An informatics consult approach for generating clinical evidence for treatment decisions

A cost-effectiveness analysis of hypertrophic cardiomyopathy sudden cardiac death risk algorithms for implantable cardioverter defibrillator decision-making

AIMS

To conduct a contemporary cost-effectiveness analysis examining the use of implantable cardioverter defibrillators (ICDs) for primary prevention in patients with hypertrophic cardiomyopathy (HCM).

METHODS

A discrete-time Markov model was used to determine the cost-effectiveness of different ICD decision-making rules for implantation. Several scenarios were investigated, including the reference scenario of implantation rates according to observed real-world practice. A 12-year time horizon with an annual cycle length was used. Transition probabilities used in the model were obtained using Bayesian analysis. The study has been reported according to the Consolidated Health Economic Evaluation Reporting Standards checklist.

RESULTS

Using a 5-year SCD risk threshold of 6% was cheaper than current practice and has marginally better total quality adjusted life years (QALYs). This is the most cost-effective of the options considered, with an incremental cost-effectiveness ratio of £834 per QALY. Sensitivity analyses highlighted that this decision is largely driven by what health-related quality of life (HRQL) is attributed to ICD patients and time horizon.

CONCLUSION

We present a timely new perspective on HCM-ICD cost-effectiveness, using methods reflecting real-world practice. While we have shown that a 6% 5-year SCD risk cut-off provides the best cohort stratification to aid ICD decision-making, this will also be influenced by the particular values of costs and HRQL for subgroups or at a local level. The process of explicitly demonstrating the main factors, which drive conclusions from such an analysis will help to inform shared decision-making in this complex area for all stakeholders concerned.

Human-centered Design of a Health Recommender System for Orthopaedic Shoulder Treatment

Background

Rich data on diverse patients and their treatments and outcomes within Electronic Health Record (EHR) systems can be used to generate real world evidence. A health recommender system (HRS) framework can be applied to a decision support system application to generate data summaries for similar patients during the clinical encounter to assist physicians and patients in making evidence-based shared treatment decisions.

Objective

A human-centered design (HCD) process was used to develop a HRS for treatment decision support in orthopaedic medicine, the Informatics Consult for Individualized Treatment (I-C-IT). We also evaluate the usability and utility of the system from the physician's perspective, focusing on elements of utility and shared decision-making in orthopaedic medicine.

Methods

The HCD process for I-C-IT included 6 steps across three phases of analysis, design, and evaluation. A team of informaticians and comparative effectiveness researchers directly engaged with orthopaedic surgeon subject matter experts in a collaborative I-C-IT prototype design process. Ten orthopaedic surgeons participated in a mixed methods evaluation of the I-C-IT prototype that was produced.

Results

The HCD process resulted in a prototype system, I-C-IT, with 14 data visualization elements and a set of design principles crucial for HRS for decision support. The overall standard system usability scale (SUS) score for the I-C-IT Webapp prototype was 88.75 indicating high usability. In addition, utility questions addressing shared decision-making found that 90% of orthopaedic surgeon respondents either strongly agreed or agreed that I-C-IT would help them make data informed decisions with their patients.

Conclusion

The HCD process produced an HRS prototype that is capable of supporting orthopaedic surgeons and patients in their information needs during clinical encounters. Future research should focus on refining I-C-IT by incorporating patient feedback in future iterative cycles of system design and evaluation.

Statins in Chronic Liver Disease: Review of the Literature and Future Role

Chronic liver disease (CLD) is a major contributor to global mortality, morbidity, and healthcare burden. Progress in pharmacotherapeutic for CLD management is lagging given its impact on the global population. While statins are indicated for the management of dyslipidemia and cardiovascular disease, their role in CLD prevention and treatment is emerging. Beyond their lipid-lowering effects, their liver-related mechanisms of action are multifactorial and include anti-inflammatory, antiproliferative, and immune-protective effects. In this review, we highlight what is known about the clinical benefits of statins in viral and nonviral etiologies of CLD and hepatocellular carcinoma (HCC), and explore key mechanisms and pathways targeted by statins. While their benefits may span the spectrum of CLD and potentially HCC treatment, their role in CLD chemoprevention is likely to have the largest impact. As emerging data suggest that genetic variants may impact their benefits, the role of statins in precision hepatology will need to be further explored.

Cumulative burden of 144 conditions, critical care hospitalisation and premature mortality across 26 adult cancers

A comprehensive evaluation of the total burden of morbidity endured by cancer survivors remains unavailable. This study quantified the burden of 144 health conditions and critical care admissions across 26 adult cancers and treatment modalities in 243,767 adults. By age 60, top conditions ranked by fold difference (cumulative burden in survivors divided by cumulative burden in controls) were haematology, immunology/infection and pulmonary conditions. Patients who had all three forms of treatment (chemotherapy, radiotherapy and surgery) experienced a high cumulative burden of late morbidities compared with patients who received radiotherapy alone. The top five cancers with the highest cumulative burden of critical care admissions by age 60 were bone (12.4 events per 100 individuals [CI: 11.6-13.1]), brain (9.0 [7.5-10.5]), spinal cord and nervous system (7.2 [6.7-7.8]), testis (6.7 [4.9-8.4]) and Hodgkin lymphoma (4.4 [3.6-5.1]). Conditions that were associated with high excess years-of-life-lost were haematological conditions (9.6 years), pulmonary conditions (8.6 years) and immunological conditions or infections (7.8 years). As the population of cancer survivors continues to grow, our results indicate that it is important to tackle long-term health consequences through enacting data-driven policies.

Comparing clinical trial population representativeness to real-world populations: an external validity analysis encompassing 43 895 trials and 5 685 738 individuals across 989 unique drugs and 286 conditions in England

BACKGROUND

Randomised controlled trials (RCTs) inform prescription guidelines, but stringent eligibility criteria exclude individuals with vulnerable characteristics, which we define as comorbidities, concomitant medication use, and vulnerabilities due to age. Poor external validity can result in inadequate treatment decision information. Our first aim was to quantify the extent of exclusion of individuals with vulnerable characteristics from RCTs for all prescription drugs. Our second aim was to quantify the prevalence of individuals with vulnerable characteristics from population electronic health records who are actively prescribed such drugs. In tandem, these two aims will allow us to assess the representativeness between RCT and real-world populations and identify vulnerable populations potentially at risk of inadequate treatment decision information. When a vulnerable population is highly excluded from RCTs but has a high prevalence of individuals actively being prescribed the same medication, there is likely to be a gap in treatment decision information. Our third aim was to investigate the use of real-world evidence in contributing towards quantifying missing treatment risk or benefit through an observational study.

METHODS

We extracted RCTs from ClinicalTrials.gov from its inception to April 28, 2021, and primary care records from the Clinical Practice Research Datalink Gold database from Jan 1, 1998, to Dec 31, 2020. We referred to the British National Formulary to classify prescription drugs into drug categories. We conducted descriptive analyses and quantified RCT exclusion and prevalence of individuals with vulnerable characteristics for comparison to identify populations without treatment decision information. Exclusion and prevalence were assessed separately for different age groups, individual clinical specialities, and for quantities of concomitant conditions by clinical specialities, where multimorbidity was defined as having two or more clinical specialties, and medications prescribed, where polypharmacy was defined as having five or more medications prescribed. Population trends of individuals with multimorbidity or polypharmacy were assessed separately by age group. We conducted an observational cohort study to validate the use of real-world evidence in contributing towards quantifying treatment risk or benefit for patients with dementia on anti-dementia drugs with and without a contraindicated clinical speciality. To do so, we identified the clinical specialities that anti-dementia drug RCTs highly excluded yet had corresponding high prevalence in the real-world population, forming the groups with highest risk of having scarce treatment decision information. Cox regression was used to assess if the risk of mortality outcomes differs between both groups.

FINDINGS

43 895 RCTs from ClinicalTrials.gov and 5 685 738 million individuals from primary care records were used. We considered 989 unique drugs and 286 conditions across 13 drug-category cohorts. For the descriptive analyses, the median RCT exclusion proportion across 13 drug categories was 81·5% (IQR 76·7-85·5) for adolescents (aged <18 years), 26·3% (IQR 21·0-29·5) for individuals older than 60 years, 40·5% (IQR 33·7-43·0) for individuals older than 70 years, and 52·9% (IQR 47·1-56·0) for individuals older than 80 years. Multimorbidity had a median exclusion proportion of 91·1% (IQR 88·9-91·8) and median prevalence of 41·0% (IQR 34·9-46·0). Concomitant medication use had a median exclusion proportion of 52·5% (IQR 50·0-53·7) and a median prevalence of 94·3% (IQR 84·3-97·2), and polypharmacy had a median prevalence of 47·7% (IQR 38·0-56·1). Population trends show increasing multimorbidity with age and consistently high polypharmacy across age groups. Populations with cardiovascular or otorhinolaryngological comorbidities had the highest risk of having scarce treatment decision information. For the observational study, populations with cardiovascular or psychiatric comorbidities had highest risk of having scarce treatment decision information. Patients with dementia with an anti-dementia prescription and contraindicated cardiovascular condition had a higher risk of mortality (hazard ratio [HR] 1·20 [95% CI 1·13-1·28 ; p<0·0001]) compared with patients with dementia without a contraindicated cardiovascular condition. Patients with dementia with comorbid delirium (HR 1·25 [95% CI 1·06-1·48]; p<0·0088), intellectual disability (HR 2·72 [95% CI 1·53-4·81]; p=0·0006), and schizophrenia and schizotypal delusional disorders (HR 1·36 [95% CI 1·02-1·82]; p=0·036) had a higher risk of mortality compared with patients with dementia without these conditions.

INTERPRETATION

Overly stringent RCT exclusion criteria do not appropriately account for the heterogeneity of vulnerable characteristics observed in real-world populations. Treatment decision information is scarce for such individuals, which might affect health outcomes. We discuss the challenges facing the inclusivity of such individuals and highlight the strength of real-world evidence as an integrative solution in complementing RCTs and increasing the completeness of evidence-based medicine assessments in evaluating the effectiveness of treatment decisions.

FUNDING

Wellcome Trust, National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre, NIHR Great Ormond Street Hospital Biomedical Research Centre, Academy of Medical Sciences, and the University College London Overseas Research Scholarship.

Mendelian randomization in pharmacogenomics: The unforeseen potentials

Mendelian randomization (MR) is an epidemiological method that uses genetic variants to proxy an exposure predicting its causal association with an outcome. It occupies a valuable niche between observational studies and randomized trials. MR applications expanded lately, facilitated by the availability of big data, to include disease risk causation prediction, supporting evidence of prior observational data, identifying new drug targets, and drug repurposing. Concurrently, the last decade witnessed the growth of pharmacogenomics (PGx) research as a cornerstone in precision medicine. PGx research, conducted at discovery and implementation levels, resulted in validated PGx biomarkers and tests. Despite many clinically relevant PGx associations that could be translated into clinical applications, worldwide implementation is lagging far behind. The current review examines the intersection zones between MR and PGx research. MR can provide supporting evidence that allows generalizing PGx findings supporting its implementation. Interchangeability, PGx research can fuel MR studies with libraries of genetic variants of validated biological relevance. Furthermore, PGx and MR exhibit a synergistic relationship in drug discovery that can accelerate identifying new targets and repurposing old drugs. Interdisciplinary research applied by PGx researchers, epidemiologists with MR experience, and data scientists' collaborations can unlock unforeseen opportunities in accelerating precision medicine acquisition.

Increased burden of cardiovascular disease in people with liver disease: unequal geographical variations, risk factors and excess years of life lost

Background

People with liver disease are at increased risk of developing cardiovascular disease (CVD), however, there has yet been an investigation of incidence burden, risk, and premature mortality across a wide range of liver conditions and cardiovascular outcomes.

Methods

We employed population-wide electronic health records (EHRs; from 1998 to 2020) consisting of almost 4 million adults to assess regional variations in disease burden of five liver conditions, alcoholic liver disease (ALD), autoimmune liver disease, chronic hepatitis B infection (HBV), chronic hepatitis C infection (HCV) and NAFLD, in England. We analysed regional differences in incidence rates for 17 manifestations of CVD in people with or without liver disease. The associations between biomarkers and comorbidities and risk of CVD in patients with liver disease were estimated using Cox models. For each liver condition, we estimated excess years of life lost (YLL) attributable to CVD (i.e., difference in YLL between people with or without CVD).

Results

The age-standardised incidence rate for any liver disease was 114.5 per 100,000 person years. The highest incidence was observed in NAFLD (85.5), followed by ALD (24.7), HCV (6.0), HBV (4.1) and autoimmune liver disease (3.7). Regionally, the North West and North East regions consistently exhibited high incidence burden. Age-specific incidence rate analyses revealed that the peak incidence for liver disease of non-viral aetiology is reached in individuals aged 50–59 years. Patients with liver disease had a two-fold higher incidence burden of CVD (2634.6 per 100,000 persons) compared to individuals without liver disease (1339.7 per 100,000 persons). When comparing across liver diseases, atrial fibrillation was the most common initial CVD presentation while hypertrophic cardiomyopathy was the least common. We noted strong positive associations between body mass index and current smoking and risk of CVD. Patients who also had diabetes, hypertension, proteinuric kidney disease, chronic kidney disease, diverticular disease and gastro-oesophageal reflex disorders had a higher risk of CVD, as do patients with low albumin, raised C-reactive protein and raised International Normalized Ratio levels. All types of CVD were associated with shorter life expectancies. When evaluating excess YLLs by age of CVD onset and by liver disease type, differences in YLLs, when comparing across CVD types, were more pronounced at younger ages.

Conclusions

We developed a public online app (https://lailab.shinyapps.io/cvd_in_liver_disease/) to showcase results interactively. We provide a blueprint that revealed previously underappreciated clinical factors related to the risk of CVD, which differed in the magnitude of effects across liver diseases. We found significant geographical variations in the burden of liver disease and CVD, highlighting the need to devise local solutions. Targeted policies and regional initiatives addressing underserved communities might help improve equity of access to CVD screening and treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03210-9.

OUP accepted manuscript

The increasing volume and richness of healthcare data collected during routine clinical practice have not yet translated into significant numbers of actionable insights that have systematically improved patient outcomes. An evidence-practice gap continues to exist in healthcare. We contest that this gap can be reduced by assessing the use of nudge theory as part of clinical decision support systems (CDSS). Deploying nudges to modify clinician behaviour and improve adherence to guideline-directed therapy represents an underused tool in bridging the evidence-practice gap. In conjunction with electronic health records (EHRs) and newer devices including artificial intelligence algorithms that are increasingly integrated within learning health systems, nudges such as CDSS alerts should be iteratively tested for all stakeholders involved in health decision-making: clinicians, researchers, and patients alike. Not only could they improve the implementation of known evidence, but the true value of nudging could lie in areas where traditional randomized controlled trials are lacking, and where clinical equipoise and variation dominate. The opportunity to test CDSS nudge alerts and their ability to standardize behaviour in the face of uncertainty may generate novel insights and improve patient outcomes in areas of clinical practice currently without a robust evidence base.