An Artificial Intelligence Approach to Predicting Unplanned Intubation Following Anterior Cervical Discectomy and Fusion

Abstract

Study Design Level III retrospective database study. Objectives The purpose of this study is to determine if machine learning algorithms are effective in predicting unplanned intubation following anterior cervical discectomy and fusion (ACDF). Methods The National Surgical Quality Initiative Program (NSQIP) was queried to select patients who had undergone ACDF. Machine learning analysis was conducted in Python and multivariate regression analysis was conducted in R. C-Statistics area under the curve (AUC) and prediction accuracy were used to measure the classifier’s effectiveness in distinguishing cases. Results In total, 54 502 patients met the study criteria. Of these patients, .51% underwent an unplanned re-intubation. Machine learning algorithms accurately classified between 72%-100% of the test cases with AUC values of between .52-.77. Multivariable regression indicated that the number of levels fused, male sex, COPD, American Society of Anesthesiologists (ASA) > 2, increased operating time, Age > 65, pre-operative weight loss, dialysis, and disseminated cancer were associated with increased risk of unplanned intubation. Conclusions The models presented here achieved high accuracy in predicting risk factors for re-intubation following ACDF surgery. Machine learning analysis may be useful in identifying patients who are at a higher risk of unplanned post-operative re-intubation and their treatment plans can be modified to prophylactically prevent respiratory compromise and consequently unplanned re-intubation.

Risk Factors for Reintubation After Anterior Cervical Spine Surgery

“…Previous retrospective studies reported that the incidence of postoperative reintubation after ACSS was 0.51%-1.0%. 1,[4][5][6][7][8][9] However, most previous reports only investigated patients undergoing ACSS for degenerative conditions such as cervical myelopathy and radiculopathy and have not included cervical spine injuries. In this series, all 3 patients who experienced reintubation were included in the trauma group; thus, no patients experienced reintubation in the nontrauma group.…”

“…[1][2][3] Postoperative airway complication rates and unplanned reintubation after ACSS are 0.1%-6.6% and 0.51%-1.0%, respectively. 1,[4][5][6][7][8][9] Despite these low incidence rates, airway obstruction can lead to catastrophic complications and result in brain death or patient mortality.…”

Limitations in Evaluating Machine Learning Models for Imbalanced Binary Outcome Classification in Spine Surgery: A Systematic Review

“…In addition to the previously mentioned errors, some papers provided poor calibration plots and omitted essential metrics. Kuris et al, Veeramani et al, and Zhang et al presented models predicting readmission, unplanned re-intubation, and short LOS, respectively, with acceptable AUROCs, accuracies, and BSs [16,27,29]. However, all three studies provided calibration plots indicating poor calibration, as the calibration curves were not in proximity to the near-perfect prediction diagonal.…”

Letter to the Editor on “an Artificial Intelligence Approach to Predicting Unplanned Intubation Following Anterior Cervical Discectomy and Fusion” by Veeramani et al

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To the Editor, We read with great interest the study by Veeramani et al on artificial intelligence approaches to predict unplanned intubation after anterior cervical discectomy and fusion. 1 We agree that respiratory compromise can be devastating and that identification of high-risk patients is of paramount importance. We commend the authors for their efforts and wish to offer our insights.

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“…Veeramani et al 1 are dealing with an imbalanced classification problem with only 283 real positive cases (.51%) and 54,219 real negative cases (99.49%). Thus, the focus during model development should be towards the positive class, as models will be inherently biased towards patients who did not require reintubation.…”