Etiology-specific variation in survival following non-traumatic spinal cord injury: a causal inference approach using data from a population-based cohort

Buzzell, Anne; Chamberlain, Jonviea D; Eriks‐Hoogland, Inge; Jordan, Xavier; Schubert, Martin; Zwahlen, Marcel; Brinkhof, Martin W. G.

doi:10.1038/s41393-020-00554-9

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…Therefore, greater medical support and the concept of “shared care” are needed to achieve positive health outcomes (Buzzell et al, 2021). Efforts to integrate care involving acute and rehabilitation stages for persons with SCI are ongoing, but fragmentation still holds, and many of these efforts have not been successful (Ho, 2016).…”

Section: Discussionmentioning

confidence: 99%

Clinical Prediction Rule Validation for Ambulation Outcome After Traumatic Spinal Cord Injury in a Spanish Population

García-Rudolph

Wright

Benito

et al. 2022

Journal of Trauma Nursing

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BACKGROUND: Predicting the ability to walk after traumatic spinal cord injury is of utmost importance in the clinical setting. Nevertheless, only a small fraction of predictive models are evaluated on their performance by other authors using external data. The Dutch Clinical Prediction Rule for long-term walking ability was developed and validated using neurological assessments performed within 15 days postinjury. However, in reality, this assessment is most often performed between 11 and 55 days. When considering a longer time from injury to neurological assessments, the Dutch Clinical Prediction Rule has only been externally validated for patients after non–traumatic spinal cord injury. OBJECTIVE: We aimed to validate the Dutch Clinical Prediction Rule with neurological assessment performed within 3–90 days after traumatic spinal cord injury, using (a) the Dutch Clinical Prediction Rule logistic regression coefficients (Equation 1); (b) the Dutch Clinical Prediction Rule weighted coefficients (Equation 2); and (c) the reestimated (using a Spanish population) weighted coefficients (Equation 3). METHODS: We conducted a retrospective (STROBE-compliant) study involving 298 adults with traumatic spinal cord injury admitted to a hospital between 2010 and 2019 in Spain. The Spinal Cord Independence Measure item-12 was used for walking assessment. RESULTS: Using Equation 1, the model yielded 86.2% overall classification accuracy, 94.5% sensitivity, and 83.4% specificity (area under the curve [AUC] = 0.939, 95% confidence interval [CI]: 0.915–0.965; p < .001). Using Equation 2 yielded 86.2% overall classification accuracy, 93.2% sensitivity, and 83.9% specificity (AUC = 0.9392, 95% CI: 0.914–0.964; p < .001). Using Equation 3 yielded 86.9% overall classification accuracy, 68.9% sensitivity, and 92.8% specificity (AUC = 0.939, 95% CI: 0.914–0.964; p < .001). CONCLUSIONS: This study validates the Dutch Clinical Prediction Rule in a Spanish traumatic spinal cord injury population with assessments performed up to 90 days postinjury with similar performance, using the original coefficients and including a reestimation of the coefficients.

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Section: Discussionmentioning

confidence: 99%

Clinical Prediction Rule Validation for Ambulation Outcome After Traumatic Spinal Cord Injury in a Spanish Population

García-Rudolph

Wright

Benito

et al. 2022

Journal of Trauma Nursing

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“…A spinal cord injury (SCI) occurs when the spinal cord is damaged either by trauma (e.g., car crash, falls; referred to as traumatic SCI [TSCI]) or through internal damage (e.g., degenerative, neoplastic, or infectious conditions; referred to as non-traumatic SCI [NTSCI]). The resulting injury can impair motor, sensory, and autonomic functions (1)(2)(3)(4), and multiple body systems can be affected. Following a SCI individuals may experience complications such as spasticity, urinary tract infections, pneumonia, pressure injuries, and pain (5)(6)(7).…”

Section: Introductionmentioning

confidence: 99%

Network analysis of multimorbidity and health outcomes among persons with spinal cord injury in Canada

Fallah,

Hong,

Wang

et al. 2024

Front. Neurol.

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IntroductionMultimorbidity, defined as the coexistence of two or more health conditions, is common in persons with spinal cord injury (SCI). Network analysis is a powerful tool to visualize and examine the relationship within complex systems. We utilized network analysis to explore the relationship between 30 secondary health conditions (SHCs) and health outcomes in persons with traumatic (TSCI) and non-traumatic SCI (NTSCI). The study objectives were to (1) apply network models to the 2011–2012 Canadian SCI Community Survey dataset to identify key variables linking the SHCs measured by the Multimorbidity Index-30 (MMI-30) to healthcare utilization (HCU), health status, and quality of life (QoL), (2) create a short form of the MMI-30 based on network analysis, and (3) compare the network-derived MMI to the MMI-30 in persons with TSCI and NTSCI.MethodsThree network models (Gaussian Graphical, Ising, and Mixed Graphical) were created and analyzed using standard network measures (e.g., network centrality). Data analyzed included demographic and injury variables (e.g., age, sex, region of residence, date, injury severity), multimorbidity (using MMI-30), HCU (using the 7-item HCU questionnaire and classified as “felt needed care was not received” [HCU-FNCNR]), health status (using the 12-item Short Form survey [SF-12] Physical and Mental Component Summary [PCS-12 and MCS-12] score), and QoL (using the 11-item Life Satisfaction questionnaire [LiSAT-11] first question and a single item QoL measure).ResultsNetwork analysis of 1,549 participants (TSCI: 1137 and NTSCI: 412) revealed strong connections between the independent nodes (30 SHCs) and the dependent nodes (HCU-FNCNR, PCS-12, MCS-12, LiSAT-11, and the QoL score). Additionally, network models identified that cancer, deep vein thrombosis/pulmonary embolism, diabetes, high blood pressure, and liver disease were isolated. Logistic regression analysis indicated the network-derived MMI-25 correlated with all health outcome measures (p <0.001) and was comparable to the MMI-30.DiscussionThe network-derived MMI-25 was comparable to the MMI-30 and was associated with inadequate HCU, lower health status, and poor QoL. The MMI-25 shows promise as a follow-up screening tool to identify persons living with SCI at risk of having poor health outcomes.

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“…The fragmentation of services and the lack of integrated care across physicians also pose a challenge to meeting the health-care needs of persons with complex medical conditions [13]. Therefore, greater medical support and the concept of 'shared care' are needed to achieve positive health outcomes [14][15][16]. Efforts have been made to reach a consensus on health concepts and modes of providing primary care for persons with SCI, but these have not been successful [4].…”

Section: Introductionmentioning

confidence: 99%

First Contact of Care for Persons With Spinal Cord Injury: A General Practitioner or a Spinal Cord Injury Specialist?

Touhami

Brach

Essig

et al. 2021

Preprint

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Background. Inter-professional collaboration between general practitioners (GPs) and spinal cord injury (SCI) specialists is needed to improve the health outcomes of persons with SCI. The objective of this study is to understand the differences in long-term care provision by GPs and SCI-specialists, by examining (1) the first contact of care for SCI health problems, (2) the morbidity profile and use of health-care services in relation to first contact, and (3) the factors associated with the choice of first contact.Methods. In this cross-sectional study based on data derived from the Swiss Spinal Cord Injury Cohort Study Community Survey 2017, the main outcome measure was the reported first contact for SCI-specific care. This information was analysed using the chi-square test and logistic regression analysis of groups based on patient characteristics, use of health-care services and secondary health conditions assessed using the Spinal Cord Injury Secondary Conditions Scale (SCI-SCS). Results. Out of 1294 respondents, 1095 reported their first contact for SCI-specific care; 56% indicated SCI-specialists and 44% specified GPs. The mean ± standard deviation of the total SCI-SCS was significantly lower in persons who initially consulted GPs (13.1±7.7) than SCI-specialists (14.6±7.3). On average, participants who first contacted a GP reported higher number of GP consultations (5.1±5.2 vs. 3.9±7.2), planned visits to ambulatory clinics (3.7±7.3 vs. 3.6±6.7) and hospital admissions (GP, 1.9±1.7 vs. 1.5±1.3), but lower number of hospital days (22.8±43.2 vs. 31.0±42.8). The likelihood to contact a GP first was significantly higher in persons ≥75 years old (OR= 4.78, 95%-CI= 1.99–11.50), Italian speakers (OR= 5.11, 95%-CI= 2.47–10.54), had incomplete lesions (OR 2.51, 95%-CI 1.78–3.54) or diabetes mellitus (OR= 1.81, 95%-CI= 1.03–3.18), but lower for those situated closer to SCI centers (OR= 0.69, 95%-CI= 0.51–0.93) or experiencing bowel dysfunction (OR= 0.71, 95%-CI= 0.52–0.98).Conclusion. The choice of first contact for SCI-specific care may be partly explained by the complementary roles of GPs and SCI specialists, but the reasons for variation are still unclear. Developing a transparent shared-care model between GPs and SCI specialists, with defined roles and responsibilities, may improve the management of secondary health conditions.

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Etiology-specific variation in survival following non-traumatic spinal cord injury: a causal inference approach using data from a population-based cohort

Cited by 7 publications

References 29 publications

Clinical Prediction Rule Validation for Ambulation Outcome After Traumatic Spinal Cord Injury in a Spanish Population

Clinical Prediction Rule Validation for Ambulation Outcome After Traumatic Spinal Cord Injury in a Spanish Population

Network analysis of multimorbidity and health outcomes among persons with spinal cord injury in Canada

First Contact of Care for Persons With Spinal Cord Injury: A General Practitioner or a Spinal Cord Injury Specialist?

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