A. F. Mannion, G. Bianchi, F. Mariaux, T. F. Fekete, R. Reitmeir, B. Moser, R. G. Whitmore, J. Ratliff, D. Haschtmann


December 2020, Volume 29, Issue 12, pp 2941 - 2952 Original Article Read Full Article 10.1007/s00586-020-06595-1

First Online: 18 September 2020

Background

The American Society of Anaesthesiologists' Physical Status Score (ASA) is a key variable in predictor models of surgical outcome and "appropriate use criteria". However, at the time when such tools are being used in decision-making, the ASA rating is typically unknown. We evaluated whether the ASA class could be predicted statistically from Charlson Comorbidy Index (CCI) scores and simple demographic variables.

Methods

Using established algorithms, the CCI was calculated from the ICD-10 comorbidity codes of 11′523 spine surgery patients (62.3 ± 14.6y) who also had anaesthetist-assigned ASA scores. These were randomly split into training (N = 8078) and test (N = 3445) samples. A logistic regression model was built based on the training sample and used to predict ASA scores for the test sample and for temporal (N = 341) and external validation (N = 171) samples.

Results

In a simple model with just CCI predicting ASA, receiver operating characteristics (ROC) analysis revealed a cut-off of CCI ≥ 1 discriminated best between being ASA ≥ 3 versus 

Conclusions

It was possible to predict ASA from CCI. In a simple model, CCI ≥ 1 best distinguished between ASA ≥ 3 and 


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