Care Data Research Articles

Association between onychomycosis and ulcerative complications in patients with diabetes: a longitudinal cohort study in Dutch general practice

IntroductionDiabetic foot ulcers are feared complications of diabetes mellitus (DM), requiring extensive treatment and hospital admissions, ultimately leading to amputation and increased mortality. Different factors contribute to the development of...

The Military-Civilian Partnership Quality Improvement Program Concept: A Process to Improve Data Collection and Outcomes Assessment.

Military-Civilian Partnerships (MCPs) are vital for maintaining the deployment readiness of military health care physicians. However, tracking their clinical activity has proven to be challenging. In this study, we introduce a locally driven process aimed at the passive collection of external clinical workload data. This process is designed to facilitate an assessment of MCP physicians' deployment readiness and the effectiveness of individual MCPs. From March 2020 to February 2023, we conducted a series of quality improvement projects at the Wright Patterson Medical Center (WPMC) to enhance our data collection efforts for MCP physicians. Our methodology encompassed several steps. First, we assessed our existing data collection processes and their outcomes to identify improvement areas. Next, we tested various data collection methods, including self-reporting, a web-based smart phone application, and an automated process based on billing or electronic health record data. Following this, we refined our data collection process, incorporating the identified improvements and systematically tracking outcomes. Finally, we evaluated the refined process in 2 different MCPs, with our primary outcome measure being the collection of monthly health care data. Our examination at the WPMC initially identified several weaknesses in our established data collection efforts. These included unclear responsibility for data collection within the Medical Group, an inadequate roster of participating MCP physicians, and underutilization of military and community resources for data collection. To address these issues, we implemented revisions to our data collection process. These revisions included establishing clear responsibility for data collection through the Office of Military-Civilian Partnerships, introducing a regular "roll call" to match physicians to MCP agreements, passively collecting data each month through civilian partner billing or information technology offices, and integrating Office of Military-Civilian Partnership efforts into regular executive committee meetings. As a result, we observed a 4-fold increase in monthly data capture at WPMC, with similar gains when the refined process was implemented at an Air Force Center for the Sustainment of Trauma and Readiness Skills site. The Military-Civilian Partnership Quality Improvement Program concept is an effective, locally driven process for enhancing the capture of external clinical workload data for military providers engaged in MCPs. Further examination of the Military-Civilian Partnership Quality Improvement Program process is needed at other institutions to validate its effectiveness and build a community of MCP champions.

Parental risk factors and children entering out-of-home care: The effects of cumulative risk and parent’s sex

BackgroundParental difficulties, including mental ill health, substance misuse, domestic violence and learning disability have been associated with children entering out-of-home care. There is also evidence that these issues may co-occur within families. Understanding how the co-occurrence of these difficulties is associated with care entry is complex because they may co-occur in the same or different household members and have different impacts on the likelihood of care entry when they occur in mothers, fathers or in single parent households. MethodAdministrative data from local authority children’s services in Wales were linked with demographic data to identify households in which children lived prior to entering care. Linkage to birth data identified biological mothers. Linkage with primary care, emergency department, hospital admissions and substance misuse services data enabled indicators of substance misuse, mental health, assaults in the home, learning disability and neurodivergence in the adults in those households to be identified. A series of multilevel binary logistic regression models were used to explore the odds of a household having one or more children entering care if risk factors were present. These considered the effects of individual risks, and cumulative risk both in individual adults in the household, and across the whole household. The effects of the number of adults, having adults with no risks and the differential impacts of risks in biological mothers, other women or men were also explored. Additional models explored these factors in single adult households. ResultsCumulative risks increased the likelihood of care entry, however this effect disappeared when individual risks were controlled for. The presence of an individual with no risks in the household acted as a protective factor. Overall, the impact of the risks on the odds of care entry was substantially greater if the risks were present in the biological mother than if they occurred in other adults (men or women) in the household. In single adult households risk factors had a much greater impact when they occurred in households headed by women as opposed to men. ConclusionSubstantial differences in the effects of risk factors in female and male adults are apparent and further research is needed to understand why this is occurring to ensure that parents are treated equally in terms of support and statutory intervention regardless of their sex.11Throughout this paper we refer to the “sex” of the parents /adults in households, as opposed to their “gender.” This is because in the analysis we carried out the sex of these individuals was determined by their GP records. We have chosen to use the term “sex,” rather than “gender,” as we believe this data is more likely to reflect their biological sex, rather the gender that the individuals identify as.

Socioeconomic inequalities in accumulation of multimorbidity in England from 2019 to 2049: a microsimulation projection study

There are socioeconomic inequalities in the prevalence of multimorbidity and its accumulation across the life course. Estimates of multimorbidity prevalence in English primary care increased by more than two-thirds from 2004 to 2019. We developed a microsimulation model to quantify current and projected multimorbidity inequalities in the English adult population. We used primary care data for adults in England from the Clinical Practice Research Datalink Aurum database between 2004 and 2019, linked to the 2015 English Index of Multiple Deprivation (IMD), to model time individuals spent in four health states (healthy, one chronic condition, basic multimorbidity [two or more chronic conditions], and complex multimorbidity [three or more chronic conditions affecting three or more body systems]) by sex, age, IMD quintile, birth cohort, and region. We applied these transition times in a stochastic dynamic continuous-time microsimulation model to Office for National Statistics population estimates for adults aged 30-90 years. We calculated projected prevalence and cumulative incident cases from 2019 to 2049 by IMD quintile, age group (younger than 65 years vs 65 years and older), and years to be lived without multimorbidity at age 30 years. Under the assumption that all chronic conditions were lifelong, and that once diagnosed there was no recovery, we projected prevalence of multimorbidity (basic or complex) increases by 34% from 53·8% in 2019 to 71·9% (95% uncertainty interval 71·8-72·0) in 2049. This rise equates to an 84% increase in the number of people with multimorbidity: from 19·2 million in 2019 to 35·3 million in 2049 (35·3 million to 35·4 million). This projected increase is greatest in the most deprived quintile, with an excess 1·07 million (1·04 million to 1·10 million) cumulative incident basic multimorbidity cases and 0·70 million (0·67 million to 0·74 million) complex multimorbidity cases over and above the projected cases for the least deprived quintile, largely driven by inequalities in those younger than 65 years. The median expected number of years to be lived without multimorbidity at age 30 years in 2019 is 15·12 years (14·62-16·01) in the least deprived IMD quintile and 12·15 years (11·61-12·60) in the most deprived IMD quintile. The number of people living with multimorbidity will probably increase substantially in the next 30 years, a continuation of past observed increases partly driven by changing population size and age structure. Inequalities in the multimorbidity burden increase at each stage of disease accumulation, and are projected to widen, particularly among the working-age population. Substantial action is needed now to address population health and to prepare health-care and social-care systems for coming decades. University of Liverpool and National Institute for Health and Care Research School for Public Health Research.

Effects of long-term HbA1c variability on serious infection risks in patients with type 2 diabetes and the influence of age, sex and ethnicity: A cohort study of primary care data

AimsLong-term HbA1c (glycated haemoglobin) variability is associated with micro- and macrovascular complications in Type 2 diabetes (T2D). We explored prospective associations between HbA1c variability and serious infections, and how these vary by HbA1c level, age, sex and ethnicity. Methods411,963 T2D patients in England, aged 18–90, alive on 01/01/2015 in the Clinical Practice Research Datalink with ≥ 4 HbA1c measurements during 2011–14. Poisson regression estimated incidence rate ratios (IRRs) for infections requiring hospitalisation during 2015–19 by HbA1c variability score (HVS) and average level, adjusting for confounders, and stratified by age, sex, ethnicity and average level. Attributable risk fractions (AF) were calculated using reference categories for variability (HVS < 20) and average level (42–48 mmol/mol). ResultsAn increased infection risk (IRR > 1.2) was seen with even modest variability (HVS ≥ 20, 73 % of T2D patients), but only at higher average levels (≥64 mmol/mol, 27 % patients). Estimated AFs were markedly greater for variability than average level (17.1 % vs. 4.1 %). Associations with variability were greater among older patients, and those with lower HbA1c levels, but not observed among Black ethnicities. ConclusionsHbA1c variability between T2D patients’ primary care visits appears to be associated with more serious infections than average level overall. Well-designed trials could test whether these associations are causal.

Adopting the RE-AIM analytic framework for rural program evaluation: experiences from the Advance Care Planning via Group Visits (ACP-GV) national evaluation.

To support rigorous evaluation across a national portfolio of grants, the United States Department of Veterans Affairs (VA) Office of Rural Health (ORH) adopted an analytic framework to guide their grantees' evaluation of initiatives that reach rural veterans and to standardize the reporting of outcomes and impacts. Advance Care Planning via Group Visits (ACP-GV), one of ORH's Enterprise-Wide Initiatives, also followed the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. ACP-GV is a national patient-centered intervention delivered in a large, veterans integrated healthcare system. This manuscript describes how RE-AIM was used to evaluate this national program and lessons learned from ORH's annual reporting feedback to ACP-GV on their use of the framework to describe evaluation impacts. We used patient, provider, and site-level administrative health care data from the VA Corporate Data Warehouse and national program management databases for federal fiscal years (FY) spanning October 1, 2018-September 30, 2023. Measures included cumulative and past FY metrics developed to assess program impacts. RE-AIM constructs included the following cumulative and annual program evaluation results. ACP-GV reached 54,167 unique veterans, including 19,032 unique rural veterans between FY 2018 to FY 2023. During FY 2023, implementation adherence to the ACP-GV model was noted in 91.7% of program completers, with 55% of these completers reporting a knowledge increase and 14% reporting a substantial knowledge increase (effectiveness). As of FY 2023, 66 ACP-GV sites were active, and 1,556 VA staff were trained in the intervention (adoption). Of the 66 active sites in FY 2023, 27 were sites previously funded by ORH and continued to offer ACP-GV after the conclusion of three years of seed funding (maintenance). Lessons learned developing RE-AIM metrics collaboratively with program developers, implementers, and evaluators allowed for a balance of clinical and scientific input in decision-making, while the ORH annual reporting feedback provided specificity and emphasis for including both cumulative, annual, and rural specific metrics. ACP-GV's use of RE-AIM metrics is a key step towards improving rural veteran health outcomes and describing real world program impacts.

Abstract 3549: PMed-TRIAL PDX database: Integrated multidimensional information to facilitate new drug development

Abstract PDX models play a crucial role in pre-clinical research for new drugs. In recent years, research based on PDX models has entered the 'Big Data Era.' On the one hand, there are numerous factors influencing drug responses, such as cancer types, genetic backgrounds, clinical treatment histories, and more. Therefore, selecting the appropriate PDX models for drug efficacy testing based on relevant information has become increasingly important. On the other hand, conducting in-depth genomics and transcriptomics analyses on PDX models allows us to gain a deeper understanding of the mechanisms of drug efficacy and drug sensitivity. After years of effort, LIDE have successfully built and maintained over 1800 PDX models from 40 different cancer types. And in this year (2023), we launched LIDE’s PMed-TRIAL PDX database website. As a comprehensive database of PDX models, it includes fundamental PDX clinical information, medication history, growth curves, and standard of care (SOC) data. It also contains whole exome sequencing data and RNA-seq data. Cancer is a complex and heterogeneous disease characterized by tumor heterogeneity, encompassing both inter-tumor diversity (between patients) and intra-tumor variability (within a single patient), thereby complicating drug responsiveness. PDX models preserve heterogeneity of individual patients, and a robust PDX cohort is essential for accurately representing inter-tumor heterogeneity. LIDE’s PDX cohorts effectively capture representative molecular subtypes across various major cancer types, highlighting their potential as a valuable resource for pre-clinical trials. LIDE’s PMed-TRIAL PDX database comes with a concise and efficient web user interface. Users can search and filter models using keywords, such as drug names, gene symbols, and other information. The website can now be accessed via the following URL (https://pmed.lidebiotech.com:8000/). Citation Format: Xinlei Chen, Youbin Guo, Loc Van, Josh Caggiula, Xiaorong Gu, Danyi Wen. PMed-TRIAL PDX database: Integrated multidimensional information to facilitate new drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3549.

Abstract 807: An ethno-phenomenology based approach to study breast cancer disparity in rural India

Abstract There are fundamental lack of right methodologies to study the breast cancer disparity in rural India. Here, we have used an ethnographic-phenomenology approach to study the disparity in breast cancer care in Assam by using retrospective database of a rural cancer care non-profit clinic, the KaviKrishna Telemedicine Care (KTC), set up 1994 to develop a CBPR based cancer care (Abstract 3342, AACR 2019). Methodology: For one and half years, the doctors, researchers, nurses, caregivers, and physician assistants of KaviKrishna Laboratory and KaviKrishna Telemedicine Care were interviewed (total number: 15), and plus literature of the lab and the clinic were studied. Then, the breast cancer patients (n=10) and the families were subjected to ethnographies-phenomenology study to find their experiences of treatment and suffering. Next, thematic analysis and grounded theory were applied to come up with novel approaches to reduce breast cancer disparity. For comparison, 5 patients from rich urban families were interviewed. Results: We found that 10/10 patients found it extremely difficult to navigate the complexity of care in urban hospitals, but found the KTC approachable and comfortable. Importantly, these 10 breast cancer patients were positively responding to our KTC based care services/focussed group discussion (https://zenodo.org/records/8062404). They revealed positive experiences in emotionally and psychologically dealing with diagnostic, surgery, and chemotherapy processes. Next, our analysis of the last 25 years of the care data at KTC showed that in rural population, the loweconomic status, inadequate access to breast cancer detection clinics, lack of awareness,indecisive treatment protocols and social stigma are the main reasons for breast cancer disparity. Grounded theory-based analysis indicates that cancer biomarker study in rural populations can be a suitable method for early detection. Whole genome sequencing of cancer tissue frombiopsy samples shall provide landscape of mutation in the entire coding as well as noncodingregions. Mutation profiling along with correlation studies from patients can lead to discoveryof novel marker(s). Primary health care clinics in rural areas can be trained to collect patientsamples, isolate genomic DNA and perform PCR for the markers (a similar model asfollowed during Covid-19). The PCR samples can be sent to non-profit advanced diagnostic center such as KaviKrishna Lab to perform sequencing to study mutation markers. The method is also cost effective to both thecare givers and the patients as compared to setting up advanced diagnostic lab in every part ofrural area. Conclusion: The community based participatory research based model developed by KTC can be an effective strategy to reduce breast cancer disparity in the rural population since the primary health clinics shall act as a bridge between geologically isolated areas and modern technology. Citation Format: Debduti Dutta, Rupam Das, Chayanika Das, Tulika Sarma, Sonali Das, Anuja Dutta, Tutumoni Baishya, Lekhika Pathak, Bikul Das. An ethno-phenomenology based approach to study breast cancer disparity in rural India [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 807.

Risk of Parkinson's disease in people aged ≥50 years with new-onset anxiety: a retrospective cohort study in UK primary care.

A history of anxiety is more common in people with Parkinson's disease (PD). The prospective risk of PD in those newly presenting with anxiety and factors that increase the risk of PD in patients with anxiety have not been investigated. To investigate the incidence of PD in people with anxiety aged ≥50 years and clinical features associated with later diagnosis of PD in people with anxiety. A retrospective cohort study using UK primary care data between 2008 and 2018, assessing patients with new-onset anxiety aged ≥50 years. Weibull survival regression models were fitted and hazard ratios (HRs) for modelling time-to-PD was estimated in those with and without anxiety, and when determining the risk of developing PD in those with anxiety. Results were adjusted for sociodemographic and lifestyle factors, and relevant physical and mental health conditions. The risk of PD increased two-fold compared with the non-anxiety group after adjustment for age, sex, social deprivation, lifestyle factors, severe mental illness, head trauma, and dementia (HR 2.1, 95% confidence interval = 1.9 to 2.4). In those with anxiety, the presence of depression, hypotension, tremor, rigidity, balance impairment, constipation, sleep disturbance, fatigue, and cognitive impairment were associated with an increased risk of developing PD. The risk of developing PD was at least doubled in people with anxiety compared with those without. The clinical features of those who developed PD can help identify patients presenting with anxiety who are in the prodromal phase of PD.

Early prediction of ventricular peritoneal shunt dependency in aneurysmal subarachnoid haemorrhage patients by recurrent neural network-based machine learning using routine intensive care unit data

Aneurysmal subarachnoid haemorrhage (aSAH) can lead to complications such as acute hydrocephalic congestion. Treatment of this acute condition often includes establishing an external ventricular drainage (EVD). However, chronic hydrocephalus develops in some patients, who then require placement of a permanent ventriculoperitoneal (VP) shunt. The aim of this study was to employ recurrent neural network (RNN)-based machine learning techniques to identify patients who require VP shunt placement at an early stage. This retrospective single-centre study included all patients who were diagnosed with aSAH and treated in the intensive care unit (ICU) between November 2010 and May 2020 (n = 602). More than 120 parameters were analysed, including routine neurocritical care data, vital signs and blood gas analyses. Various machine learning techniques, including RNNs and gradient boosting machines, were evaluated for their ability to predict VP shunt dependency. VP-shunt dependency could be predicted using an RNN after just one day of ICU stay, with an AUC-ROC of 0.77 (CI: 0.75–0.79). The accuracy of the prediction improved after four days of observation (Day 4: AUC-ROC 0.81, CI: 0.79–0.84). At that point, the accuracy of the prediction was 76% (CI: 75.98–83.09%), with a sensitivity of 85% (CI: 83–88%) and a specificity of 74% (CI: 71–78%). RNN-based machine learning has the potential to predict VP shunt dependency on Day 4 after ictus in aSAH patients using routine data collected in the ICU. The use of machine learning may allow early identification of patients with specific therapeutic needs and accelerate the execution of required procedures.

A cluster-randomized study to evaluate the effectiveness and cost-effectiveness of the Assessment of Burden of Chronic Conditions (ABCC) tool in South Tyrolean primary care for patients with COPD, asthma, type 2 diabetes, and heart failure: the ABCC South Tyrol study

BackgroundChronic diseases, such as chronic obstructive pulmonary disease (COPD), asthma, type 2 diabetes, and heart failure, often coexist and contribute to a significant burden on individuals and health systems. The Assessment of Burden of Chronic Conditions (ABCC) tool, already in routine clinical use in the Netherlands, aims to comprehensively assess and visualize disease burden, stimulate self-management, and encourage shared decision-making. This study aims to validate the German and Italian versions of the ABCC tool and evaluate its effectiveness and cost-effectiveness in the South Tyrolean Primary Care setting.MethodsThis is a cluster-randomized study involving approximately 400 patients with COPD, asthma, type 2 diabetes, and heart failure who received care from the South Tyrolean General Practices. Initially, the ABCC tool will be translated into German and Italian and validated. Subsequently, half of the participants will use the validated ABCC tool for patient-reported outcome measurement assessments, while the other half will receive usual care. The primary outcome measure is the change in the patients’ perception of the quality of care after 18 months. The secondary outcomes included changes in quality of life, self-management behavior, and healthcare utilization. The missing data will be managed using multiple imputations. Additionally, a cost-effectiveness analysis that considers the direct medical costs reimbursed by the National Health Service will be conducted.DiscussionThis study provides insights into the application, validation, and efficacy of the ABCC tool in the South Tyrolean healthcare context. The tool’s potential to enhance person-centered care, improve the quality of life, and possibly reduce healthcare costs could greatly contribute to sustainable healthcare. The challenges of implementation, such as software integration and the use of an EU data platform, will provide lessons for future international patient care data management.Trial registrationISRCTN registry, ISRCTN13531607. Registered on August 23, 2023.

Capture, Coding, and Reporting of Health Care and Medicine Information in Australian Workers' Compensation Systems.

This study aims to characterize the approaches to collecting, coding, and reporting health care and medicines data within Australian workers' compensation schemes. We conducted a cross-sectional survey of data and information professionals in major Australian workers' compensation jurisdictions. Questionnaires were developed with input from key informants and a review of existing documentation. Twenty-five participants representing regulators (40%) and insurers (60%) with representation from all Australian jurisdictions were included. Health care and medicines data sources, depth, coding standards, and reporting practices exhibited significant variability across the Australian workers' compensation schemes. Substantial variability exists in the capture, coding, and reporting of health care and medicine data in Australian workers' compensation jurisdictions. There are opportunities to advance understanding of medicines and health service delivery in these schemes through greater harmonization of data collection, data coding, and reporting.

Impact of COVID-19 national response on primary care utilisation in Singapore: an interrupted time-series analysis

Since the start of the pandemic, many national responses, such as nationwide lockdowns, have been implemented to curb the spread of COVID-19. We aim to assess the impact of Singapore’s national responses on primary care utilisation. We performed an interrupted time series using acute and chronic primary care data of 3 168 578 visits between 1 September 2019 and 31 August 2020 over four periods: before any measures were put in place, during Disease Outbreak Response System Condition (DORSCON) Orange, when Circuit Breaker was instituted, and when Circuit Breaker was lifted. We found significant mean reductions in acute and chronic primary care visits immediately following DORSCON Orange and Circuit Breaker. DORSCON Orange was associated with − 2020 mean daily visits (95% CI − 2890 to − 1150). Circuit Breaker was associated with a further − 2510 mean daily visits (95% CI − 3660 to − 1360). Primary care utilisation for acute visits remained below baseline levels even after the Circuit Breaker was lifted. These significant reductions were observed in both acute and chronic visits, with acute visits experiencing a steeper drop during DORSCON Orange. Understanding the impact of COVID-19 measures on primary care utilisation will be useful for future public health planning.

Identification of a hyperinflammatory sepsis phenotype using protein biomarker and clinical data in the ProCESS randomized trial

Sepsis is a heterogeneous syndrome and phenotypes have been proposed using clinical data. Less is known about the contribution of protein biomarkers to clinical sepsis phenotypes and their importance for treatment effects in randomized trials of resuscitation. The objective is to use both clinical and biomarker data in the Protocol-Based Care for Early Septic Shock (ProCESS) randomized trial to determine sepsis phenotypes and to test for heterogeneity of treatment effect by phenotype comparing usual care to protocolized early, goal-directed therapy(EGDT). In this secondary analysis of a subset of patients with biomarker sampling in the ProCESS trial (n = 543), we identified sepsis phenotypes prior to randomization using latent class analysis of 20 clinical and biomarker variables. Logistic regression was used to test for interaction between phenotype and treatment arm for 60-day inpatient mortality. Among 543 patients with severe sepsis or septic shock in the ProCESS trial, a 2-class model best fit the data (p = 0.01). Phenotype 1 (n = 66, 12%) had increased IL-6, ICAM, and total bilirubin and decreased platelets compared to phenotype 2 (n = 477, 88%, p < 0.01 for all). Phenotype 1 had greater 60-day inpatient mortality compared to Phenotype 2 (41% vs 16%; p < 0.01). Treatment with EGDT was associated with worse 60-day inpatient mortality compared to usual care (58% vs. 23%) in Phenotype 1 only (p-value for interaction = 0.05). The 60-day inpatient mortality was similar comparing EGDT to usual care in Phenotype 2 (16% vs. 17%). We identified 2 sepsis phenotypes using latent class analysis of clinical and protein biomarker data at randomization in the ProCESS trial. Phenotype 1 had increased inflammation, organ dysfunction and worse clinical outcomes compared to phenotype 2. Response to EGDT versus usual care differed by phenotype.

The Clinical Value of Pre-Diagnostic Thrombocytosis for the Detection of Lung Cancer in Primary Care.

Thrombocytosis is a risk marker for lung cancer in primary care. We investigated whether thrombocytosis presents pre-diagnostically for all the histological subtypes of lung cancer and its association with the stage at diagnosis. A matched cohort study used English electronic primary care data linked to the national cancer registry. Patients diagnosed with lung cancer aged ≥40 years with no prior history of malignancy were matched by age, sex, and general practice to five controls without lung cancer. Multivariable logistic regression models quantified the incidence of pre-diagnostic thrombocytosis and advanced-stage diagnoses, adjusting for COPD diagnosis, smoking status, and anti-platelet drug prescriptions. A total of 9504 cases were matched to 45,647 controls, consisting of 3260 (34%) adenocarcinomas (ADC), 2020 (21%) squamous cell carcinomas (SCC), 70 (<1%) large-cell carcinomas (LCC), and 1089 (12%) small-cell lung cancers (SCLC). The patients with lung cancer were 8.9 (95% CI 8.0-9.9) times more likely to exhibit pre-diagnostic thrombocytosis than the controls. The odds ratios were highest for the comparison between SCC and ADC (1.8, 95% CI 1.5-2.1). Thrombocytosis is associated with advanced-stage ADC and SCC but presented equally for early- and advanced-stage SCLC. Pre-diagnostic thrombocytosis may aid in the detection of all the histological subtypes in primary care.

Risk of Cancer in Patients With Diverticular Disease: A Population-Based Cohort Study

Several studies have investigated the association between diverticular disease (DD) and colorectal cancer. However, whether there is an association between DD and malignancies other than those in the colorectum remains uncertain. For the 1978-2019 period, we conducted a nationwide, population-based cohort study using national Danish health care data. We followed patients with DD for up to 20 years, beginning 1 year after the date of DD diagnosis until the first occurrence of incident cancer, emigration, death, 20 years of follow-up, or December 31, 2019. We calculated cumulative incidence proportions of cancer and standardized incidence ratios (SIRs) comparing cancer incidence among patients with DD with that in the general population. We identified 200,639 patients with DD, of whom 20,498 were diagnosed with cancer during the 1-20 years after their DD diagnosis. The SIRs were increased for most cancer sites except for those in the colorectum (SIR, 0.75; 95% confidence interval, 0.72-0.78). The highest SIRs were observed for cancers of the lung, bronchi, and trachea (SIR, 1.20; 95% confidence interval, 1.15-1.24) and kidney (SIR, 1.27; 95% confidence interval, 1.16-1.39). Our findings show an increased long-term relative risk of cancer following a diagnosis of DD. These findings are likely caused by prevalence of numerous risk factors in patients with DD that confer an increased risk of cancer. The decreased relative risk of colorectal cancer might be explained by an increased likelihood of patients with DD undergoing colonoscopy with polypectomy.

The Value of Emergency Care Data Set (ECDS) Presentation Codes for Predicting Mortality and Inpatient Admission.

Early identification of patients at higher risk of death and hospital admission is an important problem in Emergency Departments (ED). Most triage scales were developed before current electronic healthcare records were developed.The implementation of a national Emergency Care Data Set (ECDS) allows for the standardised recording of presenting complaints and the use of Electronic Patient Records (EPR) offers the potential for automated triage. The mortality risk and need for hospital admission associated with the different presenting complaints in a standardised national data set has not been previously reported. This study aimed to quantify the risks of death and hospitalisation from presenting complaints. This would be valuable in developing automated triage tools and decision support software. We conducted an observational retrospective cohort study on patients who visited a single ED in 2021. The presenting complaints related to subsequent attendances were excluded. This patient list was then manually matched with a routinely collected list of deaths. All deaths that occurred within 30 days of attendance were included. Data was collected from 84,999 patients, of which 1,159 people died within 30 days of attendance.The mortality rate was the highest in cardiac arrest [32(78.1%)], cardiac arrest due to trauma [2(50%)] and respiratory arrest [3(50%)]. Drowsy [17(12%)], hypothermia [3(13%)] and cyanosis [1(10%)] were also high-risk categories. Chest pain [34(0.6%)] was not a high-risk presenting complaint. The initial presenting complaint in ECDS may be usefulto identify people at higher and lower risk of death. This information is useful for building automated triage models.

Opinions and Beliefs of Adults with Intellectual Disabilities or Visual Impairment about Research with Routine Care Data

Research with routine care data (RCD) is not always a transparent process to the people receiving care for disabilities or impairment. This study aimed to understand the point of view on secondary use of care data for research from the people with visual or intellectual disabilities themselves. In total 36 participants from the UK and the Netherlands were interviewed (20 with intellectual disabilities, 16 with visual impairment). Transcripts were analysed inductively using the Framework Approach. Interview results showed that people from both countries and disability groups assumed that RCD research took place and saw potential contributions to care quality as grounds for legitimacy. Their themes of concern were about inaccuracy and threats to anonymity of data. Interviewees made suggestions for improving conditions under which RCD research can happen with informed consent. In addition to informing data governance policies of organisations serving people with disabilities, findings underscore the contribution that people with disabilities can make to data governance.

Designing a Block Chain Based Network for the Secure Exchange of Medical Data in Healthcare Systems

ABSTRACT An identity-based cryptosystem called signcryption is investigated in this study in order to efficiently and securely exchange healthcare information inside a data-sharing network, utilizing the idea of bilinear pairings. A distributed tamperproof database protects all health records and is uploaded to it and replicated over a group of nodes that are linked to form a peer-to-peer network. As a result, each health record is treated as an individual event that is time stamped and assigned a cryptographic hash. On a similar note, transaction blocks include all the record events. Every node in the network has a copy of the ledger of hashed records and events. Additional information such as user permission lists are also included in this health care blockchain network, which serve as instructions for the network. To guarantee the data integrity of EHRs, this article provides security for exchanging EHRs across medical institutions and inside the organization, and ultimately safe exchange of EHRs by removing a third-party trustworthy third-party provider. Various cryptographic methods are being utilized on the blockchain in order to ensure security for the exchange of health care data. Signcryption is utilized in the suggested design approach for secure health care data exchange.

Statistical analysis plan for the PRO B study: open-label, superiority randomised controlled trial of alarm-based patient-reported outcome monitoring in patients with metastatic breast cancer

BackgroundWith an increasing collection of patient-reported outcomes (PROs) to measure health-related quality of life (HRQoL) in oncological patients, there is still a lack of standardised strategies on how to interpret and use these data in patient care. Prior research has shown support for the use of digital PRO monitoring together with alarm systems to notify clinicians when the PRO values are deteriorating. This system has demonstrated advantages in improving HRQoL and increasing survival rates among oncology patients. Hence, we designed the PRO B study, a superiority multi-centre randomised controlled trial, to investigate the effects of alarm-based monitoring in metastatic breast cancer patients in Germany. The study protocol for the PRO B study was published in September 2021, and this manuscript describes a formal statistical analysis plan (SAP) for the PRO B study to improve the transparency and quality of this trial.Methods and designThe trial aimed to recruit 1000 patients with metastatic breast cancer. However, as of the completion of recruitment on June 15, 2023, we have successfully enrolled 924 patients from 52 breast cancer centres. Patients were 1:1 stratified randomised to the intervention and control groups. App-based PRO questionnaires are sent weekly to the intervention group and every 3 months to the control group. Only patients in the intervention group trigger an alarm if their PRO scores deteriorate, and they are subsequently contacted by the local care team within 48 h. The primary outcome is the fatigue score at 6 months, and secondary outcomes are other HRQoL and overall survival. Evaluation of the superiority of the intervention will be done using a linear mixed model with random intercepts for study centres.ConclusionThis detailed SAP defines the main components of the statistical analysis for the PRO B study to assist the statistician and prevent bias in selecting analysis and reporting findings. Version 1 of the SAP was finalised on January 18, 2024.Trial registrationDRKS (German Clinical Trials Register) DRKS00024015. Registered on February 15, 2021.