---
title: "Incidence, trends, and in-hospital mortality of thyroid storm in Thailand (2017–2024)"
id: "plos-one-3-epidemiology-and-mortality-rate-of-thyroid-storm-in-thailand-analysis-using-a"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-3-epidemiology-and-mortality-rate-of-thyroid-storm-in-thailand-analysis-using-a"
content_type: "clinical_feed_article"
specialty: "Endocrinology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142"
published_at: "2026-08-27T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Incidence, trends, and in-hospital mortality of thyroid storm in Thailand (2017–2024)
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-3-epidemiology-and-mortality-rate-of-thyroid-storm-in-thailand-analysis-using-a
- **Specialty:** [Endocrinology](https://medichelpline.com/clinical-feed/endocrinology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142)
- **Published At:** 2026-08-27T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This nationwide retrospective study analyzed NHSO inpatient records (2017–2024) of adults admitted with a principal diagnosis of thyrotoxicosis to estimate the **incidence** and outcomes of **thyroid storm** in Thailand. - Out of 4,418 hospitalizations for thyrotoxicosis, 1,160 (26.2%) were coded as thyroid storm and 3,258 (73.8%) as thyrotoxicosis without storm. - The average annual incidence of thyroid storm was 0.22 cases per 100,000 person-years, increasing from 0.15 in 2017 to 0.32 in 2024 (p for trend < 0.001). - **In-hospital mortality** was markedly higher for thyroid storm (18.0%) than for thyrotoxicosis without storm (1.0%); thyroid storm remained an independent predictor of death after multivariable adjustment (aOR 11.17; 95% CI 7.41–16.85). - Strongest independent predictors of death among thyroid storm admissions were **septic shock** (aOR 5.26), **cardiogenic shock** (aOR 4.90), and **acute kidney injury** (aOR 3.72). Advanced age, male sex, and chronic liver disease also independently increased mortality risk. - Median length of stay (LOS) for thyroid storm was 6 days (P25–P75 4–10); prolonged LOS was associated with pneumonia, acute kidney injury, and chronic liver disease. - Data were derived from ICD-coded principal and secondary diagnoses in the NHSO database; thyroid storm was identified by ICD-10 code E05.5. The database did not include clinical scoring systems or re-adjudicated cases. - Authors conclude that incidence is rising and that early recognition and aggressive management of precipitating factors, especially pneumonia, plus broader insurance coverage for advanced therapies, may improve survival. - Data access is restricted by NHSO and IRB; de-identified data are available on request with official approvals.
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosone%2Farticle%3Fid%3D10.1371%2Fjournal.pone.0357142) * * About * Browse * Publish * [](https://journals.plos.org/plosone/ "PLOS One") * Search [advanced search](https://journals.plos.org/plosone/search) * [Browse Topics](https://journals.plos.org/plosone/subjectAreaBrowse) Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click [here](https://github.com/PLOS/plos-thesaurus/blob/master/README.md "Link opens in new window"). [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142) [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142) * 0 [Save](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357142#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357142#savedHeader) * 0 [Citation](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357142#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357142#citedHeader) * 7 [View](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357142#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357142#viewedHeader) * 0 [Share](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357142#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357142#discussedHeader) Open Access Peer-reviewed Research Article # Epidemiology and mortality rate of thyroid storm in Thailand: Analysis using a national in-patient database * Jin Sothornwit , Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing * E-mail: jinso@kku.ac.th Affiliation Department of Medicine, Division of Endocrinology and Metabolism, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0001-6187-4033 ](https://orcid.org/0000-0001-6187-4033 "ORCID Registry") ⨯ * Suranut Charoensri, Roles Writing – review & editing Affiliation Department of Medicine, Division of Endocrinology and Metabolism, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand ⨯ * Dueanchonnee Sribenjalak, Roles Writing – review & editing Affiliation Department of Medicine, Division of Endocrinology and Metabolism, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand ⨯ * Chatlert Pongchaiyakul Roles Supervision, Writing – review & editing Affiliation Department of Medicine, Division of Endocrinology and Metabolism, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand ⨯ # Epidemiology and mortality rate of thyroid storm in Thailand: Analysis using a national in-patient database * Jin Sothornwit, * Suranut Charoensri, * Dueanchonnee Sribenjalak, * Chatlert Pongchaiyakul ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: August 27, 2026 * * [Article](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142) * [Authors](https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0357142) * [Metrics](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357142) * [Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0357142) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pone.0357142) * [Abstract](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#abstract0) * [Introduction](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#sec005) * [Materials and methods](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#sec006) * [Results](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#sec011) * [Discussion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#sec017) * [Conclusion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#sec018) * [Supporting information](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#sec019) * [Acknowledgments](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#ack) * [References](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#references) * [Reader Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0357142) * [Figures](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142) ## Abstract ### Objective To investigate the nationwide incidence, temporal trends, of thyroid storm among hospitalized patients with thyrotoxicosis in Thailand, to compare in-hospital mortality between patients with and without thyroid storm, and to identify independent predictors of mortality and healthcare resource utilization among patients with thyroid storm. ### Methods A retrospective population-based study was conducted using the National Health Security Office database (2017–2024), including adult patients hospitalized with thyrotoxicosis recorded as the principal diagnosis. Thyroid storm was identified by ICD-10 code E05.5, while other thyrotoxicosis diagnoses without thyroid storm served as the comparator group. Temporal trends in incidence were assessed by Poisson regression, and multivariable logistic regression models identified independent predictors of mortality and prolonged length of hospital stay among patients with thyroid storm. ### Results Among 4,418 admissions with thyrotoxicosis as the principal diagnosis, 1,160 (26.2%) were thyroid storm and 3,258 (73.8%) were thyrotoxicosis without thyroid storm. The average annual incidence was 0.22 cases per 100,000 person-years, rising from 0.15 (2017) to 0.32 (2024) (p for trend < 0.001). In-hospital mortality was significantly higher in patients with thyroid storm than in those without (18.0% versus 1.0%; p < 0.001). After multivariable adjustment, thyroid storm was independently associated with increased mortality (aOR 11.17; 95% CI 7.41–16.85; p < 0.001). Septic shock (aOR 5.26), cardiogenic shock (aOR 4.90), and acute kidney injury (aOR 3.72) were the strongest independent predictors of death. The median length of hospital stay was 6 days (P25-P75 4–10). ### Conclusions The incidence of thyroid storm in Thailand is rising, with a high in-hospital mortality of 18.0%. Mortality is independently driven by advanced age, male sex, chronic liver disease, and acute multi-organ complications, particularly septic shock, cardiogenic shock, and acute kidney injury. Prolonged hospitalization is largely driven by pneumonia, acute kidney injury, and chronic liver disease. Early recognition and aggressive management of precipitating factors, particularly pneumonia, and expanded insurance coverage for advanced therapies are essential to improve survival. ## Figures ![Fig 4](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.g004) ![Fig 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.g005) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.g002) ![Table 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.t001) ![Table 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.t002) ![Table 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.t003) ![Fig 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.g003) ![Fig 4](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.g004) ![Fig 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.g005) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.g002) ![Table 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357142.t001) **Citation:** Sothornwit J, Charoensri S, Sribenjalak D, Pongchaiyakul C (2026) Epidemiology and mortality rate of thyroid storm in Thailand: Analysis using a national in-patient database. PLoS One 21(8): e0357142. https://doi.org/10.1371/journal.pone.0357142 **Editor:** Phuping Sucharitakul, Tsinghua University, CHINA **Received:** May 26, 2026; **Accepted:** August 12, 2026; **Published:** August 27, 2026 **Copyright:** © 2026 Sothornwit et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. **Data Availability:** The data utilized in this study were obtained from a third-party organization, the National Health Security Office (NHSO) of Thailand. Data cannot be shared publicly due to legal regulations and ethical restrictions imposed by both the data owner (NHSO) and the Institutional Review Board, as the dataset contains potentially identifying and sensitive clinical patient information. De-identified data are available for researchers who meet the criteria for access to confidential data upon reasonable request and with official approval. Data access requests and inquiries regarding ethical approvals can be directed to the Ethics Committee for Human Research, Khon Kaen University (Contact email: echr@kku.ac.th). **Funding:** The author(s) received no specific funding for this work. **Competing interests:** The authors have declared that no competing interests exist. ## Introduction Thyroid storm represents the most extreme and life-threatening spectrum of thyrotoxicosis, characterized by systemic decompensation and multi-organ failure. [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref001)] Despite significant therapeutic advancements in critical care, thyroid storm remains associated with a formidable mortality rate, ranging from 1.2% to 30% in contemporary clinical series. [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref001)–[8](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref008)] The clinical diagnosis of this condition is predominantly based on phenotypic presentation and standardized scoring systems such as the Burch-Wartofsky Point Scale and the Japan Thyroid Association criteria. [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref001),[6](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref006),[9](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref009)] However, the rarity of the condition and the absence of definitive biochemical thresholds necessitate large-scale epidemiological investigations to refine our understanding of its natural history and prognostic determinants. [[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref005)] On a global scale, the incidence and clinical outcomes of thyroid storm exhibit significant geographic variation, with national database studies from Japan, the United States, and Germany reporting incidence rates between 0.2 and 0.7 per 100,000 person-years. [[2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref002),[6](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref006),[8](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref008)] However, data from Southeast Asia, particularly from middle-income countries with universal healthcare frameworks, remain scarce. This study therefore aims to determine the nationwide incidence, evaluate temporal trends in mortality and healthcare utilization, and identify independent predictors of in-hospital death in Thailand over an eight-year period, with the goal of informing regional management guidelines and improving clinical outcomes for this endocrine emergency. ## Materials and methods ### Study design and data source This retrospective study analyzed inpatient summary data of adult patients (aged ≥18 years) hospitalized with thyrotoxicosis in hospitals within the National Health Security Office (NHSO) network in Thailand between January 2017 and December 2024. The data were accessed for research purposes on 26 January 2026. Because the database contained only de-identified records, the authors did not have access to information that could identify individual participants during or after data collection. The inclusion criteria were adult patients (aged ≥18 years) hospitalized with a principal discharge diagnosis of thyrotoxicosis (including thyroid storm). Admissions were excluded if the patient was younger than 18 years or if thyrotoxicosis was recorded only as a secondary diagnosis without thyroid storm. Clinically, thyrotoxicosis was defined as the syndrome resulting from excess circulating thyroid hormone, and thyroid storm as its life-threatening, decompensated form characterized by thermoregulatory, cardiovascular, hepatic–gastrointestinal, and central nervous system dysfunction. In Thai clinical practice, there is no single nationally mandated diagnostic standard; the clinical diagnosis of thyroid storm is made by the treating physician, most often guided by the Burch–Wartofsky Point Scale (BWPS) and/or the Japan Thyroid Association (JTA) criteria. [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref001),[6](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref006),[9](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.ref009)] Because the NHSO database does not record these underlying clinical scores, thyroid storm and thyrotoxicosis were operationally identified based on the principal-diagnosis ICD-10 codes assigned by the attending physician, rather than being re-adjudicated by the study team. The dataset provided information on principal and secondary diagnoses (up to 22 diagnosis codes), procedures, discharge status, and length of hospital stay (LOS). Diagnoses and procedures were coded using the International Classification of Diseases, Ninth and Tenth Revisions (ICD-9 and ICD-10). Thyroid storm was operationally identified by the ICD-10 code E05.5 recorded as the principal diagnosis. Other forms of thyrotoxicosis without thyroid storm were identified based on ICD-10 codes E05.0, E05.1, E05.2, E05.3, E05.4, E05.8, E05.9, E06.0, E06.1, E06.2, E06.3, E06.4, E06.5, E06.9, and O90.5 recorded as the principal diagnosis only. For the thyrotoxicosis without storm group, etiologies were derived directly from the principal diagnosis code. For the thyroid storm group, etiologies were identified from the secondary diagnosis codes; if no specific etiological code was recorded, the case was classified as unspecified thyrotoxicosis. Comorbidities, in-hospital complications, and potential precipitating factors (e.g., pneumonia, sepsis) were identified from up to 22 secondary ICD-10 diagnosis codes recorded for the same admission, and life-sustaining procedures from ICD-9-CM procedure codes. A condition was regarded as a complication or precipitating factor when its code co-occurred within the same admission. The complete list of ICD codes used to define all study variables is provided in [S1 Table](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.s001). ### Main outcome measures The outcomes of interest included incidence, in-hospital mortality, clinical characteristics, length of hospital stay, and precipitating factors. The incidence of thyroid storm was calculated by dividing the total number of hospital discharges by the annual Thai population estimates provided by the Bureau of Registration Administration. To ensure a conservative national estimate, each hospitalization was analyzed as an independent observation. Patient characteristics included age, sex, etiologies of thyrotoxicosis, comorbidities, and associated life-sustaining procedures. ### Statistical analysis Continuous variables were expressed as mean and standard deviation (SD) or median (25th-75th percentile, P25-P75), depending on data distribution as assessed by the Kolmogorov-Smirnov test. Categorical variables were presented as numbers and percentages. Comparisons between patients with thyroid storm and those without were performed using the Mann-Whitney U test, Student’s t-test, Chi-square test, or Fisher’s exact test, as appropriate. Temporal trends in the annual hospitalization (incidence) rate were assessed by Poisson regression, with the annual case count as the dependent variable, calendar year modelled as a continuous covariate, and the log of the annual mid-year population as an offset; the p value for trend corresponds to the Wald test of the calendar-year coefficient. Univariable analyses identified factors associated with in-hospital mortality among patients with thyroid storm. Variables with p < 0.10 in univariable analyses and clinically relevant parameters were candidates for multivariable logistic regression. To avoid over-adjustment bias, variables representing intermediate outcomes in the causal pathway to death (cardiac arrest and ventricular arrhythmia) were excluded from the multivariable model. The number of predictors was limited according to the events-per-variable criterion (minimum 10 events per variable) to minimize overfitting. Multicollinearity was assessed using Variance Inflation Factors (VIF); all VIF values were below 5 ([S2 Table](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357142#pone.0357142.s002)). Model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and calibration by the Hosmer-Lemeshow goodness-of-fit test. Because length of hospital stay (LOS) data were right-skewed, the variable was summarized using the median (P25–P75) and dichotomized at the median value. Prolonged LOS was defined as a hospital stay longer than the median LOS. Multivariable logistic regression was then performed to identify factors independently associated with prolonged LOS among patients hospitalized with thyroid storm, applying the same events-per-variable criterion (minimum 10 events per variable) to minimize overfitting. All statistical tests were two-sided, with a p-value less than 0.05 considered significant. Analyses were performed using IBM SPSS Statistics version 29.0.2.0. ### Ethics approval and consent to participate The study protocol was approved by the Institutional Review Board of Khon Kaen University (IRB No. 00001189, HE691021)
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