PubMed · 9824062
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J Rapoport, D Teres, S Lemeshow. 1998. Can futility be defined numerically?. https://doi.org/10.1097/00003246-199811000-00009
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OBJECTIVE: To assess the ability of clinical or biochemical parameters to predict outcome (survival or non-survival; severe or moderate/no complication) using multiple regression analyses. DESIGN: Prospective, descriptive cohort study with no interventions SETTING: 12 surgical intensive care units of university hospitals and large community hospitals; four medical school research laboratories in eight European countries PATIENTS: 128 surgical patients with major intra-abdominal surgery admitted for at least two days to an intensive care unit MAIN OUTCOME MEASURES: Prediction of complications or survival based on analysis of clinical (Multiple Organ Dysfunction Score, Multi-Organ-Failure Score, Acute Physiology and Chronic Health Evaluation II scores) and immunological (plasma levels of endotoxin, endotoxin neutralizing capacity, IL-6, IL-8, cell associated IL-8, Fc-receptor polymorphism, soluble CD-14) parameters, with comparison of predicted and actual outcomes. RESULTS: APACHE II, MODS score, MOF score, platelets, IL-6, IL-8, ENC, cell ass. IL-8 were significantly different between survivors and non-survivors and patients with/without severe complications by univariate analysis. By multivariate analysis only MOF, MODS score, IL-6, platelets, comorbidity predicted complications with a sensitivity of 82% and a specificity of 87%. Multivariate analysis demonstrated that only APACHE II score, plasma IL-8 and complications predicted death (sensitivity 84%; specificity 90%). CONCLUSION: Immunological surrogate parameters may predict complications and death of surgical ICU patients. The use of several parameters may add to increase sensitivity and specificity in a prognostic model.
BACKGROUND: Few studies have examined mortality rates in relation to the workload of hospital staff. We investigated this issue in one adult intensive-care unit (ICU) in the UK. METHODS: We measured ICU workload per shift during each patient's stay for all admissions between 1992 and 1995 that met criteria for adjustment of mortality risk by the APACHE II equation (n=1050). APACHE II data were validated by one observer. Measures of workload in each patient's stay included occupancy, total ICU nursing requirement as defined by the UK Intensive Care Society, and the ratio of occupied to appropriately staffed beds. Over the period, staffing was appropriate for between 4.1 and 5.3 occupied beds (1.3 nurses per patient). FINDINGS: There were 337 deaths, 49 more (95% CI 34-65) than predicted by the APACHE II equation. Median occupancy was 5.8 beds, and median nursing requirement was 1.6 per patient. On multiple logistic regression analysis, adjusted mortality was more than two times higher (odds ratio 3.1 [1.9-5.0]) in patients exposed to high than in those exposed to low ICU workload, defined by average nursing requirement per occupied bed and peak occupancy; the unadjusted odds ratio for this comparison was 4.0 (2.6-6.2). After exclusion of measures of nursing requirement, adjusted mortality increased with the ratio of occupied to appropriately staffed beds during each patient's stay. All logistic regression models fitted the data satisfactorily. INTERPRETATION: Variations in mortality may be partly explained by excess ICU workload. This methodology may have implications for planning and clinical governance.
BACKGROUND: The validity of outcome report cards may depend on the ways in which they are adjusted for risk. OBJECTIVES: To compare the predictive ability of generic and disease-specific survival prediction models appropriate for use in patients with heart failure, to simulate outcome report cards by comparing survival across hospitals and adjusting for severity of illness using these models, and to assess the ways in which the results of these comparisons depend on the adjustment method. DESIGN: Analysis of data from a prospective cohort study. SETTING: A university hospital, a Veterans Affairs (VA) medical center, and a community hospital. PATIENTS: Sequential patients presenting in the emergency department with acute congestive heart failure. MEASUREMENTS: Unadjusted 30-day and 1-year mortality across hospitals and 30-day and 1-year mortality adjusted by using disease-specific survival prediction models (two sickness-at-admission models, the Cleveland Health Quality Choice model, the Congestive Heart Failure Mortality Time-Independent Predictive Instrument) and generic models (Acute Physiology and Chronic Health Evaluation [APACHE] II, APACHE III, the mortality prediction model, and the Chadson comorbidity index). RESULTS: The community hospital's unadjusted 30-day survival rate (85.0%) and the VA medical center's unadjusted 1-year survival rate (60.9%) were significantly lower than corresponding rates at the university hospital (92.7% and 67.5%, respectively). No severity model had excellent ability to discriminate patients by survival rates (all areas under the receiver-operating characteristic curve < 0.73). Whether the VA medical center, the community hospital, both, or neither had worse survival rates on simulated report cards than the university hospital depended on the prediction model used for adjustment. CONCLUSIONS: Results of simulated outcome report cards for survival in patients with congestive heart failure depend on the method used to adjust for severity.