Difficult to treat rheumatoid arthritis in a comprehensive evaluation program: frequency according to different objective evaluations.

Difficult-to-treat Rheumatoid Arthritis (RA-D2T) is a condition in which patients do not achieve the treatment target despite multiple advanced therapies, more others features. Aims: to estimate the frequency of RA-D2T in a cohort comprehensively evaluated (clinical, serology, imaging), and to analyze the associated characteristics. In a second part, the frequency of RA-D2T after 1 year of follow-up, analyzing the predictive variables at baseline and therapeutic behavior. Cross-sectional and prospective study, consecutive RA were included, then those who completed the one-year follow-up were evaluated. RA-D2T frequency was estimated (DAS28-CDAI-SDAI-Ultrasonography (US)-HAQ) at baseline and 1 year. The variables associated and those baseline predictive characteristics of D2T at 1 year, and their independent association by logistic regression were analyzed. The treatment approach was described. Two hundred seventy-six patients completed the evaluation, frequency of RA-D2T (all scores): 27.5%. Anemia, RF high titers and higher HAQ score were independent associated. At year, 125 competed follow-up. RA-D2T (all scores): 33%, D2T-US and D2T-HAQ were 14 and 18.4% (p 0.001). Predictive baseline characteristics D2T (all score): ACPA + (OR: 13.7) and X-ray erosion (OR: 2.9). D2T-US: X-ray erosion (OR: 19.7). Conventional DMARDs, corticosteroids and TNF-blockers were the drugs most used by D2T patients, Jaki were the most used in the switch. We showed different frequencies of RA-D2T according to different objective parameters (scores, images) and their association with patient characteristics. In turn, predictive variables (erosions-ACPA) for RA-D2T at 1 year were analyzed. It was shown that the Jaki were the most used drug in these patients.

Projecting hospital resource utilization during a surge using parametric bootstrapping

During the initial wave of the COVID-19 pandemic in the United States, hospitals took drastic action to ensure sufficient capacity, including canceling or postponing elective procedures, expanding the number of available intensive care beds and ventilators, and creating regional overflow hospital capacity. However, in most locations the actual number of patients did not reach the projected surge leaving available, unused hospital capacity. As a result, patients may have delayed needed care and hospitals lost substantial revenue. These initial recommendations were made based on observations and worst-case epidemiological projections, which generally assume a fixed proportion of COVID-19 patients will require hospitalization and advanced resources. This assumption has led to an overestimate of resource demand as clinical protocols improve and testing becomes more widely available throughout the course of the pandemic. Here, we present a parametric bootstrap model for forecasting the resource demands of incoming patients in the near term, and apply it to the current pandemic. We validate our approach using observed cases at UCLA Health and simulate the effect of elective procedure cancellation against worst-case pandemic scenarios. Using our approach, we show that it is unnecessary to cancel elective procedures unless the actual capacity of COVID-19 patients approaches the hospital maximum capacity. Instead, we propose a strategy of balancing the resource demands of elective procedures against projected patients by revisiting the projections regularly to maintain operating efficiency. This strategy has been in place at UCLA Health since mid-April.