Baseline body mass index among children and adults undergoing allogeneic hematopoietic cell transplantation: clinical characteristics and outcomes.

Obesity is an important public health problem that may influence the outcomes of hematopoietic cell transplantation (HCT). We studied 898 children and adults receiving first-time allogeneic hematopoietic SCTs between 2004 and 2012. Pretransplant body mass index (BMI) was classified as underweight, normal weight, overweight or obese using the WHO classification or age-adjusted BMI percentiles for children. The study population was predominantly Caucasian, and the median age was 51 years (5 months-73 years). The cumulative 3-year incidence of nonrelapse mortality (NRM) in underweight, normal weight, overweight and obese patients was 20%, 19%, 20% and 33%, respectively. Major causes of NRM were acute and chronic GVHD. The corresponding incidence of relapse was 30%, 41%, 37% and 30%, respectively. Three-year OS was 59%, 48%, 47% and 43%, respectively. Multivariate analysis showed that obesity was associated with higher NRM (hazard ratio (HR) 1.43, P=0.04) and lower relapse (HR 0.65, P=0.002). Pretransplant plasma levels of ST2 and TNFR1 biomarkers were significantly higher in obese compared with normal weight patients (P=0.04 and P=0.05, respectively). The increase in NRM observed in obese patients was partially offset by a lower incidence of relapse with no difference in OS.

Patient no-show predictive model development using multiple data sources for an effective overbooking approach.

BACKGROUND: Patient no-shows in outpatient delivery systems remain problematic. The negative impacts include underutilized medical resources, increased healthcare costs, decreased access to care, and reduced clinic efficiency and provider productivity. OBJECTIVE: To develop an evidence-based predictive model for patient no-shows, and thus improve overbooking approaches in outpatient settings to reduce the negative impact of no-shows. METHODS: Ten years of retrospective data were extracted from a scheduling system and an electronic health record system from a single general pediatrics clinic, consisting of 7,988 distinct patients and 104,799 visits along with variables regarding appointment characteristics, patient demographics, and insurance information. Descriptive statistics were used to explore the impact of variables on show or no-show status. Logistic regression was used to develop a no-show predictive model, which was then used to construct an algorithm to determine the no-show threshold that calculates a predicted show/no-show status. This approach aims to overbook an appointment where a scheduled patient is predicted to be a no-show. The approach was compared with two commonly-used overbooking approaches to demonstrate the effectiveness in terms of patient wait time, physician idle time, overtime and total cost. RESULTS: From the training dataset, the optimal error rate is 10.6% with a no-show threshold being 0.74. This threshold successfully predicts the validation dataset with an error rate of 13.9%. The proposed overbooking approach demonstrated a significant reduction of at least 6% on patient waiting, 27% on overtime, and 3% on total costs compared to other common flat-overbooking methods. CONCLUSIONS: This paper demonstrates an alternative way to accommodate overbooking, accounting for the prediction of an individual patient's show/no-show status. The predictive no-show model leads to a dynamic overbooking policy that could improve patient waiting, overtime, and total costs in a clinic day while maintaining a full scheduling capacity.