Abstract
Introduction
Spinal cord injury (SCI) is associated with increased risk of cardiovascular and respiratory morbidity, which are major contributors to rehospitalization and mortality. While overall rehospitalization after SCI is well documented, few studies have focused specifically on early cardiopulmonary readmissions or linked these outcomes with prescription patterns using nationwide registry data.
Methods
We linked the Swedish SCI register (SveReh) to the National Patient Register and Prescribed Drug Register. Adults with new-onset SCI admitted for first-time inpatient rehabilitation between 2016 and 2020 were included (n = 1,196). The cohort was followed for 12 months after discharge. Outcomes were cardiovascular (ICD I) and respiratory (ICD J) readmissions. Kaplan-Meier and Cox regression analyses examined predictors (age, neurological level, and pre-SCI drug use). Prescription changes for cardiovascular drugs (ATC C), respiratory drugs (ATC R), and antibiotics (ATC J01) were analyzed 365 days before and after SCI using McNemar’s test.
Results
Within 12 months, 42 persons (3.5%) were readmitted for cardiovascular and 43 (3.6%) for respiratory causes; >96% remained event-free. Older age predicted both cardiovascular (HR: 1.02 per year, 95% confidence interval [CI]: 1.00–1.05, p = 0.050) and respiratory readmissions (HR: 1.02, 95% CI: 1.00–1.04, p = 0.024). Neurological level was also associated with respiratory readmission, with the highest risk in C1–C4 tetraplegia. Prescription analyses (n = 1,173) showed significant increases in antibiotics (+27.7%), cardiovascular drugs (+7.2%), and respiratory drugs (+3.4%).
Conclusion
Cardiovascular and respiratory readmissions within 1 year after SCI were uncommon but strongly influenced by age, and for respiratory causes, by neurological level. Marked increases in antibiotic prescriptions highlight infection burden, while cardiovascular prescribing reflected age-related comorbidity. These findings identify subgroups who may benefit from targeted preventive follow-up.
Keywords: Spinal cord injury, Respiratory diseases, Cardiovascular disease, Public and global health tools, Drugs and diagnostics
Introduction
Spinal cord injury (SCI) is associated with a heightened risk of cardiovascular and respiratory morbidity, which contributes substantially to long-term mortality and hospital readmission. Individuals with SCI have a high prevalence of hypertension, dyslipidemia, obesity, and impaired pulmonary function, resulting in increased risk of cardiopulmonary events compared with the general population [1–3]. SCI is associated with a markedly increased risk of cardiovascular disease, with cardiometabolic risk factors emerging as early as the first weeks after injury [4, 5]. Monitoring these changes during inpatient rehabilitation is therefore critical [6]. A Swiss study from 2023 also showed that one-third of patients had high-risk cardiovascular profiles before discharge from rehabilitation [7].
Readmission is common after SCI, particularly within the first year post discharge, with urinary tract infections (UTIs), skin complications, and respiratory conditions among the leading causes [8–11]. Long-term studies confirm that secondary health conditions remain persistent drivers of readmission, with more than half of patients experiencing at least one readmission to the hospital within 10 years [12].
Pharmacological treatment is central to SCI management but frequently results in polypharmacy [10, 13]. Prescription burden increases substantially after SCI, with patients requiring more than three times as many medications compared with pre-injury levels [14], raising the risk of drug-related problems, nonadherence, and adverse interactions [15].
Together, these findings highlight the dual burden of chronic disease and secondary complications in SCI, with age, comorbidity, and injury characteristics shaping both medication needs and readmission risk. National registry data provide a unique opportunity to investigate these interrelated challenges. The aim of this study was to investigate respiratory and cardiovascular readmissions and prescription patterns in individuals with SCI in Sweden during the first year after inpatient rehabilitation.
Methods
We have previously identified all adults (≥18 years) with new-onset traumatic or nontraumatic SCI in Sweden between January 1, 2016, and December 31, 2020, using the national quality register Svenskt Register för Rehabiliteringsmedicin (SveReh) (n = 1,224) [16]. By using the unique Swedish Personal Identification Number, information on these individuals was linked by the National Board of Health and Welfare to two national registries: the National Patient Register and the National Prescribed Drug Register.
The index date was defined as the recorded date of injury in SveReh. From the National Patient Register, we extracted all inpatient discharges within 365 days of the index date, along with corresponding diagnoses coded according to International Classification of Diseases, 10th Revision (ICD-10). From the National Prescribed Drug Register, we obtained all dispensations from 365 days before the index date through 2020, restricted to the Anatomical Therapeutic Chemical (ATC) Classification groups C01–C10 (cardiovascular system), R02AB, R03, R06 (respiratory system), and J01 (antibacterials for systemic use). We received all dispensed outpatient prescription medications nationwide, but they did not include medications administered during inpatient care. Consequently, prescription data in this study reflect outpatient pharmacological treatment following discharge from inpatient rehabilitation. Antibiotic use was analyzed at the person level. Individuals were classified as antibiotic users if they had at least one dispensation within ATC group J01 during the specified period; the number of dispensations per individual was not considered. For descriptive purposes, antibiotics dispensed after SCI were further categorized by ATC code into groups commonly used for UTIs and respiratory infections. Antibiotic subgroups were not mutually exclusive, and individuals could be classified into more than one category if they received different types of antibiotics.
From the original rehabilitation cohort of 1,224 persons, 1,196 were included in the present analyses. Three persons were excluded because they were discharged from their first rehabilitation post SCI after the end of the 12-month follow-up window. An additional 25 were excluded due to missing data in the national registers. Thus, the final cohort of 1,196 persons was used for analyses of both hospital readmissions and prescription drug use. Among these, prescription follow-up after SCI was not available for 23 persons injured in 2020 who were discharged after the registry observation period ended on December 31, 2020. These persons were excluded from analyses of prescription changes, leaving 1,173 persons with complete pre- and post-SCI prescription data. For the National Patient Register, follow-up time was defined as the period from discharge after first-time rehabilitation to 365 days post SCI. For the National Prescribed Drug Register, data were available for 365 days before and after SCI but were truncated on December 31, 2020. Persons injured in 2020 therefore had shorter post-SCI follow-up; the 23 persons without any post-SCI prescription data were retained for analyses of pre-SCI use only.
Predictors
Pre-SCI prescription use of cardiovascular (ATC C) and respiratory (ATC R) drugs was used as a proxy for preexisting disease burden. For time-to-event analyses, these binary indicators (any use in the 365 days before SCI: yes/no) were included as covariates together with age at injury and neurological level (tetraplegia C1–C4, tetraplegia C5–C8, paraplegia T1-S3, or unknown).
Because follow-up time varied, we performed a sensitivity analysis restricted to individuals with at least 180 days of follow-up. This approach was used to confirm that the results were not biased by early administrative censoring. Individuals were followed from discharge after first-time inpatient rehabilitation until their first cardiovascular or respiratory readmission or the end of follow-up. Information on deaths during follow-up was not available; individuals without readmission were therefore censored at their last observed follow-up date.
Statistical analyses included descriptive statistics, McNemar’s and Chi-square tests for prescription changes, Kaplan-Meier survival functions, and Cox regression to assess predictors of cardiovascular and respiratory readmissions. Sensitivity analyses excluding persons with shorter follow-up confirmed the stability of results. All statistical analyses were performed using IBM SPSS Statistics software (version 29) for Windows (SPSS Inc., Chicago, IL, USA).
Results
Study Population
During follow-up, 82 of the 1,196 persons had at least one readmission due to cardiovascular (ICD I) or respiratory (ICD J) causes. In the Prescribed Drug Register, 991 persons (82.9%) had at least one prescription within ATC codes C, R, or J01.
The analyzed cohort of 1,196 persons (median age at onset 61 years, range 19–94) and 33.4% were women. The cohort included 344 persons with C1–C4 tetraplegia, 179 persons with C5–C8 tetraplegia, 465 persons with paraplegia, and 208 persons with unknown neurological level of injury; 54.1% traumatic SCI. Detailed cohort demographics have been reported previously [16]. For the National Patient Register, the median follow-up time was 299 days (mean 283, range 3–360), and for the National Prescribed Drug Register, the median follow-up time post SCI was 289 days (mean 265, range 2–360).
Drug Transitions
Among the 1,173 persons with complete pre- and post-SCI prescription data, cardiovascular drug use (ATC C) increased (with 11.5% initiating and 4.3% discontinuing, net +7.2%), while respiratory drug use (ATC R) showed a smaller net increase (10.2% vs. 6.8%, net +3.4%). Antibiotic use (ATC J01) increased markedly, with 36.5% initiating treatment compared to 8.8% discontinuing (net +27.7%) (Fig. 1). Significantly more persons initiated than discontinued treatment across all three drug categories (Table 1).
Fig. 1.
Net prescription changes within ATC classes C, R, and J01 before vs. after SCI. Bars represent net change (% initiations – % discontinuations) in the main cohort and the sensitivity analysis (≥180 days follow-up).
Table 1.
Prescription changes before and after SCI
| Drug group (ATC) | Analysis sample | Initiations, % (n) | Discontinuations, % (n) | Net change, % (n) | p value (McNemar) |
|---|---|---|---|---|---|
| Cardiovascular (C) | Main (n = 1,173) | 11.5 (135) | 4.3 (51) | +7.2% (84) | <0.001 |
| Sensitivity ≥180 day (n = 1,022) | 11.1 (113) | 3.3 (34) | +7.8% (79) | <0.001 | |
| Respiratory (R) | Main (n = 1,173) | 10.2 (120) | 6.8 (80) | +3.4% (40) | 0.006 |
| Sensitivity ≥180 day (n = 1,022) | 9.8 (100) | 6.6 (67) | +3.2% (33) | 0.04 | |
| Antibiotics (J01) | Main (n = 1,173) | 36.5 (428) | 8.8 (103) | +27.7% (325) | <0.001 |
| Sensitivity ≥180 day (n = 1,022) | 36.7 (375) | 7.9 (81) | +28.8% (294) | <0.001 |
Prescription changes within ATC classes C, R, and J01 before and after SCI. Initiations = none→any. Discontinuations = any→none. Net change = initiations minus discontinuations. Results are shown for the main analytic cohort (n = 1,173) and a sensitivity cohort with ≥180 days follow-up (n = 1,022).
In sensitivity analyses restricted to persons with ≥180 days of follow-up (n = 1,022), results were unchanged: cardiovascular drugs +7.8% (11.1% initiations vs. 3.3% discontinuations), respiratory drugs +3.2% (9.8% vs. 6.6%), and antibiotics +28.8% (36.7% vs. 7.9%). Comparing subgroups with <180 vs. ≥180 days of follow-up, prescription change distributions differed for cardiovascular drugs (p < 0.001) but yielded nearly identical net increases in the full cohort (+7.2%) and the ≥180-day group (+7.8%). No significant differences were observed for respiratory drugs. For antibiotics, a small but significant difference was found (p = 0.048), yet net increases were almost identical to the full cohort (27.7% vs. 28.8%). Thus, indicating that the results were robust and not materially influenced by the exclusion of persons with <180 days of follow-up (online suppl. Fig. 1; for all online suppl. material, see https://doi.org/10.1159/000551316).
Prescription changes also varied by characteristics. For cardiovascular drugs, no differences were observed by sex or neurological level, but there was a strong age gradient (p < 0.001), with initiation and continuation most common among older persons. Respiratory drug use differed significantly by sex (p < 0.001) and neurological level (p = 0.004), with men and persons with tetraplegia more likely to initiate or continue treatment, while no age association was observed. For antibiotics, no differences were seen by sex or neurological level, but there was a modest association with age (p = 0.014), with higher use in older persons. Descriptive categorization of post-SCI antibiotic dispensations showed that most individuals receiving antibiotics were treated with agents commonly used for UTIs. Across neurological injury groups, approximately 71–76% of antibiotic-treated individuals received UTI-associated antibiotics. Respiratory-associated antibiotics were prescribed to approximately 21–32% of antibiotic-treated individuals, with numerically higher proportions observed in the C1–C4 tetraplegia group.
Readmission Rates
More than 96% of persons remained free from cardiovascular (96.5%) and respiratory (96.4%) readmissions during the 12-month follow-up (Table 2). Correspondingly, mean event-free survival was 351 days for both outcomes (Kaplan-Meier analysis, 95% confidence interval [CI]: 348–354), while median survival time could not be estimated because fewer than half of the persons experienced an event. In a sensitivity analysis restricted to those with at least 180 days of follow-up (n = 1,123), findings were essentially unchanged, with a mean event-free survival of 355 days and >96% of persons remaining event-free. Three persons had multiple readmissions due to cardiovascular events (1 person had 3 readmissions and 2 persons had 2 readmissions), and among those with respiratory-related readmissions, 8 persons had multiple readmissions (1 person had 4 readmissions, 2 persons had 3 readmissions, and 5 persons had 2 readmissions).
Table 2.
Readmission outcomes
| Outcome | Events, n (%) | Event-free survival at 12 months, % | Mean survival (days, 95% CI) |
|---|---|---|---|
| Cardiovascular (ICD I) | 42 (3.5) | 96.5 | 351 (348–354) |
| Respiratory (ICD J) | 43 (3.6) | 96.4 | 351 (348–354) |
Readmission outcomes within 12 months after SCI. Event-free survival was estimated using Kaplan-Meier analysis. Median survival could not be estimated because fewer than half of the patients experienced an event.
Risk of Cardiovascular-Related Readmissions
Older age was associated with a higher risk of cardiovascular readmission, with each additional year increasing the hazard by 2.4% (HR: 1.02, 95% CI: 1.00–1.05, p = 0.050). Neurological level showed no association. Pre-SCI cardiovascular drug use showed a nonsignificant trend toward higher risk of cardiovascular readmission (HR: 1.53, 95% CI: 0.75–3.15), with wide CIs (Table 3).
Table 3.
Predictors of cardiovascular (ICD I) and respiratory (ICD J) readmissions
| aPredictor | ICD I, HR (95% CI) | p value | ICD J, HR (95% CI) | p value |
|---|---|---|---|---|
| Age (per year) | 1.02 (1.00–1.05) | 0.050 | 1.02 (1.00–1.04) | 0.024 |
| Neurological level (overall) | — | 0.45 | — | 0.027 |
| C5–C8 vs. C1–C4 | 1.22 (0.53–2.79) | 0.64 | 0.40 (0.12–1.39) | 0.15 |
| Para vs. C1–C4 | 0.75 (0.36–1.60) | 0.43 | 0.56 (0.25–1.26) | 0.16 |
| Unknown vs. C1–C4 | 0.54 (0.20–1.50) | 0.24 | 1.63 (0.79–3.33) | 0.19 |
| Pre-SCI cardiovascular drug use (ATC C) | 1.53 (0.75–3.15) | 0.25 | — | — |
| Pre-SCI respiratory drug use (ATC R) | — | — | 1.46 (0.75–2.84) | 0.27 |
Cox regression models for predictors of cardiovascular (ICD I) and respiratory (ICD J) readmissions, using continuous age. Hazard ratios (HRs) are shown with 95% confidence intervals (CIs).
Neurological level reference = C1–C4 tetraplegia.
Risk of Respiratory-Related Readmissions
Older age was independently associated with respiratory readmission, with each additional year of age increasing the hazard by 2.3% (HR: 1.02, 95% CI: 1.00–1.04, p = 0.024). Neurological level was associated with respiratory readmission in the overall model (p = 0.027), with individuals with C1–C4 tetraplegia showing the highest risk. Persons with C5–C8 tetraplegia or paraplegia showed nonsignificant trends toward lower risk compared with those with C1–C4 tetraplegia (HR: 0.40, 95% CI: 0.12–1.39, p = 0.15 and HR: 0.56, 95% CI: 0.25–1.26, p = 0.16). Pre-SCI respiratory drug use was not significantly associated with a higher risk of readmission (HR: 1.46, 95% CI: 0.75–2.84; Table 3).
Discussion
Our findings demonstrate clear shifts in prescription patterns following SCI. Antibiotic use increased substantially, reflecting the high burden of infectious complications, while cardiovascular and respiratory drug use showed more modest but consistent increases. These changes remained in the sensitivity analyses. Prescription patterns also differed by person characteristics: cardiovascular drugs followed a strong age gradient, respiratory drugs were more commonly initiated in men and those with tetraplegia, and antibiotics rose across all groups, with only a modest association with age.
The observed increase in cardiovascular drug use, most pronounced among older individuals, may reflect age-related comorbidity and cardiovascular risk rather than SCI-specific autonomic dysfunction. This pattern contrasts with a Danish study reporting a higher increase in ATC group C drugs among younger persons with spinal cord lesions, which was attributed to premature cardiovascular aging and dysregulated lipid metabolism [14]. In our cohort, the stronger association with age suggests that cardiovascular prescribing was more closely aligned with general population risk factors than with early autonomic impairment [17]. The consistent rise in antibiotic use aligns with previous studies identifying infections as a leading cause of readmission after SCI [9, 12]. Further characterization of antibiotic use indicated that the marked post-SCI increase was predominantly driven by agents commonly used for UTIs, supporting the interpretation that infectious complications related to neurogenic bladder represent a major contributor to post-SCI morbidity. Sweden is internationally recognized for relatively low outpatient antibiotic consumption and restrictive prescribing practices [18, 19]. In this context, the predominance of UTI-associated antibiotics observed in the SCI cohort is unlikely to reflect general national prescribing patterns. Although direct rate comparisons are limited by differences in population structure and morbidity, this shift in distribution supports the interpretation of a disproportionate burden of UTIs following SCI.
Respiratory-associated antibiotics were also prescribed, affecting approximately one-quarter to one-third of antibiotic-treated individuals across neurological injury levels, with numerically higher proportions in individuals with C1–C4 tetraplegia. Respiratory drug prescriptions were less affected by age but more frequent in men and in persons with tetraplegia, reflecting the greater respiratory vulnerability associated with higher-level injuries.
For readmissions, predictors differed between cardiovascular and respiratory causes. Older age was associated with cardiovascular readmissions, consistent with patterns observed in the general population, where cardiovascular morbidity increases with age [5, 17]. A slight trend toward higher readmission among persons with pre-SCI cardiovascular drug use may indicate that preexisting cardiac disease contributes to post-SCI vulnerability. In contrast, respiratory readmissions were driven by both age and neurological level, with the highest risk among older persons with high tetraplegia (C1–C4), underscoring the SCI-specific vulnerability of respiratory function when diaphragmatic and accessory muscles are impaired. These findings are consistent with prior work showing that cardiovascular autonomic impairment and reduced pulmonary function are most pronounced at higher neurological levels [1, 3, 20]. Unlike previous studies that examined overall rehospitalization after SCI [9, 12], our study focused specifically on cardiopulmonary readmissions, providing more detailed insights into the predictors of cardiovascular and respiratory complications. Age emerged as the most consistent predictor of both cardiovascular and respiratory readmissions, underscoring the importance of age-related vulnerability in the early post-SCI period. Pre-SCI use of cardiovascular and respiratory medications was included as a proxy for baseline comorbidity burden, reflecting preexisting disease rather than injury-related complications. Although this approach does not capture comorbidity severity in detail, it provides a pragmatic indicator of underlying health status in registry-based studies.
Together, these findings emphasize that post-SCI cardiovascular management should be tailored not only to neurological injury but also to age-related risk. Older people appear particularly prone to both increased cardiovascular prescribing and readmissions, highlighting the need for preventive screening and proactive risk factor management. Meanwhile, the pronounced association between high-level injuries and respiratory readmissions underscores the importance of early pulmonary monitoring and respiratory training in rehabilitation programs.
The absence of a non-SCI control group limits direct comparison with readmission rates and prescription patterns in the general population. However, the study was designed to examine variation within the SCI population.
Looking forward, advances in artificial intelligence and machine learning hold promise for improving risk prediction after SCI. A recent retrospective cohort study from the USA. SCI Model Systems [21] showed that machine learning models could predict rehospitalization and prolonged hospital stays with good accuracy. Integrating such predictive tools into clinical practice could enable earlier identification of persons of high-risk, support individualized prevention strategies, and ultimately reduce avoidable complications and healthcare utilization. As register-based data become increasingly comprehensive, similar approaches may be applied to complement traditional statistical methods and enhance post-SCI care planning.
Limitations
Several limitations must be considered. The number of readmission events was small, limiting statistical power in multivariable Cox regression analyses and resulting in wide CIs. These analyses should therefore be interpreted as exploratory, particularly for predictors that did not reach statistical significance. Follow-up for persons injured in 2020 was truncated on December 31, 2020, resulting in shorter observation time for this subgroup. Moreover, while pre-SCI medication provided a pragmatic proxy for comorbidities, it cannot fully capture the complexity of a person’s baseline health status. Information on deaths during follow-up was not available. Persons who did not experience a readmission were therefore treated as censored at their last day of follow-up. This approach may underestimate the cumulative incidence of readmission if individuals at the highest risk died before experiencing readmission, as deaths could not be distinguished from other censoring. Antibiotic indications could not be confirmed at the diagnostic level. Categorization was therefore based on ATC codes and common clinical use, and results should be interpreted as descriptive of antibiotic exposure patterns rather than confirmed infection diagnoses. Finally, the study period (2016–2020) predates the COVID-19 pandemic and therefore represents a pre-pandemic baseline for cardiopulmonary readmissions and prescribing patterns after SCI, particularly relevant for respiratory outcomes. Despite these limitations, the consistent associations observed for age and neurological level strengthen the interpretation that older persons and those with high tetraplegia are at particular risk of cardiovascular and respiratory complications after SCI, while post-SCI prescription patterns reflect both age-related comorbidity and SCI-specific risks.
Conclusion
In this nationwide SCI cohort, antibiotic prescriptions increased markedly after injury, reflecting the high burden of infectious complications, particularly UTIs. Cardiovascular and respiratory drugs showed more modest increases. Cardiovascular and respiratory readmissions within 1 year were uncommon but were consistently influenced by age, and for respiratory causes, by high neurological level, underscoring the SCI-specific vulnerability of people with tetraplegia. These findings highlight the importance of targeted preventive strategies, including infection prevention, cardiovascular risk management in older individuals, and proactive respiratory monitoring in persons with high-level injuries. Although event numbers were limited and data predate the COVID-19 era, this study provides important baseline insights into early cardiopulmonary morbidity and outpatient treatment patterns following SCI.
Acknowledgments
During the preparation of this work, the authors used ChatGPT 5 (OpenAI) to support this manuscript with language editing. The authors take full responsibility for the content of the publication.
Statement of Ethics
Ethical approval for the study, including linkage of national registries using the Swedish Personal Identification Number, and exemption from written informed consent, was obtained from the Swedish Ethical Review Authority (reference No. 2022-01300-01) in accordance with the guidelines of the Declaration of Helsinki.
Conflict of Interest Statement
C.J. has previously been, and K.S.S. is currently, a member of the steering group of the SveReh, which provided data for this study. Their roles in the steering group involve oversight of registry development and quality assurance, but did not influence the study design, data extraction, statistical analyses, interpretation of results, or manuscript preparation. Data extraction was performed without their involvement. The authors declare no other conflicts of interest.
Funding Sources
This research has been made available by contributions from the Gothenburg Competence Centre for Spinal Cord Injury, the Swedish governmental funding of clinical research (ALF): ALFGBG-1005518, and Anna Lisa och Bror Björnsson foundation, none of which have had any influence on the content of the study.
Author Contributions
C.J. analyzed the data. C.J. and K.S.S. interpreted the data and prepared and revised the manuscript content. Both authors have given their final approval of the version to be submitted.
Funding Statement
This research has been made available by contributions from the Gothenburg Competence Centre for Spinal Cord Injury, the Swedish governmental funding of clinical research (ALF): ALFGBG-1005518, and Anna Lisa och Bror Björnsson foundation, none of which have had any influence on the content of the study.
Data Availability Statement
The data that support the findings of this study are not publicly available due to legal and ethical restrictions under Swedish data protection regulations. The individual-level data contain sensitive health information that could compromise participant privacy. Access to the data requires approval from the relevant Swedish authorities, including the National Board of Health and Welfare and the Swedish Ethical Review Authority. Data are, however, available from the corresponding author C.J. upon reasonable request.
Supplementary Material.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study are not publicly available due to legal and ethical restrictions under Swedish data protection regulations. The individual-level data contain sensitive health information that could compromise participant privacy. Access to the data requires approval from the relevant Swedish authorities, including the National Board of Health and Welfare and the Swedish Ethical Review Authority. Data are, however, available from the corresponding author C.J. upon reasonable request.