Development of the Respiratory Index of Severity in Children (RISC) Score among Young Children with Respiratory Infections in South Africa

Article · January 2012with41 Reads
DOI: 10.1371/journal.pone.0027793 · Source: PubMed
Abstract
Pneumonia is a leading cause of death in children worldwide. A simple clinical score predicting the probability of death in a young child with lower respiratory tract infection (LRTI) could aid clinicians in case management and provide a standardized severity measure during epidemiologic studies. We analyzed 4,148 LRTI hospitalizations in children <24 months enrolled in a pneumococcal conjugate vaccine trial in South Africa from 1998-2001, to develop the Respiratory Index of Severity in Children (RISC). Using clinical data at admission, a multivariable logistic regression model for mortality was developed and statistically evaluated using bootstrap resampling techniques. Points were assigned to risk factors based on their coefficients in the multivariable model. A child's RISC score is the sum of points for each risk factor present. Separate models were developed for HIV-infected and non-infected children. Significant risk factors for HIV-infected and non-infected children included low oxygen saturation, chest indrawing, wheezing, and refusal to feed. The models also included age and HIV clinical classification (for HIV-infected children) or weight-for-age (for non-infected children). RISC scores ranged up to 7 points for HIV-infected or 6 points for non-infected children and correlated with probability of death (0-47%, HIV-infected; 0-14%, non-infected). Final models showed good discrimination (area under the ROC curve) and calibration (goodness-of-fit). The RISC score incorporates a simple set of risk factors that accurately discriminate between young children based on their risk of death from LRTI, and may provide an objective means to quantify severity based on the risk of mortality.
8 Figures
Development of the Respiratory Index of Severity in
Children (RISC) Score among Young Children with
Respiratory Infections in South Africa
Carrie Reed
1,2
*, Shabir A. Madhi
3,4
, Keith P. Klugman
3,4
, Locadiah Kuwanda
3
, Justin R. Ortiz
2,5
, Lyn
Finelli
1
, Alicia M. Fry
1
1Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America,
2Epidemic Intelligence Service, Office of Workforce and Career Development, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America,
3Respiratory and Meningeal Pathogens Research Unit, Department of Science and Technology/National Research Foundation, Vaccine Preventable Diseases and Medical
Research Council, University of the Witwatersrand, Bertsham, South Africa, 4Hubert Department of Public Health, Rollins School of Public Health, Emory University,
Atlanta, Georgia, United States of America, 5University of Washington, Seattle, Washington, United States of America
Abstract
Objective:
Pneumonia is a leading cause of death in children worldwide. A simple clinical score predicting the probability of
death in a young child with lower respiratory tract infection (LRTI) could aid clinicians in case management and provide a
standardized severity measure during epidemiologic studies.
Methods:
We analyzed 4,148 LRTI hospitalizations in children ,24 months enrolled in a pneumococcal conjugate vaccine
trial in South Africa from 1998–2001, to develop the Respiratory Index of Severity in Children (RISC). Using clinical data at
admission, a multivariable logistic regression model for mortality was developed and statistically evaluated using bootstrap
resampling techniques. Points were assigned to risk factors based on their coefficients in the multivariable model. A child’s
RISC score is the sum of points for each risk factor present. Separate models were developed for HIV-infected and non-
infected children.
Results:
Significant risk factors for HIV-infected and non-infected children included low oxygen saturation, chest indrawing,
wheezing, and refusal to feed. The models also included age and HIV clinical classification (for HIV-infected children) or
weight-for-age (for non-infected children). RISC scores ranged up to 7 points for HIV-infected or 6 points for non-infected
children and correlated with probability of death (0–47%, HIV-infected; 0–14%, non-infected). Final models showed good
discrimination (area under the ROC curve) and calibration (goodness-of-fit).
Conclusion:
The RISC score incorporates a simple set of risk factors that accurately discriminate between young children
based on their risk of death from LRTI, and may provide an objective means to quantify severity based on the risk of
mortality.
Citation: Reed C, Madhi SA, Klugman KP, Kuwanda L, Ortiz JR, et al. (2012) Development of the Respiratory Index of Severity in Children (RISC) Score among
Young Children with Respiratory Infections in South Africa. PLoS ONE 7(1): e27793. doi:10.1371/journal.pone.0027793
Editor: Ravi Jhaveri, Duke University School of Medicine, United States of America
Received June 8, 2011; Accepted October 25, 2011; Published January , 2012
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for
any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: The authors have no support or funding to report for this study. The funders of the original trial that supplied the data for this secondary analysis had
no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: creed1@cdc.gov
Introduction
Pneumonia is a major cause of morbidity and mortality in
children less than age five years. The World Health Organization
(WHO) estimates that lower respiratory tract infection (LRTI) is
responsible for approximately 20% of deaths in children less than
five years worldwide, 90% of which is pneumonia [1]. The highest
mortality from childhood respiratory infection is seen in resource-
limited countries and in children younger than 24 months of age [2].
In children with HIV infection, the burden of pneumonia is even
greater, with a broader range of pathogens and a case-fatality ratio
three- to six-times higher than children without HIV infection [3,4].
Effective management of pneumonia in children includes an
assessment of the severity of illness, and in many settings around
the world, this may rely primarily on signs and symptoms
ascertained at presentation. Case management in many resource-
limited settings is based on the Integrated Management of
Childhood Illness (IMCI) system developed by the World Health
Organization to aid treatment decisions for major causes of death
in children under five [5]. The IMCI classifies pneumonia, severe
pneumonia, and very severe pneumonia with simple case
definitions that include elevated respiratory rate, chest wall
indrawing, and other danger signs. These deliberately sensitive
definitions maximize the number of children identified who could
benefit from antibiotic treatment or referral to a hospital.
However, many countries report poor compliance with hospital
referral due to geographic and socioeconomic constraints,
undermining efforts to reduce childhood mortality [6,7]. More
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4
Table 1. Description and univariate analysis of risk factors for mortality among HIV non-infected and HIV-infected children ,24
months of age hospitalized with LRTI.
HIV non-infected HIV-infected
% of total % of deaths OR (95%CI) % of total % of deaths OR (95%CI)
n = 2,646 n = 33 n = 1,502 n = 265
Patient history
Age
0–2 months 18.0 27.3 3.0 (0.9–9.9)* 16.8 30.9 5.5 (3.4–9.1)**
3–12 months 58.0 60.6 2.1 (0.7–6.1)* 63.2 60.4 2.4 (1.5–3.7)**
13–23 months 24.0 12.1 1.0 (Ref) 20.0 8.7 1.0 (Ref)
Male sex 58.9 51.2 0.7 (0.4–1.5) 53.9 49.2 0.8 (0.6–1.0)*
Premature 21.0 36.4 2.2 (1.1–4.5)** 20.0 18.9 0.9 (0.6–1.3)
Received study vaccine 49.3 45.5 0.9 (0.4–1.7) 45.6 49.4 1.2 (0.9–1.6)
CDC HIV clinical classification n/a n/a n/a
Severe (C) 35.0 55.3 8.1 (4.1–16.2)**
Mild/moderate (A/B) 54.2 41.6 3.1 (1.6–6.1)**
Asymptomatic (N) 10.8 3.1 1.0 (Ref)
History of present illness
Had diarrhea 20.3 28.1 1.5 (0.7–3.4) 32.2 36.7 1.2 (1.0–1.6)*
Had vomiting 42.8 39.4 0.9 (0.4–1.7) 42.5 40.8 0.9 (0.7–1.2)
Irritable/excessive crying 37.4 28.1 0.7 (0.3–1.4) 47.6 51.7 1.2 (0.9–1.5)*
Refusing feeds 37.8 51.5 1.7 (0.9–3.4)* 41.8 50.2 1.5 (1.2–1.9)**
Seizures 3.4 9.4 3.0 (0.9–9.8)* 1.9 2.7 1.7 (0.8–3.6)
Physical exam
Abnormal temperature 27.9 27.3 0.9 (0.4–2.5) 33.9 32.8 0.9 (0.7–1.3)
O
2
saturation on room air ,90% 27.5 84.9 15.2 (5.9–39.4)** 56.2 84.5 5.6 (4.0–7.7)**
Chest indrawing 39.8 81.8 7.0 (2.9–16.9)** 65.0 84.2 3.3 (2.4–4.5)**
Intercostal recession 66.3 93.9 8.0 (1.9–33.1)** 85.1 92.5 2.4 (1.5–3.6)**
Wheezing at exam 55.0 9.1 0.1 (0.02–0.3)** 16.6 7.6 0.4 (0.3–0.6)**
Elevated respiratory rate, for age 56.9 75.8 2.4 (1.1–5.3)** 70.7 81.7 2.1 (1.5–2.9)**
.20 breaths per minute 9.7 30.0 4.2 (2.0–8.9)** 22.8 35.9 2.2 (1.6–2.8)**
Crepitations 53.4 75.0 2.6 (1.2–5.9)** 61.8 61.2 1.0 (0.8–1.3)
Bronchial breathing 3.5 15.6 5.4 (2.0–14.2)** 14.8 17.6 1.4 (0.9–1.9)*
Pulse rate .170 9.3 9.1 0.9 (0.3–3.2) 13.2 17.2 1.4 (1.0–2.0)**
Weight for age
Low (23,z-score#22) 7.9 15.2 4.8 (1.7–13.9)** 20.1 18.5 1.1 (0.7–1.5)
Very Low (z-score#23) 8.8 48.5 14.5 (6.6–31.6)** 37.5 44.2 1.5 (1.1–2.0)**
Length for age
Low (23,z-score#22) 12.0 22.6 2.7 (1.1–6.5)** 16.7 15.0 0.9 (0.6–1.3)
Very Low (z-score#23) 15.8 25.8 2.3 (1.0–5.5)** 37.1 38.6 1.1 (0.8–1.4)
Weight for length
Low (23,z-score#22) 7.1 16.1 3.5 (1.3–9.5)** 15.1 14.7 1.1 (0.7–1.5)
Very Low (z-score#23) 5.2 25.8 7.9 (3.3–18.6)** 23.7 27.5 1.3 (1.0–1.8)**
Radiology & Laboratory
Alveolar consolidation on x-ray
{
16.5 52.6 4.5 (2.1–9.9)** 37.5 35.8 1.1 (0.8–1.5)
Other infiltrates on x-ray
{
23.4 10.5 0.8 (0.3–2.1) 22.7 18.7 0.6 (0.4–0.9)**
CRP$40 mg/L
{
24.8 45.5 1.8 (0.9–3.7)* 34.5 34.0 0.9 (0.7–1.2)
*p,0.20;
**p,0.05.
{
Missing observations for .10%;
doi:10.1371/journal.pone.0027793.t001
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specific discrimination of children with LRTI based on their risk of
mortality may help refine decisions about case management, such
as the most appropriate site of treatment or the need for additional
supportive care.
Clinical prediction scores have been developed and validated to aid
clinicians in managing and treating adult patients with community-
acquired pneumonia [8,9]. These scores assign points to a variety of
patient characteristics, clinical signs and laboratory measurements to
calculate a total score which objectively discriminates patients based
on their risk of mortality [10]. No similar method has been developed
to quantify the severity of pediatric pneumonia.
A simple pediatric severity score could compliment the current
IMCI framework by predicting the probability of death in a child
who presents with respiratory illness or provide a standardized
means of quantifying severity among children during vaccine trials
and other epidemiologic studies. The objective of this study was to
develop a severity score for LRTI in children with and without
HIV infection – the Respiratory Index of Severity in Children
(RISC) – and assess its ability to predict mortality by evaluating the
performance of the scoring model.
Methods
Ethics Statement
The original study was approved by the Committee for the
Study of Human Subjects at the University of the Witwatersrand,
and permission for the trial was obtained from the Medicines
Control Council of South Africa. All participants were enrolled
after written informed consent had been obtained from a parent or
legal guardian. Secondary analysis of the original data for this
manuscript was approved by the Institutional Review Board of the
Centers for Disease Control and Prevention.
Participants
We performed a secondary analysis of data from children ,24
months of age who were enrolled in a clinical trial of the 9-valent
pneumococcal conjugate vaccine in Soweto, South Africa and
hospitalized with LRTI between 1998 and 2001. Details of the trial
have been described elsewhere [11,12]. Briefly, 39,836 children
were randomized to receive 3 doses of the 9-valent pneumococcal
conjugate vaccine or a placebo at 6, 10, and 14 weeks of age. All
children also received Haemophilus influenzae type b (Hib) vaccine.
Study staff monitored all admissions at the local hospital (Chris
Hani-Baragwanath Hospital; Soweto, South Africa) through
November 2001 and determined when study participants were
admitted with LRTI (clinical diagnosis of pneumonia or bronchi-
olitis, irrespective of radiographic features). Children were tested for
HIV infection when they were hospitalized for any reason. Details
of HIV testing have been described previously [11].
All children hospitalized with LRTI during the follow-up period
were potentially eligible for this analysis. Hospitalizations were
excluded from the analysis if the child’s HIV infection status was
unknown, data on illness history or physical exam was not
collected, or the child was $24 months at the time of admission.
Children who received the study vaccine were less likely to be
hospitalized with respiratory illness than those in the comparison
arm [11], but among those hospitalized with LRTI, vaccinated
children did not have reduced mortality compared with unvac-
cinated children. Consequently, children in both the vaccine and
comparison arms were included in this analysis.
Measures
The outcome of interest was in-hospital mortality among
children hospitalized with LRTI. As potential predictors of
mortality, we considered the following classes of variables:
Demographics, medical history, history of present illness, signs
on physical exam, growth standards, chest radiography, and C-
reactive protein levels. Information on these variables was
collected by study physicians on a standardized case report form
when a child was hospitalized. Subjective information on
symptoms occurring prior to hospitalization was obtained from
the child’s caregiver at the time of hospitalization.
For this analysis, age was categorized based on IMCI categories:
6 weeks–2 months, 3–12 months, and 12–23 months. Children
were considered to have low oxygen saturation if a pulse oximetry
reading on room air was #90%. Three growth standards were
also evaluated: weight for age, weight for length, and length for
age, categorized based on the WHO z-scores [13]. Tables of
growth standards were accessed at: http://www.who.int/child-
growth/standards/. Chest radiographs were evaluated indepen-
dently by a pediatrician and a radiologist. C-reactive protein levels
were categorized as .40 mg/L or #40 mg/L, which may
indicate bacterial pneumonia [14]. For children with HIV
infection, the clinical classification of HIV disease without CD4
count was recorded using the CDC categories – N (asymptomatic),
A (mildly symptomatic), B (moderately symptomatic), C (severely
symptomatic, AIDS-defining) [15].
Impact of HIV status
Because of the high prevalence of HIV infection in the study
population and the substantial impact of HIV infection on
mortality in young children, separate models were developed for
Table 2. Independent risk factors for mortality in children
,24 months hospitalized with LRTI, by HIV status.
aOR 95%CI p-value Score
HIV non-infected
Most severe respiratory sign:
O
2
saturation,90% 20.9 (5.0–87) ,0.01 3
Chest indrawing 4.6 (2.2–9.4) ,0.01 2
Wheezing 0.2 (0.05–0.6) ,0.01 22
Refusing feeds 1.8 (0.9–3.8) 0.10 1
Weight for age:
Low (#22 z-score) 2.5 (1.6–3.8) ,0.01 1
Very low (#23 z-score) 6.0 (2.5–14.4) ,0.01 2
HIV-infected
Most severe respiratory sign:
O
2
saturation,90% 4.8 (3.0–7.6) ,0.01 2
Chest indrawing 2.2 (1.7–2.8) ,0.01 1
Wheezing 0.6 (0.4–0.9) 0.03 21
Refusing feeds 1.5 (1.2–2.0) ,0.01 1
HIV classification
Severe (C) 5.5 (2.5–12.1) ,0.01 2
Mild or moderate (A/B) 2.3 (1.1–5.0) 0.02 1
Not symptomatic (N) 1.0 - - -
IMCI age group
#2 months 6.0 (3.5–10.4) ,0.01 2
3–12 months 2.2 (1.4–3.4) ,0.01 1
13–23 months 1.0 - - -
doi:10.1371/journal.pone.0027793.t002
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HIV-infected and non-infected children. The cohort was stratified
by HIV status and the following methods were used first to
develop a model for children without HIV infection, and then
repeated in the subset of children with HIV infection.
According to the local standard of care at the time,
antiretroviral drugs for the treatment of HIV infection were not
routinely used. Trimethoprim-sulfamethoxazole prophylaxis was
recommended for HIV-exposed children aged 6 weeks or as soon
as the child received a diagnosis of HIV infection if prophylaxis
was not previously started; prophylaxis continued past the age of 1
year in children with AIDS of clinical category B or C [16].
Identifying risk factors for mortality
Using mortality as a dichotomous outcome variable, we assessed
the relationship between each risk factor and mortality. Odds
ratios for mortality and their 95% confidence intervals, as well as
p-values, were calculated for each risk factor using logistic
regression with generalized estimating equations (GEE) to account
for the inclusion of multiple episodes among some children.
Variables with p,0.20 in univariate analysis were considered as
candidate predictors for multivariable regression. To develop the
multivariable model, we evaluated included variables for interac-
tion, and then all candidate variables were included in a GEE
model and non-significant variables were eliminated one at a time,
starting with the variable with the smallest magnitude of effect,
until all remaining variables had p,0.05 or removing an
additional variable significantly increased the 22 log likelihood
of the model.
When developing the multivariable model, we initially chose to
exclude some variables that, although associated with mortality,
could have reduced the usability of the index in some settings,
including chest radiography and laboratory measurement of CRP
levels. After selecting a multivariable model, each of these
variables was then added to assess whether they improved the
predictive performance of the model.
Developing a scoring system
A point-based scoring system was developed from the final
multivariable logistic regression model in which a number of
points was assigned to each predictor in the model by rounding
each bcoefficient to the nearest integer. For consecutive cut-offs of
the summed scores, the sensitivity, specificity and positive
predictive values for mortality were calculated.
Statistical validation
To assess the predictive accuracy of our scoring system, two
measures of model performance were evaluated – discrimination
and calibration. Models with good discrimination distinguish well
between patients with and without the outcome of interest.
Discrimination was measured using the c-statistic, or area under
the receiver operating characteristic (ROC) curve. The c-statistic
ranges from 0.5 to 1, with higher values indicating better
Table 3. RISC scoring system.
HIV NON-INFECTED CHILDREN
Severity of respiratory signs on physical exam: If O
2
#
90%: 3 points
1. Oxygen saturation = _____% else
2. Does the child have chest indrawing? Yes/No Indrawing: 2 points
3. Does the child have wheezing? Yes/No Wheezing: 22 points
4. Has the child been refusing feedings? Yes/No Refusal to feed: 1 point
Growth standards:
5. Weight for age z-score = ______ z
#2
3: 2 points
Weight = _____ kg Age = ______ months
2
2
#
z
,2
3: 1 points
z
.2
2: 0 points
Total points: _______
(Maximum: 6)
HIV-INFECTED CHILDREN
Severity of respiratory signs on physical exam: If O
2
#
90%: 2 points
1. Oxygen saturation = _____% else
2. Does the child have chest indrawing? Yes/No Indrawing: 1 point
3. Does the child have wheezing? Yes/No Wheezing: 21 points
4. Has the child been refusing feedings? Yes/No Refusal to feed: 1 point
0–2 months: 2 points
5. Age = _____ months 3–12 months: 1 points
.
12 months: 0 points
C: 2 points
6. HIV Clinical Classification = N/A/B/C A/B: 1 points
N: 0 points
Total points: _______
(Maximum: 7)
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discrimination. Calibration measures how well the predicted
probabilities of death match the actual mortality observed across
the data. Calibration was assessed using the Hosmer-Lemeshow
goodness of fit test. A low chi-square and high p-value indicates
good calibration. Calibration was also assessed visually by
graphing the observed frequencies of mortality against the
predicted probabilities of mortality at each score level.
The performance of a prediction model is generally worse in new
patients than initially observed when developing the model. This
‘optimism’ can be assessed with standard bootstrapping procedures
[17]. To assess the internal validity of our model, bootstrap samples
were drawn with replacement and of the same size as the original
sample. The multivariable model was re-estimated in 300 bootstrap
samples and each model was evaluated on the new sample and the
original data. The average difference in the c-statistic indicates the
optimism in the initially estimated discrimination [18].
All statistical analysis was performed using SAS software version
9.2 (SAS Institute, Cary, NC).
Results
Study population
From 1998 to 2001, 4,584 hospitalizations with LRTI were
observed in 3,140 children enrolled in the 9-valent pneumococcal
conjugate vaccine trial in South Africa. Episodes of LRTI were
excluded from the present study if they had unknown HIV status
(n = 101, 2.2%), if either illness history (n = 21, 0.5%) or physical
exam (n = 23, 0.5%) were not assessed, or the child was $24
months of age at the time of hospitalization (n = 319, 6.9%).
Overall, 4,148 (90.5%) LRTI hospitalizations observed during the
vaccine trial were included in this study.
Thirty-six percent (n = 1,502) of all LRTI hospitalizations
occurred among children with HIV infection. Mortality varied
greatly by HIV status, as 17.6% of all episodes in children with
HIV infection (n = 265) resulted in death, compared to 1.3% of all
episodes in children without HIV infection (n = 33).
Scoring model
Risk factors associated with mortality were identified for
children with and without HIV infection (see Table 1). In
multivariable regression (Table 2), independent risk factors for
mortality in HIV non-infected children included: low oxygen
saturation on room air, chest wall indrawing, low weight for age,
and refusal to feed. Wheezing was associated with a decreased risk
of mortality. Independent risk factors for mortality in HIV-
infected children were slightly different and included: low oxygen
saturation on room air, chest wall indrawing, and refusal to feed,
but also age, and HIV clinical classification. Wheezing was also
associated with a decreased risk of mortality in HIV-infected
children. Due to interaction between oxygen saturation and chest
indrawing in the multivariable model, we combined these factors
in the final model; chest indrawing was only associated with
increased mortality if oxygen saturation was normal.
A number of points was assigned to each of the risk factors in
the final models and is shown in Table 3. An overall score was
calculated for each episode of LRTI by adding the points for each
risk factor identified at presentation. For example, a child without
HIV infection who presented with low oxygen saturation (3 points)
and was wheezing (subtract 2 points), but was of normal weight for
age (0 points) and was not refusing feedings (0 points) would have
a total RISC score of 1 point.
The RISC scores for children ranged up to 7 points for HIV-
infected children and 6 points for non-infected children. For each
score level, the observed risk of mortality and predicted probability
of death from the model is shown in Figure 1. Among HIV non-
infected children in this population, the median RISC score was 1
(Table 4), with a corresponding risk of mortality of 0% (95%CI:
0%–0.6%) (Figure 1). Among HIV-infected children, with a higher
overall risk of mortality, the median RISC score was 4 (Table 4),
with a mortality of 14.4% (95%CI: 10.6%–18.2%).
We considered three variables as additions to the multivariable
model for HIV non-infected children to determine whether they
improved the performance of the final model. In multivariable
analysis, hepatomegaly (aOR = 3.1, 95%CI: 1.4–6.8) or the
presence of alveolar consolidation on chest radiograph
(aOR = 2.0, 95%CI: 0.9–4.7) were each independently associated
with mortality in HIV non-infected children, though the
discrimination of the multivariable model was already high and
did not improve with either addition. In addition, 198 (7.5%)
episodes would have been excluded due to missing data on chest
radiograph. Elevated CRP level was not a statistically significant
independent predictor of mortality in children without HIV
infection (OR = 1.6, 95%CI: 0.7–3.7), and excluded 908 (34.3%)
episodes. Thus, these variables were not retained in the final RISC
score, but may warrant further consideration.
Validation
The discrimination and calibration for both the HIV-infected
and non-infected models were measured to evaluate the
Figure 1. Observed mortality (with 95% confidence intervals)
and mean predicted mortality by RISC score for children
,
24
months hospitalized with LRTI, (A) without HIV infection and
(B) with HIV infection.
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performance of the RISC score. The c-statistic, or area under the
ROC curve, is a measure of model discrimination and ranges from
0.5 to 1, with higher values indicating better discrimination. In the
full dataset, the models showed good discrimination (c-statis-
tic = 0.776 for HIV-infected and 0.923 for non-infected children)
and calibration (p-value for goodness-of-fit =0.95 for HIV-infected
and 0.87 for non-infected children), although the model for HIV
non-infected children provided better discrimination of mortality
than the model for HIV-infected children (see Table 4 and
Figure 2). We also evaluated the internal validity of the RISC
model using standard bootstrapping techniques to estimate the
optimism, or possible over-fitting, in our measurement of model
discrimination. From 300 bootstrap samples, the c-statistic from
models among HIV non-infected children ranged from 0.742–
0.926, with an estimated average optimism of 0.013 in the original
data. For HIV-infected children, the c-statistic from models of 300
bootstrap samples ranged from 0.665–0.732, with an estimated
average optimism of 0.004.
Discussion
We developed and validated a clinical prediction score, the
RISC score, which uses a simple set of clinical factors to
discriminate between young children with varying risks of death
from LRTI. Variables retained in the score represent known risk
factors for severe outcomes of respiratory illness in children,
including: hypoxemia, chest indrawing, refusal to feed, malnutri-
tion, age, and stage of HIV disease [19,20,21,22,23,24,25]. We
demonstrate that a simple combination of these factors in a scoring
model provides good discrimination and calibration for predicting
mortality in young children with LRTI, without the need for
radiographic or laboratory measurements.
This severity index has several potential uses in both research
and clinical settings. In the research setting this tool may provide a
useful means of quantifying the severity of LRTI in epidemiologic
studies and clinical trials. In clinical settings, the ability to quantify
the risk of mortality may aid clinicians in the management of
young children that present with LRTI and compliment current
IMCI guidelines. Recent studies have indicated that some children
with severe pneumonia by IMCI criteria may be successfully
managed at home [26], and that in regions with barriers to
hospital referral, limiting referrals to those most in need actually
improved the overall number of children who received appropri-
ate treatment and may have reduced mortality [6]. The use of the
RISC score may help refine decisions about case management in
resource-limited countries or areas with geographic and socioeco-
nomic barriers to hospital referral by facilitating decisions about
the most appropriate site of treatment (i.e., home vs. hospital) or
the need for additional supportive care (i.e., supplemental oxygen
or intensive care).
Hypoxemia was an important risk factor among both HIV-
infected and non-infected children. While pulse oximeters may
not be currently available in all settings, our finding supports
several recent studies that have shown the importance of pulse
oximetry in guiding the use of oxygen therapy and other
supportive care for reducing the mortality in children with
respiratory infection [27,28]. In contrast, wheezing was associ-
ated with reduced mortality in both models. There have been
concerns about the potential for misclassifying pneumonia in
children with wheeze, which can also result in rapid breathing
and thus an IMCI classification of pneumonia. Studies of children
with IMCI-defined pneumonia and wheeze have found deterio-
ration was more likely in children with additional danger signs
such as chest indrawing or malnutrition [29]. In the absence of
additional danger signs, children with wheeze would have a low
RISC score, which supports suggestions that in many instances
these young children might be successfully managed without
hospitalization [29].
Table 4. Distribution of RISC scores and screening performance in children ,24 months of age with and without HIV infection,
hospitalized with LRTI in Soweto, South Africa 1998–2001.
Score Percent of total Percent Mortality (95%CI) Sensitivity* Specificity*
Positive
Predictive Value*
Negative
Predictive Value*
HIV
2
#
046.7 0.0 (0–0.2) 100% 0% 1% n/a
119.4 0.0 (0–0.6) 100% 48% 2% 100%
210.7 0.7 (0–1.7) 100% 67% 4% 100%
312.2 2.2 (0.6–3.8) 94% 78% 5% 100%
46.1 5.1 (1.6–8.5) 72% 90% 8% 100%
53.5 10.9 (4.5–17.2) 47% 96% 12% 99%
61.4 13.9 (2.6–25.2) 16% 99% 13% 99%
HIV
+
#
02.4 0.0 (0–1.7) 100% 0% 17% n/a
16.3 2.2 (0–5.1) 100% 3% 18% 100%
214.5 3.3 (0.9–5.7) 99% 10% 19% 98%
317.2 7.1 (3.9–10.2) 97% 28% 22% 97%
422.2 14.4 (10.6–18.2) 90% 50% 27% 96%
525.2 27.0 (22.4–31.5) 71% 73% 35% 92%
611.0 45.7 (38.0–53.3) 32% 93% 48% 87%
71.3 47.4 (24.9–69.8) 4% 99% 53% 83%
*For mortality, using a cutoff at the corresponding score value.
doi:10.1371/journal.pone.0027793.t004
Respiratory Index of Severity in Children
PLoS ONE | www.plosone.org 6 January 2012 | Volume 7 | Issue 1 | e27793
Predictive models are expected to perform better in the
population in which they were developed than in other
populations [30]. We used bootstrapping techniques to estimate
the possible optimism associated with the apparent performance of
the RISC model in this population. However, while the RISC
score includes recognized predictors of severe LRTI, differences in
the distribution of risk factors and/or health seeking behaviors in
different populations may impact the performance of this model at
predicting the risk of death, and will need to be evaluated. Efforts
are ongoing to validate these scoring models in other populations
of young children with respiratory illness to further define the
predicted mortality across the RISC score in a variety of settings.
Further study should also focus on evaluating the usefulness and
applicability of this tool in different settings [31].
For children with HIV infection, this study was conducted prior
to antiretroviral medication use in South Africa. Antiretroviral
medication programs can reduce the incidence and severity of
HIV-associated pneumonia in children [32], and the impact on
this score is unclear. This highlights the need for further evaluation
of this model in other populations to determine how the RISC
score may best be applied or refined in other contexts.
In order to develop a score that would have high utility in
resource-limited settings, which have the greatest burden of
childhood mortality from LRTI, we considered the effect of
radiographic and laboratory measurements only after developing a
strong multivariable model. In this population, these variables did
not provide any additional discrimination after other risk factors in
the multivariable model and thus was not included in the final
RISC score, but may warrant future consideration.
Finally, these data come from a cohort of children enrolled in a
pneumococcal vaccine trial. Although there were no differences in
mortality between children hospitalized with LRTI in either the
vaccine or comparison group, half of the children did receive
pneumococcal conjugate vaccine, and all received Hib vaccine.
Additionally, although study investigators passively monitored
hospitalizations with LRTI and did not directly participate in their
clinical care, mortality observed in a vaccine trial may underes-
timate the risk of mortality that would exist in populations with less
access to care. This underscores the need for further study of RISC
in additional populations, to understand how it might be adapted
in different settings.
The RISC score incorporates a simple set of variables that
discriminate the probability of death in children hospitalized with
LRTI. The ability to estimate a child’s risk of mortality with
limited clinical information may provide the ability to improve
outcomes for children in resource-limited settings where the
burden of pediatric pneumonia is highest. With further validation
in additional populations, the RISC score may be a tool to more
Figure 2. Distribution of RISC score stratified by mortality for children
,
24 months hospitalized with LRTI, (A) without HIV
infection and (B) with HIV infection.
doi:10.1371/journal.pone.0027793.g002
Respiratory Index of Severity in Children
PLoS ONE | www.plosone.org 7 January 2012 | Volume 7 | Issue 1 | e27793
effectively manage LRTI, a major cause of death in children
worldwide, and to better understand the impact of vaccination or
other public health interventions on the severity of childhood
respiratory illness.
Author Contributions
Conceived and designed the experiments: CR SM KPK LK LF AMF.
Analyzed the data: CR JRO. Wrote the paper: CR AMF.
References
1. Black RE, Cousens S, Johnson HL, Lawn JE, Rudan I, et al. (2010) Global,
regional, and national causes of child mortality in 2008: a systematic analysis.
Lancet 375: 1969–1987.
2. Williams BG, Gouws E, Boschi-Pinto C, Bryce J, Dye C (2002) Estimates of
world-wide distribution of child deaths from acute respiratory infections. Lancet
Infect Dis 2: 25–32.
3. Enarson PM, Gie RP, Enarson DA, Mwansambo C, Graham SM (2010) Impact
of HIV on standard case management for severe pneumonia in children. Expert
Rev Respir Med 4: 211–220.
4. Graham SM, Gibb DM (2002) HIV disease and respiratory infection in
children. Br Med Bull 61: 133–150.
5. World Health Organization (2005) Cough and difficult breathing. Pocket book
of hospital care for children: guidelines for the management of common illnesses
with limited resources. Geneva: World Health Organization.
6. Chowdhury EK, El Arifeen S, Rahman M, Hoque DE, Hossain MA, et al.
(2008) Care at first-level facilities for children with severe pneumonia in
Bangladesh: a cohort study. Lancet 372: 822–830.
7. Simoes EA, Peterson S, Gamatie Y, Kisanga FS, Mukasa G, et al. (2003)
Management of severely ill children at first-level health facilities in sub-Saharan
Africa when referral is difficult. Bull World Health Organ 81: 522–531.
8. Fine MJ, Auble TE, Yealy DM, Hanusa BH, Weissfeld LA, et al. (1997) A
prediction rule to identify low-risk patients with community-acquired pneumo-
nia. N Engl J Med 336: 243–250.
9. Lim WS, van der Eerden MM, Laing R, Boersma WG, Karalus N, et al. (2003)
Defining community acquired pneumonia severity on presentation to hospital:
an international derivation and validation study. Thorax 58: 377–382.
10. Aujesky D, Fine MJ (2008) The pneumonia severity index: a decade after the
initial derivation and validation. Clin Infect Dis 47 Suppl 3: S133–139.
11. Klugman KP, Madhi SA, Huebner RE, Kohberger R, Mbelle N, et al. (2003) A
trial of a 9-valent pneumococcal conjugate vaccine in children with and those
without HIV infection. N Engl J Med 349: 1341–1348.
12. Madhi SA, Kuwan da L, Cutland C, Klugman KP (2005) The impact of a 9-
valent pneumococcal conjugate vaccine on the public health burden of
pneumonia in HIV-infected and -uninfected children. Clin Infect Dis 40:
1511–1518.
13. WHO Multicentre Growth Reference Study Group (2006) WHO Child Growth
Standards: Methods and development: Length/height-for-age, weight-for-age,
weight-for-length, weight-for-height and body mass index-for-age. Geneva:
World Health Organization.
14. Madhi SA, Kohler M, Kuwanda L, Cutland C, Klugman KP (2006) Usef ulness
of C-reactive protein to define pneumococcal conjugate vaccine efficacy in the
prevention of pneumonia. Pediatr Infect Dis J 25: 30–36.
15. Caldwell MB, Oxtoby MJ, Simonds RJ, Lindegren ML, Rogers MR (1994) 1994
Revised Classification System for Human Immunodeficiency Virus Infection in
Children Less Than 13 Years of Age. MMWR 43: 1–10.
16. Madhi SA, Cutland C, Ismail K, O’Reilly C, Mancha A, et al. (2002)
Ineffectiveness of trimethoprim-sulfamethoxazole prophylaxis and the impor-
tance of bacterial and viral coinfections in African children with Pneumocystis
carinii pneumonia. Clin Infect Dis 35: 1120–1126.
17. Steyerberg EW, Harrell FE, Jr., Borsboom GJ, Eijkemans MJ, Vergouwe Y,
et al. (2001) Internal validation of predictive models: efficiency of some
procedures for logistic regression analysis. J Clin Epidemiol 54: 774–781.
18. Harrell FE, Jr., Lee KL, Mark DB (1996) Multivariable prognostic models: issues
in developing models, evaluating assumptions and adequacy, and measuring and
reducing errors. Stat Med 15: 361–387.
19. Young Infants Clinical Signs Study Group (2008) Clinical signs that predict
severe illness in children under age 2 months: a multicentre study. Lancet 371:
135–142.
20. Chisti MJ, Tebruegge M, La Vincente S, Graham SM, Duke T (2009)
Pneumonia in severely malnourished children in developing countries - mortality
risk, aetiology and validity of WHO clinical signs: a systematic review. Trop
Med Int Health 14: 1173–1189.
21. Duke T, Mgone J, Frank D (2001) Hypoxaemia in children with severe
pneumonia in Papua New Guinea. Int J Tuberc Lung Dis 5: 511–519.
22. Graham SM (2007) HIV-related pulmonary disorders: practice issues. Ann Trop
Paediatr 27: 243–252.
23. Lozano JM (2001) Epidemio logy of hypoxaemia in children with acute lower
respiratory infection. Int J Tuberc Lung Dis 5: 496–504.
24. McNally LM, Jeena PM, Gajee K, Thula SA, Sturm AW, et al. (2007) Effect of
age, polymicrobial disease, and maternal HIV status on treatment response and
cause of severe pneumonia in South African children: a prospective descriptive
study. Lancet 369: 1440–1451.
25. Opiyo N, English M (2011) What clinical signs best identify severe illness in
young infants aged 0–59 days in developing countries? A systematic review. Arch
Dis Child.
26. Hazir T, Fox LM, Nisar YB, Fox MP, Ashraf YP, et al. (2008) Ambulatory
short-course high-dose oral amoxicillin for treatment of severe pneumonia in
children: a randomised equivalency trial. Lancet 371: 49–56.
27. Duke T, Wandi F, Jonathan M, Matai S, Kaupa M, et al. (2008) Improved
oxygen systems for childhood pneumonia: a multihospital effectiveness study in
Papua New Guinea. Lancet 372: 1328–1333.
28. Weber MW, Mulholland EK (1998) Pulse oximetry in developing countries.
Lancet 351: 1589.
29. Hazir T, Qazi S, Nisar YB, Ansari S, Maqbool S, et al. (2004) Assessment and
management of children aged 1–59 months presenting with wheeze, fast
breathing, and/or lower chest indrawing; results of a multicentre descriptive
study in Pakistan. Arch Dis Child 89: 1049–1054.
30. Bleeker SE, Moll HA, Steyerberg EW, Donders AR, Derksen-Lubsen G, et al.
(2003) External validation is necessary in prediction research: a clinical example.
J Clin Epidemiol 56: 826–832.
31. Reilly BM, Evans AT (2006) Translating clinical research into clinical practice:
impact of using prediction rules to make decisions. Ann Intern Med 144:
201–209.
32. Gona P, Van Dyke RB, Williams PL, Dankner WM, Chernoff MC, et al. (2006)
Incidence of opportunistic and other infections in HIV-infected children in the
HAART era. JAMA 296: 292–300.
Respiratory Index of Severity in Children
PLoS ONE | www.plosone.org 8 January 2012 | Volume 7 | Issue 1 | e27793
    • Unlike in their study, we did not find heart rate or respiratory rate to be predictive of increased risk of mortality. This differs from other studies which have identified these as risk factors particularly respiratory rate[3,7,9]. Finally, Koss et al. found a 30-day mortality rate of 18.2% among patients hospitalized with pneumonia in a similar patient population of Ugandan adults[8].
    [Show abstract] [Hide abstract] ABSTRACT: Background Acute lower respiratory tract infections (LRTI) are a frequent cause of hospitalization and mortality in South Africa; however, existing respiratory severity scores may underestimate mortality risk in HIV-infected adults in resource limited settings. A simple predictive clinical score for low-resource settings could aid healthcare providers in the management of patients hospitalized with LRTI. Methods We analyzed 1,356 LRTI hospitalizations in adults aged ≥18 years enrolled in Severe Acute Respiratory Illness (SARI) surveillance in three South African hospitals from January 2010 to December 2011. Using demographic and clinical data at admission, we evaluated potential risk factors for in-hospital mortality. We evaluated three existing respiratory severity scores, CURB-65, CRB-65, and Classification Tree Analysis (CTA) Score assessing for discrimination and calibration. We then developed a new respiratory severity score using a multivariable logistic regression model for in-hospital mortality and assigned points to risk factors based on the coefficients in the multivariable model. Finally we evaluated the model statistically using bootstrap resampling techniques. ResultsOf the 1,356 patients hospitalized with LRTI, 101 (7.4%) died while hospitalized. The CURB-65, CRB-65, and CTA scores had poor calibration and demonstrated low discrimination with c-statistics of 0.594, 0.548, and 0.569 respectively. Significant risk factors for in-hospital mortality included age ≥ 45 years (A), confusion on admission (C), HIV-infection (H), and serum blood urea nitrogen >7 mmol/L (U), which were used to create the seven-point ACHU clinical predictor score. In-hospital mortality, stratified by ACHU score was: score ≤1, 2.4%, score 2, 6.4%, score 3, 11.9%, and score ≥ 4, 29.3%. Final models showed good discrimination (c-statistic 0.789) and calibration (chi-square 1.6, Hosmer-Lemeshow goodness-of-fit p-value = 0.904) and discriminated well in the bootstrap sample (average optimism of 0.003). Conclusions Existing clinical predictive scores underestimated mortality in a low resource setting with a high HIV burden. The ACHU score incorporates a simple set a risk factors that can accurately stratify patients ≥18 years of age with LRTI by in-hospital mortality risk. This score can quantify in-hospital mortality risk in an HIV-endemic, resource-limited setting with limited clinical information and if used to facilitate timely treatment may improve clinical outcomes.
    Full-text · Article · Dec 2017
    • Clinical prediction tools may aid case classification and be used to initiate earlier escalation of care in high-risk cases, rapid in-hospital triage for resuscitation and targeted therapies or intensive care admission. Two tools have been proposed to identify hospitalized children at risk of death due to acute respiratory illness: the Respiratory Index of Severity in Children (RISC) [11] and modified Respiratory Index of Severity in Children (mRISC) [12]. RISC was developed retrospectively from a dataset collected in Soweto, South Africa from 1998–2001 in hospitalized children aged 0–24 months enrolled in a pneumococcal conjugate vaccine (PCV) randomized controlled trial, post Haemophilus influenzae type b (Hib) vaccine introduction with known HIV disease status.
    [Show abstract] [Hide abstract] ABSTRACT: Background Pneumonia is the leading infectious cause of under-5 mortality in sub-Saharan Africa. Clinical prediction tools may aide case classification, triage, and allocation of hospital resources. We performed an external validation of two published prediction tools and compared this to a locally developed tool to identify children admitted with pneumonia at increased risk for in-hospital mortality in Malawi. Methods We retrospectively analyzed the performance of the Respiratory Index of Severity in Children (RISC) and modified RISC (mRISC) scores in a child pneumonia dataset prospectively collected during routine care at seven hospitals in Malawi between 2011–2014. RISC has both an HIV-infected and HIV-uninfected tool. A local score (RISC-Malawi) was developed using multivariable logistic regression with missing data multiply imputed using chained equations. Score performances were assessed using c-statistics, sensitivity, specificity, positive predictive value, negative predictive value, and likelihood statistics. Results 16,475 in-patient pneumonia episodes were recorded (case-fatality rate (CFR): 3.2%), 9,533 with complete data (CFR: 2.0%). The c-statistic for the RISC (HIV-uninfected) score, used to assess its ability to differentiate between children who survived to discharge and those that died, was 0.72. The RISC-Malawi score, using mid-upper arm circumference as an indicator of malnutrition severity, had a c-statistic of 0.79. We were unable to perform a comprehensive external validation of RISC (HIV-infected) and mRISC as both scores include parameters that were not routinely documented variables in our dataset. Conclusion In our population of Malawian children with WHO-defined pneumonia, the RISC (HIV-uninfected) score identified those at high risk for in-hospital mortality. However the refinement of parameters and resultant creation of RISC-Malawi improved performance. Next steps include prospectively studying both scores to determine if incorporation into routine care delivery can have a meaningful impact on in-hospital CFRs of children with WHO-defined pneumonia.
    Full-text · Article · Dec 2016
    • " [163] Scientific soundness " Environmental research with children should be scientifically justified, with sound research questions and valid study protocols of sufficient statistical power … " [164] Research ethics " In our opinion, it is very important for ethical reasons to state at least if informed consent was obtained and if the study was approved by the responsible REC. " [165] RCT randomized controlled trial the participating children affects all aspects of a trial, including choice of intervention and comparator, selection and measurement of relevant and valid outcomes, expected treatment effect, differences in risk/benefit profile , effects on growth and development and disease processes [21, 34, 48,50515253545556575859606162636465. Therefore, the detailed reporting of the age distribution of children in a trial is essential for the assessment of the appropriateness of age groups selected, the interventions, and outcomes, and the potential effects on growth and development.
    [Show abstract] [Hide abstract] ABSTRACT: Background: Complete and transparent reporting of clinical trial protocols and reports ensures that these documents are useful to all stakeholders, that bias is minimized, and that the research is not wasted. However, current studies repeatedly conclude that pediatric trial protocols and reports are not appropriately reported. Guidelines like SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and CONSORT (Consolidated Standards of Reporting Trials) may improve reporting, but do not offer guidance on issues unique to pediatric trials. This paper reports two systematic reviews conducted to build the evidence base for the development of pediatric reporting guideline extensions: 1) SPIRIT-Children (SPIRIT-C) for pediatric trial protocols, and 2) CONSORT-Children (CONSORT-C) for pediatric trial reports. Method: MEDLINE, the Cochrane Methodology Register, and reference lists of included studies were searched. Publications of any type were eligible if they included explicit recommendations or empirical evidence for the reporting of potential items in a pediatric protocol (SPIRIT-C systematic review) or trial report (CONSORT-C systematic review). Study characteristics, recommendations and evidence for pediatric extension items were extracted. Recurrent themes in the recommendations and evidence were identified and synthesized. All steps were conducted by two reviewers. Results: For the SPIRIT-C and CONSORT-C systematic reviews 366 and 429 publications were included, respectively. Recommendations were identified for 48 of 50 original reporting items and sub-items from SPIRIT, 15 of 20 potential SPIRIT-C reporting items, all 37 original CONSORT items and sub-items, and 16 of 22 potential CONSORT-C reporting items. The following overarching themes of evidence to support or refute the utility of reporting items were identified: transparency; reproducibility; interpretability; usefulness; internal validity; external validity; reporting bias; publication bias; accountability; scientific soundness; and research ethics. Conclusion: These systematic reviews are the first to systematically gather evidence and recommendations for the reporting of specific items in pediatric protocols and trials. They provide useful and translatable evidence on which to build pediatric extensions to the SPIRIT and CONSORT reporting guidelines. The resulting SPIRIT-C and CONSORT-C will provide guidance to the authors of pediatric protocols and reports, respectively, helping to alleviate concerns of inappropriate and inconsistent reporting, and reduce research waste.
    Full-text · Article · Dec 2015
    • Mortality rate in the included studies ranged from 3.4% to 15.3%. One study [32] reported data separatly on HIV-positive and HIV-negative children, and was included in the meta-analysis as two sub-studies. Risk of bias for included studies is reported inFig 2. Six studies were considered at high risk of bias, with lack of adjustment for confounding and other aspects of the statistical analysis being the most frequent risk of bias.
    [Show abstract] [Hide abstract] ABSTRACT: Objective: To evaluate the association between hypoxaemia and mortality from acute lower respiratory infections (ALRI) in children in low- and middle-income countries (LMIC). Design: Systematic review and meta-analysis. Study selection: Observational studies reporting on the association between hypoxaemia and death from ALRI in children below five years in LMIC. Data sources: Medline, Embase, Global Health Library, Lilacs, and Web of Science to February 2015. Risk of bias assessment: Quality In Prognosis Studies tool with minor adaptations to assess the risk of bias; funnel plots and Egger's test to evaluate publication bias. Results: Out of 11,627 papers retrieved, 18 studies from 13 countries on 20,224 children met the inclusion criteria. Twelve (66.6%) studies had either low or moderate risk of bias. Hypoxaemia defined as oxygen saturation rate (SpO2) <90% associated with significantly increased odds of death from ALRI (OR 5.47, 95% CI 3.93 to 7.63) in 12 studies on 13,936 children. An Sp02 <92% associated with a similar increased risk of mortality (OR 3.66, 95% CI 1.42 to 9.47) in 3 studies on 673 children. Sensitivity analyses (excluding studies with high risk of bias and using adjusted OR) and subgroup analyses (by: altitude, definition of ALRI, country income, HIV prevalence) did not affect results. Only one study was performed on children living at high altitude. Conclusions: The results of this review support the routine evaluation of SpO2 for identifying children with ALRI at increased risk of death. Both a Sp02 value of 92% and 90% equally identify children at increased risk of mortality. More research is needed on children living at high altitude. Policy makers in LMIC should aim at improving the regular use of pulse oximetry and the availability of oxygen in order to decrease mortality from ALRI.
    Full-text · Article · Sep 2015
    • The proportion of patients successfully oxygenated will be defined as the number of patients who achieve a saturation ≥90 % for ≥12 hours after initiating oxygen therapy. The clinical severity scores used will be the previously published and validated Lambaréné Organ Dysfunction Score (LODS) [10] and the Respiratory Index of Severity in Children (RISC) [11]. Oxygen delivery system variables will include direct costs (capital investment, operating costs, system maintenance , etcetera), details of maintenance needs, convenience of use of the systems and proportion of patients for whom the delivery system failed.
    [Show abstract] [Hide abstract] ABSTRACT: Pneumonia is a leading cause of childhood mortality globally. Oxygen therapy improves survival in children with pneumonia, yet its availability remains limited in many resource-constrained settings where most deaths occur. Solar-powered oxygen delivery could be a sustainable method to improve oxygen delivery in remote areas with restricted access to a supply chain of compressed oxygen cylinders and reliable electrical power. This study is a randomized controlled trial (RCT). Solar-powered oxygen delivery systems will be compared to a conventional method (oxygen from cylinders) in patients with hypoxemic respiratory illness. Enrollment will occur at two sites in Uganda: Jinja Regional Referral Hospital and Kambuga District Hospital. The primary outcome will be the length of hospital stay. Secondary study endpoints will be mortality, duration of supplemental oxygen therapy (time to wean oxygen), proportion of patients successfully oxygenated, delivery system failure, cost, system maintenance and convenience. The RCT will provide useful data on the feasibility and noninferiority of solar-powered oxygen delivery. This technological innovation uses freely available inputs, the sun and the air, to oxygenate children with pneumonia, and can be applied "off the grid" in remote and/or resource-constrained settings where most pneumonia deaths occur. If proven successful, solar-powered oxygen delivery systems could be scaled up and widely implemented for impact on global child mortality. Clinicaltrials.gov registration number NCT0210086 (date of registration: 27 March, 2014).
    Full-text · Article · Jul 2015
    • atients with HIV and TB were excluded.[15] A prior study conducted in the United States developed a staging system to predict mortality in HIV-infected patients with pneumonia but excluded patients with a history of TB.[16] A severity score has been developed for use in HIV-infected children with respiratory infections in resource-limited settings.[17] However, little has been done to develop prognostic tools in HIV-infected adult patients hospitalized with pneumonia in resource-limited, TB-endemic settings, particularly in sub-Saharan Africa. Thus, the aim of this study was to develop a clinical score using data that are often available at the time of initial
    [Show abstract] [Hide abstract] ABSTRACT: Pneumonia is a major cause of mortality among HIV-infected patients. Pneumonia severity scores are promising tools to assist clinicians in predicting patients’ 30-day mortality, but existing scores were developed in populations infected with neither HIV nor tuberculosis (TB) and include laboratory data that may not be available in resource-limited settings. The objective of this study was to develop a score to predict mortality in HIV-infected adults with pneumonia in TB-endemic, resource-limited settings.
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