Can the Charlson Comorbidity Index be used to predict the ASA grade in patients undergoing spine surgery?

• Published in: European spine journal Vol. 29; no. 12; pp. 2941 - 2952
• Main Authors: Mannion, A. F., Bianchi, G., Mariaux, F., Fekete, T. F., Reitmeir, R., Moser, B., Whitmore, R. G., Ratliff, J., Haschtmann, D.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020; Springer Nature B.V
• Subjects: Algorithms; Back surgery; Bone surgery; Comorbidity; Decision making; Medicine; Medicine & Public Health; Neurosurgery; Original Article; Surgical Orthopedics; Regression analysis; ASA grade; Charlson comorbidity index; Prediction algorithm; Spine surgery
• ISSN: 0940-6719; 1432-0932
• DOI: 10.1007/s00586-020-06595-1

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 < 3 (area under the curve (AUC), 0.70 ± 0.01, 95%CI,0.82–0.84). Multiple logistic regression analyses including age, sex, smoking, and BMI in addition to CCI gave better predictions of ASA (Nagelkerke’s pseudo-R 2 for predicting ASA class 1 to 4, 46.6%; for predicting ASA ≥ 3 vs.  < 3, 37.5%). AUCs for discriminating ASA ≥ 3 versus < 3 from multiple logistic regression were 0.83 ± 0.01 (95%CI, 0.82–0.84) for the training sample and 0.82 ± 0.01 (95%CI, 0.81–0.84), 0.85 ± 0.02 (95%CI, 0.80–0.89), and 0.77 ± 0.04 (95%CI,0.69–0.84) for the test, temporal and external validation samples, respectively. Calibration was adequate in all validation samples. Conclusions It was possible to predict ASA from CCI. In a simple model, CCI ≥ 1 best distinguished between ASA ≥ 3 and < 3. For a more precise prediction, regression algorithms were created based on CCI and simple demographic variables obtainable from patient interview. The availability of such algorithms may widen the utility of decision aids that rely on the ASA, where the latter is not readily available.