Association between sarcopenia and risk of major adverse cardiac and cerebrovascular events-UK Biobank database.

BACKGROUND: Few studies on the risk of incident major adverse cardiac and cerebrovascular events (MACCEs) in sarcopenia have been reported. The objective was to assess the association between presarcopenia and sarcopenia and a higher risk of MACCEs. METHODS: This study on the UK Biobank prospective cohort, used data collected between 2006 and 2021. Community-dwelling Caucasian participants aged 37 to 73 years were included if values for Handgrip Strength (HGS) and Skeletal Muscle Index (SMI) were available and if no history of MACCEs was reported. Exposure was assessed using the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria. Muscle strength was measured using HGS, and muscle mass using the SMI. Presarcopenia was defined through the two definitions available in the literature, as low HGS with normal SMI and as normal HGS with low SMI, whereas sarcopenia was defined as low HGS with low SMI. The main outcome was to determine whether presarcopenia and/or sarcopenia were predictors of MACCEs (composite events). RESULTS: A total of 406,411 included participants (women: 55.7%) were included. At baseline, there were 18,257 (4.7%) presarcopenics-subgroup n°1 (low HGS only), 7940 (2.1%) presarcopenics-subgroup n°2 (low SMI only), and 1124 (0.3%) sarcopenics. Over a median follow-up of 12.1 years (IQR: [11.4; 12.8]), 28,300 participants (7.0%) were diagnosed with at least one event. Compared to NonSarc, presarcopenic (subgroups n°1 and n°2) and sarcopenic status were significantly associated with a higher risk of MACCEs (respectively fully adjusted HRs: HR = 1.25 [95% CI: 1.19; 1.31], HR = 1.33 [95% CI: 1.23; 1.45] and HR = 1.62 [95% CI: 1.34; 1.95]). CONCLUSIONS: In a community-dwelling population, the risk of MACCEs was higher in both presarcopenic and sarcopenic participants.

Association Between Sarcopenia and Fracture Risk in a Population From the UK Biobank Database.

Studies on the fracture risk in presarcopenic and sarcopenic patients report contradictory results. The objective was to assess whether presarcopenia and sarcopenia are associated with an increase in fracture risk. We conducted a retrospective study using the UK Biobank cohort and the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria. Muscle strength was evaluated using hand-grip strength (HGS) and muscle mass using the skeletal muscle index (SMI; from bioimpedance analysis). Presarcopenia was defined through the two definitions available in the literature, as low HGS with normal SMI and as normal HGS with low SMI, and sarcopenia as low HGS and low SMI. Fracture events were recorded as "fracture" (location compatible with an osteoporotic origin) and "major osteoporotic fracture" (MOF), as listed in the FRAX tool. Associations were assessed using Cox proportional hazards models, adjusted for sarcopenia and osteoporosis risk factors. Adjusted hazard ratios (HRa ) and their 95% confidence intervals (CI) were reported. A total of 387,025 participants (women 54.4%; median age 58.0 years; interquartile range [IQR] 51.0-63.0 years) were included. At baseline, there were 18,257 (4.7%) presarcopenic participants-subgroup 1 (low HGS only), 7940 (2.1%) presarcopenic participants-subgroup 2 (low SMI only), and 1124 (0.3%) sarcopenic participants. Over a median follow-up of 12.0 years (IQR 11.4-12.6 years), 18,300 (4.7%) participants were diagnosed with at least one incident fracture. Presarcopenic (subgroups 1 and 2) and sarcopenic status were significantly associated with a higher risk of fracture (respectively adjusted HRs: HR = 1.26 [1.19-1.33], HR = 1.20 [1.11-1.30], HR = 1.30 [1.08-1.56]) and with a higher risk of MOF (respectively adjusted HRs: HR = 1.30 [1.21-1.40], HR = 1.19 [1.08-1.72], HR = 1.18 [0.93-1.49]). In a middle-aged population, the fracture and MOF risks were higher in both presarcopenic and sarcopenic participants compared with nonsarcopenic participants. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Definition of a Practical Taxonomy for Referencing Data Quality Problems in Health Care Databases.

INTRODUCTION: Health care information systems can generate and/or record huge volumes of data, some of which may be reused for research, clinical trials, or teaching. However, these databases can be affected by data quality problems; hence, an important step in the data reuse process consists in detecting and rectifying these issues. With a view to facilitating the assessment of data quality, we developed a taxonomy of data quality problems in operational databases. MATERIAL: We searched the literature for publications that mentioned "data quality problems," "data quality taxonomy," "data quality assessment," or "dirty data." The publications were then reviewed, compared, summarized, and structured using a bottom-up approach, to provide an operational taxonomy of data quality problems. The latter were illustrated with fictional examples (though based on reality) from clinical databases. RESULTS: Twelve publications were selected, and 286 instances of data quality problems were identified and were classified according to six distinct levels of granularity. We used the classification defined by Oliveira et al to structure our taxonomy. The extracted items were grouped into 53 data quality problems. DISCUSSION: This taxonomy facilitated the systematic assessment of data quality in databases by presenting the data's quality according to their granularity. The definition of this taxonomy is the first step in the data cleaning process. The subsequent steps include the definition of associated quality assessment methods and data cleaning methods. CONCLUSION: Our new taxonomy enabled the classification and illustration of 53 data quality problems found in hospital databases.

Estimating the number of usability problems affecting medical devices: modelling the discovery matrix.

BACKGROUND: Usability testing of medical devices are mandatory for market access. The testings' goal is to identify usability problems that could cause harm to the user or limit the device's effectiveness. In practice, human factor engineers study participants under actual conditions of use and list the problems encountered. This results in a binary discovery matrix in which each row corresponds to a participant, and each column corresponds to a usability problem. One of the main challenges in usability testing is estimating the total number of problems, in order to assess the completeness of the discovery process. Today's margin-based methods fit the column sums to a binomial model of problem detection. However, the discovery matrix actually observed is truncated because of undiscovered problems, which corresponds to fitting the marginal sums without the zeros. Margin-based methods fail to overcome the bias related to truncation of the matrix. The objective of the present study was to develop and test a matrix-based method for estimating the total number of usability problems. METHODS: The matrix-based model was based on the full discovery matrix (including unobserved columns) and not solely on a summary of the data (e.g. the margins). This model also circumvents a drawback of margin-based methods by simultaneously estimating the model's parameters and the total number of problems. Furthermore, the matrix-based method takes account of a heterogeneous probability of detection, which reflects a real-life setting. As suggested in the usability literature, we assumed that the probability of detection had a logit-normal distribution. RESULTS: We assessed the matrix-based method's performance in a range of settings reflecting real-life usability testing and with heterogeneous probabilities of problem detection. In our simulations, the matrix-based method improved the estimation of the number of problems (in terms of bias, consistency, and coverage probability) in a wide range of settings. We also applied our method to five real datasets from usability testing. CONCLUSIONS: Estimation models (and particularly matrix-based models) are of value in estimating and monitoring the detection process during usability testing. Matrix-based models have a solid mathematical grounding and, with a view to facilitating the decision-making process for both regulators and device manufacturers, should be incorporated into current standards.

Hospital-Acquired Hyperkalemia Events in Older Patients Are Mostly Due to Avoidable, Multifactorial, Adverse Drug Reactions.

Drug-induced hyperkalemia is a frequent and severe complication in the hospital setting. Other risk factors may also induce hyperkalemia but the combination of drugs and precipitating factors has not been extensively studied. The aim was to identify drug-induced hyperkalemia events in hospitalized older patients and to describe their combinations with precipitating factors. Two experts independently analyzed retrospective data of patients aged 75 years or more. Experts identified 471 hyperkalemia events and concluded that 379 (80.5%) were induced by drugs. The cause was multifactorial (i.e., at least one drug with a precipitating factor) in 300 (79.2%) of the 379 drug-induced hyperkalemia. Most of the drug-induced hyperkalemia events were avoidable (79.9%)-mainly because of the multifactorial cause (e.g., dosage adaptation during acute kidney injury). Drug-induced hyperkalemia events are frequently combined with precipitating factors in hospitalized older patients and their prevention should focus on these combinations.

IT-CARES: an interactive tool for case-crossover analyses of electronic medical records for patient safety.

BACKGROUND: The significant risk of adverse events following medical procedures supports a clinical epidemiological approach based on the analyses of collections of electronic medical records. Data analytical tools might help clinical epidemiologists develop more appropriate case-crossover designs for monitoring patient safety. OBJECTIVE: To develop and assess the methodological quality of an interactive tool for use by clinical epidemiologists to systematically design case-crossover analyses of large electronic medical records databases. MATERIAL AND METHODS: We developed IT-CARES, an analytical tool implementing case-crossover design, to explore the association between exposures and outcomes. The exposures and outcomes are defined by clinical epidemiologists via lists of codes entered via a user interface screen. We tested IT-CARES on data from the French national inpatient stay database, which documents diagnoses and medical procedures for 170 million inpatient stays between 2007 and 2013. We compared the results of our analysis with reference data from the literature on thromboembolic risk after delivery and bleeding risk after total hip replacement. RESULTS: IT-CARES provides a user interface with 3 columns: (i) the outcome criteria in the left-hand column, (ii) the exposure criteria in the right-hand column, and (iii) the estimated risk (odds ratios, presented in both graphical and tabular formats) in the middle column. The estimated odds ratios were consistent with the reference literature data. DISCUSSION: IT-CARES may enhance patient safety by facilitating clinical epidemiological studies of adverse events following medical procedures. The tool's usability must be evaluated and improved in further research.