Non-Thyroidal Illness and Its Impact on Outcomes in Critically Ill Patients: A Prospective Cohort Study

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This study aimed to assess the incidence of NTIS among critically ill patients and evaluate its association with clinical outcomes and mortality. Methods: A prospective cohort study was conducted in a tertiary hospital between 2019 and 2025. Adult intensive care unit (ICU) patients without known thyroid disease were included. Thyroid function tests (thyroid-stimulating hormone (TSH), free thyroxine (T4) and free triiodothyronine (T3) were measured within 24 hours of admission. Clinical data, comorbidities, and outcomes were recorded. Patients were classified as having low T3 (< 3.0 nmol/L) or normal T3 (≥ 3.0 nmol/L). Logistic regression and ROC analyses were performed to assess associations with mortality. Results: Among 180 patients (mean age 57.7 ± 18 years; 62% male), 52% had NTIS. Patients with low T3 were older and had higher rates of diabetes, cardiovascular, and pulmonary diseases (p < 0.05). They more frequently developed shock, respiratory failure, and required mechanical ventilation. ICU mortality was significantly higher among the low T3 group (43% vs. 7.5%, p < 0.001). After adjustment for age, comorbidities, and illness severity, both low T3 (OR 0.23, 95% CI 0.07–0.79; p = 0.007) and low T4 (OR 0.80, 95% CI 0.65–0.99; p = 0.03) remained independently associated with mortality, whereas TSH showed no significant association. ROC analysis demonstrated that T3 had the strongest inverse correlation with survival (AUC = 0.208, p < 0.001). Conclusion: NTIS is highly prevalent in critically ill patients and is associated with increased mortality and adverse outcomes. Low T3, in particular, reflects disease severity and may serve as a prognostic marker in the ICU. Non-thyroidal illness syndrome Low T3 Critical illness Mortality Thyroid hormones Intensive care unit Figures Figure 1 Introduction Recent studies have found that alterations in thyroid hormones are closely linked to mortality and overall prognosis among critically ill patients. These changes in thyroid hormones are referred to as euthyroid sick syndrome, nonthyroidal illness syndrome, or low triiodothyronine (T3) syndrome (Guo 2020). This resulted from disruptions in the hypothalamic-pituitary-thyroid axis, characterized by decreased T3 levels, variable thyroxine (T4) changes, and typically normal or decreased thyroid-stimulating hormone (TSH) levels. The changes in serum T3 and reverse T3 (rT3) levels depend on the severity of the illness. In mild to moderate nonthyroidal illness syndrome, T4 and TSH levels are usually normal with low free T3 and increased rT3, but in more severe or long-lasting illnesses, they are often low [ 1 , 2 ]. One of the major mechanisms behind these thyroidal hormonal alterations is the altered activity of deiodinases, the enzymes that regulate the metabolism of thyroid hormones. Decreased activity of type 1 and type 2 deiodinases in peripheral tissues reduces the conversion of T4 to the bioactive form triiodothyronine (T3), while the increased activity of deiodinase type 3 leads to the degradation of T3 and T4 to inactive metabolites such as reverse T3 (rT3) [ 2 , 3 ]. The low T3 levels are often caused by proinflammatory cytokines (such as IL-6 and TNF-alpha) and oxidative stress, especially in conditions such as sepsis, renal failure, or multi-organ failure, which contribute to the low T3 state seen in NTIS [ 1 , 3 , 4 ]. Observational studies have shown a significant association between these thyroid profile abnormalities and increased mortality risk among intensive care unit (ICU) patients, lending support for the use of thyroid hormones as prognostic markers in critical care settings [ 2 , 5 ]. The prognostic value of thyroid profiles (i.e., low T3 and abnormal TSH) has also been associated with disease severity and outcomes among critically ill populations [ 6 , 7 ]. In addition, other studies have shown that non-thyroidal illness is common in more inclusive ICUs and have identified risk factors such as sepsis and mechanical ventilation, which may aggravate thyroid dysfunction [ 1 , 3 ]. Understanding how thyroid function relates to mortality in the ICU is important to establish its clinical use as a prognostic indicator and a possible therapeutic target. Therefore, the present study aims to assess the incidence of non-thyroidal illness syndrome (NTIS) among ICU patients and to examine its association with clinical outcomes, including disease severity and mortality. Furthermore, the study seeks to identify potential risk factors that contribute to the development of NTIS in critically ill patients, to clarify its prognostic significance and potential role as a therapeutic target in critical care settings. Methods Study Design This study was a prospective cohort study of critically ill patients who were admitted to either medical or surgical ICU sections at An-Najah National University Hospital. The calculated minimum sample size was 186, based on an expected prevalence of 30% thyroid dysfunction, 80% power, and a 90% confidence interval. Inclusion criteria were critically ill patients aged 18–90 years or older admitted to the ICU. Exclusion criteria included patients with a known thyroid disease, patients being treated for hypo-or hyperthyroidism in the past, and pregnant women. Data Collection Thyroid function tests (TSH, free T3, free T4) were evaluated in the first 24 hours of ICU admission. In addition to thyroid function tests, comprehensive data were collected on various patient-related variables. Demographics such as age, gender, and body mass index (BMI) were collected at the time of ICU admission. Comorbidities such as diabetes, hypertension, cardiovascular disease, and malignancies, patients’ reasons for hospital admission and ICU admission were also recorded, along with whether patients were in shock, were in respiratory failure, or required mechanical ventilation. Blood tests obtained at ICU admission included white blood cell count (WBC), haemoglobin (HGB), platelet count (PLT), C-reactive protein (CRP), creatinine, potassium (K), sodium (Na), and international normalised ratio (INR). Outcomes were recorded based on mortality or discharge status during the ICU stay. Moreover, the duration of mechanical ventilation was observed along with the occurrence of AKI, shock, or respiratory failure post-ICU admission. Definitions Thyroid dysfunction is considered any abnormality in thyroid hormone (TSH, free T3, free T4) levels relative to reference ranges for patients with critical illness, shock was recognised as a state of inadequate blood flow that causes inadequate tissue oxygenation, which requires vasopressor support according to the Surviving Sepsis Campaign definition [ 8 ]. Respiratory failure was any inappropriate gas exchange characterised as either or both hypoxemia and hypercapnia, and AKI was defined as an unexpected increase in renal dysfunction characterised by an increase in serum creatinine and or decrease in urine output based on the definition of Kidney Disease Improving Global Outcomes (KDIGO) [ 9 ]. Statistical analysis Statistical tests were used for variables to analyse the data accordingly to assess the association between variables; for numerical variables, the independent T-test was used. Chi-square and Fisher's exact tests were conducted with categorical variables. Almost all comparisons between the study groups were assessed, adjusting for confounders with logistic regression. The confounders included variables such as the presence of comorbidities (ex, diabetes, hypertension), illness severity, and ICU treatments. For analytical purposes, patients were classified into two groups based on their serum total T3 levels: low T3 (< 3.0 nmol/L) and normal T3 (≥ 3.0 nmol/L), using the lower limit of the laboratory reference range as the clinical cut-off. A receiver operating characteristic (ROC) curve analysis was then performed to assess the diagnostic performance of various thyroid hormones, including T3, T4, and TSH, in identifying non-thyroidal illness syndrome. The ROC analysis provided the optimal cut-off values for each hormone, including a T3 threshold of 1.6 nmol/L. Area under the curve (AUC) values were calculated for T3, T4, and TSH to evaluate their sensitivity, specificity, and overall diagnostic accuracy. While this lower threshold demonstrated improved specificity, the standard reference value was retained for group comparisons to maintain clinical relevance and interpretability. All analyses were performed using SPSS (Statistical Package for the Social Sciences) Statistics version 25. Results Of the total sample of 180 ICU patients, the mean age was 57.7 ± 18 years, with 111 (62%) males. Comorbidities included diabetes (38%), hypertension (49%), cardiovascular disease (36%), chronic pulmonary disease (14%), malignancy (35%), chronic kidney disease (18%), end-stage renal disease (7%), chronic liver disease (5.6%), and smoking (28%). Patients were admitted to the surgical ICU (40%) or medical ICU (60%), with 36% having sepsis, 64% being on shock, 59% with respiratory failure, and 30% requiring mechanical ventilation. (Table 1 ) Table 1 Baseline characteristics and comparisons of critically ill ICU patients by thyroid function status. Variables Total (n = 180) Low T3 (n = 93) Normal T3 (n = 85) P-value Age, mean ± SD 57.7 ± 18 61.8 ± 18 54 ± 18 0.004 Gender, male, n (%) 111 (62) 49 (57) 60 (64) 0.4 Comorbidities Diabetes 68 (38) 39 (46) 29 (31) 0.04 Hypertension 87 (49) 46 (54) 40 (43) 0.16 Cardiovascular disease 66 (36) 39 (45) 26 (28) 0.013 Chronic pulmonary disease 25 (14) 19 (22) 5 (5.4) 0.001 Malignancy Hematological 63 (35) 17 (27%) 36 (42) 11 (30) 26 (28) 6 (23) 0.06 0.5 Chronic kidney disease 33 (18) 20 (23) 13 (14) 0.1 End-stage renal disease 13 (7) 6 (7.1) 7 (7.5) 0.8 Chronic liver disease 10 (5.6) 7 (8) 3 (3) 0.15 Smoker 50 (28) 27 ( 32) 23 (25) 0.3 Location Surgical ICU 108 (40) 38 (54) 67 (72) < 0.001 Medical ICU 72 (60) 45 (52) 26 (28) Baseline status Sepsis 66 (36) 47 (55.3) 19 (20) < 0.001 Shock 64 (64) 48 (56) 16 (17) < 0.001 Septic 31 (47) 25 (52) 6 (33) < 0.001 Hemorrhagic 15 (23) 11 (23) 4 (22) 0.057 Cardiogenic 11 (16) 6 (12) 5 (27) 0.6 Mixed 5 (7) 5 (10) - Respiratory failure 107 (59) 62 (72) 44 (47) 0.001 Mechanical ventilated 54 (30) 34 (40) 20 (21.5) 0.007 Baseline labs WBC, (×10⁹/L) 13 ± 8.3 13.1 ± 9.3 12.9 ± 7.5 0.9 HGB, (g/dL) 18 ± 2.6 10 ± 2.2 11.9 ± 2.5 < 0.001 PLT, (×10⁹/L) 228 ± 128 223 ± 147 232 ± 110 0.6 Creatinine, (mg/dL) 1.5 ± 1.7 1.78 ± 1.9 1.3 ± 1.5 0.06 CRP, (mg/L) 82 ± 110 116.7 ± 116 48 ± 94 < 0.01 INR 1.3 ± 0.4 1.4 ± 0.5 1.3 ± 0.5 0.01 SD , standard deviation; ICU , intensive care unit; WBC , white blood cell count; HGB , hemoglobin; PLT , platelet count; CRP , C-reactive protein; INR , international normalized ratio. (Table 1 location) Respiratory failure and post-operative conditions were the most common reasons for ICU admission, each accounting for 39 (22%) and 40 (22%) patients, respectively. Septic shock was 14%, and hemorrhagic shock and brain haemorrhage contributed to 16.7% and 7%, respectively. Gastrointestinal bleeding, Cardiogenic shock and stroke were less frequent, with 3%, 1.7% and 4% of patients, respectively. Other unspecified causes accounted for 33 (17%) of the cases. Univariate and multivariate analyses revealed a strong association between thyroid hormone levels and mortality. Mean T3 and T4 concentrations were significantly lower among non-survivors compared to survivors (T3: 2.3 ± 0.8 vs. 3.6 ± 1.9, P-value < 0.01; T4: 16 ± 6.1 vs. 19 ± 5.2, P-value = 0.01). After adjustment for age, comorbidities, and critical illness factors, both T3 (adjusted p = 0.007, OR 0.23–0.79) and T4 (adjusted p = 0.03, OR 0.8–0.99) remained independently associated with mortality, indicating that lower hormone levels predicted poorer outcomes. In contrast, TSH levels did not differ significantly between survivors and non-survivors (P-value = 0.7), suggesting that pituitary feedback remains blunted during severe illness. The ROC curve analysis further demonstrated that low T3 had the strongest inverse correlation with survival (AUC = 0.208, p < 0.001), followed by T4 (AUC = 0.361, P-value = 0.006), whereas TSH failed to predict mortality (AUC = 0.522, P-value = 0.664). These data confirm that thyroidal suppression, particularly reduced T3, reflects metabolic and inflammatory stress intensity and can serve as a biochemical marker of poor prognosis in the ICU. (Table 2 location) Table 2 Univariate and multivariate analysis of thyroid hormone levels and their association with mortality. Variables Total (n = 180) Alive (n = 44) Deceased (n = 146) P-value Adjusted P-value OR TSH (mIU/L) 3.1 ± 6.6 3.3 ± 7.4 2.8 ± 2.8 0.7 0.9 0.92–1.01 T3, (nmol/L) 3.3 ± 1.8 3.6 ± 1.9 2.3 ± 0.8 < 0.01 0.007 0.23–0.79 T4, (nmol/L) 18 ± 5.6 19 ± 5.2 16 ± 6.1 0.01 0.03 0.8–0.99 TSH, thyroid-stimulating hormone; T3, triiodothyronine; T4, thyroxine, OR; odds ratio; CI – confidence interval. Patients with low T3 had significantly worse clinical outcomes. The overall ICU mortality rate was 24%, but markedly higher among those with low T3 (43% vs. 7.5%, P-value < 0.001). Similarly, 28-day mortality was substantially elevated in the low T3 group (20% vs. 3%, P-value < 0.001). The incidence of acute kidney injury was also greater (22% vs. 10.8%, P-value = 0.036), and shock occurred more frequently (14% vs. 2%, P-value = 0.03), emphasising the link between hormonal suppression and organ dysfunction. Although differences in mechanical ventilation duration and ICU length of stay were not statistically significant, low T3 patients tended to require longer respiratory and hemodynamic support, consistent with more severe disease. Collectively, these outcomes reinforce that non-thyroidal illness syndrome is not merely a biochemical finding but a reflection of critical systemic compromise associated with higher morbidity and mortality. (Table 3 location) Table 3 Outcomes in critically Ill patients by thyroid function status. Outcome Total (n = 180) Low T3 (n = 93) Normal T3 (n = 85) P-value Mortality, n (%) 44 (24) 37 (43) 7 (7.5) < 0.001 28-day mortality, n (%) 20 (11) 17 (20) 3 (3) < 0.001 Mechanical ventilation, n (%) 20 (11) 12 (14) 8 (8.7) 0.3 Duration of ventilation, days 15 ± 20 11.7 ± 18 20.8 ± 48 0.4 Acute kidney injury, n (%) 29 (16) 19 (22) 10 (10.8) 0.036 Shock, n (%) 14 (8) 12 (14) 2 (2) 0.03 Respiratory failure, n (%) 30 (17) 18 (21) 12 (12.9) 0.14 ICU days 8.3 ± 17 9.1 ± 14 7.7 ± 20 0.59 ICU; intensive care unit, T3; triiodothyronine The ROC analysis demonstrated that serum T3 levels had the strongest inverse association with mortality among all thyroid parameters. The area under the curve (AUC) for T3 was 0.208 (p < 0.001), indicating that lower T3 concentrations were strongly linked to poor survival outcomes. T4 also showed a modest but significant predictive value (AUC = 0.361, p = 0.006), whereas TSH failed to demonstrate discriminatory performance (AUC = 0.522, p = 0.664). These findings support that depressed T3 and, to a lesser extent, T4 levels are reflective of disease severity and metabolic suppression during critical illness. However, the overall predictive accuracy remained limited, suggesting that thyroid hormone changes should be interpreted alongside clinical and biochemical indicators of organ dysfunction rather than as stand-alone prognostic tools. (Fig. 1) Discussion This study demonstrated that NTIS is common among critically ill patients and that low T3 is significantly associated with increased mortality and complications. The primary biochemical abnormalities were a low plasma triiodothyronine level, increased reverse T3, and either low or normal thyroxine and TSH levels [ 5 , 7 ]. NTIS is highly prevalent, according to a systematic review and meta-analysis that included 6,869 patients from 25 studies that showed a median prevalence of 58% [ 10 ]. Other observational studies showed variable prevalence, from 17% to 54% (based on low FT3) [ 3 , 5 – 7 , 11 , 12 ], and up to 80% (General definition) [ 6 , 10 , 11 ]. The occurrence and severity of NTIS were associated with poor prognosis and higher all-cause mortality. In this prospective cohort of 180 critically ill patients admitted to medical or surgical ICUs, the prevalence was similar to what is reported in the literature, but in the lower range (51.7%). The effect of low T3 on outcomes was assessed. The results showed that patients with low T3 had a significantly higher overall mortality compared to those without thyroidal illness (43% vs 7.5%, P-value < 0.001), and the 28-day mortality was also higher (20% vs 3%, P-value < 0.001). Both T3 and T4 levels were significantly associated with mortality in multivariate analysis (T3: OR 0.23–0.79; T4: OR 0.8–0.99), while TSH did not show a significant effect. Although the AUC for T3 was below 0.5, indicating an inverse association with survival, it still reflects a strong predictive relationship between low T3 and mortality (AUC = 0.208, P-value < 0.001). In addition, noting that patients with low T3 were significantly older and more likely to develop acute kidney injury and shock. These findings are similar to the literature in which low T3 is associated with worse outcomes in critical illness. Since AUC values below 0.5 indicate an inverse relationship, this suggests that lower T3 levels strongly correlate with mortality rather than protective outcomes. In a systematic review, it was found that T3 and FT3 levels were lower in non-survivors than in survivors, and NTIS was independently associated with increased risk of mortality (OR 2.21, P-value < 0.01) [ 10 ]. Similarly, Guo et al. found that patients with NTIS, defined as FT3 < 3.28 pmol/L, had higher 28-day mortality (19.5% vs 6.4%, P-value = 0.012) [ 5 ], and Patil et al. also showed that patients with low total T3 and free T3 had predicted mortalities of 47.3% (P-value < 0.001) and 47.8% (P-value = 0.003) [ 13 ], respectively. Gutch et al. Showed that free T3 was the strongest predictor of ICU mortality, even more than FT4 or even APACHE II score [ 7 ]. However, while most studies considered FT3/T3 as the most reliable prognostic marker, in this study, T3 was found to have the lowest AUC, which is against the results of several other studies. For example, Gutch et al. reported an AUC of 0.99 for FT3, higher than that for the APACHE II score [ 7 ], while Wang et al. and Chavanda et al. also showed FT3 to be the best predictor for ICU mortality [ 3 , 6 ]. The difference in results may be attributed to variation in study populations, sample size, illness severity, or methods used for hormone measurement. Regarding T4, in this study’s findings, the association with mortality are in line with some studies, as in the Vidart meta-analysis that observed a simultaneous reduction in FT3 and FT4, which was linked with higher mortality [ 10 ]. Moreover, the prognostic prediction of T4 remains inconsistent, as other studies, such as Chavanda et al., showed no significant difference in FT4 levels between survivors and non-survivors [ 6 ]. For TSH, our study did not show any significant association with mortality, which is also consistent with the majority of studies, including the Vidart meta-analysis, where no meaningful difference was found in TSH levels between survivors and non-survivors [ 10 ]. The findings of our study showed that NTIS, reflected by low T3 levels, is strongly associated with clinical indicators of severe disease and adverse outcomes. Patients classified within the low T3 group had increased incidence of major complications, including acute kidney injury (AKI) (22% vs. 10.8%), shock (14% vs. 2%), and need for mechanical ventilation (40% vs. 21.5%, P = 0.007). These results are in line with several previous studies that have shown a strong association between low T3/FT3 and disease. As in Guo et al. That reported significantly higher APACHE II and SOFA scores among patients with euthyroid sick syndrome ( P-value < 0.001 for both)[ 5 ], while Chavanda and Mane identified a negative correlation between FT3 and APACHE II (r = − 0.4083; P-value = 0.0032) [ 6 ]. Similarly, Praveen et al. found higher APACHE II scores in the NTIS group compared with euthyroid patients, and another study in Turkey had observed a strong correlation between FT3 and APACHE II (r = 0.69, P-value = 0.001) [ 14 ] Similar to our results, the study by Bello et al. observed that low FT3 was a significant predictor of prolonged mechanical ventilation [ 15 ], while Guo et al. found that serum creatinine is a mortality-related factor, supporting our observed link between NTIS and renal dysfunction. In our cohort, patients with NTIS had elevated CRP and reduced haemoglobin levels. These trends are observed in prior studies demonstrating a consistent inverse relationship between FT3 and systemic inflammation. For instance, Wang et al. found significant negative correlations between FT3 and both CRP (r = − 0.408, P-value < 0.001) [ 3 ], while Gao et al. find similar negative correlations with CRP (r = − 0.66, P-value < 0.001) and IL-6 (r = − 0.60, P-value < 0.001) in COVID-19 patients [ 5 ]. This study has several limitations that should be considered. First, it was a single-centre study with a relatively small sample size, which may limit the generalizability of the findings. Second, due to its observational and non-interventional design, a causal relationship between thyroid dysfunction and mortality cannot be established. The hormonal changes observed may reflect illness severity rather than act as independent predictors. Third, illness severity scores such as SOFA or APACHE II were not available, which might have allowed better adjustment for disease burden and organ dysfunction. Fourth, thyroid hormones were measured only once upon ICU admission; therefore, dynamic changes during recovery or deterioration were not assessed. Additionally, reverse T3 and cortisol were not evaluated, which could have provided further insight into the neuroendocrine adaptation to stress. Finally, treatment-related factors such as corticosteroid or dopamine use, which can affect thyroid hormones, were not systematically recorded. Despite these limitations, this study provides important local evidence on the prognostic impact of non-thyroidal illness among critically ill patients and highlights the need for larger multicenter studies with longitudinal hormonal assessment and integrated severity scoring. In conclusion, the results of this study confirmed that NTIS is common in critically ill patients and that low T3 is associated with increased mortality and adverse outcomes. Although the predictive accuracy of T3 for mortality in this cohort appeared less predictable than what was reported elsewhere, the consistent association across multiple studies highlights the importance of recognising NTIS as a poor prognostic marker. Overall, NTIS is common in the ICU and serves as a marker of illness severity and poor prognosis. Future multicenter studies with serial hormone measurements and integration of severity scores are warranted to validate its prognostic role. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board (IRB) of An-Najah National University and conducted in accordance with the Declaration of Helsinki. Informed consent was waived by the IRB, as data were collected from hospital records, and labs are routinely drawn from patients with no extra tests being done. Ref: Med.June.2024/10 Clinical trial number not applicable. Competing interests The authors declare that they have no competing interests. Declarations of AI Use Declarations of Helsinki for human-subject research Funding No external funding was received for this study. Author Contribution RR contributed to study design, data collection, data analysis, manuscript drafting, and approval of the final version.DA contributed to study design, supervision, critical revision, and approval of the final version.II, MS, AO, and KM contributed to data collection and approval of the final version.All authors read and approved the final manuscript. Acknowledgement The authors would like to thank the intensive care unit at An-Najah National University Hospital for their support in data collection. Data Availability The datasets used during the current study are available from the corresponding author upon request. References Bose P, Dasarathan R, Murugesan ASM, Chenthil KS. Relationship between thyroid function and ICU mortality (sick euthyroid syndrome). 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Correlation of Thyroid Hormones in the Prognosis of Critically Ill Patients. Siriraj Med J. 2021;73:161–6. https://doi.org/10.33192/Smj.2021.21 . Gutch M, Kumar S, Gupta KK. Prognostic Value of Thyroid Profile in Critical Care Condition. Indian J Endocrinol Metab. 2018;22:387–91. https://doi.org/10.4103/ijem.IJEM_20_18 . Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47:1181–247. https://doi.org/10.1007/s00134-021-06506-y . Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2024;105:S117–314. https://doi.org/10.1016/j.kint.2023.10.018 . Vidart J, Jaskulski P, Kunzler AL, Marschner RA, Silva AF, de da A, Wajner SM. 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Endokrynologia Polska. 2016;67:305–12. https://doi.org/10.5603/EP.a2016.0032 . Bello G, Ceaichisciuc I, Silva S, Antonelli M. The role of thyroid dysfunction in the critically ill: a review of the literature. Minerva Anestesiol. 2010;76:919–28. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7981571","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561131392,"identity":"24e5e9f8-8344-49cf-ad4e-875ae9d82a8d","order_by":0,"name":"Razan 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Safa","email":"","orcid":"","institution":"An-Najah National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mays","middleName":"","lastName":"Safa","suffix":""},{"id":561131395,"identity":"f6631572-e052-4216-9917-d77e6a76cf78","order_by":3,"name":"Anas Odeh","email":"","orcid":"","institution":"Cleveland Clinic","correspondingAuthor":false,"prefix":"","firstName":"Anas","middleName":"","lastName":"Odeh","suffix":""},{"id":561131396,"identity":"6946c1de-8abf-47dc-8138-413a95846e68","order_by":4,"name":"Khair Marmash","email":"","orcid":"","institution":"An-Najah National University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Khair","middleName":"","lastName":"Marmash","suffix":""},{"id":561131397,"identity":"2b678c00-6577-4c4d-b72d-c03aa2aae263","order_by":5,"name":"Dina Abugaber","email":"","orcid":"","institution":"An-Najah National University","correspondingAuthor":false,"prefix":"","firstName":"Dina","middleName":"","lastName":"Abugaber","suffix":""}],"badges":[],"createdAt":"2025-10-29 15:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7981571/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7981571/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98748751,"identity":"319f1162-7836-4555-b497-00d49e4b410b","added_by":"auto","created_at":"2025-12-22 08:56:34","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72077,"visible":true,"origin":"","legend":"","description":"","filename":"ThyroidDysfunctionmanuscripts1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7981571/v1/87702bbab0160cd48b6f3f59.docx"},{"id":98777238,"identity":"28c0a11d-9ebb-4d5f-9d9b-e4b1cca3edca","added_by":"auto","created_at":"2025-12-22 12:26:11","extension":"json","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7927,"visible":true,"origin":"","legend":"","description":"","filename":"61a8b232faf54637bad385ddded334ee.json","url":"https://assets-eu.researchsquare.com/files/rs-7981571/v1/519085a20bfad5275c8342f2.json"},{"id":98777535,"identity":"fa6ae561-ac57-4497-aec4-5804797b251f","added_by":"auto","created_at":"2025-12-22 12:27:57","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80794,"visible":true,"origin":"","legend":"","description":"","filename":"61a8b232faf54637bad385ddded334ee1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7981571/v1/220a9267b97c0c7b44b21346.xml"},{"id":98748749,"identity":"d7da002e-2135-48f4-93c7-66203243c0fb","added_by":"auto","created_at":"2025-12-22 08:56:34","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":60738,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1ROC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7981571/v1/36c95b9d4229a1ecbcccc611.pdf"},{"id":98748752,"identity":"d557e655-517e-4467-8379-084f566600c6","added_by":"auto","created_at":"2025-12-22 08:56:34","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79039,"visible":true,"origin":"","legend":"","description":"","filename":"61a8b232faf54637bad385ddded334ee1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7981571/v1/35bf53455fa61763b24eb907.xml"},{"id":98748754,"identity":"8ba33592-d133-430c-bf59-c52b15457a8f","added_by":"auto","created_at":"2025-12-22 08:56:34","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88215,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7981571/v1/5de2ae506a3839d61403fa40.html"},{"id":98748747,"identity":"188ad16f-7010-4f0d-a531-7e60136fb486","added_by":"auto","created_at":"2025-12-22 08:56:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":153868,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) curves of TSH, T3, and T4 for predicting mortality.\u003c/p\u003e\n\u003cp\u003eAUC – area under the curve; TSH; thyroid-stimulating hormone; T3; triiodothyronine; T4; thyroxine\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7981571/v1/6dc5053e1b06505734a211a8.png"},{"id":99679625,"identity":"2bb819fa-ad92-42f9-879d-9bd172afde3e","added_by":"auto","created_at":"2026-01-07 08:40:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":877235,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7981571/v1/6d11fde1-0242-4a41-b0fa-ccb6b2cf67d3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Non-Thyroidal Illness and Its Impact on Outcomes in Critically Ill Patients: A Prospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRecent studies have found that alterations in thyroid hormones are closely linked to mortality and overall prognosis among critically ill patients. These changes in thyroid hormones are referred to as euthyroid sick syndrome, nonthyroidal illness syndrome, or low triiodothyronine (T3) syndrome (Guo 2020). This resulted from disruptions in the hypothalamic-pituitary-thyroid axis, characterized by decreased T3 levels, variable thyroxine (T4) changes, and typically normal or decreased thyroid-stimulating hormone (TSH) levels. The changes in serum T3 and reverse T3 (rT3) levels depend on the severity of the illness. In mild to moderate nonthyroidal illness syndrome, T4 and TSH levels are usually normal with low free T3 and increased rT3, but in more severe or long-lasting illnesses, they are often low [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne of the major mechanisms behind these thyroidal hormonal alterations is the altered activity of deiodinases, the enzymes that regulate the metabolism of thyroid hormones. Decreased activity of type 1 and type 2 deiodinases in peripheral tissues reduces the conversion of T4 to the bioactive form triiodothyronine (T3), while the increased activity of deiodinase type 3 leads to the degradation of T3 and T4 to inactive metabolites such as reverse T3 (rT3) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The low T3 levels are often caused by proinflammatory cytokines (such as IL-6 and TNF-alpha) and oxidative stress, especially in conditions such as sepsis, renal failure, or multi-organ failure, which contribute to the low T3 state seen in NTIS [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eObservational studies have shown a significant association between these thyroid profile abnormalities and increased mortality risk among intensive care unit (ICU) patients, lending support for the use of thyroid hormones as prognostic markers in critical care settings [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The prognostic value of thyroid profiles (i.e., low T3 and abnormal TSH) has also been associated with disease severity and outcomes among critically ill populations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition, other studies have shown that non-thyroidal illness is common in more inclusive ICUs and have identified risk factors such as sepsis and mechanical ventilation, which may aggravate thyroid dysfunction [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Understanding how thyroid function relates to mortality in the ICU is important to establish its clinical use as a prognostic indicator and a possible therapeutic target. Therefore, the present study aims to assess the incidence of non-thyroidal illness syndrome (NTIS) among ICU patients and to examine its association with clinical outcomes, including disease severity and mortality. Furthermore, the study seeks to identify potential risk factors that contribute to the development of NTIS in critically ill patients, to clarify its prognostic significance and potential role as a therapeutic target in critical care settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study was a prospective cohort study of critically ill patients who were admitted to either medical or surgical ICU sections at An-Najah National University Hospital. The calculated minimum sample size was 186, based on an expected prevalence of 30% thyroid dysfunction, 80% power, and a 90% confidence interval. Inclusion criteria were critically ill patients aged 18\u0026ndash;90 years or older admitted to the ICU. Exclusion criteria included patients with a known thyroid disease, patients being treated for hypo-or hyperthyroidism in the past, and pregnant women.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eThyroid function tests (TSH, free T3, free T4) were evaluated in the first 24 hours of ICU admission. In addition to thyroid function tests, comprehensive data were collected on various patient-related variables. Demographics such as age, gender, and body mass index (BMI) were collected at the time of ICU admission. Comorbidities such as diabetes, hypertension, cardiovascular disease, and malignancies, patients\u0026rsquo; reasons for hospital admission and ICU admission were also recorded, along with whether patients were in shock, were in respiratory failure, or required mechanical ventilation. Blood tests obtained at ICU admission included white blood cell count (WBC), haemoglobin (HGB), platelet count (PLT), C-reactive protein (CRP), creatinine, potassium (K), sodium (Na), and international normalised ratio (INR). Outcomes were recorded based on mortality or discharge status during the ICU stay. Moreover, the duration of mechanical ventilation was observed along with the occurrence of AKI, shock, or respiratory failure post-ICU admission.\u003c/p\u003e\n\u003ch3\u003eDefinitions\u003c/h3\u003e\n\u003cp\u003eThyroid dysfunction is considered any abnormality in thyroid hormone (TSH, free T3, free T4) levels relative to reference ranges for patients with critical illness, shock was recognised as a state of inadequate blood flow that causes inadequate tissue oxygenation, which requires vasopressor support according to the Surviving Sepsis Campaign definition [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Respiratory failure was any inappropriate gas exchange characterised as either or both hypoxemia and hypercapnia, and AKI was defined as an unexpected increase in renal dysfunction characterised by an increase in serum creatinine and or decrease in urine output based on the definition of Kidney Disease Improving Global Outcomes (KDIGO) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical tests were used for variables to analyse the data accordingly to assess the association between variables; for numerical variables, the independent T-test was used. Chi-square and Fisher's exact tests were conducted with categorical variables. Almost all comparisons between the study groups were assessed, adjusting for confounders with logistic regression. The confounders included variables such as the presence of comorbidities (ex, diabetes, hypertension), illness severity, and ICU treatments.\u003c/p\u003e \u003cp\u003eFor analytical purposes, patients were classified into two groups based on their serum total T3 levels: low T3 (\u0026lt;\u0026thinsp;3.0 nmol/L) and normal T3 (\u0026ge;\u0026thinsp;3.0 nmol/L), using the lower limit of the laboratory reference range as the clinical cut-off. A receiver operating characteristic (ROC) curve analysis was then performed to assess the diagnostic performance of various thyroid hormones, including T3, T4, and TSH, in identifying non-thyroidal illness syndrome. The ROC analysis provided the optimal cut-off values for each hormone, including a T3 threshold of 1.6 nmol/L. Area under the curve (AUC) values were calculated for T3, T4, and TSH to evaluate their sensitivity, specificity, and overall diagnostic accuracy. While this lower threshold demonstrated improved specificity, the standard reference value was retained for group comparisons to maintain clinical relevance and interpretability.\u003c/p\u003e \u003cp\u003eAll analyses were performed using SPSS (Statistical Package for the Social Sciences) Statistics version 25.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOf the total sample of 180 ICU patients, the mean age was 57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18 years, with 111 (62%) males. Comorbidities included diabetes (38%), hypertension (49%), cardiovascular disease (36%), chronic pulmonary disease (14%), malignancy (35%), chronic kidney disease (18%), end-stage renal disease (7%), chronic liver disease (5.6%), and smoking (28%). Patients were admitted to the surgical ICU (40%) or medical ICU (60%), with 36% having sepsis, 64% being on shock, 59% with respiratory failure, and 30% requiring mechanical ventilation. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics and comparisons of critically ill ICU patients by thyroid function status.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;180)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow T3 (n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal T3 (n\u0026thinsp;=\u0026thinsp;85)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.8\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender, male, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic pulmonary disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003cp\u003eHematological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (35)\u003c/p\u003e \u003cp\u003e17 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (42)\u003c/p\u003e \u003cp\u003e11 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (28)\u003c/p\u003e \u003cp\u003e6 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnd-stage renal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic liver disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 ( 32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSurgical ICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical ICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSepsis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemorrhagic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiogenic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline labs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB, (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT, (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e228\u0026thinsp;\u0026plusmn;\u0026thinsp;128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e223\u0026thinsp;\u0026plusmn;\u0026thinsp;147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232\u0026thinsp;\u0026plusmn;\u0026thinsp;110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP, (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82\u0026thinsp;\u0026plusmn;\u0026thinsp;110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.7\u0026thinsp;\u0026plusmn;\u0026thinsp;116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u0026thinsp;\u0026plusmn;\u0026thinsp;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4 \u0026plusmn; 0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSD\u003c/b\u003e, standard deviation; \u003cb\u003eICU\u003c/b\u003e, intensive care unit; \u003cb\u003eWBC\u003c/b\u003e, white blood cell count; \u003cb\u003eHGB\u003c/b\u003e, hemoglobin; \u003cb\u003ePLT\u003c/b\u003e, platelet count; \u003cb\u003eCRP\u003c/b\u003e, C-reactive protein; \u003cb\u003eINR\u003c/b\u003e, international normalized ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e location)\u003c/p\u003e \u003cp\u003eRespiratory failure and post-operative conditions were the most common reasons for ICU admission, each accounting for 39 (22%) and 40 (22%) patients, respectively. Septic shock was 14%, and hemorrhagic shock and brain haemorrhage contributed to 16.7% and 7%, respectively. Gastrointestinal bleeding, Cardiogenic shock and stroke were less frequent, with 3%, 1.7% and 4% of patients, respectively. Other unspecified causes accounted for 33 (17%) of the cases.\u003c/p\u003e \u003cp\u003eUnivariate and multivariate analyses revealed a strong association between thyroid hormone levels and mortality. Mean T3 and T4 concentrations were significantly lower among non-survivors compared to survivors (T3: 2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 vs. 3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01; T4: 16\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1 vs. 19\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2, P-value\u0026thinsp;=\u0026thinsp;0.01). After adjustment for age, comorbidities, and critical illness factors, both T3 (adjusted p\u0026thinsp;=\u0026thinsp;0.007, OR 0.23\u0026ndash;0.79) and T4 (adjusted p\u0026thinsp;=\u0026thinsp;0.03, OR 0.8\u0026ndash;0.99) remained independently associated with mortality, indicating that lower hormone levels predicted poorer outcomes. In contrast, TSH levels did not differ significantly between survivors and non-survivors (P-value\u0026thinsp;=\u0026thinsp;0.7), suggesting that pituitary feedback remains blunted during severe illness.\u003c/p\u003e \u003cp\u003eThe ROC curve analysis further demonstrated that low T3 had the strongest inverse correlation with survival (AUC\u0026thinsp;=\u0026thinsp;0.208, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), followed by T4 (AUC\u0026thinsp;=\u0026thinsp;0.361, P-value\u0026thinsp;=\u0026thinsp;0.006), whereas TSH failed to predict mortality (AUC\u0026thinsp;=\u0026thinsp;0.522, P-value\u0026thinsp;=\u0026thinsp;0.664). These data confirm that thyroidal suppression, particularly reduced T3, reflects metabolic and inflammatory stress intensity and can serve as a biochemical marker of poor prognosis in the ICU.\u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e location)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate analysis of thyroid hormone levels and their association with mortality.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;180)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive (n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeceased (n\u0026thinsp;=\u0026thinsp;146)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH (mIU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.92\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3, (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.23\u0026ndash;0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4, (nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e18\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e19\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e16\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8\u0026ndash;0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTSH, thyroid-stimulating hormone; T3, triiodothyronine; T4, thyroxine, OR; odds ratio; CI \u0026ndash; confidence interval.\u003c/p\u003e \u003cp\u003ePatients with low T3 had significantly worse clinical outcomes. The overall ICU mortality rate was 24%, but markedly higher among those with low T3 (43% vs. 7.5%, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, 28-day mortality was substantially elevated in the low T3 group (20% vs. 3%, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The incidence of acute kidney injury was also greater (22% vs. 10.8%, P-value\u0026thinsp;=\u0026thinsp;0.036), and shock occurred more frequently (14% vs. 2%, P-value\u0026thinsp;=\u0026thinsp;0.03), emphasising the link between hormonal suppression and organ dysfunction.\u003c/p\u003e \u003cp\u003eAlthough differences in mechanical ventilation duration and ICU length of stay were not statistically significant, low T3 patients tended to require longer respiratory and hemodynamic support, consistent with more severe disease. Collectively, these outcomes reinforce that non-thyroidal illness syndrome is not merely a biochemical finding but a reflection of critical systemic compromise associated with higher morbidity and mortality.\u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e location)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOutcomes in critically Ill patients by thyroid function status.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;180)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow T3 (n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal T3 (n\u0026thinsp;=\u0026thinsp;85)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28-day mortality, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of ventilation, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute kidney injury, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShock, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory failure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.7\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eICU; intensive care unit, T3; triiodothyronine\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe ROC analysis demonstrated that serum T3 levels had the strongest inverse association with mortality among all thyroid parameters. The area under the curve (AUC) for T3 was 0.208 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that lower T3 concentrations were strongly linked to poor survival outcomes. T4 also showed a modest but significant predictive value (AUC\u0026thinsp;=\u0026thinsp;0.361, p\u0026thinsp;=\u0026thinsp;0.006), whereas TSH failed to demonstrate discriminatory performance (AUC\u0026thinsp;=\u0026thinsp;0.522, p\u0026thinsp;=\u0026thinsp;0.664). These findings support that depressed T3 and, to a lesser extent, T4 levels are reflective of disease severity and metabolic suppression during critical illness. However, the overall predictive accuracy remained limited, suggesting that thyroid hormone changes should be interpreted alongside clinical and biochemical indicators of organ dysfunction rather than as stand-alone prognostic tools. (Fig.\u0026nbsp;1)\u003c/td\u003e\u003c/tr\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated that NTIS is common among critically ill patients and that low T3 is significantly associated with increased mortality and complications. The primary biochemical abnormalities were a low plasma triiodothyronine level, increased reverse T3, and either low or normal thyroxine and TSH levels [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. NTIS is highly prevalent, according to a systematic review and meta-analysis that included 6,869 patients from 25 studies that showed a median prevalence of 58% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Other observational studies showed variable prevalence, from 17% to 54% (based on low FT3) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and up to 80% (General definition) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The occurrence and severity of NTIS were associated with poor prognosis and higher all-cause mortality.\u003c/p\u003e \u003cp\u003eIn this prospective cohort of 180 critically ill patients admitted to medical or surgical ICUs, the prevalence was similar to what is reported in the literature, but in the lower range (51.7%). The effect of low T3 on outcomes was assessed. The results showed that patients with low T3 had a significantly higher overall mortality compared to those without thyroidal illness (43% vs 7.5%, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the 28-day mortality was also higher (20% vs 3%, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both T3 and T4 levels were significantly associated with mortality in multivariate analysis (T3: OR 0.23\u0026ndash;0.79; T4: OR 0.8\u0026ndash;0.99), while TSH did not show a significant effect. Although the AUC for T3 was below 0.5, indicating an inverse association with survival, it still reflects a strong predictive relationship between low T3 and mortality (AUC\u0026thinsp;=\u0026thinsp;0.208, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, noting that patients with low T3 were significantly older and more likely to develop acute kidney injury and shock.\u003c/p\u003e \u003cp\u003eThese findings are similar to the literature in which low T3 is associated with worse outcomes in critical illness. Since AUC values below 0.5 indicate an inverse relationship, this suggests that lower T3 levels strongly correlate with mortality rather than protective outcomes. In a systematic review, it was found that T3 and FT3 levels were lower in non-survivors than in survivors, and NTIS was independently associated with increased risk of mortality (OR 2.21, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similarly, Guo et al. found that patients with NTIS, defined as FT3\u0026thinsp;\u0026lt;\u0026thinsp;3.28 pmol/L, had higher 28-day mortality (19.5% vs 6.4%, P-value\u0026thinsp;=\u0026thinsp;0.012) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and Patil et al. also showed that patients with low total T3 and free T3 had predicted mortalities of 47.3% (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 47.8% (P-value\u0026thinsp;=\u0026thinsp;0.003) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], respectively. Gutch et al. Showed that free T3 was the strongest predictor of ICU mortality, even more than FT4 or even APACHE II score [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, while most studies considered FT3/T3 as the most reliable prognostic marker, in this study, T3 was found to have the lowest AUC, which is against the results of several other studies. For example, Gutch et al. reported an AUC of 0.99 for FT3, higher than that for the APACHE II score [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], while Wang et al. and Chavanda et al. also showed FT3 to be the best predictor for ICU mortality [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The difference in results may be attributed to variation in study populations, sample size, illness severity, or methods used for hormone measurement.\u003c/p\u003e \u003cp\u003eRegarding T4, in this study\u0026rsquo;s findings, the association with mortality are in line with some studies, as in the Vidart meta-analysis that observed a simultaneous reduction in FT3 and FT4, which was linked with higher mortality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, the prognostic prediction of T4 remains inconsistent, as other studies, such as Chavanda et al., showed no significant difference in FT4 levels between survivors and non-survivors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For TSH, our study did not show any significant association with mortality, which is also consistent with the majority of studies, including the Vidart meta-analysis, where no meaningful difference was found in TSH levels between survivors and non-survivors [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings of our study showed that NTIS, reflected by low T3 levels, is strongly associated with clinical indicators of severe disease and adverse outcomes. Patients classified within the low T3 group had increased incidence of major complications, including acute kidney injury (AKI) (22% vs. 10.8%), shock (14% vs. 2%), and need for mechanical ventilation (40% vs. 21.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). These results are in line with several previous studies that have shown a strong association between low T3/FT3 and disease. As in Guo et al. That reported significantly higher APACHE II and SOFA scores among patients with euthyroid sick syndrome (\u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], while Chavanda and Mane identified a negative correlation between FT3 and APACHE II (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.4083; \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0032) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Similarly, Praveen et al. found higher APACHE II scores in the NTIS group compared with euthyroid patients, and another study in Turkey had observed a strong correlation between FT3 and APACHE II (r\u0026thinsp;=\u0026thinsp;0.69, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSimilar to our results, the study by Bello et al. observed that low FT3 was a significant predictor of prolonged mechanical ventilation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while Guo et al. found that serum creatinine is a mortality-related factor, supporting our observed link between NTIS and renal dysfunction.\u003c/p\u003e \u003cp\u003eIn our cohort, patients with NTIS had elevated CRP and reduced haemoglobin levels. These trends are observed in prior studies demonstrating a consistent inverse relationship between FT3 and systemic inflammation. For instance, Wang et al. found significant negative correlations between FT3 and both CRP (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.408, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], while Gao et al. find similar negative correlations with CRP (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.66, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and IL-6 (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.60, \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in COVID-19 patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be considered. First, it was a single-centre study with a relatively small sample size, which may limit the generalizability of the findings. Second, due to its observational and non-interventional design, a causal relationship between thyroid dysfunction and mortality cannot be established. The hormonal changes observed may reflect illness severity rather than act as independent predictors. Third, illness severity scores such as SOFA or APACHE II were not available, which might have allowed better adjustment for disease burden and organ dysfunction. Fourth, thyroid hormones were measured only once upon ICU admission; therefore, dynamic changes during recovery or deterioration were not assessed. Additionally, reverse T3 and cortisol were not evaluated, which could have provided further insight into the neuroendocrine adaptation to stress. Finally, treatment-related factors such as corticosteroid or dopamine use, which can affect thyroid hormones, were not systematically recorded. Despite these limitations, this study provides important local evidence on the prognostic impact of non-thyroidal illness among critically ill patients and highlights the need for larger multicenter studies with longitudinal hormonal assessment and integrated severity scoring.\u003c/p\u003e \u003cp\u003eIn conclusion, the results of this study confirmed that NTIS is common in critically ill patients and that low T3 is associated with increased mortality and adverse outcomes. Although the predictive accuracy of T3 for mortality in this cohort appeared less predictable than what was reported elsewhere, the consistent association across multiple studies highlights the importance of recognising NTIS as a poor prognostic marker. Overall, NTIS is common in the ICU and serves as a marker of illness severity and poor prognosis. Future multicenter studies with serial hormone measurements and integration of severity scores are warranted to validate its prognostic role.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e This study was approved by the Institutional Review Board (IRB) of An-Najah National University and conducted in accordance with the Declaration of Helsinki. Informed consent was waived by the IRB, as data were collected from hospital records, and labs are routinely drawn from patients with no extra tests being done. Ref: Med.June.2024/10\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial number\u003c/strong\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003e \u003cb\u003eof AI Use\u003c/b\u003e \u003c/p\u003e\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003e \u003cb\u003eof Helsinki for human-subject research\u003c/b\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo external funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRR contributed to study design, data collection, data analysis, manuscript drafting, and approval of the final version.DA contributed to study design, supervision, critical revision, and approval of the final version.II, MS, AO, and KM contributed to data collection and approval of the final version.All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank the intensive care unit at An-Najah National University Hospital for their support in data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used during the current study are available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBose P, Dasarathan R, Murugesan ASM, Chenthil KS. 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Non-thyroidal illness syndrome predicts outcome in adult critically ill patients: a systematic review and meta-analysis. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1530/EC-21-0504\u003c/span\u003e\u003cspan address=\"10.1530/EC-21-0504\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePraveen NS, Modi KD, Sethi BK, Murthy J, Reddy PK, Kandula S. Study of Non-Thyroidal Illness Syndrome and Its Recovery in Critically Ill Patients at a Tertiary Care Centre in South India. 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Thyroid Dysfunction in Critically ill Patients.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkbaş T, Deyneli O, S\u0026ouml;nmez FT, Akalın S. The pituitary\u0026ndash;gonadal\u0026ndash;thyroid and lactotroph axes in critically ill patients. Endokrynologia Polska. 2016;67:305\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5603/EP.a2016.0032\u003c/span\u003e\u003cspan address=\"10.5603/EP.a2016.0032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBello G, Ceaichisciuc I, Silva S, Antonelli M. The role of thyroid dysfunction in the critically ill: a review of the literature. Minerva Anestesiol. 2010;76:919\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Non-thyroidal illness syndrome, Low T3, Critical illness, Mortality, Thyroid hormones, Intensive care unit","lastPublishedDoi":"10.21203/rs.3.rs-7981571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7981571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eNon-thyroidal illness syndrome (NTIS) is a common condition in critically ill patients, yet its prognostic value remains unclear. This study aimed to assess the incidence of NTIS among critically ill patients and evaluate its association with clinical outcomes and mortality.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA prospective cohort study was conducted in a tertiary hospital between 2019 and 2025. Adult intensive care unit (ICU) patients without known thyroid disease were included. Thyroid function tests (thyroid-stimulating hormone (TSH), free thyroxine (T4) and free triiodothyronine (T3) were measured within 24 hours of admission. Clinical data, comorbidities, and outcomes were recorded. Patients were classified as having low T3 (\u0026lt;\u0026thinsp;3.0 nmol/L) or normal T3 (\u0026ge;\u0026thinsp;3.0 nmol/L). Logistic regression and ROC analyses were performed to assess associations with mortality.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eAmong 180 patients (mean age 57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18 years; 62% male), 52% had NTIS. Patients with low T3 were older and had higher rates of diabetes, cardiovascular, and pulmonary diseases (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). They more frequently developed shock, respiratory failure, and required mechanical ventilation. ICU mortality was significantly higher among the low T3 group (43% vs. 7.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjustment for age, comorbidities, and illness severity, both low T3 (OR 0.23, 95% CI 0.07\u0026ndash;0.79; p\u0026thinsp;=\u0026thinsp;0.007) and low T4 (OR 0.80, 95% CI 0.65\u0026ndash;0.99; p\u0026thinsp;=\u0026thinsp;0.03) remained independently associated with mortality, whereas TSH showed no significant association. ROC analysis demonstrated that T3 had the strongest inverse correlation with survival (AUC\u0026thinsp;=\u0026thinsp;0.208, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eNTIS is highly prevalent in critically ill patients and is associated with increased mortality and adverse outcomes. Low T3, in particular, reflects disease severity and may serve as a prognostic marker in the ICU.\u003c/p\u003e","manuscriptTitle":"Non-Thyroidal Illness and Its Impact on Outcomes in Critically Ill Patients: A Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 08:56:30","doi":"10.21203/rs.3.rs-7981571/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8d54c448-cca7-4a0c-a0dc-cabf23c283d0","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T08:40:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 08:56:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7981571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7981571","identity":"rs-7981571","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}