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Hypoglycemia Risk Score

Hypoglycemia risk is efficent practical tool to assess the 12 month risk of hypoglycemic attack in patients with type 2 diabetes.
Hypoglycemia admissions
ED visit or hospital admission for hypoglycemia at any time
0 0
1-2 1000
≥3 2000
ED visits, last 12 months
For any reason
<2 1
≥2 2
Insulin use
No 4
Yes 8
Sulfonylurea use
No 16
Yes 32
Severe or end-stage kidney disease
eGFR ≤29 by CKD-EPI Creatinine
No 64
Yes 128
<77 years 256
≥77 years 512


Measured Factor
Hypoglycemic risk score
Measured Factor Disease
  • Hypoglyceimc episodes
Measured Factor Detail
Hypoglycemia risk score is effcient method that takes into consideration 6 factors to assess the 12 month risk of getting hypoglycemia episodes.The six factors includes Hypoglycemia or normal admissions, Age, Insulin use, Sulfonylurea use, Severe or end-stage kidney disease. This tool can prevent the patients from hypoglycemic attack following targeted prevention therapy .
Body System
Measured Factor Low Impact
  • A minimun score of 0 indicates the low risk with <1% 12 month risk of hypoglycemia admission.
Measured Factor High Impact
  • A score of ≥3 states the high risk with >5% 12-month risk of hypoglycemia admission.

Result Interpretation

Ranges Ranges
  • Critical Low: 0%
  • Critical High: 0%
  • Normal: 0
  • Normal Adult Male: 0%
  • Normal Adult Female: 0%
  • Normal Pediatric: 0
  • Normal Neonate Female: 0
  • Normal Geriatric Male: 0%
  • Normal Geriatric Female: 0%
Result High Conditions
  • ≥3


Study Validation 1
The study aims to develop and validate a risk score for screening undiagnosed diabetes among Sri Lankan adults and furthermore to compare its performance with the Cambridge Risk Score (CRS), the Indian Diabetes Risk Score (IDRS) and three other Asian risk scores. Data from a representative sample of 4276 adults without diagnosed diabetes was collected. Age, waist circumference, BMI, hypertension, balanitis or vulvitis, family history of diabetes, gestational diabetes, physical activity were significantly associated with undiagnosed diabetes.  In the sample 36.3 % were above the cut-off of 31. A risk score above 31 gave a sensitivity, specificity, positive predictive value and negative predictive value of 77.9, 65.6, 9.4 and 98.3 % respectively. For Sri Lankans, the AUC for the CRS and IDRS were 0.72 and 0.66 respectively. Therefore the study concluded that non-invasive screening tool can identify 80 % of undiagnosed diabetes by selecting 40 % of Sri Lankan adults for confirmatory blood investigations.
References: 2
Study Validation 2
The study aims to develop a risk-score model, based on available clinical data to assess absolute risk of type 2 diabetes among people with impaired glucose tolerance (IGT). The risk-score model included the waist circumference, fasting plasma glucose, height, history of cardiovascular disease (CVD), variables acarbose treatment, gender, serum triglyceride level, and hypertension. When applied to people with impaired glucose tolerance in the STOP-NIDDM, the final model yielded an area under the receiver-operating-characteristic curve (AUC(ROC)) of 0.64. (AUC(ROC) of 0.84 and 0.90 when applied to FINRISK population with IGT alone and IGT and normal glucose tolerance combined, respectively; AUC(ROC) is a measure of the discriminatory power of the model (1, perfect discrimination). Hence the conclusion from the study came out to be that the STOP-NIDDM risk-score is a simple and validated tool that can identify high-risk individuals with IGT who would benefit most from type 2 diabetes or CVD prevention strategies, such as lifestyle management or early acarbose treatment.
References: 3
Study Validation 3
The cross-sectional community based study aims to find whether the individuals of 45 years and more of rural area who are in higher tertile of Indian Diabetes Risk Score i.e. of IDRS of more than 60 as compared to those who are in lower tertile i.e. of less than 30, have high frequency of hyperglycemia, impaired glucose tolerance, and manifest diabetes mellitus. For all consenting and the eligible subjects, the medical student visited three pre-identified villages and the fasting capillary blood glucose was done by One touch blood glucose monitoring system. Four simple questions and one anthropometric measurement helped in deriving the information for Indian Diabetes Risk Score from the same subject. Result of the study indicated that The Indian Diabetes Risk Score consisting of the factors like age, abdominal obesity, physical inactivity and the family history had sensitivity of 97.50% and specificity of 87.89% The study demonstrated the Indian Diabetes Risk Score (IDRS) can be reliably applied as effective tool for the mass screening of diabetes in the community.
References: 4
Study Additional 1
This study aimed to develop and validate a score to predict type 2 diabetes mellitus (T2DM) in a rural adult Chinese population. Cohort data of 12,849 participants were randomly divided into derivation (n = 11,564) and validation (n = 1285) data sets. Age, body mass index, triglycerides and fasting plasma glucose (scores 3, 12, 24 and 76, respectively) were predictors of the T2DM incident. The model accuracy was assessed by the area under the receiver operating characteristic curve (AUC), with optimal cut-off value 936 and the specificity, sensitivity, and AUC of the model was 74.0 %, 66.7%, and 0.768 (95% CI 0.760-0.776), respectively. The performance of the model was superior to the Chinese (simple), FINDRISC, Oman and IDRS models of T2DM risk but equivalent to the Framingham model, being widely applicable in a variety of populations. Therefore the model for predicting 6-year risk of T2DM could be used in a rural adult Chinese population.
References: 5
Study Additional 2
This study was done to develop and validate a simple diabetes risk score in an urban Asian Indian population with a high prevalence of diabetes. They also tested whether this score was applicable to South Asian migrants living in a different cultural context. The risk factors included in the risk score were age, BMI, waist circumference, family history of diabetes and sedentary physical activity. A risk score value of more than 21 gave a sensitivity, specificity, positive predictive value and negative predictive value of 76.6%, 59.9%, 9.4% and 97.9% in Cohort 1, 72.4%, 59%, 8.3% and 97.6% in Cohort 2 and 73.7%, 61.0%, 12.2% and 96.9% in Cohort 3, respectively. The higher distribution of risk factors in the UK means that at the same cut point the score was much more sensitive but also less specific.
References: 6
Study Additional 3
The objective of this study was to assess the validity of various T2DM risk scores in predicting the incidence of T2DM in a Swiss population-based cohort. A total of 169 patients (5.5%) developed T2DM during the follow-up and Comparing with participants who did not develop T2DM, they were more frequently male (69.8% vs 43.1%); were older (mean [SD] age, 57.1 [9.4] vs 52.3 [10.6] years); had a higher frequency of family history of T2DM (31.4% vs 19.3%) (all P < .001); and had a higher resting heart rate (69 [10] vs 67 [9] beats/min [P < .05]).  Most of the risk scores had a high AROC, specificity, and negative predictive value, while their sensitivity and positive predictive values were low. The Kahn clinical + biologic risk score has the highest receiver operating characteristic curve (AROC), but the clinical FINDRISC score may be more practical and less expensive for screening.
References: 7


  1. Karter AJ, Warton EM, Lipska KJ, et al. Development and Validation of a Tool to Identify Patients With Type 2 Diabetes at High Risk of Hypoglycemia-Related Emergency Department or Hospital Use. JAMA Intern Med. 2017;177(10):1461-1470.
  2. Katulanda P, Hill NR, Stratton I, Sheriff R, De silva SD, Matthews DR. Development and validation of a Diabetes Risk Score for screening undiagnosed diabetes in Sri Lanka (SLDRISK). BMC Endocr Disord. 2016;16(1):42.
  3. Tuomilehto J, Lindström J, Hellmich M, Lehmacher W, Westermeier T, Evers T, et al. Development and validation of a risk-score model for subjects with impaired glucose tolerance for the assessment of the risk of type 2 diabetes mellitus—The STOP-NIDDM risk-score. Diabetes research and clinical practice. 2010 Feb 1;87(2):267-74.67-74.
  4. Taksande B, Ambade M, Joshi R. External validation of Indian diabetes risk score in a rural community of central India. Journal of Diabetes mellitus. 2012 Feb 22;2(01):109.
  5. Zhang M, Zhang H, Wang C, Ren Y, Wang B, Zhang L, Yang X, Zhao Y, Han C, Pang C, Yin L. Development and validation of a risk-score model for type 2 diabetes: a cohort study of a rural adult Chinese population. Plos one. 2016 Apr 12;11(4):e0152054.
  6. Ramachandran A, Snehalatha C, Vijay V, Wareham NJ, Colagiuri S. Derivation and validation of diabetes risk score for urban Asian Indians. Diabetes research and clinical practice. 2005 Oct 1;70(1):63-70.
  7. Schmid R, Vollenweider P, Bastardot F, Waeber G, Marques-vidal P. Validation of 7 type 2 diabetes mellitus risk scores in a population-based cohort: CoLaus study. Arch Intern Med. 2012;172(2):188-9.

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