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. 2023 Jun 4;59(6):1083.
doi: 10.3390/medicina59061083.

A New Nomogram-Based Prediction Model for Postoperative Outcome after Sigmoid Resection for Diverticular Disease

Sascha Vaghiri  1 , Sarah Krieg  2 , Dimitrios Prassas  1 , Sven Heiko Loosen  2 , Christoph Roderburg  2 , Tom Luedde  2 , Wolfram Trudo Knoefel  1 , Andreas Krieg  1
Affiliations

A New Nomogram-Based Prediction Model for Postoperative Outcome after Sigmoid Resection for Diverticular Disease

Sascha Vaghiri et al. Medicina (Kaunas). .

Abstract

Background and Objectives: Sigmoid resection still bears a considerable risk of complications. The primary aim was to evaluate and incorporate influencing factors of adverse perioperative outcomes following sigmoid resection into a nomogram-based prediction model. Materials and Methods: Patients from a prospectively maintained database (2004-2022) who underwent either elective or emergency sigmoidectomy for diverticular disease were enrolled. A multivariate logistic regression model was constructed to identify patient-specific, disease-related, or surgical factors and preoperative laboratory results that may predict postoperative outcome. Results: Overall morbidity and mortality rates were 41.3% and 3.55%, respectively, in 282 included patients. Logistic regression analysis revealed preoperative hemoglobin levels (p = 0.042), ASA classification (p = 0.040), type of surgical access (p = 0.014), and operative time (p = 0.049) as significant predictors of an eventful postoperative course and enabled the establishment of a dynamic nomogram. Postoperative length of hospital stay was influenced by low preoperative hemoglobin (p = 0.018), ASA class 4 (p = 0.002), immunosuppression (p = 0.010), emergency intervention (p = 0.024), and operative time (p = 0.010). Conclusions: A nomogram-based scoring tool will help stratify risk and reduce preventable complications.

Keywords: morbidity and mortality; nomogram-based prediction; postoperative length of stay; sigmoid diverticulitis.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Nomogram for predicting the probability of an eventful postoperative course in patients undergoing sigmoid resection for diverticular disease. The nomogram (A) was constructed by multivariate regression analysis, and the calibration curve (B) revealed a well calibrated model. Leucocytes: number ×1000/µL; Hemoglobin: g/dl. Abbreviations: DM, Diabetes mellitus; DCES, Duration from current episode to surgery in days; OT, Operation time.
Figure 2
Figure 2
Dynamic nomogram for predicting the probability of an eventful postoperative course in patients undergoing sigmoid resection for diverticular disease. A helpful web-based tool for daily routine allows the user to calculate the risk for an individual eventful postoperative course.
See this image and copyright information in PMC

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