Concomitant Versus Tailored Therapy Based on Antibiotic Resistance Profiles for Helicobacter pylori Eradication: A Systematic Review and Meta‐Analysis

ABSTRACT

Rising antibiotic resistance has challenged empirical regimens for Helicobacter pylori eradication. While concomitant therapy is widely used, its effectiveness is reduced in resistant settings. Tailored therapy, guided by antimicrobial susceptibility testing, may improve outcomes. We performed a meta‐analysis of randomized controlled trials (RCTs) comparing tailored versus concomitant therapy as a first‐line treatment. We searched PubMed, Google Scholar, Cochrane, and ClinicalTrials.gov through April 2025 for RCTs enrolling treatment‐naïve adults with confirmed H. pylori infection. The primary outcome was the eradication rate by intention‐to‐treat (ITT) and per‐protocol (PP) analyses, with adverse events as a secondary outcome. We included eight RCTs with 2524 patients (1332 tailored, 1192 concomitant). Tailored therapy achieved higher eradication rates than concomitant therapy in the ITT analysis (87.4% vs. 83.2%; RR = 1.05; 95% CI: 1.00–1.10; p = 0.05) and the PP analysis (92.6% vs. 89.1%; RR = 1.04; 95% CI: 1.00–1.07; p = 0.03). Furthermore, tailored therapy was associated with a significantly lower incidence of adverse events (35.6% vs. 45.6%; RR = 0.71; 95% CI: 0.58–0.86; p = 0.0007). In conclusion, tailored therapy provides modestly higher H. pylori eradication rates and significantly fewer adverse events compared to empirical concomitant therapy. These findings support using tailored therapy as the preferred first‐line option, particularly in regions with high antibiotic resistance and as access to rapid molecular testing expands.

1. Introduction

Helicobacter pylori ( H. pylori ) infection remains a major global health burden and is a leading cause of chronic gastritis, peptic ulcer disease, and gastric cancer [1, 2]. The World Health Organization classifies H. pylori as a Class I carcinogen, making its eradication a key strategy for gastric cancer prevention [1, 3]. Although global prevalence has declined, decades of widespread antibiotic use have driven a rise in antimicrobial resistance, making persistent infections increasingly difficult to treat [4, 5].

The central challenge in managing H. pylori today is this escalating resistance. Resistance to clarithromycin—a cornerstone of traditional therapy—now exceeds the 15% threshold in many regions, rendering standard triple therapy (proton pump inhibitor [PPI], clarithromycin, and amoxicillin) unacceptably ineffective [5]. Eradication rates with this regimen often fall below the 80% clinical benchmark, leading to persistent infection, higher healthcare costs, and the risk of further resistance [5, 6]. This has necessitated the development of more robust first‐line treatment strategies.

Two main approaches have emerged: optimized empirical therapy and personalized, SGT. Concomitant therapy, a potent non‐bismuth quadruple regimen, combines a PPI, amoxicillin, clarithromycin, and a nitroimidazole (e.g., metronidazole) for 10–14 days [5]. The rationale is to overcome single‐drug resistance, assuming that dual resistance to both clarithromycin, and metronidazole remains relatively uncommon in most populations [5].

By contrast, tailored therapy represents a personalized medicine approach [7]. It relies on antimicrobial susceptibility testing (AST)—via culture‐based methods or molecular techniques such as polymerase chain reaction (PCR)—to identify the resistance profile of an individual's H. pylori strain and guide antibiotic selection [8, 9]. This strategy aims to maximize eradication efficacy while promoting antimicrobial stewardship by avoiding ineffective agents [5, 9].

The choice between these strategies has created clinical uncertainty. Individual randomized controlled trials (RCTs) directly comparing tailored versus concomitant therapy have produced conflicting findings [10, 11]. Some demonstrate the superiority of tailored therapy, particularly in regions with high dual resistance, while others report no significant difference in efficacy [11, 12]. Existing systematic reviews have also failed to resolve this ambiguity [13, 14]. This highlights a critical knowledge gap. Accordingly, the objective of this systematic review and meta‐analysis is to synthesize evidence from all available RCTs to provide a robust pooled estimate of the relative efficacy and safety of tailored versus concomitant therapy for first‐line H. pylori eradication.

2. Methods

This systematic review and meta‐analysis was conducted in accordance with the Cochrane Handbook for Systematic Reviews of Interventions and reported following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) guidelines [15, 16]. The study protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO; Registration ID: CRD420251122432).

2.1. Selection and Eligibility Criteria

Only English‐language, peer‐reviewed studies involving human participants were included. Eligibility criteria were:

1. Adults (≥ 18 years) with confirmed H. pylori infection.

2. Diagnosis confirmed by at least one of the following: (a) PCR from gastric biopsy or dual‐priming oligonucleotide PCR; (b) positive rapid urease test (RUT), histology, culture, or stool antigen test; (c) positive ¹³C‐urea breath test or anti‐ H. pylori serology with positive RUT.

3. Treatment‐naïve patients (no prior eradication therapy).

4. Some studies restricted inclusion to patients undergoing upper gastrointestinal (GI) endoscopy for dyspepsia or cancer screening, in line with national/international guidelines.

Exclusion criteria were: (1) prior eradication therapy; (2) history of gastric/esophageal surgery; (3) recent use (within 4–8 weeks) of antibiotics, bismuth, PPI, H2 receptor antagonist (H2RA), or potassium‐competitive acid blockers; (4) allergy/intolerance to antibiotics or PPI used in study regimens (e.g., amoxicillin, clarithromycin, metronidazole, levofloxacin); (5) pregnancy or lactation; (6) severe systemic comorbidities (renal, hepatic, cardiac, psychiatric), GI bleeding, or malignancy; (7) inability to discontinue concurrent medications (e.g., steroids); (8) age < 18 years (or < 19 years in one trial); (9) refusal or inability to complete study requirements. Editorials, reviews, case reports, and studies lacking relevant outcomes were also excluded.

The primary outcome was eradication rate, assessed by intention‐to‐treat (ITT) and per protocol (PP) analyses. Secondary outcomes included therapy‐related adverse events and subgroup effects by geography, regimen type (bismuth quadruple therapy [BQT] vs. non‐BQT), clarithromycin resistance, and treatment duration.

2.2. Data Sources and Search Strategy

A comprehensive search of PubMed, Google Scholar, Cochrane, and Clinicaltrials.gov was conducted from inception to April 2025. The detailed strategy is provided in Table S1. Two reviewers independently screened studies, with discrepancies resolved by a third. Duplicates were removed using Rayyan [17]. Screening was performed in two stages: titles/abstracts followed by full‐text review against predefined eligibility criteria.

2.3. Data Extraction and Quality Assessment

Two reviewers independently extracted data (baseline characteristics, demographics, outcomes) into an Excel sheet. Disagreements were resolved by consensus with a third reviewer. Risk of bias was assessed using the Cochrane tool [18]. All authors reviewed extracted data collectively, and studies were categorized by overall risk of bias.

2.4. Statistical Analysis

Analyses were conducted using RevMan 5.4.1 (Cochrane, Copenhagen, 2020). Outcomes reported in ≥ 3 studies were pooled. A random‐effects model using the DerSimonian and Laird (DL) estimator was applied to account for between‐study heterogeneity, as implemented by default in RevMan. The DL method remains widely recommended in the Cochrane Handbook for Systematic Reviews of Interventions and Borenstein et al.'s Introduction to Meta‐Analysis for datasets with moderate heterogeneity and adequate study numbers, where its performance is comparable to more complex estimators such as restricted maximum likelihood (REML). Risk ratios (RRs) with 95% confidence intervals (CIs) were generated and presented via forest plots. Funnel plots were used to assess publication bias. Heterogeneity was evaluated with I ², with ≤ 50% considered acceptable. Subgroup differences were analyzed with χ² tests. Predefined subgroup analyses included: (1) clarithromycin‐only vs. multi‐antibiotic AST; (2) tailored therapy including BQT vs. non‐BQT; (3) therapy duration (≤ 10 vs. > 10 days); and (4) geography (Asia vs. Europe).

3. Results

3.1. Study Selection

A total of 438 records were identified. After removing 46 duplicates, 392 articles were screened. Title/abstract screening excluded 349, leaving 43 for full‐text review. Thirty‐five were excluded (wrong population/design/outcome). Eight RCTs met all eligibility criteria and were included [6, 10, 11, 12, 19, 20, 21, 22]. The PRISMA flowchart (Figure 1) illustrates the selection process.

FIGURE 1.

PRISMA flow diagram. The diagram illustrates the study selection process, detailing the number of records identified, screened, assessed for eligibility, and ultimately included in the meta‐analysis.

3.2. Study and Patient Characteristics

Eight RCTs including 2524 patients were analyzed: 1332 received tailored therapy and 1192 received concomitant therapy. Six trials were conducted in East Asia and two in Europe. Tailored therapy was primarily guided by clarithromycin resistance testing; two trials also tested metronidazole and levofloxacin susceptibility. Clarithromycin‐sensitive strains were treated with triple therapy, while resistant strains received alternative regimens. Concomitant arms consistently use a 4‐drug regimen (PPI, amoxicillin, clarithromycin, metronidazole), with one trial substituting tinidazole. Baseline characteristics are shown in Table 1.

TABLE 1.

Baseline study and patient characteristics table.

First authors	Year	Country	Study design	Tailored therapy determinant	Patients (tailored/concomitant)	Antibiotics tested	Eradication regime		Eradication rate (ITT/PP)	Eradication (ITT/PP)	Eradication confirmation methods
							Tailored therapy	Concomitant therapy	Tailored therapy	Concomitant therapy
Zhou	2015	China	RCT	CLA sensitivity (E test)	318/350	CLA	CLA‐s: RAB 10 mg BID + AMX 1000 mg BID + CLA 500 mg BID, 10 days CLA‐s (het EM/PM): EPZ 20 mg BID + AMX 1000 mg BID + CLA 500 mg BID, 10 days CLA‐r (hom EM): RAB 10 mg BID + AMX 1000 mg TID + TDZ 500 mg TID, 10 days CLA‐r (het EM/PM): EPZ 20 mg BID + AMX 1000 mg TID + TDZ 500 mg TID, 10 days	EPZ 20 mg BID + AMX 1000 mg BID + CLA 500 mg BID + TDZ 500 mg BID, 10 days	88.7%/93.3%	78.3%/87.4%	13C‐urea breath test
Ong [20]	2019	South Korea	RCT	DPO‐based multiplex PCR	201/196	CLA	CLA‐s: LAN 30 mg BID + AMX 1 g BID + CLA 500 mg BID, 14 days CLA‐r: LAN 30 mg BID + AMX 1 g BID + MET 500 mg BID, 14 days	LAN 30 mg BID + AMX 1 g BID + CLA 500 mg BID + MET 500 mg BID, 14 days	81.6%/86.5%	86.2%/90.2%	13C‐urea breath test
Choi	2021	South Korea	RCT	DPO‐PCR	110/107	CLA	CLA‐s: LAN 30 mg BID + CLA 500 mg BID + AMX 1000 mg BID, 14 days CLA‐r: LAN 30 mg BID + MET 500 mg BID + BIS 300 mg QID + TET 500 mg QID, 10 days	LAN 30 mg BID + AMX 1 g BID + CLA 500 mg BID + MET 500 mg BID, 10 days	82.73%/90.10%	82.24%/91.58%	13C‐urea breath test
Kim	2022	South Korea	RCT	DPO‐PCR	145/145	CLA + MET	CLA‐s: RAB 20 mg BID + AMX 1 g BID + CLA 500 mg BID, 14 CLA‐r: RAB 20 mg BID + MET 500 mg TID + BIS 120 mg QID + TET 500 mg QID, 14 days	RAB 20 mg BID + AMX 1 g BID + CLA 500 mg BID + MET 500 mg BID, 14 days	85.5%/94.6%	82.8%/88.6%	13C‐urea breath test
Perkovic	2021	Croatia	RCT	Antimicrobial susceptibility testing (E‐test)	40/40	CLA + MET + LEV	EPZ 40 mg BID + two antimicrobial agents (AMX, CLA, TET, LEV, MET) for 14 days	EPZ 40 mg BID + AMX 1 g BID + CLA 500 mg BID + MET 500 mg BID, 14 days	92.5%/100.0%	70.0%/87.5%	ELISA‐based stool H. pylori antigen test
Lee	2024	South Korea	RCT	Culture‐based susceptibility‐guided	234/78	CLA	CLA‐s & MET‐s: LAN 30 mg BID + AMX 1000 mg BID + CLA 500 mg BID, 10 days CLA‐s & MET‐r: LAN 30 mg BID + AMX 1000 mg BID + CLA 500 mg BID, 10 days CLA‐r & MET‐s: LAN 30 mg BID + AMX 1000 mg BID + MET 500 mg BID, 10 days CLA‐r & MET‐r: LAN 30 mg BID + BIS 300 mg QID + TET 500 mg QID + MET 500 mg TID, 10 days	LAN 30 mg BID + AMX 1000 mg BID + CLA 500 mg BID + MET 500 mg BID, 10 days	84.2%/92.9%	83.3%/91.5%	13C‐urea breath test
Amiot [21]	2024	France	RCT	PCR	120/121	CLA	CLA‐s: EPZ 40 mg BID + AMX 1000 mg BID + CLA 500 mg BID, 14 days CLA‐r: EPZ 40 mg BID + AMX 1000 mg BID + LEV 500 mg BID, 14 days	EPZ 40 mg BID + AMX 1000 mg BID + CLA 500 mg BID + MET 500 mg BID, 14 days	99.2%/98.8%	95.9%/96.5%	13C‐urea breath test
Cho [22]	2025	South Korea	RCT	DPO‐PCR	164/155	CLA	CLA‐s: LAN 30 mg BID + CLA 500 mg BID + AMX 1000 mg BID, 10 days CLA‐r: LAN 30 mg BID + BIS 300 mg TID + OXYTETRACYCLINE 500 mg TID + MET 500 mg QID, 10 days	LAN 30 mg BID + CLA 500 mg BID + AMX 1000 mg BID + MET 500 mg BID, 10 days	92.0% (mITT)/92.62%	85.03% (mITT)/85.21%	13C‐urea breath test

Abbreviations: AMO, amoxicillin (also noted as AMX); APM, regimen including amoxicillin, rabeprazole, metronidazole, and clarithromycin; BID, twice daily; BIS, bismuth; BQT, bismuth quadruple therapy; CLA, clarithromycin; CLA‐r, clarithromycin resistant; CLA‐s, clarithromycin sensitive; CoT, concomitant therapy (also referred to as CT); DPO‐PCR, dual priming oligonucleotide polymerase chain reaction; ELISA, enzyme‐linked immunosorbent assay; EPZ, esomeprazole; ET, empirical therapy; ITT, intention‐to‐treat; LAN, lansoprazole; LEV, levofloxacin; LEV‐r, levofloxacin resistant; LEV‐s, levofloxacin sensitive; MET, metronidazole; MET‐r, metronidazole resistant; MET‐s, metronidazole sensitive; mITT, modified intention‐to‐treat; PAC, lansoprazole + amoxicillin + clarithromycin; PAM, lansoprazole + amoxicillin + metronidazole; PBTM, lansoprazole + bismuth potassium citrate + tetracycline + metronidazole; PP, per‐protocol; PPI, proton‐pump inhibitor; QID, four times daily; RAB, rabeprazole; RCT, randomized controlled trial; STT, standard triple therapy; TDZ, tinidazole; TET, tetracycline; TID, three times daily; TT, tailored therapy.

3.3. Quality Assessment

Four trials were at low risk of bias, three at high risk, and one raised some concerns (Figure S1A,B). Key risks were related to randomization (D1), deviations from intended interventions (D2), and selective reporting (D5).

Kim et al., Perkovic et al., and Choi et al. showed a high risk of bias in randomization due to inadequate allocation concealment descriptions, which could overestimate treatment effects through group imbalances. Additionally, concerns regarding selective reporting in Perkovic et al. and Choi et al. may introduce outcome reporting bias if favorable results are preferentially published.

3.4. Outcomes

3.4.1. ITT Eradication Rates

Meta‐analysis of 8 trials (2502 patients: 1318 tailored; 1184 concomitant) showed a borderline significant difference favoring tailored therapy (RR = 1.05; 95% CI: 1.00–1.10; p = 0.05; I ² = 58%) (Figure 2). Subgroup analyses showed no effect modification by geography (p = 0.55), BQT vs. non‐BQT (p = 0.70), clarithromycin resistance (p = 0.53), or therapy duration (p = 0.30) (Figure S2A–D). Sensitivity analysis excluding Zhou et al. reduced heterogeneity and attenuated effect size (RR = 1.03; 95% CI: 0.99–1.08; p = 0.18; I ² = 42%) (Figure S3).

FIGURE 2.

Forest plot of intention‐to‐treat (ITT) eradication rates. This plot compares the eradication rates of tailored versus concomitant therapy. Each study is represented by a risk ratio (RR) and 95% confidence interval (CI). The diamond at the bottom represents the pooled effect size, which favors tailored therapy (RR = 1.05; 95% CI: 1.00–1.10; p = 0.05).

3.4.2. PP Eradication Rates

Meta‐analysis of 8 trials (2220 patients: 1191 tailored; 1029 concomitant) demonstrated significantly higher eradication rates with tailored therapy (RR = 1.04; 95% CI: 1.00–1.07; p = 0.03; I ² = 36%) (Figure 3). No subgroup effect modification was found for geography (p = 0.63), BQT vs. non‐BQT (p = 0.48), clarithromycin resistance (p = 0.67), or duration (p = 0.47) (Figure S4A–D). Sensitivity analysis excluding Ong et al. lowered heterogeneity while maintaining effect size (RR = 1.05; 95% CI: 1.02–1.08; p = 0.002; I ² = 12%) (Figure S5), confirming robustness.

FIGURE 3.

Forest plot of per‐protocol (PP) eradication rates. This plot compares the eradication rates of tailored versus concomitant therapy in patients who completed the treatment as assigned. The pooled analysis demonstrates a statistically significant benefit for tailored therapy (RR = 1.04; 95% CI: 1.00–1.07; p = 0.03).

3.4.3. Adverse Events

Meta‐analysis of 8 trials (2439 patients: 1286 tailored; 1153 concomitant) showed significantly fewer adverse events with tailored therapy (RR = 0.71; 95% CI: 0.58–0.86; p = 0.0007; I ² = 75%) (Figure 4). Sensitivity analysis excluding Ong et al. and Kim et al. reduced heterogeneity, with a consistent effect size (RR = 0.63; 95% CI: 0.55–0.74; p < 0.00001; I ² = 26%) (Figure S6).

FIGURE 4.

Forest plot of adverse events. This plot compares the incidence of adverse events between the tailored and concomitant therapy groups. The pooled risk ratio indicates that tailored therapy was associated with significantly fewer adverse events (RR = 0.71; 95% CI: 0.58–0.86; p = 0.0007).

3.5. Publication Bias and Outlier Analysis

Visual inspection of funnel plots for all primary outcomes revealed no substantial asymmetry, suggesting a low risk of publication bias. The plots for adverse events (Figure S7), ITT (Figure S8), and PP (Figure S9) analyses all demonstrated good to excellent symmetry. Formal statistical tests for funnel plot asymmetry (such as Egger's test) were not performed, as they are underpowered with fewer than 10 studies. The robustness of the findings was further supported by leave‐one‐out sensitivity analyses.

4. Discussion

This meta‐analysis of eight RCTs (N = 2534) demonstrated that tailored therapy consistently achieved higher eradication rates than concomitant therapy, showing a borderline significant difference favoring tailored therapy in ITT analysis (87.4% vs. 83.2%; RR = 1.05; 95% CI: 1.00–1.10; p = 0.054) that approached but narrowly missed statistical significance. Importantly, the PP analysis remained statistically significant (RR 1.04; 95% CI 1.00–1.07; p = 0.03). Subgroup analyses by geography, treatment duration, and regimen type showed consistent benefit with tailored therapy, and adverse events were significantly fewer (RR 0.71; 95% CI 0.58–0.86; p = 0.0007).

Therapy duration remains a key determinant of eradication [23]. In this analysis, tailored therapy outperformed concomitant therapy in both 10‐day and 14‐day regimens. A 14‐day concomitant regimen is widely considered the standard of care and achieves higher eradication than shorter courses [24, 25, 26]. However, extending therapy increases the risk of adverse events [6, 27, 28]. In contrast, tailored therapy minimizes unnecessary antibiotic exposure, reducing toxicity, and evidence suggests even shorter courses (e.g., 7‐day regimens) may suffice [27].

Antibiotic resistance underpins these findings. Clarithromycin resistance markedly lowers eradication with triple or concomitant therapy, while metronidazole resistance has a variable effect, as higher doses or combinations may overcome it. Dual resistance severely compromises empirical regimens. Tailored therapy, guided by AST, avoids ineffective regimens and reduces the risk of further resistance. Trials show that clarithromycin‐based SGT achieves significantly higher eradication than concomitant or triple plus BQT regimens, particularly in high‐resistance settings [19]. Rising resistance to clarithromycin and metronidazole remains a major cause of treatment failure [29], and systematic reviews confirm that tailored therapy improves outcomes even in dual resistance [30]. These results reinforce the role of tailored therapy, particularly in regions with high resistance [6, 27].

Resistance patterns vary widely across regions. South Asia shows some of the highest resistance rates globally, with clarithromycin averaging 27%, similar to the Eastern Mediterranean and Western Pacific, but higher than Southeast Asia (~10%) and Europe (~18%) [31]. Metronidazole resistance is especially high in South Asia (69%), compared with 32% in Europe and 23% in the US [31]. Resistance to amoxicillin (23%), tetracycline (16%), and levofloxacin (34%) is also elevated in Asia. These disparities reflect local antibiotic use and stewardship practices, limiting the effectiveness of empirical regimens and underscoring the need for BQT or tailored therapy in high‐resistance regions.

Regarding safety, fewer adverse events with tailored therapy are consistent with prior evidence. Compliance is generally better, and although discontinuation rates are similar, overall event incidence is lower [6]. A meta‐analysis of 16 RCTs showed that tailored therapy improved tolerability and adherence compared with empirical therapy [14]. In a larger dataset of 14 600 patients, tailored therapy reduced adverse events (15% vs. 31%), improving adherence and reducing unnecessary antibiotic toxicity [13].

Despite its advantages, tailored therapy faces practical barriers that contribute to the heterogeneity observed in our analysis. Culture‐based antimicrobial susceptibility testing is invasive, costly, and time‐intensive, while molecular assays, though faster and less invasive, lack universal availability. These implementation challenges are reflected in the moderate heterogeneity of our ITT analysis (I ² = 58%), which stems from several sources: regional resistance variations (evidenced by Perkovic et al.'s European results [92.5% vs. 70.0%] versus smaller efficacy gaps in East Asian studies like Lee et al. [84.2% vs. 83.3%]), diagnostic method differences (¹³C‐urea breath test vs. stool antigen test), and regimen variability for resistant strains (bismuth quadruple vs. levofloxacin‐based therapies). The influence of Zhou et al. (whose exclusion lowered I ² to 42%) and outlier results from Ong et al. further highlight this clinical diversity.

To overcome these barriers wider access to rapid molecular diagnostics, including noninvasive stool‐based assays and affordable standardized tests, is needed. Although higher upfront costs challenge resource‐limited settings, long‐term cost‐effectiveness may favor tailored therapy through reduced retreatment. Ultimately, large multicenter RCTs are required to standardize protocols and confirm generalizability across diverse clinical settings.

Although the observed difference in eradication rates was statistically significant in the PP analysis and borderline in the ITT analysis, the absolute risk difference was modest (about 4%). The clinical significance of this improvement should be interpreted alongside local factors such as antibiotic resistance prevalence, availability and costs of antimicrobial susceptibility testing, and turnaround times. In regions with high clarithromycin or dual resistance, where empirical concomitant therapy failure rates are elevated [32], this 4% absolute benefit may be meaningful and justify the use of tailored therapy. A 2025 cost‐effectiveness study suggests that the higher initial cost of tailored therapy may be economically justified in such settings by improved outcomes and reduced retreatment needs [33]. In contrast, in settings with low resistance, the marginal gain may not outweigh the additional cost and logistical challenges associated with susceptibility testing [34].

This review has several strengths. The large sample size enabled robust subgroup analyses, and exclusive inclusion of RCTs enhances validity. Safety outcomes provide additional clinical relevance. To our knowledge, this is the most up‐to‐date meta‐analysis directly comparing tailored and concomitant therapy.

However, limitations should be acknowledged. Heterogeneity in resistance patterns, regimens, and populations may have influenced pooled results. Important effect modifiers such as smoking, comorbidities, and alcohol use were inconsistently reported. Access to SGT is limited in many regions, restricting generalizability. Finally, while statistically significant, the absolute effect size was modest and should be interpreted in the context of local resistance and safety considerations.

5. Conclusion

This meta‐analysis shows that SGT modestly improves eradication and reduces adverse events compared with empirical concomitant therapy. Where feasible, resistance testing should inform first‐line treatment, supported by investment in rapid molecular diagnostics and cost‐effectiveness analyses for broader adoption. Large multicenter RCTs are warranted to refine SGT protocols, optimize treatment duration across resistance settings, and confirm long‐term clinical and economic outcomes.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1A: Traffic light plot for quality assessment using ROB‐2 tool.

Figure S1B: Summary plot for quality assessment using ROB‐2 tool.

Figure S2A: Forest plot comparing risk ratios (RRs) for ITT between Asian and European populations.

Figure S2B: Forest plot comparing RRs for ITT between Bismuth and Non‐Bismuth arms.

Figure S2C: Forest plot comparing RRs for ITT between Clarithromycin‐only vs. Clarithromycin + other resistance groups.

Figure S2D: Forest plot comparing RRs for ITT between therapy duration ≤ 10 days and > 10 days.

Figure S3: Forest plot of ITT sensitivity analysis excluding Zhou et al. (2015), comparing tailored versus concomitant groups.

Figure S4A: Forest plot comparing RRs for PP between Asian and European populations.

Figure S4B: Forest plot comparing RRs for PP between Bismuth and Non‐Bismuth arms.

Figure S4C: Forest plot comparing RRs for PP between Clarithromycin‐only vs. Clarithromycin + other resistance groups.

Figure S4D: Forest plot comparing RRs for PP between therapy duration ≤ 10 days and > 10 days.

Figure S5: Forest plot of PP sensitivity analysis excluding Ong et al. (2019), comparing tailored versus concomitant groups.

Figure S6: Forest plot of adverse events sensitivity analysis excluding Ong et al. (2019) and Kim et al. (2022), comparing tailored versus concomitant groups.

Figure S7: Funnel plot for adverse events analysis.

Figure S8: Funnel plot for ITT eradication analysis.

Figure S9: Funnel plot for PP eradication analysis.

Khalid A., Tabish S., Burhan M., et al., “Concomitant Versus Tailored Therapy Based on Antibiotic Resistance Profiles for Helicobacter pylori Eradication: A Systematic Review and Meta‐Analysis,” JGH Open 9, no. 12 (2025): e70307, 10.1002/jgh3.70307.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.