Hypoglycemia is an important complication in patients with diabetes that has a profound impact on quality of life and self-management. While tight glycemic control remains a hallmark to lower rates of complications [1–3], the barrier of hypoglycemia is the major limiting factor in the maintenance of euglycemia [4]. Mild or self-treated episodes of hypoglycemia are common, especially in type 1 diabetes, with reported rates of two episodes per week [5]. Severe hypoglycemia, or episodes requiring external assistance, may occur at least once a year, and are a significant cause of morbidity [5–7].

Although some episodes of severe hypoglycemia are treated at home by administration of oral glucose or intramuscular glucagon by family members, the most severe episodes require ambulance or emergency department (ED) visits. Although ED visits for severe hypoglycemia represent a small percentage of the total episodes of hypoglycemia in diabetes, they do serve as a good epidemiological marker of the complication and result in significant economical and psychological costs [8].

Most data on the epidemiology of hypoglycemia are based on highly selected patients in the setting of large clinical trials [1–3, 7], and may not generalize to the general population. Accurate identification of hypoglycemia cases by International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes may help to describe the epidemiology, monitor trends, and propose interventions to improve diabetes care through national administrative databases and local chart reviews [9]. There are several ICD-9-CM coding options for hypoglycemia, some of which may represent other diagnoses. For example, diabetes with other specified manifestations (250.8) may represent hypoglycemia or diabetic lower extremity ulcers. While several prior studies have utilized ICD-9-CM codes to identify cases of hypoglycemia [10–14], only Johnson et al. described the accuracy of ICD-9-CM codes to identify hypoglycemia episodes, but with significant methodological limitations [10].

Building on this prior work, we evaluated the accuracy a new ICD-9-CM coding algorithm to identify cases of hypoglycemia presenting to the ED. We hypothesized that our proposed algorithm would identify hypoglycemia with a positive predictive value (PPV) of at least 80%.

Of the 174,134 ED visits at the three institutions during the data collection period, we identified 901 patients with candidate ICD-9-CM codes. These patients accounted for 1,139 visits with possible hypoglycemia (i.e., 0.5% of all ED visits). We abstracted 679 charts (75% of visits by unique patients) and excluded the remaining 222 patients with missing or incomplete documentation. There were no significant differences in age, sex, race/ethnicity, and distribution of candidate ICD-9-CM codes comparing abstracted and excluded patients (all p > 0.05).

Overall, the proposed algorithm had PPV of 89% (95%CI, 86–92) for detecting hypoglycemia visits. There was no significant difference in PPV when data were stratified by site or by ED disposition (p = 0.86 and 0.22, respectively). Based on the assumption that the candidate ICD-9-CM codes captured all ED presentations with hypoglycemia, we estimate that the algorithm had 97% sensitivity, >99% specificity, and >99% NPV. Assuming the algorithm missed at most 10% of hypoglycemia visits, we estimate a minimum sensitivity of 88% with NPV and specificity remaining >99%.

Post-hoc review of the algorithm performance suggested that cases identified by 962.3 demonstrated low PPV (54%), primarily representing patients with excessive insulin or oral hypoglycemic use without development of clinical or laboratory hypoglycemia. We evaluated the full list of ICD-9-CM codes for all visits coded as 962.3 and found that 10 of 13 confirmed cases of hypoglycemia had 250.8 listed as a co-diagnosis and only 1 of 11 cases without hypoglycemia had 250.8 as a co-diagnosis. When we removed 962.3 as a candidate code and relied only on coding for 250.8 in these cases, the PPV for the overall algorithm increased from 89% to 91% (95%CI, 88–93) while the estimated sensitivity remained at 97%.

In this study, we demonstrated high predictive value for detection of ED hypoglycemia visits using the new ICD-9-CM coding algorithm. To our knowledge, this is the first chart validation of the accuracy of ICD-9-CM codes for hypoglycemia, an important complication in diabetes and common ED presentation.

Our algorithm differs in several important ways from prior reports on hypoglycemia. Two separate analyses of the relationship between anti-hypertensive agents and hypoglycemia in patients with diabetes, Herings et al. and Morris et al. used ICD-9-CM codes 251.0, 251.1, and 251.2 to identify possible episodes of hypoglycemia [12, 13]. Schorr et al. broadened candidate codes to include 250.3 and 962.3 [11, 14]. Notably, none of these studies included 250.8 in their search strategies, nor accounted these possible missed episodes of hypoglycemia. In our analysis, 250.8 comprised the vast majority (83%) of hypoglycemia cases, and exclusion of this code in these prior studies likely contributed to substantial underestimates in the incidence of hypoglycemia.

Johnson, et al. first reported the utility of 250.8 in their strategy to identify ED visits for hypoglycemia [10]. In their analysis, this code also identified over half of hypoglycemia episodes and had a PPV of 73% for their case definition of hypoglycemia. Our chart validation differed in several important ways. First, Johnson et al. limited their search to first-listed ICD-9-CM codes and excluded all other diagnosis fields. We evaluated all diagnosis fields and while candidate codes in the first diagnosis field had a higher PPV, limitation to the primary diagnosis field would have excluded 27% of hypoglycemia cases. Additionally, candidate codes in the latter diagnosis fields maintained a high PPV (81%). Secondly, their validation of hypoglycemia cases involved searching of brief communications following the ED visit for symptoms consistent with hypoglycemia, which included confusion, loss of consciousness, and seizure. While these symptoms are consistent with hypoglycemia, they are nonspecific and have many other causes in addition to hypoglycemia [17]. We performed detailed chart reviews to confirm hypoglycemia visits, which allowed for more precise validation. Finally, we confirmed the importance of 250.8 in identification of the majority of hypoglycemia visits, but additionally refined the search strategy by exclusion of visits with alternate co-diagnoses. Exclusion of these cases reliably eliminated 85% of false-positive cases coded as 250.8, while eliminating only a small number of true-positive cases.

Overall, 250.8 with exclusion of alternate co-diagnoses and 251.2 comprised >95% of hypoglycemia identified by the algorithm. Although there were insufficient data to evaluate 250.0 and 250.1 in this analysis, we recommend inclusion of these codes in any search for hypoglycemia. These codes are likely to have high specificity/PPV, and their utilization may be higher in different institutions and practice environments. As anticipated, codes 270.3, 775.0 and 775.6 were rare in the ED setting but may have utility for other settings, such as maternity and neonatal units.

We hypothesized that we would not be able to determine if diabetes with other coma (250.3) were caused by hypoglycemia or diabetic ketoacidosis (DKA). There were insufficient data to evaluate this hypothesis (only one case), and charts identified with this code should be interpreted with caution. Compared to prior 100% PPV in prior analysis [10], we were surprised by the relatively low predictive value (54%) of 962.3. Based on this finding and our secondary analysis, we recommend exclusion of 962.3 and relying only on 250.8 to identify most of these associated visits. This proposed strategy will require validation in other institutions and settings.

The ICD-9-CM classification system is imperfect for case identification, as it was created for reimbursement rather than research purposes [18]. Given this inherent limitation, the proposed algorithm for hypoglycemia compares favorably to those suggested for a variety of diseases. ICD-9-CM coding accuracy for upper gastrointestinal disorders (93–95% PPV) and Clostridium difficile colitis (87% PPV) were similar [19, 20], but were more unreliable for stroke (61–79% PPV) [21], soft tissue disorders (64% PPV) [22], pneumococcal pneumonia (58% PPV) [23], traumatic brain injury (20–38% PPV) [24], and venous thromboembolism (31% PPV) [25]. Based on these comparisons, the proposed ICD-9-CM coding algorithm for hypoglycemia would be expected to perform as well or better than coding strategies for other diagnoses in accurate case identification from administrative data.

We derived and validated a new ICD-9-CM coding algorithm to identify hypoglycemia visits with high predictive validity. This algorithm improves on prior strategies to identify hypoglycemia cases in administrative data sets and will enhance the ability to study the epidemiology and design interventions for this important complication of diabetes care.