Quantifying societal burden of radiation-induced small bowel toxicity in patients with rectal cancer.

Introduction: Advancements in rectal cancer (RC) treatment not only led to an increase in lives saved but also improved quality of life (QoL). Notwithstanding these benefits, RC treatment comes at the price of gastrointestinal morbidity in many patients. Health economic modelling poses an opportunity to explore the societal burden of such side-effects. This study aims to quantify radiation-induced late small bowel (SB) toxicity in survivors of RC for Three-Dimensional Conformal Radiation Therapy (3D-CRT), Intensity Modulated Radiation Therapy (IMRT) and Intensity Modulated Radiation Therapy - Image Guided Radiation Therapy (IMRT/IGRT). Materials and methods: Materials and A model-based health economic evaluation was performed. The theoretical cohort consists of a case-mix of survivors of RC aged 25-99 years according to Belgian age-specific incidence rates. A societal perspective was adopted. The base case analysis was complemented with one-way deterministic analyses, deterministic scenario analyses and probabilistic sensitivity analysis (1,000 iterations). Results were presented as mean lifetime incremental cost (€) and utility (QALYs) per patient. Results: The analyses showed that the use of innovative radiotherapy (RT) improves lifetime QoL in survivors of RC by 0.11 QALYs and 0.05 QALYs by preferring IMRT/IGRT and IMRT over 3D-CRT, respectively. The use of IMRT/IGRT and IMRT results in an incremental cost-saving of €3,820 and €1,863 per patient, solely by radiation-induced SB toxicity, compared to 3D-CRT. Discussion and conclusion: It is important to consider late toxicity effects in decisions regarding investments and reimbursement as our analysis highlighted the potential long-term cost-savings and improved QoL of novel RT techniques in patients with rectal cancer.

Introduction of ultra-hypofractionation in breast cancer: Implications for costs and resource use.

PURPOSE: A shift towards (ultra-)hypofractionated breast irradiation can have important implications for the practice of contemporary radiation oncology. This paper presents a systematic analysis of the impact of different fractionation schedules on multiple key performance indicators, namely resource use, costs, work times, throughput and waiting times. MATERIALS AND METHODS: Time-driven activity-based costing (TD-ABC) is applied to calculate the costs and resources consumed where the perspective of the radiotherapy department in adopted. Three fractionation regimens are considered: ultra-hypofractionation (5 x 5.2 Gy, UHF), moderate hypofractionation (15 x 2.67 Gy, HF) and conventional fractionation (25 x 2 Gy, CF). Subsequently, a discrete event simulation (DES) model of the radiotherapy care pathway is developed and scenarios are compared in which the following factors are varied: distribution of fractionation regimens, patient volume and operating hours. RESULTS: The application of (U)HF can permit radiotherapy departments to reduce the use of scarce resources, realise work time and cost savings, increase throughput and reduce waiting times. The financial advantages of (U)HF are, however, reduced in cases of excess capacity and cost savings may therefore be limited in the short-term. Moreover, although an extension of operating hours has favourable effects on throughput and waiting times, it may also reduce cost differences between fractionation schedules by increasing the capacity of resources. CONCLUSION: By providing an in-depth analysis of the consequences associated with a shift towards (U)HF in breast cancer, the present study demonstrates how a DES model based on TD-ABC costing can assist radiotherapy professionals in making data-driven decisions.

Predicting return to work after traumatic brain injury using machine learning and administrative data.

BACKGROUND: Accurate patient-specific predictions on return-to-work after traumatic brain injury (TBI) can support both clinical practice and policymaking. The use of machine learning on large administrative data provides interesting opportunities to create such prognostic models. AIM: The current study assesses whether return-to-work one year after TBI can be predicted accurately from administrative data. Additionally, this study explores how model performance and feature importance change depending on whether a distinction is made between mild and moderate-to-severe TBI. METHODS: This study used a population-based dataset that combined discharge, claims and social security data of patients hospitalized with a TBI in Belgium during the year 2016. The prediction of TBI was attempted with three algorithms, elastic net logistic regression, random forest and gradient boosting and compared in their performance by their accuracy, sensitivity, specificity and area under the receiver operator curve (ROC AUC). RESULTS: The distinct modelling algorithms resulted in similar results, with 83% accuracy (ROC AUC 85%) for a binary classification of employed vs. not employed and up to 76% (ROC AUC 82%) for a multiclass operationalization of employment outcome. Modelling mild and moderate-to-severe TBI separately did not result in considerable differences in model performance and feature importance. The features of main importance for return-to-work prediction were related to pre-injury employment. DISCUSSION: While clearly offering some information beneficial for predicting return-to-work, administrative data needs to be supplemented with additional information to allow further improvement of patient-specific prognose.

Traumatic brain injury hospitalizations in Belgium: A brief overview of incidence, population characteristics, and outcomes.

Background: There is a need for complete and accurate epidemiological studies for traumatic brain injury (TBI). Secondary use of administrative data can provide country-specific population data across the full spectrum of disease. Aim: This study aims to provide a population-based overview of Belgian TBI hospital admissions as well as their health-related and employment outcomes. Methods: A combined administrative dataset with deterministic linkage at individual level was used to assess all TBI hospitalizations in Belgium during the year 2016. Discharge data were used for patient selection and description of injuries. Claims data represented the health services used by the patient and health-related follow-up beyond hospitalization. Finally, social security data gave insight in changes to employment situation. Results: A total of 17,086 patients with TBI were identified, with falls as the predominant cause of injury. Diffuse intracranial injury was the most common type of TBI and 53% had injuries to other body regions as well. In-hospital mortality was 6%. The median length of hospital stay was 2 days, with 20% being admitted to intensive care and 28% undergoing surgery. After hospitalization, 23% had inpatient rehabilitation. Among adults in the labor force pre-injury, 72% of patients with mild TBI and 59% with moderate-to-severe TBI returned to work within 1 year post-injury. Discussion: Administrative data are a valuable resource for population research. Some limitations need to be considered, however, which can in part be overcome by enrichment of administrative datasets with other data sources such as from trauma registries.

Quantifying Societal Burden of Radiation-Induced Cardiovascular Events in Breast Cancer Survivors.

Background and Purpose: Radiation-induced cardiotoxicity is an important health concern for clinicians during treatment of breast cancer (BC) patients. Underlying mechanisms are well-documented, whereas little is known about the societal impact of this long-term effect. This study aimed to quantify the additional burden of radiation-induced cardiovascular (CV) diseases in BC survivors. Materials and Methods: Conventional health economic modelling techniques were applied to estimate attributed CV-related costs and disutility in a hypothetical cohort of BC survivors. A situation in which radiotherapy caused an additional CV risk was compared with a situation in which this risk was not taken into account. Uncertainty was assessed via deterministic and probabilistic sensitivity analyses. Analyses were performed from a broad societal perspective up until 20 years after BC treatment. Results: Radiation-induced cardiotoxicity evokes a mean incremental cost of €275.10 per woman over a time horizon of 20 years after BC treatment. An additional decrement of 0.017 QALYs (per woman) might be expected when taking the radiation-induced cardiotoxic risk into account in BC survivors. Incremental costs and disutility increased with age. A scenario analysis showed that these results were more profound in women with more advanced staging. Conclusion: Our analyses suggest that with current radiation techniques, rather minor costs and disutility are to be expected from radiation-induced cardiotoxicity in BC survivors. The cost of past investments in order to achieve current mean heart dose (MHD) seems justified when considering the gains from cost and disutility reduction resulting from radiation-induced cardiovascular events. The question we might consider is whether future opportunity costs associated with investments on further technological advancements offset the expected marginal benefit from further reducing the MHD.

Quantifying injury severity for traumatic brain injury with routinely collected health data.

BACKGROUND: Routinely collected health data (RCHD) offers many opportunities for traumatic brain injury (TBI) research, in which injury severity is an important factor. OBJECTIVE: The use of clinical injury severity indices in a context of RCHD is explored, as are alternative measures created for this specific purpose. To identify useful scales for full body injury severity and TBI severity this study focuses on their performance in predicting these currently used indices, while accounting for age and comorbidities. DATA: This study utilized an extensive population-based RCHD dataset consisting of all patients with TBI admitted to any Belgian hospital in 2016. METHODS: Full body injury severity is scored based on the (New) Injury Severity Score ((N)ISS) and the ICD-based Injury Severity Score (ICISS). For TBI specifically, the Abbreviated Injury Scale (AIS) Head, Loss of Consciousness and the ICD-based Injury Severity Score for TBI injuries (ICISS) were used in the analysis. These scales were used to predict three outcome variables strongly related to injury severity: in-hospital death, admission to intensive care and length of hospital stay. For the prediction logistic regressions of the different injury severity scales and TBI severity indices were used, and error rates and the area under the receiver operating curve were evaluated visually. RESULTS: In general, the ICISS had the best predictive performance (error rate between 0.06 and 0.23; AUC between 0.82 [0.81;0.83] and 0.86 [0.85;0.86]). A clearly increasing error rate can be noticed with advancing age and accumulating comorbidity. CONCLUSION: Both for full body injury severity and TBI severity, the ICISS tends to outperform other scales. It is therefore the preferred scale for use in research on TBI in the context of RCHD. In their current form, the severity scales are not suitable for use in older populations.

Predictors of return to work after moderate-to-severe traumatic brain injury: a systematic review of current literature and recommendations for future research.

PURPOSE: To identify factors associated with employment between six months and five years after traumatic brain injury (TBI). METHODS: Using a predefined search algorithm, four electronic databases were searched for literature published between 2014 and the first half of April 2021 containing predictors of employment outcome. Data were selected in accordance with the PRISMA flow and the whole process was conducted by two reviewers who had to attain a consensus. The study results were discussed with an expert panel, in order to provide guidance for future research on this topic. RESULTS: This review found clear evidence for employment status at time of injury, occupation at time of injury, Glasgow Coma Scale, length of stay, disability level and primary payer to be predictors of return to work after TBI. CONCLUSIONS: More literature investigating in depth the functioning and environmental factors is required for further improvement of predictions, rehabilitation and policy.Implications for rehabilitationThis study identifies predictors of return to work in TBI patients, which can be used to identify patients with high risk early in the recovery process.Current literature shows difficulties with general functioning are a barrier for return to work, but gives no indication about effective therapeutic interventions.More knowledge about modifiable factors is desirable to improve rehabilitation and, thereby, employment outcomes after TBI.