Evaluating the transitions in care for children presenting with acute asthma to emergency departments: a retrospective cohort study

Background Acute asthma is a common presentation to emergency departments (EDs) worldwide and, due to overcrowding, delays in treatment often occur. This study deconstructs the total ED length of stay into stages and estimates covariate effects on transition times for children presenting with asthma. Methods We extracted ED presentations in 2019 made by children in Alberta, Canada for acute asthma. We used multivariable Cox regressions in a multistate model to model transition times among the stages of start, physician initial assessment (PIA), disposition decision, and ED departure. Results Data from 6598 patients on 8270 ED presentations were extracted. The individual PIA time was longer (i.e., HR < 1) when time to the crowding metric (hourly PIA) was above 1 h (HR = 0.32; 95% CI:0.30,0.34), for tertiary (HR = 0.65; 95% CI:0.61,0.70) and urban EDs (HR = 0.77; 95% CI:0.70,0.84), for younger patients (HR = 0.99 per year; 95% CI:0.99,1.00), and for patients triaged less urgent/non-urgent (HR = 0.89; 95% CI:0.84,0.95). It was shorter for patients arriving by ambulance (HR = 1.22; 95% CI:1.04,1.42). Times from PIA to disposition decision were longer for tertiary (HR = 0.47; 95% CI:0.44,0.51) and urban (HR = 0.69; 95% CI:0.63,0.75) EDs, for patients triaged as resuscitation/emergent (HR = 0.51; 95% CI:0.48,0.54), and for patients arriving by ambulance (HR = 0.78; 95% CI:0.70,0.87). Times from disposition decision to ED departure were longer for patients who were admitted (HR = 0.16; 95% CI:0.13,0.20) or transferred (HR = 0.42; 95% CI:0.35,0.50), and for tertiary EDs (HR = 0.93; 95% CI:0.92,0.94). Conclusions All transition times were impacted by ED presentation characteristics. The sole key patient characteristic was age and it only impacted time to PIA. ED crowding demonstrated strong effects of time to PIA but not for the transition times involving disposition decision and ED departure stages.

New Method Utilizing Continuous Time Markov Chains to Analyze Evolution in the Nine States Model of Non-Alcoholic Fatty Liver Disease

it handles the disease in more extended multistate model

New Method Utilizing Continuous Time Markov Chains to Analyze Evolution in the Nine States Model of Non-Alcoholic Fatty Liver Disease

it handles the disease in more extended multistate model

In-depth time-dependent analysis of the benefit of Allo-HSCT for elderly patients with CR1 AML: a FILO study

The benefit of allogeneic hematopoietic stem cell transplantation (Allo-HSCT) for acute myeloid leukemia (AML) patients over 60 years remains a matter of debate, notably when performed in first complete remission (CR1). In order to clarify this issue, the French Innovative Leukemia Organization (FILO) performed a 10-year real-world time-dependent analysis. The study enrolled patients between 60 and 70 years of age with AML in CR1 after intensive chemotherapy with intermediate (IR) or unfavorable (UR) risk according to the European LeukemiaNet (ELN)-2010. The impact of Allo-HSCT was analyzed through three models, respectively i) time-dependent Cox, ii) multistate for dynamic prediction and iii) super landmark. The study enrolled 369 (73%) IR and 138 (27%) UR AML patients, 203 of whom received an Allo-HSCT. Classical multivariate analysis showed that Allo-HSCT significantly improved relapse-free (RFS; Hazard Ratio/HR [95%CI]: 0.47 [0.35-0.62], p&lt;0.001) and overall (OS; HR [95%CI]: 0.56 [0.42-0.76], p&lt;0.001) survivals, independently of the ELN risk group. With the multistate model, the predicted 5-year probability for IR and UR patients to remain in CR1 without Allo-HSCT was 8% and 1%, respectively. Dynamic predictions confirmed that patients without Allo-HSCT continue to relapse over time. Finally, the super landmark model showed that Allo-HSCT significantly improved RFS (HR [95%CI]: 0.47 [0.36-0.62], p&lt;0.001) and OS (HR [95%CI]: 0.54 [0.40-0.72], p&lt;0.001). Allo-HSCT in CR1 is demonstrated here to significantly improve the outcome of fit older AML patients. Long-term RFS without Allo-HSCT is very low (&lt;10%), supporting Allo-HSCT as being the best curative option for these patients.

Prediction of hospital bed capacity during the COVID− 19 pandemic

Background Prediction of the necessary capacity of beds by ward type (e.g. ICU) is essential for planning purposes during epidemics, such as the COVID− 19 pandemic. The COVID− 19 taskforce within the Ghent University hospital made use of ten-day forecasts on the required number of beds for COVID− 19 patients across different wards. Methods The planning tool combined a Poisson model for the number of newly admitted patients on each day with a multistate model for the transitions of admitted patients to the different wards, discharge or death. These models were used to simulate the required capacity of beds by ward type over the next 10 days, along with worst-case and best-case bounds. Results Overall, the models resulted in good predictions of the required number of beds across different hospital wards. Short-term predictions were especially accurate as these are less sensitive to sudden changes in number of beds on a given ward (e.g. due to referrals). Code snippets and details on the set-up are provided to guide the reader to apply the planning tool on one’s own hospital data. Conclusions We were able to achieve a fast setup of a planning tool useful within the COVID− 19 pandemic, with a fair prediction on the needed capacity by ward type. This methodology can also be applied for other epidemics.