116 research outputs found
Predictors of early recovery of health status after intensive care
No full textOBJECTIVE: To identify factors predictive of good or poor recovery of health status and health-related quality of life (HRQOL) 90 days after admission to an intensive care unit (ICU). DESIGN AND SETTING: Prospective international multicentre study in 19 ICUs participating in the HRQOL substudy of the SAPS 3 project. INTERVENTION: The EuroQol questionnaire (EQ) was administered to discharged ICU patients 90 days after admission. A question to compare present health status with that 3 months before ICU admission (same/better/worse) was added. PATIENTS: Six hundred and eighteen patients who spent >24h in an ICU and survived for 90 days. EQ data and health comparison were available in 559 (90.5%) of them. MEASUREMENTS AND RESULTS: Patients reported their general level of health to be better (33.8%), the same (31.1%), or worse (35.1%) in comparison with baseline. Recovery was considered to be good for answers "better" or "the same". Regression analysis showed that transplantation surgery [odds ratio (OR) 0.07, 95% confidence interval (CI) 0.01-0.63], coronary artery bypass surgery without valvular repair (OR 0.39, 95% CI 0.17-0.92) and being admitted to the ICU from a ward or other location (OR 0.55, 95% CI 0.31-0.95) predicted good recovery of health. Predictors of poor recovery (all present at the time of ICU admission) were unplanned ICU admission, hypothermia, serum creatinine level >or=2mg/dl, pH<or=7.25 and metastatic cancer. CONCLUSIONS: More than 60% of ICU patients report good recovery of their health 90 days after ICU admission, depending on their illness and circumstances of ICU admission
Epidemiology of mechanical ventilation: analysis of the SAPS 3 database
No full textOBJECTIVE:
To evaluate current practice of mechanical ventilation in the ICU and the characteristics and outcomes of patients receiving it.
DESIGN:
Pre-planned sub-study of a multicenter, multinational cohort study (SAPS 3).
PATIENTS:
13,322 patients admitted to 299 intensive care units (ICUs) from 35 countries.
INTERVENTIONS:
None.
MAIN MEASUREMENTS AND RESULTS:
Patients were divided into three groups: no mechanical ventilation (MV), noninvasive MV (NIV), and invasive MV. More than half of the patients (53% [CI: 52.2-53.9%]) were mechanically ventilated at ICU admission. FIO2, VT and PEEP used during invasive MV were on average 50% (40-80%), 8 mL/kg actual body weight (6.9-9.4 mL/kg) and 5 cmH2O (3-6 cmH2O), respectively. Several invMV patients (17.3% (CI:16.4-18.3%)) were ventilated with zero PEEP (ZEEP). These patients exhibited a significantly increased risk-adjusted hospital mortality, compared with patients ventilated with higher PEEP (O/E ratio 1.12 [1.05-1.18]). NIV was used in 4.2% (CI: 3.8-4.5%) of all patients and was associated with an improved risk-adjusted outcome (OR 0.79, [0.69-0.90]).
CONCLUSION:
Ventilation mode and parameter settings for MV varied significantly across ICUs. Our results provide evidence that some ventilatory modes and settings could still be used against current evidence and recommendations. This includes ventilation with tidal volumes >8mL/kg body weight in patients with a low PaO2/FiO2 ratio and ZEEP in invMV patients. Invasive mechanical ventilation with ZEEP was associated with a worse outcome, even after controlling for severity of disease. Since our study did not document indications for MV, the association between MV settings and outcome must be viewed with caution
EuSOS: European surgical outcomes study.
No full textCommentJournal ArticleMulticenter StudyResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe
SAPS 3--From evaluation of the patient to evaluation of the intensive care unit. Part 2 : Development of a prognostic model for hospital mortality at ICU admission
No full textOBJECTIVE: To develop a model to assess severity of illness and predict vital status at hospital discharge based on ICU admission data. DESIGN: Prospective multicentre, multinational cohort study. PATIENTS AND SETTING: A total of 16,784 patients consecutively admitted to 303 intensive care units from 14 October to 15 December 2002. MEASUREMENTS AND RESULTS: ICU admission data (recorded within +/-1 h) were used, describing: prior chronic conditions and diseases; circumstances related to and physiologic derangement at ICU admission. Selection of variables for inclusion into the model used different complementary strategies. For cross-validation, the model-building procedure was run five times, using randomly selected four fifths of the sample as a development- and the remaining fifth as validation-set. Logistic regression methods were then used to reduce complexity of the model. Final estimates of regression coefficients were determined by use of multilevel logistic regression. Variables selection and weighting were further checked by bootstraping (at patient level and at ICU level). Twenty variables were selected for the final model, which exhibited good discrimination (aROC curve 0.848), without major differences across patient typologies. Calibration was also satisfactory (Hosmer-Lemeshow goodness-of-fit test H=10.56, p=0.39, C=14.29, p=0.16). Customized equations for major areas of the world were computed and demonstrate a good overall goodness-of-fit. CONCLUSIONS: The SAPS 3 admission score is able to predict vital status at hospital discharge with use of data recorded at ICU admission. Furthermore, SAPS 3 conceptually dissociates evaluation of the individual patient from evaluation of the ICU and thus allows them to be assessed at their respective reference levels
SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 1: Objectives, methods and cohort description
No full textOBJECTIVE: 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 patient
Characterizing the Risk Profiles of Intensive Care Units
No full textOBJECTIVE: To develop a new method to evaluate the performance of individual ICUs through the calculation and visualisation of risk profiles.
METHODS: The study included 102,561 patients consecutively admitted to 77 ICUs in Austria. We customized the function which predicts hospital mortality (using SAPS II) for each ICU. We then compared the risks of hospital mortality resulting from this function with the risks which would be obtained using the original function. The derived risk ratio was then plotted together with point-wise confidence intervals in order to visualise the individual risk profile of each ICU over the whole spectrum of expected hospital mortality.
MAIN MEASUREMENTS AND RESULTS: We calculated risk profiles for all ICUs in the ASDI data set according to the proposed method. We show examples how the clinical performance of ICUs may depend on the severity of illness of their patients. Both the distribution of the Hosmer-Lemeshow goodness-of-fit test statistics and the histogram of the corresponding P values demonstrated a good fit of the individual risk models.
CONCLUSIONS: Our risk profile model makes it possible to evaluate ICUs on the basis of the specific risk for patients to die compared to a reference sample over the whole spectrum of hospital mortality. Thus, ICUs at different levels of severity of illness can be directly compared, giving a clear advantage over the use of the conventional single point estimate of the overall observed-to-expected mortality ratio
Year in review in intensive care medicine: 2003. II. Brain injury, hemodynamics, gastrointestinal tract, renal failure, metabolism, trauma, and postoperative
No full textIntensive care unit caseload and workload and their association with outcomes in critically unwell patients: a large registry-based cohort analysis.
No full textBACKGROUND: Too high or too low patient volumes and work amounts may overwhelm health care professionals and obstruct processes or lead to inadequate personnel routine and process flow. We sought to evaluate, whether an association between current caseload, current workload, and outcomes exists in intensive care units (ICU). METHODS: Retrospective cohort analysis of data from an Austrian ICU registry. Data on patients aged ≥ 18 years admitted to 144 Austrian ICUs between 2013 and 2022 were included. A Cox proportional hazards model with ICU mortality as the outcome of interest adjusted with patients' respective SAPS 3, current ICU caseload (measured by ICU occupancy rates), and current ICU workload (measured by median TISS-28 per ICU) as time-dependent covariables was constructed. Subgroup analyses were performed for types of ICUs, hospital care level, and pre-COVID or intra-COVID period. RESULTS: 415 584 patient admissions to 144 ICUs were analysed. Compared to ICU caseloads of 76 to 100%, there was no significant relationship between overuse of ICU capacity and risk of death [HR (95% CI) 1.06 (0.99-1.15), p = 0.110 for > 100%], but for lower utilisation [1.09 (1.02-1.16), p = 0.008 for ≤ 50% and 1.10 (1.05-1.15), p 100% between 2020 and 2022 [1.18 (1.06-1.30), p = 0.001], i.e., the intra-COVID period. Compared to the reference category of median TISS-28 21-30, lower [0.88 (0.78-0.99), p = 0.049 for ≤ 20], but not higher workloads were significantly associated with risk of death. High workload may be associated with higher mortality in local hospitals [1.09 (1.01-1.19), p = 0.035 for 31-40, 1.28 (1.02-1.60), p = 0.033 for > 40]. CONCLUSIONS: In a system with comparably high intensive care resources and mandatory staffing levels, patients' survival chances are generally not affected by high intensive care unit caseload and workload. However, extraordinary circumstances, such as the COVID-19 pandemic, may lead to higher risk of death, if planned capacities are exceeded. High workload in ICUs in smaller hospitals with lower staffing levels may be associated with increased risk of death
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