Development of Psoriasis Assessment Tools Among Patients in the CorEvitas Psoriasis Registry

Abstract

Background
Dermatologists would benefit from an easy to use psoriasis severity assessment tool in the clinic.

Objective
To develop psoriasis assessment tools to predict PASI and Dermatology Life Quality Index (DLQI) using simple measures typically collected in clinical practice.

Methods
Data included 33 605 dermatology visits among plaque psoriasis patients enrolled in the CorEvitas Psoriasis Registry (4/15/15-7/11/20). Performance (adjusted coefficient of determination [R ² adj ], root mean square error [RMSE]) in predicting PASI and DLQI was assessed for 16 different linear regression models (specified a priori based on combinations of BSA, Investigator’s Global Assessment [IGA], itch, skin pain, patient global assessment, age, sex, BMI, comorbidity index, prior biologic use), and 2 stepwise selection models and 1 elastic net model based on 56 available variables. For each prediction model, concordance (sensitivity, specificity) of predicted PASI75, PASI90 and DLQI 0/1 with observed values was evaluated.

Results
Mean (SD) age, BSA, and PASI were 51 (14) years, 6 (11), and 4 (6), respectively; 46% were women, and 87% were biologic experienced. A model predicting PASI using BSA plus IGA performed best among a priori specified models (R ² adj = .72, RMSE = 2.93) and only marginally worse than models including additional variables (R ² adj range .64-.74, RMSE range 2.82-3.36). Models including IGA had the best concordance between predicted and observed PASI75 (sensitivity range 83-85%, specificity range 88-91%) and PASI90 (sensitivity range 76-82%, specificity range 94-98%). DLQI prediction was limited.

Conclusion
An assessment tool for psoriasis including BSA and IGA may be an ideal option to predict PASI in a clinic setting.

Full text

Original Research
Journal of Psoriasis and Psoriatic Arthritis
2023, Vol. 8(2) 74–82
© The Author(s) 2023
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/24755303231155118
journals.sagepub.com/home/jps
Development of Psoriasis Assessment Tools
Among Patients in the CorEvitas Psoriasis
Registry
Wayne P. Gulliver, MD
1
, Kyoungah See, PhD
2
, Baojin Zhu, PhD
2
, Bruce W. Konicek, MS
2
,
Ryan W. Harrison, MS
3
, Robert R. McLean, DSc, MPH
3
, Samantha J. Kerti, MS
3
,
Russel T. Burge, PhD
2,4
, and Craig L. Leonardi, MD
5
Abstract
Background: Dermatologists would benefit from an easy to use psoriasis severity assessment tool in the clinic. Objective: To
develop psoriasis assessment tools to predict PASI and Dermatology Life Quality Index (DLQI) using simple measures typically
collected in clinical practice. Methods: Data included 33 605 dermatology visits among plaque psoriasis patients enrolled in the
CorEvitas Psoriasis Registry (4/15/15-7/11/20). Performance (adjusted coefficient of determination [R
2adj
], root mean square
error [RMSE]) in predicting PASI and DLQI was assessed for 16 different linear regression models (specified a priori based on
combinations of BSA, Investigator’s Global Assessment [IGA], itch, skin pain, patient global assessment, age, sex, BMI, co-
morbidity index, prior biologic use), and 2 stepwise selection models and 1 elastic net model based on 56 available variables. For
each prediction model, concordance (sensitivity, specificity) of predicted PASI75, PASI90 and DLQI 0/1 with observed values
was evaluated. Results: Mean (SD) age, BSA, and PASI were 51 (14) years, 6 (11), and 4 (6), respectively; 46% were women, and
87% were biologic experienced. A model predicting PASI using BSA plus IGA performed best among a priori specified models
(R
2adj
= .72, RMSE = 2.93) and only marginally worse than models including additional variables (R
2adj
range .64-.74, RMSE range
2.82-3.36). Models including IGA had the best concordance between predicted and observed PASI75 (sensitivity range 83-85%,
specificity range 88-91%) and PASI90 (sensitivity range 76-82%, specificity range 94-98%). DLQI prediction was limited.
Conclusion: An assessment tool for psoriasis including BSA and IGA may be an ideal option to predict PASI in a clinic setting.
Keywords
psoriasis, PASI, DLQI, patient-reported outcome measures, psoriasis assessment tool
Introduction
There are several psoriasis disease severity clinical mea-
surement tools available, but no instrument meets all validity
criteria.
1-3
The Psoriasis Area Severity Index (PASI) is con-
sidered the gold standard for disease severity measurement in
clinical trials. It estimates disease severity using a summary
score based upon qualitative assessments of plaque redness
and thickness, and body surface area (BSA) across four body
sites. Yet the PASI has some limitations, including a lack of
consensus on interpretability, low response distribution (es-
pecially in mild –to-moderate disease (BSA 3-10%), the BSA
component is non-linear,
4-6
and is not useful in measuring
non-plaque psoriasis subsets.
7-10
Further, it is time-consuming
to ascertain, and is therefore rarely used in real-world
practice.
5,6
Simpler measures are available such as BSA and the In-
vestigator’s Global Assessment (IGA). Still, neither alone is
likely sufficient. BSA captures skin involvement but not
plaque qualities (eg, erythema, induration, scale), and the
opposite is true of IGA. Additionally, none of these measures
assess the patient’s perspective of disease, and treatment
guidelines recommend including assessment of health-related
quality of life (HRQoL).
11,12
Therefore, patient care would
benefit from a psoriasis severity assessment tool that is easy to
1
St. John’s, Newfoundland and Labrador, Memorial University of
Newfoundland, Newfoundland, Canada
2
Eli Lilly and Company, Indianapolis, IN, USA
3
CorEvitas LLC, Waltham, MA, USA
4
University of Cincinnati, Cincinnati, OH, USA
5
Central Dermatology, St. Louis, MO, USA
Corresponding Author:
Wayne P. Gulliver, MD, Memorial University of Newfoundland, St. John’s,
Newfoundland and Labrador, Newfoundland, Canada.
Email: WGulliver@newlabresearch.com

use in the clinic and gives a more comprehensive assessment
of disease burden such that it is correlated with HRQoL
measures, such as the Dermatology Life Quality Index
(DLQI).
To fill this need, a prior study evaluated an optimal pso-
riasis assessment tool (OPATÔ), based on the combination of
BSA and simple patient reported outcome measures (PROM),
as an alternative to PASI.
13
Findings suggested that a proxy
PASI score calculated using only these inputs was predictive
of an observed PASI assessment and DLQI, in 3800 patients
from three UNCOVER randomized control trials.
14
However,
this study comprised a relatively homogeneous, moderate-to-
severe psoriasis population participating in clinical trials of
ixekizumab, etanercept, or placebo. Thus, it is unclear how
well the combination of BSA and a PRO can predict PASI or
DLQI in the broader population of patients using systemic
therapy in real-world dermatologic practices.
Therefore, to address this gap, the objective of our study
was to develop an assessment tool for psoriasis utilizing data
from a real-world cohort of psoriasis patients being treated
with systemic therapies, representing a more heterogeneous
population of patients seen in dermatology practice. Our goals
were to (1) develop scales intended to enhance and ideally
optimize the assessment of psoriasis that calculate “predicted
PASI”and “predicted DLQI”based on BSA combined with
other measures that are easily ascertained in a clinic setting,
and (2) use predicted PASI and DLQI at therapy initiation and
6-months follow-up to calculate predicted PASI75, PASI90,
and DLQI 0/1, and evaluate their agreement with these ob-
served measures.
Methods
Registry Overview
The CorEvitas Psoriasis Registry is a prospective, multicenter,
observational disease-based registry launched in April 2015 in
collaboration with the National Psoriasis Foundation, the
design of which has been previously described.
15
Briefly,
patients are recruited by participating dermatologists from
private and academic practices in the US and Canada across 46
states and provinces. Patients are enrolled if they meet the
following criteria: aged ≥18 years, psoriasis diagnosed by a
dermatologist, and initiated or switched to a US Food and
Drug Administration (FDA) approved systemic psoriasis
treatment at or within 12 months of enrollment. Data is
collected from both dermatologists and patients during routine
clinical visits occurring at approximately 6-month intervals.
All participating investigators were required to obtain full
board approval for conducting research involving human
subjects. Sponsor approval and continuing review was ob-
tained through a central IRB (IntegReview, Protocol number is
Corrona-PSORIASIS-500). For academic investigative sites
that did not receive a waiver to use the central IRB, approval
was obtained from the respective governing IRBs, and
documentation of approval was submitted to the Sponsor
before initiating any study procedures. All registry patients
were required to provide written informed consent prior to
participating.
Study Population
This analysis included data from the 10 961 patients enrolled
in the registry with plaque psoriasis and no history of pustular
morphology from April 15, 2015 to July 11, 2020. These
patients contributed 33 605 registry visits to the analyses,
including both enrollment and follow-up visits.
Study Variables
Outcomes. PASI and DLQI were collected at all registry visits.
PASI considers percentage of area affected by psoriasis and
the severity of redness, thickness, and scaling of the skin, and
is measured on a scale 0-72 where a higher score indicates
greater severity.
16
The DLQI consists of 10 questions con-
cerning patients’perception of the impact of skin diseases on
different aspects of their HRQoL over the last week. The 10
items include areas such as symptoms and feelings, daily
activities, leisure, work or school, personal relationships, and
the side-effects of treatment.
17
Respondents indicate the de-
gree of problems in the past week using a 4-point Likert scale:
0 (not at all/not relevant), 1 (a little), 2 (a lot), and 3 (very
much). A total DLQI score is calculated, ranging from 0-30
with higher scores indicating worse HRQoL.
Predictors of PASI and DLQI. BSA is the percent (0-100%) of the
total area of the body affected by psoriasis.
2
IGA is a five-point
scale that provides a global clinical assessment of disease
severity ranging from 0 to 4: 0 (clear), 2 (mild), 3 (moderate),
and 4 (severe disease).
18
Patient reported outcome measures
(PROMs) included itch/pruritis and skin pain reported on a
Visual Analog Scale (VAS) of 0 (none) to 100 (very severe),
and a Patient Global Assessment (PGA) for psoriasis reported
on a VAS of 0 (very well) to 100 (very poor).
Other variables included age (years), sex, race (White vs
non-White), body mass index (BMI, kg/m
2
), the Modified
Charlson Comorbidity Index (mCCI) score,
19
and prior bio-
logic experience (biologic-na¨
ıve vs biologic-experienced).
Statistical Analysis
The data from the 33 605 registry visits were partitioned into
three mutually exclusive datasets to address study objectives.
First, patient-visits at which a biologic therapy initiation
occurred and had a subsequent 6-month follow-up visit were
identified (3267 initiation/follow-up visit pairs; 6534 total
visits), and a 40% random sample of visits with complete data
(1303 visit pairs; 2606 total visits) was selected as the dataset
to evaluate agreement between the predicted PASI and DLQI
outcomes obtained using psoriasis assessment models and
Gulliver et al. 75

measured PASI and DLQI outcomes. Next, the patient-visits
not selected for concordance analyses (n = 31 041) were
partitioned via random sample into training (60%, n = 17 185
patient-visits) and testing (40%, n = 13 856 patient-visits)
datasets for psoriasis assessment model development and
internal validation, respectively.
Of the 17 185 patient visits in the psoriasis assessment
model development training data set, 16 840 patient-visits
with complete data were used to describe patient character-
istics via means and standard deviations for continuous var-
iables and counts and percentages for categorical variables.
Additionally, these patients were used to describe the asso-
ciations among psoriasis disease severity and PROMs by
calculating Pearson correlations among BSA, PASI, IGA, itch,
skin pain, PGA, and DLQI, which were visualized as a
heatmap.
Psoriasis Assessment Model Development
To construct psoriasis assessment models to calculate
predicted PASI and DLQI, separately, we specified a priori
16 linear regression models based on different combina-
tions of the following predictors: BSA, IGA, itch, skin pain,
PGA, age, gender, BMI, mCCI, and biologic experience.
Models were specified by starting with a model with BSA
alone, followed by models adding only IGA, or each PROM
(itch, skin pain, PGA), additional models adding interac-
tions of BSA with IGA or each PROM, and finally models
adding other covariates. Three additional prediction models
were constructed utilizing variable selection methods (not
considering interactions) for the previously listed predic-
tors: two linear regressions using stepwise selection pro-
cedures (forward selection and backwards elimination) via
the Akaike information criterion (AIC), and one regularized
elastic net regression to select from all available variables
plus interactions of BSA with IGA and PROMs. Elastic net
models included a shrinkage term to reduce overfitting in
the development set to improve the predictive ability; this
penalty shrinks coefficients towards zero, and automatically
conducts variable selection. Thus, in total there were 19
different prediction models constructed. Models were run in
the training dataset, and the predictive performance of all 19
models was evaluated in the testing dataset by calculating
the adjusted coefficient of determination (R
2
adj
), root mean
square error (RMSE), and mean absolute error (MAE).
Ninety five percent confidence intervals (95% CI) for these
estimates were approximatedbyreplicatingthesample
splitting, model development, and model testing 400 times,
and utilizing the 2.5th and 97.5th percentiles as the lower
and upper bounds, respectively. Further, as a sensitivity
analysis, mixed-effects model variants of the models were
created by adding random effects for patient identification
to adjust for multiple observations per patient.
Concordance of Predicted and Measured PASI and
DLQI Outcomes
The dataset created for evaluating concordance of the psoriasis
assessment model that calculated and observed PASI and DLQI
outcomes was used to compare predicted values of PASI and
DLQI with measured values for each of the 19 models. For each
patient, predicted PASI and DLQI were calculated at both the
biologic initiation and 6-month follow-up visits, separately, using
each of the 19 prediction models from the development phase.
Using these predicted values, predicted PASI75, PASI90 and
DLQI 0/1 at 6-month follow-up were calculated. Patients
achieved PASI75/90 if their PASI decreased by 75%/90% from
baseline to follow-up. Patients achieved or maintained DLQI 0/1
if their DLQI was ≤1 at follow-up. Additionally, observed
PASI75, PASI90, and DLQI 0/1 were calculated using observed
PASI and DQLI at initiation and follow-up. The observed and
predicted values from each prediction model were compared
using sensitivity (SE), specificity (SP), negative predictive value
(NPV), and positive predictive value (PPV).
To evaluate psoriasis assessment models constructed uti-
lizing a population of patients with severe psoriasis, all models
were repeated and applied to a restricted test set of patients
with PASI ≥12, BSA ≥10, and moderate-to-severe IGA (3-4).
Ethics. This study was carried out in accordance with the
Declaration of Helsinki. All participating investigators were
required to obtain full board approval for conducting non-
interventional research involving human subjects with a
limited dataset. Sponsor approval and continuing review was
obtained through a central Institutional Review Board (IRB),
the New England Independent Review Board (NEIRB; no.
120160610). For academic investigative sites that did not
receive a waiver to use the central IRB, full board approval
was obtained from the respective governing IRBs and doc-
umentation of approval was submitted to CorEvitas, LLC
prior to the initiation of any study procedures. All patients in
the registry were required to provide written informed consent
and authorization prior to participating.
Results
Among the patient-visits with complete data in the psoriasis
assessment model training dataset, mean (SD) age, PASI, and
DLQI were 51 (14) years, 4 (6), and 4 (5) respectively; 46%
were female, 81% were White, and 87% were biologic-
experienced (Table 1). PASI was strongly correlated with
BSA (r= .78) and IGA (r= .66), and moderately correlated
with DLQI (.46), PGA (.44), itch (.47), and skin pain (.43)
(Figure 1). DLQI was moderately correlated with IGA (.50),
PGA (.57), itch (.59), skin pain (.59), and BSA (.41). Other
strong correlations were observed among PGA, itch, and skin
pain: .66 for PGA and itch, .61 for PGA and skin pain, and for
.71 itch and skin pain.
76 Journal of Psoriasis and Psoriatic Arthritis 8(2)

Psoriasis Assessment Model Development
For the a priori specified models predicting PASI, BSA alone
explained 62% of the variance in PASI (R
2
adj
=.62;95%CI=
.60, .64). Predictive performance improved only slightly with the
addition of a PRO (itch, skin pain, PGA) to the model (all R
2
adj
=
.64, RMSE range 3.32-3.34) (Ta ble 2). The model with BSA and
IGA performed marginally bettercomparedtothatwithBSA
alone (R
2
adj
= .72; RSME = 2.93). For all the a priori selected
models, including additional variables (interaction terms, age,
gender, mCCI, BMI, prior biologic use) did not improve pre-
dictive performance compared to models with only two vari-
ables. Models constructed using forward, backward, and elastic
net selection methods all performed like each other, and per-
formance was better than BSA plus PROM models, but like the
BSA plus IGA model.
For models predicting DLQI, BSA alone explained only
17% of the variance in DLQI (R
2
adj
= .17; 95%CI = .16, .18),
Table 1. Demographics and Clinical Characteristics Among Patient-Visits in the CorEvitas Psoriasis Registry; Patient-Visits With Complete
Data in the Psoriasis Assessment Model Development Training Data Set.
Total N = 16840*
Age (years), mean (SD) 51.4 (14.4)
Gender –Female, n (%) 7707 (45.8%)
Race - White n (%) 13618 (81.0%)
BMI (kg/m
2
), mean (SD) 31.0 (7.5)
BMI (kg/m
2
)-≥30 (obese) 8202 (48.7%)
Psoriasis duration (years), mean (SD) 16.7 (13.9)
mCCI, mean (SD) 1.3 (.6)
BSA (% involvement), mean (SD) 5.8 (10.9)
BSA - categorical
Mild disease [0, 3] 9518 (56.5%)
Moderate disease [3, 10] 4079 (24.2%)
Severe disease [10, 20] 1888 (11.2%)
Very severe disease [20 100] 1355 (8.0%)
PASI mean (SD) 3.5 (5.6)
PASI >10 n = 16840
n (%) 1677 (10.0%)
IGA
Clear 4364 (25.9%)
Almost clear 3716 (22.1%)
Mild 3739 (22.2%)
Moderate 4064 (24.1%)
Severe 957 (5.7%)
Number of previous biologic therapies
0 2106 (12.5%)
1 6256 (37.1%)
2+ 8478 (50.3%)
DLQI (score: 0-30), mean (SD) 4.4 (5.5)
DLQI - categorical
No Effect at all 7417 (44.2%)
Small effect 4503 (26.8%)
Moderate effect 2576 (15.3%)
Very large effect 1925 (11.5%)
Extremely large effect 373 (2.2%)
Patient global assessment, mean (SD) 27.7 (28.8)
Patient overall itch/pruritis (VAS range 0-100), mean (SD) 27.4 (31.6)
Patient overall skin pain (VAS range 0-100), mean (SD) 16.4 (25.8)
Includes only patient-visits with complete data among the 17 185 visits in the training dataset; SD, Standard Deviation; BMI, Body Mass Index; mCCI, modified
Charlson Comorbidity Index (The modified Charlson comorbidity index is calculated by giving 1 point to the following conditions: myocardial infarction,
congestive heart failure, peripheral vascular disease, TIA and/or stroke, chronic obstructive pulmonarydisease (COPD), peptic ulcer disease, diabetes, leukemia,
lymphoma, solid tumor, and liver disease); BSA, Body Surface Area; PASI, Psoriasis Area Severity Index; IGA, Investigator’s Global Assessment; DLQI,
Dermatology Life Quality Index; VAS, Visual Analogue Scale (Range 0-100).
Gulliver et al. 77

and while the addition of IGA improved performance, it was
still poor (R
2
adj
= .27; RSME = 4.60). Models with BSA plus a
PROM performed better, yet still explained only a low percent
of the variance in DLQI (all R
2
adj
= .37-.40, RMSE range 4.17-
4.28). The best performing models included the model con-
taining IGA and all other PROs (all R
2
adj
= .47, RMSE = 3.91),
and those constructed using selection methods, though test
statistics still suggested poor predictive performance (all
R
2
adj
= .48, all RMSE = 3.89) (Table 2).
Concordance of Predicted and Measured PASI and
DLQI Outcomes
When comparing observed PASI75 and PASI90 to those
predicted based on each of the constructed prediction models,
specificity and PPV were high across all models, though
sensitivity and NPV varied. Models including IGA had the
best agreement, with SE and SP exceeding 82% for PASI75
(Models 2-4: SE range = 82.7-85.1, SP range = 87.7-90.7;
Models 14-19: SE range = 82.8-85.6, SP range = 88.3-90.0)
and exceeded 76% for PASI90 (Models 2-4: SE range = 80.2-
81.9, SP range = 94.1-97.9; Models 14-19: SE range = 76.2-
80.9, SP range = 93.4-96.0) (Table 3).
When comparing observed vs predicted DLQI 0/1, sen-
sitivity and PPV were consistently high for the different
prediction models, though specificity and NPV were low.
Predicted DLQI 0/1 based on models that included PROMs
(itch, skin pain, PGA) tended to have the best agreement with
observed DLQI. Those that included IGA and all three PROs
having the highest agreement (Models 14-16: SE range =
87.1-87.5, SP range = 75.3-77.6) (Table 3).
In the subset of patients with severe psoriasis, predictive
performance of psoriasis assessment models was poor com-
pared to the full cohort. The R
2
adj
for models predicting PASI
did not exceed .45 and RMSE = 8.07 (model including all
possible covariates), and for predicting DLQI the best per-
forming model (including all possible covariates) yielded an
adjusted R
2
= .34 and RMSE = 5.48 (Supplemental Table 1).
Discussion
In our study among patients from real-world clinical practices,
psoriasis assessment models including up to 14 variables were
Figure 1. Heatmap of Pearson correlations of BSA, PASI, itch, skin pain, PGA, and DLQI among the 16 840 patient-visits in the psoriasis
assessment model development training dataset.
78 Journal of Psoriasis and Psoriatic Arthritis 8(2)

Table 2. Predictive Performance of Regression Models for Predicting PASI and DLQI Using the Psoriasis Assessment Model Development
Testing Data Set of 13 856 Patient-Visits.
PASI DLQI
Model Predictors
±
R2
adj (95%
CI)
†
RMSE (95% CI) MAE (95% CI)
±
R2
adj (95%
CI) RMSE (95% CI) MAE (95% CI)
1 BSA .62 (.60, .64) 3.43 (3.32, 3.53) 1.97 (1.93, 2.01) .17 (.16, .18) 4.90 (4.82, 5.00) 3.61 (3.58, 3.65)
2 BSA + IGA .72 (.71, .73) 2.93 (2.84, 3.03) 1.47 (1.43, 1.50) .27 (.26, .29) 4.60 (4.50, 4.68) 3.24 (3.20, 3.28)
3 BSA + IGA + BSA * IGA .74 (.72, .75) 2.85 (2.75, 2.96) 1.44 (1.40, 1.48) .28 (.26, .29) 4.58 (4.49, 4.67) 3.23 (3.19, 3.27)
4 BSA + IGA + age + gender
+ mCCI + BMI + prior
biologic experience
.72 (.71, .73) 2.92 (2.82, 3.02) 1.51 (1.47, 1.54) .29 (.28, .30) 4.54 (4.46, 4.63) 3.18 (3.14, 3.23)
5 BSA + itch .64 (.63, .66) 3.32 (3.21, 3.42) 1.85 (1.81, 1.88) .40 (.38, .41) 4.18 (4.09, 4.26) 2.85 (2.81, 2.89)
6 BSA + Itch + BSA * itch .64 (.63, .66) 3.32 (3.21, 3.42) 1.85 (1.81, 1.89) .40 (.38, .41) 4.18 (4.09, 4.26) 2.84 (2.80, 2.88)
7 BSA + Itch + age + gender
+ mCCI + BMI + prior
biologic experience
.65 (.63, .67) 3.29 (3.18, 3.39) 1.83 (1.79, 1.86) .40 (.39, .42) 4.15 (4.07, 4.24) 2.82 (2.78, 2.86)
8 BSA + skin pain .64 (.62, .66) 3.33 (3.22, 3.43) 1.88 (1.84, 1.92) .40 (.38, .41) 4.17 (4.10, 4.26) 2.91 (2.87, 2.95)
9 BSA + skin pain + BSA *
skin pain
.64 (.62, .66) 3.36 (3.22, 3.44) 1.88 (1.84, 1.92) .40 (.38, .42) 4.16 (4.09, 4.25) 2.88 (2.85, 2.92)
10 BSA + skin pain + Age +
gender + mCCI + BMI +
prior biologic experience
.64 (.63, .66) 3.30 (3.19, 3.41) 1.85 (1.81, 1.89) .41 (.39, .42) 4.14 (4.06, 4.22) 2.87 (2.83, 2.90)
11 BSA + PGA .64 (.62, .66) 3.34 (3.23, 3.44) 1.87 (1.83, 1.90) .37 (.35, .38) 4.28 (4.19, 4.37) 2.90 (2.86, 2.94)
12 BSA + PGA + BSA*PGA .64 (.62, .66) 3.34 (3.23, 3.45) 1.87 (1.83, 1.91) .37 (.35, .38) 4.28 (4.19, 4.37) 2.90 (2.86, 2.95)
13 BSA + PGA + age + gender
+ mCCI + BMI + prior
biologic experience
.64 (.62, .66) 3.31 (3.20, 3.42) 1.85 (1.80, 1.88) .38 (.36, .39) 4.25 (4.16, 4.33) 2.87 (2.83, 2.91)
14 BSA + IGA + itch + skin
pain + PGA
.72 (.71, .74) 2.92 (2.82, 3.02) 1.48 (1.45, 1.52) .47 (.46, .49) 3.91 (3.83, 3.99) 2.57 (2.53, 2.61)
15 BSA + IGA + itch + skin
pain + PGA + age +
gender + mCCI + BMI +
prior biologic experience
.72 (.71, .74) 2.90 (2.81, 3.01) 1.52 (1.48, 1.55) .48 (.46, .49) 3.89 (3.81, 3.97) 2.55 (2.52, 2.60)
16 BSA + IGA + BSA * IGA +
Itch + BSA * itch + skin
pain + BSA * skin pain +
PGA + BSA * PGA + Age
+ gender + mCCI + BMI
+ prior biologic
experience
.74 (.72, .75) 2.82 (2.73, 2.93) 1.48 (1.45, 1.52) .48 (.46, .50) 3.88 (3.80, 3.96) 2.55 (2.52, 2.60)
17 Variables‡selected from
stepwise forward
selection
.72 (.71, .74) 2.91 (2.82, 3.02) 1.52 (1.48, 1.55) .48 (.46, .49) 3.89 (3.81, 3.97) 2.55 (2.52, 2.60)
18 Variables‡selected from
stepwise backward
elimination
.72 (.71, .74) 2.91 (2.82, 3.02) 1.52 (1.48, 1.55) .48 (.46, .49) 3.89 (3.81, 3.97) 2.55 (2.52, 2.60)
19 Variables‡selected from
elastic net
.74 (.73, .75) 2.82 (2.73, 2.93) 1.48 (1.45, 1.52) .48 (.46, .50) 3.88 (3.80, 3.96) 2.56 (2.52, 2.60)
†The entire process of sample splitting, model development, and model testing was replicated 400 times; 95% confidence intervals were calculated using the
2.5th and 97.5th percentiles from these distributions; ‡Models 17-18 selected from the following variables: BSA, Itch, Skin Pain, PGA, Age, Female, mCCI, BMI,
Biologic-naive and IGA. Model 19 selected from all potential variables in model 17-18 as well as the following interactions: BSA *IGA, BSA*Itch, BSA*Skin Pain
and BSA*PGA. Models 17 and 18 selected BSA, Skin Pain, PGA, Itch, IGA, Female and Biologic-naive. Model 19 selected BSA, Skin Pain, PGA, Itch, IGA, Female,
Biologic-naive, age, mCCI, BMI, BSA*IGA, BSA*Itch, BSA*Skin Pain and BSA*PGA (all possible variables). ±Adjusted R
2
, adjusted coefficient of determination;
RMSE, root mean square error; MAE, mean absolute error; PASI, Psoriasis Area Severity Index; DLQI, Dermatology Life Quality Index; BSA, Body Surface Area;
IGA, Investigator’s Global Assessment; mCCI, modified Charlson Comorbidity Index; BMI, Body Mass Index; **Models 1-16 have been pre-specified. Calibration
for each model was assessed with calibration intercept (95% CI) and slope (95% CI) and is provided in supplemental materials. **Models 17-19 considered all
aforementioned variables, and only a subset of variables were selected as determined by stepwise forward selection (using AIC), stepwise backward elimination
(using AIC), and a regularized elastic net regression (α= .839, log(λ) = -9.47 for PASI; α= .285 and log(λ) = -22.32 for DLQI). Hyperparameters for the elastic net
model were optimized via 10-fold cross-validation. The mixture parameter (α) combines the weights of the L1 (LASSO) and L2 (Ridge) penalties, and the
shrinkage parameter (λ) indicates the extent to which regression coefficients are shrunk toward zero to reduce variation in prediction.
Gulliver et al. 79

the most predictive of PASI, though these models performed
only marginally better than a model including only BSA and
IGA, which explained greater than 70% of the variation in
PASI. When predicting PASI response based on PASI cal-
culated using the psoriasis assessment models, models that
included IGA had the best agreement with observed PASI
response, achieving the best balance between both high
(∼88%) sensitivity and specificity. The ability of models to
predict DLQI in our cohort was limited, with the best per-
forming models including several variables and explaining
less than 50% of variation in DLQI. While sensitivity for
identifying observed DLQI 0/1 based on predicted DLQI was
high (∼83%) for all models, specificity was lower, particularly
for those models with only one or two variables (∼70%).
Models including multiple PROMs had the best agreement
with DLQI 0/1.
Our findings are consistent with those among patients from
the UNCOVER clinical trials
13
which found that a proxy PASI
score calculated using only two simple data inputs (eg, BSA and
PROMs) was strongly predictive of an observed PASI assess-
ment. The prior study found its models (OPATÔ) including BSA
plus one PROM (itch, skin pain or PGA) had sensitivities and
specificities around 90% for identifying both PASI75 and
PASI90. In our study, the models that included one PROM (itch,
skin pain, or PGA) had somewhat lower predictive performance
for PASI, and our predicted PASI outcomes had lower agreement
with observed values. These findings may be due to the UN-
COVER trials including a population with more severe disease
compared to the CorEvitas Psoriasis Registry, which enrolls
patients regardless of disease severity. The UNCOVER studies
inclusion criteria required patients have BSA ≥10%, static
physician global assessment≥3, and PASI≥12. Indeed, mean
PASI in the previously published OPATÔanalysis was about 20
compared to 3.5 in the current study. Furthermore, correlations of
PASI with BSA and PROMs were stronger in the UNCOVER
cohorts, perhaps reflecting a more heterogeneous patient pop-
ulation in the CorEvitas Registry. Further, assessments may be
more tightly controlled in randomized trials vs registry patients in
clinical practice. Nevertheless, our findings indicate that an
OPAT developed and applied in a cohort more representative of
the psoriasis population, using data ascertained by dermatologists
in a clinic setting, performed well for predicting PASI and
identifying PASI response. Unlike the UNCOVER OPATÔ
models, our psoriasis assessment models considered IGA as a
predictor and found models including BSA plus IGA predicted
PASI better than those including BSA plus a PROM. Although
IGA is not a PROM, it is relatively easy to collect in the clinic,
and our data suggest that its combination with BSA may provide
a superior predictor of PASI than BSA alone, or BSA combined
with a PROM. Our findings support prior studies that have found
Table 3. Percent Agreement of Predicted PASI75, PASI90 and DLQI 0/1, Calculated Based on 19 Different Regression Models, and Observed
Outcomes Among 1303 Patient-Visits With a Biologic Initiation and Subsequent 6-Month Follow-Up Visit.
PASI75 PASI90 DLQI 0/1
Model SE
a
SP
b
PPV
c
NPV
d
SE
a
SP
b
PPV
c
NPV
d
SE
a
SP
b
PPV
c
NPV
d
1 44.7 96.5 94.0 58.5 12.1 99.0 88.2 63.8 94.8 28.4 81.8 61.8
2 84.1 87.7 89.4 81.7 81.9 94.1 89.8 89.1 84.4 59.5 87.6 52.9
3 82.7 90.7 91.7 80.9 80.3 97.9 96.1 88.6 83.8 60.1 87.7 52.2
4 85.1 89.4 90.8 82.9 80.2 95.1 91.2 88.3 83.6 58.9 87.4 51.3
5 63.3 94.0 92.9 67.5 34.1 97.7 90.3 70.0 83.9 71.3 90.8 56.7
6 62.5 94.2 93.0 67.0 32.5 97.9 90.9 69.5 83.7 71.6 90.9 56.5
7 66.5 90.9 90.0 68.8 38.9 96.9 88.7 71.5 83.8 70.2 90.5 56.0
8 56.9 93.8 91.9 63.8 21.3 97.9 86.8 66.2 89.3 67.1 90.2 64.9
9 56.9 93.8 91.9 63.8 21.3 97.9 86.8 66.2 88.9 69.8 90.9 65.0
10 62.2 91.8 90.3 66.4 33.0 97.7 89.9 69.8 90.0 66.3 90.1 66.1
11 60.5 94.2 92.8 65.9 30.4 97.8 89.8 68.9 83.9 71.3 90.8 56.7
12 61.2 94.2 92.9 66.3 32.3 97.8 90.3 69.4 84.0 70.9 90.7 56.8
13 64.2 92.7 91.5 67.8 37.2 96.7 87.9 70.9 84.5 69.9 90.5 57.0
14 84.1 88.5 90.0 81.9 80.9 93.8 89.2 88.6 87.1 77.6 93.0 64.0
15 85.4 88.3 89.9 83.1 80.0 93.4 88.4 88.1 87.5 75.9 92.5 64.1
16 82.8 90.0 91.1 81.0 76.2 96.0 92.2 86.5 87.4 75.3 92.3 63.7
17 85.6 88.4 90.1 83.2 79.6 93.8 89.1 87.9 87.5 75.9 92.5 64.1
18 85.6 88.4 90.1 83.2 79.6 93.8 89.1 87.9 87.5 75.9 92.5 64.1
19 82.9 90.0 91.1 81.1 76.4 95.8 92.0 86.6 87.4 75.3 92.3 63.7
a
SE, sensitivity (%).
b
SP, specificity (%).
c
PPV, positive predictive value (%).
d
NPV, negative predictive value (%).
80 Journal of Psoriasis and Psoriatic Arthritis 8(2)

that BSA*IGA (or Physician’s Global Assessment) is highly
correlated with PASI score, further suggesting that BSA*IGA
may be an ideal assessment of disease severity in clinical practice
as it is more practical than PASI, and is sensitive for measuring
psoriasis severity in clinical trials
20-22
and registry patients.
23
Similar to our study, regression models in the UNCOVER
study did not predict DLQI (R
2
e.65) as well as PASI. In our
study, while models including BSA plus IGA were the most
predictive of PASI, these models performed worse for pre-
dicting DLQI compared to those including PROMs. This
finding is unsurprising as itch and skin pain capture disease
symptoms that directly impact patient HRQoL and PGA re-
flects patient perception of disease impact, whereas IGA
considers only plaque appearance. Our data suggest that a
model including BSA plus all three of PROMs performed only
marginally better at predicting DLQI than those including just
one PRO, and models including additional variables did not
improve prediction.
Our study has some limitations. Generalizability to the
general population of psoriasis patients may be limited since
registry dermatologists participate voluntarily and choose
which of their patients to enroll, or if certain types of patients
(eg, sicker, or healthier) agree to enroll. Additionally, the
prediction models we developed require validation using other
datasets prior to implementation. Nevertheless, our findings
are likely more generalizable to typical patients seen in clinical
practice vs those generated from clinical trials. Furthermore,
the CorEvitas Registry included nearly 11 000 patients with
over 33 000 visits at which an extensive battery of variables
was collected, including measures not routinely available in
other real-world data sources such as claims databases.
Conclusions
Our study developed what we consider prototypes of pre-
diction models using BSA and IGA to predict PASI. External
testing and further research are required to validate these
models before they can be used in clinical practice. If and
when a model is externally validated, our findings suggest that
a prediction model including BSA and IGA may be ideal for a
psoriasis assessment to predict PASI in the clinical setting.
DLQI is more challenging to predict, though a psoriasis as-
sessment tool with BSA plus at least one PROM may provide
the best option. Once validated in other real-world patient
cohorts, a psoriasis assessment tool including a few simple
measures may provide dermatologists with a measure of
disease severity that has advantages over PASI, as it is more
easily implemented in clinical practice and might provide
additional information on patient quality of life.
Acknowledgments
The authors would like to thank all the investigators, their clinical
staff, and patients who participate in the CorEvitas Psoriasis Disease
Registry.
Declaration of Conflicting Interests
The author(s) declared the following potential conflicts of interest
with respect to the research, authorship, and/or publication of this
article: WG-Grants/research support: AbbVie, Amgen, Eli Lilly,
Novartis, Pfizer. Honoraria for Ad Boards/Invited Talks/
Consultation: AbbVie, Actelion, Amgen, Arylide, Bausch Health,
Boehringer, Celgene, Cipher, Eli Lilly, Galderma, Janssen, LEO
Pharma, Merck, Novartis, PeerVoice, Pfizer, Sanofi-Genzyme,
Tribute, UCB, Valeant. Other: Clinical trials (study fees): AbbVie,
Asana Biosciences, Astellas, Boerhinger-Ingleheim, Celgene,
CorEvitas/National Psoriasis Foundation, Devonian, Eli Lilly, Gal-
apagos, Galderma, Janssen, LEO Pharma, Novartis, Pfizer, Re-
generon, UCB; KS, BZ, BK, RB-Employee/Stock, Eli Lilly and
Company; RWH, RRM, SJK (former), Employee of CorEvitas, LLC
(formerly Corrona, LLC); CL-Consultant/Advisory Board for
Abbvie, Amgen, Boehringer-Ingelheim, Dermira, Eli Lilly, Janssen,
Leo, Pfizer, Sandoz, UCB and Vitae, Investigator for Actavis,
Abbvie, Allergan, Amgen, Boehringer-Ingelheim, Celgene, Coherus,
Cellceutix, CorEvitas, Dermira, Eli Lilly, Galderma, Glenmark,
Janssen, Leo Pharma, Merck, Novartis, Novella, Pfizer, Sandoz,
Sienna, Stiefel, UCB and Wyeth, Speaker bureau for Abbvie, Amgen,
Celgene, Eli Lilly, Janssen, Novartis, Ortho Dermatologics, Sun
Pharmaceuticals, and UCB.
Funding
The author(s) received no financial support for the research, au-
thorship, and/or publication of this article.
Ethical Approval
All participating investigators were required to obtain full board
approval for conducting research involving human subjects. Sponsor
approval and continuing review was obtained through a central IRB
(IntegReview, Protocol number is Corrona-PSORIASIS-500)... All
registry patients were required to provide written informed consent
prior to participating.
ORCID iD
Ryan W. Harrison https://orcid.org/0000-0003-4575-006X
Supplemental Material
Supplemental material for this article is available online.
References
1. Duffin KC, Papp KA, Bagel J, Levi E, Chen R, Gottlieb AB.
Evaluation of the physician global assessment and body surface
area composite tool for assessing psoriasis response to apre-
milast therapy: Results from ESTEEM 1 and ESTEEM 2.
J Drugs Dermatol. 2017;16(2):147-153.
2. Spuls PI, Lecluse LL, Poulsen ML, Bos JD, Stern RS, Nijsten T.
How good are clinical severity and outcome measures for pso-
riasis?: Quantitative evaluation in a systematic review. J Invest
Dermatol. 2010;130(4):933-943. DOI: 10.1038/jid.2009.391
Gulliver et al. 81

3. Puzenat E, Bronsard V, Prey S, et al. What are the best outcome
measures for assessing plaque psoriasis severity? A systematic
review of the literature. J Eur Acad Dermatol Venereol. 2010;24:
10-16. DOI: 10.1111/j.1468-3083.2009.03562.x
4. Psoriasis: Assessment and Management of Psoriasis. London.
2012.
5. Manalo IF, Gilbert KE, Wu JJ. An updated survey for the 2007-
2013 period of randomized controlled trials for psoriasis:
Treatment modalities, study designs, comparators, outcome
measures and sponsorship. J Eur Acad Dermatol Venereol.
2015;29(10):1945-1950. DOI: 10.1111/jdv.13089
6. Bozek A, Reich A. The reliability of three psoriasis assessment
tools: Psoriasis area and severity index, body surface area and
physician global assessment. Adv Clin Exp Med. 2017;26(5):
851-856. DOI: 10.17219/acem/69804
7. McKenna KE, Stern RS. The outcomes movement and new
measures of the severity of psoriasis. J Am Acad Dermatol.
1996;34(3):534-538. DOI: 10.1016/s0190-9622(96)90469-7
8. Harari M, Shani J, Hristakieva E, Stanimirovic A, Seidl W,
Burdo A. Clinical evaluation of a more rapid and sensitive
Psoriasis Assessment Severity Score (PASS), and its comparison
with the classic method of Psoriasis Area and Severity Index
(PASI), before and after climatotherapy at the Dead-Sea. Int J
Dermatol. 2000;39(12):913-918. DOI: 10.1046/j.1365-4362.
2000.00017.x
9. Jacobson CC, Kimball AB. Rethinking the psoriasis area and
severity index: The impact of area should be increased. Br J
Dermatol 2004;151(2):381-387. DOI: 10.1111/j.1365-2133.
2004.06035.x
10. Robinson A, Kardos M, Kimball AB. Physician Global Assessment
(PGA)andPsoriasisAreaandSeverityIndex(PASI):whydoboth?
A systematic analysis of randomized controlled trials of biologic
agents for moderate to severe plaque psoriasis. JAmAcadDer-
matol. 2012;66(3):369-375. DOI: 10.1016/j.jaad.2011.01.022
11. Menter A, Strober BE, Kaplan DH, et al. Joint AAD-NPF
guidelines of care for the management and treatment of psoriasis
with biologics. JAmAcadDermatol. 2019;80(4):1029-1072. DOI:
10.1016/j.jaad.2018.11.057
12. Smith CH, Jabbar-Lopez ZK, Yiu ZZ, et al. British Association of
Dermatologists guidelines for biologic therapy for psoriasis 2017.
Br J Dermatol. 2017;177(3):628-636. DOI: 10.1111/bjd.15665
13. Leonardi C, See K, Gallo G, et al. Psoriasis severity assessment
combining physician and patient reported outcomes: The op-
timal psoriasis assessment tool. Dermatol Ther.2021;11(4):
1249-1263. DOI: 10.1007/s13555-021-00544-6
14. Papp KA, Leonardi CL, Blauvelt A, et al. Ixekizumab treatment
for psoriasis: integrated efficacy analysis of three double-blinded,
controlled studies (UNCOVER-1, UNCOVER-2, UNCOVER-
3). Br J Dermatol. 2018;178(3):674-681. DOI: 10.1111/bjd.
16050
15. Strober B, Karki C, Mason M, et al. Characterization of disease
burden, comorbidities, and treatment use in a large, US-based
cohort: Results from the Corrona Psoriasis Registry. JAmAcad
Dermatol. 2018;78(2):323-332. DOI: 10.1016/j.jaad.2017.10.012
16. Fredriksson T, Pettersson U. Severe psoriasis–oral therapy
with a new retinoid. Dermatol.1978;157(4):238-244. DOI:
10.1159/000250839
17. Finlay AY, Khan GK. Dermatology Life Quality Index (DLQI)–
a simple practical measure for routine clinical use. Clin Exp
Dermatol. 1994;19(3):210-216. DOI: 10.1111/j.1365-2230.
1994.tb01167.x
18. Langley RG, Feldman SR, Nyirady J, van de Kerkhof P,
Papavassilis C. The 5-point Investigator’s Global Assessment
(IGA) Scale: A modified tool for evaluating plaque psoriasis
severity in clinical trials. J Dermatol Treat. 2015;26(1):23-31.
DOI: 10.3109/09546634.2013.865009
19. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new
method of classifying prognostic comorbidity in longitudinal
studies: development and validation. J Chron Dis. 1987;40(5):
373-383. DOI: 10.1016/0021-9681(87)90171-8
20. Walsh JA, Jones H, Mallbris L, et al. The Physician Global
Assessment and Body Surface Area composite tool is a simple
alternative to the Psoriasis Area and Severity Index for as-
sessment of psoriasis: post hoc analysis from PRISTINE and
PRESTA. Psoriasis (Auckl). 2018;8:65-74. DOI: 10.2147/PTT.
S169333
21. Gottlieb AB, Germino R, Herrera V, Meng X, Merola JF.
Exploration of the Product of the 5-Point Investigator’s
Global Assessment and Body Surface Area (IGA x BSA) as a
Practical Minimal Disease Activity Goal in Patients with
Moderate-to-Severe Psoriasis. Dermatology. 2019;235(4):
348-354. DOI: 10.1159/000499925
22. Gottlieb AB, Merola JF, Chen R, Levi E, DuffinKC.As-
sessing clinical response and defining minimal disease ac-
tivity in plaque psoriasis with the Physician Global
Assessment and body surface area (PGA x BSA) composite
tool: An analysis of apremilast phase 3 ESTEEM data. JAm
Acad Dermatol. 2017;77(6):1178-1180. DOI: 10.1016/j.jaad.
2017.06.162
23. Walsh JA, McFadden M, Woodcock J, et al. Product of the
Physician Global Assessment and body surface area: A simple
static measure of psoriasis severity in a longitudinal cohort. JAm
Acad Dermatol. 2013;69(6):931-937. DOI: 10.1016/j.jaad.
2013.07.040
82 Journal of Psoriasis and Psoriatic Arthritis 8(2)