Which factors influence the quality of end-of-life care in interstitial lung disease? A systematic review with narrative synthesis

Background: People dying from interstitial lung disease experience considerable symptoms and commonly die in an acute healthcare environment. However, there is limited understanding about the quality of their end-of-life care. Aim: To synthesise evidence about end-of-life care in interstitial lung disease and identify factors that influence quality of care. Design: Systematic literature review and narrative synthesis. The review protocol was prospectively registered with PROSPERO (CRD42020203197). Data sources: Five electronic healthcare databases were searched (Medline, Embase, PubMed, Scopus and Web of Science) from January 1996 to February 2021. Studies were included if they focussed on the end-of-life care or death of patients with interstitial lung disease. Quality was assessed using the Critical Appraisal Skills Programme checklist for the relevant study design. Results: A total of 4088 articles were identified by initial searches. Twenty-four met the inclusion criteria, providing evidence from 300,736 individuals across eight countries. Most patients with interstitial lung disease died in hospital, with some subjected to a high burden of investigations or life-prolonging treatments. Low levels of involvement with palliative care services and advance care planning contributed to the trend of patients dying in acute environments. This review identified a paucity of research that addressed symptom management in the last few days or weeks of life. Conclusions: There is inadequate knowledge regarding the most appropriate location for end-of-life care for people with interstitial lung disease. Early palliative care involvement can improve accordance with end-of-life care wishes. Future research should consider symptom management at the end-of-life and association with location of death.

Identifying COPD in routinely collected electronic health records: a systematic scoping review

Although routinely collected electronic health records (EHR) are widely used to examine outcomes related to chronic obstructive pulmonary disease (COPD), consensus regarding the identification of cases from electronic healthcare databases is lacking. We systematically examine and summarise approaches from the recent literature.MEDLINE via EBSCOhost was searched for COPD-related studies using EHR published from January 1, 2018 to November 30, 2019. Data were extracted relating to the case definition of COPD and determination of COPD severity and phenotypes.From 185 eligible studies, we found widespread variation in the definitions used to identify people with COPD in terms of code sets used (with 20 different code sets in use based on the ICD-10 classification alone) and requirement of additional criteria (relating to age (n=139), medication (n=31), multiplicity of events (n=21), spirometry (n=19) and smoking status (n=9)). Only 7 studies used a case definition which had been validated against a reference standard in the same dataset. Various proxies of disease severity were used since spirometry results and patient-reported outcomes were not often available.To enable the research community to draw reliable insights from electronic health records and aid comparability between studies, clear reporting and greater consistency of the definitions used to identify COPD and related outcome measures is key.

Prediction of treatment dosage and duration from free-text prescriptions: an application to ADHD medications in the Swedish prescribed drug register

BackgroundAccurate estimation of daily dosage and duration of medication use is essential to pharmacoepidemiological studies using electronic healthcare databases. However, such information is not directly available in many prescription databases, including the Swedish Prescribed Drug Register.ObjectiveTo develop and validate an algorithm for predicting prescribed daily dosage and treatment duration from free-text prescriptions, and apply the algorithm to ADHD medication prescriptions.MethodsWe developed an algorithm to predict daily dosage from free-text prescriptions using 8000 ADHD medication prescriptions as the training sample, and estimated treatment periods while taking into account several features including titration, stockpiling and non-perfect adherence. The algorithm was implemented to all ADHD medication prescriptions from the Swedish Prescribed Drug Register in 2013. A validation sample of 1000 ADHD medication prescriptions, independent of the training sample, was used to assess the accuracy for predicted daily dosage.FindingsIn the validation sample, the overall accuracy for predicting daily dosage was 96.8%. Specifically, the natural language processing model (NLP1 and NLP2) have an accuracy of 99.2% and 96.3%, respectively. In an application to ADHD medication prescriptions in 2013, young adult ADHD medication users had the highest probability of discontinuing treatments as compared with other age groups. The daily dose of methylphenidate use increased with age substantially.ConclusionsThe algorithm provides a flexible approach to estimate prescribed daily dosage and treatment duration from free-text prescriptions using register data. The algorithm showed a good performance for predicting daily dosage in external validation.Clinical implicationsThe structured output of the algorithm could serve as basis for future pharmacoepidemiological studies evaluating utilization, effectiveness, and safety of medication use, which would facilitate evidence-based treatment decision-making.

Evaluating the performance of Temporal Pattern Discovery: New application using Statins and Rhabdomyolysis in OMOP databases

Background: Temporal Pattern Discovery (TPD) is a method of signal detection using electronic healthcare databases, serving as an alternative to spontaneous reporting of adverse drug events. Here, we aimed to replicate and optimise a TPD approach previously used to assess temporal signals of statins with rhabdomyolysis (in The Health Improvement Network (THIN) database) by using the OHDSI tools designed for OMOP data sources. Methods: We used data from the Truven MarketScan US Commercial Claims and the Commercial Claims and Encounters (CCAE). Using an extension of the OHDSI ICTemporalPatternDiscovery package, we ran positive and negative controls through four analytical settings and calculated sensitivity, specificity, bias and AUC to assess performance. Results: Similar to previous findings, we noted an increase in the information component (IC) for simvastatin and rhabdomyolysis following initial exposure and throughout the surveillance window. For example, the change in IC was 0.266 for the surveillance period of 1-30 days as compared to the control period of -180 to -1days. Our modification of the existing OHDSI software allowed for faster queries and more efficient generation of chronographs. Conclusion: Our OMOP replication matched the results of the original THIN study, only simvastatin had a signal. The TPD method is a useful signal detection tool that provides a single statistic on temporal association and a graphical depiction of the temporal pattern of the drug outcome combination. It remains unclear if the method works well for rare adverse events, but it has been shown to be a useful risk identification tool for longitudinal observational databases. Future work should compare the performance of TPD with other pharmacoepidemiology methods and mining techniques of signal detection. In addition, it would be worth investigating the relative TPD performance characteristics using a variety of observational data sources.

Data-driven chronic care delivery pathways visualization Methods: protocol for a systematic review

Background Faced with increasing illness burden and costs, healthcare systems are working towards integrated care to streamline services and improve efficiency, especially for chronic conditions. Routine care delivery data stored in various electronic healthcare databases (EHD) has the potential to support chronic care coordination if information is integrated and accessible at the point of care. Care delivery pathways (CDPs) can be constructed by linking multiple data sources and extracting time-stamped healthcare utilization events and other medical data related to individual or groups of patients over specific time periods; CDPs may facilitate communication on current practice and ways of improving it. We aim to identify and describe the methods proposed to quantify and visualize CDPs. Methods A literature search was performed in PubMed (MEDLINE), Scopus, IEEE, CINAHL and EMBASE, without date restrictions. We will describe CPDs methods from 3 perspectives relevant for EHD use in long-term care: (1) clinical (what clinical information is used and how was it considered relevant?), (2) data science (how was the method developed and implemented?), and (3) behavioral (which behaviors and interactions are promoted among users and how?). Data extraction will be performed via deductive content analysis using selected frameworks, and inductive analysis to identify additional relevant features. We will compare these characteristics to identify common, infrequent, or missing features, and extract recommendations for future initiatives. Results The literature search identified 2349 entries, currently under title and abstract selection by 4 coders. This study will produce a comparison and synthesis of clinical, data, and behavioral features of CDPs methods and derive recommendations for CDP construction. Conclusions This review works towards a common basis for visualizing and quantifying CDPs across healthcare systems, an essential prerequisite for interoperable digital health. Key messages Visual feedback on health care trajectories, especially for chronic conditions, may support informed decisions and planning future care episodes to advance towards person-centered integrated care. We describe and compare technical and clinical characteristics of visual feedback methods available for care pathways, and behaviors they may promote in care planning, to inform future initiatives.

The Prevalence and Diagnostic Ratio of Familial Hypercholesterolemia (FH) and Proportion of Acute Coronary Syndrome in Japanese FH Patients in a Healthcare Record Database Study

Background. Familial hypercholesterolemia (FH) is a genetic disorder characterized by high levels of low-density lipoprotein cholesterol (LDL-C). Because of underdiagnosis, acute coronary syndrome (ACS) is often the first clinical manifestation of FH. In Japan, there are few reports on the prevalence and diagnostic ratios of FH and the proportion of ACS among FH patients in clinical settings. Methods. This retrospective, observational study used anonymized data from electronic healthcare databases between April 2001 and March 2015 of patients who had ≥2 LDL-C measurements recorded after April 2009. The index date was defined as the date of the first LDL-C measurement after April 2009. The primary endpoint was the prevalence of definite or suspected FH; secondary endpoints included the proportion of FH patients hospitalized for ACS, the proportion of patients using lipid-lowering drugs (LLDs), and LDL-C levels. Results. Of the 187,781 patients screened, 1547 had definite or suspected FH (0.8%) based on data from the entire period; 832 patients with definite (n=299, 0.16%) or suspected FH (n=533, 0.28%) before the index date were identified in the main analysis cohort. LLDs were used in 214 definite FH patients (71.6%) and 137 suspected FH patients (25.7%). Among definite or suspected FH patients with ACS (n=84) and without ACS (n=748), 32.1% and 30.1% with definite FH and 3.2% and 2.4% with suspected FH had LDL-C levels<2.6 mmol/L (<100 mg/dL), respectively. Sixty patients (7.2%) were hospitalized due to ACS at the index date. Conclusions. The prevalence of FH in this Japanese cohort of patients with ≥2 LDL-C measurements at hospitals was 0.8%, which is higher than that currently reported in epidemiological studies (0.2–0.5%). Patients with suspected FH, with or without ACS, had poorly controlled LDL-C levels and were undertreated. The proportion of FH patients who were hospitalized due to ACS was 7.2%.

TEOS: A framework for constructing operational definitions of medication adherence based on Timelines – Events – Objectives – Sources

Objectives: Managing adherence to medications is a priority for health systems worldwide. Adherence research is accumulating, yet the quality of the evidence is reduced by various methodological limitations. In particular, the heterogeneity and low accuracy of adherence measures have been highlighted in many literature reviews. Recent consensus-based guidelines advise on best practices in defining adherence (ABC) and reporting of empirical studies (EMERGE). While these guidelines highlight the importance of operational definitions in adherence measurement; such definitions are rarely included in study reports. To support researchers in their measurement decisions, we developed a structured approach to formulate operational definitions of adherence.Methods: A group of adherence and research methodology experts used theoretical, methodological and practical considerations to examine the process of applying adherence definitions to various research settings, questions and data sources. Consensus was reached through iterative reviewing of discussion summaries and framework versions.Results: We introduce TEOS, a four-component framework to guide the operationalization of adherence concepts: 1) describe treatment as four simultaneous interdependent timelines (recommended and actual use, conditional on prescribing and dispensing); 2) locate four key events along these timelines to delimit the three ABC phases (first and last recommended use, first and last actual use); 3) revisit study objectives and design to finetune research questions and assess measurement validity and reliability needs, and 4) select data sources (e.g., electronic monitoring, self-report, electronic healthcare databases) that best address measurement needs.Conclusion: Using the TEOS framework when designing research and reporting explicitly on these components can improve measurement quality.

Quantification and visualisation methods of data-driven chronic care delivery pathways: protocol for a systematic review and content analysis

IntroductionChronic conditions require long periods of care and often involve repeated interactions with multiple healthcare providers. Faced with increasing illness burden and costs, healthcare systems are currently working towards integrated care to streamline these interactions and improve efficiency. To support this, one promising resource is the information on routine care delivery stored in various electronic healthcare databases (EHD). In chronic conditions, care delivery pathways (CDPs) can be constructed by linking multiple data sources and extracting time-stamped healthcare utilisation events and other medical data related to individual or groups of patients over specific time periods; CDPs may provide insights into current practice and ways of improving it. Several methods have been proposed in recent years to quantify and visualise CDPs. We present the protocol for a systematic review aiming to describe the content and development of CDP methods, to derive common recommendations for CDP construction.Methods and analysisThis protocol followed the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols. A literature search will be performed in PubMed (MEDLINE), Scopus, IEEE, CINAHL and EMBASE, without date restrictions, to review published papers reporting data-driven chronic CDPs quantification and visualisation methods. We will describe them using several characteristics relevant for EHD use in long-term care, grouped into three domains: (1) clinical (what clinical information does the method use and how was it considered relevant?), (2) data science (what are the method’s development and implementation characteristics?) and (3) behavioural (which behaviours and interactions does the method aim to promote among users and how?). Data extraction will be performed via deductive content analysis using previously defined characteristics and accompanied by an inductive analysis to identify and code additional relevant features. Results will be presented in descriptive format and used to compare current CDPs and generate recommendations for future CDP development initiatives.Ethics and disseminationDatabase searches will be initiated in May 2019. The review is expected to be completed by February 2020. Ethical approval is not required for this review. Results will be disseminated in peer-reviewed journals and conference presentations.PROSPERO registration numberCRD42019140494.