No abstract

Background: Intensive care units represent one of the largest clinical cost centers in hospitals. Mechanical ventilation accounts for a significant share of this cost. There is a relative dearth of information quantifying the impact of ventilation on daily ICU cost. We thus determine daily costs of ICU care, incremental cost of mechanical ventilation per ICU day, and further differentiate cost by underlying diseases. Methods: Total ICU costs, length of ICU stay, and duration of mechanical ventilation of all 10,637 adult patients treated in ICUs at a German hospital in 2013 were analyzed for never-ventilated patients (N = 9181), patients ventilated at least 1 day, (N = 1455) and all patients (N = 10,637). Total ICU costs were regressed on the number of ICU days. Finally, costs were analyzed separately by ICD-10 chapter of main diagnosis. Results: Daily non-ventilated costs were €999 (95%CI €924-€1074), and ventilated costs were €1590 (95%CI €1524-€1657), a 59% increase. Costs per non-ventilated ICU day differed substantially and were lowest for endocrine, nutritional or metabolic diseases (€844), and highest for musculoskeletal diseases (€1357). Costs per ventilated ICU day were lowest for diseases of the circulatory system (€1439) and highest for cancer patients (€1594). The relative cost increase due to ventilation was highest for diseases of the respiratory system (94%) and even non-systematic for patients with musculoskeletal diseases (13%, p = 0.634). Conclusions: Results show substantial variability of ICU costs for different underlying diseases and underline mechanical ventilation as an important driver of ICU costs.

BackgroundLength of stay is a straightforward measure of hospital costs and retrospective data are widely available. However, a prospective idea of a patient’s length of stay would be required to predetermine hospital reimbursement per case based on patient classifications. The aim of this study was to analyse the predictive power of patient characteristics in terms of length of stay in a psychiatric hospital setting. A further aim was to use patient characteristics to predict episodes with extreme length of stay.MethodsThe study included all inpatient episodes admitted in 2013 to a psychiatric hospital. Zero-truncated negative binomial regression was carried out to predict length of stay. Penalized maximum likelihood logistic regressions were carried out to predict episodes experiencing extreme length of stay. Independent variables were chosen on the basis of prior research and model fit was cross-validated.ResultsA total of 738 inpatient episodes were included. Seven patient characteristics showed significant effects on length of stay. The strongest increasing effects were found in the presence of affective disorders as main diagnosis, followed by severity of disease and chronicity of disease. The strongest decreasing effects were found in danger to others, followed by the presence of substance-related disorders as main diagnosis, the daily requirement of somatic care and male gender. The squared correlation between out-of-sample predictions and observed values was 0.14. The root-mean-square-error was 40 days.ConclusionProspectively defining reimbursement per case might not be feasible in mental health because length of stay cannot be predicted by patient characteristics. Per diem systems should be used.