OBJECTIVE: To compare the recently developed "nine equivalents of nursing manpower use score" (NEMS) with the simplified Therapeutic Intervention Scoring System (TISS-28). DESIGN: Prospective single centre study. SETTING: Adult 30-bed medical-surgical intensive care unit (ICU) in a tertiary care university hospital. PATIENTS: Data from all patients admitted in 1997 to the ICU were included in the study. METHODS AND RESULTS: NEMS and TISS-28 items were recorded prospectively for each nursing shift. There were three shifts per day. The Simplified Acute Physiology Score (SAPS) II was calculated for the first 24 h of ICU stay and each patient's basic demographic data were collected. The agreement between NEMS and TISS-28 was assessed by calculating the mean difference and the standard deviation of the differences between the two measures. Further, regression techniques and Pearson's correlation were used. Altogether, 2743 patients with a total of 28,220 nursing shifts were included; 62% of the shifts were used for postoperative/trauma patients and 38% for medical patients. Mean NEMS was 26.0 +/- 8.1 and mean TISS-28 was 26.5 +/- 7.9. The scores differed by < or = 3 points in 49 % of all shifts. The bias was -0.5 +/- 5.3 (95% confidence interval -0.47 to -0.60) and the limits of agreement were -11.1 to +10.1. The relation between the two systems was NEMS = 4.7 +/- 0.8 x TISS-28 (r = 0.78, r2 = 0.62, p < 0.001). Including postoperative/trauma patients only: NEMS = 1.9 +/- 0.9 x TISS-28, for medical patients this equation was: NEMS = 6.0 + 0.8 x TISS-28. First-day SAPS II explained 11% of the variability in first-shift NEMS and 5% of the variability in first-shift TISS-28. CONCLUSIONS: This study confirms a good agreement between TISS-28 and NEMS in a large, independent sample. However, as shown by the differences between medical and postoperative/trauma patients, a change in case mix may result in different regression equations. Further, wide limits of agreement indicate that there may be a rather large variability between the two measures at the individual level.
OBJECTIVE: To compare the recently developed "nine equivalents of nursing manpower use score" (NEMS) with the simplified Therapeutic Intervention Scoring System (TISS-28). DESIGN: Prospective single centre study. SETTING: Adult 30-bed medical-surgical intensive care unit (ICU) in a tertiary care university hospital. PATIENTS: Data from all patients admitted in 1997 to the ICU were included in the study. METHODS AND RESULTS: NEMS and TISS-28 items were recorded prospectively for each nursing shift. There were three shifts per day. The Simplified Acute Physiology Score (SAPS) II was calculated for the first 24 h of ICU stay and each patient's basic demographic data were collected. The agreement between NEMS and TISS-28 was assessed by calculating the mean difference and the standard deviation of the differences between the two measures. Further, regression techniques and Pearson's correlation were used. Altogether, 2743 patients with a total of 28,220 nursing shifts were included; 62% of the shifts were used for postoperative/traumapatients and 38% for medical patients. Mean NEMS was 26.0 +/- 8.1 and mean TISS-28 was 26.5 +/- 7.9. The scores differed by < or = 3 points in 49 % of all shifts. The bias was -0.5 +/- 5.3 (95% confidence interval -0.47 to -0.60) and the limits of agreement were -11.1 to +10.1. The relation between the two systems was NEMS = 4.7 +/- 0.8 x TISS-28 (r = 0.78, r2 = 0.62, p < 0.001). Including postoperative/traumapatients only: NEMS = 1.9 +/- 0.9 x TISS-28, for medical patients this equation was: NEMS = 6.0 + 0.8 x TISS-28. First-day SAPS II explained 11% of the variability in first-shift NEMS and 5% of the variability in first-shift TISS-28. CONCLUSIONS: This study confirms a good agreement between TISS-28 and NEMS in a large, independent sample. However, as shown by the differences between medical and postoperative/traumapatients, a change in case mix may result in different regression equations. Further, wide limits of agreement indicate that there may be a rather large variability between the two measures at the individual level.
Authors: Peter Hoonakker; Pascale Carayon; Ayse Gurses; Roger Brown; Kerry McGuire; Adjhaporn Khunlertkit; James M Walker Journal: IIE Trans Healthc Syst Eng Date: 2011-10-12
Authors: Dieter P Debergh; Dries Myny; Isabelle Van Herzeele; Georges Van Maele; Dinis Reis Miranda; Francis Colardyn Journal: Intensive Care Med Date: 2012-08-09 Impact factor: 17.440
Authors: Sebastián Iribarren-Diarasarri; Felipe Aizpuru-Barandiaran; Tomás Muñoz-Martínez; Angel Loma-Osorio; Marianela Hernández-López; José María Ruiz-Zorrilla; Carlos Castillo-Arenal; Juan Luis Dudagoitia-Otaolea; Sergio Martínez-Alutiz; Cristina Vinuesa-Lozano Journal: Intensive Care Med Date: 2009-01-29 Impact factor: 17.440
Authors: Eric S Hall; Mollie R Poynton; Scott P Narus; Spencer S Jones; R Scott Evans; Michael W Varner; Sidney N Thornton Journal: J Biomed Inform Date: 2009-02-06 Impact factor: 6.317
Authors: Jean-Blaise Wasserfallen; Jean-Pierre Revelly; Davide Moro; Nicolas Gilliard; Jacques Rouge; René Chioléro Journal: Intensive Care Med Date: 2004-02-28 Impact factor: 17.440