Predicting the Long-Term Outcomes of Biologics in Psoriasis Patients Using Machine Learning

Amy X. Du; Philip Surmanowicz; Robert Gniadecki; Russ Greiner; Sepideh Emam; Simon F. Thomsen

REVIEW 5 major objections 5 minor 2 references

Predicting the Long-Term Outcomes of Biologics in Psoriasis Patients Using Machine Learning

T0 review · 5 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Routine clinical variables—age, weight, previous biologic use, disease activity—let a machine learning model predict psoriasis biologic discontinuation and its duration, with reported accuracy above 80%.

desk verdict A plausible first ML application to a psoriasis registry, but the 82% accuracy claim is likely inflated by patient-level clustering that the cross-validation ignores. read the letter →

arxiv 1908.09251 v1 pith:DCE7GH7U submitted 2019-08-25 stat.ML cs.LGq-bio.QM

Sepideh Emam , Amy X. Du , Philip Surmanowicz , Simon F. Thomsen , Russ Greiner , Robert Gniadecki This is my paper · ORCID

classification stat.MLcs.LGq-bio.QM

keywords psoriasisbiologictherapymachinelearningdrugdiscontinuationtreatmentdurationgeneralizedlinearmodelpersonalizedmedicineregistrydata

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that long-term outcomes of biologic therapy in psoriasis are predictable for individual patients from routinely available clinical variables, not just from group-level statistics. Using a Danish registry cohort of 681 patients, the authors report that a generalized linear model predicts whether a treatment series ends in discontinuation—and whether the reason is lack of efficacy, adverse events, or something else—with 82% accuracy. The same model estimates how long a patient will stay on therapy, with a mean absolute error of 4.5 months and a strong correlation between predicted and actual durations. If correct, doctors could move from trial-and-error prescribing toward a patient-specific risk estimate, and patients could be told their absolute chance of long-term success. The paper also derives a profile of the patient most likely to continue a biologic for the long term.

What carries the argument

The load-bearing mechanism is the generalized linear model (GLM), a regression framework that links input variables to a categorical or continuous outcome through a link function; here it classifies the cause of biologic discontinuation in one model and predicts treatment length in another. The inputs are 14 variables that a psoriasis clinic already records, including demographics, weight, comorbidity count, psoriatic arthritis status, methotrexate use, previous biologic exposure, baseline DLQI (dermatology life quality index) and PASI (psoriasis area and severity index), and the specific drug. Performance is estimated with 5-fold cross-validation, and agreement for treatment duration is checked with Bland-Altman analysis, mean absolute error, and Pearson correlation. An input-optimization step inverts the fitted model to identify the patient features that yield the lowest predicted withdrawal risk, producing the paper's 'ideal patient' profile, while the decision tree supplies readable rules such as infliximab being linked to inefficacy-related withdrawal within the first two years.

What would settle it

Rerun the evaluation so that all treatment records from one patient stay in the same division of the data, never split between the records used to build the model and the records used to test it. If the 82% accuracy and 0.935 correlation drop substantially under that patient-level split, the reported individual-level performance is inflated; if they hold, the concern is answered.

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Extended reading notes

Core claim

The paper's central claim is that drug discontinuation—the real-world surrogate for treatment failure—is not random noise but a predictable function of a small set of clinical features. In the Danish registry cohort of 681 patients, a generalized linear model classified the outcome of a treatment series (continuation, discontinuation due to lack of efficacy, adverse event, or other cause) with 82% accuracy, and every one of the seven algorithms tested scored above 73%. A separate generalized linear model predicted the length of the treatment series with a mean absolute error of 4.5 months and a Pearson correlation of 0.935 between predicted and actual lengths. The input-optimization analysis concludes that a biologic-naive patient under 49 years old with early-onset plaque psoriasis, no psoriatic arthritis, weight under about 99 kg, and moderate-to-severe disease activity (DLQI ≥ 16, PASI ≥ 9.4) has at least a 90% chance of remaining on treatment—compared with population averages of roughly 20% discontinuation in the first year and 50% by year three. The authors present these results as evidence that machine learning can serve as a decision-support tool for communicating expected outcomes to patients.

Load-bearing premise

The load-bearing premise is that every recorded treatment series is independent of every other; because 433 of 681 patients have more than one series, a model could be learning patient identity rather than general rules when those series are split across the records used to build the model and the records used to test it.

Editorial extensions

If this is right

A clinician could enter a patient's routine values and receive an absolute risk of stopping the biologic, plus the most likely reason, without interpreting odds ratios or hazard ratios.
A patient fitting the optimized profile can be told their chance of staying on treatment is at least 90%, well above the average population's roughly 80% one-year persistence.
The duration estimate, with a mean absolute error of 4.5 months, gives a concrete expectation for how long a prescribed biologic is likely to be continued.
Because model fitting and prediction take seconds on an ordinary laptop, the tool could be embedded in routine clinical workflows at minimal cost.
Retraining on local registry data would be needed in different health systems, since access, reimbursement, and guidelines influence drug survival.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Inference: If the data were split by patient rather than by treatment series, the reported accuracy and 0.935 correlation would likely drop, because 433 of 681 patients contributed multiple series; deployment should therefore validate at the patient level.
Inference: The ideal-patient profile is a falsifiable decision rule: a prospective cohort could check whether patients matching those criteria actually show one-year withdrawal risk of 10% or less, rather than the roughly 20% average.
Inference: The combination of a 0.935 Pearson correlation and a 4.5-month mean absolute error suggests the duration model ranks patients by persistence better than it fixes an exact stop date, so risk strata may be a safer clinical output than a point estimate.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Off the record: this is a legitimate first application of standard machine learning to a psoriasis registry, and the paper is readable and honest about several limitations. But the central accuracy claim—82% for discontinuation risk—is probably overstated because the cross-validation splits treatment series, not patients. That is the main thing to know.

What is genuinely new: the authors applied seven off-the-shelf classifiers to the Dermbio cohort and produced a patient profile for long-term biologic success (biologic-naive, under 49, early-onset plaque psoriasis, no PsA, weight <100 kg, moderate-to-severe disease). The algorithms are routine, but the empirical result on this real registry is new. The comparison across methods is sensible, and the authors are appropriately cautious about the retrospective, single-country design.

The soft spots are real. The most serious is the clustering issue. Table 1 shows 433 of 681 patients contributed more than one treatment series, and the 5-fold CV randomly partitions examples (treatment series), not patients. So the same patient's multiple series can appear in both training and test, leaking patient-specific information. That inflates the 0.815 accuracy, the 4.5-month MAE, and the r=0.935 correlation. This is not a minor technicality; it directly supports the headline claim. A patient-level split or a mixed-effects model would address it. Second, missingness is extreme—baseline PASI is only 8% complete—and the ad hoc Null filling likely turns 'missing' into a predictor. Third, the input-optimization profile is obtained by inverting the same fitted GLM, so presenting it as an independent finding overstates what it is: a description of the model's decision boundary.

On balance, the central argument does not hold up as presented. The paper would be worth serious refereeing if the authors redo the validation at the patient level, report how missingness is handled, and soften the conclusions. The topic is timely, the data are real, and the profile is clinically plausible.

This is a paper for dermatology and clinical-ML readers. I would not cite the numerical claims yet, but I would bring it to a reading group as a case study in cross-validation pitfalls. Send to peer review, but expect major revision.

Referee Report

5 major / 5 minor

Summary. The manuscript describes a machine learning analysis of the Danish Dermbio registry (681 psoriasis patients) with the goal of predicting, at the individual level, the risk and cause of biologic discontinuation and the duration of treatment. Seven supervised learning algorithms are compared using 5-fold cross-validation; the authors report accuracies between 0.73 and 0.82, with a generalized linear model (GLM) achieving 0.815, and a separate GLM yielding a mean absolute error of 4.5 months and a Pearson correlation of 0.935 for treatment duration. The paper additionally presents an 'input optimization' profile of a patient with a high chance of continuing treatment, and concludes that machine learning predicts outcomes with accuracy exceeding 80% based on a small set of routine variables.

Significance. If the reported performance estimates were obtained under a valid evaluation protocol, the work would be clinically useful because it relies on variables routinely available in practice and offers interpretable model output for patient communication. The comparison of seven algorithms on a real-world registry and the use of cross-validation in principle are strengths. However, the current analysis does not establish that the claimed predictive performance generalizes to new patients. The paper provides no code, no data availability statement, and no reproducibility artifacts. The central accuracy claim is therefore not yet supported.

major comments (5)

[Methods, Machine Learning] The 5-fold cross-validation randomly partitions treatment series, not patients, yet Table 1 shows that 433 of 681 patients contributed more than one treatment series. This allows the same patient's multiple series to appear in both training and test folds, which can inflate the reported accuracies (Table 2) and the duration correlation through patient-level leakage. The authors should re-run the analysis with patient-level or group-based cross-validation and report whether the 0.815 accuracy and the 0.935 correlation hold under that more appropriate evaluation scheme.
[Methods, Data preprocessing and feature engineering] The preprocessing section states that predictors with missing values were filled with 'Null or appropriate value indicating unavailability of the data.' Given that Table 1 reports baseline PASI completeness of only 8.25%, DLQI completeness of 38.18%, and weight completeness of 57.27%, this crude handling can create artificial patterns (missingness indicators) that drive predictions, and it does not reflect how the model would be used with incomplete real-world data. The authors need to specify the imputation procedure precisely and perform sensitivity analyses under multiple missing-data assumptions.
[Results, Predicting the treatment length] The prediction of treatment duration is presented with MAE of 4.5 months and Pearson correlation 0.935, but the text does not state whether the duration GLM was evaluated on held-out data or on the same cohort used for fitting. If the latter, the reported agreement is in-sample and does not support the claim that treatment duration can be predicted for new patients. Moreover, treatment series that are censored (patients still on therapy at the last observation) are not discussed; treating censored durations as complete outcomes biases the error metrics. The authors should evaluate the duration model with patient-level cross-validation and address censoring, for example by using time-to-event modeling or by explicitly excluding ongoing series.
[Results, Input optimization reveals patient profile] The 'input optimization' profile in the Results states '≥23 year-old at the time of diagnosis', which is inconsistent with the abstract's characterization of the profile as 'early-onset' psoriasis, since early onset typically refers to onset before age 40 or a similar threshold. In addition, the profile is derived by inverting the same fitted GLM and is presented without any validation or confidence interval; the claim that such a patient has a ≤10% discontinuation risk is not an independent finding. The abstract profile and the Results profile must be reconciled, and the profile should be treated as an exploratory model-based description unless it is externally validated.
[Methods, Data preprocessing and feature engineering] Feature selection via PCA is described as a preprocessing step before model training. If PCA is applied to the full dataset before the cross-validation split, it leaks information from the test folds into the training procedure, which can inflate accuracy. The authors should clarify that PCA, and any other preprocessing steps, are performed inside each training fold, or use a pipeline that prevents information from test examples from entering the feature-selection step.

minor comments (5)

[Title page] The affiliation contains a typo: 'Canda' should be 'Canada'.
[Methods, Performance analysis] The accuracy formula is written for binary classification (TP+TN over the total), but the outcome appears to be multinomial with at least three classes (lack of efficacy, adverse event, other, possibly with continuation as an additional class). The authors should define the multiclass accuracy used to produce Table 2.
[Results, Predicting the treatment length] The text refers to 'GLA model' where 'GLM model' is intended.
[Results] The total number of treatment series (rows) used for model training is not reported, only the number of patients. Please report the number of examples for each analysis.
[Methods, Machine Learning] Runtime values (1.4 to 53 seconds) are reported without specifying the hardware and software versions; please provide this information or move runtimes to a supplementary table.

Circularity Check

1 steps flagged · score 4.0 of 10

Input-optimization patient profile is a restatement of the fitted GLM; the main cross-validated accuracy claims are not circular.

fitted input called prediction [Results, 'Input optimization reveals patient profile associated with the best long-term response']
"Here, we were interested in knowing the characteristics under which the patient would continue the treatment with less than 10% chance of withdrawal. The results indicated that a patient who has at least 90% chance of continuing the treatments fulfill the following criteria: (1) ≥ 23 year-old at the time of diagnosis (2) ≤ 49 year-old at the time of treatment; (3) receiving ustekinumab rather than TNF inhibitor, (4) not diagnosed with psoriasis arthritis (5) baseline DLQI ≥ 16; (6) baseline PASI ≥ 9.4; (7) no previous history of biologic failure, (8) weight ≤ 98.9 kg."

This 'ideal patient' profile is not an independent empirical discovery; it is the output of input optimization applied to the same GLM that was fitted to predict discontinuation on the same Dermbio cohort. By construction, the listed characteristics are the inputs that maximize the fitted model's predicted continuation probability. Reporting them as 'the results indicated' a patient with the best long-term response is therefore a restatement of the model's learned decision surface, not a new finding validated against external or held-out data. The profile has no evidential value beyond the fitted coefficients from which it was inverted.

full rationale

The central accuracy claims—GLM discontinuation accuracy of 0.815, treatment-duration MAE of 4.5 months, and the 0.935 correlation—are obtained from 5-fold cross-validation, so the main predictive results are not circular in the narrow derivation-chain sense: test-fold outcomes are not used to fit the model. The paper's citations of prior work by the same group (refs 1, 4, 5, 17) supply the registry dataset, variable definitions, and background drug-survival estimates; they are not invoked as the evidence for the reported CV experiment, so they are not load-bearing circularity. One genuine circular element remains: the 'input optimization' patient profile is computed by inverting the fitted GLM and then presented as an established characteristic of the best-responding patient. That is a fitted quantity relabeled as a finding. A separate validity concern—the 5-fold CV randomly partitions treatment series while 433 of 681 patients contributed more than one series, so patient-level leakage may inflate the reported accuracy—is a statistical correctness issue rather than a circularity of the derivation chain, and it is not counted in this circularity score. Overall, the only circularity is the model-inversion profile presented as an empirical result, while the core accuracy claims rest on held-out evaluation.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claims rest on fitted model coefficients, the assumption that drug discontinuation measures treatment failure, an independence assumption across treatment series, and ad hoc handling of extreme missingness. No external cohort or prospective validation is provided.

free parameters (4)

GLM multinomial coefficients for discontinuation cause
Learned from the 681-patient Dermbio dataset; no coefficients are reported, but the central 82% accuracy claim depends on them.
GLM regression coefficients for treatment duration
Used for the 4.5-month MAE and 0.935 Pearson correlation; fitted on the same registry data.
Input-optimization thresholds = age <=49; weight <=98.9 kg; PASI >=9.4; DLQI >=16; no PsA; no prior biologic; ustekinumab; diagnosis age >=23
Derived from the fitted GLM decision boundary and presented as the ideal patient profile with <10% withdrawal risk, without independent validation.
Algorithm hyperparameters = not reported
Deep learning architecture, tree depths, learning rates, and SVM settings are unstated, yet the cross-algorithm comparison in Table 2 depends on these choices.

assumptions (4)

domain assumption Drug discontinuation is a valid surrogate for treatment failure (loss of efficacy or adverse events).
Used to define the outcome in the Introduction and Methods; if discontinuation reflects access, reimbursement, or physician preference, the accuracy claims would not generalize.
domain assumption Treatment series from the same patient are independent observations.
Table 1 shows 433 of 681 patients had multiple treatment series, but the 5-fold CV splits treatment series rather than patients, so same-patient records can cross train and test folds.
domain assumption Features with high missingness can be removed or filled with Null without biasing the model.
Baseline PASI was only 8.25% complete and baseline DLQI 38.18% complete; preprocessing removed some features and filled others with Null, but no imputation model or sensitivity analysis is described.
domain assumption The Dermbio registry is representative of real-world biologic outcomes beyond Denmark.
The authors note the single-country limitation and that reimbursement and guidelines influence treatment duration, which limits external representativeness.

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Cite this review

Pith. "Pith review of Predicting the Long-Term Outcomes of Biologics in Psoriasis Patients Using Machine Learning." pith.science (2026). https://pith.science/paper/DCE7GH7U

@misc{pith2026190809251,
  author       = {Pith},
  title        = {Pith review of: Predicting the Long-Term Outcomes of Biologics in Psoriasis Patients Using Machine Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DCE7GH7U}},
  note         = {Machine review of arXiv:1908.09251}
}

abstract

Background. Real-world data show that approximately 50% of psoriasis patients treated with a biologic agent will discontinue the drug because of loss of efficacy. History of previous therapy with another biologic, female sex and obesity were identified as predictors of drug discontinuations, but their individual predictive value is low. Objectives. To determine whether machine learning algorithms can produce models that can accurately predict outcomes of biologic therapy in psoriasis on individual patient level. Results. All tested machine learning algorithms could accurately predict the risk of drug discontinuation and its cause (e.g. lack of efficacy vs adverse event). The learned generalized linear model achieved diagnostic accuracy of 82%, requiring under 2 seconds per patient using the psoriasis patients dataset. Input optimization analysis established a profile of a patient who has best chances of long-term treatment success: biologic-naive patient under 49 years, early-onset plaque psoriasis without psoriatic arthritis, weight < 100 kg, and moderate-to-severe psoriasis activity (DLQI $\geq$ 16; PASI $\geq$ 10). Moreover, a different generalized linear model is used to predict the length of treatment for each patient with mean absolute error (MAE) of 4.5 months. However Pearson Correlation Coefficient indicates 0.935 linear dependencies between the actual treatment lengths and predicted ones. Conclusions. Machine learning algorithms predict the risk of drug discontinuation and treatment duration with accuracy exceeding 80%, based on a small set of predictive variables. This approach can be used as a decision-making tool, communicating expected outcomes to the patient, and development of evidence-based guidelines.

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Works this paper leans on

2 extracted references · 2 canonical work pages

[2]

25 Kakadiaris IA, Vrigkas M, Yen AA, et al

doi: 10.1093/eurheartj/ehy839 . 25 Kakadiaris IA, Vrigkas M, Yen AA, et al. Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA. J Am Heart Assoc 2018; 7 :e009476. 11

work page doi:10.1093/eurheartj/ehy839 2018
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8 Morgan DJ, Bame B, Zimand P, et al

doi: 10.1001/jamaoncol.2019.1800 . 8 Morgan DJ, Bame B, Zimand P, et al. Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions. JAMA Netw Open 2019; 2 :e190348. 9 Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in...

work page arXiv 2019

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [2]

25 Kakadiaris IA, Vrigkas M, Yen AA, et al

doi: 10.1093/eurheartj/ehy839 . 25 Kakadiaris IA, Vrigkas M, Yen AA, et al. Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA. J Am Heart Assoc 2018; 7 :e009476. 11

work page doi:10.1093/eurheartj/ehy839 2018

[2] [2019]

8 Morgan DJ, Bame B, Zimand P, et al

doi: 10.1001/jamaoncol.2019.1800 . 8 Morgan DJ, Bame B, Zimand P, et al. Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions. JAMA Netw Open 2019; 2 :e190348. 9 Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in...

work page arXiv 2019