Anonymized Dataset Research Articles

(258) LACK OF PATIENT COMPLIANCE COMPROMISES SAFETY OF VASECTOMY

Abstract Objectives Vasectomy is worldwide considered a cost-effective, definitive and safe method for male contraception and therefore one of the most frequently performed outpatient surgeries. However, repeated spermiogram controls to ascertain azoospermia are generally recommended to guarantee successful operation. This legal necessity, with all its implications, is noted in all signed informed consents basically to avert unjustified medical liability issues. Methods Members of the Austrian Andrological Working Group participate to this nationwide, retrospective data analysis for vasectomy in 2020. The primary aim was to investigate patients adherence/consultations according to the preoperative provided instructions. An anonymous dataset over 610 patients was analyzed. Results The mean patient age of was 41.60 years (SD 6.69), with the youngest male being 25 years old and the oldest male being 68 years old. A documented follow-up appointment for first spermiogram-analysis was scheduled for these patients in 96.89% (n=591), but, only 90.19% (n=533) attended to these appointments by achieving azoospermia in 86.00% (n=436). In 71 cases vital sperm could still be detected in the ejaculate (unknown in 26 cases). A second spermiogram control was scheduled for 87.24% (n=465 patients; unknown in 24 cases) and was attended by 392 patients (84.30% of patients), hereby azoospermia was achieved in 91.33% (n=358; unknown in 2 cases). Complications such as hematoma, edema, or infection were reported in only 25 patients (4.10%). Failure to achieve sperm clearance was age-independent. Conclusions Although being a safe procedure with a low complication rate, post-vasectomy follow-up lacks extensive adherence. Roughly 10% of patients missed the required post-vasectomy controls and this regardless of the informed consent signed by the patient. Due to persisting oligozoospermia in 14% of patients after the first and 9% after the second follow-up visit, patients must be encouraged to attend the previously scheduled follow-up appointments. To guarantee a successful operation and safe contraception follow-up visits are crucial. Conflicts of Interest None

Private healthcare sector spine surgery: patient and surgeon profiles from a large open medical scheme in South Africa.

Background It is generally understood that the profile of spine surgery varies by setting, based on factors such as the age profile of the population, the economic context and access to healthcare. Relatively little is known about the profile of spine surgery in South Africa, although one previous report from the public healthcare sector suggested a high burden of trauma and infection-related surgery. To our knowledge, there has been no formal investigation in the private sector. A holistic understanding of spine surgery within our setting may be beneficial for applications such as resource allocation and informing the training needs of future specialists. Objectives To provide insight into spinal surgery in the South African private healthcare sector by describing spine surgery characteristics within a large open medical scheme, including both patient and surgeon profiles. Methods This retrospective review included adult inpatient spine surgeries funded by the largest open medical scheme in South Africa between 2008 and 2017. An anonymized dataset extracted from the scheme records included patient demographics, ICD-10 codes, procedure codes and surgeon specialization. Descriptive patient and surgery data were presented. Furthermore, the association between patient and surgery factors and surgeon specialization was investigated using univariate and multivariate analyses. Results A total of 49,576 index spine surgeries were included. The largest proportion of surgeries involved members 40-59 years old (n=23,543, 48%), approximately half involved female members (n=25,293, 51%) and most were performed by neurosurgeons (n=35,439, 72%). At least 37,755 (76%) surgeries were for degenerative pathology, 2,100 (4%) for trauma and 242 (0.5%) for infection. Adjusted risk ratios (aRR) significantly associated with orthopaedic surgeon specialization included cervical spine region aRR = 0.49 (95% C.I. 0.39-0.61), trauma aRR = 1.50 (95% C.I. 1.20-1.88), deformity aRR = 1.77 (95% C.I. 1.33-2.35) and blood transfusion aRR = 1.46 (95% C.I. 1.12-1.91). Conclusion Spine surgery in South Africa's largest open medical scheme was dominated by surgery for degenerative pathology in older adults and was performed largely by neurosurgeons. This constituted a stark contrast to a previous report from the public sector and highlighted a mismatch between exposure during public sector registrar training and private practice post-specialization. The findings support the need for private-public collaboration as well as the importance of spine fellowships for all specialists intending to practice spine surgery.

Rehabilitating Homeless: Dataset and Key Insights

This paper presents a large anonymized dataset of homelessness alongside insights into the data-driven rehabilitation of homeless people. The dataset was gathered by a large non-profit organization working on rehabilitating the homeless for twenty years. This is the first dataset that we know of that contains rich information on thousands of homeless individuals seeking rehabilitation. We show how data analysis can help to make the rehabilitation of homeless people more effective and successful. Thus, we hope this paper alerts the data science community to the problem of homelessness.

Exposure to Domestic Abuse and the Subsequent Development of Atopic Disease in Women.

Exposure to domestic violence and abuse (DVA) is a global public health issue associated with substantial morbidity and mortality. There are few high-quality studies that assess the impact of DVA exposure on the development of atopic disease. To examine the association between exposure to DVA and the subsequent development of atopy. In this population-based, retrospective, open cohort study, we identified women with no history of atopic disease between January 1, 1995 and September 30, 2019 from IQVIA Medical Research Data, an anonymized UK primary care dataset. We used clinical codes to identify exposed patients (those with a code identifying exposure to DVA; n= 13,852) and unexposed patients (n= 49,036), who were matched by age and deprivation quintile. Cox proportional hazards regression was used to calculate hazard ratios (HRs) (with 95% CIs) of developing atopic disease: asthma, atopic eczema, or allergic rhinoconjunctivitis. During the study period, 967 exposed women (incidence rate, 20.10/1,000 person-years) developed atopic disease, compared with 2,607 unexposed women (incidence rate, 13.24/1,000 person-years). This translated to an adjusted HR of 1.52 (95% CI, 1.41-1.64) accounting for key confounders; asthma (adjusted HR= 1.69; 95% CI, 1.44-1.99), atopic eczema (adjusted HR= 1.40; 95% CI, 1.26-1.56), and allergic rhinoconjunctivitis (adjusted HR= 1.63; 95% CI, 1.45-1.84). Domestic violence and abuse is a significant global public health issue. These results demonstrate a significant associated risk for developing atopic disease. Public health approaches to the prevention and detection of DVA are necessary to reduce the associated ill health burden.

P-084 INDEPENDENT PATIENT DATA META-ANALYSIS OF RANDOMIZED CONTROLLED TRIALS ON PROPHYLACTIC MESH PLACEMENT FOR INCISIONAL HERNIA PREVENTION: STUDY PROTOCOL

Abstract Aim In high risk groups such as patients undergoing open abdominal aortic aneurysm (AAA) surgery, the risk of incisional hernia (IH) formation is up to 69%. IH can be prevented through prophylactic mesh augmentation (PMA). Several randomized controlled trials (RCT) have shown a significant decrease in IH formation after PMA. However, due to study design and sample size issues, implementation of PMA into daily practice is lacking. Methods International collaboration will be initiated under the guidance of the European Hernia Society. RCTs studying PMA after open AAA surgery will be identified through a literature search. Corresponding authors of the selected studies will be approached for participation by e-mail, telephone or other means. Where independent patient data (IPD) are available, a suitable data delivery agreement (DDA) will be drafted and signed upon agreement. Pseudonymised or anonymised data sets will be securely transferred and stored, and relevant study parameters will be harmonized. A 1-stage IPD meta-analysis (IPDMA) will be performed, stratified per centre, assessing PMA effectivity according to the intention-to-treat principle. If a 1-stage IPDMA is not feasible, a 2-step IPDMA will be performed, analysing each RCT separately and pooling outcomes. Results will be published in internationally recognized, peer-reviewed journals. This study protocol was registered under PROSPERO number CRD42022347881. Conclusions Gathering IPD across relevant RCTs and performing an IPDMA on the combined data will help to better establish effectiveness of PMA in preventing IH after open AAA surgery. Secondarily, subgroup analyses will allow identification of patients who will benefit from PMA most.

Association between Electronic Health Record Implementations and Hospital-Acquired Conditions in Pediatric Hospitals.

Implementing an electronic health record (EHR) is one of the most disruptive operational tasks a health system can undergo. Despite anecdotal reports of adverse events around the time of EHR implementations, there is limited corroborating research, particularly in pediatrics. We utilized data from Solutions for Patient Safety (SPS), a network of 145+ children's hospitals that share data and protocols to reduce harm in pediatric care delivery, to study the impact of EHR implementations on patient safety. Determine if there is an association between the time immediately surrounding an EHR implementation and hospital-acquired conditions (HACs) rates in pediatrics. A survey of information technology leaders at pediatric institutions identified EHR implementations occurring between 2012 and 2022. This list was cross-referenced with the SPS database to create an anonymized dataset of 27 sites comprising monthly HAC and care bundle compliance rates in the 7 months preceding and succeeding the transition. Six HACs were analyzed: central-line associated bloodstream infection (CLABSI), catheter-associated urinary tract infection (CAUTI), adverse drug events, surgical site infections (SSIs), pressure injuries (PIs), and falls, in addition to four associated care bundle compliance rates: CLABSI and CAUTI maintenance bundles, SSI bundle, and PI bundle. To determine if there was a statistically significant association with EHR implementation, the observation period was divided into three eras: "before" (months -7 to -3), "during" (months -2 to +2), and "after" go-live (months +3 to +7). Average monthly HAC and bundle compliance rates were calculated across eras. Paired t-tests were performed to compare rates between the eras. No statistically significant increase in HAC rates or decrease in bundle compliance rates was observed across the EHR implementation eras. This multisite study detected no significant increase in HACs and no decrease in preventive care bundle compliance in the months surrounding an EHR implementation.

Targeted Review and Amalgamation of Unmapped Major Trauma and Ambulance Data in Ireland: TRAUMA Study

Introduction:The trauma care system in Ireland is being re-configured to have major trauma centers for severe injuries and other sites for less severe injuries. This is to ensure patients are brought quickly to the most appropriate hospital to manage their injuries. The National Ambulance Service (NAS) electronic Patient Care Record (ePCR) records what happens to patients before they reach the hospital and the Major Trauma Audit (MTA) captures data on patients’ hospital treatment. These datasets are currently separate and if they could be joined, they would inform important decisions on which hospitals to take patients. This study aims to investigate joining these datasets to create a seamless database of the patient journey from roadside to recovery.Method:Proof of Concept–The ePCR and MTA datasets will be linked on a once-off basis. The combined anonymized dataset will then be analyzed to identify pre-hospital characteristics that determine the need to bypass smaller hospitals and bring patients to a larger major trauma center or trauma unit.Stakeholder input for ongoing dataset combination and utilization–A stakeholder consultation process will explore the best way to make a GDPR-compliant combination of datasets on an on-going basis, including geo-location data and the inclusion of patient reported outcome measures. This will incorporate the requirements of the Data Protection Commissioner, National Office of Clinical Audit, patients, clinicians, NAS, HSE and other stakeholders.Geospatial implications of major trauma services–Once ongoing data combination is approved, we will determine geospatial implications of the trauma network for prehospital care configuration and the patient journey.Results:Study results will inform prehospital service configuration to ensure safe and equitable patient management.Conclusion:The data arising from this study will capture the full trauma patient journey. This data is essential to inform policy and practice for trauma care in Ireland.

ATPP : A Mobile App Prediction System Based on Deep Marked Temporal Point Processes

Predicting the next application (app) a user will open is essential for improving the user experience, e.g., app pre-loading and app recommendation. Unlike previous solutions that only predict which app the user will open, this article predicts both the next app and the time to open it. Time prediction is essential to avoid loading the next app too early and consuming unnecessary resources on smartphones. To predict the next app and open time jointly, we model the app usage sequence as a marked temporal point process (MTPP), whose conditional intensity function can capture the probability of a new app usage event. We develop a novel data-driven MTPP-based app prediction system, named ATPP (App Temporal Point Process), which adopts a recurrent neural network architecture to learn the MTPP conditional intensity function for app prediction. ATPP adopts a set of techniques to incorporate the unique features of app prediction in our RNN architecture, including learning the correlated usage behavior of different apps by representation learning, the temporal dependency of app usage by an attention mechanism, and the location-related app usage behavior by feature extraction and fusion layer. We conduct extensive experiments on a large-scale anonymized app usage dataset to verify ATPP’s effectiveness.

Life expectancy of mesocephalic, dolichocephalic and brachycephalic dog breeds in Switzerland

Lifespan and time of death of dogs died in Switzerland between 2016 and 2020 were evaluated in order to increase the awareness of the public to animal welfare-related consequences of extreme brachycephalic breeding and to clarify the torture breeding problem of dogs suffering from brachycephalic obstructive airway syndrome (BOAS). Skull shape, body size, country of origin and altitude of the registered place of residence at the time of death were analysed in a set of anonymized data from the national animal database Amicus as potential factors influencing the life expectancy. Death rate during summer months and the altitude of the reported place of residence at death were analysed in relation to the skull shape to demonstrate the heat intolerance of brachycephalic dog breeds. The final dataset included 137 469 dogs. The average age of death of the study population was 11,8 years, mixed breeds reaching a higher average age of 12,4 years than purebred dogs with 11,5 years. Bodyweight classification, skull shape and the origin of the dogs had a significant effect on the average lifespan. Giant breeds reached with 9,0 years the lowest mean age compared to the other bodyweight categories. The mean life expectancy of brachycephalic dogs was 9,8 years, i.e., 2,1 and 1,7 years less than mesocephalic and dolichocephalic dogs, respectively. Brachycephalic dogs and dogs imported from abroad showed increased mortality at a young age.

Toward Correlated Data Trading for Private Web Browsing History

The trading of social media data has attracted wide research interests over years. In particular the trading for web browsing histories, when being applied to targeted advertising, produces tremendous economic value for data consumers. However, the disclosure of entire browsing histories, even in form of anonymous datasets, poses a huge threat to user privacy. Although some existing solutions have investigated privacy-preserving outsourcing of social media data, unfortunately, they neglected the impact on the data consumer’s utility. In this paper, we propose CEATSE, a CorrElated dAta Trading framework for various kinds of private web browSing historiEs. CEATSE first models the correlation among multiple dimensional features, and then generates the optimal feature clustering scheme. Combined with this scheme, CEATSE next incorporates a correlated data perturbation strategy on each feature cluster, in order to balance the privacy-utility tradeoff. It then quantifies each chosen data contributor’s privacy loss on optimal feature clusters. Through real-data based experiments, our analysis and evaluation results demonstrate CEATSE indeed achieves user privacy protection, the data consumer’s accuracy requirement, and truthfulness, individual rationality as well as budget balance.

Psychotropic Medication Prescribing to Patients with Dementia Admitted to Acute Hospitals in Ireland.

Psychotropic medications are commonly prescribed to people with dementia (PwD) for non-cognitive symptoms of dementia (NCSD), but have significant risks. A national audit was performed in acute hospitals in the Republic of Ireland (ROI) to establish baseline practice prior to the launch and implementation of a National Clinical Guideline on the appropriate prescribing of psychotropic medications for NCSD. The objective of this study was to analyse psychotropic prescribing patterns and compare these with international data and with existing (limited) data from a previous audit round. The pooled anonymous dataset from the second round of the Irish National Audit of Dementia Care (INAD-2) was analysed. The audit had collected retrospective data from 30 random healthcare records from each of 30 acute hospitals in 2019. Inclusion criteria were a clinical diagnosis of dementia of any type, hospital stay of 72 hours or more, and discharge or death within the audit period. Most hospitals (87%) self-audited their healthcare records, but a random sample of six healthcare records (20%) from each hospital were re-audited by a highly trained healthcare auditor. The audit tool was based on a tool used in the England and Wales National Audit of Dementia audit rounds (Royal College of Psychiatrists), adapted to the Irish healthcare setting and national priorities. In total, 893 cases were included, as one hospital could not retrieve 30 cases even within a more prolonged audit period. The sample comprised 55% females and 45% males; the median age was 84 years (interquartile range 79-88 years) and the majority (89.6%) were >75 years of age. Only 52% of healthcare records specified the type of dementia; within these, the most common diagnosis was Alzheimer's disease (45%). Most PwD (83%) were receiving psychotropic medication on admission; 40% were prescribed new or increased psychotropic medication during admission, mainly for medical indications, including end-of-life care and delirium. Anticonvulsants or cognitive enhancers were rarely prescribed for NCSD in hospital. However, new/increased antipsychotic medication was prescribed for NCSD in 11.8-17.6% of the total cohort, while 4.5-7.7% were prescribed a benzodiazepine for anxiety or NCSD. Overall, there was poor documentation of risk/benefit, or of discussion with the patient/family, and apparently inadequate review for efficacy and tolerability. Concurrently, acetylcholinesterase inhibitors appeared to be underused for cognitive impairment in the community. This audit provides baseline data on psychotropic medication prescription for NCSD in Irish hospitals prior to a specific Irish guideline on this topic. Reflecting this, most PwD were receiving psychotropic medications on admission, and many were prescribed new/increased psychotropic medication in hospital, often without evidence of appropriate decision making and prescribing processes.

Privacy-Preserving Method for Trajectory Data Publication Based on Local Preferential Anonymity

With the rapid development of mobile positioning technologies, location-based services (LBSs) have become more widely used. The amount of user location information collected and applied has increased, and if these datasets are directly released, attackers may infer other unknown locations through partial background knowledge in their possession. To solve this problem, a privacy-preserving method for trajectory data publication based on local preferential anonymity (LPA) is proposed. First, the method considers suppression, splitting, and dummy trajectory adding as candidate techniques. Second, a local preferential (LP) function based on the analysis of location loss and anonymity gain is designed to effectively select an anonymity technique for each anonymous operation. Theoretical analysis and experimental results show that the proposed method can effectively protect the privacy of trajectory data and improve the utility of anonymous datasets.

Data Resource Profile: The Multiple Sclerosis Documentation System 3D and AOK PLUS Linked Database (MSDS-AOK PLUS)

Real-world evidence in multiple sclerosis (MS) is limited by the availability of data elements in individual real-world datasets. We introduce a novel, growing database which links administrative claims and medical records from an MS patient management system, allowing for the complete capture of patient profiles. Using the AOK PLUS sickness fund and the Multiple Sclerosis Documentation System MSDS3D from the Center of Clinical Neuroscience (ZKN) in Germany, a linked MS-specific database was developed (MSDS-AOK PLUS). Patients treated at ZKN and insured by AOK PLUS were recruited and asked for informed consent. For linkage, insurance IDs were mapped to registry IDs. After the deletion of insurance IDs, an anonymized dataset was provided to a university-affiliate, IPAM e.V., for further research applications. The dataset combines a complete record of patient diagnoses, treatment, healthcare resource use, and costs (AOK PLUS), with detailed clinical parameters including functional performance and patient-reported outcomes (MSDS3D). The dataset currently captures 500 patients; however, is actively expanding. To demonstrate its potential, we present a use case describing characteristics, treatment, resource use, and costs of a patient subsample. By linking administrative claims to clinical information in medical charts, the novel MSDS-AOK PLUS database can increase the quality and scope of real-world studies in MS.

DeepAPP: A Deep Reinforcement Learning Framework for Mobile Application Usage Prediction

This paper aims to predict a set of apps a user will open on her mobile device in the next time slot. Such an information is essential for many smartphone operations, e.g., app pre-loading and content pre-caching, to improve user experience. However, it is hard to build an explicit model that accurately captures the complex environment context and predicts a set of apps at one time. This paper presents a deep reinforcement learning framework, named as DeepAPP, which learns a model-free predictive neural network from historical app usage data. Meanwhile, an online updating strategy is designed to adapt the predictive network to the time-varying app usage behavior. To transform DeepAPP into a practical deep reinforcement learning system, several challenges are addressed by developing a context representation method for complex contextual environment, a general agent for overcoming data sparsity and a lightweight personalized agent for minimizing the prediction time. Extensive experiments on a large-scale anonymized app usage dataset reveal that DeepAPP provides high accuracy (precision 70.6 percent and recall of 62.4 percent) and reduces the prediction time of the state-of-the-art by 6.58×. A field experiment of 29 participants demonstrates DeepAPP can effectively reduce launch time of apps.

Blockchains as a means to promote privacy protecting, access availing, incentive increasing, ELSI lessening DNA databases.

Not all blockchains are created equal, and many cannot accommodate all of the primary characteristics of big data: Variety, Velocity, Volume and Veracity. Currently, public blockchains are slow and clunky, it can be expensive to keep up with the velocity of genomic data production. Further, the transparent and universally accessible nature of public blockchain doesn't necessarily accommodate all of the variety of sequence data, including very private information. Bespoke private permissioned blockchains, however, can be created to optimally accommodate all of the big data features of genomic data. Further, private permissioned chains can be implemented to both protect the privacy and security of the genetic information therein, while also providing access to researchers. An NFT marketplace associated with that private chain can provide the discretized sale of anonymous and encrypted data sets while also incentivizing individuals to share their data through payments mediated by smart contracts. Private blockchains can provide a transparent chain of custody for each use of the customers' data, and validation that this data is not corrupted. However, even with all of these benefits there remain some concerns with the implementation of this new technology including the ethical, legal and social implications typically associated with DNA databases.

Predicting Undesired Treatment Outcomes With Machine Learning in Mental Health Care: Multisite Study.

Predicting which treatment will work for which patient in mental health care remains a challenge. The aim of this multisite study was 2-fold: (1) to predict patients' response to treatment in Dutch basic mental health care using commonly available data from routine care and (2) to compare the performance of these machine learning models across three different mental health care organizations in the Netherlands by using clinically interpretable models. Using anonymized data sets from three different mental health care organizations in the Netherlands (n=6452), we applied a least absolute shrinkage and selection operator regression 3 times to predict the treatment outcome. The algorithms were internally validated with cross-validation within each site and externally validated on the data from the other sites. The performance of the algorithms, measured by the area under the curve of the internal validations as well as the corresponding external validations, ranged from 0.77 to 0.80. Machine learning models provide a robust and generalizable approach in automated risk signaling technology to identify cases at risk of poor treatment outcomes. The results of this study hold substantial implications for clinical practice by demonstrating that the performance of a model derived from one site is similar when applied to another site (ie, good external validation).

A Probiotic to Improve Sleep Quality during COVID-19 Pandemic: A Questionnaire Study

Sleep quality has been related to the gut microbiome and probiotics were shown to improve sleep quality, especially in people under physical or mental stress. COVID-19 results in such mental stress, leading to sleeping disorders, especially in people working in health care. A large dataset of quality of life data from people working in pharmacies or doctors´ offices was collected before and after intake of a free sample of a multi-species probiotic during a marketing survey. 9841 anonymous datasets were available for analysis. Group differences and univariate and multivariate logistic regression analysis was performed. COVID-19 related data were obtained from publicly available datasets and matched to quality of life data via the postal code of the participants. We aimed to relate self-reported quality of life, especially sleep quality data from a marketing survey to objective criteria of COVID-19, such as incidence and mortality figures at the time of the survey. The intake of only a two-week course of a multi-species probiotic markedly improved sleep quality, mental health, vitality, stress resistance and performance. Improvement in the different categories of quality of life was influenced by sex, age, initial intestinal complaints, the initial stress level, the initial sleep quality, initial vitality and mental health. Quality of life was reduced during COVID-19 compared to pre-pandemic values of the general population. We also found some associations of changes in quality of life in relation to COVID-19 incidence rates in employees of pharmacies and doctors´ offices, however to a lesser extent than expected. Although not derived from a clinical trial, this analysis of marketing survey data provides evidence that there is a link between the gut microbiome, sleep and quality of life suggesting that gut microbiome modulation could be a valuable therapeutic approach in sleeping disorders.

Potential destination discovery for low predictability individuals based on knowledge graph

Travelers may travel to locations they have never visited, which we call potential destinations of them. Especially under a very limited observation, travelers tend to show random movement patterns and usually have a large number of potential destinations, which make them difficult to handle for mobility prediction (e.g., destination prediction). In this paper, we develop a new knowledge graph-based framework (PDPFKG) for potential destination discovery of low predictability travelers by considering trip association relationships between them. We first construct a trip knowledge graph (TKG) to model the trip scenario by entities (e.g., travelers, destinations and time information) and their relationships, in which we introduce the concept of private relationship for complexity reduction. Then a modified knowledge graph embedding algorithm is implemented to optimize the overall graph representation. Based on the trip knowledge graph embedding model (TKGEM), the possible ranking of individuals’ unobserved destinations to be chosen in the future can be obtained by calculating triples’ distance. Empirically. PDPFKG is tested using an anonymous vehicular dataset from 138 intersections equipped with video-based vehicle detection systems in Xuancheng city, China. The results show that PDPFKG outperforms baseline methods overall, and the rankings given by it have strong consistency with travelers’ behavior in choosing potential destinations on the aggregated level. Besides, experiments indicate the performance would be further improved with new valid data introduction. Finally, we provide a comprehensive discussion about the innovative points of the methodology and share some findings and understandings.

An evaluation of value-based outcomes for women admitted to a dialectical behaviour therapy integrated practice unit: a follow-up study.

An earlier evaluation (Fox et al., ) highlighted reductions in risk behaviours and restrictive practices for women admitted to low secure dialectical behaviour therapy (DBT) unit. Since then, a value-based healthcare model has been adopted. To explore changes in health, social and psychological functioning, risk, quality of life, and in incidents of violence and restrictive practices, over the initial 12-month period of admission to a specialist DBT service. Data were extracted from electronic clinical records for 41 women with emotionally unstable personality disorder admitted to a specialist integrated practice unit (IPU) providing a comprehensive DBT programme. Secondary analysis was conducted on an anonymous dataset of routinely collected outcome measures at baseline admission, and 6 and 12 months post-admission. ANOVAs and pairwise post hoc comparisons, and non-parametric equivalents, were conducted to examine changes in outcomes. Findings showed statistically significant improvements in mental health scores on the ReQOL (p<.01), global, wellbeing, problems, functioning and risk scores on the COREOM (all p<.01), and severe disturbance, emotional wellbeing, socioeconomic status, risk and need scores on the HoNOS-Secure (all p<.05). Significant reductions in risk behaviours (p<.01) and restrictive practices (p<.01) were also apparent. The most substantiative improvements were largely demonstrated over a 12-month admission period. Admission to the DBT IPU yielded significant improvements on outcomes pertaining to quality of life, psychological distress, and risk. Importantly, these are outcomes that aligned with patients' perceptions of recovery.

Single pill treatment in daily practice is associated with improved clinical outcomes and all-cause mortality in cardiovascular diseases: results from the START project

Abstract Objective Current guidelines for the management of arterial hypertension, dyslipidemia, or secondary cardiovascular (CV) prevention recommend combination drug treatments with single pill combinations (SPC). This concept is expected to improve adherence to treatment and, as a consequence, to reduce the risk of adverse CV outcomes associated with these clinical conditions. Aim of our study was to assess whether SPC are clinically superior to multi pill combination (MPC) with identical drugs in reducing CV events and al-cause mortality in a huge population under real world conditions in daily practice. Methods We analyzed an anonymized claims dataset (AOK PLUS, a statutory German sickness fund) including patients with hypertension and other CV diseases in the years 2012–2017; minimum follow-up was 1 year or until date of death, After 1:1-Propensity Score Matching (PSM), selected CV outcomes (myocardial infarction, stroke, transitory ischemic attack, coronary artery disease, heart failure, acute renal failure, cardiovascular hospitalization, all cause hospitalization) as well as all-cause mortality were compared using Incidence Rate Ratios (IRRs) and non-parametric tests. Results 50,622 patients (25,311 patients in SPC versus MPC group) aged ≥18 years treated with SPC or identical MPC were followed up for at least 1 year or until death. No significant differences in baseline characteristics were observed after PSM. Nine different clinical outcomes were compared for each group. In all comparisons, significantly lower incidence rate ratios (IRR) were identified for SPC, confirmed by comparison of Kaplan-Meier estimates: stroke (IRR=0.77; 95% CI 0.67–0.88; p&amp;lt;0.001), transitory ischemic attack (IRR=0.61; 95% CI 0.48–0.78; p&amp;lt;0.001), myocardial infarction (IRR=0.76; 95% CI 0.63–0.90; p=0.0016), coronary artery disease (IRR=0.66; 95% CI 0.57–0.77; p&amp;lt;0.001), heart failure (IRR=0.59; 95% CI 0.54–0.64; p&amp;lt;0.001), acute renal failure (IRR=0.54; 95% CI 0.56–0.64; p&amp;lt;0.001) all cause hospitalization (IRR=0.72; 95% CI 0.71–0.74; p&amp;lt;0.001), cardiovascular hospitalization (IRR=0.63; 95% CI 0.57–0.69; p&amp;lt;0.001), and all-cause mortality (IRR=0.62; 95% CI 0.57–0.68; p&amp;lt;0.001). The mean time to first events and time to death were also in favor to SPC (any event: SPC 966.052 days/median 873; MPC 846.936 days/median 647; death: SPC 1,719.424 days; MPC 1,657.248 days; log rank for both comparisons: p&amp;lt;0.001). Conclusion In clinical practice, the SPC regimen is associated with a lower incidence of CV events and lower all-cause mortality. Time to the event is also significantly longer in the SPC group compared to MPC. These results strongly support the concept of SPC and the implementation into daily practice to improve patient's prognosis. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): APONTIS PHARMA GmbH &amp; Co. KG