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R E S E A R C H Open Access
Implementing an infection control
and prevention program decreases
the incidence of healthcare-associated
infections and antibiotic resistance in
a Russian neuro-ICU
Ksenia Ershova
1*
, Ivan Savin
2
, Nataliya Kurdyumova
2
, Darren Wong
3
, Gleb Danilov
4
, Michael Shifrin
5
,
Irina Alexandrova
6
, Ekaterina Sokolova
2
, Nadezhda Fursova
7
, Vladimir Zelman
1,8
and Olga Ershova
9
Abstract
Background: The impact of infection prevention and control (IPC) programs in limited resource countries such as
Russia are largely unknown due to a lack of reliable data. The aim of this study is to evaluate the effect of an IPC
program with respect to healthcare associated infection (HAI) prevention and to define the incidence of HAIs in a
Russian ICU.
Methods: A pioneering IPC program was implemented in a neuro-ICU at Burdenko Neurosurgery Institute in 2010
and included hand hygiene, surveillance, contact precautions, patient isolation, and environmental cleaning measures.
This prospective observational cohort study lasted from 2011 to 2016, included high-risk ICU patients, and evaluated
the dynamics of incidence, etiological spectrum, and resistance profile of four types of HAIs, including subgroup
analysis of device-associated infections. Survival analysis compared patients with and without HAIs.
Results: We included 2038 high-risk patients. By 2016, HAI cumulative incidence decreased significantly for respiratory
HAIs (36.1% vs. 24.5%, p-value = 0.0003), urinary-tract HAIs (29.1% vs. 21.3%, p-value = 0.0006), and healthcare-associated
ventriculitis and meningitis (HAVM) (16% vs. 7.8%, p-value =0.004). The incidence rate of EVD-related HAVM dropped
from 22.2 to 13.5 cases per 1000 EVD-days. The proportion of invasive isolates of Klebsiella pneumoniae and Acinetobacter
baumannii resistant to carbapenems decreased 1.7 and 2 fold, respectively. HAVM significantly impaired survival and
independently increasing the probability of death by 1.43.
Conclusions: The implementation of an evidence-based IPC program in a middle-income country (Russia) was highly
effective in HAI prevention with meaningful reductions in antibiotic resistance.
Keywords: Cross infection, Intensive care unit, Infection control, Drug resistance, Survival analysis
* Correspondence: ksenia.ershova@skolkovotech.ru
1
Center for Data-Intensive Biotechnology and Biomedicine, Skolkovo Institute
of Science and Technology, Moscow, Russia
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Ershova et al. Antimicrobial Resistance and Infection Control (2018) 7:94
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Background
Infection prevention and control (IPC) programs have
been repeatedly shown to be effective at decreasing the in-
cidence of healthcare-associated infections (HAIs). A land-
mark paper on this topic in 1985 showed a 32% decrease in
the hospital infection rate after 5 years of an ongoing IPC
program [1]. In 1999 the CDC identified seven key
evidence-based elements of an effective IPC strategy in-
cluding voluntary participation of all hospitals, standardized
case definitions and protocols, targeted interventions for
high risk patient populations, risk adjusted comparisons of
infection rates across hospitals, education and adequacy of
resources, and feedback to healthcare providers [2]. The el-
ements of an IPC program have since been significantly up-
dated, forming the concept of “multimodal strategy”[3].
To prevent HAIs, the WHO recommends implementing
an IPC program in every acute healthcare facility [4].
However, according to the most-recent survey, only 29%
of 133 countries surveyed have IPC programs in all ter-
tiary hospitals [3]. In Russia, IPC programs are also not
widely used. The rate of HAIs in Russia has been heavily
underestimated for decades. In 2016 it was reported to be
approximately 0.08% (24,771 [5] cases per 31.3 million
hospitalized patients [6]) yet a concurrent meta-analysis
which included Russia reported the prevalence of HAIs at
15.5% [7]. According to the latest World Bank report,
Russia has a gross national income per capita of US
$9720, corresponding to a middle-income country [8].
Besides significant underreporting of HAIs, Russia faces
other challenges in establishing IPC programs, such as lack
of commitment, punishment-based HAI reporting systems,
lack of expertise, and inadequate allocation of resources [9].
Since the dissolution of the Soviet Union, Russia has made
some progress in adopting the IPC programs [10]. A
pioneering Russian hospital where an evidence-based IPC
program was implemented in 2010 is Burdenko National
Medical Research Center of Neurosurgery (NSI) in
Moscow. Herein we report the results of our study which
aimed to evaluate the impact of this program on HAI pre-
ventionintheICU.
Methods
Study design and healthcare facility
This study was a prospective observational cohort study
with annual interim data analyses. The study was done in
the neuro-ICU department at NSI in Moscow, Russia. NSI
is a specialized neurosurgical hospital with 300 beds that
cares for approximately 8000 patients per year, 95% of
whom undergo surgery. The NSI ICU has 38 single-bed
rooms with a flow of approximately 3000 patients per year.
Infection prevention and control program
In September 2010, an IPC program was first set up in the
neuro-ICU, inspired by the results of the European
HELICS-ICU program [11]. The protocols for our IPC
program were adopted from the 2007 CDC guidelines
[12] and included three key components: education, infec-
tion prevention measures, and surveillance (Fig. 1). The
surveillance software was designed in-house and inte-
grated in the NSI electronic health record system [13]. At
the time of initiation of this program, an antibiotic stew-
ardship program was in existence at our facility. However,
during the study period there were refinements to this
program and coordination of antibiotic stewardship initia-
tives with the infection control program.
Patients
We studied a high-risk patient population, which we de-
fined as patients who required > 48 h of care in the neuro-
surgical ICU. All of these patients were qualified to
participate in the study until discharge or death. Enroll-
ment period was between January 1st, 2011 and December
31st, 2016. Following ICU discharge, the parameters of
total length of stay and outcome were collected.
To identify cases of HAIs, we used the 2008 CDC defin-
ition [14]. Four types of HAIs were surveilled: blood-
stream, respiratory and urinary-tract infections, and
healthcare-associated ventriculitis and meningitis
(HAVM). We specifically focused on the subgroup of
device-related infections, such as central line-associated
bloodstream infections (CLABSI), ventilator-associated
pneumonia (VAP), catheter-associated urinary-tract infec-
tions (CAUTI), and external ventricular drain (EVD)-asso-
ciated HAVM. In accordance with the CDC case
definitions, an infection was considered device-related if
the patient had a device in place for > 48 h prior to devel-
oping the HAI [12].
In addition to HAIs, we monitored superficial
surgical-site infections (SSSI) after neurosurgery, and
ICU-acquired intestinal dysfunction. The latter was clinic-
ally defined by the presence of one or more of the follow-
ing gastrointestinal symptoms, as delineated in the
literature [15]: vomiting, diarrhea, absence or abnormality
of bowel sounds, bowel dilation, gastrointestinal bleeding,
or increased nasogastric aspirate volume (> 500 ml/day).
Data collection and preprocessing
Data was collected prospectively on a daily basis and in-
corporated 54 different characteristics (Additional file 1:
Table S1). The spectrum and susceptibility profile of iden-
tified organisms causing the HAIs was built for each infec-
tion type. In January of each year, interim analysis was
performed, and the results were then disseminated to NSI
staff to encourage compliance with IPC measures.
Microbiological analysis
Clinical samples were collected form patients with HAIs
and delivered to the microbiological laboratory without
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delay. Blood and CSF samples were processed using BD
BACTEC (Becton, Dickinson and Company, USA). All
samples of pure bacterial cultures underwent automated
identification by VITEK®2 (Biomerieux, France) with
standard AST Cards. Selected samples of pure bacterial
cultures were subsequently identified by MALDI-TOF
MS, MALDI Biotyper® (Bruker Daltonik GmbH,
Germany). Minimal inhibitory concentrations obtained
from VITEK®2 were interpreted in accordance with the
current CLSI guidelines [16]. A profile of antibiotic
resistance for each strain was built using the WHONET
software [17].
Statistical analysis
Statistical analysis was performed in Python3.6 using
StatsModels [18] and Scipy [19]. Categorical variables for
dichotomous events were reported as number of events of
one category with percentage and 95% confidence interval
(CI) for binomial distribution. Continuous variables were
reported as a median value with first and third quartiles
(Q1; Q3). Incidence of HAIs was calculated as a number
of cases per 100 high-risk patients or as a number of cases
per 1000 patient-days. DA-HAIs were measured as cases
per 1000 device-days. Device utilization ratio (DUR) was
calculated as proportion of device-days to patient-days.
We used Chi-square test to compare binary and categor-
ical variables and linear regression analysis to compare
continuous variables over years. In survival analysis we
used Cox regression, including HAIs, diagnosis, surgeries,
and preexisting characteristics. Log-rank test was used to
compare survival curves. P-values below 0.05 were consid-
ered statistically significant.
Results
A total of 2038 patients of all ages and both genders were
included in the study during 6 years (the study data set is
available at https://doi.org/10.5281/zenodo.1021503). The
code for data analysis is available at https://github.com/
KseniaErshova/IPC_paper.git.
Study population included 50% males, 16.9% children
under 18 years, and a patient median age of 46 [Q1;Q3:
26.0; 59.0] years. The patients were uniformly distributed
across the years by disease types, surgery types, and pa-
tient features. However, the number of lethal outcomes
and the length of stay in the ICU decreased from 2011
to 2016. The baseline characteristics of the study popu-
lation for each year and averaged over the 6 years are
shown in Table 1.
HAIs and patients’stay in the ICU
A median number of 344 [Q1;Q3: 330; 349] patients per
year accounted for a median 6998 [Q1;Q3: 6678; 7399]
patient-days per year (Additional file 1: Table S2). Since
the number of patients increased from 2011 to 2016 by
an average of 2.3% annually and the number of
patient-days gradually decreased simultaneously by 2.7%
Fig. 1 The key elements of multimodal strategy and core infection prevention and control measures in the scope of Infection Prevention and
Control (IPC) Program implemented in 2010 in neuro-ICU at Burdenko National Medical Research Center of Neurosurgery in Russia
Ershova et al. Antimicrobial Resistance and Infection Control (2018) 7:94 Page 3 of 11
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per year (from 6778 to 5809), an average patient spent
less time in the ICU, from the median of 13 days
[Q1;Q3: 7.0; 27.0] in 2011 to 8 days [Q1;Q3: 5.0; 17.0] in
2016, p-value = 0.01 (Table 1). We found that over the
six-year study, the lowest percentage of DA-HAIs was in
the HAVM group: 40.4% [95% CI 33.6–47.1]. The high-
est percentage of DA-HAIs was in healthcare-associated
bloodstream infections: 86.6% [95% CI 80.4–92.7]. Thus,
most healthcare-associated bloodstream infections were
CLABSI, whereas less than half of HAVM cases were
EVD-associated (Additional file 1: Table S3, Fig. 2).
DUR was relatively high for mechanical ventilation
(0.65 [Q1;Q3: 0.65; 0.69]), central line (0.70 [Q1;Q3:
0.66; 0.76]), and urinary catheter (0.70 [Q1;Q3: 0.67;
0.72]), but low for EVD (0.12 [Q1;Q3: 0.12; 0.13]
(Additional file 1: Table S2, Fig. 2). Although, DURs var-
ied slightly over time, we observed a significant decrease
in the number of days with respiratory HAIs: from
1643 days in 2011 to 690 in 2016 (mean annual reduc-
tion rate 11.9%, p-value = 0.038), while the number of
days with VAP remained unchanged (Fig. 2a). The num-
ber of patients with HAVM and with DA-HAVM de-
creased significantly from 2011 to 2016 (Fig. 2d).
Incidence of healthcare-associated infections
The incidence of all-cause HAIs and DA-HAIs was ana-
lyzed. The cumulative incidence of all-cause HAIs de-
creased significantly for respiratory infections (from
36.1% [95% CI 30.8–41.4] in 2011 to 24.5% [95% CI
20.0–29.0] in 2016, p-value = 0.0003), urinary tract infec-
tions (from 29.07% [95% CI 24.0–34.1] in 2011 to
21.33% [95% CI 17.0–25.6], p-value = 0.0006), and
HAVM (from 15.97% [95% CI 11.9–20.0] in 2011 to
7.78% [95% CI 5.0–10.6] in 2016, p-value = 0.004)
(Fig. 3a, Additional file 1: Table S4). Time-adjusted inci-
dence rate of all-cause HAIs identified a declining trend
for all four types of HAIs (Fig. 3c). In the group of
DA-HAIs, only the cumulative incidence of CAUTI de-
creased significantly, from 28.04 [95% CI 22.7–33.4] per
Table 1 Baseline characteristics of the study population by years
Parameters Total 2011 2012 2013 2014 2015 2016 p-value
No of pts. (%) No of pts. (%) No of pts. (%) No of pts. (%) No of pts. (%) No of pts. (%)
Patients, total 2038 313 (100%) 350 (100%) 361 (100%) 341 (100%) 326 (100%) 347 (100%) 1.000
Children 345 (16.9%) 52 (16.6%) 57 (16.3%) 58 (16.1%) 65 (19.1%) 42 (12.9%) 71 (20.5%) 0.315
Male gender 1020 (50%) 154 (49.2%) 184 (52.6%) 186 (51.5%) 168 (49.3%) 164 (50.3%) 164 (47.3%) 0.976
Diagnosis Brain trauma 255 (12.5%) 43 (13.7%) 54 (15.4%) 51 (14.1%) 41 (12.0%) 28 (8.6%) 38 (11.0%) 0.192
Brain tumor 1271 (62.4%) 185 (59.1%) 221 (63.1%) 240 (66.5%) 200 (58.7%) 209 (64.1%) 216 (62.2%) 0.911
Congenital disorders 23 (1.1%) 4 (1.3%) 5 (1.4%) 3 (0.8%) 7 (2.1%) 2 (0.6%) 2 (0.6%) 0.436
Vascular brain
diseases
454 (22.3%) 77 (24.6%) 60 (17.1%) 63 (17.5%) 89 (26.1%) 80 (24.5%) 85 (24.5%) 0.066
Other diseases 29 (1.4%) 3 (1.0%) 10 (2.9%) 4 (1.1%) 4 (1.2%) 4 (1.2%) 4 (1.2%) 0.302
Surgeries Craniotomy 1537 (75.4%) 230 (73.5%) 261 (74.6%) 279 (77.3%) 262 (76.8%) 245 (75.2%) 260 (74.9%) 0.998
INSD 650 (31.9%) 101 (32.3%) 130 (37.1%) 124 (34.3%) 112 (32.8%) 94 (28.8%) 89 (25.6%) 0.227
Endovascular surgery 194 (9.5%) 31 (9.9%) 37 (10.6%) 26 (7.2%) 40 (11.7%) 25 (7.7%) 35 (10.1%) 0.407
EETS 87 (4.3%) 13 (4.2%) 15 (4.3%) 15 (4.2%) 14 (4.1%) 15 (4.6%) 15 (4.3%) 1.000
Spinal surgery 4 (0.2%) 1 (0.3%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 2 (0.6%) 1 (0.3%) 0.377
Other surgeries 873 (42.8%) 151 (48.2%) 161 (46.0%) 156 (43.2%) 146 (42.8%) 127 (39.0%) 132 (38.0%) 0.523
Outcomes Recovery 80 (3.9%) 15 (4.8%) 14 (4.0%) 14 (3.9%) 19 (5.6%) 9 (2.8%) 9 (2.6%) 0.365
Positive dynamics 934 (45.8%) 133 (42.5%) 153 (43.7%) 170 (47.1%) 159 (46.6%) 150 (46.0%) 169 (48.7%) 0.934
No dynamics 210 (10.3%) 34 (10.9%) 41 (11.7%) 37 (10.2%) 30 (8.8%) 29 (8.9%) 39 (11.2%) 0.818
Negative dynamics 505 (24.8%) 81 (25.9%) 67 (19.1%) 78 (21.6%) 92 (27.0%) 96 (29.4%) 91 (26.2%) 0.153
Death 307 (15%) 50 (16.0%) 75 (21.4%) 62 (17.2%) 41 (12.0%) 41 (12.6%) 38 (11.0%) 0.009
Median
[Q1;Q3]
Median
[Q1;Q3]
Median
[Q1;Q3]
Median
[Q1;Q3]
Median
[Q1;Q3]
Median
[Q1;Q3]
Median
[Q1;Q3]
p-value
Age, years 46 [26.0; 59.0] 44 [25.0; 57.0] 44 [25.0; 58.0] 47 [26.0; 60.0] 44 [25.0; 57.0] 50 [30.0;
59.75]
48 [24.5; 60.5] 0.099
CCI score 3 [2.0; 5.0] 3 [2.0; 4.0] 3 [2.0; 5.0] 3 [2.0; 5.0] 3 [2.0; 4.0] 3 [2.0; 5.0] 3 [2.0; 4.0] 1.000
Length of stay in ICU,
days
10 [6.0; 22.0] 13 [7.0; 27.0] 12 [6.0; 25.0] 10 [6.0; 24.0] 8 [6.0; 22.0] 9 [6.0; 22.0] 8 [5.0; 17.0] 0.010
Abbreviations: INSD Implantation of neurosurgical devices, EETS Endoscopic endonasal transsphenoidal surgery, CCI Charlson comorbidity index
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100 patients with a urinary catheter in 2011 to 18.31
[95% CI 13.8–22.8] in 2016, p-value = 0.026 (Fig. 3b,
Additional file 1: Table S4). However, once we adjusted
incidence to the device-days at risk, EVD-associated
HAVM demonstrated a significant drop from 2011 to
2016 (22.2 vs. 13.5 cases per 1000 EVD-days,
respectively) (Fig. 3d, Additional file 1:TableS2).
Risk-adjusted incidence of VAP and CAUTI also
trended toward a decrease. The incidence rate of
CLABSI did not change and remained at the median
level of 3.7 [Q1;Q3: 3.5; 4.1] per 1000 central line-days
(Fig. 3d, Additional file 1: Table S2). Of note, in 2012
the rates of respiratory and urinary HAIs as well as
VAP and CAUTI spiked increasing 4–14% compare to
2011 (Additional file 1: Table S4). Therefore, the reduc-
tion in infection rate at the end of the study period in
2016 was more pronounced when compared to peak
rates seen in 2012.
Microbiological profile of HAIs
We observed that in 2011–2012 approximately half of
bloodstream HAIs were caused by Klebsiella pneumo-
niae and Acinetobacter baumannii. However, in 2016
the proportion of K. pneumoniae decreased to 14% from
a high of 47% in 2012 and A. baumannii did not appear
on the profile for the first time (Fig. 4a). There was a
tendency for Gram-negative species to be replaced by
Gram-positive species (Fig. 4a). For other HAIs, the etio-
logical spectrum remained relatively stable over time
(Additional file 1: Figures S1–S3).
By 2016 K. pneumoniae became more susceptible to
the most-tested antibiotics: there were significantly fewer
isolates resistant to cephalosporins, ciprofloxacin, and
imipenem as compared to 2011 (Additional file 1: Figure
S4). The proportion of imipenem-resistant K. pneumo-
niae decreased from 34.5% [95% CI 29.9–39.1] in 2011
to 20.2% [95% CI 15.6–24.8], p-value < 0.001 (Fig. 4b).
Fig. 2 Proportion of time-dependent variables (total patient days, device days, days with infection, days with device-associated infection; unstacked
area plot), number of patients, and device utilization ratio (right y-axis) for corresponding device by the years for each HAI. aHA respiratory infection
and mechanical ventilation. bHA urinary tract infection and urinary catheter. cHA bloodstream infection and central line. dHA ventriculitis and
meningitis and EVD. Number of patients in the study in each year is presented in a table below each graph. HA - healthcare-associated;
HAI - healthcare-associated infection; DA-HAI - device-associated HAI. Star (*) shows p-value > 0.05 in a linear regression analysis over years
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Dramatic changes were found in cephalosporin resistance,
e.g. in 2011 there were 90.3% isolates resistant to cefepime
[95% CI 87.4–93.1] vs. 45.6% [95% CI 39.9–51.4] in 2016,
p-value < 0.001 (Additional file 1:FigureS4).
The number of imipenem-resistant isolates of A. bau-
mannii decreased from 77.7% [95% CI 72.3–83.0] in 2011
to 38% [95% CI 30.9–45.1] in 2016, p-value < 0.001
(Fig. 4b). While the proportion of ampicillin/sulbactam--
resistant isolates increased from 48.1% [95% CI 34.8–61.5]
in 2011 to 82% [95% CI 76.2–87.9] in 2016, p-value
< 0.001, the resistance to the rest of tested antibiotics
remained virtually unchanged (Additional file 1:FigureS5).
These changes in resistance occurred with a concurrent
reduction in antibiotic utilization over the study period.
Antibiotic use was measured as antibiotic-days per 1000
patient-days. The rate of antibiotic utilization was initially
1066 antibiotic days per 1000 patient-days in 2011. This
highlights that multiple antibiotics were administered in
many patients and a high overall usage rate was in effect.
Over the six-year study period the utilization rate consist-
ently declined. In 2016 the utilization rate was 807 anti-
biotic days per 1000 patient-days.
Fig. 3 The incidence rate of HAIs in high-risk patients in 2011–2016. aCumulative incidence of HAIs, cases per 100 patients in study population.
bCumulative incidence of device-associated HAIs per 100 patients with devices. cincidence rate of all HAIs, cases per 1000 patient-days. dincidence
rate of device-associated HAIs, cases per 1000 device-days in patients with devices. Star (*) marks p-value < 0.05 in group comparison; in aand b
p-values obtained from Chi-square test, in cand d—from linear regression. In aand bshadowed area shows 95% confidence interval, in cand d—the
confidence interval for the regression estimate. Abbreviations: VAP - Ventilator-Associated Pneumonia; CLABSI - Central Line-associated Bloodstream
Infection; CAUTI - Catheter-Associated Urinary Tract Infection
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Survival analysis in patients with HAIs
Bloodstream HAIs and HAVM significantly impair
survival (log-rank p-values = 0.012 and < 0.0001, re-
spectively), Fig. 4c and 4d. In order to confirm their
influence on mortality, multifactorial survival analyses
were done by Cox regression (Additional file 1: Table S5).
We confirmed that only HAVM affected survival in-
dependently from other factors, increasing the prob-
ability of death 1.43 times (95% CI 1.03–1.98, p-value
= 0.034). Other types of HAIs did not influence sur-
vival (Additional file 1:FigureS6).BesidesHAIs,
other factors were shown to independently affect sur-
vival. While brain tumor (HR = 1.57 [95% CI 1.1–2.24],
p-value = 0.012) and implantation of neurosurgical devices
(HR = 1.59 [95% CI 1.24–2.03], p-value = 0.0002)
enhanced mortality, craniotomy decreased mortality:
HR = 0.64 [95% CI 0.48–0.87], p-value = 0.0037
(Additional file 1:FigureS7).
ICU-acquired intestinal dysfunction
The cumulative incidence of overall intestinal dysfunc-
tions dropped from 54.9% [95% CI 49.4–60.5] in 2011 to
23.9% [95% CI 19.4–28.4] in 2016, p-value < 0.001
(Additional file 1: Figure S8A, Additional file 1: Table S6).
Intestinal dysfunction impaired survival independently
increasing the probability of death 1.46 times [95% CI
1.11–1.93], p-value = 0.0069; log-rank test p-value = 0.019
(Additional file 1: Figure S8D).
Discussion
A comprehensive IPC program with a focus on hand hy-
giene and patient isolation was started in NSI’sICUin
2010 (Fig. 1a). By that time, the use of our IPC program
to prevent HAIs in the ICU became a paradigm-shifting
solution across Russia as HAI prevention strategies had
previously remained unchanged for years and had be-
come outdated [20].
Fig. 4 The surveillance results at the high-risk patient population in neuro-ICU from 2011 to 2016. athe dynamics of etiological structure of
bloodstream HAIs. bThe proportion of bacterial isolates resistant to Imipenem. cSurvival curves for patients with and without bloodstream HAIs
throughout the entire study period. dSurvival curves for patients with and without HAVM throughout the entire study period. Shadowed areas
with corresponding color at b, c and drepresent 95% confidence interval. For survival curves, number of patients at risk in each group is presented in
table below each graph. Abbreviations: MRSA - methicillin-resistant Staphylococcus aureus; CoNS - coagulase-negative Staphylococci, HAVM - healthcare-
associated ventriculitis and meningitis
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The importance of HAI prevention programs is clearly
indicated by the observation that HAIs directly deteriorate
patient survival. It was found that HAIs increased the
probability of death by 1.4–1.5 [21] and odds of mortality
increased 1.5 to 1.9-fold [22]. In our study, we found that
HAVM decreased the probability of survival by 1.43, while
other HAIs did not significantly influence survival. It has
been previously reported that HAVM increased mortality
rate approximately three times [23]. Although the exact
mechanism is not yet understood, prospective studies
have found that in ICU patients, gastrointestinal dysfunc-
tion is also an independent risk factor for increased
mortality [15]. We can postulate that the intestinal micro-
biome serves an important role in immune function and
consequently, is a well described reservoir for antibiotic
resistance [24]. Additionally, in critically ill patients intes-
tinal dysbiosis could be postulated as a potential contribu-
tor to gut translocation of pathogens and may play a role
in enteric absorption. In our study, ICU-acquired intes-
tinal dysfunction decreased the probability of survival by
1.46, which is consistent with earlier studies. The imple-
mentation of IPC initiatives and the accompanying reduc-
tion in the incidence of infections, thereby reducing the
requirement for antibiotics, can be assumed to at least in
part account for reduction in gastrointestinal dysbiosis.
This finding further highlights the potential unseen mor-
bidity impact of IPC beyond simple measures of antibiotic
utilization and resistance rates.
The implementation of the IPC program was followed
by significant reduction of HAIs in the ICU. In fact, the
impact of this program may actually be under-estimated.
Our IPC program was implemented in 9/2010 whereas
study data collection began 1/2011. Therefore, although
adherence to IPC protocols would be expected to im-
prove with greater time and familiarity, the totality of
impact of this program may be under-estimated. Key ini-
tiatives, such as early removal of indwelling catheters,
would be expected to have an immediate impact in the
reduction of nosocomial infections. Even discounting the
IPC impact in the initial months after implementation,
the fact that a sustained and continued reduction in
HAI rate occurred is both meaningful and serves as a
reinforcement of overall utility. In high-risk ICU patients
we observed a substantial decrease in HAI incidence: cu-
mulative incidence of respiratory HAIs declined by 1.47
(from 36.1 to 24.5%), urinary tract HAIs by 1.4-fold
(from 29.1 to 21.3%), HAVM by twofold (from 16 to
7.8%), CAUTI by 1.93 (from 35.4 to 18.3%) (Fig. 3), and
ICU-acquired intestinal dysfunction by 2.3 fold. These
results are consistent with previously reported evidence,
demonstrating a reduction of HAI prevalence by ap-
proximately 1.7 fold (from 11.7 to 6.8%) [25].
We also found that the risk-adjusted incidence of
EVD-related HAVM reduced 1.64 fold (from 22.2 to
13.5 cases per 1000 EVD-days) over the six-year study
period. The impact of an IPC program on decreasing
DA-HAI incidence has been previously reported. For ex-
ample, one publication reported a 2.7-fold decrease in
CAUTI episodes per 100 patients within a year after IPC
implementation [26]. However, for some HAIs, like
HAVM, such statistics are absent. In addition, the
changes in the incidence of intestinal dysfunction could
be confounded by the implementation of an advanced
nutritional protocol in 2012 at the ICU.
We did find that in 2012 the rates of several infection
subcategories did increase in comparison to 2011. The
rate of respiratory and urinary HAIs had increases ranging
from 4 to 14% compared to 2011. The reason for this in-
crease is unclear, but we postulate that this may be related
to several factors. One contributor may be that staff were
educated on the appropriate identification of HAIs and
utilized clear standardized case definitions. As staff be-
came more familiar with these definitions, they may have
been able to better identify cases leading to an apparent
increase in infection rates. Additionally, during initial im-
plementation of IPC protocols, staff underwent in-service
training and consequently there was a specific focus on
the strict adherence to protocols. However, adherence to
infection control practices may wane with time, and that
probably what happened in 2012. Therefore, continued
reinforcement of best practices along with feedback to
healthcare teams is necessary for sustained adherence to
IPC initiatives. Following the re-education of staff, a
renewed attention to IPC may have contributed to reduc-
tions seen in 2013 HAI rate.
Additionally, both the length of patients’stays in the
ICU and the incidence of patient mortality did decrease
over the study period. Although a direct causality cannot
be determined, it would be fair to postulate that the asso-
ciated decrease in HAI incidence may at least have been a
partial contributor for this reduction. Thus, a reduction in
the rate of HAIs may result in a meaningful reduction in
healthcare cost, and potential benefit in overall mortality.
However, we did not monitor all other parameters that
could have influenced the mortality and the length of stay,
thus other explanations should be investigated. Addition-
ally, we admit that the overall approach in patient treat-
ment did not change much, and the DUR did not change
for any of the devices we monitored.
The prevention of the spread of carbapenem-resistant,
Gram-negative bacteria was named the first priority of
IPC efforts by the latest WHO guidelines because these
strains pose significant threat to global health [27]. We
found firstly that the proportion of such Gram-negatives
as K. pneumoniae and A. baumannii in the spectrum of
bloodstream HAIs decreased and secondly that the re-
sistance of both pathogens to carbapenems was signifi-
cantly reduced. In our study the initial percentage of
Ershova et al. Antimicrobial Resistance and Infection Control (2018) 7:94 Page 8 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
isolates resistant to imipenem was 34.5% for K. pneumo-
nia and 77.7% for A. baumannii. By the end of the study,
the percentage decreased 1.7- and 2-fold, respectively
(Fig. 4b). The initial prevalence of carbapenem-resistant
isolates in the NSI neuro-ICU was shown to be higher
than the mean prevalence in Europe (8.1% for K. pneumo-
niae and 50% for A. baumannii), and in the U.S. (7.9% for
K. pneumoniae and 49.5% for A. baumannii)[27]. This
finding could partly be explained by the study population
because we analyzed only intensive care unit patients
which may be a higher risk population. However, we pos-
tulate these initial rates of carbapenem resistance were at
least in part due to nosocomial cross-infection of patients.
Our hypothesis is that the implementation of IPC pro-
tocols acted in a two-fold manner with an initial reduc-
tion in nosocomial patient-to-patient transmission which
consequently lead to a reduction in nosocomial infection
rate. Our most critical interventions involved implemen-
tation of contact precautions utilizing gloves, gown, and
mask, isolation of patients identified with carbapene-
mase resistance genes, and cohorting of patients with
Acinetobacter or Klebsiella (Fig. 1). These efforts were
paired with intensive environmental disinfection mea-
sures, skin antisepsis for indwelling devices, as well as
initiates focused on hand hygiene as a multi-modal strat-
egy (Fig. 1).
Of note, hand hygiene compliance was particularly dif-
ficult to implement with a compliance rate of 27% in
2011. Compliance with hand hygiene in the subsequent
years 2012 through 2016 were 40, 69, 63, 68, and 81%
respectively. The reduction in infection rate over time
could reasonably be postulated to result in a secondary
reduction in the necessity of broad spectrum antibiotic
therapy. This reduction in antibiotic utilization is under-
scored by the dramatic decline in the rate of antibiotic
utilization over the study period. It must be noted that
an antibiotic stewardship program was in existence prior
to IPC implementation. Antibiotic stewardship involved
institutional protocols for perioperative antibiotic
prophylaxis and for empiric antibiotic therapy. However,
integration of IPC protocols, including surveillance mea-
sures may have enhanced the effectiveness of antibiotic
stewardship interventions. The ultimate result was that
within the study period, our observed resistance rates
decreased to the level of global and regional estimations.
This improvement in susceptibility rates, is in contrast
to the global trend of increasing carbapenem resistance
over the past decade [27], indicating that in
limited-resource settings IPC programs can be highly ef-
fective. The programs may be especially significant in
healthcare settings with high levels of resistance where
they can serve as a cost-effective intervention leading to
a substantial clinical impact. The substantial diminution
in carbapenem resistance supports the notion that
implemented IPC strategies contain effective measures to
prevent and control the resistance to carbapenems (Fig. 1).
Moreover, this is supported by the recent WHO guidelines
which affirmed that the core components of multimodal
IPC strategy can help to prevent carbapenem resistance.
This paper reports a prospective study of the impact
of an infection control program in a high acuity limited
resource setting with regard to the reduction in HAI
risk. Such studies are limited to date but have been iden-
tified by the WHO as particularly needed [27]. Thus,
this study can help to fill this research gap providing
insight regarding an approach to implantation of these
programs and highlighting the most essential IPC
components. Our results suggest that a focus on robust
surveillance paired with isolation/infection control mea-
sures can promote a sustainable and meaningful reduc-
tion in HAI incidence and antibiotic resistance.
The current study has certain limitations. It is a
single-center study in a highly specialized ICU facility.
Thus, one should be careful when generalizing these re-
sults to other hospitals and other wards. In addition, we
only studied a cohort of high-risk patients, those staying
in the dedicated neuro-ICU for > 48 h—not the entire
ICU population. Thus, reported HAI incidences are
higher than those calculated for the entire ICU popula-
tion. However, the underlying principles of our IPC pro-
gram leading to the reduction of CAUTI, CLABSI, and
VAP would be expected to be generalizable to other hos-
pitalized settings with a similar expected impact.
One aspect that was not able to be fully evaluated
were Clostridium difficile infections (CDI). The preva-
lence of CDI, identified by a positive PCR stool assay
and compatible symptoms, was measured quarterly.
However, the quarterly rate included all patients in the
ICU at the time of a positive diagnosis and included pa-
tients that did not meet the defined criteria for high-risk
population that were studied. Additionally, the incidence
rate was low throughout the six-year period with a peak
rate of 1.5% in 2011 and a nadir of 0.9% in 2015. Notably,
patients who were transferred out of the ICU and subse-
quently developed CDI would not have been identified.
Therefore, we can postulate that IPC initiatives may result
in a reduction in CDI as the rate did decline from 2011;
however, the low overall incidence of CDI and aforemen-
tioned limitations do not allow for definitive conclusions.
By design, the study did not include a control group
(i.e. a group treated in the ICU before the IPC program
had been implemented), because HAI rates without sur-
veillance are unknown. Moreover, the decrease of HAI
incidence and length of stay in the ICU could be ex-
plained by modification of clinical practices and by re-
gression to the mean. It should be mentioned that
survival analysis in our study suffers from immortal time
bias. Patients in the HAI group are “immortal”until they
Ershova et al. Antimicrobial Resistance and Infection Control (2018) 7:94 Page 9 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
get the infection, that favors the HAI group by lowering
mortality rate in this group. Thus, HAIs have a stronger
influence on survival, posing a higher risk of death in pa-
tients once they get HAIs.
Conclusion
Implementation of an evidence-based IPC program was
strongly associated with a significant reduction in HAIs
in the neuro-ICU. Over a six-year period, there was a
decreasing HAI incidence, reduction in the prevalence
of carbapenem-resistant invasive bacterial isolates, and
consequently improved patient outcomes. Our study
supports the finding that an IPC program can be highly
effective in a middle-income country (Russia) despite the
lack of a national surveillance system and limited re-
sources. Expansion of IPC initiatives, potentially paired
with a robust antimicrobial stewardship program, should
be considered in resource limited settings as a feasible
cost-effective opportunity to achieve meaningful reduc-
tions in antibiotic resistance and HAI incidence.
Additional file
Additional file 1: Supplementary materials for the research study report
"Implementing an infection control and prevention program decreases
the incidence of healthcare-associated infections and antibiotic resistance
in a Russian neuro-ICU". (PDF 35444 kb)
Abbreviations
CAUTI: Catheter-associated urinary tract infection; CDC: Centers for Disease
Control and Prevention; CI: Confidence interval; CLABSI: Central line-associated
bloodstream infections; DA-HAI: Device-associated HAI; DUR: Device utilization
ratio; EVD: External ventricular drain; HAI: Healthcare-associated infection;
HAVM: Healthcare-associated ventriculitis and meningitis; HR: Hazard ratio;
ICU: Intensive care unit; IPC: Infection prevention and control; NSI: Burdenko
National Medical Research Center of Neurosurgery; Q1; Q3: First and third
quartiles; SSSI: Superficial surgical site infection; VAP: Ventilator-associated
pneumonia; WHO: World Health Organization
Acknowledgements
The authors gratefully acknowledge the contributions of many people who
helped to develop, support, implement, and guide this study. Special thanks
to all NSI clinicians, nurses, and administrators who patiently accepted and
complied with the IPC program, and helped to collect data. We’d like to
acknowledge the contribution of Dr. Yulia Savochkina and Dr. Svetlana
Sazykina who helped with the microbiological assay. We are grateful for the
help with data analysis to Dr. Anton Barchuk (Saint Petersburg Cancer Center),
Dr. Rashied Amini (NASA-JPL), and Oleg Khomenko (Skoltech). We thank for
providing language help and proofreading to Michael Saint-Onge (Los Angeles
Public Library), also Travis Nielsen (University of Southern California).
Availability of data and materials
The datasets generated and/or analyzed during the current study are available
in the Zenodo repository, https://doi.org/10.5281/zenodo.1021503). The code
for data analysis is available at https://github.com/KseniaErshova/IPC_paper.git.
Authors’contributions
KE and DW analyzed data and wrote the manuscript; OE and IS developed,
implemented and maintained IPC program in the ICU; NK, GD, and ES collected
data, evaluated and treated study subjects; MS developed and supported
electronic surveillance protocol; NF and IA performed microbiological testing;
VZ consulted with study design and promoted the IPC program implementation.
All authors read and approved the final manuscript.
Ethics approval and consent to participate
The NSI Review Board approved the study and granted a consent waiver status.
Informed consent from the patient was not required in this case due to non-
interventional nature of the study, indicating that the research presents no
more than minimal risk of harm to subjects and does not involve any additional
interventions besides those in the regular therapeutic regimen.
Consent for publication
All authors they have seen and approved the manuscript and granted the
consent for its publication.
Competing interests
The authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in published
maps and institutional affiliations.
Author details
1
Center for Data-Intensive Biotechnology and Biomedicine, Skolkovo Institute
of Science and Technology, Moscow, Russia.
2
Department of Intensive Care,
Burdenko National Medical Research Center of Neurosurgery, Moscow,
Russia.
3
Division of Infectious Diseases, Keck School of Medicine, University of
Southern California, Los Angeles, USA.
4
Laboratory of Biomedical Informatics,
Burdenko National Medical Research Center of Neurosurgery, Moscow,
Russia.
5
IT Department, Burdenko National Medical Research Center of
Neurosurgery, Moscow, Russia.
6
Department of Microbiology, Burdenko
National Medical Research Center of Neurosurgery, Moscow, Russia.
7
Federal
Budget Institution of Science “State Research Center for Applied
Microbiology & Biotechnology”(SRCAMB), Moscow, Russia.
8
Department of
Anesthesiology, Keck School of Medicine, University of Southern California,
Los Angeles, USA.
9
Department of Epidemiology and Infection Control,
Burdenko National Medical Research Center of Neurosurgery, Moscow, Russia.
Received: 22 February 2018 Accepted: 18 July 2018
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