Background Febrile neutropenia is a serious complication of chemotherapy. The Multinational Association for Supportive Care in Cancer (MASCC) risk index score identifies patients at low risk of serious complications. Outpatient management programs have been successfully piloted in other Australian metropolitan cancer centers. Aim To assess current management of febrile neutropenia at our regional cancer center and determine potential impacts of an outpatient management program. Method We performed a retrospective review of medical records for all patients admitted at our regional institution with febrile neutropenia between 1 January 2016, and 31 December 2018. We collected information regarding patient characteristics, determined the MASCC risk index score, and if low risk, we determined the eligibility for outpatient care and potential reduction in length of stay and cost benefit. Results A total of 98 hospital admissions were identified. Of these, 66 had a MASCC low‐risk index score. Fifty‐eight patients met the eligibility criteria for outpatient management. Seventy‐one percent were female. The most common tumor type was breast cancer. Forty‐eight percent were treated with curative intent. The median length of stay was 3 days. The median potential reduction in length of stay for each admission was 2 days. The total potential reduction in length of stay was 198 days. No admission resulted in serious complications. Conclusion This review demonstrates a significant number of hospital admission days can be avoided. We intend to conduct a prospective pilot study at our center to institute an outpatient management program for such low‐risk patients with potential reduction in hospital length of stay. This will have significant implications on health resource usage, service provision planning, and patient quality of life.
e18000 Background: Febrile neutropenia is a serious complication of chemotherapy. The Multinational Association for Supportive Care in Cancer (MASCC) risk index score can reliably identify patients with febrile neutropenia at low risk of serious complications. Outpatient management programs utilising protocol based risk stratification, daily ambulatory nursing visits, telephone follow up and early outpatient review have been successfully piloted in other Australian cancer treatment centres. Methods: We performed a retrospective review of medical records for all patients admitted at our institution with febrile neutropenia between January 1 2016 and December 31 2018. We collected information regarding patient characteristics, cancer diagnosis and treatment, determined the MASCC risk index score, and if low risk, we determined the potential eligibility for outpatient care and potential reduction in length of stay. Results: A total of 98 hospital admissions with febrile neutropenia were analysed. Of these, 66 were determined to have a MASCC low risk index score. 58 patients met the eligibility criteria for outpatient management. The median age was 67 years. 71% were female. The most common tumour type was breast cancer. 52% were treated with palliative intent. The median length of stay was 3 days. The median potential reduction in length of stay for each admission was 2 days. The total potential reduction in length of stay was 198 days. No admission resulted in serious complications indicating the safety of outpatient care. Conclusions: Febrile neutropenia is a common complication of chemotherapy and a leading cause of hospital admission. This review demonstrates a significant number of hospital admission days can be avoided with outpatient care. We intend to conduct a prospective pilot study at our centre to institute an outpatient febrile neutropenia program for such low risk groups with potential reduction in hospital bed length of stay. This has significant implications on health resource usage, service provision planning and patient quality of life.
Predictor envelopes model the response variable by using a subspace of dimension d extracted from the full space of all p input variables. Predictor envelopes have a close connection to partial least squares and enjoy improved estimation efficiency in theory. As such, predictor envelopes have become increasingly popular in Chemometrics. Often, d is much smaller than p, which seemingly enhances the interpretability of the envelope model. However, the process of estimating the envelope subspace adds complexity to the final fitted model. To better understand the complexity of predictor envelopes, we study their effective degrees of freedom (EDF) in a variety of settings. We find that in many cases a d-dimensional predictor envelope model can have far more than d + 1 EDF and often has close to p + 1. However, the EDF of a predictor envelope depend heavily on the structure of the underlying data-generating model and there are settings under which predictor envelopes can have substantially reduced model complexity.
The Tobit model has long been the standard method for regression with a leftcensored response in economics. In spite of its enduring popularity, the Tobit model has not been extended for high-dimensional regression. To fill this gap, we propose several penalized Tobit models for high-dimensional censored regression. We use Olsen's (1978) convex reparameterization of the Tobit negative log-likelihood as the basis for our models, leveraging the fact that the negative log-likelihood satisfies the quadratic majorization condition to develop a generalized coordinate descent algorithm for computing the solution path. Theoretically, we analyze the Tobit lasso and Tobit with a folded concave penalty, deriving a bound for the ℓ 2 estimation loss for the former and proving that a local linear approximation estimator for the latter possesses the strong oracle property. Through an extensive simulation study, we find that our penalized Tobit models provide more accurate predictions and parameter estimates than their least-squares counterparts on high-dimensional data with a censored response. We demonstrate the superior prediction and variable selection performance of the penalized Tobit models on Mroz's 1975 women's labor supply data.
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