AIS impairment scale, injury characteristics, intraoperative data after spinal cord injury in patients from a TRACK-SCI cohort

DOI:10.34945/F5KG6Z

DATASET CITATION

Chou A., Beattie M. S., Bresnahan J. C., Burke J. F., Almeida C. A., Dhall S. S., DiGiorgio A. M., Duong-Fernandez X., Haefeli J., Hemmerle D. D., Huie J., Kyritsis N., Manley G. T., Moncivais S., Omondi C., Pascual L. U., Singh V., Talbott J. F., Thomas L. H., Torres-Espin A., Weinstein P. R., Whetstone W. D., Pan J. Z., Ferguson A. R. (2022) AIS impairment scale, injury characteristics, intraoperative data after spinal cord injury in patients from a TRACK-SCI cohort. ODC-SCI:727 http://doi.org/10.34945/F5KG6Z

ABSTRACT

STUDY PURPOSE: The aim of the data collection was to identify predictors of whether a patient would improve on the ASIA Impairment Scale (AIS) grade between time of hospital admission and hospital discharge. Predictors investigated include injury characteristics, anesthetics and vasopressors used during surgery, MRI BASIC score, length of surgery and hospital stay, and intraoperative hemodynamics. The data were collected at a level I trauma center (Zuckerberg San Francisco General Hospital) and span both a retrospective and prospective study for the purpose of predictive modeling and external validation respectively.

DATA COLLECTED: The dataset include: (1) various injury characteristics such as the level of injury, presence of polytrauma, type of injury (e.g. penetrating, hemorrhagic, central cord, cervical, fracture, vertebral artery), and whether the patient had previous history of SCI or TBI; (2) vasopressor and anesthetic usage during surgery; (3) length of surgery and hospital stay; (4) intraoperative fraction of inspired oxygen; (5) the duration the patient was within and outside MAP goals in the ICU; (6) MRI BASIC Score; and (7) various intraoperative hemodynamics. In particular, the hemodynamics were derived from time series data of heart rate and systolic, diastolic, and mean arterial blood pressure during surgery. This includes summary statistics of mean, standard deviation, skew, and kurtosis. For mean arterial blood pressure specifically, the duration that a patient was outside a specific threshold (upper or lower) was also determined and recorded in the dataset.

CONCLUSIONS: Predictive models optimized from the data show that initial injury severity (either AIS at time of hospital admission or MRI BASIC score) were highly predictive variables for whether the patient improved in outcome by the time of hospital discharge. Additionally, intraoperative hyper- and hypotension (high and low MAP) were critical predictors; patients that spent more time in a hyper and/or hypotensive state were less likely to improve. Hypertension in particular was more predictive of worse outcome than hypotension. The lower MAP threshold predicting worse outcome was around 74-76 mmHg and the upper MAP threshold was around 103-104 mmHg.

KEYWORDS

Spinal Cord Injury; Surgery; Hemodynamic; Blood pressure; Vasopressor; AIS score; Human

PROVENANCE / ORIGINATING PUBLICATIONS

• Chou, A., Torres-Espin, A., Kyritsis, N., Huie, J.R., Khatry, S., Funk, J., Hay, J., Lofgreen, A., Shah, R., McCann, C., Pascual, L.U., Amorim, E., Weinstein, P.R., Manley, G.T., Dhall, S.S., Pan, J.Z., Bresnahan, J.C., Beattie, M.S., Whetstone, W.D., Ferguson, A.R., and the TRACK-SCI Investigators. (2021). Expert-integrated automated machine learning uncovers hemodynamic predictors in spinal cord injury. bioRxiv , 2021.09.27.461544.. doi:10.1101/2021.09.27.461544.

Originating publication (biorxiv preprint).

• Torres-Espín A., Haefeli J., Ehsanian R., Torres D., Almeida C. A., Huie J., Chou A., Dirlikov B., Suen C. G., Nielson J. L., Kyritsis N., Duong-Fernandez X., Thomas L. H., Hemmerle D. D., Morozov D., Sanderson N., Talbott J. F., Manley G. T., Dhall S. S., Whetstone W. D., Bresnahan J. C., Beattie M. S., McKenna S. L., Pan J. Z., Ferguson A. R. (2021) ASIA Impairment Scale, level of injury, intraoperative time series mean arterial pressure and heart rate after spinal cord injury in patients in a multi-site retrospective TRACK-SCI cohort: site 2 of 2. ODC-SCI:246 http://doi.org/10.34945/F5MG68. doi:10.34945/F5MG68.

Some overlap in subjects and variables (subject IDs are not matched). The provenanced dataset also includes intraoperative hemodynamics at the 5min-interval granularity from which the summary statistics were derived.

• Chou A, Torres-Espin A, Kyritsis N, Huie JR, Khatry S, Funk J, Hay J, Lofgreen A, Shah R, McCann C, Pascual LU, Amorim E, Weinstein PR, Manley GT, Dhall SS, Pan JZ, Bresnahan JC, Beattie MS, Whetstone WD, Ferguson AR; TRACK-SCI Investigators. Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome. PLoS One. 2022 Apr 7;17(4):e0265254. doi: 10.1371/journal.pone.0265254. PMID: 35390006; PMCID: PMC8989303. doi:10.1371/journal.pone.0265254.

Originating publication

RELEVANT LINKS

NOTES

DATASET INFO

Contact: Pan Jonathan (jonathan.pan@ucsf.edu), Ferguson Adam (adam.ferguson@ucsf.edu)

Lab: TRACK-SCI De-ID Lab

ODC-SCI Accession:727

Records in Dataset: 159

Fields per Record: 126

Last updated: 2022-04-25 (See changelog)

Date published: 2022-03-24

Downloads: 42

Files: 2

LICENSE

Creative Commons Attribution License (CC-BY 4.0)

FUNDING AND ACKNOWLEDGEMENTS

NIH/NINDS grants R01NS088475 (ARF), R01NS122888 (ARF), UH3NS106899 (ARF), U24NS122732 (ARF), Department of Veterans Affairs grants 1I01RX002245 (ARF) and I01RX002787 (ARF), Wings for Life Foundation (ARF), Craig H. Neilsen Foundation (ARF), Department of Defense grants SC150198 (MSB) and SC190233 (MSB), C.H. Neilsen Foundation (MSB)

CONTRIBUTORS

Chou, Austin [ORCID:0000-0003-4328-5811]

University of California, San Francisco

Beattie, Michael S. [ORCID:0000-0001-9463-3631]

University of California, San Francisco

Bresnahan, Jacqueline C. [ORCID:0000-0003-2243-7054]

University of California, San Francisco

Burke, John F. [ORCID:0000-0002-6190-5116]

University of California, San Francisco

Almeida, Carlos A. [ORCID:0000-0001-9997-8719]

University of California, San Francisco

Dhall, Sanjay S. [ORCID:0000-0002-6891-2722]

University of California, San Francisco

DiGiorgio, Anthony M. [ORCID:0000-0002-5710-691X]

University of California, San Francisco

Duong-Fernandez, Xuan [ORCID:0000-0001-8362-4166]

University of California, San Francisco

Haefeli, Jenny [ORCID:0000-0002-3546-728X]

University of California, San Francisco

Hemmerle, Debra D. [ORCID:0000-0003-2796-6107]

University of California, San Francisco

Huie, J. Russell [ORCID:0000-0001-5594-4277]

University of California, San Francisco

Kyritsis, Nikos [ORCID:0000-0001-7801-5796]

University of California, San Francisco

Manley, Geoffrey T. [ORCID:0000-0002-0926-3128]

University of California, San Francisco

Moncivais, Sara [ORCID:0000-0001-5506-9804]

University of California, San Francisco

Omondi, Cleopa [ORCID:0000-0001-8003-1146]

University of California, San Francisco

Pascual, Lisa U. [ORCID:0000-0001-6228-6217]

University of California, San Francisco

Singh, Vineeta [ORCID:0000-0003-3470-3532]

University of California, San Francisco

Talbott, Jason F. [ORCID:0000-0003-3943-5530]

University of California, San Francisco

Thomas, Leigh H. [ORCID:0000-0002-7593-0240]

University of California, San Francisco

Torres-Espin, Abel [ORCID:0000-0002-9787-8738]

University of California, San Francisco

Weinstein, Philip R. [ORCID:0000-0003-4038-745X]

University of California, San Francisco

Whetstone, William D. [ORCID:0000-0002-8568-6640]

University of California, San Francisco

Pan, Jonathan Z. [ORCID:0000-0001-5814-3707]

University of California, San Francisco

Ferguson, Adam R. [ORCID:0000-0001-7102-1608]

University of California, San Francisco