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Introduction: The disease activity associated with the drug-utilization patterns of biologic Disease Modifying Anti-Rheumatic Drugs (DMARDs) is poorly investigated in real-world studies on rheumatoid arthritis (RA) patients. To investigate the relationship between biologic DMARD initiation/discontinuations in RA patients identified in the healthcare administrative databases of Tuscany and the Disease Activity Score 28 (DAS28) reported in the medical charts. Methods: This retrospective population-based study included RA's first-ever biologic DMARD users of the Pisa University Hospital from 2014 to 2016. Patients were followed up until 31 December 2019. We evaluated the DAS28 recorded before (T0) and after (T1) the biologic DMARD initiation and before (TD0) and after (TD1) discontinuations. Patients were classified as "off-target" (DAS28 > 3.2) or "in-target" (DAS28 ≤ 3.2). We described the disease activity trends at initiation and discontinuation. Results: Ninety-five users were included (73 women, mean age 59.6). Among 70 patients (74%) with at least three DAS28 measures, 28 (40.0%) were off-target at T0 and 38 (54.3%) in-target at T1. Thirty-three (47%) patients had at least one discontinuation, among those with at least three DAS28 assessments. In the disease activity trend, disease stability or improvement was observed in 28 out of 37 (75.7%) patients at initiation and in 24 out of 37 (64.9%) at discontinuation. Discussion: Biologic DMARD discontinuations identified in the healthcare administrative databasese of Tuscany are frequently observed in situations of controlled RA disease. Further studies are warranted to confirm that these events can be used in studies using healthcare administrative databases as proxies of treatment effectiveness.
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BACKGROUND: Readmissions are hospitalizations following a previous hospitalization (called index hospitalization) of the same patient that occurred in the same facility or nursing home. They may be a consequence of the progression of the natural history of a disease, but they may also reveal a previous suboptimal stay, or ineffective management of the underlying clinical condition. Preventing avoidable readmissions has the potential to improve both a patient's quality of life, by avoiding exposure to the risks of re-hospitalization, and the financial well-being of health care systems. METHODS: We investigated the magnitude of 30 day repeat hospitalizations for the same Major Diagnostic Category (MDC) in the Azienda Ospedaliero Universitaria Pisana (AOUP) over the period from 2018 to 2021. Records were divided into only admissions, index admissions and repeated admission. The length of the stay of all groups was compared using analysis of variance and subsequent multi-comparison tests. RESULTS: Results showed a reduction in readmissions over the period examined (from 5.36% in 2018 to 4.46% in 2021), likely due to reduced access to care during the COVID-19 pandemic. We also observed that readmissions predominantly affect the male sex, older age groups, and patients with medical Diagnosis Related Groups (DRGs). The length of stay of readmissions was longer than that of index hospitalization (difference of 1.57 days, 95% CI 1.36-1.78 days, p < 0.001). The length of stay of index hospitalization is longer than that of single hospitalization (difference of 0.62 days, 95% CI 0.52-0.72 days, p < 0.001). CONCLUSIONS: A patient who goes for readmission thus has an overall hospitalization duration of almost two and a half times the length of the stay of a patient with single hospitalization, considering both index hospitalization and readmission. This represents a heavy use of hospital resources, about 10,200 more inpatient days than single hospitalizations, corresponding to a 30-bed ward working with an occupancy rate of 95%. Knowledge of readmissions is an important piece of information in health planning and a useful tool for monitoring the quality of models of patient care.
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Validation of algorithms for selecting patients from healthcare administrative databases (HAD) is recommended. This PATHFINDER study section is aimed at testing algorithms to select rheumatoid arthritis (RA) patients from Tuscan HAD (THAD) and assessing RA diagnosis time interval between the medical chart date and that of THAD. A population was extracted from THAD. The information of the medical charts at the Rheumatology Unit of Pisa University Hospital represented the reference. We included first ever users of biologic disease modifying anti-rheumatic drugs (bDMARDs) between 2014 and 2016 (index date) with at least a specialist visit at the Rheumatology Unit of the Pisa University Hospital recorded from 2013 to the index date. Out of these, we tested four index tests (algorithms): (1) RA according to hospital discharge records or emergency department admissions (ICD-9 code, 714*); (2) RA according to exemption code from co-payment (006); (3) RA according to hospital discharge records or emergency department admissions AND RA according to exemption code from co-payment; (4) RA according to hospital discharge records or emergency department admissions OR RA according to exemption code from co-payment. We estimated sensitivity, specificity, positive and negative predicted values (PPV and NPV) with 95% confidence interval (95% CI) and the RA diagnosis median time interval (interquartile range, IQR). Two sensitivity analyses were performed. Among 277 reference patients, 103 had RA. The fourth algorithm identified 96 true RA patients, PPV 0.78 (95% CI 0.70-0.85), sensitivity 0.93 (95% CI 0.86-0.97), specificity 0.84 (95% CI 0.78-0.90), and NPV 0.95 (95% CI 0.91-0.98). The sensitivity analyses confirmed performance. The time measured between the actual RA diagnosis date recorded in medical charts and that assumed in THAD was 2.2 years (IQR 0.5-8.4). In conclusion, this validation showed the fourth algorithm as the best. The time interval elapsed between the actual RA diagnosis date in medical charts and that extrapolated from THAD has to be considered in the design of future studies.