Comparison of Clinical Assessment With APACHE II for Predicting Mortality Risk in Patients Admitted to a Medical Intensive Care Unit

Kruse, James A.; Thill-Baharozian, Mary C.; Carlson, Richard W.

doi:10.1001/jama.1988.03410120085032

Cited by 203 publications

(35 citation statements)

References 22 publications

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“…An elevated number of leukocytes is considered to be important for the severely sick, which is reflected in a higher APACHE II score [15], describing the general clinical state [13,15,16]. Regardless of these facts, we found no correlation of WBC with either prognosis or clinical state after CA.…”

Section: Discussionmentioning

confidence: 53%

“…At 8.00 a.m. on the day following the CA (day 1) and the next day (day 2) the clinical state of the patients after CA was determined by means of the Glasgow Coma Scale (GCS), assessing the state of the central nervous system [13,14], and Acute Physiology and Chronic Health Evaluation II (APACHE II), describing the general clinical state [13,15,16]. Values of the scales of the patients after CA are exhibited in Table II.…”

Section: Methodsmentioning

confidence: 99%

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The role of the immuno-inflammatory response in patients after cardiac arrest

Samborska-Sablik

Sablik²,

Gaszyński³

2011

aoms

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IntroductionThe aim of the research was to assess whether concentrations of inflammatory markers in blood of patients after cardiac arrest (CA) are related to their clinical state and survival.Material and methodsForty-six patients, aged 63 ±12 years, 21 of them after out-of-hospital CA and 25 after in-hospital CA, were enrolled in the study. Twenty-five patients survived and were discharged from hospital (CA-S); 21 died during hospitalization (CA-D). The clinical state of the patients was evaluated by the Glasgow Coma Scale (GCS) and the Acute Physiology and Chronic Health Evaluation II (APACHE II). On the day immediately after CA (day 1) and on the following day (day 2) the plasma concentration of high specific C-reactive protein (hs-CRP), tumour necrosis factor (TNF)-α, interleukin-10 and interleukin-6 (Ile-6) were measured.ResultsIn CA-D patients, compared with CA-S, a significantly higher concentration of hs-CRP (on day 1, 19 ±5 vs. 15 ±4; on day 2, 21 ±3 vs. 16 ±5 mg/l, p < 0.001) and Ile-6 (on day 1, 24.9 ±19.8 vs. 9.2 ±11.3; on day 2, 24.2 ±19.7 vs. 6.9 ±6.8 IU/ml, p < 0.001) was found. The level of TNF-α was greater in CA-D on day 1 (0.42 ±0.75 vs. 0.18 ±0.21 IU/ml, p < 0.04). Concentrations of hs-CRP and Ile-6 were correlated with the scores of GCS and APACHE II. Using logistic regression analysis and ROC curves the prognostic value of hs-CRP and Ile-6 for survival was proven.ConclusionsPost-cardiac arrest immuno-inflammatory response, reflected mainly in elevated plasma concentration of hs-CRP and Ile-6, is not only correlated with patients’ clinical state but also with prediction of survival.

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Section: Discussionmentioning

confidence: 53%

Section: Methodsmentioning

confidence: 99%

The role of the immuno-inflammatory response in patients after cardiac arrest

Samborska-Sablik

Sablik²,

Gaszyński³

2011

aoms

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“…Clinical estimation is often based on subjective parameters and physicians can be overconfident in their predictive abilities [17-20]. Independent risk stratification may provide clinicians with additional information to guide clinical decision-making, but further evaluation is required.…”

Section: Discussionmentioning

confidence: 99%

A systematic review of tools for predicting severe adverse events following patient discharge from intensive care units

et al. 2013

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IntroductionThe discharge of patients from the intensive care unit (ICU) to a hospital ward is a common transition of care that is associated with error and adverse events. Risk stratification tools may help identify high-risk patients for targeted interventions, but it is unclear if proper tools have been developed.MethodsWe searched Ovid EMBASE, Ovid MEDLINE, CINAHL, PUBMED and Cochrane Central Register of Controlled Trials from the earliest available date through March 2013, plus reference lists and citations of all studies included in the systematic review. Cohort studies were selected that described the derivation, validation or clinical impact of tools for predicting medical emergency team activation, ICU readmission or mortality following patient discharge from the ICU. Data were extracted on the study design, setting, population, sample size, tool (components, measurement properties) and outcomes.ResultsThe literature search identified 9,926 citations, of which eight studies describing eight tools met the inclusion criteria. Reported outcomes included ICU readmission (n = 4 studies), hospital mortality (n = 3 studies) and both ICU readmission and hospital mortality (n = 1 studies). Seven of the tools were comprised of distinct measurable component variables, while one tool used subjective scoring of patient risk by intensive care physicians. The areas under receiver operator curves were reported for all studies and ranged from 0.66 to 0.92. A single study provided a direct comparative analysis between two tools. We did not find any studies evaluating the impact of risk prediction on processes and outcomes of care.ConclusionsEight risk stratification tools for predicting severe adverse events following patient discharge from ICU have been developed, but have undergone limited comparative evaluation. Although risk stratification tools may help clinician decision-making, further evaluation of the existing tools' effects on care is required prior to clinical implementation.

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“…In patients with cirrhosis, this “state of health” is influenced not only by liver disease severity which is well-captured by the MELD score, but by factors such as age, muscle mass, nutritional status, and non-liver related co-morbidities that also contribute to wait-list outcomes but are not captured by the MELD score. There is robust rationale for the application of this clinical judgment, as several studies have shown that clinicians can predict outcomes in hospitalized or elderly patients (4-7). However, while inclusion of clinical assessment to liver transplant decisions may be justified, it is subjective and, as such, may differ from one clinician to another and lacks transparency.…”

Section: Introductionmentioning

confidence: 99%

Frailty Predicts Waitlist Mortality in Liver Transplant Candidates

Lai

Feng

Terrault

et al. 2014

American Journal of Transplantation

416

479

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We aimed to determine whether frailty, a validated geriatric construct of increased vulnerability to physiologic stressors, predicts mortality in liver transplant (LT) candidates. Consecutive adult outpatients listed for LT with laboratory MELD≥12 at a single center (97% recruitment rate) underwent 4 frailty assessments: Fried Frailty, Short Physical Performance Battery (SPPB), Activities of Daily Living (ADL), and Instrumental ADL (IADL) scales. Competing risks models associated frailty with wait-list mortality (death/delisting for being too sick for LT). 294 listed LT patients with MELD≥12, median age 60y, and MELD 15 were followed for 12 months. By Fried Frailty score≥3, 17% were frail; 11/51 (22%) of the frail versus 25/243 (10%) of the not frail died/were delisted (p=0.03). Each 1-unit increase in the Fried Frailty score was associated with a 45% (95%CI, 4-202%) increased risk of wait-list mortality adjusted for MELD. Similarly, the adjusted risk of wait-list mortality associated with each 1-unit decrease (i.e., increasing frailty) in the SPPB (HR 1.19, 95%CI 1.07-1.32). Frailty is prevalent in LT candidates. It strongly predicts wait-list mortality, even after adjustment for liver disease severity demonstrating the applicability and importance of the frailty construct in this population.

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Comparison of Clinical Assessment With APACHE II for Predicting Mortality Risk in Patients Admitted to a Medical Intensive Care Unit

Cited by 203 publications

References 22 publications

The role of the immuno-inflammatory response in patients after cardiac arrest

The role of the immuno-inflammatory response in patients after cardiac arrest

A systematic review of tools for predicting severe adverse events following patient discharge from intensive care units

Frailty Predicts Waitlist Mortality in Liver Transplant Candidates

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