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Data availability | The datasets generated during and/or analyzed during the current study are available from the corresponding authors on reasonable request. | PMC9870900 | ||
Competing interests | The authors declare no competing interests. | PMC9870900 | ||
References | PMC9870900 | |||
Background | RECURRENCE, ATRIAL FIBRILLATION (AF) | A number of models have been reported for predicting atrial fibrillation (AF) recurrence after catheter ablation. Although many machine learning (ML) models were developed among them, black-box effect existed widely. It was always difficult to explain how variables affect model output. We sought to implement an explain... | PMC9936738 | |
Methods | Between January 2018 and December 2020, 471 consecutive patients with paroxysmal AF who had their first catheter ablation procedure were retrospectively enrolled. Patients were randomly assigned into training cohort (70%) and testing cohort (30%). The explainable ML model based on Random Forest (RF) algorithm was devel... | PMC9936738 | ||
Results | RECURRENCES, RECURRENCE | In this cohort, 135 patients experienced tachycardias recurrences. With hyperparameters adjusted, the ML model predicted AF recurrence with an area under the curve of 66.7% in the testing cohort. Summary plots listed the top 15 features in descending order and preliminary showed the association between features and out... | PMC9936738 | |
Conclusion | RECURRENCE, PAROXYSMAL ATRIAL FIBRILLATION | An explainable ML model effectively revealed its decision-making process in identifying patients with paroxysmal atrial fibrillation at high risk for recurrence after catheter ablation by listing important features, showing the impact of every feature on model output, determining appropriate thresholds and identifying ... | PMC9936738 | |
Supplementary Information | The online version contains supplementary material available at 10.1186/s12872-023-03087-0. | PMC9936738 | ||
Keywords | PMC9936738 | |||
Introduction | drug-refractory | ATRIAL FIBRILLATION (AF) | One of the primary treatment options for atrial fibrillation (AF) is rhythm control. Catheter ablation is the first-line treatment for drug-refractory paroxysmal AF, and pulmonary vein isolation, the cornerstone of AF ablation, eradicates approximately 90% of AF triggers in principle [Numerous studies have proposed a v... | PMC9936738 |
Methods | PMC9936738 | |||
Study design and population | AF, mitral stenosis, drug-refractory | MITRAL STENOSIS, PRIMARY CARDIOMYOPATHY, HYPERTHYROIDISM, HYPERTROPHIC CARDIOMYOPATHY, SICK SINUS SYNDROME | A consecutive cohort of patients was included in the study from the First Affiliated Hospital of Air Force Medical University between January 2018 and December 2020. Patients over 18 years old with drug-refractory paroxysmal AF, who received their first catheter ablation procedure were eligible. Exclusion criteria incl... | PMC9936738 |
Data collection | ATRIAL FIBRILLATION | With "Atrial Fibrillation" as the search term, all patients hospitalized in the department of cardiovascular medicine from January 2018 to December 2020 were retrieved, and basic information of patients, including age, sex and ID number, were collected. Retrieval logic written in R language was used to automatically re... | PMC9936738 | |
Feature selection | embolism | EMBOLISM, INFLAMMATION | To ensure the robustness of the ML classification model, features were selected if they: (i) characterized the cardiac function, risk of systemic embolism or the level of inflammation, or (ii) promoted the occurrence and development of AF. Laboratory and echocardiographic characteristics were considered as missing valu... | PMC9936738 |
End point and follow-up | atrial flutter, atrial tachycardia | RECURRENCE, RECURRENCE, ATRIAL TACHYCARDIA, ATRIAL FLUTTER, INFLAMMATORY RESPONSE | Recurrence of AF post-procedure was defined as the first episode of any type of atrial tachycardia, including but not limited to atrial flutter and AF, sustained for more than 30 s after the blanking period (BP). BP refers to the first three months post-procedure. In this period, any occurrence of atrial tachycardia wa... | PMC9936738 |
Explainable ML model | The Random Forest (RF) algorithm was used in the building of an explainable ML model. | PMC9936738 | ||
Classifier illustration | DTS | RF is an ensemble algorithm based on the Decision Tree (DT). The basic idea of RF is bagging: features selected into the RF model are simultaneously voted by all independent DTs in the forest, and this process conforms to the principle of majority subordination. RF integrates the results of all DTs, obtaining higher mo... | PMC9936738 | |
Model development, validation and testing | overfitted | DTS | The median of each feature was used to fill in the missing values, preserving the distribution of data to the greatest extent. However, underestimation of the weight of features using this method is possible. All patients and their specific characteristics in this cohort were randomly divided into the training and test... | PMC9936738 |
Model explanation | Shapley additive explanations (SHAP) analysis is derived from a game theory concept and has the advantage of gaining insight into the association between observations and clinical outcome [ | PMC9936738 | ||
Statistical analysis | SD | REGRESSION, RECURRENCE | Because the missing values for most laboratory examination and echocardiographic indexes were replaced with their medians, they were described as the median (IQR) and compared using Mann–Whitney U test. Continuous variables with normal distribution were expressed as the mean ± SD and compared by one-way ANOVA. Categori... | PMC9936738 |
Results | PMC9936738 | |||
Patients and baseline characteristics | tachycardias | RECURRENCES | A total of 1496 patients were screened in this study. Finally, 471 patients were enrolled. The median follow-up time was 25 months (IQR: 13–36 months). Patient follow-up totaled an aggregate of 954.3 patient-years with 135 patients (14.2/100 patient-years) experiencing tachycardias recurrences (Additional file The enti... | PMC9936738 |
Model development | DBP diastolic blood pressure | ATRIAL FIBRILLATION, RECURRENCE | Based on the 7:3 ratio, 329 and 142 patients were randomly assigned into the training and testing cohorts, respectively. Baseline characteristics are summarized in Table Baseline characteristics between training cohort and testing cohortData are mean ± SD, median (IQR) or n (%)AF duration indicates time since first AF ... | PMC9936738 |
SHAP analysis | SHAP dependence | The top 15 features with descending importance ranked by mean absolute SHAP values are shown in the summary plot (Fig. Summary plot for ML model. Summary plot Decision plot and force plot for ML model. SHAP dependence plots show the impact of a single feature on outcome prediction (Fig. The impact of single feature on ... | PMC9936738 | |
Statistical significance of thresholds | SBP systolic blood pressure | REGRESSION, ATRIAL FIBRILLATION | Statistical thresholds of continuous variables in the training cohort evaluated by Youden index were similar to those determined by SHAP analysis (Table Thresholds of continuous variables in training cohort based on Youden indexBMI body mass index, AF atrial fibrillationUnivariable Cox proportional hazard regression ba... | PMC9936738 |
Discussion | RECURRENCE | In this study, an explainable ML model was developed and tested to reveal its decision-making process in identifying patients with paroxysmal AF at high risk for recurrence after catheter ablation. The top 15 features and their specific impact on outcome prediction were revealed through SHAP analysis. ERAF showed the m... | PMC9936738 | |
Risk factors identification | RECURRENCE | Risk assessment for AF recurrence after catheter ablation remains a topic worth exploring. According to current guidelines, despite the fact that a series of prediction scores have been evaluated, their prediction ability is moderate; the most powerful predictor is ERAF [ | PMC9936738 | |
Relationship between single feature and model output | SHAP analysis could illuminate the effect of every feature on the outcome from an objective perspective. CHA | PMC9936738 | ||
Threshold determination | A threshold determines the score of each continuous variable in the clinical model, for example, age in CHAThe thresholds of laboratory examination indexes evaluated by SHAP analysis were inaccurate. For example, the threshold of NT-proBNP was 52.28 pg/ml, which was less than the upper limit of regular medical referenc... | PMC9936738 | ||
Model performance in high-dimensional data | REGRESSION | Multivariable Cox regression results showed that ERAF, AF duration and left ventricular ejection fraction were statistically significant, with an AUROC value of 0.513 (Additional file Currently, there are conflicting reports as to whether ML models perform better than traditional statistical methods. Moncada et al. [Re... | PMC9936738 | |
Explainable ML model in clinical practice | Explainable ML model can make clinical practice more accurate. When the characteristics of each patient are entered into the model, the model first predicts whether he or she is at high risk. After that, we can understand the decision-making process of the model through SHAP analysis. According to the weight of differe... | PMC9936738 | ||
Limitations | chronic obstructive pulmonary disease | CHRONIC OBSTRUCTIVE PULMONARY DISEASE | There were several limitations in this study. The sample size in our study was small. Median values were used to fill in the missing data for laboratory examinations and echocardiographic indicators, which may reduce power of test and representativeness. Potential risk factors proposed by other studies such as abnormal... | PMC9936738 |
Conclusions | RECURRENCE, PAROXYSMAL ATRIAL FIBRILLATION | This study showed that the explainable machine learning model can effectively reveal its decision-making process in identifying patients with paroxysmal atrial fibrillation at high risk for recurrence after catheter ablation by listing important features, showing the impact of every feature on model output, determining... | PMC9936738 | |
Acknowledgements | Not applicable. | PMC9936738 | ||
Author contributions | MH | FY conceived and supervised the study. YM and DZ participated in the design of the study, performed statistical analysis of the data and drafted the manuscript. JX and HP participated in clinical follow-up. JL, LG and SZ participated in data collection and verification. MH revised this paper. All of the authors read an... | PMC9936738 | |
Funding | The present work was financially supported by General program of National Natural Science Funds of China (Grant No. 81970274). | PMC9936738 | ||
Availability of data and materials | The datasets used and/or analyzed during the current study can be available from the corresponding author on reasonable request. | PMC9936738 | ||
Declarations | PMC9936738 | |||
Ethics approval and consent to participate | This study confirmed to Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement and was approved by the ethics committee of the First Affiliated Hospital of Air Force Medical University according to the principles of the Declaration of Helsinki. Written informe... | PMC9936738 | ||
Consent for publication | Not applicable. | PMC9936738 | ||
Competing interests | The authors declare no competing interests. | PMC9936738 | ||
References | PMC9936738 | |||
Background | type 2 diabetes mellitus, diabetes | DISEASE, TYPE 2 DIABETES MELLITUS, COMPLICATIONS, DIABETES | Medically underserved people with type 2 diabetes mellitus face limited access to group-based diabetes care, placing them at risk for poor disease control and complications. Immersive technology and telemedicine solutions could bridge this gap. | PMC10209787 |
Objective | diabetes | DIABETES | The purpose of this study was to compare the effectiveness of diabetes medical group visits (DMGVs) delivered in an immersive telemedicine platform versus an in-person (IP) setting and establish the noninferiority of the technology-enabled approach for changes in hemoglobin A | PMC10209787 |
Methods | TYPE 2 DIABETES MELLITUS | This study is a noninferiority randomized controlled trial conducted from February 2017 to December 2019 at an urban safety net health system and community health center. We enrolled adult women (aged ≥18 years) who self-reported African American or Black race or Hispanic or Latina ethnicity and had type 2 diabetes mel... | PMC10209787 | |
Results | Of 309 female participants (mean age 55, SD 10.6 years; n=195, 63% African American or Black; n=105, 34% Hispanic or Latina; n=151 IP; and n=158 in VW), 207 (67%) met per-protocol criteria. In the intention-to-treat analysis, we confirmed noninferiority for primary outcomes. We found similar improvements in mean HbA | PMC10209787 | ||
Conclusions | diabetes | DIABETES | In this noninferiority randomized controlled trial, immersive telemedicine was a noninferior platform for delivering diabetes care, eliciting comparable glycemic control improvement, and enhancing patient engagement, compared to IP DMGVs. | PMC10209787 |
Trial Registration | ClinicalTrials.gov NCT02726425; https://clinicaltrials.gov/ct2/show/NCT02726425 | PMC10209787 | ||
Introduction | T2DM, diabetes | TYPE 2 DIABETES MELLITUS, DIABETES | Minority and low-income women with type 2 diabetes mellitus (T2DM) face widening disparities in diabetes care and clinical outcomes, highlighting the pressing need to improve diabetes care for underserved communities [Telehealth solutions have gained unprecedented traction with the onset of the COVID-19 pandemic. Early... | PMC10209787 |
Methods | PMC10209787 | |||
Trial Design | T2DM | From February 2017 to October 2019, we recruited 17 cohorts of African American or Black or Hispanic or Latina women with uncontrolled T2DM. A total of 309 participants were enrolled and randomly assigned to the VW or IP DMGV conditions. Participants attended 8 weekly DMGVs and were followed for 6 months. | PMC10209787 | |
Participants | T2DM | Eligible participants were adult women (≥18 years) who self-identified as African American, Black, Hispanic, or Latina with uncontrolled T2DM, defined by a hemoglobin A | PMC10209787 | |
Recruitment | PMC10209787 | |||
Overview | We identified participants from Boston Medical Center and a local community health center using a weekly electronic medical record query. We contacted eligible participants with an introductory letter and follow-up call [ | PMC10209787 | ||
Randomization and Masking | After stratification by language, we used one-to-one block randomization (alternating blocks of 6 and 8) to assign participants to the VW or IP DMGV conditions. A biostatistician generated the randomization sequence, and randomization occurred after informed consent. We randomized participants prior to obtaining baseli... | PMC10209787 | ||
Intervention | diabetes | DIABETES | Assigned to cohorts of 6-12 participants based on study arm, participants convened in clinical or virtual settings for 8 weekly DMGVs. Each session lasted approximately 120 minutes and started with the completion of an intake form to document acute or chronic symptoms, health system usage, and self-management activitie... | PMC10209787 |
Outcomes | The primary outcomes were mean changes in (1) HbA | PMC10209787 | ||
Data Collection and Management | Baseline data collection included sociodemographic characteristics, HbA | PMC10209787 | ||
Sample Size Calculations | We used the average overtime change in HbA | PMC10209787 | ||
Statistical Analysis | SENSITIVITY | Sociodemographic characteristics were compared by arm using chi-square and Fisher exact tests as appropriate for categorical variables and 2 sample Accelerometry data was used to calculate participants’ mean change in physical activity behavior from baseline to 6 months. For each participant, we randomly selected the 2... | PMC10209787 | |
Ethics Approval | This study was conducted according to the CONSORT (Consolidated Standards of Reporting Trials) guidelines [ | PMC10209787 | ||
Results | PMC10209787 | |||
Fidelity | EVENTS | The 17 study cohorts were conducted with 98% fidelity to the 8-week curriculum. The median number of sessions attended by participants was 6 in the IP arm and 7 in the VW arm. Among participants who attended WIC2 DMGVs, 98.2% (1618/1648 total events) completed the clinician consult and intake forms. In the VW condition... | PMC10209787 | |
PP Results | Among the 207 participants who attended at least 6 DMGVs, within-group mean HbA | PMC10209787 | ||
Lifestyle Behaviors | diabetes | DIABETES | Self-management behaviors were assessed through a weekly self-report to detect changes in diet, exercise, and diabetes-related medication. Nearly a third of all participants (89/309, 28.8%) reported ≥1 dietary change, with a greater proportion in the VW group compared to the IP group (55/158, 34.8% vs 34/151, 22.5%).Of... | PMC10209787 |
Adverse Events | ADVERSE EVENT | One study-related severe adverse event in the VW group occurred due to emotional distress. | PMC10209787 | |
Discussion | PMC10209787 | |||
Principal Findings | depressive symptoms, depression, T2DM, diabetes distress, diabetes | DIABETES | To our knowledge, this is the first fully powered clinical trial to demonstrate the effectiveness of delivering DMGVs using an immersive 3D telemedicine platform versus IP care. Both approaches were similarly effective in reducing mean HbAOur preliminary study compared IP versus immersive DSME delivery among 89 low-inc... | PMC10209787 |
Limitations | We acknowledge several study limitations. We had a small imbalance in HbA | PMC10209787 | ||
Conclusions | Shakiyla, Diabetes, Digestive, diabetes | KIDNEY DISEASES, DIABETES, RECRUITMENT, DIABETES | Immersive technologies can reduce disparities by improving effectiveness and access to evidence-based diabetes care. We showed that when given the tools, adults from digitally underserved communities robustly adopt health technology tools with improved health outcomes. More effort is warranted to design technology tail... | PMC10209787 |
Abbreviations | diabetes | DIABETES | Consolidated Standards of Reporting Trialsdiabetes distressdiabetes medical group visitdiabetes self-management educationhemoglobin Ain-personintention-to-treatmetabolic equivalent of taskPatient Health Questionnaire-8per-protocoltype 2 diabetes mellitusvirtual worldWomen in Control 2.0 | PMC10209787 |
Data Availability | SECONDARY | The WIC2 team agrees to share deidentified individual participant data that underlie the results reported in this study, the study protocol, and the statistical analysis plan. Data will be available for 6 months following publication for 5 years. Data will only be shared with academic researchers who provide a methodol... | PMC10209787 | |
Methods | Cancer, NRS, pain | ADVANCED CANCER, CANCER | Adults with advanced cancer and scored worst pain ≥ 2/10 on a numeric rating scale (NRS) were recruited from 6 Australian oncology/palliative care outpatient services to the Stop Cancer PAIN trial (08/15-06/19). Out-of-hospital, publicly funded services, prescriptions and costs were estimated for the three months befor... | PMC9974128 |
Results | cancer type, reflux disease, GORD, pain | Overall, 212 participants had median worst pain scores of five (inter-quartile range 4). The most frequently prescribed medications were opioids (60.1%) and peptic ulcer/gastro-oesophageal reflux disease (GORD) drugs (51.6%). The total average healthcare cost in the three months before the census date was A$6,742 (95% ... | PMC9974128 | |
Conclusions | cancer, peptic, pain | CANCER | In this population with pain and cancer, there was no clear relationship between healthcare costs and pain severity. These treatment patterns requiring further exploration including the prevalence of peptic ulcer/GORD drugs, and lipid lowering agents and the higher healthcare costs for men. | PMC9974128 |
Data Availability | Cancer | CANCER | This dataset contains sensitive patient information and patient consent was not obtained to share data. To remain compliant with university, state government, and data custodian ethical requirements, access to the dataset is restricted. Further, data cannot be shared publicly by the authors because the analysis draws o... | PMC9974128 |
Introduction | Pain, cancer pain, pain, cancer-related pain, Cancer | DISEASE, ADVANCED CANCER, CANCER | Approximately 70% of patients living with advanced cancer experience pain [Pain is associated with poorer quality of life [Cancer is the leading cause of social and economic burden in Australia [However, no studies have investigated the relationship between healthcare usage and pain intensity in people with advanced ca... | PMC9974128 |
Aim | pain | ADVANCED CANCER | The aims of this study were to:identify treatment patterns and corresponding costs of healthcare resource use (government funded) for outpatients living with advanced cancer and pain;explore factors associated with healthcare costs in this population; andexamine the relationship between healthcare costs and pain intens... | PMC9974128 |
Methods | PMC9974128 | |||
The Stop Cancer PAIN Trial | cancer-related pain, cancer, cancer pain, pain, NRS, Cancer | CANCER, ADVANCED CANCER, SECONDARY, CANCER | This pragmatic, phase III, stepped-wedge, cluster randomised controlled trial investigated the effectiveness of screening and guidelines for pain with, versus without, implementation strategies for improving cancer pain.From August 2015 to June 2019, adults with cancer and pain presenting to six oncology and palliative... | PMC9974128 |
Treatment patterns and out-of-hospital healthcare resource utilisation | illness, Cancer | CANCER | The Stop Cancer PAIN Trial database was linked to routinely collected out of hospital services (Medicare Benefits Schedule) and medication data (Pharmaceutical Benefits Schedule) to explore treatment patterns and estimate healthcare resource utilisation. Ethics approval was granted by the South Western Sydney Local Hea... | PMC9974128 |
Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Schedule (PBS) | Medicare is the publicly funded universal healthcare scheme in Australia providing access to subsidised medical services for all residents [ | PMC9974128 | ||
Outcome measures | cancer | CANCER | Participants demographics and cancer diagnoses details were collected [ | PMC9974128 |
Pain Numeric Rating Scale (NRS) | pain | The pain NRS is a widely used, self-completed, validated pain scale [ | PMC9974128 | |
EORTC QLQ C15-PAL | Cancer | CANCER | The European Organisation for Research and Treatment of Cancer Quality of Life-C15-Palliative questionnaire (‘C15-PAL’) is a shortened version of the QLQ-C30, and contains 15 of the 30 original items [ | PMC9974128 |
EORTC QLU-C10D | cancer | CANCER | The C15-PAL does not provide a total score reflecting the health-related quality of life of people living with cancer and cannot be used to inform economic evaluations because this patient-reported outcome measure is not preference based, i.e. does not enable the calculation of utility values. The EORTC QLU-C10D [ | PMC9974128 |
Data linkage | Cancer | CANCER | Medicare Benefit Schedule and PBS data were requested for all consented participants. The Department of Human Services carried out probabilistic linkage to the Stop Cancer PAIN Trial ID with the MBS and PBS database based on key variables such as date of birth and Medicare number and provided anonymised data to the lea... | PMC9974128 |
Analysis | pain | Analyses were performed using SPSS for Windows version 25.0 (SPSS, Inc., Chicago, IL) and Stata version 16.0 (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC) on available data. Descriptive summary statistics were estimated for clinico-demographic variables, NRS pain and HrQO... | PMC9974128 | |
Results | PMC9974128 | |||
Sample characteristics | Cancer | CANCER | In total, 30.8% (n = 212) of patients who participated in the Stop Cancer PAIN Trial consented to having their MBS and/ or PBS data accessed. Overall, total healthcare costs and Stop Cancer PAIN Trial data were available for 186 participants (MBS costs, n = 186, PBS costs n = 188) for this study (missing trial data, n ... | PMC9974128 |
Participant demographics and clinical characteristics. | pain | IQR = inter-quartile range; NRS = pain numerical rating scale; SD = standard deviation | PMC9974128 | |
2. Factors associated with healthcare costs | pain | Spearman’s rank correlations between total healthcare costs and age, pain intensity and HrQOL and QLU-C10D scores were in the anticipated directions but weaker than expected especially for pain intensity ( | PMC9974128 | |
Correlations between total healthcare, MBS and PBS costs and age, pain intensity and health-related quality of life. | cancer, pain | CANCER | Sample sizes vary due to missing data; HrQOL = health-related quality of life; MBS = Medicare Benefits Schedule; NRS = pain numerical rating scale; PBS = Pharmaceutical Benefits Schedule; Correlations were interpreted according to Cohen’s guidelines, i.e., “strong” (≥0.51), “moderate” (0.31–0.50), “weak” (0.11–0.30), a... | PMC9974128 |
Unadjusted, mean healthcare costs by clinico-demographic characteristics. | cancer, lung cancers, pain, head and neck cancers | CANCER, LUNG CANCERS, HEAD AND NECK CANCER | KW = Kruskal-Wallis; MBS = Medicare Benefits Schedule; MWU = Mann-Whitney U; NRS = numeric rating scale; PBS = Pharmaceutical Benefits Schedule; SD = standard deviation. Shaded cells indicate statistically significant differences.Mean total MBS and PBS costs varied by cancer type. Mean total MBS costs were highest for ... | PMC9974128 |
3. Relationship between healthcare costs and pain intensity | cancer, Pain | CANCER | Pain intensity was not associated with healthcare costs after adjusting for age and sex (see Table 6 in Pain intensity was also not associated with mean total MBS costs after adjusting for age, sex and cancer type (see Table 7 in Exploratory, | PMC9974128 |
Discussion | cancer, pain | CANCER, ADVANCED CANCER, PEPTIC ULCERS, GASTROESOPHAGEAL REFLUX DISEASE | The findings suggest government funded, out-of-hospital costs are, on average, $2,247 per month for people living with advanced cancer and pain, i.e. approximately $27,000 per year, higher than recently reported MBS and PBS costs for the first 12-months following cancer diagnoses in Queensland, Australia (approximately... | PMC9974128 |
Top 10 most frequently prescribed medicines. | cancer type, cancer, breathlessness, pain | DISEASE, CANCER, ADVANCED CANCER | More than one in five people with advanced cancer and pain were prescribed a lipid modifying agent, contrary to guidance to reduce the burden of medications in advanced disease, particularly from medications such as statins which are only prescribed for long term population-level risk reduction [The remaining top ten m... | PMC9974128 |
Strengths and limitations | ADVANCED CANCER | Consent to access MBS and PBS data was granted by just under a third of study participants and findings may not reflect treatment patterns in the entire study cohort. However, unlike healthcare resource utilisation data collected using other means such as surveys, Medicare and PBS data are not prone to recall bias and ... | PMC9974128 | |
Implications for research and practice | peptic, pain | ADVANCED CANCER | The findings identify areas of treatment for outpatients with advanced cancer and pain requiring further exploration and practice change, particularly the high use of peptic ulcer/GORD drugs, lipid modifying agents and corticosteroids. Further research is needed to determine why healthcare costs were higher in men than... | PMC9974128 |
Conclusions | cancer, pain | CANCER | This study provides vital information for informing quality of care and quality use of medicines, resource allocation and developing sustainable health policy. There was no clear relationship between pain intensity and healthcare costs demonstrated in this population with pain. Investigation into the underpinning ratio... | PMC9974128 |
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