High P2X6 receptor expression in human bladder cancer predicts good survival prognosis.

As alterations in purinergic signaling have been observed in bladder diseases, we aimed to assess the potential prognostic role of purinergic receptors in bladder cancer in a translational approach based on clinical databases and in vitro data. The prognostic role of purinergic receptors in the survival of patients with bladder cancer and the expression profile of the altered P2 receptors in normal and in tumor samples were determined using The Cancer Genome Atlas databank. In T24 and RT4 human bladder cancer cell lines, the P2 purinergic receptors were characterized by RT-PCR and RT-qPCR analysis including radiotherapy exposure as treatment. The cell number and the cumulative population doubling were also assessed. The expression profile of P2X6 receptor in the cancer pathological stage and in the nodal metastasis status was in agreement with Kaplan-Meier analysis, indicating that high expression of this receptor was related to an increased survival rate in patients with bladder cancer. Of all the P2 receptors expressed on T24 cell line, P2X6 presented high expression after radiotherapy, while it was not altered in RT4 cells. In addition, irradiation promoted a decrease of T24 cell number, but did not change the cell number of RT4 after the same time and radiation dose. Along 7 days after irradiation exposure, both cells regrew. However, while P2X6 receptor was downregulated in T24 cells, it was upregulated in RT4 cells. Our findings indicated that high P2X6 receptor expression induced by radiation in T24 cell line may predict a good survival prognostic factor.

Outcome data from >10 000 multiple myeloma patients in the Danish and Swedish national registries.

OBJECTIVE: We describe real-world evidence (RWE) from the nationwide Swedish and Danish registries that provide important information on incidence and outcome in multiple myeloma (MM). METHOD: First line treatment data on more than 10.000 MM patients from Denmark and Sweden between 2005-2018 are presented. Key results from research conducted within the Swedish and Danish myeloma registries are summarized, describing subgroups of patients with comorbidity, myeloma complications, and early relapse. RESULTS: We show that national guidelines, generated on results from randomized clinical trials (RCTs) are rapidly implemented and improve overall survival (OS). We find that both the incidence of MM and the median age at diagnosis is higher in national registries compared to results from referral centres, indicating a more complete coverage. This highlights the need of validation of prognostic scoring systems and indices in e.g., SMM and high-risk MM in a real- world-population. We show that these subgroups are unlikely to be captured in RCTs with narrow inclusion and exclusion criteria, that they have worse survival, and are in need of new treatment approaches. CONCLUSION: National registries that include all MM patients are an important source of knowledge on epidemiology, treatment and outcome with implications for the planning of MM care. Despite the introduction of new and better treatments, rapidly implemented in our countries, our registries uncover subgroups of patients that still have inferior outcome. Our RWE can help to identify important research questions to be studied in further clinical trials also in patients currently not included in RCTs.

Pitfalls in the interpretation of CFTR variants in the context of incidental findings.

Whole-exome/genome sequencing analyses lead to detect disease-causing variants that are unrelated to the initial clinical question. Irrespective of any actionable gene list, only pathogenic variants should be considered. The pathogenicity of 55 cystic fibrosis transmembrane conductance regulator (CFTR) variants of known various impacts was assessed by a group of experts by comparing data from specialized databases CFTR-France and CFTR2 with those of general clinical databases ClinVar and Human Gene Mutation Database (HGMD®) Professional and data aggregators VarSome and InterVar. The assessment of cystic fibrosis (CF) variants was correct with ClinVar and HGMD® Professional while less reliable with VarSome and InterVar. Conversely, the risk of overclassifying variants as CF-causing was up to 82% with HGMD® Professional. The concordance between data aggregators was only 50%. The use of general databases and aggregators is thus associated with a substantial risk of misclassifying variants. This evaluation may be extrapolated to other disease conditions and incites to remain cautious in interpreting and disclosing incidental findings.

The Preterm Clinical Network (PCN) Database: a web-based systematic method of collecting data on the care of women at risk of preterm birth.

BACKGROUND: Despite much research effort, there is a paucity of conclusive evidence in the field of preterm birth prediction and prevention. The methods of monitoring and prevention strategies offered to women at risk vary considerably around the UK and depend on local maternity care provision. It is becoming increasingly recognised that this experience and knowledge, if captured on a larger scale, could be a utilized as a valuable source of evidence for others. The UK Preterm Clinical Network (UKPCN) was established with the aim of improving care and outcomes for women at risk of preterm birth through the sharing of a wealth of experience and knowledge, as well as the building of clinical and research collaboration. The design and development of a bespoke internet-based database was fundamental to achieving this aim. METHOD: Following consultation with UKPCN members and agreement on a minimal dataset, the Preterm Clinical Network (PCN) Database was constructed to collect data from women at risk of preterm birth and their children. Information Governance and research ethics committee approval was given for the storage of historical as well as prospectively collected data. Collaborating centres have instant access to their own records, while use of pooled data is governed by the PCN Database Access Committee. Applications are welcomed from UKPCN members and other established research groups. The results of investigations using the data are expected to provide insights into the effectiveness of current surveillance practices and preterm birth interventions on a national and international scale, as well as the generation of ideas for innovation and research. To date, 31 sites are registered as Data Collection Centres, four of which are outside the UK. CONCLUSION: This paper outlines the aims of the PCN Database along with the development process undertaken from the initial idea to live launch.

A proof-of-concept study on mortality prediction with machine learning algorithms using burn intensive care data.

INTRODUCTION: Burn injuries are a common traumatic injury. Large burns have high mortality requiring intensive care and accurate mortality predictions. To assess if machine learning (ML) could improve predictions, ML algorithms were tested and compared with the original and revised Baux score. METHODS: Admission data and mortality outcomes were collected from patients at Uppsala University Hospital Burn Centre from 2002 to 2019. Prognostic variables were selected, ML algorithms trained and predictions assessed by analysis of the area under the receiver operating characteristic curve (AUC). Comparison was made with Baux scores using DeLong test. RESULTS: A total of 17 prognostic variables were selected from 92 patients. AUCs in leave-one-out cross-validation for a decision tree model, an extreme boosting model, a random forest model, a support-vector machine (SVM) model and a generalised linear regression model (GLM) were 0.83 (95% confidence interval [CI] = 0.72-0.94), 0.92 (95% CI = 0.84-1), 0.92 (95% CI = 0.84-1), 0.92 (95% CI = 0.84-1) and 0.84 (95% CI = 0.74-0.94), respectively. AUCs for the Baux score and revised Baux score were 0.85 (95% CI = 0.75-0.95) and 0.84 (95% CI = 0.74-0.94). No significant differences were observed when comparing ML algorithms with Baux score and revised Baux score. Secondary variable selection was made to analyse model performance. CONCLUSION: This proof-of-concept study showed initial credibility in using ML algorithms to predict mortality in burn patients. The sample size was small and future studies are needed with larger sample sizes, further variable selections and prospective testing of the algorithms. LAY SUMMARY: Burn injuries are one of the most common traumatic injuries especially in countries with limited prevention and healthcare resources. To treat a patient with large burns who has been admitted to an intensive care unit, it is often necessary to assess the risk of a fatal outcome. Physicians traditionally use simplified scores to calculate risks. One commonly used score, the Baux score, uses age of the patient and the size of the burn to predict the risk of death. Adding the factor of inhalation injury, the score is then called the revised Baux score. However, there are a number of additional causes that can influence the risk of fatal outcomes that Baux scores do not take into account. Machine learning is a method of data modelling where the system learns to predict outcomes based on previous cases and is a branch of artificial intelligence. In this study we evaluated several machine learning methods for outcome prediction in patients admitted for burn injury. We gathered data on 93 patients at admission to the intensive care unit and our experiments show that machine learning methods can reach an accuracy comparable with Baux scores in calculating the risk of fatal outcomes. This study represents a proof of principle and future studies on larger patient series are required to verify our results as well as to evaluate the methods on patients in real-life situations.

A Holistic Approach for the Identification of Success Factors in Secondary Cleft Osteoplasty.

Cleft lip and palate belong to the most frequent craniofacial anomalies. Secondary osteoplasty is usually performed between 7 and 11 years with the closure of the osseus defect by autologous bone. Due to widespread occurrence of the defect in conjunction with its social significance due to possible esthetic impairments, the outcome of treatment is of substantial interest. The success of the treatment is determined by the precise rebuilding of the dental arch using autologous bone from the iliac crest. A detailed analysis of retrospective data disclosed a lack of essential and structured information to identify success factors for fast regeneration and specify the treatment. Moreover, according to the current status, no comparable process monitoring is possible during osteoplasty due to the lack of sensory systems. Therefore, a holistic approach was developed to determine the parameters for a successful treatment via the incorporation of patient data, the treatment sequences and sensor data gained by an attachable sensor module into a developed Dental Tech Space (DTS). This approach enables heterogeneous data sets to be linked inside of DTS, archiving and analysis, and is also for future considerations of respective patient-specific treatment plans.

Assessing the quality of clinical and administrative data extracted from hospitals: the General Medicine Inpatient Initiative (GEMINI) experience.

OBJECTIVE: Large clinical databases are increasingly used for research and quality improvement. We describe an approach to data quality assessment from the General Medicine Inpatient Initiative (GEMINI), which collects and standardizes administrative and clinical data from hospitals. METHODS: The GEMINI database contained 245 559 patient admissions at 7 hospitals in Ontario, Canada from 2010 to 2017. We performed 7 computational data quality checks and iteratively re-extracted data from hospitals to correct problems. Thereafter, GEMINI data were compared to data that were manually abstracted from the hospital's electronic medical record for 23 419 selected data points on a sample of 7488 patients. RESULTS: Computational checks flagged 103 potential data quality issues, which were either corrected or documented to inform future analysis. For example, we identified the inclusion of canceled radiology tests, a time shift of transfusion data, and mistakenly processing the chemical symbol for sodium ("Na") as a missing value. Manual validation identified 1 important data quality issue that was not detected by computational checks: transfusion dates and times at 1 site were unreliable. Apart from that single issue, across all data tables, GEMINI data had high overall accuracy (ranging from 98%-100%), sensitivity (95%-100%), specificity (99%-100%), positive predictive value (93%-100%), and negative predictive value (99%-100%) compared to the gold standard. DISCUSSION AND CONCLUSION: Computational data quality checks with iterative re-extraction facilitated reliable data collection from hospitals but missed 1 critical quality issue. Combining computational and manual approaches may be optimal for assessing the quality of large multisite clinical databases.

Comparison of perioperative bariatric complications using 2 large databases: does the data add up?

BACKGROUND: The Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) database is a prospective clinical database that looks at short-term (30-day) outcomes of bariatric surgery. The Texas Inpatient Public Use Data File (PUDF) is an administrative database that uses hospital discharge information to compile data on admission and discharge diagnoses. OBJECTIVE: To determine interdatabase reliability for common bariatric complications. SETTING: University hospital, United States METHODS: The Texas Inpatient PUDF and MBSAQIP were queried for patients undergoing sleeve gastrectomy and gastric bypass in the year 2015. Admission diagnoses of morbid obesity with a discharge diagnosis of bariatric surgery status and also the International Classification of Diseases 9 Clinical Modification and Current Procedural Terminology procedure codes for bariatric surgeries were queried. The same postoperative complications were examined in both databases. RESULTS: There were 137,291 patients in MBSAQIP and 9474 patients in the PUDF undergoing bariatric surgery. Patients in the PUDF had greater adjusted and unadjusted odds ratio for acute renal failure, cardiac arrest and postoperative myocardial infarction, pneumonia, progressive renal failure and postoperative sepsis. CONCLUSION: There is a significant difference in the rates of perioperative complications of bariatric surgery when different databases are used. If surgeons are to be graded or potentially financially affected by these outcome metrics, the proper use of and interpretation of data is paramount and quality monitoring organizations should not use only administrative databases as the primary method to measure quality.

Big data and machine learning in critical care: Opportunities for collaborative research.

The introduction of clinical information systems (CIS) in Intensive Care Units (ICUs) offers the possibility of storing a huge amount of machine-ready clinical data that can be used to improve patient outcomes and the allocation of resources, as well as suggest topics for randomized clinical trials. Clinicians, however, usually lack the necessary training for the analysis of large databases. In addition, there are issues referred to patient privacy and consent, and data quality. Multidisciplinary collaboration among clinicians, data engineers, machine-learning experts, statisticians, epidemiologists and other information scientists may overcome these problems. A multidisciplinary event (Critical Care Datathon) was held in Madrid (Spain) from 1 to 3 December 2017. Under the auspices of the Spanish Critical Care Society (SEMICYUC), the event was organized by the Massachusetts Institute of Technology (MIT) Critical Data Group (Cambridge, MA, USA), the Innovation Unit and Critical Care Department of San Carlos Clinic Hospital, and the Life Supporting Technologies group of Madrid Polytechnic University. After presentations referred to big data in the critical care environment, clinicians, data scientists and other health data science enthusiasts and lawyers worked in collaboration using an anonymized database (MIMIC III). Eight groups were formed to answer different clinical research questions elaborated prior to the meeting. The event produced analyses for the questions posed and outlined several future clinical research opportunities. Foundations were laid to enable future use of ICU databases in Spain, and a timeline was established for future meetings, as an example of how big data analysis tools have tremendous potential in our field.

Utilizing Big Data in Cancer Care.

Clinical research has boomed over the past decade, with the development of multiple clinical datasets that are available for retrospective review. However, data remain incomplete based on fragmented reporting, provider change, and loss of follow-up. New technologies are being developed to assist with this limitation, by joining health care systems' medical records, and tracking Medicare claims files. The future of health care will rely more heavily on these systems, and artificial intelligence to quickly pull relevant clinical and genomic data regarding particular diagnoses, as a means to personalize medicine. This article reviews current advances in management of Big Data.

Informatics Solutions for Application of Decision-Making Skills.

Critical care nurses practice in a challenging environment that requires responses to patients with complex, often unstable health conditions. The electronic health record, access to clinical data, and Clinical Decision Support Systems informed by data from clinical databases are informatics tools designed to work together to facilitate decision-making in nursing practice. The complex decision-making environment of critical care requires informatics tools that support nursing practice through integration of current evidence with clinical data. Recommendations include continuing efforts toward the development of clinical decision support tools based on patient data that include predictive models to support increased patient safety.

Implementation of SNOMED CT in an online clinical database.

A number of strategies have been published to accelerate the use of electronic health records in caring for patients across the UK. These visions of 'eHealth' have a common requirement for robust interoperability between different systems with the use of appropriate information and data standards. SNOMED CT, a comprehensive terminology that NHS England intends to adopt across all care settings by 2020, is a key component of these standards but there is currently limited experience in its use in live clinical settings. Within NHS Wales, an electronic patient record system has been developed since 2009 with a focus on a core generic clinical information model built using SNOMED CT. Our experience is that SNOMED CT is a usable and clinician-friendly terminology but that its size and scope must be considered during implementation.

Prevalence of schizophrenia and related disorders in Malaga (Spain): results using multiple clinical databases.

BACKGROUND: To calculate the 1-year prevalence of schizophrenia and related disorders in a catchment area of Malaga (Spain) and determine the prevalence by gender, dwelling (rural or urban) and socioeconomic area (deprived or non-deprived area). METHOD: This cross-sectional study comprised the mental health area covered by Carlos Haya Hospital. We used multiple large clinical databases and key informants to identify cases. RESULTS: The mean 1-year prevalence of schizophrenia and related disorders was 6.27 per 1000. It was nearly double in men (8.45 per 1000) than in women (4.26 per 1000) (p < 0.001), with a male-to-female ratio of 1.98. The rate was higher in urban (6.64 per 1000) than rural areas (3.95 per 1000) (p < 0.0001) and in socioeconomic deprived areas (7.56 per 1000) than non-deprived areas (6.12 per 1000) (p = 0.005). For the subgroup of schizophrenia, the rates were: men, 5.88 per 1000 and women, 2.2 per 1000 (p < 0.0001), with a male-to-female ratio of 2.67. The rate was also higher in urban (4.2 per 1000) than rural areas (2.49 per 1000) (p < 0.0001) and in socioeconomic deprived areas (4.49 per 1000) than non-deprived areas (3.9 per 1000) (p = 0.149). CONCLUSIONS: The use of multiple clinical sources of information not only from mental health services, but also from emergency departments, primary care and private settings revealed high prevalence rates of schizophrenia and related disorders. This diagnosis is more common in men and in cities. Such precise estimates of the prevalence of schizophrenia have important repercussions for resource allocation and policy planning.

Population-based Aarhus Sarcoma Registry: validity, completeness of registration, and incidence of bone and soft tissue sarcomas in western Denmark.

BACKGROUND: The aim of the present study was to validate the data in the Aarhus Sarcoma Registry (ASR), to determine if this registry is population-based for western Denmark, and to examine the incidence of sarcomas using validated, population-based registry data. METHODS: This study was based on patients with bone and soft tissue sarcoma treated at the Sarcoma Centre of Aarhus University Hospital between January 1, 1979 and December 31, 2008. The validation process included a review of all medical files by two researchers using a standardized form. The Danish Cancer Registry was used as a reference to assess the completeness of registration of patients in the ASR. Crude and World Health Organization age-standardized incidence, as well as age-, gender-, and year-specific incidences were estimated. RESULTS: The validation process added 385 to the 1442 patients who were registered in the ASR. Before validation, on average, 70.5% of the data for the variables was correct. Validation improved the average completeness of the registered variables from 83.7% to 99.3%. The 1827 patients in the ASR after validation include 85.3% of the patients registered in the Danish Cancer Registry. The overall World Health Organization age-standardized incidence of sarcoma in the trunk or extremities in western Denmark in the period 1979-2008 was 2.2 per 100,000, being 0.8 for bone sarcomas and 1.4 for soft tissue sarcomas. CONCLUSION: The validation process significantly improved the completeness of the variables and the quality of the ASR data. ASR is now a valuable population-based tool for epidemiological research and quality improvement in the treatment of sarcoma. It is our recommendation that documented validation of registries should be a prerequisite for publishing studies derived from them.

Data Quality Improvement in Clinical Databases Using Statistical Quality Control: Review and Case Study.

Ensuring the quality of data being collected in clinical and medical contexts is a concern for data managers and users. Quality assurance frameworks, systematic audits, and correction procedures have been proposed to enhance the accuracy and completeness of databases. Following an overview of the undertaken approaches, particularly statistical methods, the authors promote acceptance sampling plans (ASPs) and statistical process control (SPC) tools, including control charts and root cause analysis, as the technical core of the data quality improvement mechanism. They review ASP and SPC techniques and discuss their implementation in data quality evaluation and improvement. Two case studies are presented in which the authors apply some of the techniques to databases maintained by a local hospital. Finally, guidelines are proposed for which techniques are appropriate with regard to dataflow and database specifications.