Hypoglycemia

Using the electronic medical records system at each site, we searched the following ICD-9-CM codes to identify possible visits for hypoglycemia: 250.3 (diabetes with other coma), 250.8 (diabetes with other specified manifestations) 251.0 (hypoglycemic coma), 251.1 (other specified hypoglycemia), 251.2 (hypoglycemia, unspecified), 270.3 (leucine-induced hypoglycemia), 775.0 (hypoglycemia in an infant born to a diabetic mother), 775.6 (neonatal hypoglycemia), and 962.3 (poisoning by insulins and antidiabetic agents).
Given the diversity of potential ICD-9-CM codes, we searched this broad range of codes and in all diagnosis fields (up to ten listed) in an attempt to capture all possible ED hypoglycemia visits. For admitted patients, we examined only ED-based codes, to avoid inclusion of incident hypoglycemia that occurred during inpatient hospitalization. In cases where multiple candidate codes were present, we recorded only the first-listed code. The exception to this was for the more ambiguous codes 250.3 and 250.8, for which we preferentially recorded any of the other candidate codes if present. We based this strategy on detailed examination of the ICD-9-CM coding manual [9 ], review of the experience from previously reported approaches [10 (link)-14 (link)], and discussion with coding experts.
The code 250.8 may be used for other specific diabetes-associated complications in addition to hypoglycemia, including: 259.8 (secondary diabetic glycogenosis), 272.7 (diabetic lipidosis), 707.xx (ulcers of the lower extremity), 709.3 (Oppenheim-Urbach syndrome), and 730.0–730.2, 731.8 (osteomyelitis). Based on discussion with coding experts, we determined that 681.xx (cellulitis of fingers/toes), 682.xx (other cellulitis), and 686.9x (local skin infection) may also be utilized as a co-diagnoses for 250.8, although not specifically mentioned in the manual. We prospectively proposed the coding algorithm displayed in Figure 1 and validated its accuracy through chart review.
We identified all ED visits with candidate ICD-9-CM codes between July 1, 2005 and June 30, 2006 at each site, and obtained written ED charts. For patients with multiple ED visits during the data collection period, we requested only the first visit to avoid overrepresentation by certain patients. Trained research staff abstracted all charts using a standardized form, and the research group met weekly to discuss data collection and resolve abstraction issues. Additionally, two reviewers independently abstracted 10% of charts to evaluate inter-rater agreement in data collection. To enhance the reliability of our chart review, we abstracted only charts with complete ED triage assessment, nursing notes, and emergency physician notes and considered all other charts incomplete.

Two hundred and ten individuals were willing to join this study (May 10, 2021, to July 1, 2022). The exclusion criteria for the two groups were as follows: type 1 diabetes mellitus, impaired fasting glucose or impaired glucose tolerance^{58 (link)}, hypertension, hypoglycemia (blood sugar levels < 3.9 mmol/L), hyperlipidemia, serious eye diseases (e.g., blindness), symptoms of neurological conditions (e.g., cerebral infarction or hemorrhage), history of neurological abnormality (e.g., Parkinson’s disease), severe head injuries or chronic head discomfort (e.g., migraine), BMI > 31 kg/m², left- or mixed-handedness, substance (tobacco, alcohol, or psychoactive drug) abuse, taking medications that may affect cognition and memory within 6 months, specific abnormalities detected on conventional MRI scans or any other factors that may influence brain structure or function (e.g., extreme physical weakness, chronic infections, and other endocrine diseases). Patients with T2DM were diagnosed by two experienced endocrinologists following international clinical standards⁵⁹ . MCI was evaluated via Mini-Mental State Examination (MMSE) and MoCA-B (21 ≤ MoCA-B score < 26, and MMSE score > 24 were diagnosed with MCI)^{60 ,61 (link)}.
Participants with brain tumors (n = 3), neuropsychiatric diseases (n = 4) (e.g., major depression or schizophrenia), or developmental disorders (n = 4) were excluded. Finally, 37 patients with T2DM-MCI, 93 patients with T2DM-NCI, and 69 NC were enrolled in this study. The source of patients with T2DM and NC corresponded with our previous study^{37 (link)}. This study was approved by the ethics committee of The First Affiliated Hospital of Guangzhou University of Chinese Medicine (ID: NO. JY [2020] 288). Written informed consent was obtained from all participants. In addition, the study was conducted following approved guidelines.

The following obstetric and foetal outcomes were recorded. With regard to obstetric outcomes, complications, such as PE and cholestasis, type of delivery, labor induction and delivery blood loss were considered. With regard to foetal outcomes, we recorded data on foetal weight and length, Apgar score and gestational age at birth. The eventual presence of neonatal hypoglycaemia and hyperbilirubinemia requiring phototherapy, distress, congenital malformations, shoulder dystocia, trauma during birth, stillbirth, and perinatal death were recorded as well. Of note, we used the most widely definition of neonatal hypoglycaemia to diagnose it, namely a glucose concentration of <40 mg/dl (2.6 mmol/l) in late preterm and term babies more than a few hours old [12 –14 (link)], and the European Standards of Care for Newborn Health (EFCNI) to diagnose the condition of neonatal hyperbilirubinemia, namely it appears within 24 hours of birth following the detection of a bilirubin level >15 mg/dl (259 μmol/L), with an increase of >5 mg/die [15 ].
Given the absence of general agreement about the definition of macrosomia [6 (link)], for the purpose of this study, newborns weighted ˃4 kg were considered macrosomic. To identify LGA and SGA infants defined by the birth weight above the 90^th percentile and below the 10^th percentile, respectively, we referred to the Italian neonatal anthropometric values of reference [16 (link)].

First, Kendall’s correlation analyses were used to examine the presence of correlations among the parameters of glycaemic variability. Second, we plotted parameter values over time with a linear interpolation function to graphically verify the presence of temporal trends. Finally, we graphically assessed the presence of differentiated trends for the parameters of glycaemic variability with foetal study outcome. For this, we defined two groups according to whether the foetal weight was above or below the 90^th percentile, the presence and absence of neonatal hypoglycaemia and hyperbilirubinemia, as well. In Figs 1, 2 and 3, we displayed adjusted means for pre-pregnancy BMI and the administration or not of insulin therapy obtained after Anova regressions, with covariates set to the group-specific mean values. We also computed un-weighted group average (and 95% confidence intervals, CI) of GM and then performed a two-sample T-test on the equality of means (allowing for unequal variances) between those of women with adverse neonatal outcomes (LGA or Macrosomia) affected by obesity and those with physiological neonatal outcomes. All analyses were performed with the STATA software, version 17.