Drug-induced intestinal obstruction: insights from the FDA Adverse Event Reporting System

Abstract

Background:

Intestinal obstruction is a severe abdominal condition that can be triggered by various medications; however, the drugs most strongly and frequently linked to this adverse event (AE) remain insufficiently defined in the current medical literature.

Objectives:

This study sought to systematically identify and evaluate medications most strongly and consistently associated with intestinal obstruction by analyzing real-world evidence from a large pharmacovigilance database.

Design:

We conducted a retrospective, observational pharmacovigilance study.

Methods:

Reports submitted to the U.S. FDA Adverse Event Reporting System (FAERS) between January 2004 and June 2024 were analyzed. Disproportionality analyses using the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Empirical Bayesian Geometric Mean, and Information Component were conducted to identify significant safety signals associated with drug-induced intestinal obstruction.

Results:

Among 21,433,114 AE reports, 60,814 (0.28%) involved intestinal obstruction. Humira (10,356 cases) was the most frequently reported drug, followed by Remicade (2223 cases), Avastin (1580 cases), Vedolizumab (1385 cases), Clozaril (1229 cases), and Accutane (1088 cases). Disproportionality analysis revealed that the top five drugs with the highest ROR and PRR were Accutane, Teduglutide, Lonsurf, Avastin, and Lynparza. Among the top 50 drugs, 47 lacked clear labeling in their drug packaging.

Conclusion:

Our findings, derived from FAERS signal detection, identify drug-event pairs that warrant further clinical evaluation. These results should not be interpreted as evidence of causality. The high volume of reports associated with certain drugs may reflect usage patterns, underlying disease conditions, or reporting behaviors. Caution should be exercised in clinical translation.

Plain language summary

Some drugs may cause intestinal blockage: a large-scale study based on safety report data from the US FDA

Intestinal obstruction is a serious emergency, meaning that the contents of the intestine cannot pass through normally. This may lead to severe pain, infection and even more dangerous complications. Apart from well-known causes such as tumors or hernias, certain medications may also lead to intestinal obstruction, but this point is often overlooked. At present, information on which drugs are likely to cause this problem and what the risks are is very limited, mainly coming from scattered case reports. Our research aims to systematically investigate drugs associated with the risk of intestinal obstruction. We utilized the Adverse Event Reporting System (FAERS) of the U.S. Food and Drug Administration (FDA), a large database that collects reports on drug side effects worldwide. By analyzing these real-world report data, we can identify which drugs are reported abnormally frequently along with intestinal obstruction, thereby suggesting a possible association between them. The results of this study will help doctors and patients have a clearer understanding of the potential risks of intestinal blockage that drugs may bring. By identifying high-risk drugs, clinicians can be more cautious when prescribing and formulating treatment plans for patients, especially those at risk of intestinal problems, thereby helping to prevent the occurrence of this serious complication and ensuring medication safety.

Keywords

disproportionality analysis drug-induced risk FAERS database intestinal obstruction

Introduction

Intestinal obstruction, a blockage that prevents the normal flow of intestinal contents, is a common and potentially life-threatening acute abdominal condition.¹ It is broadly classified as mechanical or functional and can lead to serious complications, including ischemia, perforation, and systemic infection, significantly impacting patients’ quality of life.^2–7 In clinical practice, drugs are a significant, yet often underrecognized, contributing factor. The existing literature on drug-induced intestinal obstruction (DIIO) is limited, primarily consisting of case reports or small-scale studies, which lack the statistical power for systematic insights. While some reviews discuss drug effects on intestinal function, they offer limited analysis of DIIO-specific mechanisms and risk factors.

Clinical experience and preliminary evidence suggest several mechanisms for DIIO, including (1) inhibition of intestinal motility (e.g., by opioids and anticholinergics); (2) induction of intestinal stenosis through inflammation or fibrosis; and (3) alteration of intestinal secretions, leading to dried or viscous content.^8–12 However, clinical trials, with their strict inclusion criteria and limited sample sizes, are inadequate for fully elucidating DIIO’s real-world complexity.

Therefore, a critical knowledge gap exists in understanding the scope and nature of DIIO. To address this, large-scale, systematic pharmacovigilance studies are essential. The FDA Adverse Event Reporting System (FAERS), a leading global database of post-market adverse events (AEs), provides a valuable resource for such investigation. Mining the FAERS database allows for the identification of signals and patterns of DIIO in a real-world context, which is crucial for drug safety surveillance and clinical practice optimization.¹³

This study aims to bridge this gap by analyzing the FAERS database to identify drugs most frequently and strongly associated with intestinal obstruction, thereby aiding in the recognition and classification of DIIO risks to inform clinical decision-making.

Methods

This disproportionality analysis was conducted and reported in accordance with the recommendations of the REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS PV) statement.

Study design and reporting guidelines

This observational, retrospective pharmacovigilance study analyzed data from the FAERS database. The reporting of the study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.¹⁴ In addition, the study followed the READUS-PV guidelines.¹⁵ The completed STROBE and READUS-PV checklists are provided as Supplemental Files 1 and 2, respectively.

Data sources

This retrospective pharmacovigilance study utilizes the FAERS database (https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPDQDE-FAERS.html). FAERS supports post-market surveillance for drugs and biologics, capturing AEs and medication errors reported to the FDA. Data in FAERS are voluntarily submitted by healthcare professionals and consumers. FAERS provides valuable information, including demographics, drug details, and clinical outcomes.¹⁶

This retrospective pharmacovigilance analysis was conducted using the FAERS database. FAERS compiles reports on adverse drug events (ADEs), enabling signal detection and the quantification of drug-ADE associations.^17,18 Updated quarterly, the FAERS database includes seven datasets: Demographics (DEMO), Drug Information (DRUG), Adverse Drug Reactions (REAC), Patient Outcomes (OUCT), Report Source (RPSR), Therapy Dates (THER), and Indications (INDI). Duplicate reports inevitably occur in FAERS due to the nature of data updates. The retrieval period was set from January 1, 2004, to June 30, 2024. FDA-recommended criteria were applied to remove duplicates and enhance result reliability.^19–21 According to the FDA’s recommended deduplication reporting procedure, select the PRIMARY\_ID, CASE\_ID, and FDA\_DT fields from the DEMO table, and sort them in the order of CASE\_ID, FDA\_DT, and PRIMARY\_ID. For records with the same CASE\_ID, retain the one with the largest FDA\_DT value. In addition, for records with identical CASE\_ID and FDA\_DT, retain the one with the largest PRIMARY\_ID value.

After preprocessing, 17,956,653 DEMO reports, 65,083,962 DRUG cases, and 52,454,964 REAC records were obtained. This yielded 60,814 drug-related AE signals for intestinal obstruction (Figure 1).

Study design (identification of target AE reports)

The FAERS database uses the Medical Dictionary for Regulatory Activities (MedDRA) to code ADR (Adverse Drug Reaction) information into standardized terms, called Preferred Terms (PTs). Standardized MedDRA Queries (SMQs) are tools within MedDRA, comprising PTs that represent similar medical conditions. They are designed to retrieve cases of interest from the MedDRA-coded database and enhance ADR signal detection and assessment. SMQs offer two search types to identify target cases: wide and narrow searches. Wide searches include all PTs potentially representing the condition, while narrow searches focus on PTs closely related to it.

To ensure specificity in identifying target AE reports, this study referenced MedDRA version 26.0 and used only PTs from the “Intestinal Obstruction (SMQ)” narrow search (see Supplemental Table 1 for PTs for the identification of intestinal obstruction AE cases). Reports were classified as target cases if any PTs from Supplemental Table 1 appeared in the “Patient Outcome” field.

Data mining algorithms and data analysis

Disproportionality analysis is a data mining method for quantitatively detecting ADR signals in large pharmacovigilance databases. Using the classic 2 × 2 contingency table (Table 1), disproportionality analysis assesses the difference between the frequencies of a target drug and AE, establishing their statistical association.

Table 1.

Two-by-two contingency table for disproportionality analyses.

	Target AEs	Other AEs	Total
Drugs	A	b	a + b
Other drugs	C	d	c + d
Total	a + c	b + d	a + b + c + d

a, Number of reports containing both the target drug and target adverse drug reaction; AEs, adverse events; b, number of reports containing other adverse drug reactions of the target drug; c, number of reports containing the target adverse drug reaction of other drugs; d, number of reports containing other drugs and other adverse drug reactions.

This study utilized four approaches for ADE signal mining, namely the Reporting Odds Ratio (ROR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-Item Gamma Poisson Shrinker (MGPS) methods. The ROR method originated from the Lareb laboratory of the Netherlands Pharmacovigilance Centre, characterized by low bias and high sensitivity, and it helps reduce bias in infrequently reported events. Compared with ROR, the PRR stands out for its higher specificity. Currently, the BCPNN method represents a mature signal detection technology that has been adopted globally. The BCPNN can identify early signals even with sparse or incomplete data, and its detection results become more robust as report volume grows; nevertheless, it suffers from computational complexity and limited transparency. Moreover, the MGPS approach excels in identifying signals associated with rare AEs. Although there is no gold standard for signal detection methods, each has its own characteristics, with distinct advantages and limitations regarding applicability and feasibility in databases.^22,23 A signal was defined by meeting the following criteria:

Lower limit of 95% Confidence Interval (CI) >1

Proportional Reporting Ratio (PRR) ⩾2

Chi-squared (χ²) ⩾4

Information component (IC025) >0

Empirical Bayesian Geometric Mean (EBGM) >1

EBGM 0.5 (EBGM05) >2

Table 1 displays the 2 × 2 table for disproportionality measurement, and Table 2 outlines the calculation formulas and thresholds. Higher values of the ROR and Bayesian Confidence Propagation Neural Network (BCPNN) indicate a stronger association between the target drug and ADE.^11,24 R version 4.1.1 was used in this study for data collection, processing, and analysis.

Table 2.

Four major algorithms used for signal detection.

Algorithms	Equation	Criteria
ROR	ROR = ad/b/c	Lower limit of 95% CI > 1, N ⩾ 3
	95% CI = e^{ln(ROR) ± 1.96(1/a + 1/b + 1/c + 1/d)^0.5}
PRR	PRR = a(c + d)/c/(a + b)	PRR ⩾ 2, χ² ⩾ 4, N ⩾ 3
	χ² = [(ad − bc)^2](a + b + c + d)/[(a + b)(c + d)(a + c)(b + d)]
BCPNN	IC = log₂a(a + b + c + d)(a + c)(a + b)	IC025 > 0
	95% CI = E(IC) ± 2V(IC)^0.5
MGPS	EBGM = a(a + b + c + d)/(a + c)/(a + b)	EBGM05 > 2
	95% CI = e^{ln(EBGM) ± 1.96(1/a + 1/b + 1/c + 1/d)^0.5}

95% CI, 95% confidence interval; a, number of reports containing both the target drug and target adverse drug reaction; b, number of reports containing other adverse drug reaction of the target drug; c, number of reports containing the target adverse drug reaction of other drugs; d, number of reports containing other drugs and other adverse drug reactions; EBGM, empirical Bayesian geometric mean; EBGM05, the lower limit of 95% CI of EBGM; E(IC), the IC expectations; IC, information component; IC025, the lower limit of 95% CI of the IC; N, the number of reports; V(IC), the variance of IC; χ², Chi-squared.

Results

Basic information on AE reports

From January 1, 2004, to June 30, 2024, a total of 21,433,114 AE reports were recorded in the FAERS database. By using the PTs from Supplemental Table 1, 60,814 reports were identified as related to the AE of interest. The annual distribution of AE reports is shown in Figure 2, with 2019 recording the highest number of reports (8.2%).

Healthcare professionals were the primary source of reports, contributing 58.8% of the total (Table 3). The United States was the top reporting country, with 26,514 AE reports (43.6%) submitted (Table 3). Regarding patient demographics, female patients with DIIO accounted for 50.0%, outnumbering male patients (41.6%; Table 3). The age group 18–64.9 years had the highest number of patients (Table 3). Regarding patient outcomes, 58.9% of patients had prolonged hospitalization, and 8187 patients (13.5%) were reported to have died from DIIO (Table 3).

Table 3.

Clinical characteristics of reported drug-induced intestinal obstruction.

Characteristics	Reports	n (%)
Gender, n (%)
F	30,428	50.03%
M	25,311	41.62%
Unknown	5075	8.35%
Age, years
<18	1873	3.08%
18–64.9	23,255	38.24%
65–85	14,556	23.94%
>85	1174	1.93%
Unknown	19,956	32.81%
Weight
<50 kg	2855	4.69%
50–100 kg	15,796	25.97%
>100 kg	1740	2.86%
Unknown	40,423	66.47%
Reported person
CN	22,488	36.98%
Health professionals	35,738	58.77%
Unknown	2588	4.26%
Reported countries (top 5)
US	26,514	43.60%
CA	6214	10.22%
JP	4318	7.10%
FR	2004	3.30%
DE	1881	3.09%

CA, China; CN, consumers; DE, Germany; F, female; FR, France; JP, Japan; M, male; US, United States.

Identification of potential culprits—List of drugs

Figure 3 summarizes the top 50 drugs associated with intestinal obstruction based on the frequency of AE reports. Table 4 shows the incidence and distribution of the top 15 DIIO (see Supplemental Table 2 for the statistics and distribution of the top 50 DIIO). Humira (10,356 cases) was the most frequently reported, followed by Remicade (2223 cases), Avastin (1580 cases), Vedolizumab (1385 cases), Clozaril (1229 cases), and Accutane (1088 cases). Among these 50 drugs, 23 were listed in their package inserts as potentially causing diarrhea, which may alleviate certain types of intestinal obstruction, though these drugs are not standard treatments for the condition. Only Creon, Enbrel, and OxyContin explicitly listed the association of intestinal obstruction in their package inserts, while the remaining 47 drugs did not mention intestinal obstruction as an adverse effect. In addition, 33 drugs were listed in their package inserts as potentially causing symptoms such as abdominal pain, bloating, nausea, and vomiting, which may suggest intestinal obstruction, while 17 drugs did not include any of these symptoms in their labeling.

Table 4.

Statistics and distribution of the top 15 drug-induced intestinal obstructions.

Drugs	n	ROR (95% CI)	PRR (χ²)	EBGM (EBGM05)	IC (IC025)
Humira	10,356	5.28 (5.17–5.39)*	5.25 (29,943.67)*	4.57 (4.49)*	2.19 (0.53)*
Remicade	2223	2.57 (2.47–2.69)*	2.57 (2058.63)*	2.51 (2.43)*	1.33 (−0.34)
Avastin	1580	6.77 (6.44–7.12)*	6.73 (7521.35)*	6.58 (6.31)*	2.72 (1.05)*
Vedolizumab	1385	5.8 (5.5–6.12)*	5.76 (5342.81)*	5.66 (5.41)*	2.5 (0.84)*
Clozaril	1229	4.38 (4.14–4.63)*	4.36 (3124.32)*	4.29 (4.1)*	2.1 (0.44)*
Accutane	1088	17.39 (16.37–18.48)*	17.06 (16,185.59)*	16.78 (15.95)*	4.07 (2.4)*
Revlimid	995	1.19 (1.12–1.27)*	1.19 (29.47)	1.19 (1.13)	0.25 (−1.42)
Capecitabine	893	4.77 (4.47–5.1)*	4.75 (2609.57)*	4.7 (4.44)*	2.23 (0.57)*
Duodopa	675	2.69 (2.49–2.9)*	2.68 (706.04)*	2.67 (2.5)*	1.41 (−0.25)
Teduglutide	561	10.88 (10.01–11.83)*	10.76 (4926.56)*	10.67 (9.95)*	3.42 (1.75)*
Stelara	558	3.01 (2.77–3.27)*	3 (738.93)*	2.98 (2.78)*	1.58 (−0.09)
Dianeal	553	1.97 (1.82–2.15)*	1.97 (263.06)	1.96 (1.83)	0.97 (−0.69)
Niraparib	515	3.18 (2.91–3.47)*	3.17 (760.11)*	3.15 (2.93)*	1.66 (−0.01)
Skyrizi	508	3.9 (3.57–4.26)*	3.89 (1081.4)*	3.86 (3.59)*	1.95 (0.28)*
Carboplatin	470	2.98 (2.72–3.26)*	2.97 (609.63)*	2.95 (2.74)*	1.56 (−0.1)

An asterisk (*) indicates the statistically significant signal of the algorithm. Judging whether it is a positive signal according to the ROR algorithm (lower limit of 95% CI > 1, a ⩾ 3); Judging whether it is a positive signal according to PRR algorithm (PRR ⩾ 2, χ² ⩾ 4, a ⩾ 3); Judging whether it is a positive signal according to MGPS algorithm (EBGM05 > 2); According to BCPNN algorithm, whether it is a positive signal (IC025 > 0) is judged.

95% CI, 95% confidence interval; BCPNN, Bayesian Confidence Propagation Neural Network; EBGM, empirical Bayesian geometric mean; EBGM05, information component EBGM; IC, information component; IC025, the lower limit of 95% CI of the IC; MGPS, multi-item gamma poisson shrinker; PRR, proportional reporting ratio; ROR, reporting odds ratio; χ², chi-squared.

Ranking of drugs by reporting frequency

Among the 50 drugs, we further assessed them using four different algorithms: ROR, PRR, EBGM, and Information Component (IC). Drugs that met all four algorithmic criteria were considered strong signals, resulting in the identification of 15 strong signals. These 15 drugs were ranked by signal strength (ROR values) in descending order of ROR value, as shown in Figure 4. The top five drugs in this ranking were Accutane, Teduglutide, Lonsurf, Avastin, and Lynparza, showing a high degree of consistency across the four signal detection methods. Among these five drugs, only Teduglutide explicitly lists the risk of intestinal obstruction in its package insert. The package insert for Accutane did not mention any symptoms suggestive of intestinal obstruction.

Discussion

This study provides a comprehensive pharmacovigilance analysis of DIIO using the FAERS database. We not only identified drugs most strongly associated with DIIO but also, for the first time in a large-scale systematic review, highlighted the critical gap between strong real-world safety signals and the information in drug package inserts. We described the clinical characteristics of these AEs and, crucially, identified the drugs most strongly associated with DIIO. Many of these drugs did not list intestinal obstruction as a potential link in their package inserts, and their association with this AE remains underrecognized. DIIO is a significant AE in drug therapy, as it can cause severe harm, including death. In this study, we found that DIIO reports peaked in 2019 (8.2%), suggesting growing clinical awareness of this adverse reaction. However, a significant knowledge gap regarding this link remains. Healthcare professionals were the primary source of reports, contributing 58.8%, possibly reflecting their greater concern about this AE in clinical practice. Regarding patient outcomes, the mortality rate for intestinal obstruction was 13.5%, highlighting the significant health links of DIIO, especially since 58.9% of patients experienced prolonged hospital stays.

The clinical characteristics of DIIO showed that female patients (50.0%) were more likely to experience this AE than male patients (41.6%), with the highest number of reports coming from the 18–64.9 years age group. These patterns in gender and age distribution suggest that certain drugs may pose a higher risk for specific patient populations. Although many drugs did not explicitly list intestinal obstruction as a risk in their package inserts, we identified several drugs closely associated with this reaction, such as certain antibiotics, anesthetics, and anticancer drugs. Previous studies have shown that some drugs can induce intestinal obstruction by affecting smooth muscle function, inhibiting gut motility, or altering the gut microbiome.^11,12 Our findings strongly corroborate and extend these known mechanisms to a broader drug landscape. Thus, DIIO may be an underestimated clinical concern. Geographically, the United States accounted for 43.6% of the total reports, likely due to the widespread use of the FAERS database and well-established reporting mechanisms, highlighting differences in pharmacovigilance practices across countries.

We further analyzed the top 50 drugs associated with DIIO in the FAERS database, evaluating their package insert labeling and signal strength. Using four signal detection algorithms—ROR, PRR, EBGM, and IC—we identified 15 drugs with strong signals. The top five drugs, ranked by signal strength in descending order of ROR, were Accutane, Teduglutide, Lonsurf, Avastin, and Lynparza. Although these drugs were strongly associated with intestinal obstruction, only Teduglutide explicitly listed this risk in its package insert, while the others did not. This discrepancy underscores a potential lag in regulatory labeling and highlights the value of proactive pharmacovigilance in identifying post-market risks.

Humira, the most frequently reported drug (10,356 cases), does not explicitly list the risk of intestinal obstruction in its package insert. Previous studies suggest that immunosuppressive drugs like Humira may indirectly increase the risk of intestinal obstruction by affecting the gut immune system and weakening gut motility.²⁵ Similarly, immunosuppressive agents like Remicade and Vedolizumab have also been closely associated with DIIO.²⁶ These drugs may alter gut motility by inhibiting inflammation or modifying gut immune function, thereby increasing the risk of intestinal obstruction.

To strengthen the biological plausibility of our findings, we delve deeper into the mechanistic links of the top-signal drugs. For instance, the strongest signal from Accutane (isotretinoin) is mechanistically intriguing. Beyond its known association with inflammatory bowel disease (IBD), recent evidence suggests it can directly alter the gut microbiome composition and impair the intestinal mucosal barrier, potentially leading to a subclinical, chronic inflammation that predisposes patients to functional obstruction and adhesion formation.^27,28 This provides a plausible pathway beyond the traditional motility-centric view of DIIO.

Teduglutide and Lynparza warrant particular attention due to their drug signals. Teduglutide, used to treat short bowel syndrome, enhances nutrient absorption and improves intestinal function. However, its potential effect on gut motility could lead to intestinal obstruction.²⁹ Its primary mechanism, the potent stimulation of intestinal mucosal growth, can paradoxically lead to mechanical obstruction through hyperplasia and polyposis, a risk that is explicitly acknowledged in its label but may be under-appreciated in clinical practice.³⁰ Notably, Teduglutide’s package insert explicitly lists the risk of intestinal obstruction, alerting clinicians to this AE. By contrast, Lynparza, a PARP inhibitor, has less direct mention of intestinal obstruction but remains relevant, as chemotherapy drugs are often linked to gut dysfunction, and cancer patients are at higher risk of obstruction due to chemotherapy-induced intestinal damage.³¹ For anticancer drugs like Avastin, the mechanisms are often multifactorial, involving treatment-induced peritoneal carcinomatosis, colonic strictures from bevacizumab-related inflammation, or direct cytotoxic effects on enteric neurons and glial cells.³²

Although Accutane does not list intestinal obstruction as a risk in its package insert, studies suggest it may induce obstruction by altering the gut microbiome and impairing gut barrier function.²⁷ This finding, combined with the strong signals for other drugs, has direct implications for clinical monitoring and potential labeling updates. We recommend that for drugs with strong DIIO signals like Accutane, Lynparza, and Lonsurf, regulatory agencies and manufacturers consider updating the “Warnings and Precautions” section of the package insert. Clinicians prescribing these agents should maintain a higher index of suspicion for DIIO, especially when patients present with nonspecific gastrointestinal symptoms like abdominal pain, bloating, nausea, and vomiting. In addition, the study found that 33 drugs listed symptoms like abdominal pain, bloating, nausea, and vomiting, which suggest intestinal obstruction, while 17 drugs did not list any of these symptoms, highlighting labeling differences regarding DIIO.

In conclusion, our large-scale pharmacovigilance study establishes that DIIO is a significant and under-recognized clinical threat. We provide a systematically prioritized list of high-risk drugs—including Accutane, Lynparza, and Lonsurf—whose strong association with DIIO is not reflected in their current package inserts. The clear take-home message for clinicians is the imperative for heightened vigilance when prescribing these agents, particularly when patients report nonspecific gastrointestinal symptoms. For researchers and regulators, our findings deliver an actionable agenda: the proposed mechanistic links for top-signal drugs demand urgent validation in targeted preclinical and epidemiological studies, and the identified labeling gaps warrant formal regulatory review. Ultimately, translating these pharmacovigilance signals into validated clinical knowledge and updated safety information is the crucial next step to mitigate the substantial morbidity and mortality associated with DIIO.

Limitations

Despite identifying valuable signals regarding the association between various drugs and intestinal obstruction, this study has several limitations inherent to its data source and methodology.

First, the FAERS database, as a spontaneous reporting system, is inherently subject to reporting biases and underreporting, which may lead to either overestimation or underestimation of true associations. A key limitation is the inability to account for drug utilization patterns. The reporting frequency is influenced by a drug’s market availability, prescription volume, and duration of use, rather than solely its inherent risk. Consequently, a commonly prescribed drug may generate more reports simply due to higher exposure, potentially inflating its signal strength. Furthermore, the majority of FAERS reports originate from the United States, potentially limiting the global generalizability of our findings and introducing regional drug use disparities.

Second, imperfections in the data mining process, such as input errors and incomplete reports, may introduce analytical bias. In addition, efforts to eliminate duplicate records using event_dt, age, gender, and reporter_country resulted in the discarding of a considerable number of reports due to incomplete data. Thus, methods for duplicate report removal require further investigation.

Third, this analysis did not sufficiently account for the underlying diseases of patients, which is a profound limitation for certain drug classes. Some medications examined may be predominantly used in patients with predisposing conditions, thus confounding the association between the drug and the AE. A prime example is drugs like Humira (adalimumab) and Remicade (infliximab), which are prescribed for Crohn’s disease and other forms of IBD. Since intestinal obstruction (particularly from strictures) is a well-known complication of IBD itself, the strong signals we detected for these drugs are profoundly confounded. It is challenging to discern whether the reported obstruction is attributable to the drug’s mechanism, the natural history of the severe underlying disease, or a combination of both. This underscores that our findings for such agents should be interpreted not as evidence of causation, but as a flag for a complex clinical scenario requiring careful patient management.

Finally, the FAERS database and the disproportionality analysis method do not allow for causal inference, so the observed signals should be interpreted as hypothesis-generating. The detected associations may be confounded by concomitant medications, protopathic bias, or the natural history of the patient’s underlying disease. Further research, particularly analytical epidemiological studies (e.g., cohort or case–control studies) designed to control for these confounders, is needed to validate these findings and explore any potential causal relationships.

Conclusion

Supplemental Material

sj-docx-1-taw-10.1177_20420986251414836 – Supplemental material for Drug-induced intestinal obstruction: insights from the FDA Adverse Event Reporting System

Supplemental material, sj-docx-1-taw-10.1177_20420986251414836 for Drug-induced intestinal obstruction: insights from the FDA Adverse Event Reporting System by He Li, Yingjie Li, Lu Liu, Bing Zeng, Sihui Su, Jianjian Liu, Chun Pan and Tianlong Li in Therapeutic Advances in Drug Safety

Supplemental Material

sj-docx-2-taw-10.1177_20420986251414836 – Supplemental material for Drug-induced intestinal obstruction: insights from the FDA Adverse Event Reporting System

Supplemental material, sj-docx-2-taw-10.1177_20420986251414836 for Drug-induced intestinal obstruction: insights from the FDA Adverse Event Reporting System by He Li, Yingjie Li, Lu Liu, Bing Zeng, Sihui Su, Jianjian Liu, Chun Pan and Tianlong Li in Therapeutic Advances in Drug Safety

Supplemental Material

sj-docx-4-taw-10.1177_20420986251414836 – Supplemental material for Drug-induced intestinal obstruction: insights from the FDA Adverse Event Reporting System

Supplemental material, sj-docx-4-taw-10.1177_20420986251414836 for Drug-induced intestinal obstruction: insights from the FDA Adverse Event Reporting System by He Li, Yingjie Li, Lu Liu, Bing Zeng, Sihui Su, Jianjian Liu, Chun Pan and Tianlong Li in Therapeutic Advances in Drug Safety

Supplemental Material

sj-xlsx-3-taw-10.1177_20420986251414836 – Supplemental material for Drug-induced intestinal obstruction: insights from the FDA Adverse Event Reporting System

Supplemental material, sj-xlsx-3-taw-10.1177_20420986251414836 for Drug-induced intestinal obstruction: insights from the FDA Adverse Event Reporting System by He Li, Yingjie Li, Lu Liu, Bing Zeng, Sihui Su, Jianjian Liu, Chun Pan and Tianlong Li in Therapeutic Advances in Drug Safety

Footnotes

Acknowledgements

None.

Declarations

ORCID iD

Tianlong Li

Supplemental material

Supplemental material for this article is available online.

References

Maung

Johnson

Piper

, et al. Evaluation and management of small-bowel obstruction: an Eastern Association for the Surgery of Trauma practice management guideline. J Trauma Acute Care Surg 2012; 73(5 Suppl. 4): S362–S369.

Ten Broek

Issa

van Santbrink

, et al. Burden of adhesions in abdominal and pelvic surgery: systematic review and met-analysis. BMJ 2013; 347: f5588.

Catena

Di Saverio

Coccolini

, et al. Adhesive small bowel adhesions obstruction: evolutions in diagnosis, management and prevention. World J Gastrointest Surg 2016; 8(3): 222–231.

Vather

Trivedi

Bissett

Defining postoperative ileus: results of a systematic review and global survey. J Gastrointest Surg 2013; 17(5): 962–972.

Krielen

Stommel

MWJ

Pargmae

, et al. Adhesion-related readmissions after open and laparoscopic surgery: a retrospective cohort study (SCAR update) (published correction appears in Lancet 2020; 395(10220): 272). Lancet 2020; 395(10217): 33–41.

Sturm

Van Horn

Sternick

, et al. Indications for readmission for small bowel obstruction: a comparison of medical vs surgical service admission. Am Surg 2024; 90(7): 1872–1874.

Patel

Borad

Merchant

AM.

Comparison of outcomes following laparoscopic and open treatment of emergent small bowel obstruction: an 11-year analysis of ACS NSQIP. Surg Endosc 2018; 32(12): 4900–4911.

Camilleri

Opioid-induced constipation: challenges and therapeutic opportunities. Am J Gastroenterol 2011; 106(5): 835–843.

Farmer

Holt

Downes

, et al. Pathophysiology, diagnosis, and management of opioid-induced constipation. Lancet Gastroenterol Hepatol 2018; 3(3): 203–212.

10.

Farmer

Drewes

Chiarioni

, et al. Pathophysiology and management of opioid-induced constipation: European expert consensus statement (published correction appears in United European Gastroenterol J 2019; 7(2): 178). United European Gastroenterol J 2019; 7(1): 7–20.

11.

Bansod

Saifi

Godugu

Molecular updates on berberine in liver diseases: bench to bedside. Phytother Res 2021; 35(10): 5459–5476.

12.

Shi

Lin

Hegde

Novel insights into the mechanisms of abdominal pain in obstructive bowel disorders. Front Integr Neurosci 2018; 12: 23.

13.

Sakaeda

Tamon

Kadoyama

, et al. Data mining of the public version of the FDA adverse event reporting system. Int J Med Sci 2013; 10(7): 796–803.

14.

Von Elm

Altman

Egger

, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet 2007; 370(9596): 1453–1457.

15.

Fusaroli

Salvo

Begaud

, et al. The reporting of a disproportionality analysis for drug safety signal detection using individual case safety reports in pharmacovigilance (READUS-PV): explanation and elaboration. Drug Saf 2024; 47(6): 585–599.

16.

Duggirala

Tonning

Smith

, et al. Use of data mining at the Food and Drug Administration. J Am Med Inform Assoc 2016; 23(2): 428–434.

17.

Anand

Ensor

Trachtenberg

, et al. Osimertinib-induced cardiotoxicity: a retrospective review of the FDA adverse events reporting system (FAERS). JACC CardioOncol 2019; 1(2): 172–178.

18.

Morris

Ali

Cheng

Drug repurposing using FDA adverse event reporting system (FAERS) database. Curr Drug Targets 2024; 25(7): 454–464.

19.

Chai

Zhan

Zhao

, et al. Safety of triazole antifungals: a pharmacovigilance study from 2004 to 2021 based on FAERS. Ther Adv Drug Saf 2022; 13: 20420986221143266.

20.

Cui

Cheng

Wang

, et al. A pharmacovigilance study of etoposide in the FDA adverse event reporting system (FAERS) database, what does the real world say?. Front Pharmacol 2023; 14: 1259908.

21.

Wang

Zhang

Liu

, et al. Exploring novel adverse events of Nefecon. Kidney Int Rep 2024; 9(9): 2705–2717.

22.

Jiang

, et al. Exploring adverse events of Vilazodone: evidence from the FAERS database. BMC Psychiatry 2024; 24(1): 371.

23.

Chen

Fang

Zhao

, et al. Safety assessment of cenobamate: real-world adverse event analysis from the FAERS database. Front Pharmacol 2024; 15: 1369384.

24.

Trippe

Brendani

Meier

, et al. Identification of substandard medicines via disproportionality analysis of individual case safety reports. Drug Saf 2017; 40(4): 293–303.

25.

González-Antuña

Valdés-Delgado

Maldonado-Pérez

, et al. High serum levels of ustekinumab are associated with better clinical outcomes during maintenance treatment for inflammatory bowel disease. Therap Adv Gastroenterol 2024; 17: 17562848241271980.

26.

Sandborn

Feagan

Rutgeerts

, et al. Vedolizumab as induction and maintenance therapy for Crohn’s disease. N Engl J Med 2013; 369(8): 711–721.

27.

Sánchez-Pellicer

Navarro-Moratalla

Núñez-Delegido

, et al. Acne, microbiome, and probiotics: the gut–skin axis. Microorganisms 2022; 10(7): 1303.

28.

Miqdad

Alatta

Abdelsamad

, et al. Isotretinoin-induced inflammatory bowel disease: is there a real association? Cureus 2022; 14(10): e29825.

29.

Hill

Carter

Cohran

, et al. Safety findings in pediatric patients during long-term treatment with teduglutide for short-bowel syndrome-associated intestinal failure: pooled analysis of 4 clinical studies. J Parenter Enteral Nutr 2021; 45(7): 1456–1465.

30.

Pironi

Allard

Joly

, et al. Use of teduglutide in adults with short bowel syndrome-associated intestinal failure. Nutr Clin Pract 2024; 39(1): 141–153.

31.

Moore

Colombo

Scambia

, et al. Maintenance olaparib in patients with newly diagnosed advanced ovarian cancer. N Engl J Med 2018; 379(26): 2495–2505.

32.

Felipe de Campos-Lobato

Vogel

JD.

Enterocutaneous fistula associated with malignancy and prior radiation therapy. Clin Colon Rectal Surg 2010; 23(3): 176–181.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.03 MB

0.02 MB

0.01 MB