Jill Spitz Avrunin

Mortality probability models for patients in the intensive care unit for 48 or 72 hours: A prospective, multicenter study

ObjectiveTo develop models in the Mortality Probability Model (MPM II) system to estimate the probability of hospital mortality at 48 and 72 hrs in the intensive care unit (ICU), and to test whether the 24-hr Mortality Probability Model (MPM24), developed for use at 24 hrs in the ICU, can be used on a daily basis beyond 24 hrs. DesignA prospective, multicenter study to develop and validate models, using a cohort of consecutive admissions. SettingSix adult medical and surgical ICUs in Massachusetts and New York adjusted to reflect 137 ICUs in 12 countries. PatientsConsecutive admissions (n = 6,290) to the Massachusetts/New York ICUs were studied. Of these patients, 3,023 and 2,233 patients remained in the ICU and had complete data at 48 and 72 hrs, respectively. Patients <18 yrs of age, burn patients, coronary care patients, and cardiac surgical patients were excluded. Outcome MeasureVital status at the time of hospital discharge. ResultsThe models consist of five variables measured at the time of ICU admission and eight variables ascertained at 24-hr intervals. The 24-hr model demonstrated poor calibration and discrimination at 48 and 72 hrs. The newly developed 48− and 72-hr models—MPM48 and MPM72—contain the same 13 variables and coefficients as the MPM24. The models differ only in their constant terms, which increase in a manner that reflects the increasing probability of mortality with increasing length of stay in the ICU. These constant terms were adjusted by a factor determined from the relationship between the data from the six Massachusetts and New York ICUs and a more extensive data set, from which the ICU admission Mortality Probability Model (MPM0) and MPM24 were developed. This latter data set was assembled from ICUs in 12 countries. The MPM48 and MPM72 calibrated and discriminated well, based on goodness-of-fit tests and area under the receiver operating characteristic curve. ConclusionsModels developed for use among ICU patients at one time period are not transferable without modification to other time periods. The MPM48 and MPM72 calibrated well to their respective time periods, and they are intended for use at specific points in time. The increasing constant terms and associated increase in the probability of hospital mortality exemplify a common clinical adage that if a patients clinical profile stays the same, he or she is actually getting worse. (Crit Care Med 1994; 22:1351–1358)

The effects of a health promotion-health protection intervention on behavior change: the WellWorks Study

OBJECTIVES This study assessed the effects of a 2-year integrated health promotion-health protection work-site intervention on changes in dietary habits and cigarette smoking. METHODS A randomized, controlled intervention study used the work site as the unit of intervention and analysis; it included 24 predominantly manufacturing work sites in Massachusetts (250-2500 workers per site). Behaviors were assessed in self-administered surveys (n = 2386; completion rates = 61% at baseline, 62% at final). Three key intervention elements targeted health behavior change: (1) joint worker-management participation in program planning and implementation, (2) consultation with management on work-site environmental changes, and (3) health education programs. RESULTS Significant differences between intervention and control work sites included reductions in the percentage of calories consumed as fat (2.3% vs 1.5% kcal) and increases in servings of fruit and vegetables (10% vs 4% increase). The intervention had a significant effect on fiber consumption among skilled and unskilled laborers. No significant effects were observed for smoking cessation. CONCLUSIONS Although the size of the effects of this intervention are modest, on a populationwide basis effects of this size could have a large impact on cancer-related and coronary heart disease end points.

Validation of the mortality prediction model for ICU patients

We tested recently developed admission and 24-h models of hospital mortality on 1,997 consecutive admissions to a general medical/surgical ICU. This study population was independent of the group used to develop the models. The admission prediction model estimated each patients probability of hospital mortality based on seven routinely collected admission variables. The 24-h model utilized seven variables routinely available at 24 h in the ICU. The admission model accurately described the mortality experience of the new cohort, while the 24-h model did not.Advantages of the admission model are that it is evaluable at the time of ICU admission, is independent of ICU treatment, and can be used to stratify patients by severity of illness, thereby making ICU comparisons possible. Its excellent goodness-of-fit, correct classification rate, sensitivity, and specificity suggest that this model is now ready for multihospital testing.

Do Social Influences Contribute to Occupational Differences in Quitting Smoking and Attitudes toward Quitting

Purpose. To examine occupational differences in social influences supporting quitting smoking and their relationships to intentions and self-efficacy to quit smoking and to quitting. Design. Data were collected as part of a large worksite cancer prevention intervention trial. Setting. Forty-four worksites. Subjects. Subjects included 2626 smokers from a total baseline survey sample of 11,456 employees (response rate = 63%). Measures. Differences by job category in social support for quitting, pressure to quit smoking, rewards for quitting, and nonacceptability of smoking were measured using mixed model analysis of variance and the Cochran–Mantel–Haenszel test. Their association to self-efficacy, intention to quit, and quitting smoking was assessed using mixed model analysis of variance and linear logistic regression modeling. Results. Compared with other workers, blue-collar workers reported less pressure to quit (p = .0001), social support for quitting (p = .0001), and nonacceptability of smoking among their coworkers (p < .001). Intention to quit was associated with higher levels of social pressure to quit smoking (p = .0001) and social support for quitting (p = .002). Self-efficacy was associated with social pressure to quit (p = .0001), social support for quitting (p = .004), and perceiving greater rewards for quitting (p = .0001). Conclusions. Although these results are limited somewhat by response and attrition rates, these results suggest that differing social environments may contribute to the differences by occupational category in smoking prevalence and smoking cessation.

Explaining variability of cost using a severity-of-illness measure for ICU patients.

Factors related to hospital resource use by intensive care unit (ICU) patients, including severity of illness at admission and intensity of therapy during the first 24 ICU hours were explored in this study. Analysis was based on 2,749 patients admitted to the general medical-surgical ICU at Baystate Medical Center, Springfield, Massachusetts, between February 1,1983 and January 10, 1985. Resource use was indexed by hospital length of stay (LOS) adjusted for differences between ICU and other hospital days. Severity of illness was measured by the Mortality Prediction Model (MPM0), a validated predictor of outcome but not previously used to analyze resource consumption. Intensity of therapy was measured using the Therapeutic Intervention Scoring System (TISS). The 10% of patients with longest ICU stays were significantly different from the other 90% with respect to previous ICU use, MPM probability, and TISS score. Variability in resource use was analyzed using four diagnosis-related groups (DRGs) accounting for large numbers of ICU patients. The relationship between severity of illness and resource use was nonlinear: as severity increased from low levels, resource use increased at a decreasing rate, reached a plateau, and eventually declined. Within each DRG, MPM0 explained a statistically significant percentage of the variability in resource use.

A comparison of methods to predict mortality of intensive care unit patients.

This paper presents results of the first study explicitly designed to compare three methods for predicting hospital mortality of ICU patients: the Acute Physiology Score (APS), the Simplified Acute Physiology Score (SAPS), and the Mortality Prediction Model (MPM). With respect to sensitivity, specificity, and total correct classification rates, these methods performed comparably on a cohort of 1,997 consecutive ICU admissions. In these patients from a single hospital, the APS overestimated and the SAPS underestimated the probability of hospital mortality. The MPM probabilities most closely matched the observed outcomes. Each method holds considerable promise for assessing the severity of illness of critically ill patients. The MPM should be particularly useful for comparing ICU performance, since it is independent of ICU treatment and can be calculated at the time a patient is admitted.

Promoting mammography: results of a randomized trial of telephone counseling and a medical practice intervention

BACKGROUND Despite widespread promotion of mammography screening, a distinct minority of women have remained underusers of this effective preventive measure. We sought to measure the effects of barrier-specific telephone counseling (BSTC) and a physician-based educational intervention (MD-ED) on mammography utilization among underusers of mammography screening. DESIGN This was a randomized controlled trial. Women meeting criteria for mammography underuse at baseline (grouped by practice affiliation) were randomized to a reminder control condition (RC group received annual mailed reminders), BSTC or MD-ED interventions and followed for 3 years. Underuse was defined by failure to get two annual or biannual mammograms over a 2- to 4-year period prior to a baseline survey. PARTICIPANTS AND SETTING The study included 1655 female underusers of mammography aged 50-80 years who were members of two health maintenance organizations (HMO) in central Massachusetts. INTERVENTIONS BSTC consisted of periodic brief, scripted calls from trained counselors to women who had not had a mammogram in the preceding 15 months. Women could receive up to three annual calls during the study. MD-ED consisted of physician and office staff trainings aimed at improving counseling skills and office reminder systems. MAIN OUTCOME MEASURE Self-report of mammography use during the study period was the main outcome measure. Regular use was defined as > or =1 mammogram every 24 months. RESULTS Forty-four percent in each intervention group became regular users compared to 42% in the RC group. Among subjects who had prior but not recent mammograms at baseline, BSTC was effective (OR=1.48; 95% CI=1.04; 2. 10), and MD-ED marginally effective (OR=1.28; 95% CI=0.88, 1.85). Most recent users at baseline and few never users became regular users (61% and 17%, respectively) regardless of intervention status. CONCLUSIONS Among mammography underusers BSTC modestly increases utilization for former users at a reasonable cost (

Predicting the Outcome of Intensive Care Unit Patients

726 per additional regular user).

Two-year outcome of adult intensive care patients.

Abstract Statisticians are being asked with increasing frequency to develop models for occurrences in medical environments. Until recently, only subjective models were available to predict mortality for patients in an intensive care unit (ICU). These models were based on variables and associated weights determined by panels of medical “experts.” This article shows how multiple logistic regression (MLR) can be used to develop an objective model for prediction of hospital mortality among ICU patients. An MLR model to be applied when a patient is admitted to the ICU was developed on 737 ICU patients. The final model is based on the following variables: presence of coma or deep stupor at admission, emergency admission, cancer part of present problem, probable infection, cardiopulmonary resuscitation (CPR) prior to admission, age, and systolic blood pressure at admission. To validate this model, a new cohort of 1,997 consecutive ICU patients was entered into the study. Information was collected for the variabl...

Performance of pig heart after 30 or 120 minutes hypothermic arrest

Five hundred fifty-eight patients admitted to a general/medical surgical intensive care unit were studied 2 years after hospital discharge to determine whether they were still alive, were able to perform daily activities, and had returned to work. The overall 2-year survivorship (hospital and long-term) was 63.5%. Two-year survival was considerably lower for patients with certain condition or treatment characteristics than for others. This ranged from 14% 2-year survival for patients with 48 or more hours of coma to 82.2% for patients with no condition or treatment characteristics recorded. Once a patient was discharged alive, the 2-year cumulative survival of surgical ICU patients (84.6%) was significantly better than that of medical ICU patients (76.5%). Among ICU survivors responding to a follow-up survey, 85% were able to perform daily activities, but only 66% were working. Of the 44 patients experiencing a change in ability to perform daily activities at time of follow-up compared with pre-ICU admission, functional status of 34 (77%) improved, while 10 (23%) got worse. By comparison, of the 45 patients experiencing a change in working status, only 7 patients (16%) who did not work prior to ICU admission had returned to work, whereas the remaining 38 patients (84%) who worked prior to ICU admission were not working at time of follow-up study.