Neutrophil-to-Lymphocyte Ratio Adds Valuable Information Regarding the Presence of DKA in Children with New-Onset T1DM

Scutca, Alexandra-Cristina; Nicoară, Delia-Maria; Mărăzan, Monica; Brad, Giorgiana-Flavia; Mărginean, Otilia

doi:10.3390/jcm12010221

Cited by 5 publications

(7 citation statements)

References 42 publications

(50 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In moderate cases of DKA value, it was 3.71; 1.98-4.85. In severe cases, NLR median value was 5.77; 4.04-9.63 [95]. NLR may be a possible predictor of systemic inflammation severity in DKA [98].…”

Section: Neutrophil-to-lymphocyte Ratio (Nlr) In Dka Patientsmentioning

confidence: 90%

See 1 more Smart Citation

Detecting Diabetic Ketoacidosis with Infection: Combating a Life-Threatening Emergency with Practical Diagnostic Tools

Ahmad

Narwaria²,

Singh³

et al. 2023

Diagnostics

View full text Add to dashboard Cite

Background: Diabetic ketoacidosis (DKA) is a life-threatening acute complication of diabetes mellitus and can lead to patient demise if not immediately treated. From the recent literature, the diabetic ketoacidosis mortality rate, depending on age, is 2–5%. Insulin discontinuation and infection remain the two most common triggers for diabetic ketoacidosis. About 50% of cases of ketoacidosis result from bacterial infections like urinary tract infections and pneumonia. It is also important to diagnose the presence of infection in diabetic ketoacidosis patients to prevent the excessive use of antibiotics, which may lead to antibiotic resistance. Although performing bacterial culture is confirmatory for the presence or absence of bacterial infection, the time required to obtain the result is long. At the same time, emergency treatment needs to be started as early as possible. Methods: This narrative review examines various septic markers to identify the appropriate tools for diagnosis and to distinguish between diabetic ketoacidosis with and without infection. Electronic databases were searched using the Google engine with the keywords “Diabetes Mellitus”, “Diabetic Ketoacidosis”, “Infection with Diabetic Ketoacidosis”, “biomarkers for infection in Diabetic Ketoacidosis”, “Procalcitonin”, “Inflammatory cytokines in DKA”, “Lactic acidosis in DKA”, and “White blood cell in infection in DKA”. Results: This narrative review article presents the options for diagnosis and also aims to create awareness regarding the gravity of diabetic ketoacidosis with infection and emphasizes the importance of early diagnosis for appropriate management. Diabetes mellitus is a clinical condition that may lead to several acute and chronic complications. Acute diabetic ketoacidosis is a life-threatening condition in which an excess production of ketone bodies results in acidosis and hypovolemia. Infection is one of the most common triggers of diabetic ketoacidosis. When bacterial infection is present along with diabetic ketoacidosis, the mortality rate is even higher than for patients with diabetic ketoacidosis without infection. The symptoms and biomarkers of diabetic ketoacidosis are similar to that of infection, like fever, C reactive protein, and white blood cell count, since both create an environment of systemic inflammation. It is also essential to distinguish between the presence and absence of bacterial infection to ensure the appropriate use of antibiotics and prevent antimicrobial resistance. A bacterial culture report is confirmatory for the existence of bacterial infection, but this may take up to 24 h. Diagnosis needs to be performed approximately in the emergency room upon admission since there is a need for immediate management. Therefore, researching the possible diagnostic tools for the presence of infection in diabetic ketoacidosis patients is of great importance. Several of such biomarkers have been discussed in this research work.

show abstract

Section: Neutrophil-to-lymphocyte Ratio (Nlr) In Dka Patientsmentioning

confidence: 90%

“…NLR shows adaptive and innate immune response functions [91][92][93][94]. Several studies have found an increased NLR in patients with DKA [82,[95][96][97]. In a study, the median NLR in patients without ketoacidosis was 1.11; 0.80-1.80.…”

Section: Neutrophil-to-lymphocyte Ratio (Nlr) In Dka Patientsmentioning

confidence: 99%

Detecting Diabetic Ketoacidosis with Infection: Combating a Life-Threatening Emergency with Practical Diagnostic Tools

Ahmad

Narwaria²,

Singh³

et al. 2023

Diagnostics

View full text Add to dashboard Cite

show abstract

“…Gender Dichotomous Dichotomous (Male/Female) [14,19] Paternal level of education Ordinal (ISCED 0-7) Dichotomous (ISCED > 4 vs. ISCED ≤ 4) Higher education vs. no higher education [13,14,[21][22][23][24][25][26][27] Maternal level of education [13,14,43] Neutrophil to lymphocyte ratio Continuous Continuous [44][45][46] Mean platelet volume Continuous Continuous [47][48][49] Specifically, we adjusted disease duration and the BMI Z-score by centering them around the sample's mean for use in binary logistic regression. This process involved subtracting the sample mean from each data point within these variables, a step undertaken to diminish the numerical instability that can arise in regression models due to large variable values.…”

Section: Demographic Characteristicsmentioning

confidence: 99%

Predicting Glycemic Control in a Small Cohort of Children with Type 1 Diabetes Using Machine Learning Algorithms

Neamtu,

Negrea,

Neagu

2023

Mathematics

View full text Add to dashboard Cite

Type 1 diabetes, a chronic condition characterized by insulin deficiency, is associated with various complications and reduced life expectancy and is increasing in global prevalence. Maintaining glycaemic control in children with type 1 diabetes, as reflected by glycated hemoglobin levels (A1C), is a challenging task. The American Association of Diabetes (ADA), the Pediatric Endocrine Society, and the International Diabetes Federation (ISPAD) recommend the adoption of a harmonized A1C of <7.5% across all pediatric groups. Our retrospective study included 79 children with type 1 diabetes and aimed to identify determinants pivotal to forecasting glycemic control, focusing on a single A1C cut-off value and exploring how machine learning algorithms can enhance clinical understanding, particularly with smaller sample sizes. Bivariate analysis identified correlations between glycemic control and disease duration, body mass index (BMI) Z-score at onset, A1C at onset above 7.5 g/dL, family income, living environment, maternal education level, episodes of ketoacidosis, and elevated cholesterol or triglyceride. Binary logistic regression stressed the association of ketoacidosis episodes (β = 21.1, p < 0.01) and elevated A1C levels at onset (β = 3.12, p < 0.01) and yielded an area under the receiver operating characteristic curve (AUROC) of 0.916. Two-step clustering emphasized socioeconomic factors, as well as disease complications and comorbidities, and delineated clusters based on these traits. The classification and regression tree (CART) yielded an AUROC of 0.954, slightly outperforming binary regression, providing a comprehensive view of interactions between disease characteristics, comorbidities, and socioeconomic status. Common to all methods were predictors regarding ketoacidosis episodes, the onset of A1C levels, and family income, signifying their overarching importance in glycaemic control. While logistic regression quantified risk, CART visually elucidated complex interactions and two-step clustering exposed patient subgroups that might require different intervention strategies, highlighting how the complementary nature of these analytical methods can enrich clinical interpretation.

show abstract

“…A growing body of research focuses on CBC-derived indices in chronic diseases, including diabetes. Among these, the most evaluated index in diabetic patients has been the neutrophil-to-lymphocyte ratio (NLR) [22][23][24][25]. However, while much of the research has centered on the role of neutrophils and lymphocytes, monocytes also appear to exert a notable impact on the pathogenesis of T1DM [26][27][28][29] and its complications, such as cerebral edema and AKI [30,31].…”

Section: Introductionmentioning

confidence: 99%

Systemic Inflammatory Response Index (SIRI) as a Predictive Marker for Adverse Outcomes in Children with New-Onset Type 1 Diabetes Mellitus

Scutca,

Jugănaru,

Nicoară

et al. 2024

JCM

Self Cite

View full text Add to dashboard Cite

(1) Background: Although most cases of new-onset type 1 diabetes mellitus (T1DM) are managed without serious events, life-threatening complications do arise in a subset of patients. Our objective was to assess the correlation between elevated SIRI values and adverse events related to the onset of T1DM. (2) Methods: This retrospective study, spanning ten years, included 187 patients with new-onset T1DM divided into three groups based on SIRI tertiles. The primary outcome was the occurrence of acute complications during hospital admission, while the secondary outcome was prolonged Intensive Care Unit (ICU) admission. (3) Results: Patients with high SIRI values were more likely to experience higher disease activity, leading to longer ICU admission times and more frequent complications. Multivariate logistic regression analysis revealed that the SIRI was independently associated with acute complications (p = 0.003) and prolonged ICU length of stay (p = 0.003). Furthermore, receiver operating characteristic analysis demonstrated the SIRI’s superior predictive accuracy compared to venous pH (AUC = 0.837 and AUC = 0.811, respectively) and to the individual component cell lineages of the SIRI. (4) Conclusions: These findings emphasize the potential utility of the SIRI as a prognostic marker in identifying patients at increased risk during T1DM hospital admissions.

show abstract

Neutrophil-to-Lymphocyte Ratio Adds Valuable Information Regarding the Presence of DKA in Children with New-Onset T1DM

Cited by 5 publications

References 42 publications

Detecting Diabetic Ketoacidosis with Infection: Combating a Life-Threatening Emergency with Practical Diagnostic Tools

Detecting Diabetic Ketoacidosis with Infection: Combating a Life-Threatening Emergency with Practical Diagnostic Tools

Predicting Glycemic Control in a Small Cohort of Children with Type 1 Diabetes Using Machine Learning Algorithms

Systemic Inflammatory Response Index (SIRI) as a Predictive Marker for Adverse Outcomes in Children with New-Onset Type 1 Diabetes Mellitus

Contact Info

Product

Resources

About