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Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease.

ABSTRACT: Alkaptonuria (AKU) is an ultrarare autosomal recessive disorder (MIM 203500) that is caused byby a complex set of mutations in homogentisate 1,2-dioxygenasegene and consequent accumulation of homogentisic acid (HGA), causing a significant protein oxidation. A secondary form of amyloidosis was identified in AKU and related to high circulating serum amyloid A (SAA) levels, which are linked with inflammation and oxidative stress and might contribute to disease progression and patients' poor quality of life. Recently, we reported that inflammatory markers (SAA and chitotriosidase) and oxidative stress markers (protein thiolation index) might be disease activity markers in AKU. Thanks to an international network, we collected genotypic, phenotypic, and clinical data from more than 200 patients with AKU. These data are currently stored in our AKU database, named ApreciseKUre. In this work, we developed an algorithm able to make predictions about the oxidative status trend of each patient with AKU based on 55 predictors, namely circulating HGA, body mass index, total cholesterol, SAA, and chitotriosidase. Our general aim is to integrate the data of apparently heterogeneous patients with AKUAKU by using specific bioinformatics tools, in order to identify pivotal mechanisms involved in AKU for a preventive, predictive, and personalized medicine approach to AKU.-Cicaloni, V., Spiga, O., Dimitri, G. M., Maiocchi, R., Millucci, L., Giustarini, D., Bernardini, G., Bernini, A., Marzocchi, B., Braconi, D., Santucci, A. Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease.

SUBMITTER: Cicaloni V

PROVIDER: S-EPMC6902683 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionPatients are a valuable source of information about ways to prevent harm in healthcare, and can provide feedback about the factors that contribute to safety incidents. The Primary Care Patient Measure of Safety (PC PMOS) is a novel and validated tool that captures patient feedback on safety and can be used by primary care practice teams to identify and prevent safety incidents. The aim of this study is to assess the feasibility of PC PMOS as a tool for data-driven safety improvement and monitoring in Australian primary care.Methods and analysisFeasibility will be assessed using a mixed-methods approach to understand the enablers, barriers, acceptability, practicability, intervention fidelity and scalability of C PMOS as a tool for safety improvement across six primary care practices in the south-west region of Victoria. Patients over the age of 18 years attending their primary care practice will be invited to complete the PC PMOS when presenting for an appointment. Staff members at each practice will form a safety improvement team. Staff will then use the patient feedback to develop and implement specific safety interventions over a 6-month period. Data collection methods during the intervention period includes audio recordings of staff meetings, overt observations at training and education workshops, reflexive researcher insights, document collection and review. Data collection postintervention includes patient completion of the PC PMOS and semistructured interviews with staff. Triangulation and thematic analysis techniques will be employed to analyse the qualitative and content data. Analysis methods will use current evidence and models of healthcare culture, safety improvement and patient involvement in safety to inform the findings.Ethics and disseminationEthics approval was granted by Deakin University Human Ethics Advisory Group, Faculty of Health (HEAG-H 175_2017). Study results will be disseminated through local and international conferences and peer-reviewed publications.

Project description:BackgroundTo study patient physiology throughout a period of acute hospitalization, we sought to create accessible, standardized nationwide data at the level of the individual patient-facility-day. This methodology paper summarizes the development, organization, and characteristics of the Veterans Affairs Patient Database 2014-2017 (VAPD 2014-2017). The VAPD 2014-2017 contains acute hospitalizations from all parts of the nationwide VA healthcare system with daily physiology including clinical data (labs, vitals, medications, risk scores, etc.), intensive care unit (ICU) indicators, facility, patient, and hospitalization characteristics.MethodsThe VA data structure and database organization represents a complex multi-hospital system. We define a single-site hospitalization as one or more consecutive stays with an acute treating specialty at a single facility. The VAPD 2014-2017 is structured at the patient-facility-day level, where every patient-day in a hospital is a row with separate identification variables for facility, patient, and hospitalization. The VAPD 2014-2017 includes daily laboratory, vital signs, and inpatient medication. Such data were validated and verified through lab value range and comparison with patient charts. Sepsis, risk scores, and organ dysfunction definitions were standardized and calculated.ResultsWe identified 565,242 single-site hospitalizations (SSHs) in 2014; 558,060 SSHs in 2015; 553,961 SSHs in 2016; and 550,236 SSHs in 2017 at 141 VA hospitals. The average length of stay was four days for all study years. In-hospital mortality decreased from 2014 to 2017 (1.7 to 1.4%), 30-day readmission rates increased from 15.3% in 2014 to 15.6% in 2017; 30-day mortality also decreased from 4.4% in 2014 to 4.1% in 2017. From 2014 to 2017, there were 107,512 (4.8%) of SSHs that met the Center for Disease Control and Prevention's Electronic Health Record-based retrospective definition of sepsis.ConclusionThe VAPD 2014-2017 represents a large, standardized collection of granular data from a heterogeneous nationwide healthcare system. It is also a direct resource for studying the evolution of inpatient physiology during both acute and critical illness.

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Interactive alkaptonuria database: investigating clinical data to improve patient care in a rare disease.

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