Multiple antibiotic resistance as a risk factor for mortality and prolonged hospital stay: A cohort study among neonatal intensive care patients with hospital-acquired infections caused by gram-negative bacteria in Vietnam
Multiple antibiotic resistance as a risk factor for mortality and prolonged hospital stay: A cohort study among neonatal intensive care patients with hospital-acquired infections caused by gram-negative bacteria in Vietnam
Lynn PetersID 0 1
Linus Olson 1
Dung T. K. Khu 1
Sofia Linnros 0 1
Ngai K. Le 1
H?kan Hanberger 1
Ngoc T. B. Hoang 1
Dien M. Tran 1 2
Mattias Larsson 1
0 Global Health program, Karolinska Institutet, Stockholm, Sweden, 2 Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden, 3 Training and Research Academic Collaboration Sweden-Vietnam, Karolinska Institutet, Stockholm, Sweden, 4 Department of Neonatology, Vietnam National Children's Hospital , Hanoi , Vietnam , 5 Department of Microbiology, Vietnam National Children's Hospital , Hanoi , Vietnam , 6 Department of Clinical and Experimental Medicine, Faculty of Medicine and Health Sciences, Link o ?ping University , Link o ?ping, Sweden , 7 Research Institute for Child Health , Hanoi , Vietnam
1 Editor: Laura Folgori, St George's University of London , UNITED KINGDOM
2 Department of Surgery, Vietnam National Children's Hospital , Hanoi , Vietnam
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Funding: No authors have financial disclosure to
report. This work was supported by: Swedish
Foundation for International Cooperation in
Research and Higher Education (STINT) (All
Authors), ReAct, and Karolinska Institutet (LP, SL
doing their Master theses). The sponsors
Antibiotic resistance (ABR) is an increasing burden for global health. The prevalence of
ABR in Southeast Asia is among the highest worldwide, especially in relation to
hospitalacquired infections (HAI) in intensive care units (ICU). However, little is known about
morbidity and mortality attributable to ABR in neonates.
This study aimed to assess mortality and the length of hospitalization attributable to ABR in
gram-negative bacteria (GNB) causing HAI in a Vietnamese neonatal ICU (NICU).
We conducted a prospective cohort study (n = 296) in a NICU in Hanoi, Vietnam, from
March 2016 to October 2017. Patients isolated with HAI caused by GNB were included. The
exposure was resistance to multiple antibiotic classes, the two outcomes were mortality and
length of hospital stay (LOS). Data were analysed using two regression models, controlling
for confounders and effect modifiers such as co-morbidities, time at risk, severity of illness,
sex, age, and birthweight.
The overall case fatality rate was 44.3% and the 30 days mortality rate after infection was
31.8%. For every additional resistance to an antibiotic class, the odds of a fatal outcome
supported the study financially through the TRAC
(Training and Research Academic Collaboration)
Sweden-Vietnam. The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.Ethical
considerations. The study was approved by the
ethical board of the Vietnam Children?s Hospital in
2015. All caretakers were informed about the study
procedure and gave their consent prior to
enrollment in the study. Caretakers could withdraw
their children at any time from the study without
Competing interests: The authors have declared
that no competing interests exist.
increased by 27% and LOS increased by 2.1 days. These results were statistically
significant (p < 0.05).
ABR was identified as a significant risk factor for adverse outcomes in neonates with HAI.
These findings are generally in line with previous research in children and adults. However,
heterogeneous study designs, the neglect of important confounders and varying definitions
of ABR impair the validity, reliability, and comparability of results.
Antimicrobial resistance (AMR), in general, is the ability of a pathogen to withstand the effects
of antimicrobials. Consequently, simple infections can become lethal again without effective
tools to fight them. Antibiotic resistance (ABR) in bacteria is a consequence of selective
antibiotic pressure in the environment, in human and veterinary medicine and food production or
cross-transmission between individuals [
]. With a high number of antibiotics used in a
limited area, healthcare settings and intensive care units (ICU) in particular are one of the main
routes of emergence and transmission for resistant bacteria [
Hospital-acquired infections (HAI) are infections that occur during hospitalization and are
often caused by gram-negative bacteria (GNB) [
]. As healthcare settings facilitate the
spread of resistant bacteria, HAI are more likely to be caused by resistant bacteria than
community-acquired infections. HAI with ABR bacteria are more difficult to treat and are
associated with increased hospitalization time and mortality . AMR is estimated to be responsible
for 30,000 deaths per year in Europe [
] and 23,000 in the USA . Emergence and spread of
AMR are particularly high in low and middle-income countries (LMIC). Of all WHO regions,
it is expected to be the highest in Southeast Asia [
], leading to 38,000 deaths per year in
]. The emergence of multi- (MDR), extensive (XDR) and pan-drug resistant (PDR)
bacteria (for definition see Magiorakos et al. ) is particularly worrying since clinicians are
running out of treatment options and it has been estimated that AMR will cause 10 million
deaths per year by 2050 if we fail to take appropriate actions now [
]. AMR and especially
MDR are thus a significant burden for global health requiring immediate and coordinated
action from the global community.
Neonates (babies in their first 28 days of life) and infants (first year of life) have a premature
immune system and are a particularly vulnerable population for infectious diseases and hence
resistant bacteria [
]. In LMIC, neonatal infection rates are 3 to 20 times higher than
in high-income countries (HIC) and 40% of neonatal deaths are attributable to infectious
diseases in general [
]. Focusing on HAI specifically, rates of HAI in neonatal intensive
care units (NICU) are 9 times higher in LMIC than in HIC. They affect 20% to 50% of all
NICU admissions and fatality rates range from 12% to 52% [
]. The most common types of
HAI are pneumonia and bloodstream infections, mainly caused by GNB with increasing rates
of ABR [
So far, little is known about mortality and morbidity attributable to ABR infections in
neonates and young infants. The length of hospital stay (LOS) can serve as a measurement of
morbidity and is also associated with higher costs [
]. Previous research indicates an
increased mortality and LOS as a measurement of morbidity in neonates and infants due to
]. However, most studies focus on a single pathogen, type of infection or
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antibiotic class. Only a few studies appropriately adjust for confounders and effect modifiers,
which might bias results.
The aim of this study was to assess if multiple antibiotic resistance (MAR) is a risk factor for
mortality and prolonged LOS in patients from a NICU in Hanoi, Vietnam with HAI caused by
2. Materials and methods
Setting, data collection and inclusion criteria. A total of 327 neonates and young infants
from the NICU of the Vietnam National Children?s Hospital in Hanoi were included from
March 2016 to October 2017 (18 months). Data were collected from patient records. Patients
were considered eligible for the study if they met the following inclusion criteria: HAI
(pneumonia, bloodstream infections, surgical site infections or urinary tract infections) diagnosed
by the attending physician familiar with the clinical and laboratory parameters according to
the ECDC criteria [
] (e.g. fever, tachycardia, metabolic acidosis, elevated CRP levels,
infiltrate in case of pneumonia) caused by antibiotic-resistant bacteria and treated in isolation
care. Patients that died within 24 hours after admission were excluded, as well as those cases
with culture-negative HAI. Before cases were subsequently included in our cohort, the
diagnosis ?HAI? was verified by an experienced researcher also acquainted with the ECDC criteria.
Demographic data were collected from all included patients. Further analysis focused on
patients with an HAI caused by Klebsiella pneumonia, Pseudomonas aeruginosa, Acinetobacter
baumanii, Escherichia coli and/ or Serratia marcescens (n = 296) to exclude possible intrinsic
resistances in gram-positive as well as in fungal species.
Study design and variables. The study was designed as a prospective cohort study with
all-cause mortality and the LOS as the outcomes assessed in two models. The two endpoints
were either ?death? or ?discharged healthy?. In this setting, patients with a fatal diagnosis are
frequently withdrawn from treatment, with death at home as the most common outcome. Those
patients were added to the crude mortality rate with ?death? as outcome, the date of discharge
was defined as endpoint due to loss of follow up after discharge.
The exposure of interest was the antibiotic resistance pattern of the bacteria causing the
HAI. The definition of this variable differs across earlier studies. In order to provide
comparability within the field, a group of international experts published definitions of MDR, XDR,
and PDR for different bacterial species, based on the number of antibiotic classes a species is
resistant to. Resistance was defined as non-susceptibility to at least one agent of an antibiotic
]. The data collected was based on the local ABR testing practice for GNB and hence
did not cover all classes required to meet the definition. Therefore, it was not possible to code
the exposure as a binary variable according to this classification for MDR, XDR, and PDR. To
ensure some comparability nonetheless, we summed the number of antibiotic classes a species
was resistant to, as did Magiorakos et al. However, we used the continuous variable for the
analysis without dichotomization. The different classes (and respective agents) were:
antipseudomonal cephalosporins (ceftazidime, cefepime), antipseudomonal penicillins (piperacillin/
tazobactam, ticarcillin), antipseudomonal carbapenems (imipenem, meropenem),
antipseudomonal fluoroquinolones (ciprofloxacin, levofloxacin), aminoglycosides (amikacin, gentamicin,
tobramycin), monobactams (aztreonam), polymyxins (colistin), folate pathway inhibitors
Based on previous literature [
], we added different confounders and effect
modifiers as co-variates to our model:
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We included the effect modifiers ?age at admission? and sex. Gestational age (pre-term vs.
full-term) was strongly correlated with birthweight, causing multicollinearity. Therefore, we
picked birthweight as a continuous variable to adjust for prematurity in our model. We
included the ?time at risk? as a confounder, referring to the time from admission until a positive
culture was obtained marking the onset of the HAI. The emergence of ABR is time-dependent
and exposure to healthcare bears the risk of acquiring a resistant strain?the longer the stay,
the higher the risk [
]. Consequently, a prolonged LOS can cause ABR. However, we aimed
to assess LOS as a consequence of ABR and hence needed to control for the time at risk before
the onset of HAI.
Secondly, we included the number of common co-morbidities to adjust for underlying
diseases, namely: respiratory distress syndrome, persistent ductus arteriosus, pulmonary
hypertension, hyperbilirubinemia, congenital heart disease, intraventricular hemorrhage, epilepsy,
megacolon, bronchopulmonary dysplasia, necrotizing enterocolitis, hydrocephalus, and down
Furthermore, the severity of illness must be considered. For adults, validated and updated
scores including clinical and laboratory information are often applied. In neonates, several
scores exist(CRIB: clinical risk index for babies; SNAP[-PE]: score for neonatal acute
physiology [perinatal extension]; NMPI: neonatal mortality prognosis index; NTISS: neonatal
therapeutic intervention scoring system; NBRS: neurobiological risk score);, however, every score
has considerable disadvantages [
]: Most scores were designed in the last century and are
therefore not adjusted to the medical progress made in neonatal care. Furthermore, most
existing scores were designed to predict mortality in premature neonates with a range of problems,
not specifically to assess the severity of an infection. This explains why certain scores (CRIB,
SNAP, NMPI, SINKIN) are only validated for the first 12 or 24 hours of life, others only for
neonates with very low birthweight (CRIB, CRIB II, Berlin score) and are therefore not
applicable to our cohort with neonates and young infants, both premature and mature. Others
(SNAP, SNAP-PE, NTISS) require a vast number of parameters which is not feasible in our
setting. To control for ?severity of illness? nevertheless, we collected those parameters available
and recommended in most scoring systems, namely birthweight, gestational age, gender and
malformations, here labelled ?co-morbidities?. As mentioned earlier, these factors were entered
as confounders in the regression model, except gestational age which was later excluded
because of multicollinearity. Most scores furthermore mention respiratory failure (CRIB,
SNAP, SNAP-II, Berlin score, NMPI, SINKIN, NBRS, NTISS) as an important determinant of
the severity of illness, others low urine output (SNAP-II, SNAPPE-II, NTISS), seizures (NBRS,
SNAP, SNAP-II, NTISS) or metabolic acidosis (CRIB, CRIB II, Berlin score, NBRS, SNAP,
SNAP-II, NTISS). Most of these conditions require invasive procedures such as endotracheal
intubation, a urinary tract catheter or a venous catheter to administer treatment. Therefore,
the sum of invasive devices (endotracheal tubes, urinary tract catheters, central and peripheral
venous catheters) needed during the entire stay served as a surrogate parameter, similar as in
the NTISS, and was entered as a confounder in the regression model. In ideal circumstances,
an updated, reliable and validated score applicable to our cohort including feasible parameters
would present the most preferable approach to adjust for ?severity of illness?. However,
including those factors widely used in existing disease severity scores for neonates (birthweight,
gestational age, gender, co-morbidities) and the number of invasive devices as a proxy seems the
most feasible approach to control for ?severity of illness? given the limitations of existing
scoring systems and our setting.
Crude mortality was added as a co-variate to the model assessing LOS to avoid bias due to
the phenomenon known as ?competing risks?. In short, neonates dying during in-hospital
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treatment have a shorter LOS than those that survive. Assuming that ABR is associated with
mortality, it would wrongly be linked to a shorter LOS if not adjusted for.
Some research indicates that polymicrobial infections (infections caused by multiple
pathogens) are associated with ABR and mortality [
]. We intended to control for this possible
confounder, however, it was strongly correlated with the exposure MAR and hence excluded
because of multicollinearity.
Laboratory procedures. Laboratory information regarding culture type (tracheal aspirate
or nasopharynx culture in pneumonia, blood culture in bloodstream infections, urine culture
in urinary tract infections, wound culture in surgical site infections), culture date and result
(isolated pathogen and resistance testing) was obtained from patients? records. The procedures
respected the guidelines of the Clinical and Laboratory Standards Institute. Antibiotic
resistance was tested using the VITEK2 system (bioMe?rieux) and confirmed by broth
microdilution, since the VITEK2 system is not reliable for all substances, e.g. colistin. Intermediate
resistance was considered resistant.
Statistical analysis. Statistical analysis was performed using IBM SPSS version 21.
Demographic characteristics, type of HAI, pathogens isolated and their ABR patterns were described
with mean, median and standard deviation. The case fatality rate and the 30 days mortality
rate from admission and infection were calculated. Descriptive statistics are based on the
original data set. Prior to conducting multivariable analyses, missing data were handled using
multiple imputation. Variables were tested for normal distribution, heavily skewed variables were
log transformed to approach normality prior to the multiple imputation process and
re-transformed afterwards. Multiple imputation was performed before categorizing variables to
prevent loss of information. Since multiple imputation might weaken the influence of effect
modifiers on the outcome, the dataset was split according to the dichotomous variable
?gestational age? (pre-term vs. full-term), which was assumed to be the strongest effect modifier in
this study [
] and merged again after the multiple imputation process. The number of
datasets generated was determined using the formula suggested by Newgard and Haukoos [
aiming for a relative efficiency of at least 95%, which is considered a high rate. A table giving
an overview of the proportion of missing data in each variable is provided in the supporting
information (S1 Table).
Afterwards, two regression models were created: a logistic regression model with mortality
as a binary outcome and a linear regression model with LOS as a linear outcome, both with
MAR as exposure. The models were adjusted for the co-variates discussed earlier. All variables
were entered simultaneously. With a sample size of n = 296 and up to eight co-variates entered
in the models, the power level was sufficient to detect even small effect sizes (power level set at
]. Results were considered significant with a p-value smaller than 0.05.
Table 1 summarizes the research problems as well as the conceptual framework and the
statistical analyses used to address them.
Since linear regression is sensitive to outliers, those outliers exceeding three standard
deviations were identified (two cases in the original dataset and between no case and four cases in
the imputed sets). The Cook?s distance, however, did not show a value larger than one in any
of those cases (maximum of 0.054 in original data set and 0.092 in imputed data sets), therefore
the cases were kept in the analysis.
Ethical considerations. The study was strictly observational, no interventions were
performed, data was collected from patients? records only without influencing further treatment.
Identies of the children where not included in the data but a uniqe identifier fo each patient.
Therefore, the caretaker?s consent was not required according to the ethical board of the
Vietnam Children?s Hospital when ethical approval was given in 2015.
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Demographic characteristics. Out of 327 participants, 62.4% were male and 37.6%
female. Almost two third were born pre-term (64.2%). The main mode of delivery was vaginal
(60.9%), followed by a planned cesarean section (27.2%) and a smaller number of emergency
cesarean sections (5.8%). Table 2 displays further demographical characteristics of the study
Hospital-acquired infection and causative pathogens. The predominant HAI were
pneumonia (81.3%) and sepsis (49.2%). Sepsis referred to infections with positive blood
cultures and included both primary bloodstream infections and secondary sepsis resulting from
pneumonia. Other HAI were meningitis, gastroenteritis (0.9% each), surgical site infections
and urinary tract infections (0.3% each). The pathogens mainly isolated from the infection site
were Acinetobacter baumanii (27.9% of all isolates), Klebsiella pneumoniae (25.3%),
Pseudomonas aeruginosa (20.5%), Escherichia coli (9.0%), Serratia marcescens (2.7%), Staphylococcus
aureus (MRSA, 1.9%).
In total, 58.4% of HAI were caused by one pathogen, 27.5% were caused by two and 14.1%
by three or more pathogens.
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LOS = length of stay in days, here after infection; CFR = case fatality rate in percentage and in absolute numbers (n)
The first section displays the proportion of sepsis, pneumonia and other HAI caused by Acinetobacter baumanii, Klebsiella pneumoniae and Pseudomonas aeruginosa
respectively. The sum exceeds 100% since pneumonia and sepsis were often concurrent conditions and hospital-acquired infections (HAI) were frequently caused by
more than one pathogen. The second section describes the proportion of antibiotic resistance (ABR) to the respective antibiotic class in the isolated bacteria. HAI and
ABR are additionally displayed by treatment outcomes?the mean length of hospital stay (LOS) after infection and the case fatality rate (CFR)?for each of the bacterial
Since the study focuses on GNB, further calculations were based on HAI caused by
Acinetobacter baumanii, Klebsiella pneumonia, Pseudomonas aeruginosa, Escherichia coli or Serratia
marcescens as at least one of the isolates (n = 296, 90.5% of all cases).
Antibiotic resistance in gram-negative bacteria causing hospital-acquired infections.
The proportion of ABR in the isolates ranged from 10% (colistin resistance) to over 97%
(cephalosporin resistance). Table 3 shows the proportion of ABR to different antibiotic classes
displayed by the main isolates.
This study focuses on the effect of MAR on treatment outcome. Fig 1 displays MAR, i.e. the
number of different antibiotic classes isolated GNB were resistant to. Most patients (64%) had
HAI caused by GNB with ABR to between five and seven antibiotic classes.
Descriptive statistics of outcomes. Mean total LOS in the initial study population
(n = 327) was 38.7 days (SD 26.0 days) with a median of 30 days. Of all patients, 46.2% were
discharged in good health while 44.3% died or were withdrawn with a fatal diagnosis (case
fatality rate). Of those that died, 57.0% died within 30 days after admission and 71.7% within
30 days after infection. For the initial study population (n = 327), the 30 days mortality rate
after admission was hence 25.3% and after infection 31.8%. Table 3 summarizes the outcomes
in relation to the HAI and the GNB isolated.
All-cause mortality. A logistic regression model assessed the effect of MAR in GNB
causing HAI in neonates on the likelihood to die while adjusting for possible confounders. The
model ?2 (df = 7) = 21.877, p = 0.003, Nagelkerke?s R2 = 11.8% classified 62.7% of cases
correctly. With every increase in the score of MAR, i.e. for every additional resistance to an
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Fig 1. Multiple antibiotic resistance (MAR). Proportion of patients with hospital-acquired infections caused by
gramnegative bacteria (GNB) with antibiotic resistance (ABR) to between 0 and 8 antibiotic classes. These classes included
antipseudomonal cephalosporins, penicillins, carbapenems, fluoroquinolones, aminoglycosides, monobactams,
polymyxins, folate pathway inhibitors.
antibiotic class, the odds of a fatal outcome increased by 27% (c.f. Table 4). This result was
statistically significant (p = 0.007).
Length of hospital stay. To assess the effects of MAR on LOS, a multiple linear regression
was conducted. A significant model F = 19.884 (df = 8; p < 0.001, standard error = 21.272)
explained 41.6% (R2) of the outcome?s variance, which is a moderate proportion. The
regression coefficients are listed in Table 5.
Entered in the regression equation, LOS significantly increased by 2.1 days for every
additional resistance to an antibiotic class. Furthermore, a lower birthweight was associated with
an increased LOS, however with a smaller effect size compared to the MAR score. Mortality
was almost significantly (p = 0.052) correlated with LOS. The two significant variables account
for 11.6% (birthweight) and 2.0% (MAR score) of the outcome?s variance and therefore
influence LOS to a larger extent than other independent variables.
In a prospective cohort study, we assessed the effect of multiple antibiotic resistance (MAR) in
gram-negative bacteria (GNB) causing hospital-acquired infections (HAI) in Vietnamese
neonates and young infants on mortality and the length of hospital stay (LOS). The isolates
showed a high level of resistance against multiple antibiotic classes. Higher rates of antibiotic
8 / 18
Odds ratio (OR)
95% Confidence interval
Sex coded as 0 = male, 1 = female; time at risk as baseline; OR > 1: risk factor, OR < 1 protective factor
Adjusting for different confounders, here listed as co-variates, multiple antibiotic resistance (MAR) was a significant
risk factor for mortality, increasing the odds by 27%.
resistance were significantly associated with higher mortality and a longer hospital stay when
confounding factors were held constant.
Hospital-acquired infections and antibiotic resistance patterns and their
implications for global health
The most frequent HAI were pneumonia and sepsis. Other HAI were meningitis,
gastroenteritis, surgical site infection, and urinary tract infection. These findings were similar to previous
research in the same [
] or different settings [
Eighty-five percent of all isolates causing HAI were GNB. The bacteria mainly isolated were
Acinetobacter baumanii (28%), Klebsiella pneumoniae (25%) and Pseudomonas aeruginosa
(21%), followed by Escherichia coli (9%) and Serratia marcescens (3%). Most studies
investigating sepsis [
], pneumonia [
] or HAI in general [
] found GNB as the
main causative pathogens for neonatal HAI, fewer name gram-positive bacteria as the main
]. The exact bacteriological profile of HAI depends on the setting and the type
95% Confidence interval of B
Sex coded as 0 = male, 1 = female; B = unstandardized regression coefficient; 95% CI B = 95% confidence interval of B
Adjusting for important confounders, here listed as co-variates, the length of stay (LOS) in the hospital increased by 2.1 days with every increase in MAR score.
9 / 18
of HAI investigated. Nevertheless, Acinetobacter baumanii, Klebsiella pneumoniae,
Pseudomonas aeruginosa, and Escherichia coli were predominantly isolated in prior research, similarly to
our study [
During the last decade, research increasingly focuses on multidrug resistance (MDR),
extensive drug resistance (XDR) and pan-drug resistance (PDR) [
]. However, the use of
these terms varies widely between studies, especially before Magiorakos et al.  offered a
comprehensive definition of those classifications for common strains (Magiorakos et al. [
?MDR was defined as acquired non-susceptibility to at least one agent in three or more
antimicrobial categories, XDR was defined as non-susceptibility to at least one agent in all but two or
fewer antimicrobial categories (i.e. bacterial isolates remain susceptible to only one or two
categories) and PDR was defined as non-susceptibility to all agents in all antimicrobial
categories.?). Previous studies investigating neonatal HAI found MDR in 11% [
] and in 80% of all isolates [
]. The global reports on infections due to XDR and
even PDR GNB are increasing and a cause for concern [
]. Teerawattanapong et al. 
detected a particularly high burden of Acinetobacter spp. with MDR, XDR or PDR in Southeast
Asia compared to other regions. The exact definition suggested by Magiorakos et al. [
not be applied to the study at hand since bacteria were not tested for all required antibiotic
classes, e.g. tetracyclines since they are contraindicated in neonates. Nevertheless, 70% to over
90% of isolates were resistant to antipseudomonal penicillins, cephalosporins, carbapenems,
fluoroquinolones, aminoglycosides, and trimethoprim/ sulfamethoxazole, suggesting a high
rate of MDR and a considerable rate of XDR. However, these figures do not represent the
prevalence of ABR in the area, which is presumably lower. Since the study was conducted in a
tertiary hospital with many patients referred to due to infections, the numbers are overestimating
the true prevalence of ABR. Le et al. [
] showed in an ICU point prevalence survey of HAI in
five Vietnamese pediatric hospitals including a total of 1363 cases a carbapenem resistance
between 55% in Klebsiella pneumoniae to 71% in Acinetobacter baumanii and a prevalence of
cephalosporine resistance of 90% in Klebsiella pneumoniae.
The case fatality rate was 44.3% and similar to previous research indicating an overall
neonatal fatality of 38 to 47% [
]. Of those that died, 71.7% died within 30 days after
infection while Saleem et al. [
] report a similar proportion (70%) after only four days.
Comparing mortality as a consequence of ABR across the literature is challenging because
of vast methodological differences. Risk factor and outcome studies are frequently combined,
although not recommended [
] and study designs differ greatly. Unfortunately, not all
investigators conduct multivariable analysis to adjust for confounders or effect modifiers. Although
some papers [
] emphasize the need to control for co-morbidities, the severity of
illness and the time spent in a health facility before infection (time at risk), this is not yet
common practice, possibly leading to biased results. The co-variates included vary across the field,
sometimes exceeding the capacity of the sample size, decreasing the power.
Antibiotic resistance as a risk factor for mortality
In our study, multiple antibiotic resistance (MAR) in GNB remained a significant risk factor
for mortality in neonates with HAI after adjusting for potential confounders. This association
might be attributable to changes in the bacteria to withstand the host?s immune defense,
however, several studies report a reduced fitness in resistant strains compared to susceptible strains
]. Consistent with prior research, we assume that delayed appropriate treatment and the
absence of effective drugs in cases resistant to all tested substances contribute to the increased
mortality in our cohort since empiric treatment might fail due to ABR [
]. Data about
the exact dates and dosage of antibiotic therapy were not available to us, hence we lack strong
10 / 18
evidence to support our hypothesis. However, the authors observed that although local
recommendations for antibiotic treatment consider the prevalence of ABR in this setting, first line
antibiotics were often ineffective in patients with HAI caused by MDR pathogens.
Consequently, appropriate treatment is delayed by the time clinicians receive microbiological test
results including sensibility testing. In those cases where all antibiotics were tested resistant
(6.4%) and empirical therapy was therefore certainly inadequate, mortality was highest. In
these patients, a combination of different substances was administered, hoping for a residual
antibiotic effect in vivo.
So far, not many studies investigated mortality due to ABR in neonates. Focus often lies
upon a single type of infection, bacteria or antibiotic class, studies including multiple antibiotic
classes are rare.
In general, our study confirms previous findings. While only a few studies did not identify
ABR as a risk factor for mortality in neonates [
], the majority have found significant
associations. For instance, mortality was reported to be higher in patients infected by
ESBL-producing GNB [
] or by GNB with carbapenem resistance , increasing the odds of dying
Most research focusing on resistance to multiple antibiotic classes indicates an association
with mortality. Al Jarousha et al. [
] found a mortality rate of 37.5% in NICU patients
infected with MDR Acinetobacter spp. compared to only 12% in controls. However, controls
were uninfected, so the result might be more attributable to the infection itself than to ABR.
Folgori et al. [
] found a 30 days mortality rate of 19% in patients with HAI caused by MDR
strains compared to 13% in susceptible strains (p = 0.06) in neonates and children. Others [
reported similar results with a fatality rate of 16% in patients with sepsis caused by MDR
bacteria compared to 12% in those with susceptible strains and 8% in controls with culture negative
However, only a few studies adjust for confounders, which is essential if ABR is claimed to
be a risk factor for mortality. In two elaborate studies which included two control groups each
and adjusted for confounders, Thatrimontrichai et al. [
] reported carbapenem resistance
to be a significant risk factor for mortality in patients with infections caused by Acinetobacter
spp. The mortality was significantly higher in bloodstream infections caused by resistant
strains compared to those caused by susceptible strains (OR = 5). These results are similar to
ours, as we identified MAR as a risk factor for mortality with an OR of 1.3, however assessing a
broader spectrum of bacteria, infections, and antibiotic classes.
Prolonged length of hospital stay attributable to antibiotic resistance
Studies assessing LOS in neonates are scarce and varying with respect to the type of infection,
pathogen, and antibiotic resistance pattern. Abdel-Hady et al. [
] and Sehgal et al. [
that the hospital stay was prolonged in patients infected with ESBL-producing GNB. In a study
conducted by Thatrimontrichai et al. [
], hospital stay was longer in patients with pneumonia
caused by carbapenem-resistant Acinetobacter spp., however, the result was not significant. In
children admitted to an ICU, infections with a resistant strain seem to prolong the hospital
stay compared to infections caused by a susceptible strain in some studies [
], others found
no significant difference [
Focusing on MDR, despite the varying definition of this variable, Mauldin et al. 
assessed the LOS in patients from all age groups admitted to the general ward or ICU with
HAI. They and concluded that LOS was prolonged by 24% in patients infected with MDR
strains. Other studies including adults [
] also indicate a longer hospital stay if an
infection is caused by resistant compared to susceptible bacteria.
11 / 18
However, the vast majority of those studies did not control for confounders, especially for
the ?time at risk?. Exposure to healthcare is a risk factor to acquire colonization or infection
with ABR bacteria due to cross-transmission or selective pressure on the microbiome during
antibiotic treatment [
]. Reversely, an infection with a resistant strain can prolong the
hospital stay since it might be more difficult to treat [
]. Thus, ABR can present the
cause or the consequence of a prolonged hospital stay. To ensure that only LOS attributable to
ABR is assessed, ?time at risk?, the time before infection occurs, should be entered as a
confounder in the multivariate analysis [
]. One of the few studies controlling for ?time at risk?
and co-morbidities found increased mortality (OR 4.4) and a longer hospital stay (hazard ratio
2) in patients with MDR Pseudomonas spp. infection compared to susceptible controls .
Lower birthweight was significantly associated with increased LOS and should therefore
always be included as a confounder. Mortality was an almost significant co-variate in our
regression model, demonstrating the importance to control for this confounder when
assessing LOS. Our study, likewise adjusting for possible confounders, found an increase of LOS by
2.1 days for every additional resistance to an antibiotic class.
A strength of this study is the considerable sample size and adjustment for important
confounders such as co-morbidities and ?time at risk? as well as for possible effect modifiers. Most
studies identified birthweight or gestational age as a significant effect modifier when generally
assessing mortality in neonates. Especially preterm neonates are at a higher risk for adverse
outcomes with a compromised immune system and other underlying diseases [
future studies investigating ABR-related mortality in neonates, we hence suggest including
birthweight, gestational age, age at admission and sex as possible effect modifiers in the
Obtaining tracheal aspirate and nasopharyngeal cultures always entails a potential risk of
contamination with bacterial species other than the one or those causing pneumonia. The gold
standard in adults however, the bronchoalveolar lavage, can be harmful in severely ill neonates.
Since the bacterial species included in this study (Klebsiella pneumonia, Pseudomonas
aeruginosa, Acinetobacter baumanii, Escherichia coli and/ or Serratia marcescens) are not part of the
typical nasopharyngeal flora, the attending physicians in the vast majority of cases treated the
patients based on the assumption that the pathogens isolated using tracheal aspirates and
nasopharyngeal cultures are de facto causing the HAI. Furthermore, many neonates with
pneumonia developed a secondary sepsis that allowed us to confirm the causative pathogen in blood
Since we lacked the exact dates and dosages of drug administration, we cannot ultimately
prove if inappropriate empirical treatment is the underlying reason for the adverse outcomes
To adjust for ?severity of illness?, the number of invasive devices was used as a surrogate
parameter as well as other important factors recommended in existing scoring systems. This
approach is likely to minimize the influence of ?severity of illness? on the outcome, however, it
is no validated method and we cannot exclude residual confounding. As already discussed
earlier, a more favorable approach to control for this confounder would be a validated score
including clinical and feasible laboratory parameters. This score should preferably be assessed
before and with the onset of an HAI to adjust for the severity of the underlying disease as well
as for the severity of the HAI itself (the mortality in sepsis is presumably higher than that of a
12 / 18
simple urinary tract infection, irrespective of ABR). Another possibility might be to adjust for
the type of HAI, however, this would have reduced our sample size and with it the statistical
Our variable ?time at risk? which adjusts for previous exposure to healthcare only accounts
for the time since admission to the hospital, the time of prior hospital stays in case of referral
was not considered.
Because of missing information, we were unable to compare MDR and non-MDR bacteria
according to the definition by Magiorakos et al. [
] and introduced the variable MAR instead.
Nevertheless, we first tried the analysis with a binary variable similar to this definition. Yet no
significant results were obtained. We conclude that dichotomizing a continuous variable, such
as the number of resistances to different antibiotic classes, leads to loss of information and can
reduce the discriminatory power. This might be a reason why several studies were unable to
detect a difference between infections caused by MDR and non-MDR bacteria. Using a
continuous variable as we did might, therefore, present an alternative approach.
Antibiotic resistance in gram-negative bacteria, here measured as resistance to multiple
antibiotic classes, is a significant risk factor for mortality and prolonged hospital stay in patients
admitted to a neonatal intensive care unit with hospital-acquired infections in the Vietnamese
context. The adjustment for confounders such as co-morbidities, severity of illness and time at
risk is essential to prevent biased results, yet not common practice in the field.
In line with previous research, this study emphasizes that antibiotic resistance is an
increasing burden for global health, especially for vulnerable patients such as neonatal intensive care
patients in resource-constraint settings. Tackling antibiotic resistance must become a priority
for the global community to reduce mortality, morbidity and healthcare expenses.
S1 Table. Proportion of missing data in the variables of interest. The table shows the
percentage of missing data in each of the variables of interest across the complete dataset
(n = 327).
S1 Data. The primary data our statistical analysis is based on.
We would like to thank: The Swedish Foundation for International Cooperation in Research
and Higher Education (STINT) for the support, Training and Research Academic
Collaboration (TRAC) to have student knowledge exchange and capacity building between Sweden and
Vietnam. The Swedish research council (VR) and EU Marie Curie for support, the staff of
Vietnam National Children?s Hospital (VNCH) to let us do important research with data
collected and to our different universities for giving us time to do research. The first author wants
to thank the Else Kro?ner-Fresenius-Stiftung for a scholarship that allowed a master?s degree
and hence working on this project.
13 / 18
Conceptualization: Linus Olson, Dung T. K. Khu, Ngoc T. B. Hoang, Dien M. Tran, Mattias
Data curation: Lynn Peters, Linus Olson, Ngai K. Le, Ngoc T. B. Hoang, Mattias Larsson.
Formal analysis: Lynn Peters.
Funding acquisition: Linus Olson.
Investigation: Linus Olson, Ngai K. Le, Ngoc T. B. Hoang, Dien M. Tran, Mattias Larsson.
Methodology: Lynn Peters, Linus Olson, Sofia Linnros, Dien M. Tran, Mattias Larsson.
Project administration: Linus Olson, Dung T. K. Khu, Ngai K. Le, Mattias Larsson.
Resources: Linus Olson, Ngoc T. B. Hoang.
Supervision: Linus Olson, Ngai K. Le, Ngoc T. B. Hoang, Dien M. Tran, Mattias Larsson.
Validation: Linus Olson, Mattias Larsson.
Visualization: Lynn Peters.
Writing ? original draft: Lynn Peters.
Writing ? review & editing: Linus Olson, Dung T. K. Khu, Sofia Linnros, Ngai K. Le, H?kan
Hanberger, Ngoc T. B. Hoang, Dien M. Tran, Mattias Larsson.
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