[Cost analysis and outcome prediction with the Therapeutic Intervention Scoring System (TISS and TISS-28].
OBJECTIVE
The "Therapeutic Intervention Scoring System" (TISS) and the simplified version TISS-28 obtain the therapeutic workload in the critically ill and may be used for outcome prediction. The feasibility and applicability regarding cost analysis and outcome prediction of TISS and TISS-28 have been assessed in 303 consecutive medical patients staying longer than 24 h in the intensive care unit (ICU).


METHODS
The mean age of the enrolled patients was 62 +/- 12 years, 216 (71%) patients were male, length of ICU stay 3.7 +/- 4.7 days, and SAPS II (Simplified Acute Physiology Score) 26 +/- 13 points. The overall mortality was 44 patients (14.5%) with 25 patients (8.3%) dying while on the ICU.


RESULTS
The data collection process for TISS took significantly longer than for TISS-28. On the day of admission, the correlation of TISS and TISS-28 was excellent (r(2) = 0.91; p < 0.001). The discriminatory power as assessed by the area under the receiver operating characteristic (ROC) curve was satisfactory for TISS (0.79 +/- 0.04), TISS-28 (0.76 +/- 0.04), and SAPS II (0.77 +/- 0.04) with regard to outcome prediction. Patient-specific costs per TISS-28 point amounted to 36 euros.--and were significantly higher than the 25 euros.--calculated per TISS point. Staff costs (42%) were the most prominent cost-generating factor, and patient-specific costs contributed two thirds to the total ICU costs. There was no association of severity of illness or number of organ failure and costs. Only the length of ICU stay correlated strongly with the costs of the individual patients during the ICU stay (r(2) = 0.79; p < 0.001).


CONCLUSIONS
The faster data collection process as well as the uniformity of the system are strong clinical and scientific advantages of the TISS-28. In addition, TISS-28 is capable of calculating individual costs in an acceptable time frame. Therefore TISS-28 serves as a valuable tool for quality assurance and cost analysis purposes in the medical ICU.
Graf
Jürgen
J
Medizinische Klinik I, Universitätsklinikum Aachen. jgraf@ukaachen.de
Graf
Carmen
C
Koch
Karl-Christian
KC
Hanrath
Peter
P
Janssens
Uwe
U
D000368:Aged; D003365:Costs and Cost Analysis / Q000706:statistics & numerical data*; D003422:Critical Care / Q000191:economics*; D016638:Critical Illness / Q000191:economics* / Q000401:mortality; D005240:Feasibility Studies; D005260:Female; D005858:Germany; D006801:Humans; D007902:Length of Stay / Q000191:economics; D008297:Male; D008875:Middle Aged; D018803:Models, Economic; D009313:National Health Programs / Q000191:economics*; D011379:Prognosis; D012372:ROC Curve; D012720:Severity of Illness Index; D015996:Survival Rate