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Annals of Clinical Microbiology and Antimicrobials logoLink to Annals of Clinical Microbiology and Antimicrobials
. 2021 Jun 12;20:43. doi: 10.1186/s12941-021-00450-x

Antimicrobial susceptibility of gram-positive and gram-negative bacteria: a 5-year retrospective analysis at a multi-hospital healthcare system in Saudi Arabia

Saad Alhumaid 1,✉, Abbas Al Mutair 2,3, Zainab Al Alawi 4, Ahmad J Alzahrani 5, Mansour Tobaiqy 6, Ahmed M Alresasi 1, Ibrahim Bu-Shehab 1, Issa Al-Hadary 1, Naif Alhmeed 7, Mossa Alismail 8, Ahmed H Aldera 9, Fadhil AlHbabi 10, Haifa Al-Shammari 11, Ali A Rabaan 12, Awad Al-Omari 13,14
PMCID: PMC8196925  PMID: 34118930

Abstract

Background

Studying time-related changes in susceptible pathogens causing healthcare-associated infections (HAIs) is vital in improving local antimicrobial and infection control practices.

Objectives

Describe susceptibility patterns to several antimicrobials in gram-positive and gram-negative pathogens isolated from patients causing HAIs at three private tertiary care hospitals in Saudi Arabia over a 5-year period.

Methods

Data on trends of antimicrobial susceptibility among bacteria causing HAIs events in children and adults at three tertiary private hospitals located in Riyadh and Qassim, Saudi Arabia, were collected retrospectively between 2015 and 2019 using the surveillance data datasets.

Results

Over a 5-year period, 38,624 pathogens caused 17,539 HAI events in 17,566 patients. About 9450 (53.8%) of patients who suffered HAIs were females and the average age was 41.7 ± 14.3 years (78.1% were adults and 21.9% were children). Gram-negative pathogens were 2.3-times more likely to cause HAIs compared to gram-positive bacteria (71.9% vs. 28.1%). The ranking of causative pathogens in decreasing order was: Escherichia coli (38%), Klebsiella species (15.1%), and Staphylococcus aureus (12.6%). Gram-positive isolates were mostly susceptible to linezolid (91.8%) whereas they were resistant to ampicillin (52.6%), cefoxitin (54.2%), and doxycycline (55.9%). Gram-negative isolates were mostly sensitive to tigecycline (95%) whereas they were resistant to cefotaxime (49.5%) and cefixime (59.6%). During the 5 years, there were relatively stable susceptibility patterns to all tested antimicrobials, except for cefotaxime which shown a susceptibility reduction by 41.4%, among Escherichia coli and Klebsiella species. An increase in the susceptibility of Acinetobacter and Enterobacter and Citrobacter species to all studied antimicrobials was observed except for colistin that had a slight sensitivity reduction in 2019 by 4.3% against Acinetobacter species. However, we noted reduced sensitivity of MRSA, CoNS and Enterococcus species to gentamicin; and increased resistance of MRSA to linezolid and vancomycin.

Conclusion

The observed increase in susceptibility of gram-positive and gram-negative bacteria to studied antimicrobials is important; however, reduced sensitivity of MRSA, CoNS and Enterococcus species to gentamicin; and increased resistance of MRSA to linezolid and vancomycin is a serious threat and calls for effective antimicrobial stewardship programs.

Keywords: Antibiotics, Antimicrobials, Gram-positive, Gram-negative, Healthcare-associated infections, Rates, Saudi Arabia, Sensitivity, Susceptibility

Background

Antimicrobial resistance (AMR) is a major threat to public health imposing significant health and economic burdens on healthcare system and patients [1, 2]. Unless proactive solutions are found to address AMR, global costs are estimated to reach USD 3 trillion annually by 2050 and an additional 10 million people could die each year; cumulated costs could reach over USD 100 trillion [3]. Decreasing private sector investment in the development of new antimicrobials to treat AMR infections threatens global efforts to fight this danger; and AMR requires international attention and collaboration, because bacteria do not recognize borders. In Saudi Arabia, misuse of antimicrobials is high and complicated primarily because antibiotics are available to buy by anyone over-the-counter via the community pharmacies without a legal prescription [4]. Only two years ago, Saudi Ministry of Health has implemented a nationwide ban on the sale of antibiotics without a legal prescription; however, despite this law, dispensing antibiotics without prescription is still common [4]. Routine clinical microbiology laboratory data provide a profile of the susceptibilities of specific bacteria to antimicrobial agents for monitoring and responding to emerging antimicrobial issues. Data can be utilized to help in the selection of empirical therapy by selecting the most appropriate antibiotics before susceptibility results are available, but remains generally unexploited for purposes of epidemiological surveillance. Although Antimicrobial stewardship programs focus on antibiotic prescribing practice, it is supported by an understanding of local antibiotic susceptibility trends, which in turn depends on the availability of a reliable medical microbiology laboratory resource. The Medical Group has implemented antimicrobial stewardship (AMS) programs since January 2014 and employs various strategies to reduce inappropriate utilization of antimicrobials, minimize the emergence of AMR and lower incidence of health-care-associated infections (HAIs) and reduce cost [1, 5].

Several local studies have estimated the rates of susceptibility among gram-positive and gram-negative bacteria in Saudi Arabia [6–10], but none was comprehensive, and comparisons are complicated by variable methods and study periods that influence the findings explanation and interpretation.

Aim

This study aimed to examine patterns of antimicrobial susceptibility of gram-positive and gram-negative pathogens isolated from inpatients and outpatients causing HAIs using the surveillance data datasets collected from three HMG hospitals (Altakhassusi, Arryan and Qassim) over a 5-year period, in Saudi Arabia.

Settings

The private tertiary medical group is considered as one of the largest private healthcare providers in the Middle Eastern region. Currently, the medical group operates 14 medical facilities across Saudi Arabia, UAE and Bahrain, including 7 hospitals and 6 medical centers.

Study was conducted at three tertiary and specialized health facilities with adequate medical professional resources with 237-bed capacity, 365-bed and 150-bed capacity, respectively located in two different cities in Saudi Arabia.

These facilities provide healthcare services to a wide range of patients in various specialties and subspecialties. Yearly, the three healthcare facilities encounter over 127,364 surgical cases, nearly 1,742,144 visits to emergency departments, and over 360,587 admissions.

Methods

Study design

Data of trends in antimicrobial susceptibility among of all reports of four types of gram-positive isolates [Staphylococcus aureus, Methicillin-resistant Staphylococcus aureus (MRSA), Coagulase-negative staphylococci (CoNS) and Enterococcus species] and six types of gram-negative isolates [Escherichia coli, Klebsiella species, Pseudomonas species, Acinetobacter species, Proteus species, and Enterobacter and Citrobacter species] causing HAIs, collected from the infection control and prevention surveillance data between January 2015 and December 2019 from adult and pediatric patients in three tertiary private hospitals in Saudi Arabia, were extracted using standard customized Excel data collection sheets (Microsoft Corp, Redmond, WA, USA). The antimicrobial susceptibility patterns for selected antimicrobials were analyzed and reported.

We extracted the following patient data from the patient records meeting the inclusion criteria: age, gender, patient location (wards, intensive care units, and outpatient settings), specimen type, HAI type, organism identified, and antimicrobial susceptibility test results.

Inclusion–exclusion criteria

Data on incidence of targeted bacterial isolates causing HAIs and susceptibility trends of selected pathogens to various antimicrobials collected from medical and surgical wards, intensive care units (ICUs), emergency rooms and hospital-affiliated outpatient clinics from inpatients and outpatients with blood, urinary, rectal, cerebral spinal fluid, respiratory, saliva, nasal, cervical, lavages, wound, tissue, and semen cultures (consecutive, one per patient, per infection site) were included.

Representatives from all clinically important antimicrobial classes have been tested (ampicillin, cloxacillin, amoxicillin/clavulanic acid, piperacillin/tazobactam, cefoxitin, cefazolin, cefuroxime, cefixime, cefotaxime, ceftriaxone, ceftazidime, cefepime, ciprofloxacin, levofloxacin, ofloxacin, nitrofurantoin, erythromycin, clindamycin, trimethoprim-sulfamethoxazole, amikacin, gentamicin, doxycycline, tetracycline, vancomycin, linezolid, imipenem, meropenem, tigecycline and colistin).

Infection events and response of pathogens to antibacterials lacking microorganism and/or culture and sensitivity testing information were excluded.

Antimicrobial susceptibility testing

Species identification of isolates and their antimicrobial susceptibility profiles were obtained with different automated systems at every single laboratory of the three facilities using (VITEK®2 system, BioMariex, France), BD Phoenix system (BD Biosciences, NJ, USA), MicroScan plus (Beckman Coulter, CA, USA), and BD BACTEC system (BD Biosciences) according to manufacturers’ specifications, between 2015 and 2019, with susceptibility interpretations based on the Clinical and Laboratory Standards Institute (CLSI) broth microdilution and breakpoint criteria [11]. To ensure data compatibility, quality control was performed using control strains from the following American Type Culture Collection (ATCCs): Staphylococcus aureus ATCC 29213, Pseudomonas aeruginosa ATCC 2853, Escherichia coli ATCC 25922, Escherichia coli ATCC 35218, Klebsiella pneumoniae ATCC 27736 and Enterococcus faecalis ATCC 29212. Data are only included when the quality control test results were in acceptable ranges.

Statistical analysis

Variables that were continuous were presented as means and categorical variables were presented as frequencies and percentages. Susceptibility patterns of pathogens were presented over time. The difference in sensitivity trends between 2015 and 2019 was examined using the multivariate analysis of variance (MANOVA) and a two-sided P-values < 0.05 were considered to be statistically significant. The proportion of susceptible isolates was calculated as the sum of susceptible organisms (neither intermediately susceptible nor resistant) relative to the total number of organisms tested. SPSS (Version 25.0. Armonk, NY: IBM Corp) and Microsoft Excel Professional Plus 2019 (Microsoft Corp., Redmond, WA, USA) were used for all statistical analyses.

Our study was performed in accordance with the ethical standards of the Declaration of Helsinki and its later amendments or comparable ethical standards. Ethics approval (RC20.10.95-2) was obtained by the Ethics Committee of the coordinator center (IRB Committee of Dr. Sulaiman Al Habib Medical Group, Riyadh, Saudi Arabia).

Results

Incidence of pathogens causing HAIs and patient characteristics

A total of 41,813 pathogens were isolated over 5 years in the three of our medical group’s facilities of which 38,624 pathogens caused 17,539 HAI events in 17,566 patients. These HAIs events were contracted in HMG Hospital in Altakhassusi (6016 HAI events = 34.3%), HMG Hospital in Arryan (5893 HAI events = 33.6%) and HMG Hospital in Qassim (5630 HAI events = 32.1%). Reported HAIs varied in type: catheter-associated urinary tract infection (CAUTI) (29.4%), central line-associated bloodstream infection (CLABSI) (27.3%), surgical site infection (SSI) (26.1%) and ventilator-associated events (VAE) (17.2%). Processed samples were blood (24.7%), urinary (19.1%), respiratory (13.4%), cerebral spinal fluid (8.5%), cervical (8.2%), saliva (5.2%), nasal (5.1), rectal (4.9%), lavages (4.7%), wound (3.9%), tissue (1.4%), and semen (0.9%). These HAI events were isolated in the intensive care units (37.2%), wards (32.9%), and outpatients (29.9%). In our study, we excluded 6232 (16.1%) HAI events due to the lack of data on the antimicrobial, pathogen, and/or culture response and sensitivity testing. About 9450 (53.8%) of patients who suffered HAIs were identified as females and had a mean age of 41.7 ± 14.3 years (78.1% were adults and 21.9% were children). Of 38,624 isolates taken from clinical specimens between 2015 and 2019, 27,754 (71.9%) were gram-negative organisms and 10,870 (28.1%) were gram-positive organisms. The ranking of causative pathogens in decreasing order was: Escherichia coli (38%), Klebsiella species (15.1%), Staphylococcus aureus (12.6%), Pseudomonas species (10.1%), and Enterococcus species (5.9%) (Fig. 1).

Fig. 1.

Fig. 1

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Total frequency of isolated gram-positive and gram-negative bacteria causing healthcare-associated infections in the three facilities in Saudi Arabia (2015–2019)

Trends of susceptibility among gram-positive bacteria

A total of 79,280 gram-positive pathogen sensitivity events against 14 clinically important antimicrobials occurred at HMG Hospital in Altakhassusi (38.2%), HMG Hospital in Arryan (36.6%), and HMG Hospital in Qassim (25.2%). Gram-positive bacteria showed an overall susceptibility of ≥ 52.6%. Antimicrobial susceptibility patterns in gram-positive pathogens over time are presented in Table 1.

Table 1.

Antimicrobial susceptibility rates found in gram-positive bacteria causing healthcare-associated infections in three HMG facilities in Saudi Arabia (2015–2019)

Staphylococcus aureus 2015 2016 2017 2018 2019 Total P-value*
(N = 903 isolates) (N = 666 isolates) (N = 940 isolates) (N = 1,122 isolates) (N = 1,222 isolates) (N = 4,853 isolates)
T S S% T S S% T S S% T S S% T S S% T S S%
AMP 191 64 33.5 131 52 39.7 169 93 55 157 87 55.4 222 63 28.4 870 359 41.3 0.828
CLX 677 544 80.4 444 331 74.5 797 656 82.3 747 679 90.9 887 735 82.9 3552 2945 82.9 0.649
CTN 230 109 47.4 209 89 42.6 266 134 50.4 266 154 57.9 252 161 63.9 1223 647 52.9 0.937
CIP 664 457 68.8 446 321 72 671 498 74.2 511 453 88.6 548 487 88.9 2840 2216 78 0.993
LVX 389 288 74 228 169 74.1 552 426 77.2 588 532 90.5 700 627 89.6 2457 2042 83.1 0.864
DCN 322 153 47.5 155 87 56.1 181 93 51.4 258 124 48.1 192 139 72.4 1108 596 53.8 0.983
TCN 514 305 59.3 489 423 86.5 891 838 94.1 1001 989 98.8 1201 1079 89.8 4096 3634 88.7 0.335
GMN 888 819 92.2 641 582 90.8 889 871 98 1022 955 93.4 1193 1098 92 4633 4325 93.4 0.775
EMN 722 696 96.4 558 473 84.8 791 740 93.6 971 813 83.7 991 929 93.7 4033 3651 90.5 0.659
CMN 804 780 97 576 513 89.1 896 871 97.2 1103 967 87.7 1187 1071 90.2 4566 4202 92 0.697
NFT 441 317 71.9 334 207 62 454 371 81.7 454 321 70.7 345 266 77.1 2028 1482 73.1 0.712
TMP-SMZ 595 463 77.8 591 524 88.7 865 819 94.7 1022 947 92.7 1201 996 82.9 4274 3749 87.7 0.542
LZD 903 903 100 641 625 97.5 790 773 97.8 1110 1003 90.4 1100 1076 97.8 4544 4380 96.4 0.931
VMN 901 895 99.3 651 624 95.9 938 931 99.3 1113 1109 99.6 1200 1181 98.4 4803 4740 98.7 0.721
MRSA 2015 2016 2017 2018 2019 Total P-value*
(N = 359 isolates) (N = 335 isolates) (N = 284 isolates) (N = 443 isolates) (N = 310 isolates) (N = 1,731 isolates)
T S S% T S S% T S S% T S S% T S S% T S S%
AMP 90 13 14.4 44 8 18.2 51 7 13.7 74 15 20.3 69 21 30.4 328 64 19.5 0.814
CLX 116 72 62.1 147 86 58.5 147 81 55.1 311 138 44.4 290 141 48.6 1011 518 51.2 0.963
CTN 11 7 63.6 33 9 27.3 64 13 20.3 67 11 16.4 50 8 16 225 48 21.3 0.956
CIP 190 141 74.2 187 112 59.9 229 108 47.2 401 198 49.4 301 245 81.4 1308 804 61.5 0.924
LVX 55 31 56.4 99 46 46.5 187 75 40.1 366 131 35.8 260 115 44.2 967 398 41.2 0.693
DCN 222 110 49.5 189 122 64.6 280 141 50.4 410 191 46.6 307 154 50.2 1408 718 51 0.964
TCN 141 101 71.6 211 118 55.9 179 108 60.3 440 251 57 303 271 89.4 1274 849 66.6 0.860
GMN 291 121 41.6 277 129 46.6 151 116 76.8 432 216 50 300 284 94.7 1451 866 59.7 0.888
EMN 144 91 63.2 177 102 57.6 111 97 87.4 331 176 53.2 271 232 85.6 1034 698 67.5 0.878
CMN 220 93 42.3 254 113 44.5 161 108 67.1 411 203 49.4 300 264 88 1346 781 58 0.838
NFT 181 117 64.6 311 213 68.5 281 241 85.8 441 399 90.5 308 287 93.2 1522 1257 82.6 0.346
TMP-SMZ 233 88 37.8 268 114 42.5 229 108 47.2 409 201 49.1 290 227 78.3 1429 738 51.6 0.892
LZD 241 118 49 264 138 52.3 223 123 55.2 439 251 57.2 299 175 58.5 1466 805 54.9 0.974
VMN 299 121 40.5 285 138 48.4 258 123 47.7 440 251 57 305 175 57.4 1587 808 50.9 0.975
CoNS 2015 2016 2017 2018 2019 Total P-value*
(N = 543 isolates) (N = 276 isolates) (N = 351 isolates) (N = 426 isolates) (N = 424 isolates) (N = 2,020 isolates)
T S S% T S S% T S S% T S S% T S S% T S S%
AMP 190 31 16.3 177 21 11.9 166 33 19.9 291 42 14.4 348 51 14.7 1172 178 15.2 0.948
CLX 164 88 53.7 190 64 33.7 289 96 33.2 408 146 35.8 333 114 34.2 1384 508 36.7 0.936
CTN 491 263 53.6 270 239 99.6 340 301 88.5 420 364 86.7 409 373 91.2 1930 1570 81.3 0.962
CIP 277 122 44 257 124 48.2 300 94 31.3 360 129 35.8 340 170 50 1534 639 41.7 0.991
LVX 130 46 35.4 211 56 26.5 288 142 49.3 420 173 41.2 211 85 40.3 1260 502 39.8 0.903
DCN 511 336 65.8 271 261 96.3 349 313 89.7 410 378 92.2 420 381 90.7 1961 1669 85.1 0.951
TCN 499 169 33.9 270 211 78.1 333 283 85 420 316 75.2 424 378 89.2 1946 1357 69.7 0.930
GMN 538 292 54.3 255 176 69 348 269 77.3 418 320 76.6 420 327 77.9 1979 1384 69.9 0.982
EMN 333 95 28.5 116 51 44 270 83 30.7 339 106 31.3 198 98 49.5 1256 433 34.5 0.966
CMN 531 255 48 266 156 58.6 269 235 87.4 399 282 70.7 339 293 86.4 1804 1221 67.7 0.981
NFT 170 77 45.3 233 99 42.5 331 225 68 411 250 60.8 400 344 86 1545 995 64.4 0.819
TMP-SMZ 369 153 41.5 276 199 72.1 350 298 85.1 400 349 87.3 407 327 80.3 1802 1326 73.6 0.831
LZD 541 535 98.9 276 273 98.9 351 351 100 426 426 100 424 424 100 2018 2009 99.6 0.967
VMN 543 537 98.9 276 276 100 351 351 100 426 426 100 424 424 100 2020 2014 99.7 0.968
Enterococcus species 2015 2016 2017 2018 2019 Total P-value*
(N = 481 isolates) (N = 370 isolates) (N = 447 isolates) (N = 411 isolates) (N = 557 isolates) (N = 2266 isolates)
T S S% T S S% T S S% T S S% T S S% T S S%
AMP 460 305 66.3 330 252 76.4 422 378 89.6 377 318 84.4 533 508 95.3 2122 1761 83 0.539
CLX 469 280 59.7 340 299 87.9 430 364 84.7 388 381 98.2 499 433 86.8 2126 1757 82.6 0.618
CTN 281 61 21.7 277 79 28.5 414 110 26.6 350 124 35.4 444 151 34 1766 525 29.7 0.850
CIP 369 164 44.4 299 127 42.5 398 169 42.5 322 111 34.5 471 97 20.6 1859 668 35.9 0.965
LVX 379 243 64.1 310 182 58.7 441 305 69.2 401 275 68.6 333 188 56.5 1864 1193 64 0.928
DCN 244 32 13.1 177 20 11.3 288 44 15.3 200 59 29.5 347 67 19.3 1256 222 17.7 0.812
TCN 201 32 15.9 222 40 18 333 203 61 190 92 48.4 500 143 28.6 1446 510 35.3 0.213
GMN 298 113 37.9 191 76 39.8 430 123 28.6 310 152 49 499 183 36.7 1728 647 37.4 0.972
EMN 411 145 35.3 301 42 14 357 153 42.9 188 53 28.2 331 65 19.6 1588 458 28.8 0.824
CMN 177 28 15.8 191 45 23.6 299 61 20.4 191 77 40.3 189 81 42.9 1047 292 27.9 0.506
NFT 322 116 36 355 300 84.5 438 393 89.7 391 364 93.1 541 482 89.1 2047 1655 80.9 0.306
TMP-SMZ 280 63 22.5 339 276 81.4 409 291 71.1 336 273 81.3 534 376 70.4 1898 1279 67.4 0.646
LZD 481 470 97.7 370 370 100 447 447 100 411 411 100 557 556 99.8 2266 2254 99.5 0.874
VMN 481 237 49.3 370 361 97.6 440 435 98.9 410 364 88.8 550 537 97.6 2251 1934 85.9 0.517
Overall 2015 2016 2017 2018 2019 Total P-value*
(N = 2286 isolates) (N = 1647 isolates) (N = 2022 isolates) (N = 2402 isolates) (N = 2513 isolates) (N = 10,870 isolates)
T S S% T S S% T S S% T S S% T S S% T S S%
AMP 931 413 44.4 682 333 48.8 808 511 63.2 899 462 51.4 1172 643 54.9 4492 2362 52.6 0.971
CLX 1426 984 69 1121 780 69.6 1663 1197 72 1854 1344 72.5 2009 1423 70.8 8073 5728 71 0.874
CTN 1013 440 43.4 789 446 56.5 1084 558 51.5 1103 653 59.2 1155 693 60 5144 2790 54.2 0.933
CIP 1500 884 58.9 1189 684 57.5 1598 869 54.4 1594 891 55.9 1660 999 60.2 7541 4327 57.4 0.969
LVX 953 608 63.8 848 453 53.4 1468 948 64.6 1775 1111 62.6 1504 1015 67.5 6548 4135 63.1 0.610
DCN 1299 631 48.6 792 490 61.9 1098 591 53.8 1278 752 58.8 1266 741 58.5 5733 3205 55.9 0.939
TCN 1355 607 44.8 1192 792 66.4 1736 1432 82.5 2051 1648 80.4 2428 1871 77.1 8762 6350 72.5 0.556
GMN 2015 1345 66.7 1364 963 70.6 1818 1379 75.9 2182 1643 75.3 2412 1892 78.4 9791 7222 73.8 0.905
EMN 1610 1027 63.8 1152 668 58 1529 1073 70.2 1829 1148 62.8 1791 1324 73.9 7911 5240 66.2 0.965
CMN 1732 1156 66.7 1287 827 64.3 1625 1275 78.5 2104 1529 72.7 2015 1709 84.8 8763 6496 74.1 0.922
NFT 1114 627 56.3 1233 819 66.4 1504 1230 81.8 1697 1334 78.6 1594 1379 86.5 7142 5389 75.5 0.032
TMP-SMZ 1477 767 51.9 1474 1113 75.5 1853 1516 81.8 2167 1770 81.7 2432 1926 79.2 9403 7092 75.4 0.595
LZD 2166 2026 93.5 1551 1406 90.7 1811 1694 93.5 2386 2091 87.6 2380 2231 93.7 10,294 9448 91.8 0.875
VMN 2224 1790 80.5 1582 1399 88.4 1987 1849 92.6 2389 2163 90 2479 2357 93.5 10,661 9558 89.1 0.901
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N Number of pathogens causing healthcare-associated infections, T number of tested isolates, S number of susceptible pathogens, S (%) percentage of susceptible pathogens, MRSA methicillin-resistant Staphylococcus aureus, CoNS coagulase-negative staphylococci, AMP ampicillin, CLX cloxacillin, CTN cefoxitin, CIP ciprofloxacin, LVX levofloxacin, NFT nitrofurantoin, EMN erythromycin, CMN clindamycin, TMP-SMZ trimethoprim-sulfamethoxazole, GMN gentamicin, DCN doxycycline, TCN tetracycline, VMN vancomycin, LZD linezolid

*Multivariate analysis of variance (MANOVA) for resistance trend

Generally, the highest susceptibilities of gram-positive pathogens to antimicrobials were seen towards vancomycin and linezolid by Staphylococcus aureus, 98.7% and 96.4%; CoNS, 99.7% and 99.6%; and Enterococcus species, 99.5% and 85.9%; respectively. Moreover, Staphylococcus aureus was found to be highly sensitive to gentamicin (93.4%), clindamycin (92%), and erythromycin (90.5%); MRSA was most sensitive to nitrofurantoin (82.6%); CoNS was sensitive to doxycycline (85.1%) and cefoxitin (81.3%); and Enterococcus species was sensitive to ampicillin (83%), cloxacillin (82.6%) and nitrofurantoin (80.9%) over the 5-year period.

In opposite, lowest susceptibilities of gram-positive pathogens to antimicrobials were seen to ampicillin by CoNS, 15.2%; MRSA, 19.5%; and Staphylococcus aureus, 41.3%; respectively. Also, Enterococcus species was least susceptible to doxycycline (17.7%); and MRSA was slightly sensitive to cefoxitin (21.3%).

Tetracycline, trimethoprim-sulfamethoxazole, levofloxacin and cloxacillin retained activity against 88.7%, 87.7%, 83.1%, and 82.9% of Staphylococcus aureus isolates, respectively, whereas trimethoprim-sulfamethoxazole was active against 73.6% of the CoNS isolates.

Over the 5 years, sensitivity of nitrofurantoin to overall gram-positive bacteria was the only antimicrobial to increase significantly (30.2% increase, p-value = 0.032). Prominent insignificant increase in the susceptibility of specific gram-positive bacteria to some antimicrobials occurred in 2019 compared to 2015 by: 30.5% for Staphylococcus aureus to tetracycline; 53.1% and 45.7% for MRSA to gentamicin and clindamycin, respectively; 37.6%, 55.3%,38.4%, 38.5% and 40.7% for CoNS to cefoxitin, tetracycline, clindamycin, trimethoprim-sulfamethoxazole and nitrofurantoin, respectively; 53.1%, 47.9% and 48.3% for Enterococcus species to trimethoprim-sulfamethoxazole, nitrofurantoin and vancomycin, respectively. However, noticeable insignificant decrease in susceptibility were seen in 2019 compared to 2015 by: 47.6% for MRSA to cefoxitin; and 23.8% for Enterococcus species to ciprofloxacin.

Overall, among the studied antibiotics the gram-positive isolates were mostly sensitive to linezolid (91.8%) whereas they were resistant to ampicillin (52.6%), cefoxitin (54.2%), and doxycycline (55.9%) (Table 1).

Trends of susceptibility among gram-negative bacteria

A total of 314,624 gram-negative pathogen sensitivity events against 21 clinically important antimicrobials occurred at HMG Hospital in Altakhassusi (35.9%), HMG Hospital in Arryan (39.3%), and HMG Hospital in Qassim (24.8%). Gram-negative bacteria showed an overall susceptibility of ≥ 49.5%. Antimicrobial susceptibility patterns in gram-negative pathogens over time are presented in Table 2.

Table 2.

Antimicrobial susceptibility rates found in gram-negative bacteria causing healthcare-associated infections in three HMG facilities in Saudi Arabia (2015–2019)

Escherichia coli 2015 2016 2017 2018 2019 Total P-value*
(N = 2,481 isolates) (N = 2,458 isolates) (N = 2,509 isolates) (N = 3,085 isolates) (N = 4,149 isolates) (N = 14,682 isolates)
T S S% T S S% T S S% T S S% T S S% T S S%
AMP 1100 761 69.2 1130 793 70.2 1158 803 69.3 1100 987 89.7 1800 1654 91.9 6288 4998 79.5 0.460
AMX/CLA 1920 1679 87.4 2100 1805 86 2103 1874 89.1 2700 2326 86.1 3258 2825 86.7 12,081 10,509 87 0.835
CZN 333 126 37.8 999 841 84.2 666 470 70.6 987 631 63.9 1852 1412 76.2 4837 3480 71.9 0.683
CRX 788 660 83.8 1125 828 73.6 547 378 69.1 981 732 74.6 1987 1505 75.7 5428 4103 75.6 0.911
CFX 1968 819 41.6 645 496 76.9 752 432 57.4 896 772 86.2 879 534 60.8 5140 3053 59.4 0.981
CTM 456 253 55.5 488 282 57.8 456 238 52.2 541 290 53.6 654 274 41.9 2595 1337 51.5 0.920
CTX 2388 1764 73.9 1933 1757 90.9 1901 1610 84.7 2456 2134 86.9 3896 3007 77.2 12,574 10,272 81.7 0.682
CZM 1963 1059 53.9 1025 896 87.4 2005 1113 55.5 1754 1275 72.7 1785 1384 77.5 8532 5727 67.1 0.998
CPM 1347 1007 74.8 1987 1775 89.3 2111 1764 83.6 2666 2158 80.9 3101 2849 91.9 11,212 9553 85.2 0.846
IPM 2477 2451 99 2358 2158 91.5 2430 2395 98.6 3081 3069 99.6 4130 4022 97.4 14,476 14,095 97.4 0.676
MPM 2480 2412 97.3 2400 2238 93.3 2414 2392 99.1 3074 3048 99.2 4099 4022 98.1 14,467 14,112 97.5 0.654
PIP-TZP 2360 2270 96.2 2347 2146 91.4 2456 2322 94.5 2996 2882 96.2 3896 3533 90.7 14,055 13,153 93.6 0.805
TMP-SMZ 1456 1293 88.8 1745 1463 83.8 1736 1249 71.9 1987 1775 89.3 2898 2589 89.3 9822 8369 85.2 0.529
GMN 2223 2128 95.7 2314 2178 94.1 2300 2124 92.3 2965 2742 92.5 3991 3589 89.9 13,793 12,761 92.5 0.726
ACN 2470 2135 86.4 2389 2400 100.5 2490 2414 96.9 3030 3009 99.3 4110 4030 98.1 14,489 13,988 96.5 0.697
CIP 2001 1726 86.3 1999 1804 90.2 1800 1737 96.5 2789 2240 80.3 3008 2951 98.1 11,597 10,458 90.2 0.739
OXN 1136 785 69.1 1107 909 82.1 700 612 87.4 698 548 78.5 687 442 64.3 4328 3296 76.2 0.967
LVX 1147 785 68.4 1165 918 78.8 565 438 77.5 1365 1016 74.4 1777 1393 78.4 6019 4550 75.6 0.923
NFT 1455 1241 85.3 2411 2317 96.1 2425 2314 95.4 2905 2704 93.1 3896 3790 97.3 13,092 12,366 94.5 0.489
TGN 2377 2269 95.5 2450 2420 98.8 2500 2499 100 3083 3069 99.5 4144 4131 99.7 14,554 14,388 98.9 0.420
CLN 1365 1106 81 1130 722 63.9 789 570 72.2 2029 1016 50.1 4011 3974 99.1 9324 7388 79.2 0.120
Klebsiella species 2015 2016 2017 2018 2019 Total P-value*
(N = 1,000 isolates) (N = 839 isolates) (N = 1,299 isolates) (N = 1,357 isolates) (N = 1,356 isolates) (N = 5,851 isolates)
T S S% T S S% T S S% T S S% T S S% T S S%
AMP 300 206 68.7 222 172 77.5 221 94 42.5 801 611 76.3 512 333 65 2056 1416 68.9 0.258
AMX/CLA 654 534 81.7 654 565 86.4 906 841 92.8 1001 938 93.7 896 739 82.5 4111 3617 88 0.772
CZN 159 75 47.2 400 312 78 456 356 78.1 562 467 83.1 753 462 61.4 2330 1672 71.8 0.786
CRX 654 223 34.1 258 137 53.1 358 278 77.7 452 354 78.3 963 369 38.3 2685 1361 50.7 0.975
CFX 700 444 63.4 402 321 79.9 456 295 64.7 458 370 80.8 456 269 59 2472 1699 68.7 0.989
CTM 112 82 73.2 111 86 77.5 255 104 40.8 320 134 41.9 358 114 31.8 1156 520 45 0.997
CTX 753 543 72.1 654 507 77.5 801 770 96.1 987 887 89.9 1,196 851 71.2 4391 3558 81 0.607
CZM 656 367 55.9 456 326 71.5 687 413 60.1 454 394 86.8 756 396 52.4 3009 1896 63 0.269
CPM 550 310 56.4 654 589 90.1 1010 845 83.7 900 829 92.1 1120 814 72.7 4234 3387 80 0.821
IPM 961 778 81 832 781 93.9 1190 1113 93.5 1290 1208 93.6 1310 1116 85.2 5583 4996 89.5 0.626
MPM 800 742 92.8 765 731 95.6 1120 1104 98.6 1291 1207 93.5 1312 1104 84.1 5288 4888 92.4 0.544
PIP-TZP 751 631 84 741 664 89.6 1130 933 82.6 1140 1021 89.6 1100 906 82.4 4862 4155 85.5 0.765
TMP-SMZ 654 535 81.8 659 596 90.4 874 788 90.2 1122 903 80.5 1145 905 79 4454 3727 83.7 0.561
GMN 963 696 72.3 801 700 87.4 1122 1019 90.8 1260 1167 92.6 1258 1152 91.6 5404 4734 87.6 0.462
ACN 852 774 90.8 811 782 96.4 1125 1087 96.6 1290 1150 89.1 1322 1272 96.2 5400 5065 93.8 0.604
CIP 789 652 82.6 753 686 91.1 1100 954 86.7 1299 1068 82.2 1300 916 70.5 5241 4276 81.6 0.641
OXN 350 265 75.7 402 273 67.9 582 404 69.4 654 589 90.1 1101 789 71.7 3089 2320 75.1 0.624
LVX 300 265 88.3 582 321 55.2 452 360 79.6 542 441 81.4 900 745 82.8 2776 2132 76.8 0.870
NFT 258 137 53.1 456 322 70.6 652 505 77.5 521 478 91.7 456 379 83.1 2343 1821 77.7 0.106
TGN 753 512 68 500 356 71.2 587 417 71 530 517 97.5 874 639 73.1 3244 2441 75.2 0.984
CLN 466 378 81.1 665 654 98.3 1200 1179 98.3 529 517 97.7 1199 1023 85.3 4059 3751 92.4 0.736
Pseudomonas species 2015 2016 2017 2018 2019 Total P-value*
(N = 850 isolates) (N = 806 isolates) (N = 696 isolates) (N = 733 isolates) (N = 837 isolates) (N = 3,922 isolates)
T S S% T S S% T S S% T S S% T S S% T S S%
AMP 221 46 20.8 400 193 48.3 333 269 80.8 401 377 94 514 441 85.8 1869 1326 70.9 0.636
AMX/CLA 410 296 72.2 147 78 53.1 415 300 72.3 159 94 59.1 652 416 63.8 1783 1184 66.4 0.519
CZN 119 61 51.3 302 111 36.8 512 264 51.6 412 303 73.5 452 329 72.8 1797 1068 59.4 0.046
CRX 300 109 36.3 411 178 43.3 466 222 47.6 454 301 66.3 552 489 88.6 2183 1299 59.5 0.188
CFX 147 76 51.7 430 188 43.7 501 249 49.7 541 334 61.7 611 401 65.6 2230 1248 56 0.186
CTM 109 29 26.6 156 67 42.9 247 110 44.5 268 137 51.1 254 133 52.4 1034 476 46 0.923
CTX 358 276 77.1 547 369 67.5 240 110 45.8 260 137 52.7 219 133 60.7 1624 1025 63.1 0.746
CZM 800 676 84.5 789 686 86.9 614 495 80.6 660 480 72.7 653 558 85.5 3516 2895 82.3 0.952
CPM 598 300 50.2 658 567 86.2 611 512 83.8 614 500 81.4 678 568 83.8 3159 2447 77.5 0.961
IPM 753 541 71.8 782 614 78.5 660 574 87 603 576 95.5 801 710 88.6 3599 3015 83.8 0.925
MPM 741 564 76.1 788 617 78.3 671 570 84.9 654 582 89 822 713 86.7 3676 3046 82.9 0.944
PIP-TZP 820 631 77 799 692 86.6 680 590 86.8 721 608 84.3 810 742 91.6 3830 3263 85.2 0.956
TMP-SMZ 658 321 48.8 654 456 69.7 514 477 92.8 654 522 79.8 769 687 89.3 3249 2463 75.8 0.535
GMN 830 744 89.6 800 730 91.3 688 642 93.3 701 672 95.9 780 759 97.3 3799 3547 93.4 0.989
ACN 840 755 89.9 801 756 94.4 680 665 97.8 699 686 98.1 820 784 95.6 3840 3646 94.9 0.990
CIP 801 631 78.8 771 646 83.8 670 604 90.1 652 597 91.6 700 667 95.3 3594 145 87.5 0.997
OXN 80 13 16.3 154 71 46.1 230 110 47.8 325 187 57.5 400 191 47.8 1189 572 48.1 0.030
LVX 775 608 78.5 658 492 74.8 677 369 54.5 541 359 66.4 521 381 73.1 3172 2209 69.6 0.921
NFT 302 111 36.8 321 128 39.9 321 177 55.1 428 201 47 451 290 64.3 1823 907 49.8 0.491
TGN 346 339 98 420 409 97.4 511 498 97.5 541 533 98.5 681 678 99.6 2499 2457 98.3 0.142
CLN 711 706 99.3 670 661 98.7 587 571 97.3 301 291 96.7 328 321 97.9 2597 2550 98.2 0.853
Acinetobacter species 2015 2016 2017 2018 2019 Total P-value*
(N = 611 isolates) (N = 275 isolates) (N = 280 isolates) (N = 262 isolates) (N = 182 isolates) (N = 1,610 isolates)
T S S% T S S% T S S% T S S% T S S% T S S%
AMP 90 33 36.7 150 74 49.3 320 110 34.4 264 130 49.2 275 142 51.6 1099 489 44.5 0.262
AMX/CLA 110 29 26.4 120 53 44.2 168 76 45.2 191 91 47.6 201 103 51.2 790 352 44.6 0.291
CZN 60 19 31.7 60 21 35 96 29 30.2 67 34 50.7 79 44 55.7 362 147 40.6 0.538
CRX 75 24 32 82 36 43.9 78 33 42.3 86 41 47.7 98 53 54.1 419 187 44.6 0.480
CFX 64 11 17.2 70 24 34.3 77 23 29.9 76 36 47.4 88 47 53.4 375 141 37.6 0.101
CTM 58 10 17.2 35 10 28.6 34 12 35.3 39 17 43.6 47 26 55.3 213 75 35.2 0.551
CTX 86 18 20.9 31 14 45.2 33 22 66.7 71 29 40.8 76 38 50 297 121 40.7 0.478
CZM 64 14 21.9 43 21 48.8 79 33 41.8 98 46 46.9 98 51 52 382 165 43.2 0.523
CPM 23 3 13 29 11 37.9 39 17 43.6 70 36 51.4 81 43 53.1 242 110 45.5 0.573
IPM 240 62 25.8 78 35 44.9 108 50 46.3 130 70 53.8 157 79 50.3 713 296 41.5 0.450
MPM 149 35 23.5 127 62 48.8 100 50 50 129 70 54.3 171 91 53.2 676 308 45.6 0.112
PIP-TZP 97 17 17.5 89 44 49.4 70 38 54.3 90 47 52.2 110 59 53.6 456 205 45 0.550
TMP-SMZ 310 75 24.2 348 172 49.4 287 141 49.1 220 115 52.3 300 177 59 1465 680 46.4 0.420
GMN 378 93 24.6 186 144 77.4 190 88 46.3 171 91 53.2 240 127 52.9 1165 543 46.6 0.660
ACN 220 49 22.3 189 84 44.4 121 56 46.3 155 80 51.6 168 94 56 853 363 42.6 0.413
CIP 127 29 22.8 114 49 43 130 59 45.4 125 67 53.6 148 78 52.7 644 282 43.8 0.415
OXN 89 21 23.6 94 36 38.3 111 48 43.2 122 67 54.9 139 73 52.5 555 245 44.1 0.322
LVX 40 8 20 17 8 47.1 36 17 47.2 40 24 60 58 33 56.9 191 90 47.1 0.697
NFT 128 41 32 321 77 24 310 140 45.2 290 151 52.1 287 151 52.6 1336 560 41.9 0.102
TGN 369 350 94.9 197 188 95.4 142 139 97.9 148 138 93.2 113 108 95.6 969 923 95.3 0.022
CLN 401 381 95 134 124 92.5 169 164 97 120 116 96.7 129 117 90.7 953 902 94.6 0.089
Proteus species 2015 2016 2017 2018 2019 Total P-value*
(N = 212 isolates) (N = 192 isolates) (N = 150 isolates) (N = 135 isolates) (N = 197 isolates) (N = 886 isolates)
T S S% T S S% T S S% T S S% T S S% T S S%
AMP 200 92 46 186 89 47.8 74 40 54.1 99 59 59.6 181 100 55.2 740 380 51.4 0.527
AMX/CLA 201 143 71.1 187 146 78.1 147 100 68 129 114 88.4 170 155 91.2 834 658 78.9 0.797
CZN 107 48 44.9 180 99 55 140 126 90 125 101 80.8 190 132 69.5 742 506 68.2 0.828
CRX 83 35 42.2 178 44 24.7 91 51 56 113 74 65.5 130 81 62.3 595 285 47.9 0.863
CFX 60 19 31.7 62 27 43.5 89 49 55.1 128 77 60.2 191 110 57.6 530 282 53.2 0.266
CTM 112 36 32.1 81 40 49.4 78 52 66.7 97 64 66 141 76 53.9 509 268 52.7 0.760
CTX 203 112 55.2 179 133 74.3 130 121 93.1 127 100 78.7 180 161 89.4 819 627 76.6 0.878
CZM 150 77 51.3 150 70 46.7 136 115 84.6 131 79 60.3 174 116 66.7 741 457 61.7 0.913
CPM 90 41 45.6 128 63 49.2 137 71 51.8 131 118 90.1 179 127 70.9 665 420 63.2 0.692
IPM 130 61 46.9 167 78 46.7 141 102 72.3 133 127 95.5 160 140 87.5 731 508 69.5 0.619
MPM 200 121 60.5 189 150 79.4 149 130 87.2 121 101 83.5 189 179 94.7 848 681 80.3 0.821
PIP-TZP 199 113 56.8 180 129 71.7 147 122 83 129 119 92.2 179 161 89.9 834 644 77.2 0.935
TMP-SMZ 131 64 48.9 179 78 43.6 140 98 70 118 100 84.7 189 109 57.7 757 449 59.3 0.598
GMN 141 88 62.4 167 109 65.3 145 140 96.6 120 102 85 170 148 87.1 743 587 79 0.417
ACN 198 115 58.1 169 138 81.7 140 131 93.6 129 117 90.7 181 173 95.6 817 674 82.5 0.871
CIP 191 93 48.7 187 108 57.8 135 133 98.5 131 122 93.1 179 147 82.1 823 603 73.3 0.775
OXN 76 30 39.5 71 39 54.9 70 40 57.1 109 61 56 131 77 58.8 457 247 54 0.588
LVX 40 17 42.5 40 22 55 66 37 56.1 97 55 56.7 137 81 59.1 380 212 55.8 0.218
NFT 197 91 46.2 180 119 66.1 135 131 97 127 110 86.6 187 140 74.9 826 591 71.5 0.871
TGN 190 176 92.6 188 178 94.7 139 134 96.4 130 123 94.6 191 188 98.4 838 799 95.3 0.759
CLN 179 166 92.7 183 178 97.3 140 133 95 129 124 96.1 177 173 97.7 808 774 95.8 0.687
Enterobacter and Citrobacter species 2015 2016 2017 2018 2019 Total P-value*
(N = 162 isolates) (N = 135 isolates) (N = 85 isolates) (N = 124 isolates) (N = 297 isolates) (N = 803 isolates)
T S S% T S S% T S S% T S S% T S S% T S S%
AMP 66 32 48.5 90 40 44.4 60 54 90 90 70 77.8 184 165 89.7 490 361 73.7 0.023
AMX/CLA 39 17 43.6 36 11 30.6 60 29 48.3 66 40 60.6 83 60 72.3 284 157 55.3 0.084
CZN 41 21 51.2 58 27 46.6 51 31 60.8 118 60 50.8 159 141 88.7 427 280 65.6 0.068
CRX 58 27 46.6 78 33 42.3 78 41 52.6 120 66 55 87 71 81.6 421 238 56.5 0.003
CFX 60 21 35 70 36 51.4 77 47 61 85 46 54.1 79 53 67.1 371 203 54.7 0.801
CTM 81 33 40.7 89 44 49.4 69 53 76.8 80 59 73.8 93 67 72 412 256 62.1 0.803
CTX 41 23 56.1 89 46 51.7 80 66 82.5 100 79 79 157 129 82.2 467 343 73.4 0.548
CZM 49 24 49 102 56 54.9 83 62 74.7 81 66 81.5 107 93 86.9 422 301 71.3 0.409
CPM 88 45 51.1 128 72 56.3 80 79 98.8 122 114 93.4 151 126 83.4 569 436 76.6 0.791
IPM 132 69 52.3 130 80 61.5 85 81 95.3 120 105 87.5 180 154 85.6 647 489 75.6 0.171
MPM 141 75 53.2 131 82 62.6 84 80 95.2 119 105 88.2 221 161 72.9 696 503 72.3 0.596
PIP-TZP 120 60 50 110 67 60.9 78 61 78.2 121 116 95.9 160 135 84.4 589 439 74.5 0.180
TMP-SMZ 128 65 50.8 122 73 59.8 79 63 79.7 118 101 85.6 174 144 82.8 621 446 71.8 0.378
GMN 100 59 59 112 61 54.5 80 66 82.5 122 101 82.8 188 154 81.9 602 441 73.3 0.417
ACN 160 85 53.1 125 75 60 81 70 86.4 123 114 92.7 229 160 69.9 718 504 70.2 0.666
CIP 134 77 57.5 130 81 62.3 84 69 82.1 123 117 95.1 189 149 78.8 660 493 74.7 0.428
OXN 130 72 55.4 133 96 72.2 84 60 71.4 115 96 83.5 153 131 85.6 615 455 74 0.868
LVX 60 33 55 98 48 49 67 57 85.1 94 81 86.2 147 116 78.9 466 335 71.9 0.166
NFT 63 26 41.3 91 76 83.5 81 63 77.8 88 54 61.4 151 118 78.1 474 337 71.1 0.430
TGN 149 136 91.3 127 120 94.5 83 80 96.4 114 110 96.5 288 278 96.5 761 724 95.1 0.180
CLN 143 122 85.3 130 123 94.6 76 71 93.4 116 111 95.7 280 279 99.6 745 706 94.8 0.370
Overall 2015 2016 2017 2018 2019 Total P-value*
(N = 5,316 isolates) (N = 4,705 isolates) (N = 5,019 isolates) (N = 5,696 isolates) (N = 7,018 isolates) (N = 27,754 isolates)
T S S% T S S% T S S% T S S% T S S% T S S%
AMP 1977 1170 59.2 2178 1361 62.5 2166 1370 63.3 2755 2234 81.1 3466 2835 81.8 12,542 8970 71.5 0.683
AMX/CLA 3334 2698 80.9 3244 2658 81.9 3799 3220 84.8 4246 3603 84.9 5260 4298 81.7 19,883 16,477 82.9 0.976
CZN 819 350 42.7 1999 1411 70.6 1921 1276 66.4 2271 1596 70.3 3485 2520 72.3 10,495 7153 68.2 0.375
CRX 1958 1078 55.1 2132 1256 58.9 1618 1003 62 2206 1568 71.1 3817 2568 67.3 11,731 7473 63.7 0.662
CFX 2999 1390 46.3 1679 1092 65 1952 1095 56.1 2184 1635 74.9 2304 1414 61.4 11,118 6626 59.6 0.961
CTM 928 443 47.7 960 529 55.1 1139 569 50 1345 701 52.1 1547 690 44.6 5919 2932 49.5 0.911
CTX 3829 2736 71.5 3433 2826 82.3 3185 2699 84.7 4001 3366 84.1 5724 4319 75.5 20,172 15,946 79.1 0.975
CZM 3682 2217 60.2 2565 2055 80.1 3604 2231 61.9 3178 2340 73.6 3573 2598 72.7 16,602 11,441 68.9 0.998
CPM 2696 1706 63.3 3584 3077 85.9 3988 3288 82.4 4503 3755 83.4 5310 4527 85.3 20,081 16,353 81.4 0.866
IPM 4693 3962 84.4 4347 3746 86.2 4614 4315 93.5 5357 5155 96.2 6738 6221 92.3 25,749 23,399 90.9 0.965
MPM 4511 3949 87.5 4400 3880 88.2 4538 4326 95.3 5388 5113 94.9 6814 6270 92 25,651 23,538 91.8 0.966
PIP-TZP 4347 3722 85.6 4266 3742 87.7 4561 4066 89.1 5197 4793 92.2 6255 5536 88.5 24,626 21,859 88.8 0.981
TMP-SMZ 3337 2353 70.5 3707 2838 76.6 3630 2816 77.6 4219 3516 83.3 5475 4611 84.2 20,368 16,134 79.2 0.869
GMN 4635 3808 82.2 4380 3922 89.5 4525 4079 90.1 5339 4875 91.3 6627 5929 89.5 25,506 22,613 88.7 0.967
ACN 4740 3913 82.6 4484 4235 94.4 4637 4423 95.4 5426 5156 95 6830 6513 95.4 26,117 24,240 92.8 0.960
CIP 4043 3208 79.3 3954 3374 85.3 3919 3556 90.7 5119 4211 82.3 5524 4908 88.8 22,559 19,257 85.4 0.971
OXN 1861 1186 63.7 1961 1424 72.6 1777 1274 71.7 2023 1548 76.5 2611 1703 65.2 10,233 7135 69.7 0.985
LVX 2362 1716 72.7 2560 1809 70.7 1863 1278 68.6 2679 1976 73.8 3540 2749 77.7 13,004 9528 73.3 0.847
NFT 2403 1647 68.5 3780 3039 80.4 3924 3330 84.9 4359 3698 84.8 5428 4868 89.7 19,894 16,582 83.4 0.921
TGN 4184 3782 90.4 3882 3671 94.6 3962 3767 95.1 4546 4490 98.8 6291 6022 95.7 22,865 21,732 95 0.968
CLN 3265 2859 87.6 2912 2462 84.5 2961 2688 90.8 3224 2175 67.5 6124 5887 96.1 18,486 16,071 86.9 0.608
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N number of pathogens causing healthcare-associated infections, T number of tested isolates, S number of susceptible pathogens, S (%) percentage of susceptible pathogens, AMP ampicillin, AMX/CLA amoxicillin/clavulanic acid, CZN cefazolin, CRX cefuroxime, CFX cefixime, CTM cefotaxime, CTX ceftriaxone, CZM ceftazidime, CPM cefepime, IPM imipenem, MPM meropenem, PIP-TZP piperacillin/tazobactam, TMP-SMZ trimethoprim-sulfamethoxazole, GMN gentamicin, ACN amikacin, CIP ciprofloxacin, OXN ofloxacin, LVX levofloxacin, NFT nitrofurantoin, TGN tigecycline, CLN colistin

*Multivariate analysis of variance (MANOVA) for resistance trend

Generally, the highest susceptibilities of gram-negative pathogens to antimicrobials were seen towards: tigecycline, meropenem, imipenem and amikacin by Escherichia coli, 98.9%, 97.5%, 97.4% and 96.5%, respectively; amikacin, meropenem and colistin by Klebsiella species, 93.8%, 92.4% and 92.4%, respectively; tigecycline, colistin, amikacin and gentamicin by Pseudomonas species, 98.3%, 98.2%, 94.9% and 93.4%, respectively; tigecycline and colistin by Acinetobacter species, 95.3% and 94.6%, respectively; colistin and tigecycline by Proteus species, 95.8% and 95.3%, respectively; and tigecycline and colistin by Enterobacter and Citrobacter sepcies,95.1% and 94.8%, respectively.

Moreover, Escherichia coli was found to be highly sensitive to nitrofurantoin (94.5%), piperacillin-tazobactam (93.6%), gentamicin (92.5%) and ciprofloxacin (90.5%); against Klebsiella species, imipenem, amoxicillin/clavulanic acid, gentamicin and piperacillin-tazobactam retained susceptibility > 85%; Pseudomonas species were sensitive to ciprofloxacin (87.5%), piperacillin-tazobactam (85.2%), imipenem (83.8%), meropenem (82.9%) and ceftazidime (82.3%); Proteus species were sensitive to amikacin (82.5%) and meropenem (80.3%); and Enterobacter and Citrobacter species were sensitive by ≥ 70% to most of the tested antimicrobials over the 5-year period.

In contrary, lowest susceptibilities of gram-negative pathogens to antimicrobials were seen to cefotaxime and cefixime by Acinetobacter species, 35.2% and 37.6%, respectively. Acinetobacter species shown low sensitivity of ≥ 40% almost to all antimicrobials; and both Klebsiella and Pseudomonas species were slightly sensitive to cefotaxime (45% and 46%, respectively).

Over the 5 years, sensitivity of cefazolin and ofloxacin to Pseudomonas species increased significantly (21.5% and 31.5% increase, p-values = 0.046 and 0.030; respectively). The small sensitivity increase of Acinetobacter species towards tigecycline was found to be significant (0.4% increase, p-value = 0.022). In addition, large difference in susceptibility were found for both ampicillin and cefuroxime towards Enterobacter and Citrobacter species (41.2% and 35.1% increase, p-values = 0.023 and 0.003; respectively).

Prominent insignificant increase in the susceptibility of specific gram-negative bacteria to some antimicrobials occurred in 2019 compared to 2015 by: 38.4% for Escherichia coli to cefazolin; 30% for Klebsiella species to nitrofurantoin; and 65%, 52.3%, 40.6%, 33.6% and 31.5% for Pseudomonas species to ampicillin, cefuroxime, trimethoprim-sulfamethoxazole, cefepime and ofloxacin, respectively.

For a 5-year difference, sensitivity of Acinetobacter species to antimicrobials shown many insignificant increases: (rate of sensitivity increase: for cefepime, 40%; for cefotaxime, 38.1%; for levofloxacin, 36.9%; for cefixime, 36.2%; for piperacillin-tazobactam, 36.1%; for trimethoprim-sulfamethoxazole, 34.8%; for amikacin, 33.7%; and for ceftazidime, 30.2%.

Enterobacter and Citrobacter species exhibited most of the sensitivity increase changes to antimicrobials of all gram-negative isolates. In 2019 compared to 2015, Enterobacter and Citrobacter species susceptibility increased insignificantly by: 41.2% for ampicillin; 37.9% for ceftazidime; 37.5% for cefazolin; 36.9% for nitrofurantoin; 35.1% for cefuroxime; 34.4% for piperacillin-tazobactam; 33.3% for imipenem; 32.3% for cefepime; 32.1% for cefixime; 32% for trimethoprim-sulfamethoxazole; 31.3% for cefotaxime; and 30.2% for ofloxacin. However, a big insignificant decrease in susceptibility was seen in 2019 compared to 2015 by cefotaxime for Klebsiella species (41.4%).

Overall, among the studied antibiotics the gram-negative isolates were mostly sensitive to tigecycline (95%) whereas they were resistant to cefotaxime (49.5%) and cefixime (59.6%) (Table 2).

Discussion

This retrospective study describes the distribution of pathogens causing HAIs and susceptibility patterns for a very high number of samples collected from both the ward and clinics in Saudi Arabia from 2015 to 2019, with an emphasis on the antibiotic classes frequently utilized to treat common infections given by a huge national surveillance program. The most commonly encountered organisms were Escherichia coli, Klebsiella species, and Staphylococcus aureus. Though various studies have previously described susceptibility rates in several infectious isolates, Saudi data are limited either to single-center studies [7, 12–20] or to research concentrating on the susceptibility to single or double antimicrobial classes [21–26].

One of the vital findings of the data analysis of this study was the significant increase of sensitivity for overall gram-positive bacteria to nitrofurantoin over the 5 years (30.2% increase, p-value = 0.032). Interestingly, the susceptibility of Staphylococcus aureus to tetracycline; MRSA to gentamicin and clindamycin; CoNS to cefoxitin, tetracycline, clindamycin, trimethoprim-sulfamethoxazole and nitrofurantoin; and Enterococcus species to trimethoprim-sulfamethoxazole, nitrofurantoin and vancomycin; increased insignificantly over time by ≥ 30% although this was likely due to the change of followed guidelines used for antimicrobial susceptibility testing at the Medical Group facilities, a shift from the Clinical and Laboratory Standards Institute (CLSI) to the European Committee on Antimicrobial Susceptibility Testing (EUCAST) [11, 27].

A comparison of the current results with findings from previous studies can offer some validation of the findings of this present study and identify methodological distinctions in their approaches. As expected, HAI events were more frequent in the ICUs (37.2%) compared with non-ICU locations [HAI events in wards and outpatients were 32.9% and 29.9%, respectively], a finding which was previously described in local studies [10, 28] and may reflect the epicenter role of ICU in both infections and antimicrobial resistance. The predominant isolates to cause HAIs were gram-negative organisms (71.9% vs. 28.1%); this finding was similar with many Saudi studies made in different cities in Saudi Arabia including Riyadh [6, 28, 29], Makkah [30, 31], Dhahran [32], Bisha [33], and Aljouf [10]; with the majority being Escherichia coli (38%) accounting approximately for 52.9% of the gram-negative bacterial growth in line with previous national studies [7, 29, 30, 32, 34]. The second predominant isolates of the gram-negative organisms were the Klebsiella species (15.1%), this finding was similar to the bacterial isolates prevalence studies from Dhahran [7], and Riyadh [6]. The proportions of Klebsiella were 17.2% in Dhahran [7], and 14.7% in Riyadh [6]. The culture rate in our study for Proteus species (2.3%) was comparable to previously reported rates in two different studies in Riyadh (1.2% and 1.8%) [6, 35]. Also, the incidence of Acinetobacter species in our study was very close to the rate reported before (4.2% vs 5.5%) [6]; in contrast to the much higher rates found in two separate studies in Riyadh (31.7% and 25.3%) [35, 36]. Our prevalence of Staphylococcus aureus was similar to the rate described in a previous report done in Riyadh (12.6% vs 13.9%) [6]. We report a lower rate of MRSA (15.9%) compared to two previous studies made in Riyadh (24.4% and 30.3%, respectively) [28] but similar to the rate reported before in another study in Riyadh (17.5%) [29]. We report a higher susceptibility of Enterococcus species to vancomycin (85.9% vs 79.7%) compared to one study in Riyadh [6]. In our study, proportion of Pseudomonas species that caused HAIs is less than what was reported in Riyadh (10.1% vs 15.4%) [6]; however, our prevalence was in agreement to the bacterial frequency in a study from Dhahran (12.8%) [7]. Frequency of CoNS in causing HAIs in this study is in line with a study from Riyadh (5.2% vs 6.5%) [6] but much lower than the rate reported previously in a study in Riyadh (28.4%) [29]. In our study, incidence of Enterococcus species as causative pathogens for HAIs is almost half of the reported rate by a study in Riyadh (4.5% vs 8.6%) [6]; however, rate was in parallel to the prevalence reported in other study in Riyadh (5.9%) [28] but contradicts with the rate reported in another study in Riyadh (15.8%) [29].

Our data analysis regarding the susceptibility patterns of antimicrobials confirm or contradict the findings of previous local studies. For example, Pseudomonas and Acinetobacter were most susceptible to colistin and amikacin in a study in Riyadh [29], whereas in our study, tigecycline and colistin had higher susceptibility rates. On the other hand, Escherichia coli, Klebsiella pneumonia, Enterobacter and Citrobacter species were most sensitive to amikacin, imipenem and meropenem [29], whereas in our study, Escherichia coli and Enterobacter and Citrobacter species were most susceptible to tigecycline, and Klebsiella species was most susceptible to amikacin. However, our study support the finding that CoNS were most susceptible to vancomycin and linezolid [29] and we found the susceptibility of Staphylococcus aureus to clindamycin and trimethoprim-sulfamethoxazole were almost identical to the results of the aforementioned study (92% vs 94% and 87.7 vs 87%, respectively). This is might be due the fact that the sample was drawn from tertiary private hospitals in Saudi Arabia where the level of environmental hygiene is higher and staff are highly restricted to infection control practices.

Linezolid and vancomycin had the best susceptibility profile to Staphylococcus aureus, CoNS, and Enterococcus species while gentamicin shown low sensitivity towards MRSA, CoNS and enterococcus species. In the context of emergence of resistance of malicious gram-positive bacteria to gentamicin, linezolid and vancomycin have become effective alternatives to gentamicin treatment frequently associated with nephrotoxicity [37]. Linezolid and vancomycin are active against the most serious gram-positive bacteria, including streptococci, vancomycin-resistant enterococci (VRE) and MRSA [38]. Nevertheless, we noted a low rate of susceptibility of linezolid and vancomycin against MRSA (54.9% and 50.9%, respectively) likely because of antibiotic selection pressure and possibly a reflection of selective reporting of susceptibility testing; this finding contradicts those of a recent study in Riyadh, which identified a 100%-sensitivity of both antimicrobial agents for MRSA [29]. The relatively lower susceptibility in gram-positive bacteria in the current study may be reflecting Saudi prescription trends in recent years that overuse fluoroquinolones [1, 39] and carbapenems [40, 41] at the expenses of other broad-spectrum such as linezolid and vancomycin due to increased availability and reduced cost of these drugs. However, nitrofurantoin maintained the greatest efficacy against MRSA in our study (82.6%); supporting the finding of a recent study in Aseer that shown 100% susceptibility of MRSA to nitrofurantoin [42].

Over the 5-year period, it is interesting to note imipenem and meropenem either retained its activity or shown susceptibility increase patterns towards all the studied gram-negative pathogens except for imipenem which was less sensitive in 2019 by 1.6% against Escherichia coli and for meropenem that shown a minor sensitivity reduction by 8.6% to Klebsiella species. Previous studies from Saudi found high susceptibility of Pseudomonas to carbapenems [7, 41]; however, in other local studies, the susceptibility of Pseudomonas to meropenem declined over a five-year period [29] and nonsusceptibility of Acinetobacter and Pseudomonas aeruginosa to carbapenems was very high (68.3% and 76%) [6, 41]. Furthermore, there were relatively stable susceptibility patterns to all tested antimicrobials, except for cefotaxime which shown a susceptibility reduction by 41.4%, among Escherichia coli and Klebsiella species; in opposite to the finding of a local study in Dhahran that shown a reduction trend in the susceptibility of antibiotics to Escherichia coli and Klebsiella species [7]. Moreover, we observed an increase in the susceptibility of Acinetobacter and Enterobacter and Citrobacter species to all studied antimicrobials except for colistin that had a slight sensitivity reduction by 4.3% against Acinetobacter species. This can be considered as a part of the success of the combating strategies implemented since January 2014 at the medical settings to reduce further emergence and spread of AMR, lower the percentage of HAIs and MDR organisms, and save on needless healthcare expenses [1].

Significant differences in antibiogram findings between different healthcare facilities and regions may suggest differences in populations of the served patients, patterns of antimicrobial use, or deficiencies in hospital infection control and hygiene practices that could be further explored.

Limitations

This study had a few limitations. Firstly, the retrospective design and the risk of misclassification and selection bias. For instance, even though the laboratories follow the highest standards, there may be a possibility that some isolates had some contaminants. Furthermore, since all three hospitals in this study are tertiary care hospitals, they receive more complicated cases that may be caused by resistant pathogens which may not indicate the antibiotic susceptibility trend and microbiology of the general population. Nevertheless, our study’s findings will add to local and global data on antimicrobial susceptibility, especially with highly threatening infections.

Conclusion

Systematic collection and analysis of routine clinical laboratory data is important in assessing the antimicrobial resistance burden. Nationwide surveillance is urgently needed to provide policy makers, antimicrobial stewardship committees, infection preventionists, microbiologists, and epidemiologists with essential information to guide proper action plans. The observed increase in susceptibility of gram-positive and gram-negative bacteria to studied antimicrobials is important; however, reduced sensitivity of MRSA, CoNS and Enterococcus species to gentamicin; and increased resistance of MRSA to linezolid and vancomycin is a serious threat and calls for effective antimicrobial stewardship programs.

Acknowledgements

The authors would like to thank the Microbiology staff at Altakhassusi, Arryan and Qassim HMG Hospitals in Riyadh and Qassim, Saudi Arabia, for identifying all the isolates and for their assistance with data collection. We would also like to thank the reviewers for very helpful and valuable comments and suggestions for improving the paper.

Abbreviations

AMS

Antimicrobial Stewardship

HAIs

Healthcare Associated Infections

MRSA

Methicillin-resistant Staphylococcus aureus

VRE

Vancomycin-resistant Enterococci

CoNS

Coagulase-negative Staphylococci

CLABSI

Central line associated bloodstream infection

CAUTI

Catheter-associated urinary tract infection

SSI

Surgical site infection

VAE

Ventilator-associated events

HMG

Habib Medical Group

AMR

Antimicrobial resistance

MANOVA

Multivariate analysis of variance

ICUs

Intensive care units

CLSI

Clinical and Laboratory Standards Institute

EUCAST

European Committee on Antimicrobial Susceptibility Testing

ATCC

American Type Culture Collection

Authors’ contributions

Conceptualization, SA, AA and ZA; methodology, SA, AA and AAO; formal analysis, SA, ZA, and AAR; data curation, SA; writing—original draft preparation, SA, AA and AAR; writing—review and editing, SA, AA, ZA, AAR, AJA, AMA, IB, IA, MT, NA, MA, AHA, FA, HA and AAO; supervision, SA, AA and AAO; project administration, SA and AA. All authors read and approved the final manuscript.

Funding

This research received no external funding.

Availability of data and materials

Data are available upon request, please contact author for data requests.

Declarations

Ethics approval and consent to participate

Ethical approval of the current study was obtained from Dr. Sulaiman Habib Institutional Review Board (RC20.10.95-2).

Consent for publication

All authors agreed to this publication.

Competing of interest

The authors have no conflicts of interest to declare.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Saad Alhumaid, Email: saalhumaid@moh.gov.sa.

Abbas Al Mutair, Email: abbas.almutair@almoosahospital.com.sa.

Zainab Al Alawi, Email: dr_z.alawi@hotmail.com.

Ahmad J. Alzahrani, Email: ajalzahrani@imamu.edu.sa

Mansour Tobaiqy, Email: mtobaiqy@uj.edu.sa.

Ahmed M. Alresasi, Email: aalresasi@moh.gov.sa

Ibrahim Bu-Shehab, Email: ibushehab@moh.gov.sa.

Issa Al-Hadary, Email: ialhadary@moh.gov.sa.

Naif Alhmeed, Email: nalhmeed@moh.gov.sa.

Mossa Alismail, Email: moalsmail@moh.gov.sa.

Ahmed H. Aldera, Email: ahaldera@moh.gov.sa

Fadhil AlHbabi, fadelhh2@googlemail.com.

Haifa Al-Shammari, Email: dr_hf_ksa@hotmail.com.

Ali A. Rabaan, Email: ali.rabaan@jhah.com

Awad Al-Omari, Email: research.center@drsulaimanalhabib.com.

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