Introduction. Many cancer survivors suffer from a combination of disease-and treatment-related morbidities and complaints after primary treatment. There is a growing evidence base for the effectiveness of monodimensional rehabilitation interventions; in practice, however, patients often participate in multidimensional programs. This study systematically reviews evidence regarding effectiveness of multidimensional rehabilitation programs for cancer survivors and cost-effectiveness of cancer rehabilitation in general.Methods. The published literature was systematically reviewed. Data were extracted using standardized forms and were summarized narratively.Results. Sixteen effectiveness and six cost-effectiveness studies were included. Multidimensional rehabilitation programs were found to be effective, but not more effective than monodimensional interventions, and not on all outcome measures. Effect sizes for quality of life were in the

Background Procalcitonin is a biomarker that supports clinical decision-making on when to initiate and discontinue antibiotic therapy. Several cost (-effectiveness) analyses have been conducted on Procalcitonin-guided antibiotic stewardship, but none mainly based on US originated data. Objective To compare effectiveness and costs of a Procalcitonin-algorithm versus standard care to guide antibiotic prescription for patients hospitalized with a diagnosis of suspected sepsis or lower respiratory tract infection in the US. Methods A previously published health economic decision model was used to compare the costs and effects of Procalcitonin-guided care. The analysis considered the societal and hospital perspective with a time horizon covering the length of hospital stay. The main outcomes were total costs per patient, including treatment costs and productivity losses, the number of patients with antibiotic resistance or C . difficile infections, and costs per antibiotic day avoided. Results Procalcitonin -guided care for hospitalized patients with suspected sepsis and lower respiratory tract infection is associated with a reduction in antibiotic days, a shorter length of stay on the regular ward and the intensive care unit, shorter duration of mechanical ventilation, and fewer patients at risk for antibiotic resistant or C . difficile infection. Total costs in the Procalcitonin-group compared to standard care were reduced by 26.0% in sepsis and 17.7% in lower respiratory tract infection (total incremental costs of −$11,311 per patient and −$2,867 per patient respectively). Conclusions Using a Procalcitonin-algorithm to guide antibiotic use in sepsis and hospitalised lower respiratory tract infection patients is expected to generate cost-savings to the hospital and lower rates of antibiotic resistance and C . difficile infections.

Patients can be prescribed CBT or, based on individual preferences, PE.

A141annually. Two-way sensitivity analyses were performed by varying values for key parameters in the model. RESULTS: Under base-case scenario, gain of QALYs per patient associated with exemestane (29 QALYS) was higher compared with tamoxifen (28 QALYS) and raloxifene (28 QALYS). The cost of gaining one QALY with exemestane ($44,723) was found to be low compared to tamoxifen ($93,053) and raloxifene ($70,940). These results were robust to the two-way sensitivity analyses performed. CONCLUSIONS: Results suggested that switching to exemestane after 2 years of primary treatment with tamoxifen was costeffective.

Medical decision-making is revolutionizing with the introduction of artificial intelligence and machine learning. Yet, traditional algorithms using biomarkers to optimize drug treatment continue to be important and necessary. In this context, early diagnosis and rational antimicrobial therapy of sepsis and lower respiratory tract infections (LRTI) are vital to prevent morbidity and mortality. In this study we report an original cost-effectiveness analysis (CEA) of using a procalcitonin (PCT)-based decision algorithm to guide antibiotic prescription for hospitalized sepsis and LRTI patients versus standard care. We conducted a CEA using a decision-tree model before and after the implementation of PCTguided antibiotic stewardship (ABS) using real-world U.S. hospital-specific data. The CEA included societal and hospital perspectives with the time horizon covering the length of hospital stay. The main outcomes were average total costs per patient, and numbers of patients with Clostridium difficile and antibiotic resistance (ABR) infections. We found that health care with the PCT decision algorithm for hospitalized sepsis and LRTI patients resulted in shorter length of stay, reduced antibiotic use, fewer mechanical ventilation days, and lower numbers of patients with C. difficile and ABR infections. The PCT-guided health care resulted in cost savings of $25,611 (49% reduction from standard care) for sepsis and $3630 (23% reduction) for LRTI, on average per patient. In conclusion, the PCT decision algorithm for ABS in sepsis and LRTI might offer cost savings in comparison with standard care in a U.S. hospital context. To the best of our knowledge, this is the first health economic analysis on PCT implementation using U.S. real-world data. We suggest that future CEA studies in other U.S. and worldwide settings are warranted in the current age when PCT and other decision algorithms are increasingly deployed in precision therapeutics and evidence-based medicine.