INTRODUCTION

Pregnancy induces physiological and structural changes in the urinary tract, such as urethral dilation and reduced bladder tone, which increase urinary stasis and risk of urinary tract infections (UTIs) [1] . UTIs, primarily caused by enteric bacteria like Escherichia coli (60–80% of cases), are more prevalent in pregnant women (20%) than non-pregnant individuals, often leading to hospitalization. These infections are exacerbated by glycosuria and hormonal shifts, peaking between weeks 22–24 of gestation. Concurrently, bacterial vaginosis (BV), an inflammatory condition linked to preterm birth and miscarriage, disrupts the vaginal microbiome dominated by Lactobacillus species [2] that maintain acidic conditions to inhibit pathogens  [3]. BV is characterized by overgrowth of Gram-negative E. coli, Klebsiella spp. and Gram-positive bacteria Staphylococcus aureus, Streptococcus agalactiae, many exhibiting antibiotic resistance. Opportunistic pathogens like Staphylococcus epidermidis and Enterococcus faecalis further colonize the vaginal tract, leveraging virulence factors (e.g., biofilm formation, adhesion proteins) to evade host defenses. With 75% of women experiencing vaginal infections—often exacerbated by inappropriate antibiotic useunderstanding resistance patterns is critical [4]. Urinary tract infections (UTIs), bacterial vaginosis (BV), and wound infections are prevalent among women and are of particular concern in pregnant populations, where they can lead to serious complications. The treatment of these infections is increasingly challenged by the emergence of multidrug-resistant (MDR), pan drug-resistant (PDR), and extensively drug-resistant (XDR) pathogens, highlighting the urgent need for improved infection control and therapeutic strategies [5].Urinary tract infections (UTIs) Urinary tract infections include a spectrum of disorders ranging from those affecting the lower urinary tract, like asymptomatic bacteriuria and cystitis to those affecting the kidney such as pyelonephritis [6].Wound infections Surgical site infection (SSI) or wound infection is an infection that develops within 30 days of a surgical operation that involves the skin, subcutaneous tissue, or soft tissue [7].Bacterial vaginosis (BV) BV is the most common cause of vaginal infections in women of reproductive age, with prevalence rates varying between 10% to 30% globally, depending on geographical and socioeconomic factors. This condition is often asymptomatic but can cause abnormal vaginal discharge, discomfort, and a characteristic "fishy" odor [3].The extensive use of antibiotics in medicine has driven the emergence and global spread of antibiotics resistance (AR) in both pathogenic and commensal bacteria. This crisis is exacerbated by the lack of new antibiotics and the rapid dissemination of resistance genes through horizontal gene transfer (HGT), fueled by selective pressure from environmental antibiotic exposure. Notably, the overlap of antibiotics used in humans has accelerated the spread of AR genes across bacteria linked to the food chain, creating reservoirs of resistance [8]. Resistance is often not due to de novo mutations but to reinfection by resistant strains from a patient’s own microbiota. Machine learning–based personalized antibiotic recommendations, informed by past infection histories, could mitigate recurrence and slow the spread of resistant pathogens [9]. Several commonly used antibiotics, such as amikacin, gentamicin, and ceftazidime, are integral in treating Gram-negative bacterial infections. However, increasing resistance to these agents has been observed, particularly among Enterobacteriaceae and Pseudomonas aeruginosa, complicating clinical management [10]. To standardize resistance classifications, multidrug resistance (MDR), extensively drug-resistant (XDR), and pan-drug-resistant (PDR) phenotypes are defined as nonsusceptibility to ≥1 agent in ≥3 antimicrobial categories, ≤2 categories, and all categories, respectively [11].

METHODS

Sample Collection

 

A total of (1074) samples were collected from urine, vagina and wound from (Rizgari and Maternity) hospitals and (Malaikat al rahma) laboratory from married women in Erbil city between January 2021-october 2024. After collection, all bacterial isolates were subjected to a series of confirming tests. Selection of appropriate samples for bacterial vaginosis detection involves careful consideration of the sample type. For urine samples, midstream collection is recommended to reduce contamination, ensuring the area around the urethral opening is cleaned with water or a mild sterile solution if necessary. High vaginal swabs (HVS) should be obtained using sterile techniques for accurate results. For wound swabs, the surrounding area must be carefully cleaned and prepared before sample collection. Proper handling and transport of all samples are essential to maintain their integrity for diagnostic testing [4].

 

Antimicrobial Susceptibility Test by Vitek 2 Compact System

 

The system includes an advanced expert system that analyzes minimum inhibitory concentration patterns and detects phenotypes for most organisms tested. This helps optimize laboratory efficiency for lean laboratory management. Rapid results allow clinicians to discontinue empiric therapy and prescribe targeted therapy, resulting in improved patient outcomes and enhanced antibiotic stewardship [12]. With its ability to provide accurate "fingerprint" recognition of bacterial resistance mechanisms and phenotypes, the advanced expert system is a critical component of Vitek2 technology. The Vitek2 card contains 64microwells. Each well contains identification substrates or antimicrobial. Vitek2 offers a comprehensive menu for the identification and antibiotic susceptibility testing of organisms. The Vitek2 test card is sealed, which minimizes aerosols, spills, and personal contamination. Disposable waste is reduced by more than 80% over micro titer methods [13].

Ethical Consideration

The bacterial strains used in this study were isolated from the routine clinical specimens and verbal consent was obtained from the patients. This study was approved by the Scientific and Research Ethics Committee at the College of Health Sciences/ Hawler Medical University.

Statistical Analysis

Data entry and statistical analysis were performed using SPSS v.23 software. Comparisons were made using Pearson Chi-square. A p-value of < 0.05 was considered indicative of a statistically significant difference and p-value < 0.01 was considered indicative of a highly statistically significant difference

 

Results

Frequency of bacteria isolated from women according to years

 

Out of 1074 isolates only 150 were positive from patients as in table (3-1). The analysis of bacteria distribution from 2021 to 2024 illustrates significant fluctuations in positivity. In 2022, the year with the lowest positivity, only 5 out of 128 samples (3.9%) tested positive, while 123 samples (96.09%) were negative. This was followed by 2021, where 14 out of 88 samples (15.9%) tested positive, resulting in 74 negative samples (84.1%). In 2024, the positivity increased to 51 out of 458 samples (11.13%), with 407 samples (88.86%) testing negative. The year 2023 saw the highest positivity, with 80 out of 400 samples (20%) testing positive, while 320 samples (80%) were negative. Over the entire study period from 2021 to 2024, there were a total of 150 positive samples (13.96%) and 924 negative samples (86.03%). Statical analysis showed that is significant correlation (P= 0.04) as showed in table (1) and figure (1).

 

 

 

 

Table (1): Frequency of bacteria isolated from women according to years

Years	Positive	%	Negative	%	Total	%	P(value)
2021	14	15.9	74	84	88	8.19
2022	5	3.9	123	96.09	128	11.91
2023	80	20	320	80	400	37.24
2024	51	11.13	407	88.86	458	42.64
Total	150	13.96	924	86.03	1074	100	P = 0.04

%= Percentage             *If P value ≤ 0.05 is significant

 

 

Figure (1):  Frequency of bacteria isolated from women according to years

 

Distribution of bacteria among different samples

Out of 150 positive samples, 80 (53.3%) were urine samples, 67 (44.7%) were high vaginal swabs (H.V.S), and 3 (2%) were wound samples, as shown in Table 3.2. The results were analyzed based on age groups.  In urine samples, seven age groups were identified: the 0–10 age group had 0 cases (0%), 11–20 had 13 (18.7%), 21–30 had the highest frequency with 27 cases (18%), 31–40 had 25 (16.7%), 41–50 had 10 (6.7%), 51–60 had 2 (1.3%), and 61–70 had 3 (2%).  In H.V.S samples, the distribution across seven age groups was as follows: 11–20 had 11 cases (7.3%), 21–30 had the highest frequency with 25 cases (16.7%), 31–40 had 18 (12%), 41–50 had 9 (6%), 51–60 had 4 (2.7%), while the 0–10 and 61–70 age groups had 0 cases (0%).  In wound samples, only the 21–30 age group showed 3 cases (2%), while the other age groups (0–10, 11–20, 31–40, 41–50, 51–60, and 61–70) had 0 cases (0%) as showed is table (2).

 

Table (2) : Distribution of bacteria among different sample

Specimen	Ages groups	NO (%)
Urine (80)	0-10	0 (0%)
	11-20	13 (8.7%)
	21-30	27 (18%)
	31-40	25 (16.7%)
	41-50	10 (6.7%)
	51-60	2 (1.3%)
	61-70	3 (2%)
	Total	80 (53.3%)
H.V.S(67)	0-10	0 (0%)
	11-20	11 (7.3%)
	21-30	25 (16.7%)
	31-40	18 (12%)
	41-50	9 (6%)
	51-60	4 (2.7%)
	61-70	0 (0%)
	Total	67 (44.7%)
Wound (3)	0-10	0 (0%)
	11-20	0 (0%)
	21-30	3 (2%)
	31-40	0 (0%)
	41-50	0 (0%)
	51-60	0 (0%)
	61-70	0 (0%)
	Total	3 (2%)

*NO = Number, % = Percentage

 

 

Distribution of bacteria in different ages according to different years

The distribution of bacterial vaginosis. In 2021 was seen mostly among the (21-30) having 5/14(35.7%) and in two age groups as same (11-20) and (31-40) that having 3/14(21.4%) and in (41-50) having 2/14(14.3%), in (51-60) having 1/14(7.14%).  meanwhile in 2022 it was different, it was seen mostly among the (21-30) having 2/5(40%) and it was seen among three age groups as same (11-20), (31-40), (41-50) all of them having 1/5(20%). In 2023 was mostly seen in (21-30) that have 27/80(33.8%). and lastly in 2024 it was seen among four age groups in (11-20) having 7/51(13.7%) and (21-30) having 22/51(43.1%) and (31-40) having 17/51(33.3%),(41-50) having 5/51(9.8%) as showed in table (3).   

 

 

 

Table (3):  Distribution of bacteria in different ages according to different years

Years	0-10  NO  %	11-20  N0  %	21-30  NO  %	Ages  31-40   NO   %	41-50  NO  %	51-60  NO  %	61-70  NO  %	Total  N0  %
2021	0 0%	3 21.4%	5 35.7%	3 21.4%	2 14.3%	1 7.14%	0 0%	14 9.3%
2022	0 0%	1 20%	2 40%	1 20%	1 20%	0 0%	0 0%	5 3.33%
2023	0 0%	13 16.25%	27 33.8%	22 28%	10 13%	5 6.3%	3 3.8%	80 53.3%
2024	0 0%	7 13.7%	22 43.1%	17 33.3%	5 9.8%	0 0%	0 0%	51 34%
Total	0 0%	24 16%	56 37.3%	43 28.7%	18 12%	6 4%	3 2%	150 100%

* NO= Number, %= Percentage

 

Antibiotics sensitivity and resistance patterns across specimen types

The findings from the antibiotics sensitivity and resistance data indicate that Meropenem stands out as the most effective antibiotic, showing high sensitivity in H.V.S. (92.5%) and urine (95%), though its effectiveness drops in wound specimens (33.3% sensitivity). Amikacin shows moderate effectiveness in urine (60%) but is less effective in H.V.S. (59.7%) and wound specimens (66.7%). Ciprofloxacin and Levofloxacin demonstrate high sensitivity in H.V.S. (76.1%) but show moderate resistance in urine (62.5% and 53.8%, respectively). In wound specimens, both exhibit significant resistance (66.7%). Gentamicin and Ceftazidime show high resistance rates across all specimen types. Ceftazidime has (75%) resistance in urine and (100%) resistance in wound specimens, while Gentamicin exhibits (70%) resistance in urine and (66.7%) resistance in wound specimens. Piperacillin and Trimethoprim demonstrate very low sensitivity, particularly in urine (16.2% and 12.5%, respectively), and no sensitivity was observed in wound samples. In conclusion, Meropenem remains the most reliable treatment across specimen types, while Amikacin and Levofloxacin show moderate effectiveness, especially in H.V.S. and urine as showed in table (4).

 

 

 

 

 

 

 

 

Table (4): Antibiotics Sensitivity and Resistance Patterns Across Specimen Types

specimen	Antibiotics used	S No. %	R No. %
Urine (80)	Meropenem Amikacin Levofloxacin Ciprofloxacin Gentamicin Ceftazidime Piperacillin Trimethoprim	76 (95%) 48 (60%) 37 (46.2%) 30 (37.5%) 24 (30%) 20 (25%) 13 (16.2%) 10 (12.5%)	4 (5%) 32 (40%) 43 (53.8%) 50 (62.5%) 56 (70%) 60 (75%) 67 (83.8%) 70 (87.5%)
H.V.S (67)	Meropenem Levofloxacin Ciprofloxacin Amikacin Gentamicin Trimethoprim Piperacillin Ceftazidime	62 (92.5%) 51 (76.1%) 43 (64.1%) 40 (59.7%) 37 (55.2%) 19 (28.3%) 17 (25.3%) 16 (23.8%)	5 (7.5%) 16 (23.8%) 24 (35.8%) 27 (40.3%) 30 (44.7%) 48 (71.6%) 50 (74.62%) 51 (76.1%)
Wound (3)	Amikacin Levofloxacin Gentamicin Ciprofloxacin Meropenem Ceftazidime Piperacillin Trimethoprim	2 (66.7%) 2 (66.7%) 1 (33.3%) 1 (33.3%) 1 (33.3%) 0 (0%) 0 (0%) 0 (0%)	1 (33.3%) 1(33.3%) 2 (66.7%) 2 (66.7%) 2 (66.7%) 3 (100%) 3 (100%) 3 (100%)

*S= sensitive, R= resistance, NO= number, %= Percentage

Distribution of gram-negative bacteria isolated from women among years

The analysis of Gram-negative bacterial isolates from women (2021–2024) revealed Escherichia coli as the predominant pathogen, constituting 83.11% (64/77) of total isolates, with a notable temporal surge in 2023 (57.81% of E. coli cases). Klebsiella spp. ranked second in prevalence (10.38%, 8/77), exclusively detected in 2023–2024. Klebsiella pneumoniae (3.89%, 3/77) demonstrated a decline post-2021, where it accounted for 66.66% of its total cases. Rare isolates included Raoultella ornithinolytica and Acinetobacter pittii (1.29% each, 1/77), detected only in 2024 and 2023, respectively. Temporal distribution highlighted a marked increase in bacterial isolation during 2023 (57.14% of total cases), driven predominantly by E. coli. Percentages reflect species-specific annual contributions to their cumulative totals, underscoring E. coli’s persistent dominance and the sporadic emergence of secondary pathogens as showed in table (5).

 

Table (5): Distribution of gram-negative bacteria isolated from women among years

Isolated Bacteria	2021 NO (%)	2022 NO (%)	2023 NO (%)	2024 NO (%)	Total NO (%)
E.coli	6 9.37%	1 1.56%	37 57.81%	20 31.25%	64 83.11%
Klebsiella spp.	0 0%	0 0%	5 62.5%	3 37.5%	8 10.38%
Klebsiella Pneumoniae	2 66.66%	0 0%	1 33.33%	0 0%	3 3.89%
Acinetobacter pittii	0 0%	0 0%	1 100%	0 0%	1 1.29%
Raoultella ornithinolytica	0 0%	0 0%	0 0%	1 100%	1 1.29%
Total	8 10.38%	1 1.29%	44 57.14%	24 31.16%	77 100%

* NO= number, %= Percentage

 Distribution of gram-positive bacteria isolated from women among years

The results showed that among the 73 positive samples, Staphylococcus spp. had the highest frequency, with 28/47 (59.85%) in 2023 and 19/47 (40.42%) in 2024. Streptococcus spp. followed with 4/7 (57.14%) in 2024 and 3/7 (42.85%) in 2023. Staphylococcus epidermidis appeared 3/8 (37.5%) in both 2021 and 2023, and 1/8 (12.5%) in 2022 and 2024. Staphylococcus haemolyticus was detected 3/5 (60%) in 2023 and 1/5 (20%) in both 2021 and 2022. Enterococcus faecalis appeared 1/2 (50%) in 2021 and 2022, while Staphylococcus aureus was seen 2/2 (100%) in 2024. Staphylococcus pseudintermedius and Streptococcus agalactiae were the least frequent, each recorded 1/1 (100%) in 2021 and 2022, respectively as in table (6).

Table (6): Distribution of gram-positive bacteria isolated from women among years

Isolated Bacteria	2021 NO (%)	2022 NO (%)	2023 NO (%)	2024 NO (%)	Total NO (%)
Staphylococcus spp.	0 0%	0 0%	28 59.75%	19 40.42%	47 64.38%
Staphylococcus epidermidis	3 37.5%	1 12.5%	3 37.5%	1 12.5%	8 10.95%
Streptococcus spp.	0 0%	0 0%	3 42.85%	4 57.14%	7 9.58%
Staphylococcus haemolyticus	1 20%	1 20%	3 60%	0 0%	5 6.84%
Staphylococcus aureus	0 0%	0 0%	0 0%	2 100%	2 2.73%
Enterococcus faecalis	1 50%	1 50%	0 0%	0 0%	2 2.73%
Streptococcus agalactiae	0 0%	1 100%	0 0%	0 0%	1 1.36%
Staphylococcus pseudintermedius	1 100%	0 0%	0 0%	0 0%	1 1.36%
Total	6 8.21%	4 5.47%	37 50.68%	26 35.61%	73 100%

* NO= number, %= Percentage

 

Gram negative bacteria isolated according to specimen

In gram negative bacteria only 77-gram negative bacteria, (48 sample is urine and 29 sample was H.V.S), while other bacteria that infected the patient in urine sample like Raoultella ornithinolytica which is about 1(2.08%), and Klebsiella about 5(10.41%). In H.V. S sample, same as the urine sample E. coli was the most common bacteria that infected the patients about 22(75.86%) bacteria. While other bacteria that infected the patients like Acinetobacter pitti about 3(3.44%), Kleibsella pneumoniae 3(10.34%), and Kleibsella spp. about 3(10.34%) bacteria those infected the patients in H.V.S sample as showed in table (7).

Table (7): Gram negative bacteria isolated according to specimen

NO.	Specimen	Bacteria isolates NO, (%)
1.	Urine (48)	E. coli 42 (87.5%)
		Klebsiella 5 (10.41%)
		Raoultella ornithinolytica 1 (2.08%)
2.	H. V. S (29)	E. coli 22 (75.86%)
		Klebsiella pneumoniae 3 (10.34%)
		Klebsiella spp. 3 (10.34%)
		Acinetobacter pittii 1 (3.44%)

 * NO= number, %= Percentage

Gram positive bacteria isolated according to specimen

In gram positive bacteria only 73-gram positive bacteria  (32 urine sample, 38 H.V.S sample and 3 wound sample). The findings from gram positive sample in urine specimen indicate that Staphylococcus Spp. was the most common bacteria that can infected the patients across all bacteria in urine specimen, about 21(65.62%) bacteria. While other bacteria that infected the patients like Staph- aureus, Staphylococcus haemolyticus, Staphylococcus epidermis, Streptococcus agalactiae and Enterococcus faecalis which are about 2(6.25 %), 1(3.12%), 6(18.75%), 1(3.12%) and 1(3.12%). In H.V.S sample, same as the urine sample Staphylococcus spp. was the most common bacteria that infected the patients about 23(60.52%) bacteria. While other bacteria that infected the patients such as staphylococcus haemolyticus about 4(10.52%), staphylococcus epidermidis 2(5.26%), staphylococcus faecalis and staphylococcus spp. 1(2.63%), streptococcus 7(18.42%). While in Wound sample we have just staphylococcus spp. about 3(100%) bacteria that can infected the patient as in table (8).

 

 

Table (8): Gram positive bacteria isolated according to specimen

NO.	Specimen	Bacteria isolates NO, (%)
1.	Urine (32)	Staphylococcus spp. 21(65.62%)
		Staphylococcus epidermidis 6 (18.75%)
		Staphylococcus aureus 2(6.25%)
		Streptococcus agalactiae 1 (3.12%)
		Staphylococcus haemolyticus 1 (3.12%)
		Enterococcus faecalis 1(3.12%)
2.	H. V. S (38)	Staphylococcus spp. 23 (60.52%)
		Staphylococcus haemolyticus 4 (10.52%)
		Staphylococcus epidermidis 2 (5.26%)
		Enterococcus faecalis 1 (2.63%)
		Staphylococcus pseudintermedius 1 (2.63%)
		Streptococcus spp. 7 (18.42%)
3.	Wound (3)	Staphylococcus.spp. 3 (100%)

*NO= number, % = percentage

Antibiotics Susceptibility patterns tests for (77) Gram negative bacteria isolated

Out of 150 (13.97%) cases 77 (7.17%) were positive isolates of Gram-negative bacteria in 2024 were screened for their antibiotics susceptibility to eight antibiotics, widely used antibiotics, including different class with different generation. the results were interpreted according to standard value by clinical and laboratory standard of antimicrobial sensitivity testing. It is obvious that Gram negative bacteria isolates showed high resistance (85.71%) to Trimethoprim, (77.92%) to ceftazidime, On the other hand, the lowest resistance were (10.39%) to Meropenem, (27.27 %) to Amikacin, (38.96%) to Levofloxacin and (44.16%) to Ciprofloxacin as in table. Statistical analysis showed that is non- significant correlation (P= 0.03) between Gram negative bacteria and different types of Antibiotics as showed in table (9).

 

 

 

 

 

 

Table (9): Antibiotics Susceptibility patterns tests for (77) Gram negative bacteria 2021-2024

Class of antibiotics	Tested antibiotics	S NO, %	R NO, %	P value
Carbapenem	Meropenem	69(89.61%)	8(10.39%)
Aminoglycoside	Amikacin Gentamicin	56(72.73%) 29(37.66%)	21(27.27%) 48(62.34%)
Fluoroquinolone	Ciprofloxacin Levofloxacin	43(55.84%) 47(61.04%)	34(44.16%) 30(38.96%)
Penicillin	Piperacillin	22(28.57%)	55(71.43%)
Cephalosporin	Ceftazidime	17(22.08%)	60(77.92%)
Antifolate	Trimethoprim	11(14.29%)	66(85.71%)	P=0.03

*S= sensitive, R= resistance, NO= number, %= Percentage        * P ≤ 0.05 significant

Antibiotics Susceptibility patterns tests for (73) Gram -positive bacteria isolated

Out of 150 (13.97%) cases 73 (6.78%) were positive isolates of Gram-positive bacteria in 2024 were screened for their antibiotics susceptibility to eight antibiotics, widely used antibiotics, including different class with different generation. the results were interpreted according to standard value by clinical and laboratory standard of antimicrobial sensitivity testing. It is obvious that Gram positive bacteria isolates showed high resistance (89.04%) to Piperacillin, (75.34%) to Trimethoprim, (74%) to Ceftazidime On the other hand, the lowest resistance were (4.11%) to Meropenem, (41.1%) to Levofloxacin, (53.42%) to Amikacin and (54.79%) to Gentamicin as in table .Statistical analysis showed that is non- significant correlation ( P= 0.04 ) between Gram positive bacteria and different types of Antibiotics as showed in table (10).

 

 

 

 

 

 

 

 

 

 

 

Table (10): Antibiotics Susceptibility patterns tests for (73) Gram positive bacteria 2021-2024

Class of antibiotics	Tested antibiotics	S NO,%	R NO,%	P value
Carbapenem	Meropenem	70(95.89%)	3(4.11%)
Fluoroquinolone	Ciprofloxacin Levofloxacin	31(42.47%) 43(58.9%)	42(57.53%) 30(41.1%)
Aminoglycoside	Amikacin Gentamicin	34(46.57%) 33(45.21%)	39(53.42%) 40(54.79%)
Cephalosporin	Ceftazidime	19(26%)	54(74%)
Antifolate	Trimethoprim	18(24.66%)	55(75.34%)
Penicillin	Piperacillin	8(10.96%)	65(89.04%)	P= 0.04

*S= sensitive, R= resistance, NO= number, %= Percentage               * P≤ 0.05 significant

Resistance rate of Gram-negative bacteria isolated

The result in the table showed that Acinetobacter pittii mainly resistance to Piperacillin, Ceftazidime and ciprofloxacin 1/1 (100%), for Escherichia coli showed resistance to Trimethoprim 56/64 (87.5%) then followed by Ceftazidime 49/64 (76.56%), for Klebsiella spp. showed highly resistance to Ceftazidime percentage of resistance 8/8 (100%) followed by Gentamicin 7/8 (87.5%) and for Klebsiella pneumonia showed high resistant to Piperacillin and Trimethoprim percentage of resistant 3/3 (100%) lastly for Raoultella ornithinolytica showed resistance to Gentamicin, Piperacillin, ceftazidime, Trimethoprim, and Levofloxacin have percentage of resistance 1/1 (100%)  as in table (11) .

 

 

 

 

 

 

 

 

 

 

 

 

 

Table (11): Resistance rate of Gram-negative bacteria isolated

E. coli (64) NO, %

Raoultella ornithinolytica (1) NO, %

Klebsiella (8) NO, %

Klebsiella  Pneumonia (3) NO, %

Acinetobacter pittii (1) NO, %

 

 

 

 

Antibiotics
Amikacin	15 23.44%	3 37.5%	2 66.67%	/	/
Gentamicin	40 59.7%	7 87.5%	/	/	1 100%
Ceftazidime	49 76.56%	8 100%	1 33.33%	1 100%	1 100%
Ciprofloxacin	30 46.88%	3 37.5%	/	1 100%	/
Levofloxacin	24 37.5%	4 50%	1 33.33%	/	1 100%
Meropenem	6 9.38%	1 14.29%	1 33.33%	/	/
Piperacillin	44 68.75%	6 75%	3 100%	1 100%	1 100%
Trimethoprim	56 87.5%	5 62.5%	3 100%	1 100%	1 100%

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

*NO= number, %= percentage, / = not resistant  

 

Resistance rate of Gram-positive bacteria isolated

The results for Gram-positive in the table showed that Enterococcus faecalis mainly resistance to Piperacillin, Trimethoprim, Ceftazidime and ciprofloxacin1/1 ( 100%), for Staphylococcus pseudintermedius showed high resistance to Gentamicin, Ceftazidime, Ciprofloxacin, Levofloxacin, Piperacillin, Trimethoprim with resistant range of 1/1 (100%) , for Staphylococcus epidermidis showed high resistance to Ceftazidime 8/8 (100%) then followed by Piperacillin 7/8 (87.5%) and ciprofloxacin of 6/8 (75%) , for Staphylococcus haemolyticus showed highly resistance to Piperacillin 5/5 (100%) followed by Gentamicin and ceftazidime 4/5 ( 80%) and for Staphylococcus aureus showed high resistant to Piperacillin, Trimethoprim, ciprofloxacin and ceftazidime  2/2 (100%) for Staphylococcus spp.  showed high resistance to Piperacillin 41/47 (87.23%), Trimethoprim 38/47 (80.85%), and for Streptococcus agalactiae high resistance to Amikacin, Gentamicin, Ceftazidime, Ciprofloxacin, Piperacillin and Trimethoprim 1/1 (100%), and lastly for Streptococcus spp. showed high resistance to Amikacin, Levofloxacin, Piperacillin and Trimethoprim 6/7 (85.71%) as in table (12).

 

 

 

Table (12): Resistance rate of Gram-positive bacteria isolated

 

 

Antibiotics
Amikacin	20 42.5%	6 75%	6 85.7%	/	1 50%	2 100%	1 100%	/
Gentamicin	29 61.7%	2 25%	2 28.6%	1 100%	/	1 50%	1 100%	1 100%
Ceftazidime	32 68.1%	8 100%	4 57.1%	1 100%	2 100%	2 100%	1 100%	1 100%
Ciprofloxacin	23 48.9%	6 75%	4 57.1%	1 100%	2 100%	2 100%	1 100%	1 100%
Levofloxacin	17 36.2%	2 25%	6 85.7%	1 100%	1 50%	1 50%	/	1 100%
Meropenem	2 4.3%	/	1 14.3%	/	/	/	/	/
Piperacillin	41 87.2%	7 87.5%	6 85.7%	1 100%	2 100%	2 100%	1 100%	1 100%
Trimethoprim	38 80.8%	4 50%	6 85.7%	1 100%	2 100%	2 100%	1 100%	1 100%

Staphylococcus epidermidis (8) NO, %

Staphylococcus spp. (47) NO, %

Streptococcus spp. (7) NO, %

Staphylococcus haemolyticus (5) NO, %

Staphylococcus aureus (2) NO, %

Enterococcus faecalis (2) NO,%

Streptococcus agalactiae (1) NO, %

Staphylococcus pseudintermedius (1) NO, %

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

* NO= number, %= percentage,  /= not resistant

 

The analysis of bacteria isolated from pregnant and non-pregnant patients highlights the distribution of gram-negative and gram-positive bacteria

Among the gram-negative bacteria, Escherichia coli (E. coli) was the most prevalent, constituting 83% of total isolates, with 23 cases (74%) in pregnant patients and 41 cases (89%) in non-pregnant women. Klebsiella spp. accounted for 10.3% of isolates, split equally between pregnant (4 cases, 10%) and non-pregnant women (4 cases, 10.8%). In contrast, Raoultella ornithinolytica and Acinetobacter pittii were the least frequent, each accounting for only 1.3% of total gram-negative isolates, with cases occurring exclusively in pregnant patients as showed in table (13).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table (13): The analysis of bacteria isolated from pregnant and non-pregnant patients highlights

 

 

Isolated Gram negative bacteria	Pregnant		Non- pregnant		Total
	No.	%	No.	%	No.	%
Escherichia coli	23	74%	41	89%	64	83%
Klebsiella spp.	4	10%	4	10.8%	8	10.3%
Klebsiella pneumoniae	2	5%	1	2.7%	3	3.9%
Raoultella ornithinolytica	1	2.5%	0	0%	1	1.3%
Acinetobacter pittii	1	2.5%	0	0%	1	1.3%
Total	31	52%	46	48%	77	100%

 *NO= number       %= percentage                  

 

The analysis of bacteria isolated from pregnant and non-pregnant patients highlights the distribution of gram-positive bacteria

Gram-positive bacteria showed a different distribution, with Staphylococcus spp. dominating the isolates at 64.3%, including 25 cases (64.1%) from pregnant patients and 22 cases (64.7%) from non-pregnant patients. Staphylococcus epidermidis was the second most frequent gram-positive isolate at 10.9%, with 4 cases (10.2%) in pregnant patients and 4 cases (11.8%) in non-pregnant patients. Streptococcus accounted for 9.6% of total gram-positive isolates, with a higher prevalence in non-pregnant patients (5 cases, 14.7%) compared to pregnant patients (2 cases, 5.1%) as showed in table (14).

 

 

 

 

 

 

Table (14): The analysis of bacteria isolated from pregnant and non-pregnant patients highlights the

Isolated Gram positive bacteria	Pregnant		Non pregnant		Total
	No.	%	No.	%	No.	%
Staphylococcus spp.	25	64.1%	22	64.7%	47	64.3%
Staphylococcus epidermidis	4	10.2%	4	11.8%	8	10.9%
Streptococcus spp.	2	5.1%	5	14.7%	7	9.6%
Staphylococcus haemolyticus	2	5.1%	3	8.9%	5	6.8%
Staphylococcus pseudintermedius	1	2.6%	0	0%	1	1.4%
Staphylococcus aureus	2	5.1%	0	0%	2	2.7%
Enterococcus faecalis	2	5.1%	0	0%	2	2.7%
Staphylococcus pseudintermedius	1	2.6%	0	0%	1	1.4%
Total	39	53.4%	34	46.6%	73	100%

distribution of gram-positive bacteria

*NO= number       %= percentage    

Relation between antibiotics susceptibility and pregnancy status in female

Out of 150 (13.97%) positive cases 70 (6.52%) cases were for pregnant women and 80 (7.45%) cases were for non-pregnant women. The antibiotics susceptibility test showed a higher rate of resistant among pregnant women comparing to non-pregnant ones, highest resistance is among pregnant women for Trimethoprim with resistant percentage of 67/70 (95.71%) followed by Ceftazidime with resistant percentage of 65/70 (92.86%), piperacillin with resistant range of 61/70(87.14%). While in non-pregnant cases the highest resistant is piperacillin with resistant percentage of 59/80 (73.75%) followed by Trimethoprim 54/80 (67.5%) as in table (15).

Table (15): Relation between antibiotics susceptibility and pregnant & non-pregnant women

Antibiotics	Non-pregnan women S                           R No, %                 No, %		Pregnant women S                       R No, %               No, %		Total   No (%)
meropenem	76 95%	4 5%	63 90%	7 10%	150 (100%)
amikacin	58 72.5%	22 27.5%	32 45.71%	38 54.23%	150 (100%)
Ciprofloxacin	57 71.25%	23 28.75%	17 24.29%	53 75.71%	150 (100%)
levofloxacin	55 68.75%	25 31.25%	35 50%	35 50%	150 (100%)
gentamicin	49 61.25%	31 38.75%	13 18.57%	57 81.42%	150 (100%)
ceftazidime	31 38.75	49 61.25%	5 7.14%	65 92.86%	150 (100%)
trimethoprim	26 32.5%	54 67.5%	3 4.29%	67 95.71%	150 (100%)
piperacillin	21 26.5%	59 73.75%	9 12.86%	61 87.14%	150(100%)

*S= sensitive, R= resistant, NO= number, %= percentage    

Distribution of MDR, XDR, PDR among Gram-negative bacteria

Antimicrobial susceptibility testing was performed on 77 Gram-negative bacterial isolates. The results show that the highest MDR percentages were 1/1 (100%) for Acinetobacter pittii and Raoultella ornithinolytica, 2/3 (66.67%) for Klebsiella pneumonia, and 62.5% for Escherichia coli and Klebsiella spp. Klebsiella pneumonia 1/3 (33.33%) had the greatest XRD resistance proportion, followed by Escherichia coli 18/64 (28.12%) and Klebsiella spp. 2/8 (62.5%). According to the chart below, PDR had the lowest resistant percentage overall, with 7 out of 77 cases Escherichia coli 6/64 (9.38%) and Klebsiella spp. 1/8 (12.5%) as in table (16) and figure (2).

 

Table (3-16) Distribution of MDR, XDR, PDR among Gram-negative bacteria

Bacteria isolated	MDR NO, (%)	XDR NO, (%)	PDR NO, (%)
E. coli (64)	40 (62.5%)	18 (28.12%)	6 (9.38%)
Klebsiella spp. (8)	5 (62.5%)	2 (25%)	1 (12.5%)
Klebsiella pneumonia (3)	2 (66.67%)	1 (33.33%)	0 (0%)
Acinetobacter pittii (1)	1 (100%)	0 (0%)	0 (0%)
Raoultella ornithinolytica (1)	1 (100%)	0 (0%)	0 (0%)

*NO=number,%=percentage   

 

Figure (2): incidence of MDR, XDR and PDR strains of each species of Gram-negative bacteria isolated (n = 77)

 

 

Distribution of MDR, XDR, PDR among Gram-positive bacteria

A total of 73 Gram-positive bacterial isolates were obtained and subjected for antimicrobial susceptibility testing here is the results, Staphylococcus spp. had the highest MDR percentage 31/47 (65.96%), followed by Enterococcus faecalis and Staphylococcus aureus 1/2 (50%) , Streptococcus spp. 3/7 (42.86%), then Staphylococcus haemolyticus 2/5 (40%), Staphylococcus epidermidis 2/8 (25%).While XRD highest resistant percentage was Staphylococcus pseudintermedius and Streptococcus agalactiae 1/1 (100%) followed by Staphylococcus epidermidis 6/8 (75%), Staphylococcus haemolyticus 3/5 (60%). PDR had the lowest resistant percentage in total 3 out of 73 cases Streptococcus spp. 1/7 (14.28%) and Staphylococcus spp. 2/47 (4.25%) as in table (17) and figure (3).

Bacteria isolated	MDR NO, (%)	XDR NO, (%)	PDR NO, (%)
Staphylococcus spp. (47)	31 (65.96%)	14 (29.79%)	2 (4.25%)
Staphylococcus epidermidis (8)	2 (25%)	6 (75%)	0 (0%)
Streptococcus spp. (7)	3 (42.86%)	3 (42.86%)	1 (14.28%)
Staphylococcus haemolyticus (5)	2 (40%)	3 (60%)	0 (0%)
Staphylococcus aureus (2)	1 (50%)	1 (50%)	0 (0%)
Enterococcus faecalis (2)	1 (50%)	1 (50%)	0 (0%)
Streptococcus agalactiae (1)	0 (0%)	1 (100%)	0 (0%)
Staphylococcus pseudintermedius (1)	0 (0%)	1 (100%)	0 (0%)

Table (17) Distribution of MDR, XDR, PDR among Gram-positive bacteria

*NO= number, %= percentage    

 

Figure (3-3): incidence of MDR, XDR and PDR strains of each species of Gram-positive bacteria isolated (n = 73)

 

Comparison of Antimicrobial Resistance (MDR, XDR, and PDR) Based on Pregnancy Status among Gram- negative bacteria

The table illustrates the relationship between pregnancy status and antimicrobial resistance, including MDR (Multidrug Resistance), XDR (Extensively Drug Resistance), and PDR (Pan-Drug Resistance). Among pregnant individuals, 18 cases (23.4%) exhibited MDR, 9 cases (11.7%) showed XDR, and 4 cases (5.19%) were PDR, with a total of 31 cases (40.26%). In non-pregnant individuals, 31 cases (40.26%) had MDR, 12 cases (15.56%) exhibited XDR, and 3 cases (3.9%) were PDR, with a total of 46 cases (59.74%). Overall, there were 49 cases (63.66%) with MDR, 21 cases (27.3%) with XDR, and 7 cases (9.09%) with PDR, across a total of 77 cases. This data indicates a higher prevalence of MDR in non-pregnant individuals compared to pregnant ones, while XDR and PDR percentages are relatively similar across both groups as shows in table (18) and figure (4).

Table (18): Comparison of Antimicrobial Resistance (MDR, XDR, and PDR) Based on Pregnancy Status among Gram- negative bacteria

Pregnancy status	MDR No.     %	XDR No.     %	PDR No.     %	Total No.     %
Non-pregnant	31    40.26%	12     15.6%	3     3.9%	46     59.74%
Pregnant	18     23.4%	9     11.7%	4     5.19%	31     40.26%
Total	49     63.66%	21     27.3%	7     9.09%	77     100%

*NO= number       %= percentage    

 

Figure (4): Distribution of Antimicrobial Resistance Categories (MDR, XDR, PDR) among Gram-negative bacteria

 

Comparison of Antimicrobial Resistance (MDR, XDR, and PDR) Based on Pregnancy Status among Gram-positive bacteria

Among Gram-positive bacteria, 28.8% (21/73) of isolates from pregnant individuals exhibited multidrug resistance (MDR), compared to 26% (19/73) in non-pregnant individuals. Extensively drug-resistant (XDR) phenotypes were observed in 23.3% (17/73) of pregnant individuals and 17.8% (13/73) of non-pregnant individuals. Pan drug-resistant (PDR) isolates were rare, with 1.4% (1/73) in pregnant and 2.7% (2/73) in non-pregnant groups. Overall, 53.4% (39/73) of resistant isolates originated from pregnant individuals, while 46.6% (34/73) were from non-pregnant individuals. Total resistance rates across all categories were 54.8% (40/73) for MDR, 41.1% (30/73) for XDR, and 4.1% (3/73) for PDR as in table (19) and figure (5).

Table (19): Comparison of Antimicrobial Resistance (MDR, XDR, and PDR) Based on Pregnancy Status among Gram- positive bacteria

Pregnancy status	MDR No.     %	XDR No.     %	PDR No.     %	Total No.     %
Pregnant	21     28.8%	17     23.3%	1     1.4%	39     53.4%
Non-pregnant	19    26%	13     17.8%	2     2.7%	34     46.6%
Total	40     54.8%	30     41.1%	3     4.1%	73     100%

*NO= number       %= percentage    

 

 

Figure (5): Distribution of Antimicrobial Resistance Categories (MDR, XDR, PDR) among Gram-positive bacteria.

 

 

DISCUSSION

 

Out of 1,074 isolates, only 150 were positive, including 67 high vaginal swabs (HVS) from married women patients in different hospitals in Erbil city (excluding unmarried women), in the study showed that the lowest positivity rate was in 2022, with only 3.9% (5 out of 128) of samples testing positive, while the highest was in 2023, with 20% (80 out of 400) positive. These differences might be due to changes in testing methods, sample collection, or the characteristics of the population. In comparison, other studies also show different infection rates. For example, in [14] study, which out of 2345 total samples 504 (21.49%) were positive at Geneva, Switzerland.

Among gram-negative bacteria, it was noted that E. coli was identified in 42/48 (87.5%) of urine samples and in 22/29 (75.86%) of high vaginal swabs (HVS), while Klebsiella pneumoniae was detected at a lower frequency., aligning with studies from Baghdad by [15], which reported E. coli as the leading cause of urogenital infections (83% prevalence). Its persistence across years underscores its role in bacterial vaginosis due to its ability to evade immune responses. Klebsiella pneumoniae (10.3%), known for multidrug resistance, was less frequent but clinically significant, consistent with findings from Italy [16] reporting 12% prevalence. The emergence of Raoultella ornithinolytica (2.2%) in 2024 mirrors trends in Iraq [17], suggesting evolving epidemiology. Staphylococcus spp. dominated Gram-positive isolates (64.3%), particularly in HVS samples, consistent with Baghdad [18], where Staphylococcus spp. accounted for 58% of gynecological infections. Streptococcus spp. (9.6%) showed higher prevalence in non-pregnant women.

In the study found that urinary tract infections (UTIs) and vaginal infections were most common in women aged 21–30 years (18% in urine samples; 16.7% in vaginal swabs), followed by those aged 31–40 years (16.7% in urine; 12% in vaginal swabs). This matches earlier research by [19], which linked higher infection risks in reproductive-age women to hormonal changes and shifts in vaginal bacteria. However, a study in Zakho, Iraq [20] reported different age trends: bacterial vaginosis (BV) was highest in women under 20 (41.67%) and 40–50 (37.93%). This difference could be due to factors like local demographics or healthcare practices.

The findings from this study highlight significant variations in antibiotics resistance patterns among different specimen types collected in Kurdistan, Iraq. The results emphasize the effectiveness of Meropenem as the most potent antibiotic, with high sensitivity in both high vaginal swabs (H.V.S.) (92.5%) and urine specimens (95%), though its sensitivity decreases in wound infections (33.3%). This aligns with the findings of [21], who identified Meropenem as the most effective antibiotic against various pathogens, showing high efficacy across different specimen types. Amikacin also exhibits moderate effectiveness in our study, particularly in urine samples (60%), but shows slightly lower efficacy in H.V.S. (59.7%) and wound specimens (66.7%). This finding is in agreement with [22], who observed similar moderate effectiveness of Amikacin against clinical isolates, though they also reported variable efficacy in different specimen types. Piperacillin and Trimethoprim exhibited poor sensitivity in our study, especially in urine samples (16.2% and 12.5%, respectively), with no effectiveness in wound isolates. [23] reported a similar resistance pattern, particularly for Trimethoprim.

The antibiotics susceptibility patterns of gram-negative and gram-positive bacterial isolates in this study, revealed critical resistance trends. Meropenem demonstrated the lowest resistance rates (10.39% in gram-negative, 4.11% in gram-positive isolates), reaffirming its role as a last-line antibiotics. This aligns with [20] in Zakho, Iraq, who reported 10% Meropenem resistance in genitourinary infections. In contrast, Ciprofloxacin resistance was alarmingly high (44.16% in Gram-negative, 57.53% in Gram-positive isolates), consistent with regional studies showing similar fluoroquinolone resistance [20]. Notably, Acinetobacter pittii, Klebsiella pneumoniae, and Raoultella ornithinolytica exhibited complete resistance (100%) to multiple antibiotics, including Piperacillin and Ceftazidime. Escherichia coli, while less resistant than these pathogens, still showed concerning resistance to Ceftazidime (76.56%) and Piperacillin (68.75%). Among gram-positive bacteria, Streptococcus agalactiae, Staphylococcus aureus, and Staphylococcus pseudintermedius displayed 100% resistance to Piperacillin, Trimethoprim, and Ciprofloxacin, while Staphylococcus epidermidis showed comparatively lower resistance (75–87.5%). These findings mirror [24] in Baghdad, where E. coli and Klebsiella pneumoniae were fully resistant to Piperacillin, and Staphylococcus aureus showed total resistance to cephalosporins.

The results reveal distinct patterns in the prevalence of bacterial isolates, providing insights into the microbial landscape of such infections. Among Gram-negative bacteria, Escherichia coli was the most prevalent, accounting for 83% of total isolates, with 74% in pregnant and 89% in non-pregnant patients. This finding aligns with the study by [25], which reported Escherichia coli as the most frequent isolate in cases of aerobic vaginitis, constituting 32.4% of isolates. For gram-positive bacteria, Staphylococcus species were the dominant isolate, accounting for (64.3%) of total cases, with nearly equal distributions in pregnant (64.1%) and non-pregnant (64.7%) groups. This observation is consistent with the findings of [26] who identified Staphylococcus aureus as the most prevalent vaginal pathogen, contributing to 57.33% of isolates in their study. The ability of Staphylococcus spp. to form biofilms and exhibit antibiotics resistance underscores their role in persistent infections. The agreement between this study and existing literature validates the findings and demonstrates the global relevance of these bacterial pathogens in vaginal infections in pregnant and non-pregnant women.

  Antibiotics resistance patterns may be influenced by pregnant status, according to the study. Higher resistance rates were seen in isolates from pregnant women, with (95.71%) to Trimethoprim, (92.86%) to Ceftazidime, (87.14%) showing resistance to Piperacillin, Reduced resistance rates were seen in isolates from non-pregnant women, with (67.5%) and (73.75%), respectively, to Trimethoprim and Piperacillin. These variations could be explained by hormonal or physiological changes that occur during pregnancy and impact antibiotics efficacy or infection susceptibility. In order to ensure successful treatment while avoiding resistance. The study’s results are consistent with those of [27], who found that pregnant women had a greater rate of antibiotics resistance than non-pregnant women. For example, their study found that pregnant women had a greater rate of Piperacillin resistance (70.9%) than non-pregnant women (56.25%) for E. coli. Furthermore, Amikacin resistance was found in (29%) of pregnant women compared to (18.5%) of non-pregnant women. These findings provide additional evidence to the idea that patterns of antibiotics resistance may be influenced by pregnancy, maybe as a result of physiological modifications that alter the susceptibility of bacteria to treatment.

Significant resistance trends were also revealed by antimicrobial susceptibility testing of 77 Gram-negative and 73 Gram-positive bacterial isolates. Raoultella ornithinolytica and Acinetobacter pittii had the highest MDR rates (100%) among Gram-negative bacteria, followed by Escherichia coli and Klebsiella pneumonia (62.5%) and (66.67%) respectively. XDR resistance was also significant in Klebsiella pneumonia (33.33%) and Escherichia coli (28.12%). While PDR rates were lower, with Escherichia coli (9.38%) and Klebsiella spp. (12.5%) exhibiting limited resistance, The highest MDR frequencies were found in gram-positive bacteria (65.96%), with Staphylococcus pseudintermedius and Streptococcus agalactiae showing the highest XDR resistance (both 100%). PDR resistance was uncommon in gram-positive isolates; the lowest percentages were found in Streptococcus spp. (14.28%) and Staphylococcus spp. (4.25%). These results agreed with  the  results  of a  study  conducted  by  [11] which discovered that among women overall, (60%) of E. coli were multidrug-resistant (MDR), (21.24%) were XDR, and (1.77%) were PDR. Additionally, a different study by [27] found that all bacteria exhibit multidrug resistance (MDR) in pregnant women as compared to non-pregnant women. For example, Escherichia coli and Klebsiella spp. showed higher rates of MDR in pregnant women (24.3%) and (21.51%), respectively.

The study reveals a higher prevalence of MDR in non-pregnant individuals (40.26% vs. 23.4% in pregnant individuals), while XDR and PDR rates are relatively similar between groups. This aligns with studies suggesting that non-pregnant populations may experience greater antibiotics exposure due to fewer restrictions on antibiotics use, leading to higher MDR prevalence. Similarly, research on aerobic vaginitis in Iraqi women found that 60% of isolates were MDR and 40% XDR, underscoring widespread resistance in non-pregnant populations [24], which likely contribute to the observed MDR disparity. In gram-positive bacteria indicates a slightly higher prevalence of MDR (28.8%) and XDR (23.3%) in pregnant individuals compared to non-pregnant counterparts, though PDR remains low in both groups. This contrasts with some studies but resonates with others, particularly in settings where pregnancy-related physiological changes (e.g., hormonal shifts, urinary stasis) may predispose women to recurrent infections and selective antibiotics pressure. For example, a study in Saudi Arabia found that 57% of UTIs in pregnant women were caused by E. coli, with Gentamicin and Piperacillin-tazobactam showing the highest sensitivity, suggesting region-specific resistance dynamics [28].

 

 

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