OBJECTIVE: This review aimed to summarize the current evidence base of reported utility values for chemotherapy-related ADEs.
METHODS: A structured electronic search combining terms for utility, utility valuation methods and generic terms for cancer treatment was conducted in MEDLINE and EMBASE in June 2011. Inclusion criteria were: (1) elicitation of utility values for chemotherapy-related ADEs and (2) primary data. Two reviewers identified studies and extracted data independently. Any disagreements were resolved by a third reviewer.
RESULTS: Eighteen studies met the inclusion criteria from the 853 abstracts initially identified, collectively reporting 218 utility values for chemotherapy-related ADEs. All 18 studies used short descriptions (vignettes) to obtain the utility values, with nine studies presenting the vignettes used in the valuation exercises. Of the 218 utility values, 178 were elicited using standard gamble (SG) or time trade-off (TTO) approaches, while 40 were elicited using visual analogue scales (VAS). There were 169 utility values of specific chemotherapy-related ADEs (with the top ten being anaemia [34 values], nausea and/or vomiting [32 values], neuropathy [21 values], neutropenia [12 values], diarrhoea [12 values], stomatitis [10 values], fatigue [8 values], alopecia [7 values], hand-foot syndrome [5 values] and skin reaction [5 values]) and 49 of non-specific chemotherapy-related adverse events. In most cases, it was difficult to directly compare the utility values as various definitions and study-specific vignettes were used for the ADEs of interest.
LIMITATIONS: This review was designed to provide an overall description of existing literature reporting utility values for chemotherapy-related ADEs. The findings were not exhaustive and were limited to publications that could be identified using the search strategy employed and those reported in the English language.
CONCLUSIONS: This review identified wide ranges in the utility values reported for broad categories of specific chemotherapy-related ADEs. There were difficulties in comparing the values directly as various study-specific definitions were used for these ADEs and most studies did not make the vignettes used in the valuation exercises available. It is recommended that a basic minimum requirement be developed for the transparent reporting of study designs eliciting utility values, incorporating key criteria such as reporting how the vignettes were developed and presenting the vignettes used in the valuation tasks as well as valuing and reporting the utility values of the ADE-free base states. It is also recommended, in the future, for studies valuing the utilities of chemotherapy-related ADEs to define the ADEs according to the National Cancer Institute (NCI) definitions for chemotherapy-related ADEs as the use of the same definition across studies would ease the comparison and selection of utility values and make the overall inclusion of adverse events within economic models of chemotherapy agents much more straightforward.
Methods: A systematic literature search was performed in 5 databases for articles published between 2002 and 2021. Studies that compared adherence enhancing interventions implemented by healthcare professionals with a comparison group were included. Relevant data on study characteristics were extracted. Medication adherence and clinical outcomes between intervention and control arms were compared.
Results: Nine studies were included in two randomised controlled trials, four cohort studies, and three before-and-after comparison studies. All the included studies incorporated complex interventions, including intensive education or consultation with pharmacists, nurses or multidisciplinary team, in combination with one or more other strategies such as structured follow-up, written materials or video, psychotherapy, medication reminder or treatment diary, with the overall goal of monitoring and improving TKI adherence. Most (7 out of 9) studies demonstrated significantly better adherence to TKIs in the intervention group than the comparison group. The relative proportion of participants who adhered to TKIs ranged from 1.22 to 2.42. The improvement in the rate of TKI doses taken/received ranged from 1.5% to 7.1%. Only one study showed a significant association between intervention and clinical outcomes, with a 22.6% higher major molecular response rate and improvement in 6 out of 20 subscales of health-related quality-of-life.
Conclusion: Complex interventions delivered by healthcare professionals showed improvement in adherence to TKIs in CML patients. Further studies are required to clarify the cost-effectiveness of adherence-enhancing interventions.
METHODS: A parallel RCT was conducted in two hospitals in Malaysia, where 129 CML patients were randomised to MMS or control (usual care) groups using a stratified 1:1 block randomisation method. The 6-month MMS included three face-to-face medication use reviews, CML and TKI-related education, two follow-up telephone conversations, a printed information booklet and two adherence aids. Medication adherence (primary outcome), molecular responses and health-related quality of life (HRQoL) scores were assessed at baseline, 6th and 12th month. Medication adherence and HRQoL were assessed using medication possession ratio and the European Organisation for Research and Treatment in Cancer questionnaire (EORTC_QLQ30_CML24) respectively.
RESULTS: The MMS group (n = 65) showed significantly higher adherence to TKIs than the control group (n = 64) at 6th month (81.5% vs 56.3%; p = 0.002), but not at 12th month (72.6% vs 60.3%; p = 0.147). In addition, a significantly higher proportion of participants in the MMS group achieved major molecular response at 6th month (58.5% vs 35.9%; p = 0.010), but not at 12th month (66.2% vs 51.6%; p = 0.092). Significant deep molecular response was also obtained at 12th month (24.6% vs 10.9%; p = 0.042). Six out of 20 subscales of EORTC-QLQ30-CML24 were significantly better in the MMS group.
CONCLUSIONS: The MMS improved CML patients' adherence to TKI as well as achieved better clinical outcomes.
TRIAL REGISTRATION: Clinicaltrial.gov (ID: NCT03090477).
METHODS: This study included participants from the intervention arm of a randomised controlled trial which was conducted to evaluate the effects of pharmacist-led interventions on CML patients treated with TKIs. Participants were recruited and followed up in the haematology clinics of two hospitals in Malaysia from March 2017 to January 2019. A pharmacist identified DRPs and helped to resolve them. Patients were followed-up for six months, and their DRPs were assessed based on the Pharmaceutical Care Network Europe Classification for DRP v7.0. The identified DRPs, the pharmacist's interventions, and the acceptance and outcomes of the interventions were recorded. A Poisson multivariable regression model was used to analyse factors associated with the number of identified DRPs per participant.
RESULTS: A total of 198 DRPs were identified from 65 CML patients. The median number of DRPs per participants was 3 (interquartile range: 2, 4). Most participants (97%) had at least one DRP, which included adverse drug events (45.5%), treatment ineffectiveness (31.5%) and patients' treatment concerns or dissatisfaction (23%). The 228 causes of DRPs identified comprised the following: lack of disease or treatment information, or outcome monitoring (47.8%), inappropriate drug use processes (23.2%), inappropriate patient behaviour (19.9%), suboptimal drug selection (6.1%), suboptimal dose selection (2.6%) and logistic issues in dispensing (0.4%). The number of concomitant medications was significantly associated with the number of DRPs (adjusted Odds Ratio: 1.100; 95% CI: 1.005, 1.205; p = 0.040). Overall, 233 interventions were made. These included providing patient education on disease states or TKI-related side effects (75.1%) and recommending appropriate instructions for taking medications (7.7%). Of the 233 interventions, 94.4% were accepted and 83.7% were implemented by the prescriber or patient. A total of 154 DRPs (77.3%) were resolved.
CONCLUSIONS: The pharmacist-led interventions among CML patients managed to identify various DRPs, were well accepted by both TKI prescribers and patients, and had a high success rate of resolving the DRPs.
OBJECTIVES: This study aimed to provide a personalized surgical recommendation that enables more confidence in advising patients to pursue surgical treatment.
METHODS: We enrolled 328 patients with uPA harboring KCNJ5 mutations (n = 158) or not (n = 170) who had undergone adrenalectomy. Eighty-seven features were collected, including demographics, various blood and urine test results, and clinical comorbidities. We designed 2 versions of the prediction model: one for institutes with complete blood tests (full version), and the other for institutes that may not be equipped with comprehensive testing facilities (condensed version).
RESULTS: The results show that in the full version, the Light Gradient Boosting Machine outperformed other classifiers, achieving area under the curve and accuracy values of 0.905 and 0.864, respectively. The Light Gradient Boosting Machine also showed excellent performance in the condensed version, achieving area under the curve and accuracy values of 0.867 and 0.803, respectively.
CONCLUSIONS: We simplified the preoperative diagnosis of KCNJ5 mutations successfully using machine learning. The proposed lightweight tool that requires only baseline characteristics and blood/urine test results can be widely applied and can aid personalized prediction during preoperative counseling for patients with uPA.