[Severity scores for childhood psoriasis: A systematic literature review]

J Lavaud1, E Mahé1

  • 1Service de dermatologie, hôpital Victor-Dupouy, 69 rue du Lieutenant-Colonel Prud'hon, 95100 Argenteuil, France.

Insights

Pediatric psoriasis severity scores lack validation and standardization, hindering effective treatment. Child-specific thresholds are needed for accurate assessment and comparison in childhood psoriasis management.

Area of Science:

  • Dermatology
  • Pediatrics
  • Clinical Research

Background:

  • Childhood psoriasis affects 0.5-1% of European children, significantly impacting quality of life.
  • Systemic treatments are increasingly available for moderate-to-severe pediatric psoriasis.
  • Lack of standardized severity assessment tools complicates management.

Purpose of the Study:

  • To examine the utilization of clinical severity scores and quality-of-life measures in pediatric psoriasis.
  • To identify current practices and gaps in the assessment of childhood psoriasis severity.

Main Methods:

  • Systematic literature review of PubMed and Embase databases.
  • Keywords included "psoriasis" with pediatric terms and various severity/quality-of-life score terms.
  • Analysis of selected articles for score usage, definitions, and thresholds.

Main Results:

  • 78 articles were selected, with Psoriasis Area Severity Index (PASI) being the most common score (74.4%).
  • Body Surface Area (BSA) and Physician's Global Assessment (PGA) were also frequently used.
  • Significant heterogeneity in severity definitions and lack of defined thresholds were observed for both severity and quality-of-life scores (e.g., CDLQI, DLQI, Peds-QL).

Conclusions:

  • Current pediatric psoriasis severity scores are often adapted from adult measures without specific validation.
  • Pediatric-specific factors are not adequately considered in existing scoring systems.
  • There is a critical need to validate and define child-specific severity and quality-of-life thresholds for better management and research comparability.
Abstract

Related Concept Videos

Review and Preview01:10

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
8.3K
Review and Preview01:13

Review and Preview

Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
10.9K
Introduction to z Scores01:06

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
11.0K
Introduction to z Scores01:05

Introduction to z Scores

A z score (or standardized value) is measured in units of the standard deviation. It indicates how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
1.3K
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
18.4K
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
14.7K