FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1967760375> ?p ?o ?g. }

• W1967760375 endingPage "163" @default.
• W1967760375 startingPage "161" @default.
• W1967760375 abstract "The development of intensive patient care has increased our ability to monitor, diagnose, and treat critically ill and injured patients; consequently, critical care practice has grown substantially in the past 4 decades. However, there is a strong perception by some that intensive care unit (ICU) efforts introduce an inappropriate financial burden. Intensive care unit costs are high and account for a disproportionately large fraction of health care resources.1Halpern NA Pastores SM Greenstein RJ Critical care medicine in the Unites States 1985-2000: an analysis of bed numbers, use, and costs.Crit Care Med. 2004; 32: 1254-1259Crossref PubMed Scopus (364) Google Scholar Furthermore, the true impact of the delivery of such services has yet to be defined. The detractors perceive that ICU care is a way of prolonging life without improving long-term survival and thus is a waste of resources. In addition, there is increasing recognition of the wide variation in health care practices and, more importantly, of the potential effect of this variance on health care delivery and outcomes.2Glance LG Osler TM Dick A Rating the quality of intensive care units: is it a function of the intensive care unit scoring system?.Crit Care Med. 2002; 30: 1976-1982Crossref PubMed Scopus (101) Google Scholar Consequently, critical care is under increasing pressure to improve ICU performance and quality of care in ways that will reduce costs. As a result, clinical decisions must often be based on a simultaneous evaluation of clinical outcomes and resource consumption. The ability to identify critically ill patients who will not survive to hospital discharge may yield substantial cost savings. A variety of instruments are now available for severity evaluation and outcome prediction in critical care. Outcome prediction models are usually classified as disease specific (ie, applied to patients with a particular condition) or generic (ie, designed to be applied to all critically ill patients). These instruments typically assign points according to severity of illness and purport to predict an outcome, providing the user with a numeric estimate of the probabilities of an outcome (eg, in-hospital mortality) for that patient or group of patients.3Cohen NH Assessing futility of medical interventions—is it futile? [editorial].Crit Care Med. 2003; 31: 646-648Crossref PubMed Scopus (12) Google Scholar The most common outcome prediction models used in adult intensive care include the Mortality Probability Model (MPM) II, the Simplified Acute Physiology Score (SAPS) II, and the Acute Physiology and Chronic Health Evaluation (APACHE) II and APACHE III.4Beck DH Taylor BL Millar B Smith GB Prediction of outcome from intensive care: a prospective cohort study comparing Acute Physiology and Chronic Health Evaluation II and III prognostic systems in a United Kingdom intensive care unit.Crit Care Med. 1997; 25: 9-15Crossref PubMed Scopus (126) Google Scholar In general, these systems are based on logistic regression models that use a set of clinical and physiological variables-evaluated and registered on admission or on the first day of intensive care-to predict hospital mortality. These models differ considerably in the number and types of variables used, as well as the time frame for data collection. For instance, the APACHE III system consists of 2 parts-an APACHE III score, which is based on 17 physiological variables, age, and chronic health status, and the predictive equation APACHE III, a series of predicted equations linked to ICU admission diagnosis, patient selection criteria, and the APACHE III database.5Knaus WA Wagner DP Draper EA et al.The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults.Chest. 1991; 100: 1619-1636Crossref PubMed Scopus (3054) Google Scholar These predictive systems are being used more commonly. At the patient level, severity scores describe severity of illness and are being used in some centers to support clinical decision making and to help guide discussions with patients and families concerning withdrawal of life support. At the ICU level, these outcome prediction models compare actual and expected outcome for groups of patients (ie, the standardized mortality ratio); they are used to compare ICU performance, to ensure that patient groups in research studies have similar severity of illness, to determine optimal allocation of critical care resources, and to evaluate the effect of new therapies, procedures, or ICU organization.6Moreno R Matos R The “new” scores: what problems have been fixed, and what remain?.Curr Opin Crit Care. 2000; 6: 158-165Crossref Scopus (16) Google Scholar Appropriate use of scoring systems depends on several factors, including consistent and accurate data collection and interpretation. Periodic system calibration helps define how closely mortality prognosis fits the observed mortality. These systems have been validated by 2 techniques. The first, cross-validation, evaluates the goodness of fit of the model by examining a model's calibration and discrimination on a subset of the data set different from the subset used to develop the model. The second, external validation, evaluates the goodness of fit of the model on an entirely new data set.7Mourouga P Goldfrad C Rowan KM Does it fit? is it good? assessment of scoring systems.Curr Opin Crit Care. 2000; 6: 176-180Crossref Scopus (23) Google Scholar In the current issue of Mayo Clinic Proceedings, Berge et al8Berge KH Maiers DR Schreiner DP et al.Resource utilization and outcome in gravely ill intensive care unit patients with predicted in-hospital mortality rates of 95% or higher by APACHE III scores: the relationship with physician and family expectations.Mayo Clin Proc. 2005; 80Abstract Full Text Full Text PDF PubMed Scopus (28) Google Scholar report the results of a retrospective study that evaluated the process of care, resource utilization, and outcomes in a cohort of 248 gravely ill patients as defined by the APACHE III scoring system model. Their study highlights the difficulties of applying scoring systems for clinical management of individual patients. Most investigators acknowledge the limitations of APACHE III and other scoring systems for predicting individual prognosis based on physiological data collected during the first 24 hours of the patient's ICU stay. Other investigators have proposed prognostic models that incorporate daily APACHE III values from subsequent days for predicting mortality. Conceptually, an approach that includes changes in physiological variables over time should more accurately predict the chance of survival. In a novel approach, Berge et al used a 95% or higher probability of hospital death APACHE III prediction score obtained on 2 consecutive days (median time after ICU admission, 4.5 days; range, 1–76 days) rather than the APACHE III score obtained on the first day of ICU care or a daily prognostic model. Surprisingly, there was a striking difference between the observed and the expected survival rates (23% vs =5%). Several factors may explain such difference. First, when data are not obtained at approximately the same time in the course of an acute illness or within a similar period, prognostic estimates will be inaccurate because physiological measures reflect different phases of critical illness.9Escarce JJ Kelley MA Admission source to the medical intensive care unit predicts hospital death independent of APACHE II score.JAMA. 1990; 264: 2389-2394Crossref PubMed Scopus (152) Google Scholar, 10Borlase BC Baxter JK Kenney PR Forse RA Benotti PN Blackburn GL Elective intrahospital admissions versus acute interhospital transfers to a surgical intensive care unit: cost and outcome prediction.J Trauma. 1991; 31: 915-918Crossref PubMed Scopus (50) Google Scholar Second, for an individual patient, mortality predictions calculated with use of scoring systems are often inaccurate in populations other than those in which the scales were developed. Moreover, the application of a different model to the same patient often results in different outcome predictions. Mackenzie et al11Mackenzie SJ Kendrick SW Howie JC From severity scores to health gain—a difficult road but one worth traveling.Curr Opin Crit Care. 2000; 6: 181-186Crossref Scopus (6) Google Scholar summarized the factors that may be responsible for significant variations in standardized mortality ratios: (1) the characteristics of the health care system, (2) the characteristics of the individual population, (3) the patterns of care in an individual ICU, (4) the intrinsic deficiencies of the model, (5) inconsistent applications of the model, (6) the size of the study population, and (7) variations in the quality of care in the ICU and/or the hospital. Several studies have evaluated the ability of critical care providers to predict both futility of care and outcomes. Theoretically, prognostic scoring systems should offer more accurate predictions than an individual physician's judgment because they are based in large databases, as opposed to being limited to a single clinician's experience. As an aid to the process of clinical decision making, evidence suggests that outcome prediction models perform better than clinicians in prognostic predictions.12Watts CM Knaus WA The case for using objective scoring systems to predict intensive care unit outcome.Crit Care Clin. 1994; 10: 73-89PubMed Google Scholar Esserman et al13Esserman L Belkora J Lenert L Potentially ineffective care: a new outcome to assess the limits of critical care.JAMA. 1995; 274: 1544-1551Crossref PubMed Scopus (96) Google Scholar developed a model based on the product of APACHE III risk estimates on ICU days 1 and 5 to identify patients receiving potentially ineffective care, whereas Afessa et al14Afessa B Keegan MT Mohammad Z Finkielman JD Peters SG Identifying potentially ineffective care in the sickest critically ill patients on the third ICU day.Chest. 2004; 126: 1905-1909Crossref PubMed Scopus (39) Google Scholar identified potentially ineffective care in the sickest critically ill patients with a predicted hospital mortality rate of 80% or higher by showing an increase in APACHE III score on the third ICU day compared with the first ICU day. Berge et al evaluated whether an APACHE III score that would predict an in-hospital mortality of 95% or higher on 2 consecutive days could also be used to identify patients for whom care would be potentially ineffective or futile and would potentially use more resources. They discovered that their use of APACHE III scores woefully underestimated observed survival at the time of hospital discharge. Often, physicians are confronted with decisions about what type of care to offer, how aggressively to pursue diagnostic and therapeutic interventions, and the potential futility of these actions. Clinicians often include input from patients, families, surrogates, and members of the health care team in the decision-making process.3Cohen NH Assessing futility of medical interventions—is it futile? [editorial].Crit Care Med. 2003; 31: 646-648Crossref PubMed Scopus (12) Google Scholar The preconceived notion is that, in the sickest critically ill patient, the process of care and resource utilization are often driven by the family's unrealistic expectations. In the study by Berge et al, unrealistic expectations of a good outcome correlated with increased resource utilization without obvious survival benefit. These investigators confirmed previous observations that the sickest critically ill patients consume a high proportion of resources. However, they were unable to ascertain whether the higher resource utilization was physician or family driven. This study also confirmed previous studies that showed that clinicians have differing opinions about futility of care, often interpreting the same information in diverging ways. The study by Berge et al provides further evidence that quantitative risk estimates should not be determinative in clinical decision making. Current prognostic systems will never be able to predict outcome with a 100% specificity, and therefore they will never be indicative of absolute irreversibility of disease or impossibility of survival.12Watts CM Knaus WA The case for using objective scoring systems to predict intensive care unit outcome.Crit Care Clin. 1994; 10: 73-89PubMed Google Scholar However, generic ICU prognostic systems can be useful in comparing ICU performance with respect to a wide variety of end points, including ICU mortality. Unless APACHE III or other scoring systems can be shown to retain the same level of predictive power across different ICUs with different mixes and survival rates or can be calibrated to different ICU populations, it is unlikely that current prognostic systems will be used as the single basis to withdraw care or allocate resources. As stated by Becker and Zimmerman,15Becker RB Zimmerman JE ICU scoring systems allow prediction of patient outcomes and comparison of ICU performance.Crit Care Clin. 1996; 12: 503-514Abstract Full Text Full Text PDF PubMed Scopus (47) Google Scholar “Future research will improve the accuracy of individual patient predictions but, even with the highest degree of precision, such predictions are only useful in support of, and not as a substitute for, good clinical judgment.” Resource Utilization and Outcome in Gravely Ill Intensive Care Unit Patients With Predicted In-hospital Mortality Rates of 95% or Higher by APACHE III Scores: The Relationship With Physician and Family ExpectationsMayo Clinic ProceedingsVol. 80Issue 2PreviewTo assess resource utilization and outcome in gravely ill patients admitted to an intensive care unit (ICU) and the potential association with health care workers' and family members' expectations. Full-Text PDF" @default.
• W1967760375 created "2016-06-24" @default.
• W1967760375 creator A5020321122 @default.
• W1967760375 creator A5075261717 @default.
• W1967760375 date "2005-02-01" @default.
• W1967760375 modified "2023-09-26" @default.
• W1967760375 title "Predicting Patient Outcomes, Futility, and Resource Utilization in the Intensive Care Unit: The Role of Severity Scoring Systems and General Outcome Prediction Models" @default.
• W1967760375 cites W1968931847 @default.
• W1967760375 cites W1981648877 @default.
• W1967760375 cites W2030560642 @default.
• W1967760375 cites W2033925475 @default.
• W1967760375 cites W2035460408 @default.
• W1967760375 cites W2052255514 @default.
• W1967760375 cites W2055171500 @default.
• W1967760375 cites W2058170334 @default.
• W1967760375 cites W2065253150 @default.
• W1967760375 cites W2077253436 @default.
• W1967760375 cites W2083835986 @default.
• W1967760375 cites W2131008061 @default.
• W1967760375 cites W21511533 @default.
• W1967760375 cites W4247323564 @default.
• W1967760375 cites W4361865037 @default.
• W1967760375 cites W2093486714 @default.
• W1967760375 doi "https://doi.org/10.4065/80.2.161" @default.
• W1967760375 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/15704768" @default.
• W1967760375 hasPublicationYear "2005" @default.
• W1967760375 type Work @default.
• W1967760375 sameAs 1967760375 @default.
• W1967760375 citedByCount "20" @default.
• W1967760375 countsByYear W19677603752012 @default.
• W1967760375 countsByYear W19677603752020 @default.
• W1967760375 countsByYear W19677603752021 @default.
• W1967760375 countsByYear W19677603752022 @default.
• W1967760375 crossrefType "journal-article" @default.
• W1967760375 hasAuthorship W1967760375A5020321122 @default.
• W1967760375 hasAuthorship W1967760375A5075261717 @default.
• W1967760375 hasBestOaLocation W19677603751 @default.
• W1967760375 hasConcept C126322002 @default.
• W1967760375 hasConcept C144237770 @default.
• W1967760375 hasConcept C148220186 @default.
• W1967760375 hasConcept C17744445 @default.
• W1967760375 hasConcept C177713679 @default.
• W1967760375 hasConcept C194828623 @default.
• W1967760375 hasConcept C199539241 @default.
• W1967760375 hasConcept C2776376669 @default.
• W1967760375 hasConcept C2779473830 @default.
• W1967760375 hasConcept C30036603 @default.
• W1967760375 hasConcept C33923547 @default.
• W1967760375 hasConcept C71924100 @default.
• W1967760375 hasConceptScore W1967760375C126322002 @default.
• W1967760375 hasConceptScore W1967760375C144237770 @default.
• W1967760375 hasConceptScore W1967760375C148220186 @default.
• W1967760375 hasConceptScore W1967760375C17744445 @default.
• W1967760375 hasConceptScore W1967760375C177713679 @default.
• W1967760375 hasConceptScore W1967760375C194828623 @default.
• W1967760375 hasConceptScore W1967760375C199539241 @default.
• W1967760375 hasConceptScore W1967760375C2776376669 @default.
• W1967760375 hasConceptScore W1967760375C2779473830 @default.
• W1967760375 hasConceptScore W1967760375C30036603 @default.
• W1967760375 hasConceptScore W1967760375C33923547 @default.
• W1967760375 hasConceptScore W1967760375C71924100 @default.
• W1967760375 hasIssue "2" @default.
• W1967760375 hasLocation W19677603751 @default.
• W1967760375 hasLocation W19677603752 @default.
• W1967760375 hasOpenAccess W1967760375 @default.
• W1967760375 hasPrimaryLocation W19677603751 @default.
• W1967760375 hasRelatedWork W2080135567 @default.
• W1967760375 hasRelatedWork W2219713661 @default.
• W1967760375 hasRelatedWork W2411411256 @default.
• W1967760375 hasRelatedWork W2426559221 @default.
• W1967760375 hasRelatedWork W2792512684 @default.
• W1967760375 hasRelatedWork W2887591203 @default.
• W1967760375 hasRelatedWork W3115034772 @default.
• W1967760375 hasRelatedWork W3198778831 @default.
• W1967760375 hasRelatedWork W4246440493 @default.
• W1967760375 hasRelatedWork W2090307362 @default.
• W1967760375 hasVolume "80" @default.
• W1967760375 isParatext "false" @default.
• W1967760375 isRetracted "false" @default.
• W1967760375 magId "1967760375" @default.
• W1967760375 workType "article" @default.