Article Text

Download PDFPDF

THU0682 Popgen-ossa: development of an organ specific self assessment (OSSA) for interdisciplinary documentation of patient reported clinical outcomes
  1. R Zeuner1,
  2. U Gsell2,
  3. M Hübenthal3,
  4. S Schreiber4,
  5. A Franke3,
  6. M Laudes4,
  7. JO Schröder1
  1. 1Section of Rheumatology, Clinic for Internal Medicine I, University Hospital Schleswig-Holstein, Campus Kiel, Kiel
  2. 2Klinik Eichstätt, Eichstätt
  3. 3Institute of Clinical Molecular Biology
  4. 4Clinic for Internal Medicine I, University Hospital Schleswig-Holstein, Campus Kiel, Kiel, Germany


Background Patient reported outcome measures are comprised of either sets of questionnaires or patient global assessment based on visual analogue scale (VAS). These patient-reported outcome measures lack accuracy and/or clinical feasibility when comparing heterogeneous patient groups with different diseases, or when characterizing patients with systemic disease involving different organ systems.

Objectives Developing a clinical feasible patient-reported outcome measure based VAS assessment of different organ systems.

Methods Patients were asked to rate their health status in a 10cm VAS (0–100%) concerning their global health as well as of different organ systems, namely heart, lung, muscle and joints, gastro-intestinal, metabolic, uro-genital, skin, neuro-psychiatric, eyes and ears. All VA-scales were “anchored”. Patients were advised to rate their health status below 75% if they felt “medical action is needed”, they should rate the health status <50% in case of a “strong need for medical action” and <25% in case of a “medical emergency”.

336 patients from different outpatient clinics (cardiologic, pneumologic, gastro-intestinal, nephrologic, neurologic, dermatologic, rheumatologic, ophthalmologic and obesity outpatient clinic) as well as patients from internal emergency clinics and a general practitioner clinic were evaluated. Both, patients and the attending physicians completed the Popgen-OSSA. In addition the attending physician was asked to document ranking of the 5 most important diagnoses of the patient.

Statistical analysis was carried out using non-parametric testing. Furthermore, to predict main diagnoses based on patients's as well as physician's OSSA state-of-the-art machine learning tools, namely support vector machines (SVMs), were applied. To assess model performance multi-class AUC (area under the ROC curve) according to Hand and Till (2001) was estimated based on repeated cross validation (10 folds, 5 repeats), optimizing the SVM's hyperparamters using grid search.

Results The test showed a good reproducibility. With a mean percentage of 74±0.98 SE and 66±1.17 SE, respectively, the physicians OSSA rating was significantly higher than the rating of the patients (pwilcoxon<0.001). Models predicting main diagnoses were constructed and estimated to perform with multi-class AUCs of 63.5% and 73.4% based on patient's and physician's OSSA, respectively.

Conclusions In this preliminary trial with low sample size the Popgen-OSSA showed a good reproducibility and allowed a correct allocation of the patient's clinical problem to involved organ system by SVM analysis with multi-class AUC of up to 73.4%. These data merit further investigation and development of the Popgen-OSSA on larger patient cohorts.


  1. David J. Hand and Robert J. Till (2001). A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems. Machine Learning 45(2), p. 171–186. DOI: 10.1023/A:1010920819831.


Disclosure of Interest R. Zeuner Grant/research support from: Pfizer, Novartis, UCB, U. Gsell: None declared, M. Hübenthal: None declared, S. Schreiber: None declared, A. Franke: None declared, M. Laudes Grant/research support from: Roche, Lilly, MSD, J. Schröder: None declared

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.