This study evaluated a modified, timed version of the "Get-Up and Go" Test (Mathias et al, 1986) in 60 patients referred to a Geriatric Day Hospital (mean age 79.5 years). The patient is observed and timed while he rises from an arm chair, walks 3 meters, turns, walks back, and sits down again. The results indicate that the time score is (1) reliable (inter-rater and intra-rater); (2) correlates well with log-transformed scores on the Berg Balance Scale (r = -0.81), gait speed (r = -0.61) and Barthel Index of ADL (r = -0.78); and (3) appears to predict the patient's ability to go outside alone safely. These data suggest that the timed "Up & Go" test is a reliable and valid test for quantifying functional mobility that may also be useful in following clinical change over time. The test is quick, requires no special equipment or training, and is easily included as part of the routine medical examination.

The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons

Objective. —To develop and validate a new Simplified Acute Physiology Score, the SAPS II, from a large sample of surgical and medical patients, and to provide a method to convert the score to a probability of hospital mortality. Design and Setting. —The SAPS II and the probability of hospital mortality were developed and validated using data from consecutive admissions to 137 adult medical and/or surgical intensive care units in 12 countries. Patients. —The 13 152 patients were randomly divided into developmental (65%) and validation (35%) samples. Patients younger than 18 years, burn patients, coronary care patients, and cardiac surgery patients were excluded. Outcome Measure. —Vital status at hospital discharge. Results. —The SAPS II includes only 17 variables: 12 physiology variables, age, type of admission (scheduled surgical, unscheduled surgical, or medical), and three underlying disease variables (acquired immunodeficiency syndrome, metastatic cancer, and hematologic malignancy). Goodness-of-fit tests indicated that the model performed well in the developmental sample and validated well in an independent sample of patients (P=.883 andP=.104 in the developmental and validation samples, respectively). The area under the receiver operating characteristic curve was 0.88 in the developmental sample and 0.86 in the validation sample. Conclusion. —The SAPS II, based on a large international sample of patients, provides an estimate of the risk of death without having to specify a primary diagnosis. This is a starting point for future evaluation of the efficiency of intensive care units. (JAMA. 1993;270:2957-2963)

https://jamanetwork.com/HttpHandlers/ArticlePdfHandler.ashx?journal=JAMA&articleId=409979&pdfFileName=jama_270_24_035.pdf

A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study

Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines.

Statistical methods in cancer research

Reliability, the consistency of a test or measurement, is frequently quantified in the movement sciences literature. A common metric is the intraclass correlation coefficient (ICC). In addition, the SEM, which can be calculated from the ICC, is also frequently reported in reliability studies. However, there are several versions of the ICC, and confusion exists in the movement sciences regarding which ICC to use. Further, the utility of the SEM is not fully appreciated. In this review, the basics of classic reliability theory are addressed in the context of choosing and interpreting an ICC. The primary distinction between ICC equations is argued to be one concerning the inclusion (equations 2,1 and 2,k) or exclusion (equations 3,1 and 3,k) of systematic error in the denominator of the ICC equation. Inferential tests of mean differences, which are performed in the process of deriving the necessary variance components for the calculation of ICC values, are useful to determine if systematic error is present. If so, the measurement schedule should be modified (removing trials where learning and/or fatigue effects are present) to remove systematic error, and ICC equations that only consider random error may be safely used. The use of ICC values is discussed in the context of estimating the effects of measurement error on sample size, statistical power, and correlation attenuation. Finally, calculation and application of the SEM are discussed. It is shown how the SEM and its variants can be used to construct confidence intervals for individual scores and to determine the minimal difference needed to be exhibited for one to be confident that a true change in performance of an individual has occurred.

P. Armitage

Papers

The Design and Analysis of Clinical Experiments.