Discovering hidden patterns: Association rules for cardiovascular diseases in type 2 diabetes mellitus.

BACKGROUND: It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM) and coronary artery disease (CAD), and studies are able to correlate their relationships with available biological and clinical evidence. The aim of the current study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features relevant to these diseases. ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care. AIM: To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery. METHODS: This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi, involving a total of 300 consented subjects categorized into three groups: CAD with diabetes, CAD without diabetes, and healthy controls, with 100 subjects in each group. The participants were enrolled from the Cardiology IPD & OPD for the sample collection. The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value. RESULTS: The clinical dataset comprised 35 attributes from enrolled subjects. The analysis produced rules with a maximum branching factor of 4 and a rule length of 5, necessitating a 1% probability increase for enhancement. Prominent patterns emerged, highlighting strong links between health indicators and diabetes likelihood, particularly elevated HbA1C and random blood sugar levels. The ARM technique identified individuals with a random blood sugar level > 175 and HbA1C > 6.6 are likely in the "CAD-with-diabetes" group, offering valuable insights into health indicators and influencing factors on disease outcomes. CONCLUSION: The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes. Implying artificial intelligence techniques with medical data, we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.

Target association rule mining to explore novel paediatric illness patterns in emergency settings.

BACKGROUND AND AIMS: To assess the hospitalized sick children admitted to the pediatric emergency department (ED) and to find new patterns of clinical and laboratory attributes using association rule mining (ARM). METHODS: In this observational study, 158 children with median (IQR) age 11 months and a PRISM III score of 5 (2-9) were enrolled. Hotspot data mining method was applied to assess clinical attributes, lab investigations and pre-defined outcome parameters of children and their association in sick hospitalized children aged 1 month to 12 years. RESULTS: We obtained 30 rules with value for outcome as discharge is given attributes as follows: duration of hospitalization > 4 days, lactate > 1.2 mmol/L, platelet = 3.67/µL, dur_ventil = 0 h, serum K = 5.2 mmol/L, SBP = 120 mmHg, pCO2 = 41.9 mmHg, PaO2 = 163 mmHg, age = 92 months, heart rate > 114-159 per minute, temperature > 98 °F, GCS (Glasgow Coma Scale) > 7-14, gas K = 4.14 mmol/L, gas Na = 138.1 mmol/L, BUN (Blood Urea Nitrogen) = 18.69 mg/dL, Diagnosis > 1-718, Creatinine = 1.2 mg/dL, serum Na = 148 mmol/L, shock = 2, Glucose = 144 mg/dL, Mg(i) > 0.23 meq/L, BUN > 6.54 mg/dL. CONCLUSION: ARM is an effective data analysis technique to find meaningful patterns using clinical features with actual numbers in pediatric critical illness. It can prove to be important while analysing the association of clinical attributes with disease pattern, its features, and therapeutic or intervention success patterns.

Ionized Blood Magnesium in Sick Children: An Overlooked Electrolyte.

INTRODUCTION: Magnesium is a less frequently monitored electrolyte in critically ill patients. Hypomagnesemia is associated with increased need for mechanical ventilation, mortality and prolonged ICU stay. The present study was undertaken to identify the proportion of children with abnormal magnesium levels and correlate it with disease outcome. METHODS: This observational study included children aged 1 month to 12 years hospitalized at the emergency room. Heparinized blood was collected for determination of ionized magnesium, ionized calcium, sodium, potassium and lactate using Stat Profile Prime Plus (Nova Biomedical, Waltham, MA, USA). Clinical outcomes for duration of hospitalization, and death or discharge were recorded. RESULTS: A total of 154 (102 males) children with median (IQR) age of 11 (4, 49.75) months were enrolled. Sixty one (39.6%) had ionized magnesium levels below 0.42 mmol/l, 63 (40.9%) had normal levels and 30 (19.4%) had hypermagnesemia (>0.59 mmol/l). Hypomagnesemia was associated with hypocalcemia (p < 0.001), hyponatremia (p < 0.001) and hypokalemia (p < 0.02). A higher proportion of children with hypermagnesemia required ventilation than hypomagnesemia (26% vs. 9%) and succumbed (35% vs. 20%), respectively; p > 0.05. Ninety-three (60.3%) had hypocalcemia and 10 (6.5%) children had hypercalcemia. There was good correlation between ionized calcium and magnesium values (r = 0.72, p < 0.001). CONCLUSION: Both hypomagnesemia and hypermagnesemia were seen in critically ill children. Patients with hypomagnesemia had significantly higher proportion of other electrolyte abnormalities.