Occurrence of major postoperative complications in onco-geriatric surgical patients (PREOP Score)
Scoring system for major 30-day postoperative complications, developed in a relatively large cohort of onco-geriatric surgical patients.
Research authors: Huisman MG, Audisio RA, Ugolini G, Montroni I, Vigano A, Spiliotis J, Stabilini C, de Liguori Carino N, Farinella E, Stanojevic G, Veering BT, Reed MW, Somasundar PS, de Bock GH, van Leeuwen BL.
Details Formula Study characteristics Files & References
Model author
Model ID
Revision date
MeSH terms
  • Surgical Oncology
  • Assessments, Geriatric
  • Postoperative Complications
  • Clinical Prediction Rule
  • Model type
    Linear model (Calculation)
    No Formula defined yet
    Condition Formula

    Additional information

    The aim of this study was to investigate the predictive ability of screening tools regarding the occurrence of major postoperative complications in onco-geriatric surgical patients and to propose a scoring system.

    328 patients 70 years undergoing surgery for solid tumors were prospectively recruited. Preoperatively, twelve screening tools were administered. Primary endpoint was the incidence of major complications within 30 days. Odds ratios (OR) and 95% confidence intervals (95% CI) were estimated using logistic regression. A scoring system was derived from multivariate logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was applied to evaluate model performance.

    At a median age of 76 years, 61 patients (18.6%) experienced major complications. In multivariate analysis, Timed Up and Go (TUG), ASA-classification and Nutritional Risk Screening (NRS) were predictors of major complications (TUG>20 OR 3.1, 95% CI 1.1-8.6; ASA3 OR 2.8, 95% CI 1.2-6.3; NRSimpaired OR 3.3, 95% CI 1.6-6.8). The scoring system, including TUG, ASA, NRS, gender and type of surgery, showed good accuracy (AUC: 0.81, 95% CI 0.75e0.86). The negative predictive value with a cut-off point >8 was 93.8% and the positive predictive value was 40.3%.

    A substantial number of patients experience major postoperative complications. TUG, ASA and NRS are screening tools predictive of the occurrence of major postoperative complications and, together with gender and type of surgery, compose a good scoring system.

    Huisman MG, Audisio RA, Ugolini G, et al. Screening for predictors of adverse outcome in onco-geriatric surgical patients: A multicenter prospective cohort study. 2015;41(7):844-51.

    Study Population

    Total population size: 328
    Males: {{ model.numberOfMales }}
    Females: {{ model.numberOfFemales }}

    Continuous characteristics

    Name LL Q1 Median Q3 UL Unit
    Age 70 76 96 years

    Categorical characteristics

    Name Subset / Group Nr. of patients
    Living situation Independent/family 323
    Resedential care/nursing home 2
    Hemoglobin level ≥ 12 g/dl 198
    < 12 g/dl 110
    Surgery Minor 105
    Major 223
    Cancer site Breast 81
    Colorectal 121
    Gastric 22
    Gynaecological 19
    Pancreas and biliary tract 34
    Renal and bladder 23
    Soft tissue and skin 18
    Remaining 12
    Tumor stage Stage 0 / benign 19
    Stage 1 75
    Stage 2 83
    Stage 3 65
    Stage 4 53
    Unknown 33
    Age 70-74 120
    75-79 103
    80-84 72
    ≥ 85 33

    Related files

    Calculated PREOP score:

    {{ resultSubheader }}

    {{ model.survival.PITTitle }}

    {{ model.survival.YNETitle }}

    Notes are only visible in the result download and will not be saved by Evidencio

    Calculated PREOP score: points

    {{ resultSubheader }}

    Outcome stratification

    Result interval {{ additionalResult.min }} to {{ additionalResult.max }}

    Conditional information

    Result interpretation

    How this model should be used:
    The PREOP scoring system can easily be implemented into daily practice as a screening measure, to support the judgment of the clinician. The high negative predictive value indicates that the scoring system can exclude the fit elderly from further evaluation, whilst a positive score might indicate that a more comprehensive assessment by a geriatrician or by means of a multidisciplinary meeting is indicated.

    Strenghts and weaknesses:
    A strength of the PREOP-study is its prospective and comprehensive design. A large number of medical centers participated, which further enhances the generalisability of the results. Selection bias is a limitation of the underlying study as inclusion of a consecutive series of patients cannot be guaranteed. Furthermore, cultural differences could have influenced the reporting of results and answers to questionnaires.

    Assessement of model performance:
    The AUC for this individual risk score was 0.81 (95% CI: 0.75-0.86). Based on the ROC, a cut-off point was set at >8, with a sensitivity of 78.7% and a specificity of 73.4%. Positive and negatieve predictive values were 40.3% and 93.8%, respectively.


    {{ file.classification }}

    Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.

    Please enter a comment of rating
    Comments are visible to anyone

    Model feedback

    No feedback yet 1 Comment {{ model.comments.length }} Comments
    Not rated | On {{ comment.created_at }} {{ comment.user.username }} a no longer registered author wrote:

    Please sign in to enable Evidencio print features

    In order to use the Evidencio print features, you need to be logged in.
    If you don't have an Evidencio Community Account you can create your free personal account at:


    Printed results - Examples {{ new Date().toLocaleString() }}

    Evidencio Community Account Benefits

    With an Evidencio Community account you can:

    • Create and publish your own prediction models.
    • Share your prediction models with your colleagues, research group, organization or the world.
    • Review and provide feedback on models that have been shared with you.
    • Validate your models and validate models from other users.
    • Find models based on Title, Keyword, Author, Institute, or MeSH classification.
    • Use and save prediction models and their data.
    • Use patient specific protocols and guidelines based on sequential models and decision trees.
    • Stay up-to-date with new models in your field as they are published.
    • Create your own lists of favorite models and topics.
    A personal Evidencio account is free, with no strings attached! Join us and help create clarity, transparency, and efficiency in the creation, validation, and use of medical prediction models.

    Disclaimer: Calculations alone should never dictate patient care, and are no substitute for professional judgement.