Impact of clinical and sociodemographic factors on fatigue among patients with substance use disorder: a cohort study from Norway for the period 2016-2020

Jørn Henrik Vold
for the INTRO-HCV Study Group

Rolf Gjestad
for the INTRO-HCV Study Group

Christer F Aas
for the INTRO-HCV Study Group

Fatemeh Chalabianloo
for the INTRO-HCV Study Group

Svetlana Skurtveit
for the INTRO-HCV Study Group

Else-Marie Løberg
for the INTRO-HCV Study Group

Kjell Arne Johansson
for the INTRO-HCV Study Group

Lars Thore Fadnes
for the INTRO-HCV Study Group

Impact of clinical and sociodemographic factors on fatigue among patients with substance use disorder: a cohort study from Norway for the period 2016-2020
10.1186/s13011-020-00334-xR E S E A R C H Open Access
Background: The impact of clinical and sociodemographic factors on fatigue remains unknown among patients with substance use disorders (SUD). This study aims to evaluate fatigue among patients with SUD using a nine-item fatigue severity scale (FSS-9) and identify the impact that clinical and sociodemographic factorssuch as injecting substance use, chronic infectious diseases, liver fibrosis, opioid agonist therapy (OAT), debt difficulties, and housing situationhave on fatigue.Methods: We used data from a cohort of patients with SUD in Norway with annual health assessments surveying FSS-9 and some clinical and sociodemographic factors. A total of 915 FSS-9 measurements were collected from 654 patients during the period 2016-2020. We defined baseline as the first annual health assessment when the health assessments were listed chronologically. Time was defined as years from baseline. We used a linear mixed model to analyse whether the clinical and sociodemographic factors affected the FSS-9 sum score, presented with beta coefficients (β) with 95% confidence intervals (CI).Results:The mean sum score of the FSS-9 was 43 (standard deviation: 16) at baseline. Females compared with males (adjusted mean difference of FSS-9 sum score: 4.1, 95% CI: 1.3-7.0), having debt difficulties compared with having no debt difficulties (2.9;0.4-5.3), and frequent use of benzodiazepines (5.7;3.0-8.4) or amphetamines (-5.0;-8.0--2.0) compared to less frequent or no use of these substances changed the FSS-9 baseline sum score. The other clinical and sociodemographic factors did not predict any clinically relevant change in the FSS-9 sum score from baseline to the following health assessments.Conclusion: Patients with SUD suffer from high levels of fatigue. Female patients, patients with debt difficulties, and those with extensive use of benzodiazepines are at particular risk of being fatigued. This should be taken into consideration when planning health services.Hematological and biochemical samples, median (IQR)Hemoglobin(g/dl) 14 (13-15) 14 (13-15) Estimated glomerulus filtration rate (ml/min/1.73 m 2 ) 104 (89-122) 105 (91-124) C-reactive protein (mg/L) 4 (1-9) 3 (1-8) Aspartate transaminase (U/L) 31 (23-50) 40 (30-65) Vold et al. Substance Abuse Treatment, Prevention, and Policy (2020) 15:93

Background

Patients with Substance Use Disorders (SUD) suffer from a broad range of health-related difficulties that may contribute to fatigue [1][2][3]. Fatigue presents itself as a persistent and overwhelming feeling of exhaustion and loss of energy. The condition is mainly associated with chronic diseases and may mitigate treatment adherence and exacerbate comorbid disorders [4,5]. In SUD populations, a myriad of external factors can interact with fatigue and affect these patients' general well-being [6][7][8].

Injecting substance use, internal organ dysfunctions (predominantly kidney and liver diseases), mental disorders, as well as low income, unemployment, and homelessness are some of the external factors that interact with fatigue. Despite this, relatively little attention has been paid to the extent of fatigue and how much various external factors influence fatigue among patients with SUD. Therefore, understanding the key factors affecting fatigue is essential to improve treatment outcomes and adherence in this population.

Fatigue is associated with several sociodemographic and clinical factors. Among patients with the Hepatitis C Virus (HCV) infection, 50-70% have reported fatigue [9][10][11], while 33-88% of those with the Human Immunodeficiency Virus (HIV) infection have presented the same symptom [12]. A more uncertain prevalence of fatigue is seen among patients with the Hepatitis B Virus (HBV) [13,14]. In addition, females, patients with lower educational levels, and those with opioid use disorders undergoing Opioid Agonist Therapy (OAT) with methadone or buprenorphine generally have a greater risk of fatigue [15,16]. Disentangling the effects of the potential factors influencing fatigue in patients with SUD is essential for individualised treatment and developing clinical guidelines.

Fatigue is a subjective concept, and various definitions and instruments are used in the literature to capture it, which makes interpretations more complicated [17][18][19]. The nine-item fatigue severity scale (FSS-9) is a wellknown questionnaire used to quantify fatigue treatment effects. It shows excellent validity and reliability across various chronic neurological and infectious diseases, such as multiple sclerosis [17], HCV infection [20], stroke [5], and Parkinson's disease [21]. The fact that FSS-9 shows a high consistency across various chronic diseases makes it particularly suitable to estimate fatigue among patients suffering from SUD with complex and challenging comorbidities.

Thus, this prospective cohort study aims to investigate fatigue using the nine-item Fatigue severity scale (FSS-9) among patients with substance use disorders (SUDs) and predict the impact of sociodemographic and clinical factors on FSS-9, including educational level, housing situation, debt difficulties, chronic infectious diseases, injecting substance use, substance use, liver fibrosis, and kidney disease. Moreover, we estimate: 1) using annual health assessments, the FSS-9 sum score and whether and to what extent the sociodemographic and clinical factors impact this score; 2) the impact of sociodemographic and clinical factors on changes in the FSS-9 sum score from the first health assessment to the following annual health assessments; 3) for two separate subgroupspatients receiving methadone as opioid agonist therapy (OAT) and those receiving buprenorphine as OATthe FSS-9 sum score, whether and to what extent the sociodemographic and clinical factors affect the FSS-9 sum score at baseline, and any changes in the FSS-9 sum score from the first health assessment to the following annual health assessments.

Methods

Data source

We used data from a cohort nested to the INTRO-HCV trial on patients with SUD in Bergen and Stavanger, Norway [22]. We collected data from May 2016 to January 2020, and recruited patients on OAT from outpatient clinics in Bergen and Stavanger, as well as patients with various SUDs receiving primary healthcare from the municipality clinics in the city of Bergen.

Data collections

All included patients were assessed yearly with a health assessment, including FSS-9 measurements, sociodemographic data, and current substance use. Additionally, blood samples and liver fibrosis measurements using transient elastography were conducted. We collected all data in a health register using electronic data collection software (Checkware®) under research nurses' supervision. All the clinical data, including information regarding OAT, OAT medication, substance use, and possible comorbid clinical conditions, were collected from the electronic medical record.

Study sample

We included 915 FSS-9 measurements from 654 patients in the study period. In total, 225 had follow-up data and conducted the health assessment, including the FSS-9 questionnaire, twice (n = 188) or thrice (n = 37), providing 487 repeated measurements. The median time interval between the baseline health assessments, and any subsequent assessments in the same patients, including FSS-9 measurements, was 11 months (interquartile range (IQR): 9-14) (Additional file 1).

Measuring fatigue

We measured fatigue during the last week using FSS-9, including items considering: mental and physical functioning, motivation, carrying out duties, and interference with work, family, or social life. An FSS-9 measurement was completed when all nine items in the questionnaires were entirely conducted during an annual health assessment. The FSS-9 items were answered on a Likert scale ranging from 1 (no fatigue) to 7 (worst fatigue)that demonstrates the fatigue level. A high score of FSS-9 items notes a high level of fatigue, while a mean FSS-9 item score greater than 4.0 revealed severe fatigue. The data collection software only allowed valid responses to each question and prompted empty questions before submission to minimise missing data. The FSS-9 was also translated and back-translated from the US-English version into Norwegian by qualified native Norwegianspeaking translators (Additional file 2) [23].

Measuring liver stiffness and assessing blood samples

We assessed liver stiffness using transient elastography (Fibroscan®) to reveal liver fibrosis and cirrhosis. The elastography was reported as a median score of 10 measurements conducted by research nurses. A liver stiffness above 10 kilopascals (kPa) was defined as liver fibrosis, while a value above 12.5 kPa indicated liver cirrhosis [24]. We also collected blood samples, including hemoglobin, thrombocytes, C -reactive protein, aspartate aminotransferase, estimated glomerular filtration rate, hepatitis B surface antigen, HIV antigen/antibodies, HCV antibodies, and HCV polymerase chain reaction (HCV PCR) during the annual health assessment. Liver stiffness was estimated by calculating the AST to platelet ratio index (APRI) score and using transient elastography (Fibroscan®) (Additional file 3). Moreover, the hematological and biochemical samples were analysed to detect anemia (Hemoglobin), infection or inflammation (Creactive protein), kidney disease (estimated glomerular filtration rate), liver disease (APRI), or chronic infectious diseases (HIV, HCV, and HBV), which could affect the FSS-9 score. Both elastography and blood samples were examined annually and simultaneously when conducting the annual health assessments. We analysed the blood samples at the Department of Laboratory Medicine, Haukeland University Hospital, Bergen, Norway, and at the Department of Medical Biochemistry and Microbiology, Stavanger University Hospital, Stavanger, Norway (accredited by ISO-standard 15,189).

Definition of study variables, including sociodemographic and clinical factors

We defined baseline for patients as the first annual health assessment that included an FSS-9 measurement when we listed the health assessments chronologically.

We dealt with each FSS-9 measurement as a sum score by summarising the value (one to seven) from each item and as a mean score calculated by dividing the sum score by nine (nine items). We defined being on OAT according to whether patients received buprenorphine or methadone (OAT opioids) at baseline. Further, in accordance with the World Health Organization's standards, we calculated the daily dose of received OAT opioids as a ratio between the received dose per day divided by the expected mean dose of OAT opioids (buprenorphine 18 mg, buprenorphine-naloxone 18/4.5 mg or methadone 90 mg) [25]. We categorised educational level into five groups: 'not completed primary school,' 'completed primary school (nine years),' 'completed high school (12 years),' 'three or fewer years of college or university' or 'more than three years of college or university.' Patients' housing situations in the 30 days prior to the FSS-9 measurement were classified into two groups: "stable" and "unstable." The latter category involved patients who had lived on the street, in a homeless shelter, or with family and friends during the past 30 days. Others who had a more permanent residence were classified as having a stable housing situation. Debt difficulties were defined as striving with paying off legal or illegal debt due to a constrained private economy. We set 'injecting substance use' as having injected at any time during the past 12 months, whereas frequent substance use was categorised as consuming at least one of the substance groups, including 'benzodiazepines or zhypnotics,' 'cannabis,' 'stimulants (amphetamines or cocaine),' 'alcohol,' and 'heroin or other illicit opioids', more than weekly during the 12 months prior to a health assessment. Patients who did not use substances or used them less than weekly during the past 12 months were categorised as having 'no frequent use of substance'. Having chronic infectious diseases was defined as detecting HCV PCR (HCV), hepatitis B surface antigen (HBV), or HIV antigen/antibodies (HIV) in the blood samples. For HCV PCR, we used the Helmert contrast in order to classify patients into two groupstransmitted and nontransmittedand further into two subgroups: whether patients have a low viral load (< 800,000 IU/ml) or high viral load (≥ 800,000 IU/ml). By this two-fold division, we investigated whether the level of viral HCV load was associated with changes in the fatigue level.

Statistical analyses

We used Stata/SE 16.0 (StataCorp, TX, USA) for descriptive analysis and IBM SPSS version 26.0 for expectation-maximisation imputation and linear mixed model analyses. The threshold for statistical significance was set to P < 0.05 for all analyses unless otherwise stated. In all analyses, we defined time as years from baseline.

We dealt with any missing values concerning sociodemographic and clinical factorssuch as educational level, housing situation, debt difficulties, receiving OAT, OAT opioid dose ratio, injecting substance use, substance use, and the results of defined blood samples and transient elastographyas 'missing at random' when running expectation-maximisation imputation. We identified missing values in 2.6% in these factors and all were replaced with estimated values by imputation.

The FSS-9 sum score at baseline was calculated by summarising the nine items' points. Linear mixed model analyses were used to investigate whether the sociodemographic and clinical factors affected the FSS-9 sum score and to what extent they impacted any changes in the score from baseline to following the health assessments. First, the factor variables were analysed separately as outcome variables as a function of the time (time from baseline). We did not identify substantial significant changes in the sociodemographic and clinical factors between the annual health assessments (data not shown). Thus, baseline levels were used as stable predictors in the prediction of the level and changes in FSS-9. We specified the linear mixed models as a random intercept fixed slope regression model. The estimator was set to Restricted Maximum Likelihood. To explore whether predictors predicted changes in outcome, the interactions between these factors and time were added to the model. The full information maximum likelihood ensured that all available FSS-9 sum score measurements were used. Additionally, we presented sub-group analyses for OAT patients using methadone or buprenorphine, respectively. For these analyses, we added the OAT opioid ratio as a predictor. The potential correlations between sociodemographic and clinical factors and fatigue are presented in Additional file 4. We performed a sensitivity analysis by adding Bonferroni corrected pvalues to adjust for Type I errors in all analyses.

Ethics approval and consent to participate

The study is reviewed and approved by the Regional Ethical Committee for Health Research West, Norway (REK Vest 2017/51). Each patient provided written informed consent prior to enrolling in the study.

Results

Patients characteristics at baseline

Seventy-one percent of patients were male, and the mean age was 43 years (standard deviation (SD): 11 years) at baseline (Table 1). Six percent had not completed primary school, or 44% had primary school as their highest educational level. 82% received OAT, of which 60% received buprenorphine or buprenorphine-naloxone as an OAT opioid. Further, 13% had an unstable housing situation in the last 30 days leading up to the FSS-9 measurement. 73% had used at least one substance weekly during the past 12 months.

FSS-9 sum scores at baseline

The mean sum score for the FSS-9 was 43 (SD: 16), representing a mean score for the FSS-9 items of 4.8 (2.6) ( Table 2). A total of 69% of patients had severe fatigue. The mean FSS-9 sum score was slightly leftskewed (skewness: − 0.7) and tended towards a flattened distribution (kurtosis: 2.4).

The mean scores for the FSS-9 were 43 (SD: 16) for patients receiving methadone and 43 (17) for those using buprenorphine (Additional file 5), corresponding to a mean score for the FSS-9 items of 4.8 (1.8) for patients receiving methadone and 4.7 (1.9) for those using buprenorphine. Severe fatigue was identified in 77% of patients receiving methadone and 67% of those using buprenorphine. In these two sub-groups, the FSS-9 sum scores were slightly leftskewed (skewness: − 1.0 (methadone group), − 0.6 (buprenorphine group)) and flattened distributed (kurtosis: 3.0 (methadone group), 2.2 (buprenorphine group)).

The sociodemographic and clinical factors' impact on the FSS-9 sum score at baseline and the factors' influence on changes in the FSS-9 sum score from baseline to the following annual health assessments At baseline, we found that the FSS-9 sum score was higher for females than males (adjusted mean FSS-9 sum score difference: 4.1, CI 1.3;7.0, p = 0.005), for patients with debt difficulties compared with those without debt difficulties (2.9, CI 0.4;5.3, p = 0.022), and for patients with frequent benzodiazepine use compared with those with less frequent or no use (5.7, CI 3.0;8.4, p < 0.001) ( Table 3). In contrast, the FSS-9 sum score was lower for patients with frequent stimulant use than those with less frequent or no use (− 5.0, CI -8.0;-2.0, p = 0.001). Moreover, we saw a small non-clinical significant reduction of the FSS-9 sum score from baseline to the following annual health assessments for patients with frequent benzodiazepine use compared to those with less frequent or no use (− 4.4, CI -8.2;-0.7, p = 0.021) and for patients having significant liver fibrosis or cirrhosis measured by transient elastography compared with those with nonsignificant fibrosis or normal liver stiffness (− 5.5, CI -9.9;-1.0, p = 0.016). With Bonferroni corrected p-values, we only found that patients with frequent benzodiazepine or stimulant use compared with those with less frequent or no use of these substances changed the fatigue levels at baseline.

The sociodemographic and clinical factors' impact on changes in the FSS-9 sum score from baseline to the following annual health assessments among patients on OAT Among patients receiving methadone as an OAT opioid, we found that the FSS-9 sum score was higher for  (29) 55 (24) Highest educational level, n (%)

Not completed primary school 40 (6)  15 (7) Completed primary school (9 years) 286 (44) 105 (47) Completed high school (12 years . Among patients receiving buprenorphine as an OAT opioid, we found that patients with frequent alcohol use had higher the FSS-9 sum score (4.8, CI 0.2;9.3, p = 0.039), while patients with frequent stimulant use had lower the FSS-9 score (− 5.0, CI -9.9;-0.1, p = 0.047) compared with patients with less frequent or no use of these substances at baseline (Additional file 7). For both subgroups, no sociodemographic and clinical factors were clinically associated with substantial changes in the FSS-9 sum score from baseline to the following annual health assessments. With Bonferroni corrected p-values, we did not identify any predictors that changed the fatigue score at baseline and between the annual health assessments.

Discussion

This study showed that 69% of SUD patients had severe fatigue symptoms. The sociodemographic and clinical factors that substantially contributed to higher fatigue scores at baseline were females compared with males (four points), frequent benzodiazepine use compared with less frequent or no use (six points), and debt difficulties compared with no debt difficulties (three points). However, the fatigue score was five points lower for patients with frequent stimulant use than those with less frequent or no use. For patients using buprenorphine as an OAT opioid, we found five points lower fatigue score for patients with frequent stimulant use and five points higher fatigue score for patients with frequent alcohol The table displays the sociodemographic and clinical characteristics for all included patients, and for patients with two or more health assessments, including FSS-9 measurements at baseline FSS-9 nine-item fatigue severity scale (Likert scale), IQR interquartile range, kPa kilopascal, OAT opioid agonist therapy, SD standard deviation a OAT opioid ratio is a ratio between the received dose of OAT opioids per day and the expected median daily dose (18 mg buprenorphine, 18/4.5 mg buprenorphine-naloxone or 90 mg methadone). A ratio on 1.0 indicates that patients received the expected daily dose; b A stable housing situation was defined as having owned or rented housing situation or being imprisoned; c Unstable housing situation was defined as living in a homeless shelter, with family or friends, or on the street; d Frequent substance use was defined as using substance at least weekly during the past 12 months Table 2 Mean (Standard deviation (SD)) item scores for single items on FSS-9 at baseline and follow-up  Follow-up: FSS-9 score on the last health assessment during the study period among patients with two or more annual health assessments; FSS-9 nine-item fatigue severity scale (Likert scale ranging from 1 (no fatigue) to 7 (worst fatigue)), I Item, SD standard deviation use compared with those with less frequent or no use of these substances at baseline. For patients receiving methadone as an OAT opioid, the fatigue score was higher for females than males (seven points), for patients with frequent benzodiazepine use compared with those with less frequent or no use (six points), for patients with debt difficulties compared with those without difficulties with debt (five points), and for patients with a high versus a low viral load of HCV (32 points) at baseline. The latter finding suggesting an extreme difference between a high and a low viral load of HCV was surprising. Other studies assessing HCV viral load and correlation based on clinical and histological features have also not found HCV viral load to impact other related outcomes [26][27][28]. This finding is likely related to random variability within the data, and it should be interpreted with caution. Moreover, no sociodemographic and clinical factors were associated with clinically significant changes in the fatigue score from baseline to the following health assessments.   In the present study, patients with SUD had a mean fatigue score (4.8) comparable to some of the most severe chronic diseases. In recent studies, patients infected with HIV or HCV, or those co-infected with both of them, have a mean FSS-9 score that ranged from 3.3 to 4.5 [10,29,30]. Patients with myasthenia gravis have reported a comparable fatigue score of 4.7 [31], and similarly, so have patients who have suffered from a stroke at least 6 months after the stroke onset (4.8) [32]. One can assume that a high prevalence of underlying mental disorders, extensive polysubstance use, and lower social status could have attributed to the high level of fatigue in the SUD population.

We found that females were weakly more fatigued than males. For both genders, patients with frequent use of benzodiazepines were weakly more fatigued than those with less frequent or no use. Recent studies evaluating fatigue in the general population and patients with chronic disorders have demonstrated similar gender differences in fatigue levels [15,23,31,33]. Gender inequalities regarding household responsibilities and caring for the family have generally been highlighted as explanations for females' fatigue levels [34]. Additionally, females with SUD may be worse off than males in many domains. They may have less financial resources, experience more physical trauma caused by exchanging sex for drugs and money, and face more stigma related to family failures [34]. Moreover, in the general population and among patients with SUDs, females are more likely to use benzodiazepines than males, with a similar tendency found in different countries [2,[35][36][37][38]. Females' higher prevalence of anxiety disorders, sleeping disorders and the fact that they are more likely to seek medical care may contribute to more prescriptions of hypnotics and anxiolytics, such as benzodiazepines and zhypnotics [39][40][41]. One can believe that these medical, psychological, and social challenges may overall explain the gender gap concerning a higher fatigue level among females in the SUD population.

Our findings revealed that patients with frequent use of benzodiazepines were weakly more fatigued than those with less frequent or no use. Among patients with frequent use of stimulants, we found that they were less fatigued than those with less frequent or no use. These group differences were overall small. In the general population, previous studies assessing the effect of benzodiazepine use have suggested that patients using benzodiazepines have a lower quality of life, selfreported physical health, and more disability than those not using benzodiazepines [42,43]. This is parallel with the higher fatigue levels shown in the present study. Furthermore, using stimulants, particularly illicit amphetamines, is generally associated with poor mental health and stimulant withdrawal symptoms in the SUD populations [44,45]. A temporary sense of better self-perceived mental health and fewer withdrawal symptoms may arise when consuming stimulant substances, which contributes to a temporary reduction of fatigue compared to the experience without stimulants. However, our trend analyses indicate that the fatigue levels remained stable over time when the frequent use of stimulants is persistent.

The present study showed no clear associations between fatigue and chronic infectious diseases or kidney disease. For patients with HBV, HIV or end-stage kidney disease, the low prevalence of HBV and HIV and a mean renal function within the normal range could explain why no associations with fatigue were detected. Furthermore, we are surprised that SUD patients with HCV infections did not demonstrate a higher fatigue level compared to patients with SUD without HCV infection, considering that the prevalence of fatigue is up to 70% in populations with HCV [9][10][11], which is considerably higher than in general population [23]. However, the large extent of polysubstance use in the present population (75%) could have temporarily displaced the HCV infection's change on fatigue.

We found that 77% of patients using methadone as an OAT opioid and 67% of those using buprenorphine had severe fatigue symptoms. Four sociodemographic and clinical factors significantly changed fatigue levels among methadone users, while two factors influenced fatigue among those using buprenorphine. Methadone is a full opioid agonist associated with more euphoria and analgesia than the partial opioid agonist buprenorphine [46]. In a quantitative study evaluating patients' experience of using methadone and buprenorphine in OAT, unwanted physical effects, for example, over-sedation, were particularly pointed out in some methadone cases [47]. These effects might explain methadone users' high prevalence of severe fatigue symptoms and why more sociodemographic and clinical factors influenced methadone users' the fatigue levels than those using buprenorphine.

Overall, no single sociodemographic and clinical factor was associated with substantial changes in fatigue at baseline or over time in the SUD population. This signifies that fatigue was substantially constant between patients. However, the mean fatigue level significantly exceeded the threshold for severe fatigue in the SUD population, which underlies the importance of identifying patients who simultaneously have several sociodemographic and clinical risk factors for severe fatigue. Identifying these patients and treating the underlying causes of fatigue should be the way to reduce the fatigue in the population.

Strengths and limitations

This study has several strengths. We have included 654 patients with SUD that usually are difficult to reach in health care. Of those, 225 patients were followed up by two or three annual health assessments, making longitudinal analyses possible. This study does, however, have some limitations. First, the patients were mainly recruited from outpatient clinics receiving OAT. The majority had opioid dependence, although this was often combined with other dependencies, which could affect the generalisability of our results to other SUD populations. Second, we had a prospective follow-up of only a third of those patients recruited at baseline. This also causes weakness in our results and makes it necessary to carefully interpret the longitudinal analyses. Third, due to clinical challenges, including systematic and patient delays, the annual health assessments were not precisely conducted one year after the previous health assessment. This may affect the interpretation of the predicted fatigue level changes from baseline. Fourth, due to data imputation and inclusion of up to 42 predictors in the linear mixed model analyses, there is a risk of Type I error. We dealt with this by presenting the Bonferroni corrections to all p-values in the analyses.

Conclusion

The present study shows a high symptom burden of fatigue among patients with SUDs, particularly among females, patients with debt difficulties, and those with extensive use of benzodiazepines. Identifying severe fatigue and considering fatigue in the follow-ups could help optimise SUD treatment for these patients. Policymakers could take this into consideration when planning health services.

Supplementary Information

The online version contains supplementary material available at https://doi. org/10.1186/s13011-020-00334-x.

Additional file 1. The number of months from baseline to the second or third health assessment. No.: Number of patients; SD: Standard deviation; ref.: Reference. The table displays the number of patients with one, two, and three health assessments, including a nine-item Fatigue Severity Scale score. The table displays the time between baseline and the second and third health assessments.

Additional file 2. The US-English and the Norwegian versions of FSS-9. Description: Legends: FSS-9; Nine-item Fatigue Severity Scale. All items in the FSS-9 are ranged as a Likert scale from 1 to 7, where 1 indicates "strongly disagree" and 7 "strongly agree".

Additional file 3. Aspartate aminotransferase to platelet ratio index (APRI). Description: APRI: Aspartate aminotransferase to platelet ratio index. The figure displays the APRI score equation. The AST upper limit of normal range was defined as 45 IU/L (male) and 35 IU/L (female).

Additional file 4. Potential correlations between sociodemographic and clinical factors and fatigue. The figure shows that potential sociodemographic and clinical comorbidities may affect fatigue among patients with substance use disorders.

Additional file 5. Mean (SD) item scores for single items on the FSS-9 at baseline and follow-up. Legend: FSS-9: Nine-item Fatigue Severity Scale; I: Item; OAT: Opioid agonist therapy; SD: Standard Deviation. Follow-up: The FSS-9 score on the last health assessment during the study period among patients with two or more annual health assessments. Mean (SD) item scores for single items on the FSS-9 for patients using methadone or buprenorphine as OAT opioid.

Additional file 6. Linear mixed model of fatigue (FSS-9) adjusted for sociodemographic and clinical factors among patients receiving methadone as an OAT opioid at baseline (N = 209). Legends: APRI: Aspartate transaminase to platelet ratio index; CI: Confidence interval; CRP: C-reactive protein; FSS-9: Nine-item Fatigue Severity Scale; eGFR: estimated glomerular filtration rate; HIV: Human Immunodeficiency virus; kPa: Kilopascal; OAT: Opioid Agonist Therapy. 1) Age per 10 years was centred according to mean age (43 years) in the study sample at baseline. 2) Includes amphetamine or cocaine use. The OAT opioid ratio: a ratio between the received dose of OAT opioids per day divided by the expected mean dose of OAT opioids (buprenorphine 18 mg, buprenorphine-naloxone 18/4.5 mg or methadone 90 mg). The educational level: highest level of education was coded 0-4 with 4 as the highest educational level. Unstable housing situation: living on the street, homeless shelter, or with family and friends at any time in the past 30 days prior to the health assessment. Debt difficulties: struggling with repaying current illegal and legal debt. Injecting substance use: Having injected a substance in the past 12 months prior to the health assessment. Frequent use of substances: at least weekly during the past 12 months prior to the health assessment. Viral load of HCV: From − 0.5 to 0.5, where the range ≥ − 0.5 to < 0 represents the low viral load (HCV PCR < 800,000 IE/ml), and the range from ≤0.5 to > 0 identifies the high viral load (HCV PCR ≥ 800,000 IE/ml). Zero (0) defined patients without HCV infection. When using the Bonferroni corrected p-values (α altered = 0.05 / 43 = 0.0012), the predictors did not affect the FSS-9 sum score significantly. The table displays a linear mixed model analysis (Restricted Maximum Likelihood regression) evaluating sociodemographic and clinical factors' (predictors) changes in the FSS-9 sum score at baseline and their influence on changes in the FSS-9 sum score (time trend) per year from baseline among patients receiving methadone as an OAT opioid. The predictors' effect estimates and time trends estimate adjusted mean differences in the FSS-9 sum score. Additional file 7. Linear mixed model of fatigue (FSS-9) adjusted for sociodemographic and clinical factors among patients receiving buprenorphine as an OAT opioid at baseline (N = 321). Legends: APRI: Aspartate transaminase to platelet ratio index; CI: Confidence interval; CRP: C-reactive protein; FSS-9: Nine-item Fatigue Severity Scale; eGFR: estimated glomerular filtration rate; HIV: Human Immunodeficiency virus; kPa: Kilopascal; OAT: Opioid Agonist Therapy. 1) Age per 10 years was centred according to mean age (43 years) in the study sample at baseline. 2) Includes amphetamine or cocaine use. The OAT opioid ratio: a ratio between the received dose of OAT opioids per day divided by the expected mean dose of OAT opioids (buprenorphine 18 mg, buprenorphine-naloxone 18/4.5 mg or methadone 90 mg). The educational level: highest level of education was coded 0-4 with 4 as the highest educational level. Unstable housing situation: living on the street, homeless shelter, or with family and friends at any time during the past 30 days prior to the health assessment. Debt difficulties: struggling with repaying current illegal and legal debt. Injecting substance use: Having injected a substance during the past 12 months prior to the health assessment. Frequent use of substances: at least weekly during the past 12 months prior to the health assessment. Viral load of HCV: From − 0.5 to 0.5, where the range ≥ − 0.5 to < 0 represents the low viral load (HCV PCR < 800,000 IE/ml), and the range ≤ 0.5 to > 0 identifies the high viral load (HCV PCR ≥ 800,000 IE/ml). Zero (0) defined patients without HCV infection. When using the Bonferroni corrected p-values (α altered = 0.05 / 43 = 0.0012), the predictors did not affect the FSS-9 sum score significantly. The table displays a linear mixed model analysis (Restricted Maximum Likelihood regression) evaluating sociodemographic and clinical factors' (predictors) changes in the FSS-9 sum score at baseline and their influence on changes in the FSS-9 sum score (time trend) per year from baseline among patients receiving buprenorphine as an OAT opioid. The predictors' effect estimates and time trends estimate adjusted mean differences in the FSS-9 sum score.

table displays a linear mixed model analysis (Restricted Maximum Likelihood regression) evaluating sociodemographic and clinical factors' (predictors)   changes in the FSS-9 sum score at baseline and the predictors' influence on changes in the FSS-9 sum score (time trend) per year from baseline. The predictors' effect estimates and time trends estimate adjusted mean differences in the FSS-9 sum score APRI aspartate transaminase to platelet ratio index, CI confidence interval, CRP C-reactive protein, FSS-9 nine-item fatigue severity scale, eGFR estimated glomerular filtration rate, HIV human immunodeficiency virus, kPa Kilopascal, OAT opioid agonist therapy 1) Age per 10 years was centred according to mean age (43 years) in the study sample at baseline. 2) Includes amphetamine or cocaine use. The educational level: highest level of education was coded 0-4 with 4 as the highest educational level. Unstable housing situation: living on the street, homeless shelter, or with family and friends at any time during the past 30 days prior to the health assessment. Debt difficulties: struggling with repaying current illegal and legal debt. Injecting substance use: Having injected substance during the past 12 months prior to the health assessment. Frequent use of substances: at least weekly during the past 12 months prior to the health assessment. Viral load of HCV: From − 0.5 to 0.5, where the range ≥ − 0.5 to < 0 represents the low viral load (HCV PCR < 800,000 IE/ ml), and the range ≤ 0.5 to > 0 identifies the high viral load (HCV PCR ≥ 800,000 IE/ml). Zero (0) defined patients without HCV infection a) Statistically significant results when using Bonferroni corrected p-values (α altered = 0.05 / 41 = 0.0012)

Table 1
1Sociodemographic and clinical characteristics at baseline for all patients and for patients with more than one annual health assessmentAll
patients
(N = 654)

Patients with
≥2 health assessments
(N = 225)

Table 1
1Sociodemographic and clinical characteristics at baseline for all patients and for patients with more than one annual health assessment (Continued)All
patients
(N = 654)

Patients with
≥2 health assessments
(N = 225)

Table 3
3Linear mixed model of fatigue (FSS-9) adjusted for sociodemographic and clinical factors (N = 654)Fixed effects

Effect estimate
Time trend (per year)

Estimate (95% CI)
p-value
Slope (95% CI)
p-value

FSS-9 sum score
42 (26-58)
< .001
3.6 (−23.5-30.7)
0.792

Vold et al. Substance Abuse Treatment, Prevention, and Policy (2020) 15:93
AcknowledgementsWe thank the devoted clinical staff for their enthusiasm during the planning of the project. We also thank Nina Elisabeth Eltvik and Christer Kleppe for their valuable help and input during the planning and preparation phases.Authors' contributionsJørn Henrik Vold and Rolf Gjestad have led the study design, analysis, and article preparation. Christer F. Aas, Fatemeh Chalabianloo, Svetlana Skurtveit, Else-Marie Løberg, Kjell Arne Johansson, and Lars Thore Fadnes have contributed by leading the study design, analysis, and article preparation. All authors have read and approved the final article.FundingThis work was supported by The Norwegian Research Council (BEHANDLING, contract no 269855) and the Western Norway Regional Health Authority («Åpen prosjektstøtte») with the Department of Addiction Medicine, Haukeland University Hospital, Bergen, Norway as responsible institution. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Two of the authors are funded from the research grant BEHANDLING related to the project INTRO-HCV from the Norwegian Research Council. The other authors are funded by their respective affiliations.Availability of data and materialsNo additional data are available due to data protection requirements.Ethics approval and consent to participateThe study has been reviewed and approved by the Regional Ethical Committee for Health Research (REC) West, Norway (reference number: 2017/51/REK Vest, dated 29.03.2017/20.04.2017). Each patient provided written informed consent prior to enrolling in the study.Consent for publicationParticipants have consented for publication.Competing interestsNot applicable.
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Safety enhancement in adult body computed tomography scanning: comparison of iodixanol versus iohexol

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Safety enhancement in adult body computed tomography scanning: comparison of iodixanol versus iohexol
10.1186/s40064-016-1754-zXiao et al. SpringerPlus (2016) 5:148 RESEARCHIodixanolIohexolBody computed tomography (CT)Adverse events
Background: The aim of this study was to compare the safety and intra-individual contrast enhancement of lowosmolar monomeric iohexol and the iso-osmolar dimeric iodixanol in body computed tomography (CT) scanning.Methods:In this single center, double-blind, prospective study, a total of 2000 consecutive patients undergoing adult body CT scanning were prospectively enrolled, with 1000 patients assigned to iodixanol and 1000 patients assigned to iohexol. In both groups, the contrast medium was injected at the rate of 3.5 ml/s. Subjective assessment of image quality for each image was determined using a 3-grading scale by three reviewers. Patients were monitored with questioning and vital signs before injection, immediately after injection, and at 24 and 48 h. Extensive laboratory evaluation also was performed.Results:Laboratory results showed no significant difference across groups. There were also no detectable differences in image quality between the two contrast groups in this study. The total adverse reactions occurred in less than 1 % of individuals receiving iodixanol comparing to 2.5 % in iohexol group (P < 0.05). Among them, only 0.7 % patients injecting iodixanol suffered immediate events, comparing to 2 % patients in iohexol group. In all, 0.2 % patients with iodixanol and 0.5 % with iohexol had late allergic reaction. Further, No deaths occurred in any of the two groups.Conclusions:The iso-osmolar iodixanol provides image quality compared with that of iohexol, with lower incidence of adverse events.

Background

The intravenous injection of contrast materials for medical imaging is usually a safe procedure and has been widely used throughout the world, over the last few decades (Casserbaum 1965). However, some individuals may undergo discomfort, experience allergy-like reactions or even adverse events (Becher et al. 2005). The hyper-osmolality is believed to be an important factor for systemic adverse reactions of contrast agents and responsible for local symptom from contrast media extravasations (Lee et al. 1996). More efficacious and safe contrast agents have been extensive developed to benefit the patients' care in terms of their decreasing osmolality.

Iodixanol (Hengrui, Jiangsu, China) is a water-soluble, nonionic, dimeric, isosmolar radiographic material for intravascular injection (Spencer and Goa 1996). Preclinical researched have detected better toxicological pharmacological and physicochemical properties of iodixanol (Wilcox et al. 1987). A latter double blind, randomized, prospective, multi-center study has concluded that iohexol was more nephrotoxic among high risk groups, compared with the iso-osmolar, agent iodixanol (Doerfler et al. 1999). Numerous clinical trials also demonstrated comparable safety and efficacy in body computed tomography usage (Lee et al. 1996). Iohexol is another extensively used low-osmolar monomer non-ionic contrast medium, which is considered to safe, efficient and be well-tolerated for the head and body computed tomography (Almen 1983).

Therefore, we try to perform an intra-individual evaluation of the degrees of image quality and safety enhancement achieved by iohexol and iodixanol in body computed tomography (CT) scanning under the standardized conditions. Patients were assigned to contrast medium groups randomly.

Materials and methods

Patients

This was a randomized study conducted from April 2014 to 2015 at Chongqing City Chinese Medicine Hospital. The consecutive patients, who were scheduled to undergo a follow-up contrast-enhanced CT for the assessment of pain or the evaluation of suspected masses in organs, were prospectively enrolled in this study. In brief, nine variables were assigned as exclusion criteria: (1) a known history of hypersensitivity to iodine, (2) pregnancy, (3) lactation, (4) clinically unstable condition, (5) having received contrast media <7 days before the procedure, (6) cardiogenic shock, (7) known renal dysfunction, (8) pulmonary edema, (9) mechanical ventilator support. The subjects were randomized into two groups by the permuted block randomization method. Subjects allocated to the experimental arm underwent body computed tomography scanning using the contrast medium of iodixanol, whereas iohexol was used in the controlled arm of the trial.

Methods

The radiology nurse established the heparin lock intravenous line before injection. Both of the contrast material were pre-warmed to 37 °C prior for intravascular use. The regional difference in contrast agent wash-in and wash-out time constants were identified as the main mechanism of delayed contrast enhancement on scan images. The highest attenuation difference for idoixanol on delayed contrast-enhanced images was achieved 4 min post injection comparing to 3 min for iohexol. In this trial, 1000 individuals were injected 100 ml iohexol 300 mg I/ml, while the other 1000 participants received intravenously 100 ml iodixanol 300 mg I/ml. The whole participants were monitored through completion of injections and throughout the scanning. In addition, these patients were further interviewed at the end of the test immediately, at 1 h, as well as at 24 h, concerning any local or systemic syndrome. The radiology nurse recorded vital signs at the time of interview. Urine and blood specimens were also obtained at the aforementioned time. The CTDIvol was 13.82 mGy for the 80 kV scan protocol.

Laboratory analysis

This analysis was performed on both venous blood samples and voided urine samples, which were collected in a blinded fashion and measured by auto-analyzer of the clinical laboratory in Chongqing City Chinese Medicine Hospital. Generally, blood samples included tests for potassium chloride, platelet count, alanine aminotransferase, white blood cells, eosinophils, lymphocytes, aspartate aminotransferase, hemoglobin, red blood cells, sodium, blood urea nitrogen, creatinine, basophils, hematocrit, lactate dehydrogenase, and neutrophils. Urine samples usually contain occult blood (red blood cells), glucose, pH, specific gravity, and protein.

Assessment of contrast enhancement on images

All participants were instructed to practice breath holding before scanning which was all performed on a second-generation 64-slice scanner. Three experienced CT radiologists, each with more than 5 years' work experience was allocated in a group, to evaluate the images independently. Contrast quality grades should arrive at consensus for each diagnostic item by discussion. None radiologists had been involved in the patients recruiting. The radiologists were fully unaware of the original examination interpretation results and the specific contrast material injected. Contrast quality grades which is referenced as diagnostic images quality in this passage, which could be classified into three distinct grades as follows: Grade one is optimal, the one that provides optimal information to make a definitive decision. Grade two is the suboptimal choice, which provides less definitive information to make a suboptimal decision, which was also taken, if the images enhancement could not arrive at the optimal conditions in any aspect. Grade three is definitely not determinative, could not offer sufficient information to make the diagnosis. Results of this trial would be presented for all participants recruited in this research.

Contrast agent reactions

Based on the reaction severity according to the criteria, patients' reactions to the contrast enhanced CT could be graded as mild, moderate, or severe (Pintassilgo Santos et al. 2009). Discomfort related to the injection was classified into three grades as follows: mild, moderate, or severe, with the definition of subjective feeling, which resulted from physiology and psychology. Mild contrast reactions including rash, emesis, peculiar taste, itching, did not require any treatment. Moderate events were of any aforementioned in more advanced degree and required close monitoring and immediate therapy. Serious reactions were regarded as life-threatening events and more robust solutions were further required. All incidents accompanied with the material injection were recorded in this institution. For each adverse event, both supervising CT radiologists and technologists involved, completed the incidents reports, which contain the personal characteristics, contrast medium as well as date of the scanning and the events descriptions. These reactions could be classified into either immediate or delayed event according to the events onset time. Immediate events were those that occur within 1 h and were reported by experimentalists nearby or patients. Delayed reactions were subsequent clinical symptoms up to 7 days from time of intravenous use. The patients or their companions reported these information to us voluntarily.

Statistical analysis

Quantitative variables were summarized by use of descriptive statistics, expressed as mean ± SD, while categorical variables were expressed as frequencies or percentages. Personal characteristics were compared between two contrast media by Student's t test. Fisher's exact test was used to evaluate the frequency of adverse events and discomfort. P values <0.05 were considered statistically significant difference. Statistical analysis was performed by spss version 18.0 for Mac.

Ethics statement

This study was approved by local institutional review boards, and written informed consents were obtained from all patients before they were randomly assigned.

Results

Patients' characteristics

2702 participants were assessed for eligibility between April 2014 and 2015. Among these, 318 individuals met at least one of the specified exclusion criteria in this study, while 388 patients declined to participate the research. The other participants (n = 2004) were randomly assigned either to an iohexol or iodixanol program (Fig. 1). One were excluded, among these enrolled participants, on account of a serious hypersensitivity event in iohexol group and three were excluded because randomization assignment was not match the preferred contrast medium (one in iohexol and two in iodixanol group) of their own. No significant difference was detected between two groups for the demographic characteristics, as shown in Table 1.

Image enhancement and quality

With regard to the overall enhancement, the diagnostic images were performed in CT room, which deployed the same monitors and similar machines. These images were diagnosed and classified by onsite-experienced investigators into three grades. As Table 2 shown, only 11 participate were regarded as Grade 3 in iodixanol group while 21 patients were considered as Grade 3 in iohexol group. On the contrary, the diagnostic images in iodixanol group was evaluated as Grade 1 for 80.9 % (809/1000) of the individuals and 82.6 % (826/1000) of the patients in the iohexol group. In Grade 2163 images were considered suboptimal in iodixanol group and 170 in iohexol. No significant difference was detected in image quality between contrast medium among the grades (P > 0.05, Table 1).

Comparison of the time duration with vascular attenuation values equal to or above 300 HU revealed no statistically significantly difference between iohexol and iodixanol for aorta (350 vs 293; P = 0.03, Table 3).

Laboratory results

In brief, we did not detect any significant difference between the two groups in all laboratory results measured. Neither clinically significant changes nor any relevant trends, which demonstrated toxicity attributable to any contrast agents, were founded in these specific laboratory results (data not shown). However, among these patients, thirty patients in iohexol group had deviated from the maximum limits of normal reference range, and turned into normal in 3 days post-injection. The increase in serum creatinine was considered to be clinically relevant by radiologists. Ten patients in iodixanol group had increase in serum creatinine and decreased to normal reference range in 3 days post-injection. However, one participant receiving iodixanol got 1.3 mg/dl in creatinine level on the day prior to the research, and 1.8 mg/dl after 4 days. He received low dose gentamycin, and ampicillin therapy with the decrease of creatinine 2 days later. Summary results for creatinine are presented in Table 4.

Safety results

The overall incidence of adverse reaction was low for iodixanol (0.9 %). A nearly threefold increase in the overall adverse events frequency was detected with iohexol (2.5 %), which was significant (P < 0.05, Fisher's exact test). Both of the immediate adverse events and delayed ones experienced increased in iohexol groups compared with iodixanol, while the changes were not significant (P > 0.05). Though immediate adverse reactions predominated in both contrast materials, the rate of delayed adverse reactions was much greater with iohexol group (P > 0.05). For both mediums, most of the adverse reactions were of mild severity (87 % iohexol vs 89 % iodixanol). For moderate events combined, the percentage of iodixanol was higher than iohexol (2.4 % iohexol vs 1 % iodixanol). Futher, only one patients suffered severe side effects after injecting iohexol. For both agents, neither a monthly nor a seasonal trend in the incidence of adverse reactions during the study period was included in the study. The results of this study are summarized in Table 5.

The profile of adverse reactions is similar in two groups. Almost all adverse reactions had a cutaneous component (75 % in iohexol vs 90 % in iodixanol, data not shown), with most commonly urticaria. However, respiratory and gastrointestinal was the next most frequent symptom, and the frequency of them was higher in iohexol group. In addition, two severe reaction was observed with iohexol while none was found with iodixanol. In this particular event, both of the patients developed erythemas immediately, have profound hypotension, loss of consciousness at the injection of iohexol. Luckily, they responded to administration of oxygen,     Mean values of all procedures stayed ordinarily within a single standard deviation of means. The five patients who showed greater ranges were diagnosed to be clinically non-relevant by two cardiologists. No significant of vital signs and the relation between types of contrast material injected could be detected.

In the follow-up research, one patient had severe skin rash 2 days after injecting iohexol. The skin lesion had pruritus, edema, erythema, urticaria, and exanthema. Receiving low dose (0.5 mg/kg) oral prednisolone therapy result in the resolution of skin lesion 1 week later. All researches were successfully completed in all of the enrolled individuals.

Discussion

The data of this research detected similar diagnostic quality enhancement by iohexol and iodixanol. The comparison was performed by paralleled total iodine amounts and identical iodine delivery rates under standardized conditions in intra-individuals. The overall diagnostic quality was evaluated as optimal in 80.9 % (809/1000) of the individuals receiving iodixanol and 82.6 % (826/1000) of the groups injecting iohexol. We did not detect any significant difference in diagnostic image enhancement among the three grades between contrast medium (P < 0.05). In addition, The impact of iodixanol also persisted longer than the other agent. The result is similar to the detection of Elmstahl et al. (2007). The detection might also indicate that the increased quality enhancement observed with iodixanol injection might provide improved images diagnosis and detailed descriptions of several organic disease. More importantly, the iso-osmotic agent might be more efficient and the smaller doses of iodixanol could pose similar diagnostic enhancement compared with larger doses of the other one when imaging some specific organ.

There are distinct types of complications following intravenous use: soft tissue injury, direct chemical toxicity, nephrotoxicity, and anaphylactic reactions (Leow et al. 2015;Li et al. 2015;McAlister and Kissane 1990). Nearly 2 % of the patients in this study experience adverse reactions related with intravenous contrast medium, similar to the dictation of Ho et al (Ho et al. 2007). Late reactions are reported with frequencies nearly 20 %. Our observation indicates that compared to iohexol, iodixanol is associated with a lower frequency of late reaction (P < 0.05). These results suggested that there exists the increased frequency of late reaction with hypotonic contrast medium compared to hyperosmotic contrast medium. This finding is also consistent with the literature that suggests that delayed reactions are more common with nonionic dimers than with monomers (Webb et al. 2003). Definitely, the physiological background of delayed reaction is largely unknown. In this study, the incidence of allergic reaction was 2 % among all paticipants, slightly less than previous study (Li et al. 2015). In all, 0.2 % patients with iodixanol and 0.5 % with iohexol had late allergic reaction. These results is similar to the report that the adverse reaction was minimal for patients receiving iodixanol compared with individuals injecting iohexol (Chalmers and Jackson 1999). During this study, no significant differences were account for the different frequency of adverse reactions in the methods of reporting adverse events with either contrast medium.

Contrast nephropathy (CN) is the complex complications of arteriography action, which could be defined as the acute renal function impairment, caused by contrast material injection (McCullough et al. 1997;Lindholt 2003). The clinical presentation of CN is distinct, having a temporal relation with the high-risk patients groups and the onset of a serum creatinine levels increase within the 24 h to 5 days following the injection. CN could be diagnosed if the value of serum creatinine is rising by 0.5 mg/ dl or greater than 25 % of baseline or more. A previous study has demonstrated that iohexol was slightly more nephrotoxic compared with iodixanol (Chalmers and Jackson 1999). Accordingly, in this research, 10 % patients receiving iodixanol had an increase in serum creatinine greater than 10 % of baseline in the week following scanning, compared with 15 % in the iohexol group (P > 0.05). Several previous studies have demonstrated that contrast medium could pose direct cytotoxic effects on renal structures, such as glomerulus, kidney tubules. Until now, several hypothesis have been proposed to explore the further mechanisms, including the discovery of disturbed renal perfusion/hypoxia, the renal tubular epithelium caused by direct toxicity of contrast medium, or  (Gyoten 1998;Hizoh and Haller 2002;Duarte et al. 1999;Tervahartiala et al. 1993). However, there is no evidence that the use of any medicine could provide any significant prophylactic benefit, based on these mechanisms. The serious complications in the high-risk patients' groups, including anaphylactic shock, acute renal failure, still should be managed by hemodialysis. Thus, further measures and researches about cytokines and complement profiles are needed in sequential studies. A limitation of our research, consistent with previous contrast material clinical trials, is the comparatively small patients population with special species of chronic disease, such as diabetes and hypertension without CKD. The previous studies has indicated that non-diabetic patients with preexisting renal insufficiency and patients with diabetes mellitus are conditions clinically carrying a high risk of nephrotoxicity events resulted by contrast material (Gavant and Siegle 1992). In this research, the data of body CT scanning revealed that iodixanol is superior to iohexol in safe and efficacy in the concentrations tested with 15 diabetes mellitus and 10 renal insufficiencies. In the iodixanol group, none diabetic patient with renal insufficiency had CN while five diabetes mellitus with normal renal function experience CN in the iohexol group. However, there were no significant difference in serum creatinine levels, and no death adverse reactions occurred in these overall samples. Our experience is mirrored by that of others who have used iodixanol for excretory urography (Gavant and Siegle 1992). Although these special patient populations were small, and the broad safety conclusions of these two contrast agents are beyond the scope or intent of this study, we do suppose that further researches are needed to add to the validity of this study in terms of applicability to clinical practice.

Above all, there were no apparent differences in the diagnostic images enhancement. The incidence of adverse reactions with the universal use of iodixanol for CT were lower compared with iohexol and within the range of previously reported incidences for other contrast material. In addition, in our experience, the rate of immediate and delayed adverse reactions were significantly greater with iohexol than iodixanol. No seasonal influence was detected to affect the incidence of adverse events compared with either contrast material. Additional studies will be needed to further illuminate the iodixanol clinical properties.

Fig. 1
1Flow of patients through the study

Table 1
1Patient demographic informationSafety parameter
Iodixol
Iohexol
P value

n = 1000
n = 1000

Male, n (%)
549 (54.9)
631 (63.1)
0.782

Age (years)
52 ± 13.3
55 ± 13.6
0.652

BMI (kg/m 2 )
25.2 ± 3.1
24.9 ± 2.8
0.263

Baseline SCr (mg/dl)
1.46 ± 1.2
1.53 ± 0.72
0.397

Baseline CrCl (ml/min)
47.51 ± 4.3
49.21 ± 5.7
0.421

Table 2
2Diagnostic image qualityIodixanol
Iohexol
P value

n = 1000
n = 1000

Grade 1
826 (82.6 %)
809 (80.9 %)

Grade 2
163 (16.3 %)
170 (17 %)
0.178

Grade 3
11 (1.1 %)
21 (2.1 %)

Table 3
3CT HU for aortaIodixanol
Iohexol
P value

HU
350 ± 25.7
293 ± 18.6
0.032

Table 4
4Summary values for patient creatinine levelsSerum creatinine (mg/dl) Iodixanol group Iohexol group P value

Baseline level
1.46 ± 1.23
1.53 ± 0.72
0.397

At day 2
1.43 ± 1.52
1.56 ± 0.68
0.432

At day 3
1.43 ± 1.61
1.58 ± 0.59
0.451

>10 % rise in creatinine
14
27
0.028

>25 % rise in creatinine
1
1
1

Table 5
5Patients safety resultsapoptosis of glomerular, and altered glomerular functionSafety parameter
Iodixanol
Iohexol
P value

Total

Adverse events
9
25
0.04

Immediate events
7
20
0.018

Delayed events
2
5
0.452

Severity

Mild
8
22
NA

Moderate
1
2
NA

Severe
0
1
NA

Authors' contributions PZ designed research; YX, GZ, XL, CP and CL performed research and wrote the paper. All authors read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests. The authors alone are responsible for the content and writing of the paper.Received: 31 August 2015 Accepted: 12 February 2016
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