During acute infectious diseases, glycemic status is an important metabolic control that affects clinical outcomes. Infection initiates a stress response which includes an increase in secretion of catecholamines, cortisol, glucagon, and inflammatory mediators, leading to insulin resistance and enhanced hepatic glucose production [1]. Thus, hyperglycemia can happen to people with and without known diabetes. In acutely ill patients, there has been a consistent association between stress-related hyperglycemia and an increased risk for mortality, longer hospital stays, and higher health care utilization [2]. Long-term hyperglycemia may also lead to impaired innate and adaptive immune function, through effects on neutrophil chemotaxis and phagocytosis, oxidative killing, complement activity and vascular function, which can result in increased susceptibility to infection and a poor clinical outcome. Additionally, recent evidence has shown that the glycemia-outcome relationship may be better framed as the acute glucose hit in relation to the patient's baseline glycemic control, rather than just a single glucose reading [4].

There are multiple acute infections observed to have a relationship between glycemic status and the severity of the infection. Stress hyperglycemia is common even in the youngest children in critical condition and has been linked to worse outcome [5]. In adults with sepsis, the relationship between recent acute hyperglycemia and higher short-term mortality has been demonstrated by meta-analysis in non-diabetic patients, suggesting that glucose increase may be a marker for the severity of the systemic disease [6]. Similarly, a higher stress-hyperglycemia ratio is associated with greater mortality among patients with sepsis admitted to critical care units [7]. COVID-19 also has shown worse outcomes of infectious disease in patients with diabetes and abnormal glucose metabolism, which reinforces the link between glycemic derangements and COVID-19 outcomes [8].

Due to the high variability in glucose metabolism, immune function, comorbidity burden and physiological reserve across the lifespan, the relationship between glycemic status and severity of infection could be modified by age. In children, stress hyperglycemia can happen in the acute physiologic reaction but in adults it can be compounded by the presence of undiagnosed diabetes, existing diabetes, or metabolic syndrome [5,9]. Immunosenescence, multimorbidity, frailty and physiological reserve are risk factors associated with older adults and their susceptibility to severe infections [10]. In elderly patients with pneumonia, an association has been shown with high admission blood glucose and high stress hyperglycemia (SH) ratio and mortality, but the relationship may vary by diabetes status [11,12]. A comparison of glycemic status with age thus gives a more comprehensive picture in whether glucose abnormalities are consistently related with severity of the infectious disease in both age groups.

Research Objective

To determine the association between glycemic status and severity of acute infectious diseases across different age groups.

Study Design and Setting A systematic retrospective analytical cross-sectional study was carried out at Pakistan, which is a tertiary care teaching hospital. This study involved patients seen in the emergency department, medical, pediatric and inpatient departments for acute infectious diseases. Study Duration The study was carried out for one year from January 2025 to December 2025. Eligible patients' medical records during this time were reviewed. Study Population and Sample Size Patients in the study population had different ages, acute infectious diseases, as confirmed by clinical and laboratory examinations, and documented blood glucose levels at the time of clinical evaluation. A complete sample of 400 patients was included. A sample size of 400 was determined using a 50% expected proportion, 95% confidence level and a 5% margin of error, rounded to a total sample of 400 to enable comparisons between age groups. Non-probability consecutive sampling was used to conduct the sampling. Eligibility Criteria Patients with an acute infectious disease (AID), as well as documented glycemic status, and sufficient clinical data for assessment of disease severity were included. Patients in whom there was incomplete history, patients who had concurrent infections that could not be identified as the main diagnosis, and patients with conditions and medications that had a significant impact on blood glucose levels, other than the acute infection, were excluded. Study Variables and Age Groups Blood glucose, HbA1c (if available) and documented diabetes status were used as the primary independent variable (glycemic status). Severity of acute infectious disease was the main outcome variable. The possible confounding factors were age, sex, diabetes mellitus, disease comorbidities, and infection type. Participants were divided into 3 age groups: <18 years, 18 – 59 years and ≥60 years. Assessment of Glycemic Status and Disease Severity Initial blood glucose levels were obtained from medical record; HbA1c and history of diabetes, if available. Clinical and outcome measures such as shock/hyperdynamic instability, respiratory failure, organ dysfunction, intensive care admission, mechanical ventilation, vasopressor requirement and in-hospital mortality were used to document severity of disease. If applicable, disease specific severity criteria were established. Patients were then stratified as non-severe vs severe disease based on predetermined criteria. Data Collection and Ethical considerations. A structured proforma was used for data extraction, comprising demographic, clinical, laboratory, treatment and outcome variables. Data was reviewed prior to analysis for completeness and consistency. The study was approved by the Ethics Committee of PIMS, Islamabad. Patient confidentiality was ensured and the analytical data was anonymised. Statistical Analysis IBM SPSS Statistics version 26.0 was used to analyze data. The data were presented as mean ± standard deviation and/or frequencies (%), as appropriate, for continuous variables and presented as median (interquartile range) for categorical variables. The distribution of glycemic status and disease-severity outcomes was compared between the three age groups that were predefined: <18 years, 18-59 years and ≥60 years. Comparisons were made using the independent-samples t-test or Mann–Whitney U test between two groups and the one-way ANOVA or Kruskal–Wallis test when comparing the three age groups depending on whether the data distributed normally or not. The association between categorical variables was determined using either chi-square or Fisher's exact test. To assess if glycemic status was independently associated with SAID, multivariable binary logistic regression was used, adjusting for other potential confounding factors such as age group, sex, diabetes mellitus, comorbidities, and type of infection. To see if the relationship of glycemic status to disease severity was different by age group, the interaction between age group and glycemic status was examined. Data was presented as ORs (95% CI) and p<0.05 was defined as significant.

A total of 400 patients were included in the study. Of these, 72 (18.00%) were aged <18 years, 228 (57.00%) were aged 18–59 years, and 100 (25.00%) were ≥60 years. Males comprised 218 (54.50%) participants and females 182 (45.50%). Diabetes mellitus was documented in 92 (23.00%) patients, while comorbidities were present in 126 (31.50%) patients. Respiratory infections were the most common infection type, accounting for 130 (32.50%) cases (Table 1).

Table 1. Demographic and Clinical Characteristics of Study Participants

Glycemic status differed significantly across the three age groups. Normal glycemic status was observed in 50 (69.44%) patients aged <18 years, 100 (43.86%) aged 18–59 years, and 30 (30.00%) aged ≥60 years. Diabetes/hyperglycemia was more frequent among patients aged ≥60 years [38 (38.00%)] than those aged 18–59 years [48 (21.05%)] and <18 years [4 (5.56%)]. This association was statistically significant (χ²=35.74, df=4, p<0.001) (Table 2).

Table 2. Association Between Age Group and Glycemic Status

Disease severity also varied significantly according to age group. Severe disease was identified in 18 (25.00%) patients aged <18 years, 75 (32.89%) patients aged 18–59 years, and 47 (47.00%) patients aged ≥60 years. Overall, 140 (35.00%) patients had severe disease compared with 260 (65.00%) who had non-severe disease (χ²=10.87, df=2, p=0.004) (Table 3).

Table 3. Association Between Age Group and Disease Severity

A significant association was observed between glycemic status and disease severity. Severe disease occurred in 35 (19.44%) patients with normal glycemic status, compared with 50 (38.46%) patients with elevated/stress hyperglycemia and 55 (61.11%) patients with diabetes/hyperglycemia. The association was statistically significant (χ²=45.62, df=2, p<0.001) (Table 4).

Table 4. Association Between Glycemic Status and Disease Severity

Within the individual age groups, glycemic status was significantly associated with disease severity among patients aged 18–59 years (χ²=14.86, df=2, p=0.001) and ≥60 years (χ²=8.31, df=2, p=0.016), whereas the association did not reach statistical significance among patients aged <18 years (χ²=4.72, df=2, p=0.094). Among patients aged ≥60 years with diabetes/hyperglycemia, 26 (68.42%) had severe disease compared with 9 (30.00%) with normal glycemic status (Table 5).

Table 5. Association Between Glycemic Status and Disease Severity Across Different Age Groups

Patients with severe disease had significantly higher glycemic parameters than those with non-severe disease. Mean admission blood glucose was 156.8±52.7 mg/dL in severe disease compared with 118.6±31.4 mg/dL in non-severe disease (t=−8.11, p<0.001). Mean HbA1c was also higher in the severe group (6.6±1.4% vs. 5.8±0.9%; t=−6.35, p<0.001) (Table 6).

Table 6. Comparison of Glycemic Parameters Between Non-Severe and Severe Disease Groups

*Include only if stress-hyperglycemia ratio was actually calculated in your study.

Among the clinical indicators of severe infection, organ dysfunction was observed in 74 (18.50%) patients, respiratory failure in 61 (15.25%), ICU admission in 58 (14.50%), hemodynamic instability/shock in 48 (12.00%), vasopressor requirement in 37 (9.25%), mechanical ventilation in 29 (7.25%), and in-hospital mortality in 24 (6.00%) patients (Table 7).

Table 7. Clinical Indicators of Severe Acute Infectious Disease

After adjustment for potential confounders, diabetes/hyperglycemia remained independently associated with severe disease (adjusted OR=2.76, 95% CI: 1.62–4.69, p<0.001), while elevated/stress hyperglycemia was also associated with increased odds of severe disease (adjusted OR=1.84, 95% CI: 1.18–2.88, p=0.007). Age ≥60 years and presence of comorbidities were additional independent predictors of severe disease (Table 8).

Table 8. Multivariable Logistic Regression for Factors Associated With Severe Disease

Overall model: Likelihood-ratio χ² = 58.47, df = 7, p <0.001; Nagelkerke R² = 0.214.

The interaction analysis suggested that the relationship between glycemic status and disease severity differed according to age group. The overall interaction was statistically significant (likelihood-ratio χ²=9.86, df=4, p=0.043), with the strongest association observed between diabetes/hyperglycemia and severe disease among patients aged ≥60 years (adjusted OR=3.17, 95% CI: 1.42–7.08, p=0.008) (Table 9).

Table 9. Interaction Between Age Group and Glycemic Status in Predicting Severe Disease

Interaction test: Likelihood-ratio χ² = 9.86, df = 4, p = 0.043.

In the present study, we showed that there was a significant relationship between the severity of acute infectious diseases and glycemic status at different ages. Among 400 patients, 140 (35.0%) developed severe disease, while severe disease increased progressively from 25.0% in patients aged <18 years to 32.89% in those aged 18–59 years and 47.0% in those aged ≥60 years (χ²=10.87, p=0.004). This age-related phenomenon is consistent with evidence that older patients are at higher risk of severe infection due to multimorbidity, diminished physiological reserve and immune dysfunction. Most significantly, our results replicate previous observations but are made at the level of multiple acute infectious conditions. Mortality was also strongly related to metabolic and clinical markers of stress in elderly patients with severe CAP, again highlighting the impact of metabolic abnormalities in older patients with infection [13]. An important discovery was that glycemic status varied greatly by age group. There was an increase in number of participants with normal glycemia from children (<18 years) to adults (18-59 years): from 69.44% to 43.86% and from adults (18-59 years) to older adults (≥60 years); from 43.86% to 30.0% (χ²=35.74, p<0.001). This trend of increasing prevalence with age is consistent with the biology; diabetes and impaired glucose regulation increase with age. However, these pediatric results are also clinically significant as there were 25.0% of children in our cohort with elevated/stress hyperglycemia, while only 5.56% were classified as diabetes/hyperglycemia. Recent emergency department (ED) research conducted with children revealed that stress hyperglycemia was correlated with increased admission and intensive care unit (ICU) admission and mortality rates, consistent with the idea that acute glucose elevation is a physiological rather than a true diabetes status [14]. The strongest overall association was that between glycemic status and the severity of the disease. Between the groups with normal glycemia, elevated/stress hyperglycemia, and diabetes/hyperglycemia, severe disease was seen in 19.44% of patients, 38.46%, and 61.11% respectively (χ²=45.62, p<0.001). The amount of glucose in admission (156.8±52.7 vs. 118.6±31.4 mg/dL, p<0.001) was also significantly higher in severe than non-severe disease cases. This is similar to prospective sepsis data in which severe admission hyperglycemia was independently associated with 30-day mortality both in those with and without diabetes [15]. Likewise, a recent meta-analysis of 53,073 non-diabetic septic patients found that stress-induced hyperglycemia was linked to a double mortality rate (RR=1.75, 95% CI 1.45–2.11) [16]. Therefore, hyperglycemia seems to be an important marker of acute disease burden, not just an incidental biochemical abnormality, as our findings suggest. Our study also showed that the association between glycemic status with severity remained in certain age groups. Severe disease was seen in 18.0% of patients with normal glycemia, 37.5% of patients with elevated/stress hyperglycemia, and 56.25% of patients with diabetes/hyperglycemia (p=0.001) among those aged 18–59 years. Among those aged ≥60 years, the corresponding proportions were 30.0%, 37.5%, and 68.42%, respectively (p=0.016). The association was not significant in children (p=0.094); likely due to fewer participants (72 children) and fewer children with a diabetes/hyperglycemia diagnosis (four). However, the pediatric literature does indicate a link between glucose and poor outcome: In a large study of 44,905 children who presented to emergency departments, the frequency of children admitted to the intensive care unit (ICU) and the rate of death were found to be higher in children with stress hyperglycemia [14]. Thus, lack of statistical significance in our pediatric subgroup must not be read as absence of an association! This was further supported by independent analysis. Elevated/stress hyperglycemia was associated with 1.84-fold higher odds of severe disease (95% confidence interval [CI] 1.18-2.88; p=0.007) and diabetes/hyperglycemia with 2.76-fold higher odds (95% CI 1.62-4.69; p<0.001) after adjustment. Age ≥60 years (OR=2.31, 95% CI 1.26–4.24; p=0.007) and comorbidity (OR=1.67, 95% CI 1.08–2.59; p=0.021) were also independent predictors. These findings are consistent with pooled analysis of COVID-19 data, which indicated about a 2-fold increased risk of severe disease or death for patients with diabetes, in the present study, which was much larger, thus incorporating multiple acute infections [17]. Of great interest, this association persisted after adjusting for diabetes and other potential confounders, which means that acute glycemic dysregulation may offer prognostic data beyond that of diabetes. Lastly, according to the interaction analysis, the relationship between glycemic status and disease severity was modified by age. There was a statistically significant interaction for the overall population (likelihood-ratio χ²=9.86, df=4, p=0.043), with diabetes/hyperglycemia having the greatest interaction in individuals older than 60 years (OR=3.17, 95% CI 1.42–7.08; p=0.008). This is especially similar to recent data in elderly patients with severe pneumonia, where the stress hyperglycemia ratio was an independent predictor of death (increased risk in the absence of diabetes) [18,19]. This is supported by another recent systematic review and meta-analysis of patients with sepsis in the ICU which found that a higher stress-hyperglycemia ratio was linked to an increased in-hospital mortality (RR=2.11, 95% CI 1.79–2.50) [20]. Together, our findings provide a strong argument for glycemic status as a potentially important tool for early risk stratification in older adults who have an acute infection. However, since this was a retrospective cross-sectional study, a cause–effect relationship cannot be identified, and prospective studies based on the use of standardized glucose and HbA1C thresholds are needed. Strengths and Limitations One of the strengths of this study is that the association between glycemic status and the severity of acute infectious diseases could be evaluated in three clinically relevant age groups and overall, such that the overall and age-specific relations could be assessed. The study population consisted of 400 patients with a wide range of acute infections, and several different indices of glycemic status were included in the study: blood glucose on admission, HbA1C, documented diagnosis of diabetes, stress hyperglycemia, disease severity was assessed by clinically relevant outcomes: shock, respiratory failure, organ dysfunction, admission to the intensive care unit, mechanical ventilation, need for vasopressors, and mortality. The analysis was further strengthened by the use of multivariable logistic regression to account for important confounders and by assessment of age–glycemia interaction for added information regarding the effect of age on the association. There are, however, a few factors to take into account. The retrospective cross sectional study design makes it difficult to make temporal or causal inferences regarding the relationship between glycemic abnormalities and disease severity. The study was undertaken in a single tertiary-care hospital, which might not be generalizable to other healthcare facilities. Clinical variability may have arisen as a result of the heterogenous nature of the group of infectious diseases, where disease mechanisms and criteria for severity vary between infections. Further, HbA1c was only available when documented, which could introduce information bias; and the acute glucose measurement could have been affected by nutritional status, medications, and severity-related stress responses. The relatively small number of children (n=72) and the small number of children who had diabetes/hyperglycemia (n=4) may also have reduced statistical power for age-specific analyses. Last, residual confounding by unmeasured factors like nutritional status, exposure to corticosteroids, treatment of the infection and length of illness cannot be ruled out.

This study shows that there was a high association between abnormal glycemic status and severity of acute infectious diseases, particularly among those with diabetes/hyperglycemia. Patients with elevated or abnormal glucose levels were more likely to have severe infection; blood glucose and HbA1c were more elevated in severe infection. Elevated/stress hyperglycemia and diabetes/hyperglycemia were both independently associated with severe disease after potential confounder adjustment. Other factors that were important predictors for disease severity were older age and the presence of comorbidities. In addition, there was significant interaction between age and glycemic status, implying that the negative relationship between glycemic abnormalities and severity of infectious disease may be stronger in older patients.

The results suggest that together with the traditional clinical parameters, assessment of glycemic status may give useful information for early risk stratification in patients with acute infectious disease. Future multicenter trials with a common definition of stress hyperglycemia, glycemic status and common disease severity criteria are recommended to confirm the findings and to assess the role of timely recognition of glycemic abnormalities and their management in the clinical course of disease.

Olariu E, Pooley N, Danel A, Miret M, Preiser JC. A systematic scoping review on the consequences of stress-related hyperglycaemia. PLoS One. 2018;13(4):e0194952. doi:10.1371/journal.pone.0194952. https://doi.org/10.1371/journal.pone.0194952 2. Berbudi A, Rahmadika N, Tjahjadi AI, Ruslami R. Type 2 diabetes and its impact on the immune system. Curr Diabetes Rev. 2020;16(5):442-449. doi:10.2174/1573399815666191024085838. 3. Knapp S. Diabetes and infection: is there a link?—A mini-review. Gerontology. 2013;59(2):99-104. DOI: https://doi.org/10.1159/000345107 4. Kim T, Choi SH. Diabetes mellitus and infectious diseases: current evidence and clinical implications. Diabetes Metab J. 2025;49(5):915-933. doi:10.4093/dmj.2025.0508. https://doi.org/10.4093/dmj.2025.0508 5. Srinivasan V. Stress hyperglycemia in pediatric critical illness: the intensive care unit adds to the stress! J Diabetes Sci Technol. 2012;6(1):37-47. doi:10.1177/193229681200600106. 6. Huang J, Li J, Chen X, Gu J. Stress-induced hyperglycemia and mortality of non-diabetic patients with sepsis: a meta-analysis. Front Endocrinol (Lausanne). 2025;16:1688494. doi:10.3389/fendo.2025.1688494. 7. Xie H, Hao T, Qi R, Zhang L, An Y, Jia D, et al. Association between stress hyperglycemia ratio and all-cause mortality among ICU patients with sepsis: a systematic review and meta-analysis. Front Med. 2026;12:1741993. doi:10.3389/fmed.2025.1741993. 8. Huang I, Lim MA, Pranata R. Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia: a systematic review, meta-analysis, and meta-regression. Diabetes Metab Syndr. 2020;14(4):395-403. doi:10.1016/j.dsx.2020.04.018. 9. Reisi M, Keivanfar M, Mostofizadeh N, Madihi Y, Esmaeilian Y, Hashemi E. Correlation between stable hyperglycemia and mortality in children admitted to the pediatric intensive care unit of Imam Hossein Hospital. Adv Biomed Res. 2021;10:2. doi:10.4103/abr.abr_31_20. 10. Norman DC. Clinical features of infection in older adults. Clin Geriatr Med. 2016;32(3):433-441. doi:10.1016/j.cger.2016.02.005.(PubMed)DOI: https://doi.org/10.1016/j.cger.2016.02.005 11. Akirov A, Shimon I. The prognostic significance of admission blood glucose levels in elderly patients with pneumonia (GAP Study). J Diabetes Complications. 2016;30(5):845-851. doi:10.1016/j.jdiacomp.2016.03.021. 12. Miao L, Shen X, Du Z, Liao J. Stress hyperglycemia ratio and its influence on mortality in elderly patients with severe community-acquired pneumonia: a retrospective study. Aging Clin Exp Res. 2024;36(1):175. doi:10.1007/s40520-024-02831-6. 13. Miao, L., Shen, X., Du, Z., & Liao, J. (2024). Stress hyperglycemia ratio and its influence on mortality in elderly patients with severe community-acquired pneumonia: A retrospective study. Aging Clinical and Experimental Research, 36, 175. https://doi.org/10.1007/s40520-024-02831-6 14. Bae W, Ahn MB. Association between Stress Hyperglycemia and Adverse Outcomes in Children Visiting the Pediatric Emergency Department. Children (Basel). 2022 Apr 2;9(4):505. doi: 10.3390/children9040505. 15. van Vught, L. A., Wiewel, M. A., Klein Klouwenberg, P. M. C., et al. (2016). Admission hyperglycemia in critically ill sepsis patients: Association with outcome and host response. Critical Care Medicine, 44(7), 1338–1346. https://doi.org/10.1097/CCM.0000000000001650 16. Huang, J., Li, J., Chen, X., & Gu, J. (2025). Stress-induced hyperglycemia and mortality of non-diabetic patients with sepsis: A meta-analysis. Frontiers in Endocrinology, 16, 1688494. https://doi.org/10.3389/fendo.2025.1688494 17. Aggarwal, G., Lippi, G., Lavie, C. J., Henry, B. M., & Sanchis-Gomar, F. (2020). Diabetes mellitus association with coronavirus disease 2019 (COVID-19) severity and mortality: A pooled analysis. Journal of Diabetes, 12(11), 851–855. https://doi.org/10.1111/1753-0407.13091 18. Kazi, A. K., Jatoi, A., & Hashmani, H. (2025). An Empirical Evaluation of Technology Acceptance Model in Mobile Devices in Healthcare Industry. Journal of Development and Social Sciences, 6(3), 707–714. https://doi.org/10.47205/jdss.2025(6-III)60 19. Jatoi, A., Bibi, M., Hashmani, H., & Ramish, M. S. (2025). Awareness of Patients’ Rights and the Respect and Dignity Experienced at Hospitals in Karachi, Pakistan. International Journal of Trends and Innovations in Business & Social Sciences, 3(3), 232–243. https://doi.org/10.5281/zenodo.17359794 20. Xie H, Hao T, Qi R, Zhang L, An Y, Jia D, Wang H, Niu W, Han X, Sha Y, Yang L, Zhang Z. Association between stress hyperglycemia ratio and all-cause mortality among ICU patients with sepsis: a systematic review and meta-analysis. Front Med (Lausanne). 2026 Jan 5;12:1741993. doi: 10.3389/fmed.2025.1741993. Olariu E, Pooley N, Danel A, Miret M, Preiser JC. A systematic scoping review on the consequences of stress-related hyperglycaemia. PLoS One. 2018;13(4):e0194952. doi:10.1371/journal.pone.0194952. https://doi.org/10.1371/journal.pone.0194952 2. Berbudi A, Rahmadika N, Tjahjadi AI, Ruslami R. Type 2 diabetes and its impact on the immune system. Curr Diabetes Rev. 2020;16(5):442-449. doi:10.2174/1573399815666191024085838. 3. Knapp S. Diabetes and infection: is there a link?—A mini-review. Gerontology. 2013;59(2):99-104. DOI: https://doi.org/10.1159/000345107 4. Kim T, Choi SH. Diabetes mellitus and infectious diseases: current evidence and clinical implications. Diabetes Metab J. 2025;49(5):915-933. doi:10.4093/dmj.2025.0508. https://doi.org/10.4093/dmj.2025.0508 5. Srinivasan V. Stress hyperglycemia in pediatric critical illness: the intensive care unit adds to the stress! J Diabetes Sci Technol. 2012;6(1):37-47. doi:10.1177/193229681200600106. 6. Huang J, Li J, Chen X, Gu J. Stress-induced hyperglycemia and mortality of non-diabetic patients with sepsis: a meta-analysis. Front Endocrinol (Lausanne). 2025;16:1688494. doi:10.3389/fendo.2025.1688494. 7. Xie H, Hao T, Qi R, Zhang L, An Y, Jia D, et al. Association between stress hyperglycemia ratio and all-cause mortality among ICU patients with sepsis: a systematic review and meta-analysis. Front Med. 2026;12:1741993. doi:10.3389/fmed.2025.1741993. 8. Huang I, Lim MA, Pranata R. Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia: a systematic review, meta-analysis, and meta-regression. Diabetes Metab Syndr. 2020;14(4):395-403. doi:10.1016/j.dsx.2020.04.018. 9. Reisi M, Keivanfar M, Mostofizadeh N, Madihi Y, Esmaeilian Y, Hashemi E. Correlation between stable hyperglycemia and mortality in children admitted to the pediatric intensive care unit of Imam Hossein Hospital. Adv Biomed Res. 2021;10:2. doi:10.4103/abr.abr_31_20. 10. Norman DC. Clinical features of infection in older adults. Clin Geriatr Med. 2016;32(3):433-441. doi:10.1016/j.cger.2016.02.005.(PubMed)DOI: https://doi.org/10.1016/j.cger.2016.02.005 11. Akirov A, Shimon I. The prognostic significance of admission blood glucose levels in elderly patients with pneumonia (GAP Study). J Diabetes Complications. 2016;30(5):845-851. doi:10.1016/j.jdiacomp.2016.03.021. 12. Miao L, Shen X, Du Z, Liao J. Stress hyperglycemia ratio and its influence on mortality in elderly patients with severe community-acquired pneumonia: a retrospective study. Aging Clin Exp Res. 2024;36(1):175. doi:10.1007/s40520-024-02831-6. 13. Miao, L., Shen, X., Du, Z., & Liao, J. (2024). Stress hyperglycemia ratio and its influence on mortality in elderly patients with severe community-acquired pneumonia: A retrospective study. Aging Clinical and Experimental Research, 36, 175. https://doi.org/10.1007/s40520-024-02831-6 14. Bae W, Ahn MB. Association between Stress Hyperglycemia and Adverse Outcomes in Children Visiting the Pediatric Emergency Department. Children (Basel). 2022 Apr 2;9(4):505. doi: 10.3390/children9040505. 15. van Vught, L. A., Wiewel, M. A., Klein Klouwenberg, P. M. C., et al. (2016). Admission hyperglycemia in critically ill sepsis patients: Association with outcome and host response. Critical Care Medicine, 44(7), 1338–1346. https://doi.org/10.1097/CCM.0000000000001650 16. Huang, J., Li, J., Chen, X., & Gu, J. (2025). Stress-induced hyperglycemia and mortality of non-diabetic patients with sepsis: A meta-analysis. Frontiers in Endocrinology, 16, 1688494. https://doi.org/10.3389/fendo.2025.1688494 17. Aggarwal, G., Lippi, G., Lavie, C. J., Henry, B. M., & Sanchis-Gomar, F. (2020). Diabetes mellitus association with coronavirus disease 2019 (COVID-19) severity and mortality: A pooled analysis. Journal of Diabetes, 12(11), 851–855. https://doi.org/10.1111/1753-0407.13091 18. Kazi, A. K., Jatoi, A., & Hashmani, H. (2025). An Empirical Evaluation of Technology Acceptance Model in Mobile Devices in Healthcare Industry. Journal of Development and Social Sciences, 6(3), 707–714. https://doi.org/10.47205/jdss.2025(6-III)60 19. Jatoi, A., Bibi, M., Hashmani, H., & Ramish, M. S. (2025). Awareness of Patients’ Rights and the Respect and Dignity Experienced at Hospitals in Karachi, Pakistan. International Journal of Trends and Innovations in Business & Social Sciences, 3(3), 232–243. https://doi.org/10.5281/zenodo.17359794 20. Xie H, Hao T, Qi R, Zhang L, An Y, Jia D, Wang H, Niu W, Han X, Sha Y, Yang L, Zhang Z. Association between stress hyperglycemia ratio and all-cause mortality among ICU patients with sepsis: a systematic review and meta-analysis. Front Med (Lausanne). 2026 Jan 5;12:1741993. doi: 10.3389/fmed.2025.1741993.