title: Algorithm-guided treatment of depression reduces treatment costs--results from the randomized controlled German Algorithm Project (GAPII).
abstract: BACKGROUND
  The German Algorithm Project, Phase 2 (GAP2) revealed that a standardized stepwise treatment regimen (SSTR) results in better treatment outcomes than treatment as usual (TAU) in depressed inpatients. The objective of this study was a health economic evaluation of SSTR based on a cost effectiveness analysis (CEA).
  
  
  METHODS
  GAP2 was a randomized controlled study with 148 patients. In an intention to treat (ITT) analysis direct treatment costs for study duration (SD) and total time in hospital (TTH; enrolment to discharge) were calculated based on daily hospital charges followed by a CEA to calculate cost expenditure per remitted patient.
  
  
  RESULTS
  Treatment costs in SSTR compared to TAU were significantly lower for SD (SSTR: 10 830 € ± 8 632 €, TAU: 15 202 € ± 12 483 €; p = 0.026) and did not differ significantly for TTH (SSTR: 21 561 € ± 16 162 €; TAU: 18 248 € ± 13 454; p = 0.208). CEA revealed that the costs per remission in SSTR were significantly lower for SD (SSTR: 20 035 € ± 15 970 €; SSTR: 38 793 € ± 31 853 €; p<0.0001) and TTH (SSTR: 31 285 € ± 23 451 €; TAU: 38 581 € ± 28 449 €, p = 0.041).
  
  
  CONCLUSIONS
  Indirect costs were not assessed. Different dropout rates in TAU and SSTR complicated interpretation of data.
  
  
  CONCLUSIONS
  An SSTR-based algorithm results in a superior cost effectiveness at no significant extra costs. Implementation of treatment algorithms in inpatient-care may help reduce treatment costs.
authors.affiliation: Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Mitte, Germany.
authors.forename: Roland
authors.initials: R
authors.lastname: Ricken
authors.forename: Katja
authors.initials: K
authors.lastname: Wiethoff
authors.forename: Thomas
authors.initials: T
authors.lastname: Reinhold
authors.forename: Kathrin
authors.initials: K
authors.lastname: Schietsch
authors.forename: Thomas
authors.initials: T
authors.lastname: Stamm
authors.forename: Julia
authors.initials: J
authors.lastname: Kiermeir
authors.forename: Peter
authors.initials: P
authors.lastname: Neu
authors.forename: Andreas
authors.initials: A
authors.lastname: Heinz
authors.forename: Michael
authors.initials: M
authors.lastname: Bauer
authors.forename: Mazda
authors.initials: M
authors.lastname: Adli
mesh_terms: D000328:Adult; D000465:Algorithms; D002985:Clinical Protocols; D003362:Cost-Benefit Analysis; D003365:Costs and Cost Analysis; D003863:Depression / Q000628:therapy; D003866:Depressive Disorder / Q000191:economics* / Q000628:therapy*; D005260:Female; D005500:Follow-Up Studies; D017048:Health Care Costs / Q000706:statistics & numerical data*; D005102:Health Expenditures; D006760:Hospitalization; D006801:Humans; D007297:Inpatients; D057194:Intention to Treat Analysis; D008297:Male; D008875:Middle Aged; D010352:Patient Dropouts; D016896:Treatment Outcome