Critical Care in the Emergency Department A Physiologic Assessment and Outcome Evaluation

OBJECTIVES: The changing landscape of health care in this country has seen an increase in the delivery of care to critically ill patients in the emergency department (ED). However, methodologies to assess care and outcomes similar to those used in the intensive care unit (ICU) are currently lacking in this setting. This study examined the impact of ED intervention on morbidity and mortality using the Acute Physiology and Chronic Health Evaluation (APACHE II), the Simplified Acute Physiology Score (SAPS II), and the Multiple Organ Dysfunction Score (MODS). METHODS: This was a prospective, observational cohort study over a three-month period. Critically ill adult patients presenting to a large urban ED and requiring ICU admission were enrolled. APACHE II, SAPS II, and MODS scores and predicted mortality were obtained at ED admission, ED discharge, and 24, 48, and 72 hours in the ICU. In-hospital mortality was recorded. RESULTS: Eighty-one patients aged 64 +/- 18 years were enrolled during the study period, with a 30.9% in-hospital mortality. The ED length of stay was 5.9 +/- 2.7 hours and the hospital length of stay was 12.2 +/- 16.6 days. Nine (11.1%) patients initially accepted for ICU admission were later admitted to the general ward after ED intervention. Septic shock was the predominant admitting diagnosis. At ED admission, there was a significantly higher APACHE II score in nonsurvivors (23.0 +/- 6.0) vs survivors (19.8 +/- 6.5, p = 0.04), while there was no significant difference in SAPS II or MODS scores. The APACHE II, SAPS II, and MODS scores were significantly lower in survivors than nonsurvivors throughout the hospital stay (p 相似文献   

Prognostic performance and customization of the SAPS II: results of a multicenter Austrian study

OBJECTIVES: To evaluate the prognostic performance of the original Simplified Acute Physiology Score (SAPS) II in Austrian intensive care patients and to evaluate the impact of customization. DESIGN: Analysis of the database of a multicenter study. SETTING: Nine adult medical, surgical, and mixed intensive care units (ICUs) in Austria. PATIENTS: A total of 1733 patients consecutively admitted to the ICUs. MEASUREMENTS AND RESULTS: The database included admission data, SAPS II, length of stay, and hospital mortality. The Hosmer-Lemeshow goodness-of-fit test for the SAPS II showed a lack of uniformity of fit (H = 89.1, 10 df, p < 0.0001; C = 91.8, 10 df, p < 0.0001). Subgroup analysis showed good performance in patients with cardiovascular (medical and surgical) diseases as the primary reasons for admission. A new predictive equation was derived by means of the logistic regression. Goodness-of-fit was excellent for the customized model (SAPS IIAM) (H = 11.2, 9 df, p = 0.33, C = 11.6, 9 df, p = 0.24). The mean standardized mortality ratio (SMR) changed from 0.81 +/- 0.26 to 0.93 +/- 0.29 with customization. CONCLUSIONS: SAPS II was not well calibrated when applied to all patients. However, it performed well for patients with cardiovascular diseases as the primary reason for admission and may thus be applied to these patients. Standardized mortality ratios that are calculated from scoring systems without known calibration must be viewed with skepticism.     

Plasma glutamine depletion and patient outcome in acute ICU admissions

OBJECTIVE: To evaluate whether low plasma glutamine (PG) is related to severity of illness, and actual and predicted hospital mortality. DESIGN: Prospective cohort study. SETTING: 18-bed closed format general intensive care unit (ICU) of a teaching hospital. PATIENTS: Cohort of 80 seriously ill patients non-electively admitted to the ICU. INTERVENTIONS: Blood sampling for the determination of PG at ICU admission. MEASUREMENTS AND RESULTS: Severity of illness and predicted mortality were calculated using the locally validated APACHE II, SAPS II, and MPM II 0 and 24 systems. Illness scores, and actual and predicted hospital mortality were compared between patients with total PG < 0.420 mmol/l ("low PG") and patients with PG > or = 0.420 mmol/l. Mean total PG was 0.523 mmol/l, range 0.220-1.780 mmol/l. Low PG (n = 25) was associated with higher age (P = 0.03), shock as primary diagnosis, and higher actual hospital mortality (60 % vs 29 %, P = 0.01). Normal to high PG was associated with high plasma creatine phosphokinase (P = 0.007) There was a nonsignificant trend towards higher severity of illness scores and predicted mortality rates in the low PG group. The presence of low PG significantly improved mortality prediction when added as a factor to the APACHE II predicted mortality rate (P = 0.02). CONCLUSIONS: Low PG at acute ICU admission is related to higher age, shock as primary diagnosis, and higher hospital mortality. Low PG represents a risk of poor outcome, not fully reflected in the presently used mortality prediction systems.     

Validation of severity scoring systems SAPS II and APACHE II in a single-center population

OBJECTIVE: To validate two severity scoring systems, the Simplified Acute Physiology Score (SAPS II) and Acute Physiology and Chronic Health Evaluation (APACHE II), in a single-center ICU population. DESIGN AND SETTING: Prospective data collection in a two four-bed multidisciplinary ICUs of a teaching hospital. PATIENTS AND METHODS: Data were collected in ICU over 4 years on 1,721 consecutively admitted patients (aged 18 years or older, no transferrals, ICU stay at least 24 h) regarding SAPS II, APACHE II, predicted hospital mortality, and survival upon hospital discharge. RESULTS: At the predicted risk of 0.5, sensitivity was 39.4 % for SAPS II and 31.6 % for APACHE II, specificity 95.6 % and 97.2 %, and correct classification rate 85.6 % and 85.5 %, respectively. The area under the ROC curve was higher than 0.8 for both models. The goodness-of-fit statistic showed no significant difference between observed and predicted hospital mortality (H = 7.62 for SAPS II, H = 3.87 for APACHE II; and C = 9.32 and C = 5.05, respectively). Observed hospital mortality of patients with risk of death higher than 60 % was overpredicted by SAPS II and underpredicted by APACHE II. The observed hospital mortality was significantly higher than that predicted by the models in medical patients and in those admitted from the ward. CONCLUSIONS: This study validates both SAPS II and APACHE II scores in an ICU population comprised mainly of surgical patients. The type of ICU admission and the location in the hospital before ICU admission influence the predictive ability of the models.     

Is the SAPS II score valid in surgical intensive care unit patients?

Aims and objectives We investigated the performance of the simplified acute physiology score II (SAPS II) in a large cohort of surgical intensive care unit (ICU) patients and tested the hypothesis that customization of the score would improve the uniformity of fit in subgroups of surgical ICU patients. Methods Retrospective analysis of prospectively collected data from all 12 938 patients admitted to a postoperative ICU between January 2004 and January 2009. Probabilities of hospital death were calculated for original and customized (C1‐SAPS II and C2‐SAPS II) scores. A priori subgroups were defined according to age, probability of death according to the SAPS II score, ICU length of stay (LOS), surgical procedures and type of admission. Results The median ICU LOS was 1 (1–3) day. ICU and hospital mortality rates were 5.8% and 10.3%, respectively. Discrimination of the SAPS II was moderate [area under receiver operating characteristic curve (aROC) = 0.76 (0.75–0.78)], but calibration was poor. This model markedly overestimated hospital mortality rates [standardized mortality rate: 0.35 (0.33–0.37)]. First‐level customization (C1‐SAPS II) did not improve discrimination in the whole cohort or the subgroups, but calibration improved in some subgroups. Second‐level customization (C2‐SAPS II) improved discrimination in the whole cohort [aROC = 0.82 (0.79–0.85)] and most of the subgroups (aROC range 0.65–86). Calibration in this model (C2‐SAPS II) improved in the whole cohort and in subgroups except in patients with ICU LOS 4–14 days and those undergoing neuro‐ or gastrointestinal surgery. Conclusions In this large cohort of surgical ICU patients, performance of the original SAPS II model was generally poor. Although second‐level customization improved discrimination and calibration in the whole cohort and most of the subgroups, it failed to simultaneously improve calibration in the subgroups stratified according to the type of surgery, age or ICU LOS.     

Validation of the SAPS 3 admission prognostic model in patients with cancer in need of intensive care

Objectives To validate the SAPS 3 admission prognostic model in patients with cancer admitted to the intensive care unit (ICU).Design Cohort study.Setting Ten-bed medical–surgical oncologic ICU.Patients and participants Nine hundred and fifty-two consecutive patients admitted over a 3-year period.Interventions None.Measurements and results Data were prospectively collected at admission of ICU. SAPS II and SAPS 3 scores with respective estimated mortality rates were calculated. Discrimination was assessed by area under receiver operating characteristic (AUROC) curves and calibration by Hosmer–Lemeshow goodness-of-fit test. The mean age was 58.3 ± 23.1 years; there were 471 (49%) scheduled surgical, 348 (37%) medical and 133 (14%) emergency surgical patients. ICU and hospital mortality rates were 24.6% and 33.5%, respectively. The mean SAPS 3 and SAPS II scores were 52.3 ± 18.5 points and 35.3 ± 20.7 points, respectively. All prognostic models showed excellent discrimination (AUROC ≥ 0.8). The calibration of SAPS II was poor (p < 0.001). However, the calibration of standard SAPS 3 and its customized equation for Central and South American (CSA) countries were appropriate (p > 0.05). SAPS II and standard SAPS 3 prognostic models tended somewhat to underestimate the observed mortality (SMR > 1). However, when the customized equation was used, the estimated mortality was closer to the observed mortality [SMR = 0.95 (95% CI = 0.84–1.07)]. Similar results were observed when scheduled surgical patients were excluded.Conclusions The SAPS 3 admission prognostic model at ICU admission, in particular its customized equation for CSA, was accurate in our cohort of critically ill patients with cancer.This work was performed at the Intensive Care Unit, Instituto Nacional de Cancer, Rio de Janeiro, Brazil. Financial support: institutional departmental funds. Conflicts of interest: none.     

Comparison of Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score II (SAPS II) scoring systems in a single Greek intensive care unit

OBJECTIVE: To evaluate Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II scoring systems in a single intensive care unit (ICU), independent from the ICUs of the developmental sample; and to compare the performance of APACHE II and SAPS II by means of statistical analyses in such a clinical setting. DESIGN: Prospective, cohort study. SETTING: A single ICU in a Greek university hospital. PATIENTS: In a time interval of 5 yrs, data for 681 patients admitted to our ICU were collected. The original exclusion criteria of both systems were employed. Patients <17 yrs of age were dropped from the study to keep compatibility with both systems. Eventually, a total of 661 patients were included in the analysis. INTERVENTIONS: Demographics, clinical parameters essential for the calculation of APACHE II and SAPS II scores, and risk of hospital death were recorded. Patient vital status was followed up to hospital discharge. MEASUREMENTS AND MAIN RESULTS: Both systems showed poor calibration and underestimated mortality but had good discriminative power, with SAPS II performing better than APACHE II. The evaluation of uniformity of fit in various subgroups for both systems confirmed the pattern of underprediction of mortality from both models and the better performance of APACHE II over our data sample. CONCLUSIONS: APACHE II and SAPS II failed to predict mortality in a population sample other than the one used for their development. APACHE II performed better than SAPS II. Validation in such a population is essential. Because there is a great variation in clinical and other patient characteristics among ICUs, it is doubtful that one system can be validated in all types of populations to be used for comparisons among different ICUs.     

The predictive value of four scoring systems in liver transplant recipients

Objective To compare 4 general severity classification scoring systems concerning prognosis of outcome in 123 liver transplant recipients. The compared scoring systems were: the mortality prediction model (admission model and 24 h model); the simplified acute physiology score; the acute physiology and chronic health evaluation (Apache II) and the acute organ systems failre score. Design Retrospective, consecutive sample. Setting Adult intensive care unit in a university hospital. Patients 123 adult liver allograft recipients after admission to the intensive care unit. Measurements and main results The scoring systems were calculated as described by the authors to classify the severity of illness after admission of the allograft recipients to the intensive care unit. The mean and median values of survivors and the group of patients, that died during hospital stay were compared. Receiver-operating characteristics were plotted for all scoring systems and the areas under the curves of receiver-operating characteristics were calculated. The predictive value of the 4 scoring systems was tested using a variety of sensitivity analyses. The mortality prediction model (24 h model) was found to have a high significance (p<0.001) in predicting mortality and showed the greatest area under the curve (0.829). Simplified acute physiology score (p<0.001) and acute physiology and chronic health evaluation (Apache II) (p<0.01) had a high significance as well, but did not hit the level of prognosis of mortality prediction model, as shown in the area under the curves. Accordingly, sensitivity was highest in MPM-24 h (83%), followed by SAPS (72%) and Apache II (71%). MPM-24h had a total misclassification rate of 22% (SAPS=32%, Apache II=33%). MPM-admission failed in predicting mortality (sensitivity=52%). Organ systems failure score seemed not to be useful in liver transplant recipients. Conclusion General disease classification systems, such as the mortality prediction model, simplified acute physiology score or acute physiology and chronic health evaluation are good mortality prediction models in patients after liver transplantation. We suggest that there is no need for improvement of a special scoring system.     

SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 1: Objectives, methods and cohort description

OBJECTIVE: Risk adjustment systems now in use were developed more than a decade ago and lack prognostic performance. Objective of the SAPS 3 study was to collect data about risk factors and outcomes in a heterogeneous cohort of intensive care unit (ICU) patients, in order to develop a new, improved model for risk adjustment. DESIGN: Prospective multicentre, multinational cohort study. PATIENTS AND SETTING: A total of 19,577 patients consecutively admitted to 307 ICUs from 14 October to 15 December 2002. MEASUREMENTS AND RESULTS: Data were collected at ICU admission, on days 1, 2 and 3, and the last day of the ICU stay. Data included sociodemographics, chronic conditions, diagnostic information, physiological derangement at ICU admission, number and severity of organ dysfunctions, length of ICU and hospital stay, and vital status at ICU and hospital discharge. Data reliability was tested with use of kappa statistics and intraclass-correlation coefficients, which were >0.85 for the majority of variables. Completeness of the data was also satisfactory, with 1 [0-3] SAPS II parameter missing per patient. Prognostic performance of the SAPS II was poor, with significant differences between observed and expected mortality rates for the overall cohort and four (of seven) defined regions, and poor calibration for most tested subgroups. CONCLUSIONS: The SAPS 3 study was able to provide a high-quality multinational database, reflecting heterogeneity of current ICU case-mix and typology. The poor performance of SAPS II in this cohort underscores the need for development of a new risk adjustment system for critically ill patients.     

Soluble L-selectin levels predict survival in sepsis

OBJECTIVE: To evaluate serum soluble L-selectin as a prognostic factor for survival in patients with sepsis. DESIGN: A prospective study of mortality in patients with sepsis whose serum levels of sL-selectin were measured on admission to an intensive care unit (ICU) and 4 days later. Follow-up data on mortality were obtained from the Danish Central Office of Civil Registration. SETTING: A tertiary referral university hospital ICU in Copenhagen. PATIENTS: Sixty-three patients meeting the criteria for systemic inflammatory response syndrome (SIRS) with a suspected or verified infection in one or more major organs, and 14 control subjects. MEASUREMENTS AND RESULTS: On admission to the ICU the Simplified Acute Physiology Score (SAPS) II was calculated, and relevant microbial cultures were performed. Mortality was registered at various follow-up points: 7 days after admission, at discharge from hospital, and 3 and 12 months after admission. Serum sL-selectin levels were significantly lower in the patients than in the controls. Sepsis nonsurvivors had significantly lower levels than survivors. Efficiency analysis and receiver operation characteristics showed that the ideal cutoff point for sL-selectin as a test for sepsis survival was 470 ng/ml. The accumulated mortality in patients with subnormal sL-selectin levels on admission was significantly increased. No correlation was found between clinical or paraclinical markers, including SAPS II and sL-selectin, and no relationship to the microbial diagnosis was found. CONCLUSIONS: Serum sL-selectin is a predictor of survival in patients with sepsis. Those admitted with low sL-selectin (<470 ng/ml) are characterized by a high mortality within the subsequent 12-month period.     

Evaluation of an interdisciplinary data set for national intensive care unit assessment.

OBJECTIVES: To evaluate the ability of an interdisciplinary data set (recently defined by the Austrian Working Group for the Standardization of a Documentation System for Intensive Care [ASDI]) to assess intensive care units (ICUs) by means of the Simplified Acute Physiology Score II (SAPS II) for the severity of illness and the simplified Therapeutic Intervention Scoring System (TISS-28) for the level of provided care. DESIGN: A prospective, multicentric study. SETTING: Nine adult medical, surgical, and mixed ICUs in Austria. PATIENTS: A total of 1234 patients consecutively admitted to the ICUs. INTERVENTIONS: Collection of data for the ASDI data set. MEASUREMENTS AND MAIN RESULTS: The overall mean SAPS II score was 33.1+/-2.1 points. SAPS II overestimated hospital mortality by predicting mortality of 22.2%+/-2.9%, whereas observed mortality was only 16.8%+/-2.2%. The Hosmer-Lemeshow goodness-of-fit test for SAPS II scores showed lacking uniformity of fit (H = 53.78, 8 degrees of freedom; p < .0001). TISS-28 scores were recorded on 8616 days (30.6+/-1.5 points). TISS-28 scores were higher in nonsurvivors than in survivors (30.4+/-0.9 vs. 25.7+/-0.4, respectively; p < .05). No significant correlation between mean TISS-28 per patient per unit on the day of admission and mean predicted hospital mortality (r2 = .23; p < .54) or standardized mortality ratio per unit (r2 = -.22; p < .56) was found. CONCLUSIONS: Implementation of an interdisciplinary data set for ICUs provided data with which to evaluate performance in terms of severity of illness and provided care. The SAPS II did not accurately predict outcomes in Austrian ICUs and must, therefore, be customized for this population. A combination of indicators for both severity of illness and amount of provided care is necessary to evaluate ICU performance. Further data acquisition is needed to customize the SAPS II and to validate the TISS-28.     

The ability of the Simplified Acute Physiology Score (SAPS II) to predict outcome in coronary care patients

Objective: To evaluate the applicability of the Simplified Acute Physiology Score (SAPS II) for coronary care patients. Design: Prospective observational cohort study. Setting: Medical ICU of a community teaching hospital. Patients: 1587 consecutive patients admitted over a period of 18 months. Measurements and main results: Patients were divided in two groups according to the primary admission diagnosis: general medical intensive care (ICU) patients and intensive coronary care (CCU) patients. Score prediction was tested using criteria suitable to evaluate the discrimination and calibration properties of SAPS II. Mean SAPS II score was 31.6 (± 20.1) in ICU and 28.3 (± 15.5) in CCU patients (p = 0.06), mean risk of death 0.206 and 0.134 (p = 0.001), and observed hospital mortality 17.8 vs 10.3 %. The area under the receiver operating characteristic curve was 0.888 in ICU and 0.908 in CCU patients (p = 0.5). The correlation between predicted and observed hospital mortality was 0.62 (p = 0.001) in ICU and 0.66 (p = 0.001) in CCU patients. The calibration curves did not differ from each other. The probability of death in survivors and nonsurvivors was equally distributed in ICU and CCU patients (p = 0.5). Conclusion: We conclude that SAPS II is applicable to CCU patients in our unit. Received: 30 October 1996 Accepted: 7 August 1997     

Sampling rate causes bias in APACHE II and SAPS II scores

OBJECTIVE: To study the effect of sampling rate of laboratory and haemodynamic data on severity scorings and predicted risk of hospital death. DESIGN: Prospective study. SETTING: Medical-surgical intensive care unit (ICU) with 23 beds in a university hospital. PATIENTS: Sixty-nine consecutive emergency admission patients. INTERVENTIONS: Blood samples were drawn from indwelling arterial lines for the laboratory tests of all variables contained in the APACHE II and SAPS II scores at 2-hourly intervals from the time of admission up to 24 h or earlier discharge or death of the patient. Haemodynamic data and temperature were collected either manually by the attending nurse once an hour or as 2-min median values automatically using a Clinical Information Management System (CIMS, Clinisoft, Datex-Ohmeda, Helsinki, Finland). Three sets of severity scores were obtained. (1) "Traditional" scores (haemodynamic data from manual records and laboratory values from tests taken at admission and subsequently on clinical basis only). (2) "CIMS" scores (haemodynamic data from 2-min median values and laboratory values prescribed on clinical indication) and (3) "High rate" scores (haemodynamic data from 2-min median values and laboratory values at 2-hourly intervals). Probability of hospital death was calculated using the SAPS II and APACHE II scores, respectively. RESULTS: Increasing the sampling rate of haemodynamic monitoring interval to 2-min from once per hour resulted in 7.8 % and 11.5 % increases (p < 0.001) in the APACHE II and SAPS II scores, respectively. The combined effect of increased sampling rate of haemodynamic and laboratory tests on the APACHE II and SAPS II scores was 14.4 % and 14.5 % compared to traditional scores (p < 0.001), respectively. The probability of hospital death increased from 0.23 and 0.21 ("traditional" SAPS II and APACHE II) to 0.31 and 0.25 ("high rate" SAPS II and APACHE II), respectively, and, because eight patients died, standardised mortality ratio (SMR) decreased from 0.53 to 0.41 (SAPS II) and from 0.60 to 0.50 (APACHE II). CONCLUSIONS: Increased sampling rate results in higher scores and lower SMR. Comparisons between hospitals using severity scores are biased due to differences in the sampling rates.     

Severe pneumonia due to Legionella pneumophila: prognostic factors,impact of delayed appropriate antimicrobial therapy

OBJECTIVE: To compare the outcome of patients with severe Legionella pneumonia (LP) according to the presence or absence of prognostic factors currently reported in the literature and delays in initiating fluoroquinolones and macrolides. DESIGN: Retrospective clinical investigation. SETTING: Intensive care unit (ICU) of an university hospital. PATIENTS: Forty-three consecutive cases with no previous treatment with a macrolide or a fluoroquinolone. MEASUREMENTS AND MAIN RESULTS: The 14 (33%) patients who died of LP were compared with the 29 survivors. Thirty-eight patients (88%) received a fluoroquinolone in combination with a macrolide agent, two patients erythromycin alone and three ofloxacin alone. In univariate analysis, SAPS II more than 46 ( p=0.006) and intubation requirement ( p=0.012) were associated with a higher mortality whereas the administration of fluoroquinolones ( p=0.011) or erythromycin ( p=0.044) within 8 h of arrival on the ICU was associated with better survival. By logistic regression analysis, SAPS II score more than 46 [odds ratio (OR) 8.69; 95% confidence interval (CI) 1.15-66.7; p=0.036], duration of symptoms prior to ICU admission longer than 5 days (OR 7.46; 95% CI 1.17-47.6) were independent risk factors for death. Fluoroquinolone administration within 8 h of ICU arrival (OR 0.16; 95% CI 0.03-0.96; p=0.035) was associated with a reduced mortality. CONCLUSIONS: SAPS II score higher than 46, duration of symptoms prior to ICU admission longer than 5 days and intubation were associated with increased mortality. Initiation of fluoroquinolone therapy within 8 h of ICU admission significantly reduced mortality.     

Accuracy of a composite score using daily SAPS II and LOD scores for predicting hospital mortality in ICU patients hospitalized for more than 72 h

In most databases used to build general severity scores the median duration of intensive care unit (ICU) stay is less than 3 days. Consequently, these scores are not the most appropriate tools for measuring prognosis in studies dealing with ICU patients hospitalized for more than 72 h. PURPOSE: To develop a new prognostic model based on a general severity score (SAPS II), an organ dysfunction score (LOD) and evolution of both scores during the first 3 days of ICU stay. DESIGN: Prospective multicenter study. SETTING: Twenty-eight intensive care units (ICUs) in France. PATIENTS: A training data-set was created with four ICUs during an 18-month period (893 patients). Seventy percent of the patients were medical (628) aged 66 years. The median SAPS II was 38. The ICU and hospital mortality rates were 22.7% and 30%, respectively. Forty-seven percent (420 patients) were transferred from hospital wards. In this population, the calibration (Hosmer-Lemeshow chi-square: 37.4, P = 0.001) and the discrimination [area under the ROC curves: 0.744 (95 % CI: 0.714-0.773)] of the original SAPS II were relatively poor. A validation data set was created with a random panel of 24 French ICUs during March 1999 (312 patients). MEASUREMENTS AND MAIN RESULTS: The LOD and SAPS II scores were calculated during the first (SAPS1, LOD1), second (SAPS2, LOD2), and third (SAPS3, LOD3) calendar days. The LOD and SAPS scores alterations were assigned the value "1" when scores increased with time and "0" otherwise. A multivariable logistic regression model was used to select variables measured during the first three calendar days, and independently associated with death. Selected variables were: SAPS II at admission [OR: 1.04 (95 % CI: 1.027-1.053) per point], LOD [OR: 1.16 (95 % CI: 1.085-1.253) per point], transfer from ward [OR: 1.74 (95 % CI: 1.25-2.42)], as well as SAPS3-SAPS2 alterations [OR: 1.516 (95 % CI: 1.04-2.22)], and LOD3-LOD2 alterations [OR: 2.00 (95 % CI: 1.29-3.11)]. The final model has good calibration and discrimination properties in the training data set [area under the ROC curve: 0.794 (95 % CI: 0.766-0.820), Hosmer-Lemeshow C statistic: 5.56, P = 0.7]. In the validation data set, the model maintained good accuracy [area under the ROC curve: 0.826 (95 % CI: 0.780-0.867), Hosmer-Lemeshow C statistic: 7.14, P = 0.5]. CONCLUSIONS: The new model using SAPS II and LOD and their evolution during the first calendar days has good discrimination and calibration properties. We propose its use for benchmarking and evaluating the over-risk of death associated with ICU-acquired nosocomial infections.     

Determinants of post-intensive care mortality in high-level treated critically ill patients

Objective To assess the predictive ability of preillness and illness variables, impact of care, and discharge variables on the post-intensive care mortality.Setting and patients 5,805 patients treated with high intensity of care in 89 ICUs in 12 European countries (EURICUS-I study) surviving ICU stay.Methods Case-mix was split in training sample (logistic regression model for post-ICU mortality: discrimination assessed by area under ROC curve) and in testing sample. Time to death was studied by Cox regression model validated with bootstrap sampling on the unsplit case-mix.Results There were 5,805 high-intensity patients discharged to ward and 423 who died in hospital. Significant odds ratios were observed for source of admission, medical/surgical unscheduled admission, each year age, each SAPSII point, each consecutive day in high-intensity treatment, and each NEMS point on the last ICU day. Time to death in ward was significantly shortened by different source of admission; age over 78 years, medical/unscheduled surgical admission; SAPSII score without age, comorbidity and type of admission over 16 points; more than 2 days in high-intensity treatment; all days spent in high treatment; respiratory, cardiovascular, and renal support at discharge; and last ICU day NEMS higher than 27 pointsConclusions Worse outcome is associated with the physiological reserve before admission in the ICU, type of illness, intensity of care required, and the clinical stability and/or the grade of nursing dependence at discharge.This study was supported in part, by the Foundation for Research on Intensive Care in Europe (FRICE) and by a grant from the Commission of the European communities (BMH1-CT93-1340)     

Effectiveness of SAPS III to predict hospital mortality for post-cardiac arrest patients

Purpose

The mortality for patients admitted to intensive care unit (ICU) after cardiac arrest (CA) remains high despite advances in resuscitation and post-resuscitation care. The Simplified Acute Physiology Score (SAPS) III is the only score that can predict hospital mortality within an hour of admission to ICU. The objective was to evaluate the performance of SAPS III to predict mortality for post-CA patients.

Methods

This retrospective single-center observational study included all patients admitted to ICU after CA between August 2010 and March 2013. The calibration (standardized mortality ratio [SMR]) and the discrimination of SAPS III (area under the curve [AUC] for receiver operating characteristic [ROC]) were measured. Univariate logistic regression tested the relationship between death and scores for SAPS III, SAPS II, Sequential Organ Failure Assessment (SOFA) Score and Out-of-Hospital Cardiac Arrests (OHCA) score. Independent factors associated with mortality were determined.

Results

One-hundred twenty-four patients including 97 out-of-hospital CA were included. In-hospital mortality was 69%. The SAPS III was unable to predict mortality (SMRSAPS III: 1.26) and was less discriminating than other scores (AUCSAPSIII: 0.62 [0.51, 0.73] vs. AUCSAPSII: 0.75 [0.66, 0.84], AUCSOFA: 0.72 [0.63, 0.81], AUCOHCA: 0.84 [0.77, 0.91]). An early return of spontaneous circulation, early resuscitation care and initial ventricular arrhythmia were associated with a better prognosis.

Conclusions

The SAPS III did not predict mortality in patients admitted to ICU after CA. The amount of time before specialized CPR, the low-flow interval and the absence of an initial ventricular arrhythmia appeared to be independently associated with mortality and these factors should be used to predict mortality for these patients.     

Comparison of three severity scores for critically ill cancer patients

Objective To compare three scoring systems, the Acute Physiology and Chronic Health Evaluation (APACHE) II, the Simplified Acute Physiology Score (SAPS) II and a modified Mortality Probability Model II (ICU cancer mortality model, ICMM) for their prognostic value for mortality during hospital stay in a group of cancer patients admitted to a medical ICU.Design Prospective cohort study.Setting Medical ICU of a tertiary care hospital.Patients Two hundred forty-two consecutive cancer patients admitted to the ICU.Measurements and results Variables included in APACHE II, SAPS II and the ICMM scores as well as demographic data were assessed during the first 24 h of stay in the ICU. Hospital mortality was measured; it was 44%. Calibration for all three scoring systems was acceptable, SAPS II yielded a significantly superior discrimination between survivors and non-survivors. The areas under the receiver operating characteristic curves were 0.776 for APACHE II, 0.825 for SAPS II and 0.698 for the ICMM.Conclusion The SAPS II was superior to APACHE II and ICMM. The newly developed ICMM does not improve mortality prediction in critically ill cancer patients.     

Acute kidney injury in the intensive care unit according to RIFLE

OBJECTIVES: To apply the RIFLE criteria "risk," "injury," and "failure" for severity of acute kidney injury to patients admitted to the intensive care unit and to evaluate the significance of other prognostic factors. DESIGN: Retrospective analysis of the Riyadh Intensive Care Program database. SETTING: Riyadh Intensive Care Unit Program database of 41,972 patients admitted to 22 intensive care units in the United Kingdom and Germany between 1989 and 1999. PATIENTS: Acute kidney injury as defined by the RIFLE classification occurred in 15,019 (35.8%) patients; 7,207 (17.2%) patients were at risk, 4,613 (11%) had injury, and 3,199 (7.6%) had failure. It was found that 797 (2.3%) patients had end-stage dialysis-dependent renal failure when admitted to an intensive care unit. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS:: Patients with risk, injury, and failure classifications had hospital mortality rates of 20.9%, 45.6%, and 56.8%, respectively, compared with 8.4% among patients without acute kidney injury. Independent risk factors for hospital mortality were age (odds ratio 1.02); Acute Physiology and Chronic Health Evaluation II score on admission to intensive care unit (odds ratio 1.10); presence of preexisting end-stage disease (odds ratio 1.17); mechanical ventilation (odds ratio 1.52); RIFLE categories risk (odds ratio 1.40), injury (odds ratio 1.96), and failure (odds ratio 1.59); maximum number of failed organs (odds ratio 2.13); admission after emergency surgery (odds ratio 3.08); and nonsurgical admission (odds ratio 3.92). Renal replacement therapy for acute kidney injury was not an independent risk factor for hospital mortality. CONCLUSIONS: The RIFLE classification was suitable for the definition of acute kidney injury in intensive care units. There was an association between acute kidney injury and hospital outcome, but associated organ failure, nonsurgical admission, and admission after emergency surgery had a greater impact on prognosis than severity of acute kidney injury.     

The performance of SAPS II in a cohort of patients admitted to 99 Italian ICUs: Results from GiViTI

Objective To assess the validity of SAPS II (new Simplified Acute Physiology Score) in a cohort of patients admitted to a large sample of Italian intensive care units (ICU).Design and setting The ability of the SAPS II scoring system to predict the probability of hospital mortality was assessed with calibration and discrimination measures obtained using published coefficients. A new logistic regression equation was then developed and further formal calibration and discrimination measures were estimated for the customized model.Patients From the 2202 consecutive patients recruited during a 1-month period in 99 ICUs, a total of 1393 patients were included in this validation study.Results When the parameters based on the standard model were applied, the expected probability of mortality did not fit those actually observed in the cohort (p<0.001), although it showed satisfactory discrimination (area under the receiver operating characteristic curve=0.80). Such lack of fit yields an overall under prediction of mortality (observed/expected ratio=1.14) that reflects a uniform pattern across a preselected set of subgroups. Customization allowed new mortality estimates to be calculated, with satisfactory calibration (p=0.82) and a more uniform pattern across subgroups.Conclusions SAPS II maintained its validity in an independent sample of patients recruited in a large network of Italian ICUs only after appropriate adaptation (first-level customization). Whether the determinants of this relatively poor performance are related to differences in unmeasured case-mix, methods of application, or quality of care delivered is a matter for discussion that cannot be solved with the data presently available. However, these findings suggest that caution is warranted before implementing the standard SAPS II scoring system parameters outside formal research projects.GiViTI: Gruppo italiano per la Valutazione degli interventi in Terapia Intensiva (A complete list of study participants appears in Appendix 2)