment of
consciousness (GCS < 8) increase mortality by 7.8 times as
compared with those mild level of impaired
consciousness [AOR = 7.75 (1.1–54), CI = 95%]. Patients who
needed MV were at 17.6 times increased risk of mortality
than those who did not need MV [AOR = 17.6 (2.2–14.3),
CI = 95%]. Regarding treatment with inotropes, patients
who needed inotropes were at increased risk of
mortality by 10 times than those who did not need inotropes
[AOR = 10.4 (3.7–29), CI = 95%]. Mortality was high in
those patients who had an infectious diagnosis than
noninfectious and long ICU stay than short ICU stay, which
was statistically significant. With regard to the admission
diagnosis, case fatality was high in those patients with
septic shock and acute flaccid paralysis (Additional file 1:
The purpose of this study was to assess pattern and
treatment outcome, and its predictors of children
admitted to PICU, in ARH. Mean age of the admitted
patient was (62.99 ± 60.94) month, with 62.5% of them
under 5 years and M: F ratio was 1.2:1. The mean ages
of admitted patients were higher in our ICU than
studies done in Greece (54.26 ± 49.93 months) and India
(40.01 ± 45.79 months [
]. This may be due to the
wide age (2 weeks–18 years) distribution of patients
admitted to our ICU. However, the age distribution of
ICU admission of which, the majority were under five
(62.5%), was similar to studies done in India, 53–72.4%
were under five [
]. The preponderance of male sex
(53.8%) was similar to study done in Ethiopia − 54.7% 
and Brazil − 55.2% [
], but somehow lower than studies
done in India, Nepal, and Greek (54–61.1%) [
The majority of admitted patients were medical (85.3%)
with the commonly affected system, respiratory (90/400
pts., 22.5%) and CNS (83/400 pts., 20.75%). The
demographic profile of our patient was similar to studies done
in Greek, of which major cause of admission was due to
pathologic emergencies (69.8%) and respiratory system
(22.3%) involvement [
]. This was opposite to studies
done in Jimma, Ethiopia, and Tanzania where surgical
and trauma patients represent a large proportion of PICU
6, 7, 15
]. This difference may be due to common
ICU for all cases and all age groups.
However, the common cause of under-five ICU
admission was infectious (79.1%) with the most
common being pneumonia, septic shock, meningitis and
croup which was similar to studies done in Ethiopia
and Tanzania [
3, 4, 6, 7
]. Thirty-four patients died
giving a mortality rate of 8.5%. The reported mortality
rate varies from 2.1 to 41% [
6, 7, 9, 10, 12–16
] with the
highest mortality in developing countries due to lack of
]. Even though mortality in our patients
was within the reference range, it’s relatively high
compared to recent studies done in India [
] and China
], that was 2.1 and 6.5% respectively. This is related
to inadequate resources similar to other developing
]. However, this was lower than studies
done in other sub-Saharan countries (40–42%) [
6, 7, 17,
]. This may be due to the presence of isolated PICU
(others use common ICU for all age) and burn unit of
which burns contribute to high case fatality rate for
those who have common ICU for all cases .
Patients with comorbid illness were at higher risk
of mortality (p = 0.002) than those without (14.8% vs.
3.2%), which is similar to studies done in Nepal and
12, 14, 16
The mortality rate of our PICU was 8.5%, with the most
common cause of admission and death being
infectious causes mostly affecting respiratory and CNS. The
statistically significant predictors of mortality in this
study were: the presence of comorbid illness, need for
MV, need for inotropes, low GCS level, infectious
disease and duration of ICU stay. The need for ventilation
and inotropes indicates that these patients were in an
advanced stage of a disease (Additional file 2).
The Federal Ministry of Health and Regional Health
bureaus in collaboration with ARH should invest on
creating awareness in the community on the
preventing mechanism, such as, improving vaccine coverage,
hygiene, and educating the population about the disease
so that they will get treatment in the early stages of a
disease. The regional health bureaus must also consider
expanding PICU in the region to address critically ill
Ayder Referral Hospital has also to work more on PICU
providing a high-quality intensive care to critically ill
patients focusing on increase ICU capacity.
Limitation of the study
The limitations of this study include the retrospective
design; the mortality rate may be falsely low in the
presence of limited resources, due to a significant number
of patients were not analyzed because their data was
incomplete. This provides quantitative data in the
available resources, but not on the specific quality of care
Additional file 1: Table S1. Patients’ outcome at the end of ICU stay.
Table S2. Mortality across diagnostic categories (N = 34). Table S3.
Admission diagnosis versus age. Table S4. Socio demographic and clinical
profile versus outcome of children admitted to PICU in Mekelle, North
Additional file 2: Annex 1. Data collection format (questioner).
ARH: Ayder Referral Hospital; CNS: central nervous system; MOF:
multipleorgan failure; ICU: intensive care unit; PICU: pediatric intensive care unit; WHO:
World Health Organization; LOS: length of hospital stay; USA: United States
of America; GCS: Glasgow coma scale; MV: mechanical ventilation; AFP: acute
flaccid paralysis; AOR: adjusted odds ratio; COR: crude odds ratio.
This work was carried out in collaboration between all authors. HH
contributed to the conception and design of the study, acquired, analyzed and
interpreted the data, and drafted and revised the manuscript. TH and AM
designed, reviewed the data. TG was involved in preparation and critical
revision of manuscript. All authors read and approved the final manuscript.
I would like to thank God for all things done for me in my life. Furthermore,
I would like to acknowledge Mekelle University, college of health sciences
department of pediatrics for giving me this opportunity. Our thank goes to
the data collectors for their unreserved contribution to the success of this
Finally, we would like also to thank my family, Mr. Ashenafi, Teklu, Haftom,
and My Friends for unlimited support.
The authors declare that they have no competing interests.
Availability of data and materials
Please contact author for data requests.
Consent for publication
Ethics approval and consent to participate
Ethical clearance waived from Mekelle University, College of Health Sciences,
and Institutional Research Review Board (IRRB) with 0625/2015 IRRB Number
given to this research).
Permission received from the medical director of the hospital before the
commencement of the study.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
1. Manimala R , Suhasini T. Organization of intensive care unit and predicting outcome of critical illness , India. Indian J Anesth . 2003 ; 47 ( 5 ): 328 - 37 .
2. Elisabeth D , Riviello S , Stephen L , et al. Critical care in resource-poor settings , Kenya. Crit Care Med . 2011 ; 39 : 860 - 7 .
3. Bryce J , Boschi-Pinto C , Shibuya K , Black RE . WHO estimates of the causes of death in children? Lancet . 2005 ; 365 ( 9465 ): 1147 - 52 .
4. You D , et al. Levels, and trends in under-5 mortality, 1990 - 2008 . Lancet. 2009 ; 375 : 100 - 3 .
5. Donna M , Chris S . Child health, part 1: overview of problems, trends and strategies for improvement . Glob Child Health . 2013 ; 450 ( 34 ): 1 - 74 .
6. Teshome A , Mullu G , Girma G , et al. The epidemiological profile of pediatric patients admitted to the general intensive care unit in an Ethiopian University Hospital , Ethiopia. Int J Gen Med . 2015 ; 8 : 63 - 7 .
7. Hendry R , Juma A , Salum J , et al. Disease pattern and clinical outcomes of patients admitted in intensive care units of Tertiary Referral Hospital, Tanzania . BMC Int Health Hum Rights . 2014 ; 8 : 1 - 8 .
8. Adudu OP , Ogunrin OA , Adudu OG . Morbidity and mortality patterns among neurological patients in the intensive care unit of a Tertiary Health Facility , Annals of African Medicine. Nigeria . 2007 ; 6 ( 4 ): 174 - 9 .
9. Abhulimhen L , Suneel K , Nanda K. morbidity pattern and outcome of patients admitted into intensive care in India . Indian J Clin Med . 2014 ; 5 : 1 - 5 .
10. Volakli E , Sdougka M , Tamiolaki M , et al. Demographic profile and outcome analysis of pediatric intensive care patients , Thssaloniki, Greece. Hippokratia. 2011 ; 15 ( 4 ): 316 - 22 .
11. Utkarsh S , Arti S , Roopa H , et al. Epidemological study of morbidity pattern of critically ill children admitted in child intensive therapy unit . Indian Med Gazette . 2012 : 233 .
12. Gauri S , Basant K , Anil T , et al. Admission patterns and outcome in a pediatric intensive care unit , Nepal. Br J Med Med Res . 2014 ; 4 ( 30 ): 4939 - 45 .
13. Michel G , Evandro B , Mombelli F , et al. Addmission sources and mortality in pediatric intensive care unit , Brazil. Indian J Crit Med . 2012 ; 16 ( 2 ): 81 - 6 .
14. Wuyan L . Risk factors for death in pediatric intensive care unit , University of Hong Kong Chinese, 2010 to 2013 . 2014 .
15. Mestrovic J , Polic B , Mestrovic M , et al. Functional outcome of children treated in intensive care unit . J Pediatria . 2008 ; 84 ( 3 ): 232 - 6 .
16. Srinivas M , Hannah W. Clinical review: international comparisons in critical care , USA, 2012 . BMC Crit Care. 2012 ; 16 : 218 .
17. Adudu OP , Ogunrin OA , Adudu OG . Morbidity and mortality patterns among neurological patients in the intensive care unit of a tertiary health facility . Ann Afr Med . 2007 ; 6 ( 4 ): 174 - 9 .
18. Raymond S , Japhet M , Ramesh M , et al. Paediatric injuries at Bugando Medical Centre in Northwestern Tanzania: a prospective review of 150 cases . J Trauma Manag Outcomes. 2013 ; 7 : 10 .