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

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Abstract

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-R2 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.

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Fig. 1
Fig. 2

Notes

  1. Three of the comorbidities in the original index of 19 conditions described by Charlson [26], “leukemia”, “lymphoma” and “non-metastatic tumor”, were subsumed under the “any malignancy” category by Deyo et al. [27] when developing the first ICD-coding algorithm; the 17-item index is the one most commonly used today.

  2. The equations are obtainable from the authors as algorithms/prediction tools in Microsoft Excel or FilemakerPro.

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Acknowledgments

We would like to thank Jon Lurie and Adam Pearson (Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA) for sharing their data allowing us to perform the external validation analyses.

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Authors and Affiliations

  1. Spine Center, Schulthess Klinik, Lengghalde 2, 8008, Zurich, Switzerland

    A. F. Mannion, G. Bianchi, F. Mariaux, T. F. Fekete, R. Reitmeir & D. Haschtmann

  2. Department of Anaesthesia, Schulthess Klinik, Lengghalde 2, 8008, Zurich, Switzerland

    B. Moser

  3. Department of Anesthesia, Spital Limmattal, Urdorferstrasse 100, 8952, Schlieren, Switzerland

    B. Moser

  4. Lahey Clinic, Tufts University School of Medicine, Burlington, MA, 01805, USA

    R. G. Whitmore

  5. Department of Neurosurgery, Stanford University, Palo Alto, CA, 94304-5979, USA

    J. Ratliff

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Mannion, A.F., Bianchi, G., Mariaux, F. et al. Can the Charlson Comorbidity Index be used to predict the ASA grade in patients undergoing spine surgery?. Eur Spine J 29, 2941–2952 (2020). https://doi.org/10.1007/s00586-020-06595-1

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