Liver transplantation in the most severely ill cirrhotic patients: A multicenter study in acute-on-chronic liver failure grade 3
TL;DR: LT strongly influences the survival of patients with cirrhosis and ACLF-3 with a 1-year survival similar to that of Patients with a lower grade of ACLF, suggesting that patients with ACLf-3 should be rapidly referred to a specific liver ICU.
About: This article is published in Journal of Hepatology.The article was published on 2017-10-01. It has received 242 citations till now. The article focuses on the topics: Liver transplantation & Transplantation.
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TL;DR: The panel of experts, having emphasised the importance of initiating aetiologic treatment for any degree of hepatic disease at the earliest possible stage, extended its work to all the complications of cirrhosis which had not been covered by the European Association for the Study of the Liver guidelines.
1,534 citations
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...This emphasises the need for special management when transplanting patients with ACLF-3, with repeated systematic screening for infection and careful monitoring of renal and respiratory parameters.(434) Another point is that some patients with ACLF are potentially too sick for LT....
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...Thus, more data is needed to determine medical futility in patients with ALCF.(425,434) However, if LT is contraindicated or not available for patients with organ failures ≥4 or CLIF-C ACLFs >64 at days 3–7 after diagnosis of ACLF-3, the intensive organ support should be discontinued owing to futility....
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...Data on outcome of patients with ACLF treated with liver LT are scarce but nonetheless, patient survival at three-months after LT is about 80%, much higher than what would be anticipated if patients were not transplanted.(425,433,434) Almost all patients with ACLF-3 developed complications after LT, especially pulmonary, renal and infectious, compared to patients with no ACLF, or ACLF-1 and -2....
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278 citations
TL;DR: High mortality among patients with ACLF-3 on the liver transplant waitlist, even among those with lower MELD-Na scores is found, and Liver transplantation increases odds of survival for these patients, particularly if performed within 30 days of placement on the waitlist.
196 citations
TL;DR: It is shown that survival chances for more than 30 days in those with three or more organ failures are less than 8%, however, if a liver transplant is performed quickly, the survival chances are very high with one-year survival ranging from 84% with three organ failures to 81% with 5-6 organ failures.
124 citations
TL;DR: Patients with ACLF and high CLIF-C ACLF score after 48 hours of intensive care may reach a threshold of futility for further ongoing intensive support, and the best treatment options in this scenario remain to be determined but may include palliative care.
Abstract: Acute-on-chronic liver failure (ACLF) is a severe complication of cirrhosis and is defined by organ failure and high rates of short-term mortality. Patients with ACLF are managed with multiorgan support in the intensive care unit (ICU). Currently, it is unclear when this supportive care becomes futile, particularly in patients who are not candidates for liver transplant. The aim of this study was to determine whether the currently available prognostic scores can identify patients with ACLF in whom prolonged ICU care is likely to be futile despite maximal treatment efforts. Data of 202 consecutive patients with ACLF admitted to the ICU at the Royal Free Hospital London between 2005 and 2012 were retrospectively analyzed. Prognostic scores for chronic liver diseases, such as Child-Pugh, Model for End-Stage Liver Disease (MELD), European Foundation for the study of chronic liver failure (CLIF-C) organ failure (OF), and CLIF-C ACLF, were calculated 48 hours after ICU admission and correlated with patient outcome after 28 days. The CLIF-C ACLF score, compared with all other scores, most accurately predicted 28-day mortality, with an area under the receiver operator characteristic of 0.8 (CLIF-C OF, 0.75; MELD, 0.68; Child-Pugh, 0.66). A CLIF-C ACLF score cutoff ≥ 70 identified patients with a 100% mortality within 28 days. These patients had elevated inflammatory parameters representing a systemic inflammatory response, most often renal failure, compared with patients below this cutoff. Patients with ACLF and high CLIF-C ACLF score (≥ 70) after 48 hours of intensive care may reach a threshold of futility for further ongoing intensive support. The best treatment options in this scenario remain to be determined but may include palliative care.
96 citations
References
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TL;DR: The ESICM developed a so-called sepsis-related organ failure assessment (SOFA) score to describe quantitatively and as objectively as possible the degree of organ dysfunction/failure over time in groups of patients or even in individual patients.
Abstract: Multiple organ failure (MOF) is a major cause of morbidity and mortali ty in the critically ill patient. Emerging in the 1970s, the concept of MOF was linked to modern developments in intensive care medicine [1]. Although an uncontrolled infection can lead to MOF [2], such a phenomenon is not always found. A number of mediators and the persistence of tissue hypoxia have been incriminated in the development of MOF [3]. The gut has been cited as a possible \"moto r \" of MOF [4]. Nevertheless, our knowledge regarding the pathophysiology of MOF remains limited. Furthermore, the development of new therapeutic interventions aiming at a reduction of the incidence and severity of organ failure calls for a better definition of the severity of organ dysfunction/failure to quantify the severity of illness. Accordingly, it is important to set some simple but objective criteria to define the degree of organ dysfunction/failure. The evolution of our knowledge of organ dysfunction/failure led us to establish several principles: 1. Organ dysfunction/failure is a process rather than an event. Hence, it should be seen as a continuum and should not be described simply as \"present\" or \"absent~' Hence, the assessment should be based on a scale. 2. The time factor is fundamental for several reasons: (a) Development and similarly resolution of organ failure may take some time. Patients dying early may not have time to develop organ dysfunction/failure. (b) The time course of organ dysfunction/failure can be mult imodal during a complex clinical course, what is sometimes referred to as a \"multiple-hit\" scenario. (c) Time evaluation allows a greater understanding of the disease process as a natural process or under the influence of therapeutic interventions. The collection of data on a daily basis seems adequate. 3. The evaluation of organ dysfunction/failure should be based on a limited number of simple but objective variables that are easily and routinely measured in every institution. The collection of this information should not impose any intervention beyond what is routinely performed in every ICU. The variables used should as much as possible be independent of therapy, since therapeutic management may vary from one institution to another and even from one patient to another (Table 1). Until recently, none of the existing systems describing organ failure met these criteria, since they were based on categorial definitions or described organ failure as present or absent [5-7] . The ESICM organized a consensus meeting in Paris in October 1994 to create a so-called sepsis-related organ failure assessment (SOFA) score, to describe quantitatively and as objectively as possible the degree of organ dysfunction/failure over time in groups of patients or even in individual patients (Fig. 1). There are two major applications of such a SOFA score: 1. To improve our Understanding of the natural history of organ dysfunction/failure and the interrelation between the failure of the various organs.
8,538 citations
TL;DR: In this article, an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, which are particularly needed for binary, ordinal, and time-to-event outcomes.
Abstract: Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
7,879 citations
TL;DR: 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, and is a starting point for future evaluation of the efficiency of intensive care units.
Abstract: 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)
5,836 citations
24 Aug 2005
TL;DR: An easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes.
Abstract: Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
4,905 citations
"Liver transplantation in the most s..." refers methods in this paper
...Univariable Cox proportional hazards models were also used to assess the value of the existing futility score in predicting one-year survival; the Harrell’s c index of agreement was also calculated [18]....
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TL;DR: In patients with severe ARDS, early application of prolonged prone-positioning sessions significantly decreased 28-day and 90-day mortality.
Abstract: In this multicenter, prospective, randomized, controlled trial, we randomly as signed 466 patients with severe ARDS to undergo prone-positioning sessions of at least 16 hours or to be left in the supine position. Severe ARDS was defined as a ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen (Fio2) of less than 150 mm Hg, with an F io2 of at least 0.6, a positive end-expira tory pressure of at least 5 cm of water, and a tidal volume close to 6 ml per kilogram of predicted body weight. The primary outcome was the proportion of patients who died from any cause within 28 days after inclusion. Results A total of 237 patients were assigned to the prone group, and 229 patients were as signed to the supine group. The 28-day mortality was 16.0% in the prone group and 32.8% in the supine group (P<0.001). The hazard ratio for death with prone position ing was 0.39 (95% confidence interval [CI], 0.25 to 0.63). Unadjusted 90-day mortal ity was 23.6% in the prone group versus 41.0% in the supine group (P<0.001), with a hazard ratio of 0.44 (95% CI, 0.29 to 0.67). The incidence of complications did not differ significantly between the groups, except for the incidence of cardiac arrests, which was higher in the supine group. Conclusions In patients with severe ARDS, early application of prolonged prone-positioning ses sions significantly decreased 28-day and 90-day mortality. (Funded by the Programme Hospitalier de Recherche Clinique National 2006 and 2010 of the French Ministry of Health; PROSEVA ClinicalTrials.gov number, NCT00527813.)
2,897 citations