Decision tree–based machine learning analysis of intraoperative vasopressor use to optimize neurological improvement in acute spinal cord injury

Author:

Agarwal Nitin1,Aabedi Alexander A.1,Torres-Espin Abel123,Chou Austin123,Wozny Thomas A.1,Mummaneni Praveen V.12,Burke John F.1,Ferguson Adam R.1234,Kyritsis Nikos123,Dhall Sanjay S.123,Weinstein Philip R.12,Duong-Fernandez Xuan123,Pan Jonathan15,Singh Vineeta16,Hemmerle Debra D.123,Talbott Jason F.37,Whetstone William D.8,Bresnahan Jacqueline C.123,Manley Geoffrey T.123,Beattie Michael S.1234,DiGiorgio Anthony M.123

Affiliation:

1. Department of Neurological Surgery, University of California, San Francisco;

2. Weill Institute for Neurosciences, Brain and Spinal Injury Center, University of California, San Francisco;

3. Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco;

4. San Francisco Veterans Affairs Healthcare System, San Francisco; and

5. Department of Anesthesia and Perioperative Care, University of California, San Francisco;

6. Department of Neurology, University of California, San Francisco;

7. Department of Radiology and Biomedical Imaging, University of California, San Francisco;

8. Department of Emergency Medicine, University of California, San Francisco, California

Abstract

OBJECTIVE Previous work has shown that maintaining mean arterial pressures (MAPs) between 76 and 104 mm Hg intraoperatively is associated with improved neurological function at discharge in patients with acute spinal cord injury (SCI). However, whether temporary fluctuations in MAPs outside of this range can be tolerated without impairment of recovery is unknown. This retrospective study builds on previous work by implementing machine learning to derive clinically actionable thresholds for intraoperative MAP management guided by neurological outcomes. METHODS Seventy-four surgically treated patients were retrospectively analyzed as part of a longitudinal study assessing outcomes following SCI. Each patient underwent intraoperative hemodynamic monitoring with recordings at 5-minute intervals for a cumulative 28,594 minutes, resulting in 5718 unique data points for each parameter. The type of vasopressor used, dose, drug-related complications, average intraoperative MAP, and time spent in an extreme MAP range (< 76 mm Hg or > 104 mm Hg) were collected. Outcomes were evaluated by measuring the change in American Spinal Injury Association Impairment Scale (AIS) grade over the course of acute hospitalization. Features most predictive of an improvement in AIS grade were determined statistically by generating random forests with 10,000 iterations. Recursive partitioning was used to establish clinically intuitive thresholds for the top features. RESULTS At discharge, a significant improvement in AIS grade was noted by an average of 0.71 levels (p = 0.002). The hemodynamic parameters most important in predicting improvement were the amount of time intraoperative MAPs were in extreme ranges and the average intraoperative MAP. Patients with average intraoperative MAPs between 80 and 96 mm Hg throughout surgery had improved AIS grades at discharge. All patients with average intraoperative MAP > 96.3 mm Hg had no improvement. A threshold of 93 minutes spent in an extreme MAP range was identified after which the chance of neurological improvement significantly declined. Finally, the use of dopamine as compared to norepinephrine was associated with higher rates of significant cardiovascular complications (50% vs 25%, p < 0.001). CONCLUSIONS An average intraoperative MAP value between 80 and 96 mm Hg was associated with improved outcome, corroborating previous results and supporting the clinical verifiability of the model. Additionally, an accumulated time of 93 minutes or longer outside of the MAP range of 76–104 mm Hg is associated with worse neurological function at discharge among patients undergoing emergency surgical intervention for acute SCI.

Publisher

Journal of Neurosurgery Publishing Group (JNSPG)

Subject

Neurology (clinical),General Medicine,Surgery

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