Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery

Abstract

Meta-analyses suggest that the published literature represents only a small minority of the total data collected in biomedical research, with most becoming ‘dark data’ unreported in the literature. Dark data is due to publication bias toward novel results that confirm investigator hypotheses and omission of data that do not. Publication bias contributes to scientific irreproducibility and failures in bench-to-bedside translation. Sharing dark data by making it Findable, Accessible, Interoperable, and Reusable (FAIR) may reduce the burden of irreproducible science by increasing transparency and support data-driven discoveries beyond the lifecycle of the original study. We illustrate feasibility of dark data sharing by recovering original raw data from the Multicenter Animal Spinal Cord Injury Study (MASCIS), an NIH-funded multi-site preclinical drug trial conducted in the 1990s that tested efficacy of several therapies after a spinal cord injury (SCI). The original drug treatments did not produce clear positive results and MASCIS data were stored in boxes for more than two decades. The goal of the present study was to independently confirm published machine learning findings that perioperative blood pressure is a major predictor of SCI neuromotor outcome (Nielson et al., 2015). We recovered, digitized, and curated the data from 1125 rats from MASCIS. Analyses indicated that high perioperative blood pressure at the time of SCI is associated with poorer health and worse neuromotor outcomes in more severe SCI, whereas low perioperative blood pressure is associated with poorer health and worse neuromotor outcome in moderate SCI. These findings confirm and expand prior results that a narrow window of blood-pressure control optimizes outcome, and demonstrate the value of recovering dark data for assessing reproducibility of findings with implications for precision therapeutic approaches.

Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome

“…Previous blood pressure management studies have primarily focused on hypotension as a contributor to worse patient outcome [ 58 – 60 ] despite the increased risk of cardiovascular and cerebrovascular complications as a result of hypertension [ 61 ]. Notably, the importance of perioperative hypertension for SCI outcome has been observed both clinically and preclinically [ 26 – 28 , 62 ]; this is the first analysis to suggest that hypertension is more predictive of worse outcome than hypotension, thus proposing that careful MAP management should strive to avoid hypertension while minimizing hypotension. Future prospective clinical studies should extend the verifiability of the findings throughout broader SCI patient care such as during treatment in the emergency room and intensive care unit.…”

Promoting FAIR Data Through Community-driven Agile Design: the Open Data Commons for Spinal Cord Injury (odc-sci.org)

“…As the SCI community has demonstrated, even in the absence of tightly defined knowledge engineering, it may be possible to extract new knowledge from semi-structured data if modern machine learning analytics are leveraged. Indeed, Nielson et al 2015 andAlmeida et al (2021; in the present issue) demonstrates the utility of analyzing FAIR data even from archival laboratory data (25 years ago) to develop and externally validate new predictors of long term neuromotor recovery. The continuing development of the SCI data commons through odc-sci.org and broader neurocommons efforts will promote ever increasing knowledge through FAIR data sharing across individual researchers, laboratories, species, and perhaps even disease domains.…”

Expert-integrated automated machine learning uncovers hemodynamic predictors in spinal cord injury

“…Previous blood pressure management studies have primarily focused on hypotension as a contributor to worse patient outcome 58–60 despite the increased risk of cardiovascular and cerebrovascular complications as a result of hypertension 61 . Notably, the importance of perioperative hypertension for SCI outcome has been observed both clinically and preclinically 26–28,62 ; this is the first analysis to suggest that hypertension is more predictive of worse outcome than hypotension, thus proposing that careful MAP management should strive to avoid hypertension while minimizing hypotension. Future prospective clinical studies should extend the verifiability of the findings throughout broader SCI patient care such as during treatment in the emergency room and intensive care unit.…”