scholarly journals Identification of preoperative predictors for acute postsurgical pain and for pain at three months after surgery: a prospective observational study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Regina L. M. van Boekel ◽  
Ewald M. Bronkhorst ◽  
Lilian Vloet ◽  
Monique A. M. Steegers ◽  
Kris C. P. Vissers

AbstractIdentifying patients at risk is the start of adequate perioperative pain management. We aimed to identify preoperative predictors for acute postsurgical pain (APSP) and for pain at 3 months after surgery to develop prediction models. In a prospective observational study, we collected preoperative predictors and the movement-evoked numerical rating scale (NRS-MEP) of postoperative pain at day 1, 2, 3, 7, week 1, 6 and 3 months after surgery from patients with a range of surgical procedures. Regression analyses of data of 2258 surgical in- and outpatients showed that independent predictors for APSP using the mean NRS-MEP over the first three days after surgery were hospital admittance, female sex, higher preoperative pain, younger age, pain catastrophizing, anxiety, higher score on functional disability, highest categories of expected pain, medical specialty, unknown wound size, and wound size > 10 cm compared to wound size ≤ 10 cm (RMSE = 2.11). For pain at three months, the only predictors were preoperative pain and a higher score on functional disability (RMSE = 1.69). Adding pain trajectories improved the prediction of pain at three months (RMSE = 1.37). Our clinically applicable prediction models can be used preoperatively to identify patients at risk, as well as in the direct postoperative period.

2020 ◽  
Vol 4 ◽  
pp. 239784732097863
Author(s):  
Stanley E Lazic ◽  
Dominic P Williams

Predicting the safety of a drug from preclinical data is a major challenge in drug discovery, and progressing an unsafe compound into the clinic puts patients at risk and wastes resources. In drug safety pharmacology and related fields, methods and analytical decisions known to provide poor predictions are common and include creating arbitrary thresholds, binning continuous values, giving all assays equal weight, and multiple reuse of information. In addition, the metrics used to evaluate models often omit important criteria and models’ performance on new data are often not assessed rigorously. Prediction models with these problems are unlikely to perform well, and published models suffer from many of these issues. We describe these problems in detail, demonstrate their negative consequences, and propose simple solutions that are standard in other disciplines where predictive modelling is used.


2013 ◽  
Vol 472 (5) ◽  
pp. 1409-1415 ◽  
Author(s):  
Patricia M. Lavand’homme ◽  
Irina Grosu ◽  
Marie-Noëlle France ◽  
Emmanuel Thienpont

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