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{{Short description|Measured values that are relatively normal for a particular medical test}}
{{Reference ranges}}
In [[medicine]] and [[health]]-related fields, a '''reference range''' or '''reference interval''' is the [[range (statistics)|range]] or the [[interval (mathematics)|interval]] of values that is deemed normal for a [[physiology|physiologicphysiological]] measurement in healthy persons (for example, the amount of [[creatinine]] in the [[blood]], or the [[blood gas tension|partial pressure of oxygen]]). It is a basis for comparison (a [[frame of reference]]) for a [[physician]] or other [[health professional]] to interpret a set of test results for a particular patient. Some important reference ranges in medicine are [[reference ranges for blood tests]] and [[Urinalysis#Target parameters|reference ranges for urine tests]].
 
The standard definition of a reference range (usually referred to if not otherwise specified) originates in what is most prevalent in a [[reference group]] taken from the general (i.e. total) population. This is the general reference range. However, there are also ''optimal health ranges'' (ranges that appear to have the optimal health impact) and ranges for particular conditions or statuses (such as pregnancy reference ranges for hormone levels).
 
Values '''within the reference range''' ('''WRR''') are those within the [[normal distribution]] and are thus often described as '''within normal limits''' ('''WNL'''). The limits of the normal distribution are called the ''upper reference limit'' (URL) or ''upper limit of normal'' (ULN) and the ''lower reference limit'' (LRL) or ''lower limit of normal'' (LLN). In [[health care]]–related publishing, [[style guide|style sheets]] sometimes prefer the word ''reference'' over the word ''normal'' to prevent the nontechnical [[word sense|senses]] of ''normal'' from being conflated with the statistical sense. Values outside a reference range are not [[wikt:in and of itself#Adverb|necessarily]] pathologic, and they are not necessarily abnormal in any sense other than statistically. Nonetheless, they are indicators of probable pathosis. Sometimes the underlying cause is obvious; in other cases, challenging [[differential diagnosis]] is required to determine what is wrong and thus how to treat it.
 
A '''cutoff''' or '''threshold''' is a limit used for [[binary classification]], mainly between normal versus pathological (or probably pathological). Establishment methods for cutoffs include using an upper or a lower limit of a reference range.
 
== Standard definition ==
The standard definition of a reference range for a particular measurement is defined as the interval between which 95% of values of a reference population fall into, in such a way that 2.5% of the time a value will be less than the lower limit of this interval, and 2.5% of the time it will be larger than the upper limit of this interval, whatever the distribution of these values.<ref>[https://books.google.com/books?id=Je_pJfb2r0cC&pg=PA19 Page 19] in: {{cite book |author=Stephen K. Bangert MA MB BChir MSc MBA FRCPath; William J. Marshall MA MSc MBBS FRCP FRCPath FRCPEdin FIBiol; Marshall, William Leonard |title=Clinical biochemistry: metabolic and clinical aspects |publisher=Churchill Livingstone/Elsevier |location=Philadelphia |year=2008 |isbn=978-0-443-10186-1 }}</ref>
 
Reference ranges that are given by this definition are sometimes referred as ''standard ranges''.
 
Since a range is a defined statistical value ([[Range (statistics)]]) that describes the interval between the smallest and largest values, many, including the International Federation of Clinical Chemistry prefer to use the expression reference interval rather than reference range.<ref>{{cite journal |last1=Dybkaer |first1=R |title=International federation of clinical chemistry (IFCC)1),2) the theory of reference values. Part 6. Presentation of observed values related to reference values. |journal=Journal of Clinical Chemistry and Clinical Biochemistry |date=November 1982 |volume=20 |issue=11 |pages=841–5 |pmid=7153721}}</ref>
 
Regarding the target population, if not otherwise specified, a standard reference range generally denotes the one in healthy individuals, or without any known condition that directly affects the ranges being established. These are likewise established using reference groups from the healthy population, and are sometimes termed ''normal ranges'' or ''normal values'' (and sometimes "usual" ranges/values). However, using the term ''normal'' may not be appropriate as not everyone outside the interval is abnormal, and people who have a particular condition may still fall within this interval.
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===Establishment methods===
Methods for establishing reference ranges arecan mainlybe based on assuming a [[normal distribution]] or a [[log-normal distribution]], or directly from percentages of interest, as detailed respectively in following sections. When establishing reference ranges from bilateral organs (e.g., vision or hearing), both results from the same individual can be used, although intra-subject correlation must be taken into account.<ref>{{cite journal |last1=Davis |first1=C.Q. |last2=Hamilton |first2=R. |title=Reference ranges for clinical electrophysiology of vision |journal=Doc Ophthalmol |date=2021 |volume=143 |issue=2 |pages=155–170 |doi=10.1007/s10633-021-09831-1|pmc=8494724 |pmid=33880667 |doi-access=free }}</ref>
 
====Normal distribution====
{{further|68–95–99.7 rule}}
[[File:Standard deviation diagram.svg|thumb|350px|When assuming a normal distribution, the reference range is obtained by measuring the values in a reference group and taking two standard deviations either side of the mean. This encompasses ~95% of the total population.]]
 
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However, in the real world, neither the population mean nor the population standard deviation are known. They both need to be estimated from a sample, whose size can be designated ''n''. The population standard deviation is estimated by the sample standard deviation and the population mean is estimated by the sample mean (also called mean or [[arithmetic mean]]). To account for these estimations, the 95% [[prediction interval]] (95% PI) is calculated as:
 
: {{math|1= 95% PI = mean ± ''t''{{sub|0.975,''n''&minus;1}}&middot;·{{sqrt|(''n''+1)/''n''}}&middot;·sd}},
 
where <math>t_{0.975,n-1}</math> is the 97.5% quantile of a [[Student's t-distribution]] with ''n''&minus;1 [[Degrees of freedom (statistics)|degrees of freedom]].
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This method is often acceptably accurate if the standard deviation, as compared to the mean, is not very large. A more accurate method is to perform the calculations on logarithmized values, as described in separate section later.
 
The following example of this (''not'' logarithmized) method is based on values of [[fasting plasma glucose]] taken from a reference group of 12 subjects:<ref name=Keevil1998>[http://www.clinchem.org/cgi/content-nw/full/44/7/1535/T1 Table 1. Subject characteristics] in: {{Cite journal | last1 = Keevil | first1 = B. G. | last2 = Kilpatrick | first2 = E. S. | last3 = Nichols | first3 = S. P. | last4 = Maylor | first4 = P. W. | title = Biological variation of cystatin C: Implications for the assessment of glomerular filtration rate | journal = Clinical Chemistry | volume = 44 | issue = 7 | pages = 1535–1539 | year = 1998 | doi = 10.1093/clinchem/44.7.1535 | pmid = 9665434| doi-access = free }}</ref>
 
{|class="wikitable"
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The 90% ''confidence interval of a standard reference range limit'' as estimated assuming a normal distribution can be calculated by:<ref>[https://books.google.com/books?id=p7XwAwAAQBAJ&pg=PA65 Page 65] in: {{cite book|title=Tietz Fundamentals of Clinical Chemistry and Molecular Diagnostics|author=Carl A. Burtis, David E. Bruns|edition=7|publisher=Elsevier Health Sciences|year=2014|isbn=9780323292061}}</ref>
 
: Lower limit of the confidence interval = percentile limit - 2.81 &times;× {{frac|''SD''|{{sqrt|''n''}}}}
 
: Upper limit of the confidence interval = percentile limit + 2.81 &times;× {{frac|''SD''|{{sqrt|''n''}}}},
 
where SD is the standard deviation, and n is the number of samples.
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Taking the example from the previous section, the number of samples is 12 and the standard deviation is 0.42&nbsp;mmol/L, resulting in:
 
:''Lower limit of the confidence interval'' of the ''lower limit of the standard reference range'' = 4.4 - 2.81 &times;× {{frac|0.42|{{sqrt|12}}}} ≈ 4.1
 
:''Upper limit of the confidence interval'' of the ''lower limit of the standard reference range'' = 4.4 + 2.81 &times;× {{frac|0.42|{{sqrt|12}}}} ≈ 4.7
 
Thus, the lower limit of the reference range can be written as 4.4 (90% CI 4.1-41–4.7) mmol/L.
 
Likewise, with similar calculations, the upper limit of the reference range can be written as 6.3 (90% CI 6.0-60–6.6) mmol/L.
 
These confidence intervals reflect [[random error]], but do not compensate for [[systematic error]], which in this case can arise from, for example, the reference group not having fasted long enough before blood sampling.
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====Log-normal distribution====
[[Image:PDF-log normal distributions.svg|thumb|Some functions of [[log-normal distribution]] (here shown with the measurements non-logarithmized), with the same means - ''μ'' (as calculated after logarithmizing) but different standard deviations - ''σ'' (after logarithmizing).]]
In reality, biological parameters tend to have a [[log-normal distribution]],<ref>{{cite book | last = Huxley | first = Julian S. | year = 1932 | title = Problems of relative growth | publisher = London | oclc = 476909537 | isbn = 978-0-486-61114-3 }}</ref> rather than the arithmetical normal distribution (whichor is generally referred to as normalGaussian distribution without any further specification).
 
An explanation for this log-normal distribution for biological parameters is: The event where a sample has half the value of the mean or median tends to have almost equal probability to occur as the event where a sample has twice the value of the mean or median. Also, only a log-normal distribution can compensate for the inability of almost all biological parameters to be of [[negative number]]s (at least when measured on [[absolute scale]]s), with the consequence that there is no definite limit to the size of outliers (extreme values) on the high side, but, on the other hand, they can never be less than zero, resulting in a positive [[skewness]].
 
As shown in diagram at right, this phenomenon has relatively small effect if the standard deviation (as compared to the mean) is relatively small, as it makes the log-normal distribution appear similar to an arithmeticala normal distribution. Thus, the arithmetical normal distribution may be more appropriate to use with small standard deviations for convenience, and the log-normal distribution with large standard deviations.
 
In a log-normal distribution, the [[geometric standard deviation]]s and [[geometric mean]] more accurately estimate the 95% prediction interval than their arithmetic counterparts.
 
=====Necessity=====
Reference ranges for substances that are usually within relatively narrow limits (coefficient of variation less than 0.213, as detailed below) such as [[electrolytes]] can be estimated by assuming normal distribution, whereas reference ranges for those that vary significantly (coefficient of variation generally over 0.213) such as most [[hormones]]<ref name="pmid19758299">{{cite journal| author=Levitt H, Smith KG, Rosner MH| title=Variability in calcium, phosphorus, and parathyroid hormone in patients on hemodialysis. | journal=Hemodial Int | year= 2009 | volume= 13 | issue= 4 | pages= 518–25 | pmid=19758299 | doi=10.1111/j.1542-4758.2009.00393.x | pmc= | s2cid=24963421 | url=https://pubmed.ncbi.nlm.nih.gov/19758299 }}</ref> are more accurately established by log-normal distribution.
The necessity to establish a reference range by log-normal distribution rather than arithmetic normal distribution can be regarded as depending on how much difference it would make to ''not'' do so, which can be described as the ratio:▼
 
▲The necessity to establish a reference range by log-normal distribution rather than arithmetic normal distribution can be regarded as depending on how much difference it would make to ''not'' do so, which can be described as the ratio:
 
:{{math|1=Difference ratio = {{sfrac| {{mabs| Limit{{sub|log-normal}} - Limit{{sub|normal}} }} | Limit{{sub|log-normal}} }} }}
 
where:
* ''Limit<sub>log-normal</sub>'' is the (lower or upper) limit as estimated by assuming log-normal distribution
* ''Limit<sub>normal</sub>'' is the (lower or upper) limit as estimated by assuming arithmetically normal distribution.
 
[[File:Diagram of coefficient of variation versus deviation in reference ranges erroneously not established by log-normal distribution.png|thumb|350px|Coefficient of variation versus deviation in reference ranges established by assuming arithmetic normal distribution when there is actually a log-normal distribution.]]
 
This difference can be put solely in relation to the [[coefficient of variation]], as in the diagram at right, where:
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where:
* ''s.d.'' is the arithmetic standard deviation
* ''m'' is the arithmetic mean
 
In practice, it can be regarded as necessary to use the establishment methods of a log-normal distribution if the difference ratio becomes more than 0.1, meaning that a (lower or upper) limit estimated from an assumed arithmetically normal distribution would be more than 10% different from the corresponding limit as estimated from a (more accurate) log-normal distribution. As seen in the diagram, a difference ratio of 0.1 is reached for the lower limit at a coefficient of variation of 0.213 (or 21.3%), and for the upper limit at a coefficient of variation at 0.413 (41.3%). The lower limit is more affected by increasing coefficient of variation, and its "critical" coefficient of variation of 0.213 corresponds to a ratio of (upper limit)/(lower limit) of 2.43, so as a rule of thumb, if the upper limit is more than 2.4 times the lower limit when estimated by assuming arithmetically normal distribution, then it should be considered to do the calculations again by log-normal distribution.
 
Taking the example from previous section, the arithmetic standard deviation (s.d.) is estimated at 0.42 and the arithmetic mean (m) is estimated at 5.33. Thus the coefficient of variation is 0.079. This is less than both 0.213 and 0.413, and thus both the lower and upper limit of fasting blood glucose can most likely be estimated by assuming arithmetically normal distribution. More specifically, the coefficient of variation of 0.079 corresponds to a difference ratio of 0.01 (1%) for the lower limit and 0.007 (0.7%) for the upper limit.
 
=====From logarithmized sample values=====
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=====From arithmetic mean and variance=====
An alternative method of establishing a reference range with the assumption of log-normal distribution is to use the arithmetic mean and arithmetic value of standard deviation. This is somewhat more tedious to perform, but may be useful for example in cases where a study that establishes a reference range presents only the arithmetic mean and standard deviation, while leaving out the source data. If the original assumption of arithmetically normal distribution is shown to be less appropriate than the log-normal one, then, using the arithmetic mean and standard deviation may be the only available parameters to correctdetermine the reference range.
 
By assuming that the [[expected value]] can represent the arithmetic mean in this case, the parameters ''μ<sub>log</sub>'' and ''σ<sub>log</sub>'' can be estimated from the arithmetic mean (''m'') and standard deviation (''s.d.'') as:
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====Example====
{{Hatnote|MethodThe method is described in further detail inat [[Differentialdifferential diagnosis]].}}
Let's say, for example, that an individual takes a test that measures the [[ionized calcium]] in the blood, resulting in a value of 1.30&nbsp;mmol/L, and a reference group that appropriately represents the individual has established a reference range of 1.05 to 1.25&nbsp;mmol/L. The individual's value is higher than the upper limit of the reference range, and therefore has less than 2.5% probability of being a result of random variability, constituting a strong indication to make a [[differential diagnosis]] of possible causative conditions.
 
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:{{math|1= Standard score (''z'') = {{sfrac|{{mabs | (Mean) - (individual measurement) }} |s.d.}} = {{sfrac|{{mabs | 1.15 - 1.30 }}|0.05}} = {{sfrac|0.15|0.05}} = 3}}.
 
The probability that a value is of so much larger value than the mean as having a standard score of 3 corresponds to a probability of approximately 0.14% (given by {{math|(100% &minus; 99.7%)/2}}, with 99.7% here being given from the [[68-95-9968–95–99.7 rule]]).
 
Using the same probabilities that the hypercalcemia would have occurred in the first place by the other candidate conditions, the probability that hypercalcemia would have occurred in the first place is 0.00335, and given the fact that hypercalcemia ''has occurred'' gives adjusted probabilities of 37.3%, 6.0%, 14.9% and 41.8%, respectively, for primary hyperparathyroidism, cancer, other conditions and no disease.
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==One-sided cut-off values==
In many cases, only one side of the range is usually of interest, such as with markers of pathology including [[CA19-9|cancer antigen 19-9]], where it is generally without any clinical significance to have a value below what is usual in the population. Therefore, such targets are often given with only one limit of the reference range given, and, strictly, such values are rather ''cut-off values'' or ''threshold values''.
 
They may represent both standard ranges and optimal health ranges. Also, they may represent an appropriate value to distinguish healthy person from a specific disease, although this gives additional variability by different diseases being distinguished. For example, for [[NT-proBNP]], a lower cut-off value is used in distinguishing healthy babies from those with [[acyanotic heart disease]], compared to the cut-off value used in distinguishing healthy babies from those with [[congenital nonspherocytic anemia]].<ref name=Moses2011>[http://www.medscape.com/viewarticle/735842_3 Screening for Congenital Heart Disease with NT-proBNP: Results] By Emmanuel Jairaj Moses, Sharifah A.I. Mokhtar, Amir Hamzah, Basir Selvam Abdullah, and Narazah Mohd Yusoff. Laboratory Medicine. 2011;42(2):75-8075–80. American Society for Clinical Pathology</ref>
 
==General drawbacks==
For standard as well as optimal health ranges, and cut-offs, sources of [[Accuracy and precision|inaccuracy and imprecision]] include:
 
* Instruments and lab techniques used, or how the measurements are interpreted by observers. These may apply both to the instruments etc. used to establish the reference ranges and the instruments, etc. used to acquire the value for the individual to whom these ranges is applied. To compensate, individual laboratories should have their own lab ranges to account for the instruments used in the laboratory.
* [[Risk factor#General determinants(epidemiology)|Determinants]] such as age, diet, etc. that are not compensated for. Optimally, there should be reference ranges from a reference group that is as similar as possible to each individual they are applied to, but it's is practically impossible to compensate for every single determinant, often not even when the reference ranges are established from multiple measurements of the same individual they are applied to, because of [[test-retest reliability|test-retest]] variability.
 
Also, reference ranges tend to give the impression of definite thresholds that clearly separate "good" or "bad" values, while in reality there are generally continuously increasing risks with increased distance from usual or optimal values.
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With this and uncompensated factors in mind, the ideal interpretation method of a test result would rather consist of a comparison of what would be expected or optimal in the individual when taking all factors and conditions of that individual into account, rather than strictly classifying the values as "good" or "bad" by using reference ranges from other people.
 
In a recent paper, Rappoport et al.<ref>{{cite biorxivbioRxiv|last1=Rappoport|first1=Nadav|last2=Paik|first2=Hyojung|last3=Oskotsky|first3=Boris|last4=Tor|first4=Ruth|last5=Ziv|first5=Elad|last6=Zaitlen|first6=Noah|last7=Butte|first7=Atul J.|date=2017-11-04|title=Creating ethnicity-specific reference intervals for lab tests from EHR data|biorxiv=10.1101/213892}}</ref> described a novel way to redefine reference range from an [[Electronicelectronic health record]] system. In such a system, a higher population resolution can be achieved (e.g., age, sex, race and ethnicity-specific).
 
==Examples==
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* [[Reference ranges for urine tests]]
 
== See also ==
* [[Clinical pathology]]
* [[Joint Committee for Traceability in Laboratory Medicine]]
* [[Medical technologist]]
* [[Reference ranges for blood tests]]
 
==References==
{{Academic peer reviewed|Q44275619|doi-access=free}}
{{Reflist|30em}}
 
==Further reading==
* The procedures and vocabulary referring to reference intervals: CLSI (Committee for Laboratory Standards Institute) and IFCC (International Federation of Clinical Chemistry) ''CLSI - Defining, Establishing, and Verifying Reference Intervals in the Laboratory; Approved guideline'' - Third Edition. Document C28-A3 ({{ISBN|1-56238-682-4}})Wayne, PA, USA, 2008
* [http://www.biostat.envt.fr/reference-value-advisor/ Reference Value Advisor] : A free set of Excel macros allowing the determination of reference intervals in accordance with the CLSI procedures. Based on: {{Cite journal | last1 = Geffré | first1 = A. | last2 = Concordet | first2 = D. | last3 = Braun | first3 = J. P. | last4 = Trumel | first4 = C. | title = Reference Value Advisor: A new freeware set of macroinstructions to calculate reference intervals with Microsoft Excel | journal = Veterinary Clinical Pathology | volume = 40 | issue = 1 | pages = 107–112 | year = 2011 | pmid = 21366659 | doi = 10.1111/j.1939-165X.2011.00287.x| url = https://hal.inrae.fr/hal-02649142/file/VCP%20-%20GEFFRE%20A%20-%20reference%20value%20-%20.2011_1.pdf }}
 
{{Blood tests}}
{{Urine tests}}
{{CSF tests}}
{{Authority control}}
 
[[Category:Research]]
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