Characteristics, preventability, management, and predictors of adverse drug reaction-related hospital admissions: a cross-sectional study in a Thai tertiary care hospital

Warisara Jiamsathit; Thanyamai Sapsinpan; Phrompassorn Khumwatrungruang; Anthony R Cox; Narumol Jarernsiripornkul

doi:10.1080/20523211.2026.2734106

. 2026 Oct 1;19(1):2734106. doi: 10.1080/20523211.2026.2734106

Characteristics, preventability, management, and predictors of adverse drug reaction-related hospital admissions: a cross-sectional study in a Thai tertiary care hospital

Warisara Jiamsathit ^a,^b, Thanyamai Sapsinpan ^b, Phrompassorn Khumwatrungruang ^b, Anthony R Cox ^c, Narumol Jarernsiripornkul ^b,^CONTACT

PMCID: PMC13637779 PMID: 42836209

ABSTRACT

Background

Adverse drug reaction (ADR)-related hospital admissions may be fatal and increase substantial healthcare costs, but several ADRs are preventable. However, studies on preventable ADRs in Thailand remain limited, particularly with respect to their management and prevention. Aim To determine the frequency, characteristics, and preventability of ADR-related hospital admission, and to evaluate the management, prevention strategies, and associated factors.

Methods

A cross-sectional study was conducted among patients admitted to a tertiary care teaching hospital. Data were extracted from the hospital’s medical record database for a 9-month period. ADR-related hospital admissions were identified using the International Statistical Classification of Diseases and Related Health Problems, 10^th Revision (ICD-10).

Results

During the study period, 29,927 patients were admitted to the hospital. Of these, 730 admissions were identified as ADR-related based on ICD-10 codes, and 200 patients were included using purposive sampling. The most frequently reported ADRs involved cardiovascular disorders (50.5%), and nervous system disorders (17.0%). The most common suspected drugs were warfarin (38.0%) and paracetamol (5.0%). Approximately 67.1% of ADRs were classified as probable or definite, and 66.0% were considered preventable. The primary management strategy was discontinuation of the suspected drug (82.5%), while discharge counselling was the most commonly employed preventive measure (56.0%). Inappropriate dosage was independently associated with the preventability of ADR-related hospital admissions (OR 0.007; 95% CI 0.001–0.074; p < 0.001), and age groups of 36–65 years and more than 65 years were independently associated with documented ADR-related hospital admissions (OR 7.688; 95% CI 1.533–38.548; p = 0.013 and OR 9.441; 95% CI 1.668–53.422; p = 0.011, respectively).

Conclusions

Warfarin was the most frequently implicated drug in ADR-related hospital admissions. Inappropriate dosing was significantly associated with preventable ADR-related hospitalisations. Implementing targeted strategies, such as dose optimisation and appropriate clinical monitoring, may enhance medication safety and reduce preventable ADRs.

KEYWORDS: hospital admission, adverse drug reaction, preventable, hospitalization

Background

Adverse drug reactions (ADRs) represent a major public health concern, occurring in about 6–10% of patients and contributing significantly to morbidity and mortality (Geer et al., 2016; Lazarou et al., 1998). A meta-analysis reported that the proportion of patients hospitalised as a result of ADRs has been reported to range from 0.2% to 41.3% (Beijer & de Blaey, 2002), and a systematic review showed age-related differences in ADR prevalence, with rates of 4.1% in children, 6.3% in adults, and 10.7% in elderly patients (Kongkaew et al., 2008). More than 5% of hospital admissions due to ADRs are fatal, representing about 0.1% of all hospitalisations (Carrasco-Garrido et al., 2010). The incidence of ADR-related hospital admissions has been reported to range from 0.5% to 12.8% in several international studies (Alexopoulou et al., 2008; Komagamine & Kobayashi, 2019; Kongkaew et al., 2008; Patel et al., 2007), whereas in Thailand the incidence is 2.74% (Siltharm et al., 2017). ADR-related hospitalisations raise healthcare costs and place a substantial financial burden on healthcare systems (Carrasco-Garrido et al., 2010; Rottenkolber et al., 2011). Because ADRs are associated with higher healthcare expenditures, effective prevention strategies may substantially reduce the overall economic burden on the healthcare system (Geer et al., 2016; Perrone et al., 2014). Furthermore, an estimated 4.3–66.7% of hospital admissions due to ADRs could potentially be prevented (Alexopoulou et al., 2008; Beijer & de Blaey, 2002; Komagamine & Kobayashi, 2019; Maříková et al., 2021; Winterstein et al., 2002).

The common characteristics associated with ADR-related hospital admission included reporting abnormal laboratory parameters (80%), lack of therapeutic drug monitoring (41–67%), inappropriate prescribing or dosing (27.8–51.0%), poor patient adherence (33%), and clinically significant drug–drug interactions (26%) (Franceschi et al., 2008; McDonnell & Jacobs, 2002). Many of these factors are preventable and reducing them could lower the incidence of ADR-related hospital admissions. Therefore, healthcare professionals (HCPs) play a key role in managing or preventing ADR-related hospital admissions by selecting medications tailored to individual patients, carefully assessing potential drug interactions, adjusting doses appropriately, and providing effective medication counselling (Komagamine & Kobayashi, 2019; Maříková et al., 2021; McDonnell & Jacobs, 2002).

Previous studies have shown that the drug classes most often associated with ADR-related hospital admissions were cardiovascular agents (10.1–45.7%), NSAIDs (11.7–14.6%), and antineoplastic or immunosuppressive drugs (15.7–21.5%) (Carrasco-Garrido et al., 2010; Kongkaew et al., 2008; Patel et al., 2007). In Thailand, ADR-related hospital admissions most commonly involved hormones and their synthetic substitutes or antagonists (23.8%), systemic antibiotics (13.2%), drugs affecting blood components (12.6%), and analgesic, antipyretic, and anti-inflammatory agents (6.5%) (Siltharm et al., 2017). The most commonly reported clinical manifestations leading to hospitalisation in previous studies included hemorrhagic complications (20.0–37.3%), central nervous system disorders (11.3%), metabolic disturbances (10.8%), renal complications (10.8%), and dermatologic reactions (8.1%) (Alexopoulou et al., 2008; Bénard-Laribière et al., 2015; Mejía et al., 2020).

Polypharmacy is a well-established predictor of ADRs. Patients receiving five or more medications were over three times more likely to experience an ADR-related hospital admission than those receiving fewer medications (OR 3.17, 95% CI 2.12–4.74) (Komagamine & Kobayashi, 2019). Similarly, the use of four or more medications was significantly associated with an increased risk of ADR-related hospitalisation (p = 0.011) (Alexopoulou et al., 2008). This risk was especially marked in older adults, who were more likely to require multiple medications (Laatikainen et al., 2016). Moreover, the majority of preventable ADRs were related to inappropriate dosing, lack of serum drug concentration monitoring, prior drug allergy histories, clinically significant drug–drug interactions, and anticoagulant use (McDonnell & Jacobs, 2002; Pearson et al., 1994; Seeger et al., 1998). There remains a limited number of studies on preventable ADRs, particularly regarding their management and prevention. Therefore, this study focused on the evaluation of ADR-related hospital admissions and the management and prevention of ADRs. This study aimed to determine the frequency, characteristics, and preventability of ADR-related hospital admissions, and to evaluate management and prevention methods of ADR-related hospital admissions as well as factors affecting these aspects.

Methods

Study design, setting, and sample

This was a cross-sectional study using retrospective data collected from inpatients admitted to a tertiary care teaching hospital in northeastern Thailand between January 2021 and September 2021.

Participants

Eligible participants were hospitalised patients with reported ADR-related admissions between 1 January 2021 and 30 September 2021. The International Statistical Classification of Diseases and Related Health Problems, 10^th Revision (ICD-10), was used to identify patients with ADRs associated with hospital admissions from the hospital’s medical records database. Twenty ICD-10 codes for external causes mortality and morbidity (Y40-Y59: drugs, medicaments, and biological substances causing adverse effects in therapeutic use) and fifteen ICD-10 codes for poisoning by drugs, medicaments and biological substances (T36-T50) were applied. Patients with incomplete or unavailable medical records were excluded. Potential ADR cases were initially identified using predefined ICD-10 diagnosis codes recorded in the hospital electronic medical record system. Although this approach provides a standardised and feasible method for identifying potential ADR-related admissions, ICD-10 coding alone may not capture all ADR cases because of under-recognition, incomplete documentation and coding in routine clinical practice. Consequently, some eligible cases may have been missed. Sample size was calculated using the Cochrane equation (Cochran, 1977) with a 5% margin of error, and 9.9% of ADR-related hospital admissions (Komagamine & Kobayashi, 2019). A minimum of 138 patients were required. Considering a 20% rate of unavailable medical records, as reported in a previous study (Kampichit et al., 2018), the total required sample size was at least 166 patients. Therefore, 200 patients were included in this study using purposive sampling.

Data collection form development and testing

The data collection form was designed and developed by the research team based on the study’s objectives. The content of the data collection form was validated by two HCPs with expertise in the field of ADRs. Content validity of the questionnaire was evaluated using the index of item-objective congruence (IOC > 0.5 for each item). The final version consisted of four main sections, as follows:

Section 1: Demographic information, including gender, age, diagnosis, history of drug allergy, history of dietary supplement use, dates of admission and discharge, concomitant diseases, and concurrent medications.

Section 2: Open-ended questions which cover ADRs-related hospital admission data including ICD-10, vital sign, clinical symptom, the name of suspected drugs, timeline, severity assessment, causality assessment, and preventability assessment.

Section 3: Management of ADRs, including dechallenge, rechallenge, discontinuation of concomitant medications, dosage adjustment, or switching to alternative medications. It also includes determining preventive measures such as medical record documentation, sticker charts, or discharge counselling.

Data collection

The final data collection form was used by the researchers to collect data on ADR-related hospital admissions from patients’ medical records at the study hospital. This study collected data between 1 December 2021 and 31 March 2022. Study participants included inpatients who had reports of ADR-related hospital admissions between 1 January 2021 and 30 September 2021. Patients with reports of ADR-related hospital admissions between 1 January 2021 and 30 September 2021 were identified using the International Statistical Classification of Diseases and Health-Related Problems (ICD-10). After obtaining the list of patients according to ICD-10 codes, we reviewed their medical records and the hospital database to retrieve ADR-related hospital admission information. Patients’ hospital numbers (HN) were then converted into code numbers to maintain confidentiality, and only the research team had access to the data.

Additional ADR-related hospital admission data were retrieved from the electronic medical record (EMR) system. After potential ADR cases were identified using ICD-10 diagnosis codes, complete electronic medical records were manually reviewed using a standardised data extraction form. Information was extracted from physician progress notes, admission records, discharge summaries, medication administration records, laboratory findings, and other relevant clinical documentation. The severity of ADRs was evaluated by researchers using the Hartwig Scale. This scale classified ADRs into seven severity levels, with levels 1–2 defined as mild, levels 3–4 as moderate, and levels 5–7 as severe. The severity of ADRs was subsequently classified as mild, moderate, or severe. Mild ADRs resolved spontaneously without treatment; moderate ADRs required therapeutic intervention or extended the hospital stay by at least one day; and severe ADRs resulted in life-threatening conditions, permanent disability, or death (Hartwig et al., 1992). Naranjo’s algorithm (Naranjo et al., 1981) and the World Health Organization Uppsala Monitoring Centre (WHO-UMC) criteria (The Uppsala Monitoring Centre, 2005) were used for causality assessment. Naranjo’s algorithm classified causality into four categories: definite (score > 9), probable (score 5–8), possible (score 1–4), and doubtful (score < 0) (Naranjo et al., 1981). The WHO criteria classified causality into four categories: certain, probable/likely, possible, and unlikely (The Uppsala Monitoring Centre., 2005). The Schumock and Thornton criteria (Schumock & Thornton, 1992) were used for the ADR preventability assessment. These criteria consisted of seven questions; if at least one question was answered ‘yes,’ the adverse drug reaction was classified as a preventable ADR. Agreement between Naranjo’s algorithm and WHO criteria was assessed using Cohen’s kappa (κ). Independent assessment of Hartwig, Naranjo, WHO-UMC tools was performed by two trained pharmacist researchers. These standardised tools were used to reduce variability in clinical judgment during the assessment process. Although a formal inter-rater reliability analysis was not performed, disagreements encountered during the assessment were discussed among the trained pharmacist researchers until consensus was achieved.

Data analysis

All valid data collection forms were analysed using IBM SPSS for Windows version 26.0. Demographic data, frequencies, and types of ADR-related hospital admission were analysed using descriptive statistics. Pearson’s chi-squared test and Fisher’s exact test were used to compare subgroups for categorical data, and independent-sample t-tests were used for continuous data. The Mann–Whitney U test was used to assess the independence of non-parametric variables. Agreement between Naranjo’s algorithm and WHO-UMC Causality Assessment was judged to be acceptable on the basis of a Cohen’s kappa (κ). Cohen’s kappa (κ) was interpreted as follows: poor < 0, slight = 0.01–0.20, fair = 0.21–0.40, moderate= 0.41–0.60, substantial = 0.61–0.80, and almost perfect agreement = 0.81–1.00 (Viera & Garrett, 2005). Univariate analysis of the factors related to the outcome variables was conducted using Pearson’s chi-squared test. Prior to multivariable logistic regression, multicollinearity among independent variables was assessed using variance inflation factors (VIF). The explanatory power of the model was evaluated using the Nagelkerke pseudo R². Data completeness was assessed before analysis, and complete-case analysis was performed. Variables associated with preventable ADRs and causes of ADR-related hospital admissions at p-values ≤ 0.25 in the univariate analyses were entered into a multivariate analysis. Multiple logistic regression was used to determine independent factors related to the preventable ADRs and causes of ADR-related hospital admissions. The results with p-values <0.05 were considered as statistically significant.

Results

A total of 29,927 patients were admitted to the hospital due to ADRs. Of these, 730 patients were identified using ICD-10 codes, of whom 200 were eligible and included in this study (Figure 1). The demographic characteristics of the respondents are presented in Table 1. Approximately two-thirds of the respondents were female (n = 127, 63.5%). Patients aged more than 65 years accounted for 38.0% of the study population (n = 76). Over half of the patients had two or fewer comorbidities (n = 107, 53.5%) and received five or more concomitant medications (n = 118, 59.0%). Most patients reported no use of herbal products or dietary supplements (n = 195, 97.5%), no alcohol consumption (n = 183, 91.5%), and no smoking (n = 188, 94.0%). Severe ADRs were the most frequently reported severity level among ADR-related hospital admissions (n = 186, 93.0%) and most ADR-related hospital admissions occurred at normal doses (n = 157, 78.5%).

A flow diagram showing 29927 hospital admissions filtered to 730 screened patients, 200 included, then split into 158 and 42 groups. The figure shows a vertical flow diagram summarizing how patients move from initial hospital admissions into the final study groups. At the top, a rounded rectangle states that 29927 patients are admitted to the hospital between 1 January and 30 September 2021. A connecting line leads down to a second rounded rectangle noting 730 patients identified using predefined International Classification of Diseases, Tenth Revision codes for possible adverse drug reaction related admissions. To the right, a side box labeled excluded 530 patients lists 2 reasons with bullet points: not adverse drug reaction related hospital admission 465 and medical records unavailable 65. A line continues downward to a central box labeled included in the study 200. From this box, a horizontal line splits into 2 branches. The left branch ends in a box stating adverse drug reaction as the primary cause of hospital admission (documented adverse drug reactions) 158. The right branch ends in a box stating adverse drug reaction as a contributing cause of hospital admission (potential adverse drug reactions) 42. All data are approximate. — Flow diagram of participant selection.

Table 1.

Demographic characteristics of respondents with ADR-related hospital admission, categorised by the causes of hospital admission (n = 200).

Variables	Number of patients (%)			p-value^a
Variables	Documented ADRs^c (n = 158)	Potential ADRs^d (n = 42)	Total (n = 200)	p-value^a
Gender
Male	53 (33.5)	20 (47.6)	73 (36.5)	0.092
Female	105 (66.5)	22 (52.4)	127 (63.5)	0.092
Age (years)
≤ 35	63 (39.9)	2 (4.8)	65 (32.5)	<0.001*
36–65	45 (28.5)	14 (33.3)	59 (29.5)
> 65	50 (31.6)	26 (61.9)	76 (38.0)
Mean ± SD	47.0 ± 24.36	66.4 ± 16.39	51.1 ± 24.22
Number of comorbidities
0–2	97 (61.4)	10 (23.8)	107 (53.5)	<0.001*
≥ 3	61 (38.6)	32 (76.2)	93 (46.5)
Mean ± SD	2.2 ± 1.66	3.7 ± 1.66	2.5 ± 1.76
Number of concomitant drugs
0–1	32 (20.3)	2 (4.8)	34 (17.0)	0.‏004*
2–4	42 (26.6)	6 (14.3)	48 (24.0)
≥ 5	84 (53.2)	34 (81.0)	118 (59.0)
Mean ± SD	5.7 ± 4.55	7.7 ± 3.8	6.1 ± 4.48
Use of herb and dietary supplement
Yes	4 (2.5)	1 (2.4)	5 (2.5)	1.‏000^b
No	154 (97.5)	41 (97.6)	195 (97.5)	1.‏000^b
Alcohol Drinking
Yes	15 (9.5)	2 (4.8)	17 (8.5)	0.‏534^b
No	143 (90.5)	40 (95.2)	183 (91.5)	0.‏534^b
Smoking
Yes	9 (5.7)	3 (7.1)	12 (6.0)	0.718^b
No	149 (94.3)	39 (92.9)	188 (94.0)	0.718^b
Onset of ADRs
< 1 h	17 (10.8)	0 (0.0)	17 (8.5)	0.034^b*
1 h to < 24 h	46 (29.1)	1 (2.4)	47 (23.5)
1 d to < 1 month	21 (13.3)	4 (9.5)	25 (12.5)
≥ 1 month	18 (11.4)	3 (7.1)	21 (10.5)
Duration of ADRs improvement (days)
< 1	26 (16.5)	0 (0.0)	26 (13.0)	0.001*
1–3	82 (51.9)	33 (78.6)	115 (57.5)
4–6	30 (19.0)	4 (9.5)	34 (17.0)
≥ 7	16 (10.1)	1 (2.4)	17 (8.5)
Length of stay (days)
1–3	54 (34.2)	8 (19.0)	62 (31.0)	0.278
4–6	45 (28.5)	15 (35.7)	60 (30.0)
7–9	24 (15.2)	9 (21.4)	33 (16.5)
≥ 10	35 (22.2)	10 (23.8)	45 (22.5)

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^{^a}

Pearson’s chi-square test.

^{^b}

Fisher’s exact test.

^{^c}

Patients hospitalised for ADRs whose presenting symptoms were directly caused by medications, as evidenced by medical record documentation or definitive diagnostic test results.

^{^d}

Patients hospitalised for ADRs whose presenting symptoms were possibly attributable to medications but for which alternative causes were identified.

*Significant difference at the level of <0.05.

Of the 390 respondents, the three most frequently reported ADRs classified by system organ class (SOC) were injury, poisoning and procedural complications (n = 82, 41.0%), including warfarin overactivity (n = 45, 22.5%); vascular disorders (n = 33, 16.5%), including bleeding (n = 32, 16.0%); and metabolism and nutrition disorders (n = 19, 9.5%), including hypoglycemia (n = 10, 5.0%), respectively (Table 2). Furthermore, the most common suspected drugs with their reported ADRs classified by anatomical therapeutic chemical (ATC) system were as follows: blood and blood-forming organs – warfarin (n = 76, 38.0%) induced warfarin overactivity (n = 45, 22.5%); nervous system – paracetamol (n = 10, 5.0%) induced overdose (n = 9, 4.5%) and lorazepam (n = 7, 3.5%) induced overdose (n = 7, 3.5%); alimentary tract and metabolism – insulin (n = 7, 3.5%) induced hypoglycemia (n = 7, 3.5%), respectively (Table 3).

Table 2.

Top five most common System Organ Classes (SOCs) of ADRs among patients with ADR-related hospital admissions.

System of ADRs on SOC	Number of patients (%) n = 200	Reported ADRs, N (%)
Injury, poisoning and procedural complications	82 (41.0)	Warfarin overactivity (45, 22.5%), paracetamol overdose (9, 4.5%), lorazepam overdose (5, 2.5%), clonazepam overdose (4, 2.0%), amitriptyline overdose (3, 1.5%)
Vascular disorders	33 (16.5)	Bleeding^a (32, 16.0%), hypotension (1, 0.5%)
Metabolism and nutrition disorders	19 (9.5)	Hypoglycemia (10, 5.0%), hyperglycemia (4, 2.0%), lactic acidosis (2, 1.0%), hypokalemia (1, 0.5%), tumour lysis syndrome (1, 0.5%), metabolic acidosis (1, 0.5%)
Blood and lymphatic system disorders	17 (8.5)	Febrile neutropenia (4, 2.0%), thrombocytopenia (3, 1.5%), bicytopenia (3, 1.5%), pancytopenia (3, 1.5%), anemia (2, 1.0%)
Immune system disorders	16 (8.0)	Anaphylaxis (14, 7.0%), DRESS syndrome (2, 1.0%)

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^{^a}

Bleeding included hemorrhage at various anatomical sites.

Note: DRESS: Drug Rash with Eosinophilia and Systemic Symptoms; A patient may experience more than one ADR and may have more than one suspected medication associated with an ADR-related hospital admission.

Table 3.

Top five most common suspected drugs causing ADR-related hospital admission, based on the top five most common drug groups classified by the Anatomical Therapeutic Chemical (ATC) system.

Suspected drug on ATC	Number of patients (%) n = 200	Suspected drug reported, N (%)
Blood and blood forming organs	79 (39.5)	Warfarin (76, 38.0%), clopidogrel (2, 1.0%), apixaban (1, 0.5%)
Nervous system	48 (24.0)	Paracetamol (10, 5.0%), lorazepam (7, 3.5%), sertraline (5, 2.5%), clonazepam (5, 2.5%), amitriptyline (3, 1.5%), fluoxetine (3, 1.5%)
Antineoplastic and immunomodulating agents	39 (19.5)	Doxorubicin (4, 2.0%), vincristine (4, 2.0%), cytarabine (3, 1.5%), gemcitabine (3, 1.5%), ifosfamide (3, 1.5%), carboplatin (3, 1.5%), cyclophosphamide (3, 1.5%)
Alimentary tract and metabolism	19 (9.5)	Insulin (7, 3.5%), metformin (4, 2.0%), glipizide (4, 2.0%), dexamethasone (1, 0.5%), metoclopramide (1, 0.5%), omeprazole (1, 0.5%), sulfasalazine (1, 0.5%)
Anti-infective for systemic use	17 (8.5)	Rifampicin (4, 2.0%), COVID-19 vaccine (3, 1.5%), cotrimoxazole (2, 1.0%), pyrazinamide (2, 1.0%), isoniazid (2, 1.0%)

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Note: A patient may experience more than one ADR and may have more than one suspected medication associated with an ADR-related hospital admission.

Based on the Naranjo algorithm, 2 ADRs (0.9%) were identified as definite, while 184 (80.7%) were considered probable and 42 (18.4%) possible. Using the WHO criteria, 2 suspected ADRs (0.9%) were classified as certain, 162 (71.0%) as probable, and 64 (28.1%) as possible. Overall, 153 of the 228 suspected ADRs (67.1%) were consistently identified as true ADRs by both assessment methods. Nonetheless, the level of agreement between the Naranjo algorithm and the WHO criteria was moderate (κ = 0.485, p < 0.001) (Table 4).

Table 4.

Agreement of causality assessment between Naranjo's algorithm and WHO-UMC causality assessment (n = 228)^a.

Causality assessment tool		Number of ADRs (%)			Total
		WHO-UMC causality assessment
		Possible	Probable	Certain
Naranjo’s algorithm	Possible	31 (48.4)	11 (6.8)	0 (0.0)	42 (18.4)
	Probable	33 (51.6)	151 (93.2)	0 (0.0)	184 (80.7)
	Definite	0 (0.0)	0 (0.0)	2 (100.0)	2 (0.9)
	Total	64 (28.1)	162 (71.0)	2 (0.9)	228 (100.0)
Kappa		0.485
p-value		<0.001^b*

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^{^a}

A single patient may experience more than one ADR or may use more than one medication suspected of causing ADR-related hospital admissions.

^{^b}

Fisher’s exact test.

* Significant difference at the level of < 0.05.

The most common methods of ADR management by physicians were stop the suspected drug (n = 165, 82.5%), use another drug for treating ADRs (n = 117, 58.5%) and stop the concomitant drug (n = 62, 31.0%). The most common method of ADR prevention by HCPs was discharge counselling (n = 112, 56.0%). Only 3.2% (n = 5) of patients were received allergy card (Table 5). Based on the Schumock and Thornton criteria, 132 of the 200 patients admitted due to ADRs (66.0%) were considered preventable. Of these, 101 (76.5%) were directly caused by medications, whereas 31 (23.5%) were possibly medication-related with alternative causes identified.

Table 5.

Management and prevention of ADRs by HCPs, categorised by causes of hospital admission.

Method of ADR management	Number of patients (%)
Method of ADR management	Documented ADRs^a (n = 158)	Potential ADRs^b (n = 42)	Total (n = 200)
Stop the suspected drug	132 (83.5)	33 (78.6)	165 (82.5)
Use another drug for treating ADRs	101 (63.9)	16 (38.1)	117 (58.5)
Stop the concomitant drugs	49 (31.0)	13 (31.0)	62 (31.0)
Continue the suspected drug	11 (7.0)	6 (14.3)	17 (8.5)
Rechallenge the suspected drug	2 (1.3)	0 (0.0)	2 (1.0)
Method of ADR prevention	Number of patient (%)
	Documented ADRs^a (n = 158)	Potential ADRs^b (n = 42)	Total (n = 200)
Provide discharge counselling	87 (55.0)	25 (59.5)	112 (56.0)
Record in medical notes	7 (4.4)	2 (4.8)	9 (4.5)
Give allergy card to patients	5 (3.2)	0 (0.0)	5 (2.5)

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^{^a}

Patients hospitalised for ADRs whose presenting symptoms were directly caused by medications, as evidenced by medical record documentation or definitive diagnostic test results.

^{^b}

Patients hospitalised for ADRs whose presenting symptoms were possibly attributable to medications but for which alternative causes were identified.

The univariate analysis regarding factors related to the preventability of ADR showed that dosage (p < 0.001) was significantly associated with preventability of ADR-related hospital admissions. Variables entered into the multiple logistic regression analysis (Backward; likelihood ratio method) included the number of comorbidities, number of concomitant drugs, onset of ADRs, caused of ADRs and dosage. Multiple logistic regression analysis identified dosage (OR 0.007; 95% CI 0.001-0.074; p < 0.001) as the factor independently associated with the preventability of ADR-related hospital admissions. In addition, the univariate analysis regarding factors related to documented ADR-related hospital admissions showed that age (p < 0.001), the number of comorbidities (p < 0.001), number of concomitant drugs (p = 0.004), causality assessment by Naranjo’s algorithm (p = 0.002), and WHO-UMC causality assessment (p = 0.046) were significantly associated with documented ADR-related hospital admissions. Variables entered into the multiple logistic regression analysis (Backward; likelihood ratio method) included gender, age, number of comorbidities, number of concomitant drugs, causality assessment by Naranjo’s algorithm and WHO-UMC causality assessment. Multiple logistic regression analysis identified age groups of 36-65 years and >65 years as independently associated with documented ADR-related hospital admissions (OR 7.688; 95% CI 1.533–38.548; p = 0.013 and OR 9.441; 95% CI 1.668–53.422; p = 0.011, respectively) (Table 6). No evidence of multicollinearity was observed (all VIF < 4.0).‏ The Nagelkerke R² was 0.563 (Preventability) and 0.‏257 (Causes of ADRs).

Table 6.

Multiple logistic regression analysis of the factors affecting the preventability and causes of ADR-related hospital admissions (n = 200).

Variables^a	Number of patients; N (%)		Adjusted OR			p-value
	Preventability^b			95% CI
	Preventable (n = 132)	Unpreventable (n = 68)		lower	upper
Number of comorbidities
0–2	65 (49.2)	42 (61.8)	1
≥3	67 (50.8)	26 (38.2)	0.305	0.079	1.185	0.086
Number of concomitant drugs
0–1	17 (12.9)	17 (25.0)	1
2–4	36 (27.3)	12 (17.6)	0.171	0.019	1.506	0.112
≥5	79 (59.8)	39 (57.4)	0.174	0.015	2.015	0.162
Onset of ADRs (n = 110)
<1 h	11 (15.7)	6 (15.0)	1
1 h to < 24 h	36 (51.4)	11 (27.5)	1.030	0.105	10.092	0.980
1 d to < 1 month	13 (18.6)	12 (30.0)	1.376	0.102	18.629	0.810
≥1 month	10 (14.3)	11 (27.5)	1.543	0.102	23.352	0.754
Causes of ADRs
Documented ADRs^c	101 (76.5)	57 (83.8)	1
Potential ADRs^d	31 (23.5)	11 (16.2)	4.158	0.669	25.837	0.126
Dosage
Normal dose	90 (68.2)	67 (98.5)	1
Overdose	42 (31.8)	1 (1.5)	0.007	0.001	0.074	<0.001*
Variables^e	Number of patients; N (%)		Adjusted OR			p-value
	Causes of ADRs			95% CI
	Documented ADRs^c (n = 158)	Potential ADRs^d (n = 42)		lower	upper
Gender
Male	53 (33.5)	20 (47.6)	1
Female	105 (66.5)	22 (52.4)	0.785	0.361	1.707	0.541
Age (years)
≤ 35	63 (39.9)	2 (4.8)	1
36–65	45 (28.5)	14 (33.3)	7.688	1.533	38.548	0.013*
> 65	50 (31.6)	26 (61.9)	9.441	1.668	53.422	0.011*
Number of comorbidities
0–2	97 (61.4)	10 (23.8)	1
≥ 3	61 (38.6)	32 (76.2)	1.834	0.669	5.022	0.238
Naranjo’s algorithm (n = 228)
Possible	41 (22.3)	1 (2.3)	1
Probable and definite	143 (77.7)	43 (97.7)	7.475	0.951	58.742	0.056
Preventability
Preventable	101 (63.9)	31 (73.8)	1
Unpreventable	57 (36.1)	11 (26.2)	0.635	0.276	1.462	0.286

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^{^a}

Variables included in the multiple logistic regression analysis (Backward; LR method) are number of comorbidities, number of concomitant drugs, onset of ADRs, causes of ADRs, and dosage.

^{^b}

Preventability assessment was assessed using the Schumock and Thornton criteria.

^{^c}

Documented ADRs: Patients hospitalised for ADRs whose presenting symptoms were directly caused by medications, as evidenced by medical record documentation or definitive diagnostic test results.

^{^d}

Potential ADRs: Patients hospitalised for ADRs whose presenting symptoms were possibly attributable to medications but for which alternative causes were identified.

^{^e}

Variables included in the multiple logistic regression analysis (Backward; LR method) are gender, age, number of comorbidities, number of concomitant drugs, causality assessment by Naranjo’s algorithm and WHO-UMC causality assessment and preventability of ADRs-related hospital admissions.

*The level of significant difference <0.05.

Discussion

In this cross-sectional study using retrospective medical records identified through ICD-10 codes, the incidence of ADR-related hospital admissions is 0.67%. This finding is comparable to the study by Patel et al., which reported an incidence of 0.5% using a similar retrospective design that also relied on ICD-10 coding to identify cases (Patel et al., 2007). In contrast, Pirmohamed et al. and Laribiere et al. reported higher incidences of 6.5% (Pirmohamed et al., 2004) and 3.6% (Bénard-Laribière et al., 2015), respectively, likely reflecting differences in study design, as both studies were conducted prospectively. Likewise, a study from Thailand by Siltharm et al. reported a higher incidence of 2.74%, possibly due to the broader range of ICD-10 codes employed in that study compared with ours (Siltharm et al., 2017).

Blood and blood-forming agents accounted for the highest proportion of ADR-related hospital admissions, followed by drugs acting on the nervous system and antineoplastic/immunomodulating agents. This pattern is in line with previous studies, in which antithrombotic and antineoplastic agents were identified as the most frequent contributors to ADR-related hospital admissions (Bénard-Laribière et al., 2015; Carrasco-Garrido et al., 2010; Cosgrave et al., 2025; Kauppila et al., 2021; Mejía et al., 2020). In contrast, some studies have reported antimicrobial agents as the most common cause of ADRs, which may reflect differences in study populations and settings. In this study, drugs in the blood and blood-forming organs category were responsible for warfarin overactivity in 22.5% of patients and bleeding in 15.5%. This is consistent with the findings of a previous study, which reported that vitamin K antagonists were the most common drugs responsible for hospital admission due to hemorrhage or elevated INR, with an incidence of 0.5% (95% CI, 0.2–0.8) (Bénard-Laribière et al., 2015). Another study similarly reported that anticoagulants, particularly warfarin, was the most frequent causative drug relative to their use (Davies et al., 2009). In addition, Kauppila et al. reported that warfarin was a major cause of hospitalisation and is considered a high-risk medication (Kauppila et al., 2021). Therefore, close monitoring for ADRs and strategies to reduce complications associated with warfarin use are essential and require collaboration among HCPs (Snipelisky & Kusumoto, 2013).

Interestingly, although this study did not perform age-stratified subgroup analyses, previous studies have demonstrated age-specific patterns of ADR-related hospital admissions (Cosgrave et al., 2025; Franceschi et al., 2008; Gallagher et al., 2011; Gholami et al., 2015; Maříková et al., 2021; Temple et al., 2004). Several medication classes are consistently reported to be associated with ADR-related hospitalisations, including anti-infectives for systemic use; nervous system drugs (e.g. paracetamol, NSAIDs, and opioids); cardiovascular agents (e.g. diuretics); and drugs affecting the alimentary tract and metabolism (e.g. hypoglycemic agents). The patterns of medications implicated in ADRs differ between pediatric and elderly populations. In children, in addition to antineoplastic and immunomodulating agents, antibiotics and analgesics are frequently implicated in ADRs (Gallagher et al., 2011; Gholami et al., 2015; Temple et al., 2004). In contrast, among older adults, in addition to antithrombotic agents, cardiovascular medications such as diuretics and digoxin, as well as NSAIDs, are commonly implicated (Cosgrave et al., 2025; Franceschi et al., 2008; Maříková et al., 2021). These differences likely reflect age-related disease patterns and medication use, with higher antibiotic use in children and greater use of chronic disease-related medications in older adults. Moreover, ADRs related to hypoglycemic agents have been reported across both middle-aged and elderly populations. These findings highlight the importance of targeted ADR monitoring, particularly for medication classes frequently associated with ADRs, to reduce ADR-related hospital admissions and improve medication safety.

Causality assessment of ADR-related hospital admissions using the Naranjo algorithm and the WHO-UMC criteria showed that most cases were classified as probable or definite (81.6%) and probable or certain (71.9%), respectively. These findings are consistent with those reported by Gholami et al., who also found that the majority of ADR-related hospital admissions were classified as probable or definite (88.9%) using the Naranjo algorithm and probable or certain (81.5%) using the WHO-UMC criteria (Gholami et al., 2015). This study demonstrated moderate agreement between the Naranjo algorithm and the WHO criteria for causality assessment (κ = 0.485). This finding is consistent with a recent study that also reported moderate agreement (κ = 0.463) (Misra et al., 2025b ). In contrast, other studies report only fair agreement between the two methods (κ = 0.22 and κ = 0.143) (Belhekar et al., 2014; More et al., 2024), suggesting uncertainty about the reliability of these tools. Ongoing training and efforts to standardise causality assessment methods are needed to improve accuracy and consistency.

Several previous studies have shown that ADR-related hospital admissions are often preventable (Al Damen & Basheti, 2019; Kauppila et al., 2021; McDonnell & Jacobs, 2002). In our study, 66% of ADR-related admissions were deemed preventable, which is consistent with previously reported estimates (26.4–66.7%) (Al Damen & Basheti, 2019; Alexopoulou et al., 2008; Beijer & de Blaey, 2002; Bénard-Laribière et al., 2015; Keche et al., 2021; Komagamine & Kobayashi, 2019; McDonnell & Jacobs, 2002; Misra et al., 2025a). Notably, previous evidence suggests that ADRs in elderly patients may be preventable in up to 87.9% of cases (Beijer & de Blaey, 2002). These findings indicate that more than half of ADR-related hospital admissions could potentially be avoided. Previous studies have identified inappropriate drug use, incorrect dosing, self-medication, medication errors, abrupt withdrawal, drug–drug interaction, and inadequate pharmacogenetic testing as common potentially preventable causes of ADRs (Bénard-Laribière et al., 2015; Maříková et al., 2021; Misra et al., 2025a). Consistent with these findings, inappropriate dosing was a key preventable cause of ADRs in this study. This study showed that the most common methods of ADR management used by HCPs were discontinuation of the suspected drug and the use of other medications to treat the reaction, which is consistent with findings from previous studies (Adisa & Omitogun, 2019; Srisuriyachanchai et al., 2022). The most frequently reported method of ADR prevention by HCPs was discharge counselling. This is also in line with previous studies demonstrating that preventive strategies commonly include patient counselling, educational programmes, and the use of supportive tools (Srisuriyachanchai et al., 2025; Teo & Walsh, 2016). However, only 3.2% of patients in this study received an allergy card, which contrasts with previous study reporting much higher rates (52.3%) (Jiamsathit et al., 2025). This difference is likely explained by the types of ADRs observed. Previous study focused primarily on severe cutaneous adverse reactions (SCARs), for which issuing an allergy card is mandatory for all affected patients. In contrast, some ADRs in this study were type A reactions, which are dose dependent and related to the pharmacological action of the drug, and therefore may not routinely require an allergy card. Thus, several activities can help improve the prevention of ADRs. Targeted strategies – such as improved medication review, careful review of allergy histories and contraindications, strengthening physicians’ clinical judgment and prescribing decisions, monitoring high-risk medications, screening for drug–drug interactions, incorporating pharmacogenetic testing, enhanced patient counselling, promoting patient adherence, and early recognition of ADR symptoms – are essential to reduce ADR-related morbidity and healthcare utilisation (Beijer & de Blaey, 2002; Kauppila et al., 2021; McDonnell & Jacobs, 2002; Saokaew et al., 2014; Schnipper et al., 2006).

Increasing age was independently associated with a higher risk of ADR-related hospital admission, consistent with previous evidence (Al Damen & Basheti, 2019; Alexopoulou et al., 2008; Davies et al., 2009; Perrone et al., 2014; Rydberg et al., 2016). Although the number of concomitant medications associated with preventability of ADR-related hospital admissions in univariate analysis did not reach statistical significance (p = 0.059), a greater medication burden showed a tendency towards an association with preventable ADRs. Prior studies had similarly identified polypharmacy and prolonged length of hospital stay as significant contributors to ADR-related hospital admissions (Al Damen & Basheti, 2019; Alexopoulou et al., 2008; Davies et al., 2009; Perrone et al., 2014; Rydberg et al., 2016). In this study, inappropriate dosing was the strongest determinant of ADR-related hospital admission among preventable ADRs, in agreement with earlier reports (McDonnell & Jacobs, 2002; Seeger et al., 1998). Additional factors associated with preventable ADR-related hospital admissions included a history of drug allergy and the use of anticoagulant or thrombolytic agents (Pearson et al., 1994; Seeger et al., 1998). Furthermore, insufficient monitoring, inappropriate dosing, and drug–drug interactions have been consistently identified as key determinants of ADR preventability (Al Damen & Basheti, 2019; McDonnell & Jacobs, 2002).

These findings informed the development of targeted strategies designed to minimise ADRs and reduce ADR-related hospital admissions. Such strategies include optimisation of dosing regimens, implementation of adequate monitoring – such as therapeutic drug monitoring and relevant laboratory assessments – the use of structured screening approaches (e.g. ADR self-screening tools or pharmacogenetic testing), and systematic, accessible review of medication use and drug allergy histories (Al Damen & Basheti, 2019; Bénard-Laribière et al., 2015; Maříková et al., 2021; McDonnell & Jacobs, 2002; Misra et al., 2025b). In addition, advances in digital health technologies including artificial intelligence, machine learning, natural language processing, and large language models may further strengthen ADR prediction and monitoring processes. Optimising clinical decision support systems integrated into electronic health records can assist HCPs in identifying potential drug interactions, allergies, contraindications, and inappropriate dosing at the point of care (Bate & Hobbiger, 2021; Chisholm-Burns et al., 2010; Hansen et al., 2011; Kaushal et al., 2003).

In Thailand, strategies to reduce ADR-related hospital admissions should include dose adjustment for special populations, careful risk–benefit assessment of combination therapy, enhanced monitoring of narrow therapeutic index drugs and patients at risk of drug–drug interaction. Implementation of screening tools, increased ADR monitoring during and after hospitalisation, and ongoing patient education are also essential. The integration of these preventive measures for avoidable ADRs has the potential to substantially reduce ADR-related hospital admissions, thereby enhancing medication safety, improving patient quality of life, and reducing healthcare expenditures.

Strengths and limitations

A key strength of this study is that it was conducted in a tertiary-care teaching hospital in northeastern Thailand, which serves a large patient population and encompasses a wide range of medication regimens, thereby increasing the likelihood of capturing a diverse spectrum of ADRs. The use of established assessment tools for ADR causality, severity, and preventability including the Naranjo algorithm, WHO-UMC criteria, the Hartwig Severity Assessment Scale, and the Schumock and Thornton Preventability Scale, was a potential strength of our methodology. Furthermore, the inclusion of management and prevention strategies enhanced the clinical relevance of our findings. Several limitations should be acknowledged. First, the retrospective design, which relied on data extracted from patients’ medical records, may have resulted in incomplete or missing information. Second, as the study was conducted at a single tertiary-care hospital in northeastern Thailand, the findings may have limited generalizability to other healthcare settings or regions within the country. Third, this study is the use of purposive sampling, which may have introduced selection bias and limited the generalizability of the findings. Future studies using probability sampling and recruiting participants from multiple healthcare settings are recommended to improve external validity. Fourth, a formal assessment of inter-rater reliability was not performed, however standardised ADR assessment tools and consensus discussions were used to minimise variability. Fifth, although the Schumock and Thornton criteria are widely used and facilitate standardised assessment of ADR preventability, the binary classification system would simplify complex clinical scenarios but potentially overestimate preventable ADRs. Future studies may consider additional methods to further evaluate ADR preventability. Sixth, potential ADR-related hospitalisations were initially identified using ICD-10 codes. Although this approach is practical and enables standardised case identification, ICD-10 coding alone may underestimate the true incidence of ADRs because of under-recognition, incomplete clinical documentation and coding. A systematic review by Hohl et al. reported that the sensitivity of ICD-10 codes for identifying adverse drug events varies substantially across healthcare settings and coding practices (Hohl et al., 2014). In the present study, this limitation was partially addressed by conducting detailed medical record reviews and standardised ADR causality assessments for all potentially eligible cases identified through ICD-10 screening. Nevertheless, some ADR-related admissions may have remained undetected if they were not assigned the relevant ICD-10 codes.

Conclusion

Cardiovascular disorders were the most common clinical manifestations of ADR-related hospital admissions, with warfarin being the most frequently implicated drug. Discontinuation of the suspected medication and substitution with alternative therapy were the primary management approaches, while discharge counselling served as the main preventive strategy. Approximately two-thirds of ADR-related hospital admissions were considered preventable, with inappropriate dosage significantly associated with the preventability of ADR-related hospital admissions. These results highlight the importance of implementing targeted interventions to reduce ADR occurrence and related hospitalisations. Strategies such as dose optimisation, appropriate clinical monitoring, structured screening methods, and review of medication use and documented drug allergies have the potential to enhance medication safety, improve patients’ quality of life, and lower healthcare costs.

Acknowledgements

The authors would like to thank the medical records unit for facilitating access to patient information. They are also grateful to all healthcare professionals and patients who participated in the study. Warisara Jiamsathit: Conceptualisation; Methodology; Software; Validation; Formal analysis; Data curation; Writing – Original Draft and Review & Editing; Visualisation. Thanyamai Sapsinpan: Formal analysis. Phrompassorn Khumwatrungruang: Formal analysis. Anthony R. Cox: Investigation; Writing – Review & Editing; Supervision. Narumol Jarernsiripornkul: Conceptualisation; Methodology; Software; Validation; Investigation; Resources; Writing – Review & Editing; Visualisation; Supervision; Project administration; Funding acquisition. This work was conducted with the utilisation of Al (Chat GPT-5.5) to assist in language editing for improving readability and ensuring concise language use. All generated content was carefully reviewed and edited by the authors before inclusion in the manuscript to ensure accuracy and originality.

Ethics approval

This study was approved by the Khon Kaen University Ethics Committee for Human Research (Number HE641581 on 14 November 2021).

Disclosure statement

No potential conflict of interest was reported by the authors.

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[CIT0053] Viera, A. J., & Garrett, J. M. (2005). Understanding interobserver agreement: The kappa statistic. Family Medicine, 37(5), 360–363. [PubMed] [Google Scholar]

[CIT0054] Winterstein, A. G., Sauer, B. C., Hepler, C. D., & Poole, C. (2002). Preventable drug-related hospital admissions. Annals of Pharmacotherapy, 36(7-8), 1238–1248. 10.1345/aph.1A225 [DOI] [PubMed] [Google Scholar]

PERMALINK

Characteristics, preventability, management, and predictors of adverse drug reaction-related hospital admissions: a cross-sectional study in a Thai tertiary care hospital

Warisara Jiamsathit

Thanyamai Sapsinpan

Phrompassorn Khumwatrungruang

Anthony R Cox

Narumol Jarernsiripornkul

Roles

ABSTRACT

Background

Methods

Results

Conclusions

Background

Methods

Study design, setting, and sample

Participants

Data collection form development and testing

Data collection

Data analysis

Results

Figure 1.

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

Table 6.

Discussion

Strengths and limitations

Conclusion

Acknowledgements

Ethics approval

Disclosure statement

References

ACTIONS

PERMALINK

RESOURCES

Cite

Add to Collections

PERMALINK

Characteristics, preventability, management, and predictors of adverse drug reaction-related hospital admissions: a cross-sectional study in a Thai tertiary care hospital

Warisara Jiamsathit

Thanyamai Sapsinpan

Phrompassorn Khumwatrungruang

Anthony R Cox

Narumol Jarernsiripornkul

Roles

ABSTRACT

Background

Methods

Results

Conclusions

Background

Methods

Study design, setting, and sample

Participants

Data collection form development and testing

Data collection

Data analysis

Results

Figure 1.

Table 1.

Table 2.

Table 3.

Table 4.

Table 5.

Table 6.

Discussion

Strengths and limitations

Conclusion

Acknowledgements

Ethics approval

Disclosure statement

References

ACTIONS

PERMALINK

RESOURCES

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