Indian Journal of Endocrinology and Metabolism

REVIEW ARTICLE
Year
: 2013  |  Volume : 17  |  Issue : 9  |  Page : 601--607

Online risk engines and scoring tools in endocrinology


Partha Pratim Chakraborty1, Sujoy Ghosh1, Sanjay Kalra2,  
1 Department of Endocrinology, Institute of Post Graduate Medical Education and Research, Calcutta, India
2 Department of Endocrinology, Bharti Hospital and BRIDE, Karnal, Haryana, India

Correspondence Address:
Sujoy Ghosh
Department of Endocrinology, IPGME&R, Kolkata
India

Abstract

With evolution of evidence-based medicine, risk prediction equations have been formulated and validated. Such risk engines and scoring systems are able to predict disease outcome and risks of possible complications with varying degrees of accuracy. From health policy makers point of view it helps in appropriate disbursement of available resources for greatest benefit of population at risk. Understandably, the accuracy of prediction of different risk engines and scoring systems are highly variable and has several limitations. Each risk engine or clinical scoring tool is derived from data obtained from a particular population and its results are not generalizable and hence its ability to predict risk/outcome in a different population with differences in ethnicity, ages, and differences in distribution of risk factors over time both within and between populations. These scoring systems and risk engines to begin with were available for manual calculations and references/use of formula and paper charts were essential. However, with evolution of information technology such calculations became easier to make with use of online web-based tools. In recent times with advancement of android technology, easy to download apps (applications) has helped further to have the benefits of these online risk engines and scoring systems at our finger tips.



How to cite this article:
Chakraborty PP, Ghosh S, Kalra S. Online risk engines and scoring tools in endocrinology.Indian J Endocr Metab 2013;17:601-607


How to cite this URL:
Chakraborty PP, Ghosh S, Kalra S. Online risk engines and scoring tools in endocrinology. Indian J Endocr Metab [serial online] 2013 [cited 2021 Jun 19 ];17:601-607
Available from: https://www.ijem.in/text.asp?2013/17/9/601/123544


Full Text

 Introduction



Treating physicians and patients would like to have a clear vision of magnitude of risk and possible outcome of different medical conditions. Such risk estimates and predictions were often made by physicians in the past on the basis of their own clinical experiences. However, such predictions were often biased and influenced by several factors.

With evolution of evidence-based medicine, greater weightage was given to statistics and population-based studies. Multifactorial risk analysis and logistic regression analysis are made to determine the weightage of individual risk factors for prediction of outcome. Once such risk prediction equations are formulated, they are then put to test in subsequent validation studies.

Such risk engines and scoring systems are able to predict disease outcome and risks of possible complications with varying degrees of accuracy.

From health policy makers point of view it helps in appropriate disbursement of available resources for greatest benefit of population at risk.

These scoring systems and risk engines to begin with were available for manual calculations and references/use of formula and paper charts were essential. However with evolution of information technology, such calculations became easier to make with use of online web-based tools. In recent times with advancement of android technology, easy to download apps (applications) has helped further to have the benefits of these online risk engines and scoring systems at our finger tips.

Several different online risk engines and clinical scoring systems relevant to endocrinology have been popularized. We shall discuss few of the more relevant ones in the subsequent sections of this article.

 Framingham Risk Score



The Framingham Risk Score is a widely used gender-specific algorithm which estimates the 10-year cardiovascular risk of a given individual. The score was developed based on data obtained from the famous Framingham Heart Study, to estimate the 10-year risk of developing coronary heart disease (CHD). [1] In order to assess the 10-year cardiovascular disease risk, cerebrovascular events, peripheral artery disease and heart failure were subsequently added as disease outcomes for the 2008 Framingham Risk Score. This was in addition to the risk of CHD. [2]

Because the Framingham risk score gives an indication of the likely benefits of primary prevention, it is considered to be useful for both the concerned patient and the treating physician. It helps to identify patients at increased risk for future cardiovascular events and helps in re-emphasizing the need to incorporate lifestyle modification and preventive medical treatment and patient education in high risk patients. [3]

The components of the score are:

AgeGenderTotal cholesterol in mmol/LCigarette smokingHigh density lipoprotein (HDL) cholesterol in mmol/LSystolic blood pressure in mmHgMedication for hypertension.

CHD risk at 10 years in percent can be calculated with the help of the Framingham Risk Score. Individuals with low risk have a 10% or less CHD risk, with intermediate risk have a 10-20%, and with high risk 20% or more CHD risk at 10 years. However, it should be remembered that these categorizations are arbitrary. There is increasing evidence as well that the Framingham risk equations are unable to provide accurate estimations of absolute risk in individuals from different populations. Risk estimates derived from cohort studies such as Framingham and the recent Systematic Coronary Risk Evaluation (SCORE) project; do not have the flexibility to incorporate regional, socioeconomic, and temporal differences in disease rates. [4] The Framingham risk engine has also been criticized for not being able to predict outcomes in patients with diabetes. In the European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk study, it overestimated the risks of CHD in individuals with diabetes. [5]

In addition to the CHD risk at 10 years, the Framingham data set has been used in several other risk prediction engines. These include cardiovascular disease (30 year risk), congestive heart failure risk, diabetes risk engine, atrial fibrillation (10 year risk) prediction, hypertension risk score, intermittent claudication, and stroke prediction scores. [6]

 Uk Prospective Diabetes Study (UKPDS) Risk Engine



Risk calculators based on equations from the Framingham Heart Study tend to underestimate risks for people with diabetes as this study included relatively few diabetic subjects. The UKPDS Risk Engine is a type 2 diabetes specific risk calculator based on 53,000 patients years of data from the UKPDS, which also provides an approximate 'margin of error' for each estimate. [7]

The UKPDS risk engine provides risk estimates and 95% confidence intervals, in individuals with type 2 diabetes not known to have heart disease, for:

Nonfatal and fatal CHDFatal CHDNonfatal and fatal strokeFatal stroke.

These can be calculated for any given duration of type 2 diabetes based on current age, sex, ethnicity, smoking status, presence or absence of atrial fibrillation, and levels of HbA1c, systolic blood pressure, total cholesterol, and HDL cholesterol. [8]

 Frax Score



FRAX is a computer-based algorithm that provides models for the assessment of 10-year fracture probability taking into consideration a number of clinical risk factors. [9] Until recently, the decision to treat osteoporosis was entirely based on the T-score of the bone mineral density (BMD). [10] However, it was recognized subsequently that the future risk of fracture also depends heavily on a number of risk factors and the Dual-energy X-ray absorptiometry (DEXA) machine may not be available widely. The FRAX tool, developed by World Health Organization (WHO) is based on individual patient models that integrate the clinical risk factors with the BMD at the femoral neck. The FRAX algorithms give the 10-year probability of a hip fracture and the 10-year probability of a major osteoporotic fracture (clinical spine, forearm, hip or shoulder fracture). National Osteoporosis Foundation Guide recommends treating patients with FRA × 10-year risk scores of ≥ 3% for hip fracture or ≥ 20% for major osteoporotic fracture, to reduce their fracture risk. [11]

The FRAX takes into account the following risk factors:

BMD (the maker of the DEXA equipment and the actual femoral neck BMD in g/cm 2 . Alternatively, the T-score based on the NHANES III female reference data. In patients without a BMD, the field should be left blank).Age (the model accepts ages between 40 and 90 years. If ages below or above are entered, the program will compute probabilities at 40 and 90 year, respectively)GenderHeightWeightPrevious fragility fracture (a previous fracture in adult life occurring spontaneously, or a fracture arising from trauma which, in a healthy individual, would not have resulted in a fracture)Parental history of hip fractureGlucocorticoid treatment (the patient is currently exposed to oral glucocorticoids or has been exposed to oral glucocorticoids for more than 3 months at a dose of prednisolone of 5 mg daily or more or equivalent doses of other glucocorticoids)Current smokingAlcohol intake of 3 or more units per day (a unit of alcohol varies slightly in different countries from 8 to 10 g of alcohol. This is equivalent to a standard glass of beer (285 ml), a single measure of spirits (30 ml), a medium-sized glass of wine (120 ml), or 1 measure of an aperitif (60 ml))Rheumatoid arthritisOther secondary causes of osteoporosis (these include type 1 diabetes, osteogenesis imperfecta in adults, untreated long-standing hyperthyroidism, hypogonadism, or premature menopause (<45 years), chronic malnutrition or malabsorption, and chronic liver disease)

Limitations of FRAX

The FRAX has its own sets of limitation as well. [12] For example, it does not accommodate all the known risk factors for osteoporosis like risk factors for repeated falls and in patients aged less than 40 years or more than 90 years, the calculation may not be correct. It also does not take into account the detail of the risk factors it considers like the number of cigarettes per day and duration smoking, number of prior fragility fractures, dose response effects of glucocorticoids, and alcohol. It also does not consider the biochemical markers of bone turnover and the widely used quantitative ultrasound. Moreover, this model is relevant only for untreated patients. Till now only limited country models available in the online calculator. Lastly, it should be kept in mind that the calculation depends on adequacy of epidemiological information and it does not replace clinical judgment. Many treating physicians confuse the prediction scores of FRAX with the recommendations for treatment. The FRAX predicts the risk of fractures, but it cannot predict the efficacy of a treatment.

 Qfracture



QFracture is used to estimate an individual's future risk of developing a hip fracture or a major osteoporotic fracture (hip, spine, wrist, or shoulder) over the next 10 years. The algorithm is used to assess patients aged between 30 and 99 years unless they have already had an osteoporotic fracture. [13]

The following factors are needed to calculate a QFracture score in men and women:

AgeSexEthnicitySmoking status (nonsmoker, ex-smoker, light, moderate, heavy)Alcohol useType 1 or 2 diabetesParental history of hip fracture/osteoporosisNursing or care home residenceHistory of prior osteoporotic (wrist, spine, hip, or shoulder) fractureHistory of fallsDementiaCancerAsthma or chronic obstructive pulmonary disease (COPD)Cardiovascular diseaseChronic liver diseaseChronic kidney diseaseParkinson's disease.

Rheumatoid arthritis or systemic lupus erythematosis (SLE)

Gastrointestinal malabsorption (including Crohn's disease, ulcerative colitis, celiac disease, steatorrhea, blind loop syndrome)

Epilepsy or use of anticonvulsantsUse of antidepressants (at least 2 scripts in last 6 months)Use of corticosteroids (at least 2 scripts in last 6 months)Body mass index.

Additional factors are used for women only

Use of estrogen only hormone replacement therapy. Endocrine problems (thyrotoxicosis, primary, or secondary hyperparathyroidism, Cushing's syndrome).

QFracture has its own sets of limitations. It estimate fracture risk up to the 9 th decade and use 10-year fracture risk, which may underestimate short-term risk in old-home residents, who have a mean age of approximately 85 years and a life expectancy of less than 5 years. QFracture was derived from a particular population that may not reflect the ethnic diversity, and also make assumptions across different racial and ethnic groups which may not be valid. It is also difficult to memorize all the component of the tool. Further work is therefore needed to determine whether this risk assessment tools is accurate and reliable in predicting fracture risk in different ethnic groups across the globe. [14]

 Macis Score



Papillary thyroid cancer or papillary thyroid carcinoma (PTC) is the most common histological type of thyroid cancer constituting about 75-85% of all thyroid cancer cases. Depending on source, the overall 5-year survival rate for papillary thyroid cancer is more than 95% and a 10-year survival rate of 93%. [15]

To prognosticate individual cases in a more specific way, there are at least 13 known scoring systems in PTC. Among them some are used more often like the AGES ( A ge, G rade, E xtent of disease, S ize), AMES (Age, M etastasis, E xtent of disease, S ize), and the MACIS, which is probably the most reliable staging method available. The MACIS score was developed by the Mayo Clinic based on careful evaluation of a large group of patients and was developed to determine the prognosis of patients with papillary thyroid cancer.

MAICS is the abbreviation of the several factors taken into account to predict survival in PTC. The factors which are considered in MACIS scores are: 1. M etastasis (distant) or spread of the cancer to areas outside the neck, 2. A ge of the patient at the time the tumor was discovered, 3. C ompleteness of surgical resection (or removal) of the tumor, 4. I nvasion into surrounding areas of the neck as seen by the naked eye, and 5. S ize of the tumor.

The final prognostic score is calculated as per the following formula:

MACIS = 3.1 (if aged less than or equal to 39 years) or 0.08 × age (if aged greater than or equal to 40 years), + 0.3 × tumor size (in centimeters), +1 (if incompletely resected), +1 (if locally invasive), +3 (if distant metastases present).

Twenty-year cause-specific survival rates for patients in PTC based on the MACIS score are:

MACIS < 6: 99%; MACIS 6-6.99: 89%; MACIS 7-7.99: 56%; MACIS 8+: 24%. [16]

MACIS score however, has not been used and accepted widely across the globe for a number of reasons. It does not take into consideration the histological variants of the tumor, though some of them (tall cell, columnar cell, and insular type) are associated with poorer prognosis. It can also be used for follicular thyroid cancers and is better than the other classifications and scoring systems like tumor, node, metastasis (TNM); age, grade, extent, size (AGES); and age, metastases, extent, size (AMES) prognostication scores. For computation of MACIS score, a detailed operative note and a meticulous macroscopic and microscopic description of the surgically removed specimen is required, which may not be available in all patients. Lastly, it may appear complicated to some of the users for daily clinical usage and their practicality has been questioned. [17],[18]

 Thyroid Clinical Activity Score (CAS)



It is a score to decide on immunosuppressive treatment and to predict the outcome of such treatment in Graves' ophthalmopathy (GO). GO is an inflammatory orbitopathy that develops in association with autoimmune thyroid disorders. Thyroid-associated orbitopathy (TAO), thyroid eye disease, and Graves' orbitopathy are other names used for GO. Approximately half of patients with Graves' hyperthyroidism have clinical signs and/or symptoms suggestive of GO, and 5% of them suffer from severe disease.

The 10 components of CAS

Painful feeling behind the globe over last 4 weeksPain with eye movement during last 4 weeksRedness of the eyelidsRedness of the conjunctivaSwelling of the eyelidsChemosis (edema of the conjunctiva)Swollen caruncle (flesh body at medial angle of eye)Increase in proptosis ≥ 2 mm during a period of 3 monthsDecreased eye movements ≥ 5° any directionDecreased visual acuity ≥ 1 line on Snellen chart

A 7-point scale (excluding the last three elements) is used when no previous assessment is available. GO is considered active in patients with a CAS ≥ 3. CAS has a high specificity and high positive predictive value in predicting the therapeutic outcome of immunosuppressive treatment and radiotherapy.CAS ≥ 4 had an 80% chance of a favorable outcome after treatment. [19]

 Burch-Wartofsky Score



The Burch-Wartofsky Score is a point scale that helps to assess the probability of thyroid storm independently of the level of thyroid hormones. [20] It is solely based on clinical and physical criteria and as a result can be used at bedside. The components and the points are as follows:

Body temperature (°F):

99-99.9: 5

100-100.9: 10

101-101.9: 15

102 -102.9: 20

103-103.9: 25

≥104: 30Central nervous effects

Absent: 0

Mild (agitation): 10

Moderate (delirium, psychosis, extreme lethargy): 20

Severe (seizures, coma): 30Hepatogastroinestinal dysfunction

Absent: 0

Moderate (diarrhea, nausea, vomiting, abdominal pain): 10

Severe (unexplained jaundice): 20Cardiovascular dysfunction:

Pulse frequency (beats/min)

90-109: 5

110-119: 10

120-129: 15

130-139: 20 ≥

140: 25

Congestive heart failure:

Absent: 0

Mild (pedal edemas): 5

Moderate (bibasilar rales): 10

Severe (pulmonary edema): 15

Atrial fibrillation:

Absent: 0

Present: 10Precipitating event:

Absent: 0

Present: 10

The summed up point values deliver the score. A score of ≥ 45 is highly suggestive of a thyroid storm in a patient, score between 25 and 44 is suggestive of impending thyroid storm, and if the score is less than 25 the thyroid storm is unlikely. [21] However, this score is supposed to be neither evidence based nor validated. In addition, the scoring system is rather complex, making it difficult to memorize precisely.

 Thyroid Events Amsterdam (THEA) Score



This is used for prediction of progression to overt hypothyroidism or hyperthyroidism in female relatives of patients with autoimmune thyroid disease (AITD). The baseline assessment is done with measurement of serum thyrotropin (TSH), free thyroxine (FT4), and thyroid peroxidase (TPO) antibody levels as well as evaluation for the presence and levels of Yersinia enterocolitica antibodies.

For TSH the risk starts to increase at values > 2.0 mIU/L. The TPO antibody has a level-dependent relationship with the risk which also depends on the family background (with the greatest risk to subjects having at least two relatives with Hashimoto disease).

A numerical score, the THEA score, was designed to predict events by weighting these three risk factors proportionately to their relative risks (maximum score, 21): low (0 - 7), medium (8 - 10), high (11 - 15), and very high (16 - 21). These THEA scores were associated with observed event rates. [22]

Though this simple predictive score was developed to estimate the 5-year risk of overt hypothyroidism or hyperthyroidism in female relatives of patients with AITD, in view of the small number of observed events, the THEA score needs further validation with larger studies.

 Score System for Predicting Congenital Hypothyroidsm (CH)



The classical picture of CH with characteristic clinical features manifests usually by the age of 3 months, but by that time the child develops irreversible neurological damage. A number of nonspecific signs and symptoms can be noticed during the first weeks of life, which may help to establish the diagnosis of CH early in neonatally screened, but not confirmed newborns. This scoring system helps the clinicians to pick up the affected babies earlier than 3 weeks of age and gives an opportunity to replace levothyroxine early in course of the disease. [23],[24]

Method to calculate

[INLINE:1]

The cut-off value for predicting CH is 6. It can predict CH in 99% of cases correctly.

 Pitutary Apoplexy Score (PAS)



This scoring system is used to quantify the neuro-ophthalmic deficits and predict the outcome in surgically and conservatively managed patients with pituitary apoplexy.

Components

[INLINE:2]

A patient with a PAS ≥ 4 should be managed by emergency surgical intervention. PAS can also be a useful tool to monitor patient being managed conservatively and progressive increase in the score during the period of observation may prompt consideration for surgery. [25] However, as suggested by the UK guideline development group, clinical outcomes of management decisions based on PAS needs validation by prospective studies.

 Calculation of Bioavailable and Free Testosterone (Bio T and Ft) in Men



Testosterone and dihydrotestosterone (DHT) circulate in plasma unbound (free approximately 2-3%), bound to specific plasma proteins (sex hormone-binding globulin (SHBG)), and weakly bound to nonspecific proteins such as albumin. The SHBG-bound fraction is biologically inactive because of the high binding affinity of SHBG for testosterone. FT measures the free fraction, bioT includes free plus weakly bound to albumin.

Formula: Free androgen index = (total testosterone/SHBG) × 100

Merits: These calculated parameters more accurately reflect the level of bioactive testosterone than does the sole measurement of total serum testosterone. Estimation of serum concentrations of FT and bioT by calculation is an inexpensive and uncomplicated method.

Limitations

Algorithms to calculate FT and bioT must be revalidated in the local setting, otherwise over- or underestimation of FT and bioT concentrations can occur. Additionally, confounding of the results by SHBG concentrations may be introduced.

 Nonalcoholic Fatty Liver Disease (Nafld) Score



NAFLD fibrosis score is used to assess the degree of liver fibrosis in nonalcoholic fatty liver disease, which is commonly associated with obesity, metabolic syndrome, and type 2 diabetes. [26] Obesity, diabetes mellitus, platelet count of ≤ 200 × 10 3 /L, advanced age, and aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio ≥ 0.8 are the risk factors of advanced fibrosis.

Components

Age (years)

Body mass index (BMI; kg/m 2 )

Insulin-like growth factor (IGF)/diabetes

AST

ALT

Platelets (×10 9 /L)

Albumin (g/dL)

This is calculated by the following formula: (-) 1.675 + 0.037 × age (years) + 0.094 × BMI (kg/m 2 ) + 1.13 × IFG/diabetes (yes = 1, no = 0) + 0.99 × AST/ALT ratio - 0.013 × platelet (×10 9 /L) - 0.66 × albumin (g/dL)

Result

NAFLD score < −1.455 = F0-F2 (mild/moderate fibrosis)

NAFLD score − 1.455 to 0.675 = indeterminate score

NAFLD score > 0.675 = F3-F4 (advanced fibrosis)

This noninvasive method is capable of ruling out advanced hepatic fibrosis and significantly reduces the need for liver biopsies in patients with NAFLD. Pathology involving the entire liver is reflected in this score. However, it does not differentiate NASH from the other causes of liver injury. Further research is needed to validate the benefit of the score to predict complications or death in NAFLD patients in different ethnic groups.

 Conclusions



Understandably the accuracy of prediction of different risk engines and scoring systems are highly variable and has several limitations. Each risk engine or clinical scoring tool is derived from data obtained from a particular population and its results are not generalizable and hence its ability to predict risk/outcome in a different population with differences in ethnicity, ages, and differences in distribution of risk factors over time both within and between populations.

In spite of these short comings, these risk engines and scoring systems are useful tools in prognostication and also in treatment decisions, particularly for priotization of intensification of treatment modalities especially in high risk populations.

References

1Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation 1998;97:1837-47.
2D'Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: The Framingham Heart Study. Circulation 2008;117:743-53.
3Wilson PW. Estimation of cardiovascular risk in an individual patient without known cardiovascular disease. In: Basow DS, editor. UpToDate [Textbook of Medicine]. Netherlands: Massachusetts Medical Society, and Wolters Kluwer Publishers; 2010.
4Conroy RM, Pyorala K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE project. Eur Heart J 2003;24:987-1003.
5Simmons RK, Coleman RL, Price HC, Holman RR, Khaw KT, Wareham NJ, et al. Performance of the UK Prospective Diabetes Study Risk Engine and the Framingham Risk Equations in Estimating Cardiovascular Disease in the EPIC- Norfolk Cohort. Diabetes Care 2009;32:708-13.
6Available from: http://www.framinghamheartstudy.org/risk/index.html [Last accessed 2013 Sept 17].
7Hayes AJ, Leal J, Gray AM, Holman RR, Clarke PM. UKPDS Outcomes Model 2: A new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia 2013;56:1925-33.
8Available from: http://www.dtu.o ×.ac.uk/riskengine/[Last accessed 2013 Sept 17].
9Available from: http://www.shef.ac.uk/FRAX [Last accessed 2013 Sept 17].
10World Health Organization. Assessment of osteoporosis at the primary health care level. Geneva: WHO; 2007. Available from: www.who.int/chp/topics/rheumatic/en/index.html [Last accessed 2013 Sept 17].
11Siris ES, Baim S, Nattiv A. Primary care use of FRAX: Absolute fracture risk assessment in postmenopausal women and older men. Postgrad Med 2010;122:82-90.
12Kanis JA, Oden A, Johansson H, Borgström F, Ström O, McCloskey E. FRAX and its applications to clinical practice. Bone 2009;44:734-43.
13Available from: http://www.qfracture.org/index.php [Last accessed 2013 Sept 17].
14Hippisley-Cox J, Coupland C. Derivation and validation of updated QFracture algorithm to predict risk of osteoporotic fracture in primary care in the United Kingdom: Prospective open cohort study. BMJ 2012;344:e3427.
15Numbers from National Cancer Database in the US. In: Grunewald F, Biersack HJ, Grunewald F, editors. Thyroid Cancer. Berlin: Springer; 2005.
16Hay ID, Bergstralh EJ, Goellner JR, Ebersold JR, Grant CS. Predicting outcome in papillary thyroid carcinoma: Development of a reliable prognostic scoring system in a cohort of 1779 patients surgically treated at one institution during 1940 through 1989. Surgery 1993;114:1050-7.
17Brierley JD, Panzarella T, Tsang RW, Gospodarowicz MK, O›Sullivan B. A comparison of different staging systems. Predictability of patient outcome: Thyroid carcinoma as an example. Cancer 1997;79:2414-23.
18Passler C, Prager G, Scheuba C, Kaserer K, Zettinig G, Niederle B. Application of staging systems for differentiated thyroid carcinoma in an endemic goiter region with iodine substitution. Ann Surg 2003;207:227-34.
19Mourits MP, Prummel MF, Wiersinga WM, Koornneef L. Clinical activity score as a guide in the management of patients with Graves› ophthalmopathy. Clin Endocrinol (Oxf) 1997;47:9-14.
20Burch HB, Wartofsky L. Life-threatening thyrotoxicosis. Thyrotoxic storm. Endocrinol Metab Clin North Am 1993;22:263-77.
21Available from: http://www.formatio-reticularis.de/burch-wartofsky.html [Last accessed 2013 Sept 17].
22Strieder TG, Tijssen JG, Wenzel BE, Endert E, Wiersinga WM. Prediction of progression to overt hypothyroidism or hyperthyroidism in female relatives of patients with autoimmune thyroid disease using the Thyroid Events Amsterdam (THEA) score. Arch Intern Med 2008;168:1657-63.
23Muzsnai Á. Some aspects for optimalization of hormone substitution in congenital hypothyroidism. (Hungarian) PhD thesis. Budapest; 1991.
24Péter F, Muzsnai A. Congenital disorders of the thyroid: Hypo/hyper. Endocrinol Metab Clin North Am 2009;38:491-507.
25Eddy NL, Rajasekaran S, Han TS, Theodoraki A, Drake W, Vanderpump M, et al. An objective scoring tool in the management of patients with pituitary apoplexy. Clin Endocrinol (Oxf) 2011;75:723.
26Angulo P, Hui JM, Marchesini G, Bugianesi E, George J, Farrell GC, et al. The NAFLD fibrosis score: A noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology 2007;45:846-54.