antibiotics
ArticleA COVID-19 Silver Lining—Decline in Antibiotic Resistance inIschemic Leg Ulcers during the Pandemic: A 6-Year Retrospective
Study from a Regional Tertiary Hospital (2017–2022)
Amaraporn Rerkasem 1,2,† , Pak Thaichana 1,† , Nuttida Bunsermvicha 3 , Rawee Nopparatkailas 4 , Supapong Arwon 3 ,Saranat Orrapin 3 , Termpong Reanpang 3 , Poon Apichartpiyakul 3 , Saritphat Orrapin 5 ,Boonying Siribumrungwong 5 , Nongkran Lumjuan 6 , Kittipan Rerkasem 1,3, * and José G. B. Derraik 1,7,8, *
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2

3

4

5

6

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Citation: Rerkasem, A.; Thaichana, P.;
Bunsermvicha, N.; Nopparatkailas, R.;
Arwon, S.; Orrapin, S.; Reanpang, T.;
Apichartpiyakul, P.; Orrapin, S.;
Siribumrungwong, B.; et al. A
COVID-19 Silver Lining—Decline in
Antibiotic Resistance in Ischemic Leg
Ulcers during the Pandemic: A 6-Year
Retrospective Study from a Regional
Tertiary Hospital (2017–2022).
Antibiotics 2024, 13, 35. https://doi.org/10.3390/antibiotics13010035
Academic Editors: Mirela
Loredana Grigoraş, Cristian Oancea
and Cosmin Citu
Received: 28 October 2023
Revised: 13 December 2023
Accepted: 21 December 2023
Published: 29 December 2023

Copyright: © 2023 by the authors.

8*†

Environmental-Occupational Health Sciences and Non-Communicable Diseases Research Center, ResearchInstitute for Health Sciences, Chiang Mai University, Chiang Mai 50200, Thailand;
amaraporn.rer@cmu.ac.th (A.R.); pak.th@cmu.ac.th (P.T.)
Research Center for Infectious Disease and Substance Use, Research Institute for Health Sciences, Chiang Mai
University, Chiang Mai 50200, Thailand
Clinical Surgical Research Center, Department of Surgery, Faculty of Medicine, Chiang Mai University,
Chiang Mai 50200, Thailand; nuttida.b@cmu.ac.th (N.B.); supapong.arworn@cmu.ac.th (S.A.);
saranat.orrapin@cmu.ac.th (S.O.); termpong.reanpang@cmu.ac.th (T.R.); poon.ap@cmu.ac.th (P.A.)
Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand;
rawee.nop@cmu.ac.th
Department of Surgery, Faculty of Medicine, Thammasat University, Rangsit Campus, Pathum Thani 12120,
Thailand; orrapins@staff.tu.ac.th (S.O.); boonying@tu.ac.th (B.S.)
Research Center for Molecular and Cell Biology, Research Institute for Health Sciences, Chiang Mai
University, Chiang Mai 50200, Thailand; nongkran.l@cmu.ac.th
Department of Women’s and Children’s Health, Uppsala University, 75185 Uppsala, Sweden
Department of Pediatrics: Child and Youth Health, Faculty of Medical and Health Sciences, University of
Auckland, Auckland 1142, New ZealandCorrespondence: kittipan.r@cmu.ac.th (K.R.); j.derraik@auckland.ac.nz (J.G.B.D.)
These authors contributed equally to this work.

Abstract: Antibiotic resistance (AR) associated with chronic limb-threatening ischemia (CLTI) poses
additional challenges for the management of ischemic leg ulcers, increasing the likelihood of severe
outcomes. This study assessed AR prevalence in bacteria isolated from CLTI-associated leg ulcers
before (1 January 2017–10 March 2020; n = 69) and during (11 March 2020–31 December 2022; n = 59)
the COVID-19 pandemic from patients admitted with positive wound cultures to a regional hospitalin Chiang Mai (Thailand). There was a marked reduction in AR rates from 78% pre-pandemic to 42%
during the pandemic (p < 0.0001), with rates of polymicrobial infections 22 percentage points lower(from 61% to 39%, respectively; p = 0.014). There were reduced AR rates to amoxicillin/clavulanate(from 42% to 4%; p < 0.0001) and ampicillin (from 16% to 2%; p = 0.017), as well as multidrug
resistance (19% to 8%; p = 0.026). Factors associated with increased AR odds were polymicrobial
infections (adjusted odds ratio (aOR) 5.6 (95% CI 2.1, 15.0); p = 0.001), gram-negative bacteria (aOR
7.0 (95% CI 2.4, 20.5); p < 0.001), and prior use of antibiotics (aOR 11.9 (95% CI 1.1, 128.2); p = 0.041).
Improvements in infection control measures and hygiene practices in the community during the
pandemic were likely key factors contributing to lower AR rates. Thus, strategic public healthinterventions, including community education on hygiene and the informed use of antibiotics, may
be crucial in mitigating the challenges posed by AR in CLTI. Further, advocating for more judicious
use of empirical antibiotics in clinical settings can balance effective treatment against AR development,
thereby improving patient outcomes.

Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://

Keywords: bacteria; chronic limb-threatening ischemia; community health; ischemic leg ulcer;
empirical antibiotic use; gram-negative; gram-positive; Gram staining; infection control; multidrug
resistance; peripheral arterial disease; polymicrobial infection; public health; susceptibility

creativecommons.org/licenses/by/4.0/).

Antibiotics 2024, 13, 35. https://doi.org/10.3390/antibiotics13010035

https://www.mdpi.com/journal/antibiotics

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1. Introduction
Chronic limb-threatening ischemia (CLTI) is the advanced stage of atherosclerosisinduced chronic occlusive peripheral arterial disease (PAD) [1]. PAD results from severe
blockage of lower-limb arteries, resulting in reduced blood perfusion; this may lead to tissue
damage, unhealed ulcers, necrosis, and an increased susceptibility to infections [1–3]. The
co-occurrence of infection and ischemia poses a substantial risk to the patient, potentiallyleading to major limb amputations or even fatality [4]. Severe limb infections extend totendons, ligaments, bones, and even the proximal limb, requiring aggressive debridement
and potentially leading to limb loss. As a result, CLTI has a 1-year mortality rate of 25%,
and approximately 30% of CLTI patients require lower limb amputation [5].
Concurrent infections present at ulcer sites and the presence of necrotic tissue markedly
affect treatment outcomes, particularly following revascularization surgery [6,7]. When superimposed infections occur in ischemic leg ulcers, treatment typically consists of multiple
courses of high-potency, empirical, broad-spectrum antibiotics over an extended period
before referral to a tertiary care center. However, the compromised blood flow in ischemic
tissue hinders adequate antibiotic delivery, facilitating polymicrobial infections and theproliferation of highly virulent organisms [8]. Consequently, superimposed infectionsin ischemic leg ulcers increase the risk of multidrug-resistant (MDR) pathogens occurring [9,10], potentially leading to life-threatening untreatable infections [8]. Additionally,
MDR pathogens can evolve into extensively drug-resistant strains, requiring aggressive
treatment regimens involving combinations of different drugs [11–13].
A systematic review and meta-analysis included 148 studies and approximately
363,000 patients covering 2019–2021 [14]. It reported a 5% prevalence of bacterial coinfections and an 18% prevalence of secondary bacterial infections among hospitalized
COVID-19 patients [14]. However, only 42 of the included studies reported comprehensive
data on antimicrobial susceptibility, with 61% of recorded bacterial infections involving resistant pathogens [14]. Another meta-analysis of 23 studies yielded inconclusive results [15],but there have been reports of increased antibiotic resistance during the COVID-19 pandemic [16,17]. In contrast, several studies have reported reductions in antibiotic-resistanceprevalence during the pandemic [18–20]. The uncertainties regarding COVID-19 diagnosis
and concerns over bacterial co-infections or secondary infections likely underpinned substantial but inappropriate prescription of antibiotics [15]. While the latter might have led toincreased antibiotic resistance, the evidence remains conflicting and, thus, inconclusive.
For the physician, ischemic leg ulcers are a concern as they frequently lead to severe
pain, systemic inflammation, and a high risk of treatment failure, potentially leading tohospitalization and the need for both antibiotic therapy and surgical interventions [3]. In
2020–2022, during the COVID-19 pandemic, inevitable delays occurred in accessing minor
clinical procedures, such as special dressing or debridement, due to the strict screening
protocols that restricted hospital entry and exit and limited non-urgent admissions [21]. The
consequences of delayed treatment were exacerbated by the demands on medical resources
for COVID-19 patients, which put substantial pressure on healthcare systems [22,23]. In thiscontext, physicians frequently resorted to empiric broad-spectrum antibiotics to mitigate therisks of deep infection and potential limb loss. However, this practice led to adverse publichealth consequences, such as variations in antibiotic-resistant genes, as well as changes inthe spectrum of pathogens and the severity of associated foot infections. Conversely, it wassuggested that the COVID-19 pandemic would not substantially increase the prevalenceof antimicrobial resistance in most countries, owing to enhanced infection prevention andcontrol efforts in community and healthcare settings [24].
Although several studies have explored changes in antibiotic resistance associatedwith diabetes-related foot ulcers during the COVID-19 pandemic [16,21], to our knowledge,
no studies have specifically reported the prevalence and spectrum of bacteria isolated
from ischemic leg ulcers. Therefore, we examined the infection rates of antibiotic-resistantbacteria in ischemic leg ulcers among PAD patients admitted to a tertiary medical center
before and during the COVID-19 pandemic. In addition, we aimed to characterize theAntibiotics 2024, 13, x FOR PEER REVIEW

Antibiotics 2024, 13, 35

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isolated from ischemic leg ulcers. Therefore, we examined the infection rates of antibioticresistant bacteria in ischemic leg ulcers among PAD patients admitted to a tertiary medical
3 of 18
center before and during the COVID-19 pandemic. In addition, we aimed to characterize
the recorded bacterial taxa, including the antibiotics they were resistant to. Lastly, we
exploredbacterial
the associatedrisk factors
characteristics
of resistantthe infected
wounds.
recordedtaxa, including
theandantibiotics
they were
to. Lastly,
we explored
the associated risk factors and characteristics of the infected wounds.
2. Results
2.2.1.
Results
Study Population
2.1. Study Population
During the six years covered by this study, 197 patients were admitted with CLTI of
During classthe six
coveredthis study,
197 patientsand
were
with
CLTI of
Rutherford≥5,years
including106bypatients
pre-COVID-19
91admitted
during the
pandemic,
Rutherford
class
≥
5,
including106
patients
pre-COVID-19
and91
duringthepandemic,
but 37 and 32 patients, respectively, were subsequently excluded (Figure 1). Thus, 69 andbut
37 and 32respectively,
patients, respectively,
were
subsequently(Figure1). Thus,
69 and59 patients,
met the main
inclusion
criteriaexcludedof confirmed
chronic
ischemicleg
59 patients, respectively, met the main inclusion criteria of confirmed chronic ischemic leg
ulcers with signs of infection and positive wound cultures (Figure 1).
ulcers with signs of infection and positive wound cultures (Figure 1).

Figure1.1.Flow
Flowdiagram
diagramshowing
showingthe
therecruitment
patients
intostudy,
including
the reasons
Figurerecruitment
of ofpatients
into
thisthisstudy,
including
the reasonsforfor exclusion.
exclusion.

The
Thedemographic
demographiccharacteristics
characteristicsof
ofthe
thestudy
studypopulation
populationare
areshown
shownininTableTable1.1.Most
Most
patients
patientswere
weremalemale(62%)(62%) andand elderly,
elderly,withwithaamedian
medianage
ageof
of69
69years
years(range(range35–99
35–99 years)
years)(Table(Table 1).
1). Pre-pandemic
Pre-pandemic andand pandemic
pandemic patientspatients had
had similar
similar demographic
demographic andand clinicalclinicalcharacteristics,
characteristics,except
exceptfor
forhigher
higherrates
rates ofof revascularization
revascularization procedures
procedures during
during thethe COVIDCOVID19
19pandemic
pandemic(51(51vs.
vs.32%;
32%;pp== 0.029).
0.029). There
There waswas also
also indication
indicationthat
thatthethemedian
medianduration
durationofofulceration
beforehospitalization
waslongerinthepre-pandemic
comparedtothepandemic
ulceration before hospitalization was longer in the pre-pandemic comparedto theperiod(30 vs.
20 days,respectively;
p = 0.052) (Table1). Importantly,
rates of systemic
pandemic
period(30 vs.
20 days, respectively;
p = 0.052)(Table 1). Importantly,
rates ofinflammatory
response syndrome(SIRS) and(SIRS)
severity
ofseverity
wound infections
similarinsystemic inflammatory
response syndrome
andof wound were
infections
were
the
two periods(Table1). (Table 1).
similarin the two
periods

Antibiotics 2024, 13, 35

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Table 1. Demographic and clinical characteristics of patients admitted to hospital with CLTIassociated ischemic leg ulcers before (1 January 2017–10 March 2020) and during (11 March 2020–31
December 2022) the COVID-19 pandemic.
Parameters

Overall

Pre-COVID-19

COVID-19

pA

nAge (years)
Male
Major atherosclerotic risk factors
Diabetes mellitus
Hypertension
Dyslipidemia
Current smoking
Chronic kidney disease (stages III, IV, V) B
Receiving RRT
Manifestation of SIRS C
Temperature >38 ◦ C
Tachycardia >90 bpm
Tachypnea >20 bpm
Abnormal leukocytes D
Positive SIRS score
CLTI leg ulcer parameters
History of previous ulcers (recurrent ulcer)
Duration of active ischemic ulcer/gangrene (days)
Severity of ulcer and infection E
Wound grade128
69 [61, 77]
79 (62%)

69
67 [60, 76]
37 (54%)59
70 [61, 78]
42 (71%)

0.45
0.88

83 (65%)93 (73%)47 (37%)
6 (5%)
68 (53%)33 (26%)41 (59%)49 (71%)22 (32%)3 (4%)
39 (57%)20 (29%)42 (71%)44 (75%)25 (42%)3 (5%)
29 (49%)13 (22%)

0.17
0.65
0.22
>0.99
0.41
0.37

25 (20%)46 (36%)14 (11%)52 (41%)48 (38%)13 (19%)27 (39%)10 (14%)25 (36%)25 (36%)

12 (20%)19 (32%)
4 (7%)
27 (46%)23 (39%)

0.83
0.42
0.26
0.27
0.75

40 (31%)30 [14, 68]

22 (32%)30 [14, 90]

18 (31%)
20 [14, 60]

0.87
0.052

9 (7%)
77 (60%)42 (33%)46 (36%)36 (28%)46 (36%)15 (12%)71 (55%)42 (33%)3 (2%)
6 (4%)
119 (93%)41 (32%)121 (95%)

8 (11%)
37 (54%)24 (35%)28 (41%)22 (32%)19 (27%)8 (12%)
38 (55%)23 (33%)
3 (4%)
3 (4%)
63 (92%)25 (36%)64 (93%)

1 (2%)
40 (68%)18 (30%)18 (30%)14 (24%)27 (46%)
7 (12%)
33 (56%)
19 (32%)–3 (5%)
56 (95%)16 (27%)57 (97%)0.058

62 (48%)40 (31%)38 (55%)25 (36%)24 (41%)
15 (25%)

0.10
0.19

50 (39%)52 (41%)122 (95%)
102 (84%)
20 (16%)23 (33%)22 (32%)66 (96%)56 (85%)10 (15%)27 (46%)30 (51%)56 (95%)46 (82%)10 (18%)

0.15
0.029
>0.99
0.69

Ischemic grade

Infection grade

Clinical stage

Presence of osteomyelitis
Presence of wound gangrene
Hospitalization and treatments
Any hospitalizations within 6 months
Referral from health services for CLTI
Surgical treatments at this admission
Debridement
Revascularization
Underwent amputation
Amputation levels

1
2
3
1
2
3
1
2
3
2
3
4

Minor
Major

0.10

0.99

0.41

0.27
0.45

Continuous data are reported as median [quartile 1, quartile 3] and categorical data as n (%). CLTI, chronic
limb-threatening ischemia; RRT, renal replacement therapy; SIRS, systemic inflammatory response syndrome.
A Derived from chi-square tests, two-sample t-test, or non-parametric Mann–Whitney U comparing the two study
periods, with the p-value for the statistically significant differences (at p < 0.05) shown in bold. B Staging of chronic
kidney disease defined by an eGFR reduction <60 mL/min/1.73 m2 . C Patients were diagnosed with SIRS when
they met any two or more of the following criteria: body temperature, heart rate, respiratory rate, and whiteblood cell count. D Leukocyte count: leukopenia (<4000/mm3 ), leukocytosis (>12,000/mm3 ), or ≥10% immatureneutrophils (band forms). E The SVS-WIfI classification grades CLTI based on perfusion (0–3), wound extent (0–3),
and superadded infection (0–3), with 0 indicating absence and 3 indicating severe; the total score (summing thesecomponents) is associated with the risk of major amputations.

Antibiotics 2024, 13, 35

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2.2. Infection Types and Prescribed Antibiotics
Microbiological analyses showed a markedly higher rate of polymicrobial infections
pre-pandemic compared to the pandemic period (61% vs. 39%, respectively, or +22%
(95% CI 5, 39%); p = 0.014) (Table 2). All but one patient was prescribed an antibiotic at
admission (Table 2). Over two-thirds of patients (68%) across the six years had exclusivegram-negative infections, with similar rates in the two periods (69% vs. 67%, respectively;
Table 2). However, rates of mixed infections were more than two-fold higher pre-pandemic(23% vs. 10% or +13.0% (95% CI 0.4, 26%); p = 0.043).
Table 2. Bacterial infection types and prescribed antibiotics in patients admitted to hospital with
CLTI-associated ischemic leg ulcers before (1 January 2017–10 March 2020) and during (11 March
2020–31 December 2022) the COVID-19 pandemic.
pA

Parameters

N

Pre-COVID-19

COVID-19

n

128

69

59

63 (49%)65 (51%)27 (39%)42 (61%)36 (61%)23 (39%)0.014

19 (15%)87 (68%)22 (17%)7 (10%)
46 (67%)16 (23%)

12 (20%)41 (70%)6 (10%)

0.067

121 (95%)
103 (85%)
18 (15%)127 (99%)
96 (75%)83 (69%)10 (8%)
15 (12%)66 (96%)55 (83%)
11 (17%)68 (99%)49 (71%)44 (64%)6 (9%)
6 (9%)

55 (93%)48 (87%)7 (13%)
59 (100%)
47 (80%)39 (66%)4 (7%)
6 (15%)

Infection type
Monomicrobial
Polymicrobial
Gram staining
Only gram-positive
Only gram-negative
Mixed infection
Empirical antibiotics
Previous treatment B
Prescription by
Treatment during this study C

General practitioner
Specialist
Any
Clindamycin
Ciprofloxacin
Penicillin
Cephalosporin

0.043 D
0.70
0.54
>0.99
0.26
0.78
0.75
0.43

Data are n (%). A Derived from chi-square or normal approximation tests comparing the two study periods, withthe p-value for the statistically significant difference (p < 0.05) shown in bold. B Antibiotic treatment informationrefers to the period before the first infection included in this study. C The antibiotics used were not mutually
exclusive; the same patient might have received more than one. D Comparing mixed infections vs. others.

Overall, 95% of patients received empirical antibiotic treatment before wound culture,with little change between periods (Table 2). Among the empirical treatment regimens,
clindamycin and ciprofloxacin were the most prescribed antibiotics (75% and 69%, respectively), without evidence of rate differences between periods (Table 2). Similarly, combined
treatment rates with ciprofloxacin and clindamycin did not change (62% pre-pandemic and
64% during the pandemic).
2.3. Differences in AR Rates
The six-year rate of patients with at least one bacterial taxon with AR cultured froma leg ulcer was 61.7% (79/128). However, there were marked differences between study
periods (Figure 2). Pre-pandemic, the three-year AR rate was 78% (54/69) compared to42% (25/59) during the COVID-19 pandemic (+36% (95% CI 20, 52%); p < 0.0001) (Figure 2).
Notably, while there was relatively limited variation in yearly AR rates pre-pandemic, rates
progressively increased throughout the COVID-19 pandemic (29% in 2020, 43% in 2021,
and 50% in 2022; Figure 2). From the 128 patients, 225 bacterial samples were isolated, of
which 79.1% were gram-negative and 20.9% were gram-positive.

Antibiotics 2024, 13, x FOR PEER REVIEW

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Antibiotics 2024, 13, 35

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Figure 2. Yearly (A) and overall (B) prevalence of antibiotic resistance in 128 patients with CLTIFigure 2.leg
Yearly(A)and (1overall(B)2017–10
prevalenceof 2020)
antibiotic
resistancein 128 2020–31
patients December
with CLTIassociatedulcersbeforeJanuaryMarchand during(11 March
associatedleg
ulcersbefore(1
January
2017–10
March
2020)
and
during(11
March
2020–312022) the COVID-19 pandemic.
December 2022) the COVID-19 pandemic.

When data on the 225 bacterial samples tested were compared between the two
Whendata on thebacterial
samples tested werewere
comparedthe vs.
two
periods,
pre-pandemic
AR225
rates
for amoxicillin/clavulanate
10-foldbetween
higher (42%periods,
pre-pandemic
AR
rates
for
amoxicillin/clavulanate
were
10-fold
higher(42%
vs.
4%; +38% (95% CI 27, 51%); p < 0.0001) and for ampicillin 8-fold higher (16% vs. 2%;
4%; +38%(95%
CI 27, p51%);
p < 0.0001)
andDespite
for ampicillin
8-fold higherlower(16% numbers
vs. 2%; +14%
+14%
(95% CI
5, 24%);= 0.017)(Table 3).
the proportionallyof(95% CI 5, 24%);isolates
p = 0.017)(Table3). Despite
proportionallylower numbers
antibiotic-resistantduringthe pandemic
forthesevenof the 12 antibiotics
tested, noof
antibiotic-resistantisolates differences
during the were
pandemic
for sevenof theantibiotics
tested,3).
noother
statistically significant
observed
betweenthe12
twoperiods (Table
other statistically significant diﬀerences were observed between the two periods (Table 3).
Table 3. Prevalence of antibiotic resistance in bacteria isolated from CLTI-associated leg ulcers
Table(13.January
Prevalenceof March
antibiotic
resistancein (11
bacteria
CLTI-associatedleg ulcers
before2017–102020)
and during
Marchisolated
2020–31from
December2022) the COVID-19
before(1
January
2017–10
March
2020)
and
during(11
March
2020–31
December2022)
the
COVIDpandemic.
19 pandemic.
Antibiotic
Antibiotic

Overall
Overall

Pre-COVID-19
Pre-COVID-19

COVID-19
COVID-19

Difference
Difference

pp

−4%
(−33,
24%)
>0.99
−4%
(−33,
24%)
>0.99
14%
(5, (5,
24%)
0.017
14%
24%)
0.017
8% (−19, 35%)
0.64
8% (−19, 35%)
0.64
8% (−19, 35%)
0.64
8%
(−19,
35%)
0.64
−2% (−10, 6%)
0.53
−2%
(−10,
6%)
0.53
11% (0, 22%)
0.12
22%)
0.12
3%11%
(−2,(0,
9%)
0.49
3% 3%
(−8,(−2,
15%)
0.62
9%)
0.49
−7%3%
(−(−8,
15, 1%)
0.28
15%)
0.62
38% (27, 51%)
<0.0001−7% (−15, 1%)0.28−2% (−15, 11%)>0.99
38% (27, 51%)
<0.0001−3% (−26, 20%)
>0.99
Trimethoprim/sulfamethoxazole A
41/173 (24%)
25/110 (23%)
16/63 (25%)−2% (−15, 11%)>0.99
Data for individual periods or the overall study represent the proportions of isolated pathogens with resistance
Penicillin
2/24
(8%)
1/14
(7%)
1/10
(10%)
−3%
(−26,
20%)
>0.99
to a given antibiotic out of those tested. Antibiotic resistance rates between the two periods were compared
DataFisher’sfor individualor thestudy
represent
the proportionsof isolated
using
exact tests,periods
and p-values
for overall
statisticallysignificant
differences(at p < 0.05) are
shown in pathogens
bold. The
differencesin prevalence
betweenperiods arepercentage
points resistanceand the respective
95% confidencewith resistanceto a given
antibiotic
outprovidedof thoseas
tested.
Antibiotic
rates between
the two
A Antibiotics used in combination due to their synergistic
intervals.
limb-threatening
ischemia.
periodsCLTI,
werechronic
comparedusing Fisher’s
exact tests,
and p-values for statistically significant diﬀerences
effect.
(at p < 0.05) are shown in bold. The diﬀerences in prevalence between periods are provided aspercentage
pointsthe respective
95% confidence
intervals.
CLTI,
chronic
There were
33 andbacterial
isolates (14.7%)
consideredof greater
clinicalandlimb-threatening
public health
A Antibiotics used in combination due to their synergistic eﬀect.
ischemia.
relevance (Table 4). Consistent with the above-described findings, the pre-pandemic 3-year

Oxacillin
Oxacillin
Ampicillin
Ampicillin
Clindamycin
Clindamycin
Erythromycin
Erythromycin
Carbapenems
Carbapenems
Cephalosporins
A
Cephalosporins
Piperacillin/tazobactam
Fluoroquinolones
Piperacillin/tazobactam A
Aminoglycoside
Fluoroquinolones
A
Amoxicillin/clavulanate
AminoglycosideA
Trimethoprim/sulfamethoxazole
Amoxicillin/clavulanate A
Penicillin

3/22
3/22(14%)(14%)
13/116
13/116(11%)(11%)
5/30 (17%)
5/30 (17%)
5/30 (17%)
5/30 (17%)
11/169(7%)
11/169(7%)
32/178 (18%)
32/178(5%)
(18%)
8/169
54/224
8/169(24%)(5%)
19/215(9%)
54/224 (24%)
39/134 (29%)
19/215 (9%)
41/173 (24%)
39/134 (29%)
2/24 (8%)

1/9
1/9(11%)
(11%)
12/73
12/73(16%)(16%)
3/14 (21%)
3/14 (21%)
3/14 (21%)
3/14 (21%)
6/107
(6%)
6/107
(6%)
25/114 (22%)
25/114(6%)
(22%)
6/107
35/137
6/107(26%)
(6%)
9/130
(6%)
35/137 (26%)
37/89 (42%)
9/130 (6%)
25/110 (23%)
37/89 (42%)
1/14 (7%)

2/13
(15%)
2/13
(15%)
1/43
(2%)
1/43
(2%)
2/16 (13%)
2/16 (13%)
2/16 (13%)
2/16
(13%)
5/62
(8%)
5/62
(8%)
7/64 (11%)
7/64
(11%)
2/62
(3%)
19/87
(23%)
2/62
(3%)
10/85
(13%)
19/87 (23%)
2/45 (4%)
10/85 (13%)
16/63 (25%)
2/45 (4%)
1/10 (10%)

rate of MDR bacteria was markedly higher compared to the pandemic rate (18.8% vs. 8.0%;+10.8% (95% CI 2.1, 19.5%); p = 0.026) (Table 4). Nonetheless, most of these bacteria wererelatively rare in our study population, ranging from 0.4% to 1.3%, except for the higher
prevalence of ESBL Escherichia coli (4.9%; 11/225), ESBL Proteus spp. (3.6%; 8/225), and
ESBL Klebsiella spp. (3.1%; 7/225) (Table 4).

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Table 4. Clinically important antibiotic-resistant bacteria isolated from CLTI-associated leg ulcers
before (1 January 2017–10 March 2020) and during (11 March 2020–31 December 2022) the COVID-19
pandemic.
Bacteria with Antibiotic Resistance

Overall

Pre-COVID-19

COVID-19

n
MRSA
MRSE
VRE
ESBL Escherichia coli
ESBL Klebsiella spp.
ESBL Pseudomonas spp.
ESBL Proteus spp.
Carbapenem-resistant Escherichia coli
Carbapenem resistant Klebsiella spp.
Carbapenem resistant Pseudomonas spp.
Carbapenem resistant Proteus spp.
Carbapenem resistant Acinetobacter spp.
Multidrug resistant (MDR)

225
1 (0.4%)2 (0.9%)1 (0.4%)11 (4.9%)7 (3.1%)1 (0.4%)8 (3.6%)
1 (0.4%)1 (0.4%)2 (0.9%)3 (1.3%)1 (0.4%)33 (14.7%)

138
1 (0.7%)–1 (0.7%)9 (6.5%)5 (3.6%)–7 (5.1%)––1 (0.7%)1 (0.7%)2 (1.4%)26 (18.8%)

87–
2 (2.3%)–2 (2.3%)2 (2.3%)
1 (1.1%)1 (1.1%)1 (1.1%)1 (1.1%)1 (1.1%)2 (2.3%)–7 (8.0%) *Data for the overall study or individual bacterial taxon or group represent the number and respective proportionout of the total number tested. ESBL, extended-spectrum β-lactamase-producing bacteria, including those resistantto third-generation cephalosporins, as defined by Thailand’s National Antimicrobial Resistance SurveillanceCenter; MDR, multidrug-resistant bacteria non-susceptible to at least one drug in three or more antibiotic categories, as defined by the Clinical Laboratory Standards Institute; MRSA, Methicillin-resistant Staphylococcus aureus;
MRSE, Methicillin-resistant Staphylococcus epidermidis; VRE, Vancomycin-resistant Enterococcus spp.; * p = 0.026 fora difference in MDR prevalence between the two periods, derived from a Fisher’s exact test.

2.4. Gram-Positive Bacteria
The analysis of the 47 samples of gram-positive bacteria showed that overall AR rates
in the pre-pandemic and pandemic periods were 29% and 22%, respectively (Table 5).
Although the proportion of antibiotic-resistant isolates seemed to be lower for four of theten drugs tested during the pandemic, no statistically significant differences were detected
between the two periods (Table 5).
The overall AR rate among gram-positive bacteria was 26% (12/47), with at least one
isolated taxon displaying AR against one out of 10 antibiotics (Table 5 and Supplementary
Table S2). Staphylococcus aureus was the predominant gram-positive bacterium isolated(40%; 19/47), with 16% (3/19) of cultures found to be antibiotic-resistant (Supplementary
Table S2 and Table 5). Enterococcus spp. was the second most common gram-positive taxon(n = 17; 36%) with a 35% AR rate (6/17; Supplementary Table S2 and Table 5). These two
taxa comprised 77% (36/47) of gram-positive isolates (Supplementary Table S2 and Table 5).
The antibiotics with the greatest AR rates among gram-positive bacteria were clindamycin
and erythromycin (both 17% (5/30); Supplementary Table S2 and Table 5). Among thetwo Staphylococcus epidermidis isolates, AR was observed against six of the seven antibiotics
tested (Supplementary Table S2 and Table 5).

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Table 5. Antibiotic resistance in gram-positive bacteria isolated from CLTI-associated leg ulcers before (1 January 2017–10 March 2020) and during (11 March 2020–31
December 2022) the COVID-19 pandemic.
Pathogens n (%)

Overall

Oxacillin

Ampicillin

Erythromycin

Trimethoprim/

DuringPre

During3/9(33%)

0/10 *
(nil)

1/9
(11%)

0/10
(nil)

Enterococcus spp. (E. faecalis,E. faecium, E. avium)(n = 17; 36%)

3/10
(30%)

3/7
(43%)

ND

1/10
(10%)

1/7
(14%)

ND

β-hemolytic Streptococci(n = 7; 15%)

0/4
(nil)

0/3
(nil)

ND

0/4
(nil)

0/3
(nil)

ND

0/4
(nil)

0/3
(nil)

0/4
(nil)

0/3
(nil)

Staphylococcus epidermidis(n = 2; 4%)–

2/2(100%)

ND

ND

–

2/2
(100%)–

2/2(100%)

α-hemolytic Streptococci(n = 1; 2%)

1/1(100%)

–

ND

ND

1/1
(100%)–

1/1(100%)–

Staphylococcus haemolyticus(n = 1; 2%)

–

0/1
(nil)

–

0/1
(nil)

ND

ND

–

0/1
(nil)

–

0/1
(nil)

–

0/1
(nil)

7/24
(29%)

5/23
(22%)

1/9
(11%)

2/13
(15%)

3/14
(21%)

2/16
(13%)

3/14
(21%)

2/16
(13%)

1/9
(11%)

2/13
(15%)

ND

2/2
(100%)
ND

1/14
(7%)

1/10
(10%)

Pre

DuringPre

DuringPre

DuringPre

During

1/9
(11%)

0/10
(nil)

2/9
(22%)

0/10
(nil)

2/9
(22%)

0/10
(nil)

1/9
(11%)

0/10
(nil)

1/9
(11%)

ND

0/10
(0%)

Penicillin

Sulfamethoxazole A

Pre–

DuringClindamycin

Staphylococcus aureus(n = 19; 40%)Total
(n = 47)

Pre

Fusidic Acid

ND

Pre

Aminoglycoside

During
ND

Fluoroquinolones

Vancomycin

Pre

DuringPre

DuringPre

During

0/9
(nil)

0/10
(nil)

0/9
(nil)

0/10
(nil)

0/9
(nil)

0/10
(nil)

ND

1/10
(10%)

1/7
(14%)

2/10
(20%)

2/7
(29%)

0/10
(nil)

0/7
(nil)

1/10
(10%)

0/7
(0%)

ND

0/4
(nil)

0/3
(nil)

0/4
(nil)

0/3
(nil)

0/4
(nil)

0/3
(0%)

0/4
(0%)

0/3
(0%)

–

1/2
(50%)

–

1/2
(50%)

0/2
(0%)

–

0/1
(nil)

–

2/2
(100%)

–

ND

ND

ND

ND

ND
1/14
(7%)

1/10
(10%)

ND

–

0/1
(nil)

–

0/1
(0%)

–

0/1
(0%)

2/23
(9%)

3/23
(13%)

0/23
(0%)

1/23
(4%)

1/26
(4%)

0/21
(0%)

CLTI, chronic limb-threatening ischemia; ND, no susceptibility testing performed. Data within cells are n/N (%), where N is the number of isolates tested against a given antibiotic(columns), n is the number with antibiotic resistance, and (%) is the corresponding proportion. A Antibiotics used in combination due to their synergistic effect. * p < 0.05 for thedifference in antibiotic resistance prevalence between the two periods (shown in bold) derived from Fisher’s exact test. Note that comparisons were only examined for the overall counts
for given bacterial taxa with isolates in both periods, the total counts for individual antibiotics, and the total overall.

Antibiotics 2024, 13, 35

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2.5. Gram-Negative Bacteria
In Table 6, the 178 gram-negative bacterial samples tested were compared between
the two periods. The overall pre-pandemic AR rate was higher than the pandemic rate
(61% vs. 39%, respectively; +22% (95% CI 7, 37%); p = 0.007). This observation aligns withthe markedly higher pre-pandemic AR rates for specific antibiotics: ampicillin was 19-foldhigher (19% vs. 0%; +19% (95% CI 9, 29%); p = 0.006) and amoxicillin/clavulanate was
10-fold higher (42% vs. 4%; +38% (95% CI 27, 51%); p < 0.0001) (Table 6). Among individual
gram-negative bacteria, there was a detectable AR rate reduction for Morganella morganii
despite very few pandemic isolates (n = 3), as all ten pre-pandemic isolates tested were
antibiotic-resistant (33% vs. 100%; +67% (95% CI 13, 100%); p = 0.014) (Table 6).
Over half of the individual gram-negative bacterial cultures (53%; 94/178) wereresistant to at least one of the eight antibiotics tested (Table 6 and Supplementary Table S3).
This AR rate was markedly higher compared to the observed 26% rate for gram-positivebacteria (+27% (95% CI 13, 42%); p < 0.001). Further, 12 out of 13 gram-negative bacterial
taxa in the study were resistant to at least two antibiotics (Supplementary Table S3 andTable 6). Escherichia coli and Pseudomonas aeruginosa were the most common gram-negative
bacteria isolated, each making up 15% of the overall records for this group (27/178 and26/178, respectively; Supplementary Table S3 and Table 6). Notably, the AR rate against
at least one antibiotic for both Escherichia coli and Morganella morganii was 85% (23/27
and 11/13, respectively), with 77% of Morganella morganii found to be resistant against
amoxicillin/clavulanate (10/13) (Supplementary Table S3 and Table 6). The highest AR
rates were recorded against fluoroquinolones (30%; 53/178) and amoxicillin/clavulanate(29%; 39/134) (Supplementary Table S3 and Table 6). Importantly, among Escherichia
coli, AR was recorded against all eight antibiotics tested and against seven antibiotics for
Citrobacter freundii and Klebsiella pneumoniae (Supplementary Table S3 and Table 6).
2.6. AR Risk Factors
In univariable analyses, the odds ratio of AR during the COVID-19 pandemic wasnearly five-fold lower compared to the pre-pandemic period (OR 0.21; p < 0.001) (Table 7).
Other risk factors associated with increased odds of AR were having a polymicrobial
infection (OR 3.37 vs. monomicrobial; p = 0.002) or a wound infection with gram-negative
bacteria alone (OR 2.57; p = 0.015) and hospitalization within 6 months before admission(OR 2.10; p = 0.038) (Table 7).
The multivariable logistic regression yielded adjusted odds ratios (aOR) identical tothe univariable analysis, also showing odds of AR approximately five-fold lower during the
pandemic period (0.21 (95% CI 0.08, 0.51); p = 0.001) (Table 7). Risk factors that remained
associated with increased odds of AR were a polymicrobial infection (aOR 5.58; p = 0.001)
or a gram-negative only infection (aOR 6.98; p < 0.001) (Table 7). In addition, there weremarkedly higher odds of AR in association with previous empirical antibiotic treatment(aOR 11.9; p = 0.041) and increased respiratory rate at admission (aOR 6.17; p = 0.047)(Table 7).

Antibiotics 2024, 13, 35

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Table 6. AR in gram-negative bacteria from CLTI-associated leg ulcers before (1 January 2017–10 March 2020) and during (11 March 2020–31 December 2022) the
COVID-19 pandemic.
Bacterial Taxa

Overall

Carbapenems

Cephalosporins

Piperacillin/
Tazobactam A

Fluoroquinolones

Aminoglycosides

Ampicillin

Amoxicillin/
Clavulanate A

Trimethoprim/
Sulfamethoxazole A

Pre

DuringPre

DuringPre

DuringPre

DuringPre

DuringPre

DuringPre

DuringPre

DuringPre

DuringEscherichia coli (n = 27; 15%)

15/18
(83%)

8/9
(89%)

0/18
(nil)

1/9
(11%)

9/18
(50%)

2/9
(22%)

0/18
(nil)

1/9
(11%)

12/18
(67%)

7/9
(78%)

1/18
(6%)

3/9
(33%)

3/18
(17%)

0/9
(nil)

1/18
(6%)

1/9
(11%)

8/18
(44%)

4/9
(44%)

Pseudomonas aeruginosa (n = 26;
15%)

1/12
(8%)

1/14
(7%)

1/12
(8%)

1/14
(7%)

0/12
(nil)

1/14
(7%)

0/12
(nil)

0/14
(nil)

0/12
(nil)

1/14
(7%)

0/12
(nil)

1/14
(7%)

Proteus mirabilis (n = 17; 10%)

7/12
(58%)

2/5
(40%)

0/12
(nil)

0/5
(nil)

5/12
(42%)

1/5
(20%)

0/12
(nil)

0/5
(nil)

5/12
(42%)

2/5
(40%)

1/12
(8%)

1/5
(20%)

0/5
(nil)

2/12
(17%)

0/5
(nil)

7/12
(58%)

2/5
(40%)

Citrobacter freundii (n = 16; 9%)

6/9
(67%)

2/7
(29%)

1/9
(11%)

0/7
(nil)

2/9
(22%)

0/7
(nil)

2/9
(22%)

1/7
(14%)

1/9
(11%)

1/7
(14%)

0/9
(nil)

1/7
(14%)

0/7
(nil)

0/7
(nil)

2/9
(22%)

2/7
(28%)

Klebsiella pneumoniae (n = 15; 8%)

6/9
(67%)

4/6
(67%)

0/9
(nil)

1/6
(17%)

4/9
(44%)

2/6
(33%)

2/9
(22%)

0/6
(nil)

5/9
(55%)

4/6
(67%)

0/9
(nil)

0/6
(nil)

1/9
(11%)

0/6
(nil)

4/9
(44%)

0/6
(nil)

5/9
(55%)

4/6
(67%)

Morganella morganii (n = 13; 7%)

10/10(100%)

1/3 *
(33%)

0/10
(nil)

0/3
(nil)

2/10
(20%)

0/3
(nil)

0/10
(nil)

0/3
(nil)

1/10
(10%)

1/3
(33%)

0/10
(nil)

0/3
(nil)

2/10
(20%)

0/3
(nil)

10/10
(100%)

0/3
(nil)

0/10
(nil)

1/3
(33%)

Stenotrophomonas maltophila (e = 9;
5%)

2/7
(29%)

1/2
(50%)

0/7
(nil)

1/2
(50%)

2/7
(29%)

0/2
(nil)

0/7
(nil)

0/2
(nil)

Serratia marcescens (n = 8; 4%)

6/6(100%)

0/2
(nil)

0/6
(nil)

0/2
(nil)

0/6
(nil)

0/2
(nil)

0/6
(nil)

0/2
(nil)

0/6
(nil)

0/2
(nil)

0/6
(nil)

0/2
(nil)

Proteus vulgaris (n = 7; 4%)

1/3
(33%)

2/4
(50%)

0/3
(nil)

2/4
(50%)

1/3
(33%)

0/4
(nil)

0/3
(nil)

0/4
(nil)

1/3
(33%)

0/4
(nil)

1/3
(33%)

0/4
(nil)

Enterobacter cloacae (n = 6; 3%)

2/2(100%)

1/4
(25%)

0/2
(nil)

0/4
(nil)

0/2
(nil)

0/4
(nil)

0/2
(nil)

0/4
(nil)

0/2
(nil)

1/4
(25%)

0/2
(nil)

0/4
(nil)

Klebsiella oxytoca (n = 6; 3%)

2/5
(40%)

0/1
(nil)

0/5
(nil)

0/1
(nil)

1/5
(20%)

0/1
(nil)

0/5
(nil)

0/1
(nil)

2/5
(40%)

0/1
(nil)

0/5
(nil)

0/1
(nil)

Acinetobacter baumannii (n = 4;2%)

2/2
(100%)

0/2
(nil)

2/2
(100%)

0/2
(nil)

0/2
(nil)

0/2
(nil)

2/2
(100%)

0/2
(nil)

2/2
(100%)

0/2
(nil)

2/2
(100%)

0/2(nil)

Nonfermenting gram-negative
bacilli (n = 4; 2%)

0/3
(nil)

1/1
(100%)

0/3
(nil)

0/1
(nil)

0/3
(nil)

0/1
(nil)

0/3
(nil)

0/1
(nil)

0/3
(nil)

1/1
(100%)

0/3
(nil)

1/1
(100%)

Others (n = 20; 11%) B

9/16
(56%)

2/4
(50%)

2/16
(13%)

0/4
(nil)

1/15
(7%)

0/4
(nil)

0/16
(nil)

0/4
(nil)

4/16
(25%)

0/4
(nil)

2/16
(13%)

0/4
(nil)

1/5
(20%)

0/4
(nil)

6/15
(40%)

Total (n = 178)

69/114(61%)

25/64 **
(39%)

6/107
(6%)

5/62
(8%)

25/114
(22%)

7/64
(11%)

6/107
(6%)

2/62
(3%)

35/114
(31%)

18/64
(28%)

7/107
(6%)

7/62
(13%)

11/59
(19%)

0/33 **
(nil)

37/89
(42%)

ND

ND

ND
2/12
(17%)

ND

ND

ND

ND

ND

ND
6/6
(100%)

0/2
(nil)

0/6
(nil)

0/2
(nil)

0/3
(nil)

0/4
(nil)

1/3
(33%)

0/4
(nil)

ND

2/2
(100%)

0/4
(nil)

0/2
(nil)

1/4
(25%)

ND

0/5
(nil)

0/1
(nil)

1/5
(20%)

0/1
(nil)

ND

0/2
(nil)

0/2
(nil)

ND

0/3
(nil)

0/1
(nil)

1/4
(25%)

0/15
(nil)

0/4
(nil)

2/45 ****(4%)

24/101
(24%)

14/50
(28%)

ND
1/3
(33%)

0/3
(nil)

1/2
(50%)

0/2
(nil)
ND

CLTI, chronic limb-threatening ischemia; ND, no susceptibility testing performed. Data within cells are n/N (%), where N is the number of isolates tested against a given antibiotic(columns), n is the number with antibiotic resistance, and (%) is the corresponding proportion. A Antibiotics used in combination due to their synergistic effect. B This group included
bacteria with fewer than 3 isolates, namely Achromobacter xylosoxidans, Acinetobacter johnsonii, Aeromonas hydrophila, Citrobacter koseri, Delftia acidovorans, Haemophilus parainfluenzae, Proteus
penneri, Providencia rettgeri, Providencia stuartii, and Pseudomonas putida. * p < 0.05, ** p < 0.01, and **** p < 0.0001 for a difference in AR prevalence between the two periods (shown inbold) derived from Fisher’s exact tests. Note that comparisons were only examined for the overall counts for given bacterial taxa, the total counts for individual antibiotics, and the total
overall.

Antibiotics 2024, 13, 35

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Table 7. Risk factors for antibiotic resistance among the study population.
Parameter

OR (95% CI)

paOR (95% CI)

pStudy period (COVID-19 vs. prior)
Age ≥ 60 years
Male
Diabetes mellitus
Renal replacement therapy
High fever (body temperature > 38 ◦ C)
Heart rate (beats per minute)Respiratory rate > 20 (breaths per min)
Leukocytosis (WBC > 12,000/mm3 )Recurrent ulcer on the same limb
Duration ulcer ≥ 3 months
Limb infection grade 3 A
Wound grade 3 A
Ischemic grade 3 A
Presence of osteomyelitis
Any hospitalization within 6 months before admission
Referral from health services for CLTI
Polymicrobial infection
Gram-negative infection alone
Mixed infection with gram-negative and gram-positive
Previous empirical antibiotic treatment

0.21 (0.10, 0.46)
0.98 (0.42, 2.30)
1.80 (0.87, 3.74)
1.12 (0.53, 2.35)
1.60 (0.69, 3.73)
1.40 (0.56, 3.56)
1.09 (0.52, 2.30)
2.48 (0.66, 9.38)
0.75 (0.36, 1.55)
1.05 (0.48, 2.27)
1.51 (0.62, 3.64)
1.05 (0.50, 2.20)
1.01 (0.47, 2.16)
0.71 (0.34, 1.49)
0.95 (0.44, 2.05)
2.10 (1.01, 4.36)
1.69 (0.76, 3.76)
3.37 (1.59, 7.13)
2.57 (1.20, 5.52)
1.41 (0.53, 3.74)
1.79 (0.79, 4.06)<0.001
0.97
0.11
0.77
0.28
0.47
0.82
0.18
0.44
0.90
0.36
0.89
0.98
0.37
0.91
0.038
0.20
0.002
0.015
0.494
0.16

0.21 (0.08, 0.51)

0.001

6.17 (1.02, 37.3)

0.047

5.58 (2.08, 15.0)
6.98 (2.38, 20.5)

0.001<0.001

11.9 (1.11, 128)

0.041

Data are the odds ratio (OR) or adjusted odds ratio (aOR) and their 95% confidence intervals derived from
univariable or multivariable logistic regressions, respectively. Statistically significant associations (at p < 0.05)
are shown in bold. CLTI, chronic limb-threatening ischemia; WBC, white blood cell count. A The SVS-WIfI
classification grades CLTI based on perfusion (0–3), wound extent (0–3), and superadded infection (0–3), with 0
indicating absence and 3 indicating severe; the total score (summing these components) is associated with the riskof major amputations.

3. Discussion
The COVID-19 pandemic has profoundly affected healthcare services worldwide,
particularly regarding resource distribution and, thus, patient care. However, this study
shows that the pandemic was associated with markedly lower AR rates among patients
admitted to our tertiary hospital in Chiang Mai with infected ischemic leg ulcers due
to CLTI. These were accompanied by considerably lower AR rates for certain antibiotics(e.g., amoxicillin/clavulanate and ampicillin) and lower rates of polymicrobial and mixed
infections, while the rate of MDR infections more than halved. The latter is particularly
notable as MDR bacteria are associated with more severe infections and worse patient
outcomes [6].
Our observations are corroborated by previous studies consistently showing decreases
in AR rates among inpatients during the pandemic, such as in Italy [18,20], Turkey [19], andSingapore [25]. These were largely attributed to improved hospital infection prevention andcontrol measures [18–20]. Practices that reportedly mitigate the risk of nosocomial infections
with AR pathogens include screening potential COVID cases, segregating patients withrespiratory symptoms, enforcing physical distancing between beds, mandating masks, andintensifying disinfection protocols [25]. Further evidence that lower AR rates in Chiang
Mai were likely due to COVID-19 measures was provided by the steady increase in yearly
AR rates throughout the 3-year study period following the pandemic declaration in March
2020.
Importantly, apart from the pandemic-associated reduction in rates of nosocomial
infections (for pathogens other than SARS-CoV-2), our study also indicated reductions inhome- and community-acquired AR infection rates. Our samples were collected directlyfrom wound sites at the time of patient admission, which indicates frequent bacterialcolonization of wounds acquired in the community. Thus, it is likely that many CLTI
infections with secondary complications originate from community settings. Several factors
may contribute to community-acquired AR infections, such as inadequate wound care,

Antibiotics 2024, 13, 35

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inadequate use and/or overuse of antibiotics [26], exposure to infected sources, and poor
hygiene practices [27]. In addition, AR bacteria can also increase in community settings via
contaminated food and water [28].
Additional strong evidence of marked reductions in community transmission of common pathogens during the pandemic was reported in New Zealand [29]. During thewinter of 2020, stringent government-imposed lockdowns led to the near complete elimination of influenza virus transmission, when surveillance data showed a 99.9% reduction
in influenza cases compared to previous years [29]. Our study’s apparent reduction incommunity-acquired infections with antibiotic-resistant bacteria may be partially attributed
to government-imposed measures and heightened public awareness following the COVID19 pandemic declaration in March 2020 [30]. In Thailand, the government imposed a rangeof measures, including the inception of the Centre for COVID-19 Situation Administration(CCSA), the imposition of lockdowns, border controls, quarantine protocols, and publichygiene campaigns [30]. The success of the collaborative effort between government initiatives and public compliance halted community transmission of SARS-CoV-2 during thefirst wave, achieving 220 consecutive days with no confirmed cases of community transmission [31]. While the CCSA was officially closed in October 2020 and lockdowns werelifted, several key preventative measures remained in place to some extent throughout thethree-year duration of the pandemic. These included physical distancing, mask-wearing,
regular handwashing, frequent antigen self-testing, and continuous risk communication to
the public [31].
Clinical practice guidelines for ischemic leg infections are mostly based on studies ofdiabetic foot infections [32]. Here, we examined bacterial pathogens associated with CLTI,
which are less explored. Our results suggest different infection patterns in CLTI-associatedleg ulcers compared to diabetic foot infections. Regarding Gram stain characteristics, diabetic foot infections appear to be colonized mostly by gram-positive bacteria (≈75%) [10,32]
compared to 21% among our patients and ≈42% in a Romanian study [33]. Notably, earlystage wounds are mainly infected by gram-positive bacteria, such as Staphylococcus spp.
and Streptococcus spp., which come from normal skin flora [34]. In contrast, chronic wounds,
if deprived of oxygen due to arterial insufficiency, can lead to prolonged infections, often colonized by gram-negative bacterial genera such as Pseudomonas, Acinetobacter, andStenotrophomonas [35]. In our study, Staphylococcus aureus and Enterococcus spp. were themain gram-positive bacteria recorded. Still, infections were primarily associated withgram-negative bacteria, particularly Escherichia coli and Pseudomonas aeruginosa.
The choice of antibiotic treatment in CLTI often relies on expert opinion rather thanevidence-based recommendations [6,33]. While there seems to be no consensus on the
optimal antibiotic regimen for CLTI, empirical antibiotic therapy is the most common
approach for such infections, targeting the most likely pathogens before the exact bacterium
species is identified. However, these infections can lead to complications such as failed
revascularization, resulting in the incomplete healing of ischemic lesions and subsequently
increasing the risk of major limb amputations [36,37]. A previous study showed that
infected CLTI patients who received empirical antibiotics before revascularization had
amputation and death rates similar to those without infection [36]. Therefore, it is concerning that we observed relatively high AR rates for commonly used empirical antibiotics
for CLTI, especially ciprofloxacin, clindamycin, and amoxicillin/clavulanate. Antibiotic
resistance to fluoroquinolone and amoxicillin/clavulanate was approximately 30% among
gram-negative bacteria. While robust conclusions cannot be drawn from routinely collected retrospective data, 95% of our patients hospitalized with CLTI reported a historyof previous antibiotic treatment, mostly prescribed by general practitioners. Therefore,
our findings suggest that excessive use of empirical antibiotics was likely an important
contributor to antibiotic resistance in CLTI, especially when empirical therapy involves
ineffective antibiotics against the causative organism(s) [10]. As a result, future studies
should compare previous empirical antibiotic treatments with the laboratory results ofantibiotic resistance in patients with CLTI.

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A limitation of our study was its retrospective design involving a single center with arelatively limited number of cases. While our hospital served as a tertiary center during the
pandemic, we faced a manageable number of COVID-19 inpatients compared to secondaryor provincial hospitals. Therefore, caution is required when generalizing our results. In
addition, our cross-sectional study examined data collected only at the patient’s hospital
admission, which might have biased our observations regarding antibiotic-resistance rates
in the absence of longitudinal data. Further, we did not evaluate how different wounddressing and care approaches might have affected secondary infections. Lastly, several
bacterial taxa (e.g., Citrobacter freundii and Klebsiella pneumoniae) exhibit natural resistanceto specific antibiotics [38,39], which would likely affect susceptibility assessments, asreported, for example, for Proteus vulgaris and Enterobacter cloacae to aminoglycosides andAcinetobacter baumannii to fluoroquinolones [38,40,41]. However, distinguishing between
natural and acquired resistance is challenging. While underlying natural resistance is mostunlikely to have yielded Type I errors (i.e., concluding that AR rates changed when they didnot), such bacterial taxa might have masked certain changes in AR rates, leading to Type IIerrors. Despite these considerations, our research is among the initial efforts to compare
AR in ischemic limbs pre- and post-COVID-19. Additionally, comprehensive descriptionsof antibiotic resistance and the related bacterial taxa in CLTI-associated leg ulcer infections
within Thailand had not been previously explored.
4. Conclusions
We observed markedly lower rates of antibiotic resistance among patients admitted
to our regional hospital with infected ischemic leg ulcers due to CLTI. We speculate that
improvements in infection control measures and hygiene practices in the community during
the pandemic were likely key factors contributing to lower AR rates. Thus, strategic publichealth interventions, including community education on hygiene and the informed use ofantibiotics, may be crucial in mitigating the challenges posed by antibiotic resistance in CLTI.
Further, advocating for more judicious use of empirical antibiotics in clinical settings canbalance effective treatment against AR development, thereby improving patient outcomes.
5. Material and Methods
5.1. Ethics Approval
This study was approved by the Research Ethics Committee of the Faculty of Medicine,
Chiang Mai University (SUR-2565-09322). Informed consent from individual patients was
not necessary, as this was a retrospective audit of data from routine clinical care based on
de-identified patient data.
5.2. Study Design
This study was conducted at the Maharaj Nakorn Chiang Mai Hospital, a teaching
hospital affiliated with the Faculty of Medicine at Chiang Mai University (Chiang Mai,
Thailand). It was the first university hospital established outside Bangkok, and it is
an important regional medical center providing tertiary care in Northern Thailand. Thishospital is well-equipped to manage complex medical cases, including CLTI and AR. Patient
data for this study were sourced from the Department of Vascular Surgery, spanning six
years from 1 January 2017 to 31 December 2022.
We extracted data from electronic medical records, including admission and discharge
summaries, laboratory data, and microbiological diagnostic information for infected ulcers.
Eligible participants were all patients with PAD assessed for CLTI and diagnosed withchronic ischemic leg ulcers using Rutherford categories before admission [3]. PAD was
diagnosed based on abnormal pedal pulse palpation due to chronic atherosclerosis andconfirmed by poor arterial perfusion using at least one of the following methods [1,42,43]:■■■

ankle-brachial index (ABI)
toe pressure measurements
transcutaneous oxygen measurement (TCOM)

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color Doppler ultrasoundcomputed tomographic angiography (CTA)
The 6-stage Rutherford classification was used to categorize PAD severity [1]:

■
■
■
■
■
■
■

0—asymptomatic
1—mild claudication
2—moderate claudication
3—severe claudication
4—ischemic rest pain
5—minor tissue loss
6—major tissue loss

CLTI, indicative of advanced PAD stage, was defined as persistent foot pain or tissue
necrosis for at least two weeks. In this study, ischemic leg ulcers were specifically defined as
CLTI cases classified under the Rutherford Classification stage ≥ 5. Hemodynamic criteria
for CLTI included [3,44]:■■■

resting ankle systolic pressure < 50–70 mmHg
toe pressure < 40 mmHg in non-diabetic or <50 mmHg in diabetic patients
TCOM < 30 mmHg

Using the guidelines from the Infectious Diseases Society of America/International
Working Group on Diabetic Foot (IDSA/IWGDF) [32], we classified the severity of infectedischemic wounds based on the presence of at least two of the following:
■
■
■
■
■

local swelling or induration
erythema (redness) >0.5 cm around the wound
local tenderness or pain
local hyperthermia
purulent discharge not attributable to other conditions (e.g., trauma, gout, or venous
stasisThe depth of infection into bone structures was evaluated using the probe-to-bone
test, with positive results leading to further investigation by X-ray for osteomyelitis diagnosis. The Society of Vascular Surgery’s Wound, Ischemia, and Foot Infection (SVS-WIfI)
classification system [2] was used to evaluate the overall severity of ulcers and infections.
Information regarding patient demographics, medical history, clinical evaluation, anddiagnosis were extracted at the first admission date. Revascularization refers to open
surgical bypass, endarterectomy, and endovascular procedure. Minor limb amputations
were defined as the transverse removal of any part of the lower limb at the level of theankle joint or below; major amputations were classified as any amputation above the ankle
joint up to the thigh [32,45].
For patients diagnosed with CLTI multiple times throughout the study period, only
the data from their first admission were included in this study. Other exclusion criteria
were the absence of systemic or local symptoms, lack of signs of infection alongside positive
wound cultures, and instances where specimens were contaminated during handling
and/or processing.
5.3. Specimens and Microbiology
Bacterial pathogens were primarily identified through microbiological tests on pus
samples or wound biopsies collected within the first few days of admission and before theadministration of antibiotics at our hospital. It should be noted, however, that most patients
had likely already received empirical antibiotics from primary healthcare providers before
admission to our facility. We included only the first isolates per patient to prevent the overestimation of microbiological isolations resulting from multiple samples within the same
patients. Inconsistencies were resolved using wound biopsy results. Bacterial identification
was performed using MALDI-TOF [46], and antibiotic susceptibility was tested using theVitek2 system (bioMerieux, Marcy L’Etoile, France). The results were interpreted according
to the Clinical and Laboratory Standards Institute (CLSI) guidelines [47].

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The levels of antibiotic resistance of each identified pathogen were categorized into
susceptible, intermediate, and resistant phenotypes, with intermediate results treated assusceptible. We assessed AR risks across various antibiotic groups and identified numerous bacterial pathogens resistant to antibiotics commonly used in routine clinicalpractice. These pathogens pose a considerable threat to public health, leading to prolonged
illness and, in some cases, death, as well as increased healthcare costs. To assess therisk of AR across antibiotic groups (penicillins, clindamycin, macrolides, sulfonamides,
cephalosporins, quinolones, and aminoglycosides), we considered a loss of susceptibility
affecting the bactericidal or bacteriostatic properties of an antibiotic agent, as indicatedby laboratory results [8,12,16]. Subsequently, we analyzed the most frequently isolated
agents over the study period, including Methicillin-resistant Staphylococcus aureus (MRSA),
Methicillin-resistant Staphylococcus epidermidis (MRSE), extended-spectrum β-lactamase(ESBL)-producing bacteria (including those resistant to third-generation cephalosporin),
Vancomycin-resistant Enterococcus (VRE), Carbapenem-resistant Enterobacterales (CRE,
including Escherichia coli, Klebsiella spp., and Proteus spp.), Carbapenem-resistant Pseudomonas spp., and Carbapenem-resistant Acinetobacter spp. Multidrug resistance (MDR)
was defined as acquired non-susceptibility to at least one agent in a minimum of three
antibiotic categories, following the criteria established by the Clinical Laboratory Standards
Institute [13].
5.4. Outcomes
The rates of antibiotic resistance in infected CLTI patients were compared between
the COVID-19 pandemic and pre-pandemic years, determined based on the WHO’s declaration on 11 March 2020 [48]. The primary outcome was the rate of AR, defined as thenumber of patients with at least one antibiotic-resistant pathogen in their infected leg
wound, stratified by the period before and during the COVID-19 pandemic. We selected
the potential risk factors likely to influence AR outcomes independently, as previously
reported [9,49]. These factors were assessed at hospital admission and coded as binary variables to simplify the comparison of descriptors and AR results: gender (male vs. female);age (≥60 vs. <60 years); diabetes mellitus (with vs. without); renal replacement therapy(undergoing vs. not undergoing); fever (≤38 vs. >38 ◦ C) [32]; respiratory rate (≤20 vs.
>20 breaths/min) [32], leukocytosis (≤12,000 vs. >12,000 cells/mm3 ) [32]; recurrent ulcer
on the same limb (yes vs. no); duration of ulcer (<3 vs. ≥3 months); limb infection grade(<3vs. >3) [32]; wound grade (<3 vs. 3) [32]; ischemic grade (<3 vs. 3) [32]; osteomyelitis (withvs. without) [32]; hospitalization within six months before admission (yes vs. no); referralcase (yes vs. no); infection type (polymicrobial vs. monomicrobial); Gram stain (negativeor positive vs mixed infection); any empirical antibiotic treatment in the last 30 days (yesvs. no).
5.5. Statistical Analysis
Differences in baseline clinical data between the two periods were evaluated using
two-sample t-tests or the non-parametric Mann–Whitney U test for continuous data andchi-squared tests or Fisher’s exact tests for categorical data, as appropriate. The AR rates
before and during the COVID-19 pandemic were compared using Fisher’s exact tests. The
95% confidence intervals (CI) for the differences between the two rates were derived using
the normal approximation test for two proportions. Continuous data approximating anormal distribution were reported as the mean ± standard deviation (SD). Skewed data
were reported as the median [quartile 1, quartile 3]. A multivariable logistic regression
was used to explore the risk factors for infected ischemic leg ulcers in CLTI patientswith antibiotic-resistant bacteria. A backward stepwise selection procedure was used to
identify the predictors associated with AR likelihood. Variables with a p-value ≥ 0.1 were
removed from the model at each step, and variables with a p-value < 0.05 were retained.
Multicollinearity among risk factors was checked by the variance inflation factor. All
tests were 2-sided with statistical significance set at p < 0.05. Statistical analyses wereAntibiotics 2024, 13, 35

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performed using STATA (16.1, Stata Corp LLC, College Station, TX, USA) and Minitab
v21.4 (Pennsylvania State University, State College, PA, USA).
Supplementary Materials: The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/antibiotics13010035/s1; Table S1: List of antibiotics and theirrespective classes against which gram-positive and gram-negative bacteria were tested for resistancein our study; Table S2: Antibiotic resistance and susceptibility of gram-positive bacteria isolated
from leg ulcers in patients admitted to hospital with CLTI over the six-year study period (2017–2022);
Table S3: Antibiotic resistance and susceptibility of gram-negative bacteria isolated from leg ulcers inpatients admitted to hospital with CLTI over the six-year study period (2017–2022).
Author Contributions: Conceptualization, A.R., B.S., J.G.B.D., K.R. and S.O. (Saritphat Orrapin);
Methodology, A.R., B.S., J.G.B.D., K.R. and S.O. (Saritphat Orrapin); Data collection, all authors;
Data validation, all authors; Formal analysis, A.R., J.G.B.D., K.R. and P.T.; Writing—original draft
preparation, A.R., J.G.B.D., K.R. and P.T.; Writing—review and editing, all authors; Supervision,
J.G.B.D. and K.R.; Funding acquisition, A.R. and K.R. All authors have read and approved the
published version of the manuscript.
Funding: José G. B. Derraik was partially supported by Chiang Mai University.
Institutional Review Board Statement: Ethics approval for this study was provided by the Research
Ethics Committee of the Faculty of Medicine, Chiang Mai University (SUR-2565-09322).
Informed Consent Statement: Patient consent was not necessary due to the study’s retrospective
nature and the use of de-identified data.
Data Availability Statement: The data supporting this study are available within the article. Raw
data supporting this study’s findings are available from the corresponding author upon reasonable
request, conditional on the necessary ethics approval of the statistical analysis plan.
Acknowledgments: This research was partially supported by Chiang Mai University. Amaraporn
Rerkasem and Pak Thaichana contributed equally to this study as the first authors. ChatGPT-4(OpenAI, San Francisco, CA, USA) was used to assist with the translation and initial revision of text
from Thai to English. In addition, ChatGPT-4 and Claude 2 (Anthropic, San Francisco, CA, USA)
were used for additional editorial revision before manuscript submission.
Conflicts of Interest: The authors declare no conflicts of interest.

References
1.

2.
3.
4.
5.
6.
7.
8.

9.

Aboyans, V.; Ricco, J.B.; Bartelink, M.E.L.; Björck, M.; Brodmann, M.; Cohnert, T.; Collet, J.P.; Czerny, M.; De Carlo, M.; Debus, S.;et al. 2017 ESC Guidelines on the Diagnosis and Treatment of Peripheral Arterial Diseases, in collaboration with the European
Society for Vascular Surgery (ESVS): Document covering atherosclerotic disease of extracranial carotid and vertebral, mesenteric,renal, upper and lower extremity arteries. Endorsed by: The European Stroke Organization (ESO), The Task Force for the
Diagnosis and Treatment of Peripheral Arterial Diseases of the European Society of Cardiology (ESC) and of the European Society
for Vascular Surgery (ESVS). Eur. Heart. J. 2018, 39, 763–816. [CrossRef] [PubMed]
Conte, M.S.; Bradbury, A.W.; Kolh, P.; White, J.V.; Dick, F.; Fitridge, R.; Mills, J.L.; Ricco, J.B.; Suresh, K.R.; Murad, M.H. Global
vascular guidelines on the management of chronic limb-threatening ischemia. J. Vasc. Surg. 2019, 69, 3S–125S.e40. [CrossRef]
Sidawy, A.N.; Perler, B.A. Rutherford’s Vascular Surgery and Endovascular Therapy; Elsevier Health Sciences: Philadephia, PA, USA,
2022.
Azuma, N. The diagnostic classification of critical limb ischemia. Ann. Vasc. Dis. 2018, 11, 449–457. [CrossRef] [PubMed]
Ventoruzzo, G.; Mazzitelli, G.; Ruzzi, U.; Liistro, F.; Scatena, A.; Martelli, E. Limb salvage and survival in chronic limb-threatening
ischemia: The need for a fast-track team-based approach. J. Clin. Med. 2023, 12, 6081. [CrossRef]
Izumi, Y. Countermeasures against infection in critical limb ischemia treatments. Ann. Vasc. Dis. 2018, 11, 277–280. [CrossRef][PubMed]Tautenhahn, J.; Lobmann, R.; Koenig, B.; Halloul, Z.; Lippert, H.; Buerger, T. The influence of polymorbidity, revascularization,
and wound therapy on the healing of arterial ulceration. Vasc. Health Risk Manag. 2008, 4, 683–689. [CrossRef]
Matta-Gutiérrez, G.; García-Morales, E.; García-Álvarez, Y.; Álvaro-Afonso, F.J.; Molines-Barroso, R.J.; Lázaro-Martínez, J.L. The
influence of multidrug-resistant bacteria on clinical outcomes of diabetic foot ulcers: A systematic review. J. Clin. Med. 2021, 10,1948. [CrossRef] [PubMed]
Xia, W.; He, W.; Luo, T.; Tang, N. Risk factors for multidrug-resistant bacterial infections in patients with diabetic foot ulcers: A
systematic review and meta-analysis. Ann. Palliat. Med. 2021, 10, 12618–12630. [CrossRef]

Antibiotics 2024, 13, 35

10.

11.
12.
13.

14.

15.

16.

17.

18.

19.

20.
21.
22.

23.

24.
25.

26.

27.
28.
29.

30.
31.
32.

17 of 18

Salm, J.; Bohme, T.; Noory, E.; Beschorner, U.; Kramer, T.S.; Westermann, D.; Zeller, T. Arterial leg ulcers—Bacterial patterns,
antimicrobial resistance and clinical characteristics, a retrospective single-centre cohort, 2012–2021. PLoS ONE 2023, 18, e0290103.
[CrossRef]
Bansal, E.; Garg, A.; Bhatia, S.; Attri, A.K.; Chander, J. Spectrum of microbial flora in diabetic foot ulcers. Indian J. Pathol. Microbiol.
2008, 51, 204–208. [CrossRef]
Boyanova, L.; Mitov, I. Antibiotic resistance rates in causative agents of infections in diabetic patients: Rising concerns. Expert Rev.
Anti-Infect. Ther. 2013, 11, 411–420. [CrossRef] [PubMed]
Magiorakos, A.P.; Srinivasan, A.; Carey, R.B.; Carmeli, Y.; Falagas, M.E.; Giske, C.G.; Harbarth, S.; Hindler, J.F.; Kahlmeter, G.;
Olsson-Liljequist, B.; et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: An international expert
proposal for interim standard definitions for acquired resistance. Clin. Microbiol. Infect. 2012, 18, 268–281. [CrossRef] [PubMed]
Langford, B.J.; So, M.; Simeonova, M.; Leung, V.; Lo, J.; Kan, T.; Raybardhan, S.; Sapin, M.E.; Mponponsuo, K.; Farrell, A.; et al.
Antimicrobial resistance in patients with COVID-19: A systematic review and meta-analysis. Lancet Microbe 2023, 4, e179–e191.
[CrossRef] [PubMed]
Langford, B.J.; Soucy, J.R.; Leung, V.; So, M.; Kwan, A.T.H.; Portnoff, J.S.; Bertagnolio, S.; Raybardhan, S.; MacFadden, D.R.;
Daneman, N. Antibiotic resistance associated with the COVID-19 pandemic: A systematic review and meta-analysis. Clin.
Microbiol. Infect. 2023, 29, 302–309. [CrossRef] [PubMed]
Caruso, P.; Maiorino, M.I.; Macera, M.; Signoriello, G.; Castellano, L.; Scappaticcio, L.; Longo, M.; Gicchino, M.; Campitiello, F.;
Bellastella, G.; et al. Antibiotic resistance in diabetic foot infection: How it changed with COVID-19 pandemic in a tertiary carecenter. Diabetes Res. Clin. Pract. 2021, 175, 108797. [CrossRef] [PubMed]
Petrakis, V.; Panopoulou, M.; Rafailidis, P.; Lemonakis, N.; Lazaridis, G.; Terzi, I.; Papazoglou, D.; Panagopoulos, P. The impactof the COVID-19 pandemic on antimicrobial resistance and management of bloodstream infections. Pathogens 2023, 12, 780.
[CrossRef] [PubMed]
Micozzi, A.; Assanto, G.M.; Cesini, L.; Minotti, C.; Cartoni, C.; Capria, S.; Ciotti, G.; Alunni Fegatelli, D.; Donzelli, L.; Martelli,M.; et al. Reduced transmission of Klebsiella pneumoniae carbapenemase-producing K. pneumoniae (KPC-KP) in patients withhaematological malignancies hospitalized in an Italian hospital during the COVID-19 pandemic. JAC Antimicrob. Resist. 2021, 3,
dlab167. [CrossRef]
Guven, D.C.; Eroglu, I.; Ismayilov, R.; Ulusoydan, E.; Aktepe, O.H.; Telli Dizman, G.; Arik, Z.; Dizdar, O.; Unal, S.; Metan, G.; et al.
Lesson learned from the pandemic: Isolation and hygiene measures for COVID-19 could reduce the nosocomial infection rates inoncology wards. J. Oncol. Pharm. Pract. 2022, 28, 1807–1811. [CrossRef]
Bentivegna, E.; Luciani, M.; Arcari, L.; Santino, I.; Simmaco, M.; Martelletti, P. Reduction of multidrug-resistant (MDR) bacterial
infections during the COVID-19 pandemic: A retrospective study. Int. J. Environ. Res. Public Health 2021, 18, 1003. [CrossRef]
Tao, F.; Tang, X.; Tao, H.; Luo, Y.; Cao, H.; Xiang, W.; Zhao, Y.; Jin, L. Surgical treatment of diabetic foot ulcers during the
COVID-19 pandemic in China. J. Diabetes Complicat. 2020, 34, 107622. [CrossRef]
Saleem, Z.; Haseeb, A.; Godman, B.; Batool, N.; Altaf, U.; Ahsan, U.; Khan, F.U.; Mustafa, Z.U.; Nadeem, M.U.; Farrukh, M.J.; et al.
Point prevalence survey of antimicrobial use during the COVID-19 pandemic among different hospitals in Pakistan: Findings
and implications. Antibiotics 2023, 12, 70. [CrossRef] [PubMed]
Smith, M.; Vaughan Sarrazin, M.; Wang, X.; Nordby, P.; Yu, M.; DeLonay, A.J.; Jaffery, J. Risk from delayed or missed care andnon-COVID-19 outcomes for older patients with chronic conditions during the pandemic. J. Am. Geriatr. Soc. 2022, 70, 1314–1324.
[CrossRef] [PubMed]
Collignon, P.; Beggs, J.J. CON: COVID-19 will not result in increased antimicrobial resistance prevalence. JAC Antimicrob. Resist.
2020, 2, dlaa051. [CrossRef] [PubMed]
Wee, L.E.I.; Conceicao, E.P.; Tan, J.Y.; Magesparan, K.D.; Amin, I.B.M.; Ismail, B.B.S.; Toh, H.X.; Jin, P.; Zhang, J.; Wee, E.G.L.; et al.
Unintended consequences of infection prevention and control measures during COVID-19 pandemic. Am. J. Infect. Control 2021,
49, 469–477. [CrossRef] [PubMed]
Luu, L.; Muhsin, A. A retrospective study of the overuse of extended-spectrum antibiotics in patients with community-acquired
pneumonia with risk for methicillin-resistant Staphylococcus aureus and/or Pseudomonas aeruginosa. Cureus 2022, 14, e31126.
[CrossRef]
Goossens, H.; Sprenger, M.J. Community acquired infections and bacterial resistance. BMJ 1998, 317, 654–657. [CrossRef]
Levy, S.B.; Marshall, B. Antibacterial resistance worldwide: Causes, challenges and responses. Nat. Med. 2004, 10, S122–S129.
[CrossRef]
Huang, Q.S.; Wood, T.; Jelley, L.; Jennings, T.; Jefferies, S.; Daniells, K.; Nesdale, A.; Dowell, T.; Turner, N.; Campbell-Stokes,
P.; et al. Impact of the COVID-19 nonpharmaceutical interventions on influenza and other respiratory viral infections in New
Zealand. Nat. Commun. 2021, 12, 1001. [CrossRef]
Plipat, T. Lessons from Thailand’s response to the COVID-19 pandemic. Thai J Public Health 2020, 50, 268–277.
Wilasang, C.; Jitsuk, N.C.; Sararat, C.; Modchang, C. Reconstruction of the transmission dynamics of the first COVID-19 epidemic
wave in Thailand. Sci. Rep. 2022, 12, 2002. [CrossRef]
Lipsky, B.A.; Senneville, É.; Abbas, Z.G.; Aragón-Sánchez, J.; Diggle, M.; Embil, J.M.; Kono, S.; Lavery, L.A.; Malone, M.; van
Asten, S.A.; et al. Guidelines on the diagnosis and treatment of foot infection in persons with diabetes (IWGDF 2019 update).
Diabetes Metab. Res. Rev. 2020, 36 (Suppl. S1), e3280. [CrossRef] [PubMed]

Antibiotics 2024, 13, 35

33.
34.
35.

36.

37.
38.
39.

40.
41.
42.

43.
44.
45.
46.
47.

48.
49.

18 of 18

IluT, , P.A. Antibiotic susceptibility and resistance of bacterial pathogens in chronic leg ulcers: A retrospective cohort study.
Farmacia 2023, 71, 38–43. [CrossRef]
Chiller, K.; Selkin, B.A.; Murakawa, G.J. Skin microflora and bacterial infections of the skin. J. Investig. Dermatol. Symp. Proc. 2001,
6, 170–174. [CrossRef] [PubMed]
Nahid, M.A.; Griffin, J.M.; Lustik, M.B.; Hayes, J.J.; Fong, K.S.K.; Horseman, T.S.; Menguito, M.; Snesrud, E.C.; Barnhill, J.C.;
Washington, M.A. A longitudinal evaluation of the bacterial pathogens colonizing chronic non-healing wound sites at a UnitedStates military treatment facility in the Pacific Region. Infect. Drug. Resist. 2021, 14, 1–10. [CrossRef] [PubMed]
Jaccard, Y.; Walther, S.; Anderson, S.; Tauber, M.; Kummer, O.; Baumgartner, R.; Diehm, N.; Dörffler-Melly, J.; Baumgartner, I.
Influence of secondary infection on amputation in chronic critical limb ischemia. Eur. J. Vasc. Endovasc. Surg. 2007, 33, 605–609.
[CrossRef] [PubMed]
Kummer, O.; Widmer, M.K.; Pluss, S.; Willenberg, T.; Vogele, J.; Mahler, F.; Baumgartner, I. Does infection affect amputation ratein chronic critical leg ischemia? Vasa 2003, 32, 18–21. [CrossRef] [PubMed]
Reygaert, W.C. An overview of the antimicrobial resistance mechanisms of bacteria. AIMS Microbiol. 2018, 4, 482–501. [CrossRef]
Uddin, T.M.; Chakraborty, A.J.; Khusro, A.; Zidan, B.M.R.M.; Mitra, S.; Emran, T.B.; Dhama, K.; Ripon, M.K.H.; Gajdács, M.;
Sahibzada, M.U.K.; et al. Antibiotic resistance in microbes: History, mechanisms, therapeutic strategies and future prospects. J.
Infect. Public Health 2021, 14, 1750–1766. [CrossRef]
Davin-Regli, A.; Pagès, J.-M. Enterobacter aerogenes and Enterobacter cloacae; versatile bacterial pathogens confronting antibiotic
treatment. Front. Microbiol. 2015, 6, 392. [CrossRef]
Kyriakidis, I.; Vasileiou, E.; Pana, Z.D.; Tragiannidis, A. Acinetobacter baumannii antibiotic resistance mechanisms. Pathogens 2021,
10, 373. [CrossRef]
Aboyans, V.; Criqui, M.H.; Abraham, P.; Allison, M.A.; Creager, M.A.; Diehm, C.; Fowkes, F.G.; Hiatt, W.R.; Jönsson, B.; Lacroix,
P.; et al. Measurement and interpretation of the ankle-brachial index: A scientific statement from the American Heart Association.
Circulation 2012, 126, 2890–2909. [CrossRef] [PubMed]
Norgren, L.; Hiatt, W.R.; Dormandy, J.A.; Nehler, M.R.; Harris, K.A.; Fowkes, F.G. Inter-Society Consensus for the Management of
Peripheral Arterial Disease (TASC II). J. Vasc. Surg. 2007, 45 (Suppl. S), S5–S67. [CrossRef] [PubMed]
Rutherford, R.B.; Baker, J.D.; Ernst, C.; Johnston, K.W.; Porter, J.M.; Ahn, S.; Jones, D.N. Recommended standards for reports
dealing with lower extremity ischemia: Revised version. J. Vasc. Surg. 1997, 26, 517–538. [CrossRef] [PubMed]
Jones, N.J.; Harding, K. 2015 International Working Group on the Diabetic Foot Guidance on the prevention and management offoot problems in diabetes. Int. Wound J. 2015, 12, 373–374. [CrossRef] [PubMed]
Singhal, N.; Kumar, M.; Kanaujia, P.K.; Virdi, J.S. MALDI-TOF mass spectrometry: An emerging technology for microbial
identification and diagnosis. Front. Microbiol. 2015, 6, 791. [CrossRef] [PubMed]
Carey, R.B.; Bhattacharyya, S.; Kehl, S.C.; Matukas, L.M.; Pentella, M.A.; Salfinger, M.; Schuetz, A.N. Practical guidance for clinicalmicrobiology laboratories: Implementing a quality management system in the medical microbiology laboratory. Clin. Microbiol.
Rev. 2018, 31, e00062-17. [CrossRef]
Cucinotta, D.; Vanelli, M. WHO declares COVID-19 a pandemic. Acta Biomed. 2020, 91, 157–160. [CrossRef]
Sibbald, R.G.; Orsted, H.; Schultz, G.S.; Coutts, P.; Keast, D. Preparing the wound bed 2003: Focus on infection and inflammation.
Ostomy Wound Manag. 2003, 49, 24–51.

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