Incidence and Outcomes of Cardiopulmonary Resuscitation in ICUs: Retrospective Cohort Analysis.

OBJECTIVES: We aim to describe incidence and outcomes of cardiopulmonary resuscitation (CPR) efforts and their outcomes in ICUs and their changes over time. DESIGN: Retrospective cohort analysis. SETTING: Patient data documented in the Austrian Center for Documentation and Quality Assurance in Intensive Care database. PATIENTS: Adult patients (age ≥ 18 yr) admitted to Austrian ICUs between 2005 and 2019. INTERVENTIONS: None. MEASUREMENTS ANDN MAIN RESULTS: Information on CPR was deduced from the Therapeutic Intervention Scoring System. End points were overall occurrence rate of CPR in the ICU and CPR for unexpected cardiac arrest after the first day of ICU stay as well as survival to discharge from the ICU and the hospital. Incidence and outcomes of ICU-CPR were compared between 2005 and 2009, 2010 and 2014, and 2015 and 2019 using chi-square test. A total of 525,518 first admissions and readmissions to ICU of 494,555 individual patients were included; of these, 72,585 patients (14.7%) died in hospital. ICU-CPR was performed in 20,668 (3.9%) admissions at least once; first events occurred on the first day of ICU admission in 15,266 cases (73.9%). ICU-CPR was first performed later during ICU stay in 5,402 admissions (1.0%). The incidence of ICU-CPR decreased slightly from 4.4% between 2005 and 2009, 3.9% between 2010 and 2014, and 3.7% between 2015 and 2019 ( p < 0.001). A total of 7,078 (34.5%) of 20,499 patients who received ICU-CPR survived until hospital discharge. Survival rates varied slightly over the observation period; 59,164 (12.0%) of all patients died during hospital stay without ever receiving CPR in the ICU. CONCLUSIONS: The incidence of ICU-CPR is approximately 40 in 1,000 admissions overall and approximately 10 in 1,000 admissions after the day of ICU admission. Short-term survival is approximately four out of 10 patients who receive ICU-CPR.

Association of Acute Kidney Injury Receiving Kidney Replacement Therapy With Prognosis of Critically Ill Patients With and Without Cancer: A Retrospective Study.

OBJECTIVES: To assess outcomes of cancer patients receiving kidney replacement therapy due to acute kidney injury in ICUs and compare these with other patient groups receiving kidney replacement therapy in ICUs. DESIGN: Retrospective registry analysis. SETTING: Prospectively collected database of 296,424 ICU patients. PATIENTS: Patients with and without solid cancer with acute kidney injury necessitating kidney replacement therapy were identified and compared with those without acute kidney injury necessitating kidney replacement therapy. INTERVENTIONS: Descriptive statistics were used to ascertain prevalence of acute kidney injury necessitating kidney replacement therapy and solid cancer in ICU patients. Association of acute kidney injury necessitating kidney replacement therapy and cancer with prognosis was assessed using logistic regression analysis. To compare the attributable mortality of acute kidney injury necessitating kidney replacement therapy, 20,154 noncancer patients and 2,411 cancer patients without acute kidney injury necessitating kidney replacement therapy were matched with 12,827 noncancer patients and 1,079 cancer patients with acute kidney injury necessitating kidney replacement therapy. MEASUREMENTS AND MAIN RESULTS: Thirty-five thousand three hundred fifty-six ICU patients (11.9%) had solid cancer. Acute kidney injury necessitating kidney replacement therapy was present in 1,408 (4.0%) cancer patients and 13,637 (5.2%) noncancer patients. Crude ICU and hospital mortality was higher in the cancer group (646 [45.9%] vs 4,674 [34.3%], p < 0.001, and 787 [55.9%] vs 5,935 [43.5%], p < 0.001). In multivariable logistic regression analyses, odds ratio (95% CI) for hospital mortality was 1.73 (1.62-1.85) for cancer compared with no cancer 3.57 (3.32-3.83) for acute kidney injury necessitating kidney replacement therapy and 1.07 (0.86-1.33) for their interaction. In the matched subcohort, attributable hospital mortality of acute kidney injury necessitating kidney replacement therapy was 56.7% in noncancer patients and 48.0% in cancer patients. CONCLUSIONS: Occurrence rate of acute kidney injury necessitating kidney replacement therapy and prognosis in ICU patients with solid cancer are comparable with other ICU patient groups. In cancer, acute kidney injury necessitating kidney replacement therapy is associated with higher crude hospital mortality. However, the specific attributable mortality conveyed by acute kidney injury necessitating kidney replacement therapy is actually lower in cancer patients than in noncancer patients. Diagnosis of cancer per se does not justify withholding kidney replacement therapy.

Association of Body Mass Index and Outcome in Acutely Ill Patients With Chronic Kidney Disease Requiring Intensive Care Therapy.

OBJECTIVE: An association of body mass index (BMI) and outcome, the "obesity paradox," has been described in patients with chronic kidney disease (CKD) and end-stage renal disease. We sought to assess whether a potential beneficial effect of a high body mass is also seen in CKD patients with critical illness. METHODS: In a retrospective analysis of a prospectively collected database of 123,416 patients from 107 Austrian intensive care units (ICUs) in whom BMI was available, the association of 6 groups of BMI and hospital mortality was assessed in 12,206 patients with CKD 3-5 by univariate and multivariate logistic regression analyses. RESULTS: Patients with CKD were sicker, had a longer ICU stay, and had a higher ICU and hospital mortality than those without. The association of BMI and outcome in CKD patients indicated a U-shaped curve with the highest mortality in patients with BMI <20 and ≥40, and the lowest with a BMI between ≥25 and <40. This relationship was also significant in a multivariate analysis adjusted for severity of illness assessed by Simplified Acute Physiology Score III score, age, gender, admission diagnosis, and pre-existing comorbidities. It was not found in patients with CKD 5 on renal replacement therapy, in patients below 60 years of age, and those with diabetes mellitus requiring insulin treatment. CONCLUSIONS: BMI is associated with better outcomes in CKD 3-5 patients who have acquired acute intermittent diseases and are admitted to an ICU, but not those requiring renal replacement therapy. This higher tolerance to acute disease processes may in part explain the "obesity paradox" observed in CKD patients.

Weekends affect mortality risk and chance of discharge in critically ill patients: a retrospective study in the Austrian registry for intensive care.

BACKGROUND: In this study, we primarily investigated whether ICU admission or ICU stay at weekends (Saturday and Sunday) is associated with a different risk of ICU mortality or chance of ICU discharge than ICU admission or ICU stay on weekdays (Monday to Friday). Secondarily, we analysed whether weekend ICU admission or ICU stay influences risk of hospital mortality or chance of hospital discharge. METHODS: A retrospective study was performed for all adult patients admitted to 119 ICUs participating in the benchmarking project of the Austrian Centre for Documentation and Quality Assurance in Intensive Care (ASDI) between 2012 and 2015. Readmissions to the ICU during the same hospital stay were excluded. RESULTS: In a multivariable competing risk analysis, a strong weekend effect was observed. Patients admitted to ICUs on Saturday or Sunday had a higher mortality risk after adjustment for severity of illness by Simplified Acute Physiology Score (SAPS) 3, year, month of the year, type of admission, ICU, and weekday of death or discharge. Hazard ratios (95% confidence interval) for death in the ICU following admission on a Saturday or Sunday compared with Wednesday were 1.15 (1.08-1.23) and 1.11 (1.03-1.18), respectively. Lower hazard ratios were observed for dying on a Saturday (0.93 (0.87-1.00)) or Sunday (0.85 (0.80-0.91)) compared with Wednesday. This is probably related to the reduced chance of being discharged from the ICU at the weekend (0.63 (0.62-064) for Saturday and 0.56 (0.55-0.57) for Sunday). Similar results were found for hospital mortality and hospital discharge following ICU admission. CONCLUSIONS: Patients admitted to ICUs at weekends are at increased risk of death in both the ICU and the hospital even after rigorous adjustment for severity of illness. Conversely, death in the ICU and discharge from the ICU are significantly less likely at weekends.

Association of immediate versus delayed extubation of patients admitted to intensive care units postoperatively and outcomes: A retrospective study.

AIM OF THIS STUDY: This study seeks to investigate, whether extubation of tracheally intubated patients admitted to intensive care units (ICU) postoperatively either immediately at the day of admission (day 1) or delayed at the first postoperative day (day 2) is associated with differences in outcomes. MATERIALS AND METHODS: We performed a retrospective analysis of data from an Austrian ICU registry. Adult patients admitted between January 1st, 2012 and December 31st, 2019 following elective and emergency surgery, who were intubated at the day 1 and were extubated at day 1 or day 2, were included. We performed logistic regression analyses for in-hospital mortality and over-sedation or agitation following extubation. RESULTS: 52 982 patients constituted the main study population. 1 231 (3.3%) patients extubated at day 1 and 958 (5.9%) at day 2 died in hospital, 464 (1.3%) patients extubated at day 1 and 613 (3.8%) at day 2 demonstrated agitation or over-sedation after extubation during ICU stay; OR (95% CI) for in-hospital mortality were OR 1.17 (1.01-1.35, p = 0.031) and OR 2.15 (1.75-2.65, p<0.001) for agitation or over-sedation. CONCLUSIONS: We conclude that immediate extubation as soon as deemed feasible by clinicians is associated with favourable outcomes and may thus be considered preferable in tracheally intubated patients admitted to ICU postoperatively.

The Influence of Geography, Religion, Religiosity and Institutional Factors on Worldwide End-of-Life Care for the Critically Ill: The WELPICUS Study.

PURPOSE: To evaluate the association between provider religion and religiosity and consensus about end-of-life care and explore if geographical and institutional factors contribute to variability in practice. MATERIALS AND METHODS: Using a modified Delphi method 22 end-of-life issues consisting of 35 definitions and 46 statements were evaluated in 32 countries in North America, South America, Eastern Europe, Western Europe, Asia, Australia and South Africa. A multidisciplinary, expert group from specialties treating patients at the end-of-life within each participating institution assessed the association between 7 key statements and geography, religion, religiosity and institutional factors likely influencing the development of consensus. RESULTS: Of 3049 participants, 1366 (45%) responded. Mean age of respondents was 45 ± 9 years and 55% were females. Following 2 Delphi rounds, consensus was obtained for 77 (95%) of 81 definitions and statements. There was a significant difference in responses across geographical regions. South African and North American respondents were more likely to encourage patients to write advance directives. Fewer Eastern European and Asian respondents agreed with withdrawing life-sustaining treatments without consent of patients or surrogates. While respondent's religion, years in practice or institution did not affect their agreement, religiosity, physician specialty and responsibility for end-of-life decisions did. CONCLUSIONS: Variability in agreement with key consensus statements about end-of-life care is related primarily to differences among providers, with provider-level variations related to differences in religiosity and specialty. Geography also plays a role in influencing some end-of-life practices. This information may help understanding ethical dilemmas and developing culturally sensitive end-of-life care strategies.

Patient volume affects outcome in critically ill patients.

CONTEXT: A positive relationship between patient volume and outcome has been demonstrated for a variety of clinical conditions and procedures, but the evidence is sparse for critically ill patients. OBJECTIVE: To evaluate the relationship between patient volume and outcome in a large cohort of critically ill patients. DESIGN: Prospective multicenter cohort study, January 1998 through December 2005. SETTING: 40 intensive care units in Austria. PATIENTS: A total of 83,259 consecutively admitted patients. MAIN OUTCOME MEASURES: Structural quality of participating ICUs was evaluated using a questionnaire and merged with the prospectively collected data. Volume related indices were then calculated, representing patient turnover, occupancy rate, nursing workload and diagnostic variability. RESULTS: Univariate analysis revealed that several volume variables were associated with outcome: more patients treated per year per bed in the intensive care unit and more patients treated in the same diagnostic category reduced the risk of dying in the hospital (odds ratios, 0.967 and 0.991 for each additional 10 patients treated, respectively). In contrast, an increase in the patient-to-nurse ratio and an increase in the number of diagnostic categories were associated with increased mortality rates. Multivariate analysis confirmed these results. The relationship between the number of patients treated in the same diagnostic category and their outcomes showed not a linear but a U shape, with increasing mortality rates below and above a certain patient volume. CONCLUSIONS: Our results provide evidence for a relationship between patient volume and outcome in critically ill patients. Besides the total number of patients, diagnostic variability plays an important role. The relationship between volume and outcome seems, however, to be complex and to be influenced by other variables, such as workload of nursing staff.

Time of Day and its Association with Risk of Death and Chance of Discharge in Critically Ill Patients: A Retrospective Study.

Outcomes following admission to intensive care units (ICU) may vary with time and day. This study investigated associations between time of day and risk of ICU mortality and chance of ICU discharge in acute ICU admissions. Adult patients (age ≥ 18 years) who were admitted to ICUs participating in the Austrian intensive care database due to medical or surgical urgencies and emergencies between January 2012 and December 2016 were included in this retrospective study. Readmissions were excluded. Statistical analysis was conducted using the Fine-and-Gray proportional subdistribution hazards model concerning ICU mortality and ICU discharge within 30 days adjusted for SAPS 3 score. 110,628 admissions were analysed. ICU admission during late night and early morning was associated with increased hazards for ICU mortality; HR: 1.17; 95% CI: 1.08-1.28 for 00:00-03:59, HR: 1.16; 95% CI: 1.05-1.29 for 04:00-07:59. Risk of death in the ICU decreased over the day; lowest HR: 0.475, 95% CI: 0.432-0.522 for 00:00-03:59. Hazards for discharge from the ICU dropped sharply after 16:00; lowest HR: 0.024; 95% CI: 0.019-0.029 for 00:00-03:59. We conclude that there are "time effects" in ICUs. These findings may spark further quality improvement efforts.

Sepsis mortality prediction based on predisposition, infection and response.

OBJECTIVE: To empirically test, based on a large multicenter, multinational database, whether a modified PIRO (predisposition, insult, response, and organ dysfunction) concept could be applied to predict mortality in patients with infection and sepsis. DESIGN: Substudy of a multicenter multinational cohort study (SAPS 3). PATIENTS: A total of 2,628 patients with signs of infection or sepsis who stayed in the ICU for >48 h. Three boxes of variables were defined, according to the PIRO concept. Box 1 (Predisposition) contained information about the patient's condition before ICU admission. Box 2 (Injury) contained information about the infection at ICU admission. Box 3 (Response) was defined as the response to the infection, expressed as a Sequential Organ Failure Assessment score after 48 h. INTERVENTIONS: None. MAIN MEASUREMENTS AND RESULTS: Most of the infections were community acquired (59.6%); 32.5% were hospital acquired. The median age of the patients was 65 (50-75) years, and 41.1% were female. About 22% (n=576) of the patients presented with infection only, 36.3% (n=953) with signs of sepsis, 23.6% (n=619) with severe sepsis, and 18.3% (n=480) with septic shock. Hospital mortality was 40.6% overall, greater in those with septic shock (52.5%) than in those with infection (34.7%). Several factors related to predisposition, infection and response were associated with hospital mortality. CONCLUSION: The proposed three-level system, by using objectively defined criteria for risk of mortality in sepsis, could be used by physicians to stratify patients at ICU admission or shortly thereafter, contributing to a better selection of management according to the risk of death.

Development of demographics and outcome of very old critically ill patients admitted to intensive care units.

PURPOSE: To evaluate the development of demographics and outcome of very old (>80 years) critically ill patients admitted to intensive care units. SETTING: All consecutive patients admitted to 41 Austrian intensive care units (ICUs) over an 11-year period. METHODS: We performed a retrospective cohort study of prospectively collected data. To compare parameters over time, patients were divided into three groups (group I from 1998 until 2001, group II from 2002 to 2004, and group III from 2005 to 2008). RESULTS: A total of 17,126 patients older than 80 years of age were admitted over the study period. The proportion of very old patients increased from 11.5% (I) to 15.3% (III) with a significant higher prevalence of females in all groups (on average 63.2%). Severity of illness also increased over time, even when corrected for age. Use of noninvasive mechanical ventilation increased over the years. However, risk-adjusted mortality rates [observed-to-expected (O/E) ratios] decreased from 1.14 [confidence interval (CI) 1.11-1.18] to 1.02 (CI 0.99-1.05). This improvement in outcome was confirmed on multivariate analysis: for every year delay in ICU admission, the odds to die decreased by 3%. Moreover, females exhibited a better outcome compared with males. CONCLUSIONS: The relative and absolute numbers of very old patients increased over the study period, as did the severity of illness. Despite this, risk-adjusted hospital mortality improved over the study period. Females dominated in the very old patients and exhibited moreover a better outcome compared with males.

Impact of body mass on incidence and prognosis of acute kidney injury requiring renal replacement therapy.

PURPOSE: In patients with chronic kidney disease, survival has been shown to be better with increasing body mass, an observation which was termed the "obesity paradox". To investigate if such an effect would also be present in patients with acute kidney injury (AKI), we analysed the impact of body mass on the prognosis of intensive care patients with severe AKI requiring renal replacement therapy. METHODS: A total of 5,232 patients with AKI requiring renal replacement therapy from 53 Austrian ICUs were analysed. RESULTS: Patients were divided into one of five BMI groups: underweight, normal, overweight, obese and morbid obese. The incidence of AKI increased with increasing body mass from underweight, normal (5.4%) to morbid obese (11.8%). Moreover, adjusted odds ratios to develop AKI were significantly increased for all groups (reference group: normal). Risk-adjusted hospital mortality rates followed a U-shaped pattern, with the lowest mortality in obese patients (BMI of > or = 30 < 35). Multivariate analysis (with adjustment for severity of illness, sex, reason for admission and comorbidities) confirmed these results: obese patients presented with a significantly reduced probability to die in the hospital [odds ratio 0.81 (0.66-0.98)]. CONCLUSIONS: Obesity is an independent risk factor for developing AKI. Our results provide further evidence that body mass impacts on survival of patients with AKI requiring renal replacement therapy. Obese patients seem to have a survival benefit compared to underweight or normal weight patients.

Incidence and prognosis of dysnatremias present on ICU admission.

PURPOSE: Dysnatremias are common in patients admitted to the intensive care unit (ICU). Whether the presence of disorders of sodium balance on ICU admission is independently associated with excess mortality is unknown. We hypothesized that dysnatremias at the time of ICU admission are independent risk factors for increased mortality in critically ill patients. METHODS: We conducted a retrospective study in 77 medical, surgical, and mixed ICUs in Austria, with a database of 151,486 adults admitted consecutively over a period of 10 years (1998-2007). RESULTS: Most patients (114,170, 75.4%) had normal sodium levels (135 < or = Na < or = 145 mmol/L) on ICU admission. The frequencies of borderline (130 < or = Na < 135 mmol/L), mild (125 < or = Na < 130 mmol/L), and severe hyponatremia (Na < 125 mmol/L) were 13.8%, 2.7%, and 1.2%, respectively. The frequencies of borderline (145 < Na < or = 150 mmol/L), mild (150 < Na < or = 155 mmol/L), and severe hypernatremia (Na > 155 mmol/L) were 5.1%, 1.2%, and 0.6%, respectively. All types and grades of dysnatremia were associated with increased raw and risk-adjusted hospital mortality ratios. Multiple logistic regression analysis showed an independent mortality risk rising with increasing severity of both hyponatremia and hypernatremia. Odds ratios and 95% confidence interval (CI) for borderline, mild, and severe hyponatremia were 1.32 (1.25-1.39), 1.89 (1.71-2.09), and 1.81 (1.56-2.10), respectively. Odds ratios and 95% CI for borderline, mild, and severe hypernatremia were 1.48 (1.36-1.61), 2.32 (1.98-2.73), and 3.64 (2.88-4.61), respectively. CONCLUSIONS: Our results suggest that both hypo- and hypernatremia present on admission to the ICU are independent risk factors for poor prognosis.

Impact of nosocomial infections on clinical outcome and resource consumption in critically ill patients.

OBJECTIVE: Nosocomial infections still present a major problem in intensive care units (ICUs), accounting for prolonged ICU and hospital stays and worsened outcomes. There exist differences in the literature regarding the impact of nosocomial infections on attributable mortality and resource consumption. The aim of this study was to observe these effects in a large cohort of critically ill patients. PATIENTS AND SETTINGS: Thirty-four Austrian ICUs participated in the study by documenting all nosocomial infections from 1 June to 30 November 2003 according to the Hospital in Europe Link for Infection Control through Surveillance (HELICS) protocol. MEASUREMENTS AND RESULTS: Of 2,392 patients with a length-of-stay (LOS) >2 days, 683 (28.6%) developed at least one nosocomial infection. The most common infection was pneumonia (n = 456), followed by central venous catheter (CVC) infections (n = 101). Risk-adjusted mortality rates (standardized mortality ratios) were significantly increased for infected patients [0.91 (0.83-0.99) vs. 0.68 (0.61-0.74)]. Significant attributable risk-adjusted mortality was found for patients with pneumonia, combined infections (both 32%) and CVC-related infections (26%). LOS in the ICU increased significantly for all infections. CONCLUSIONS: We conclude that significant attributable mortality for several nosocomial infections exists in a large cohort of critically ill patients, with the highest impact occurring in those with microbiologically diagnosed pneumonia and combined infections. All infections were associated with an increased resource consumption. Effective infection control measures could improve both clinical outcome and proper and effective use of ICU resources.

Characterizing the risk profiles of intensive care units.

OBJECTIVE: 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.

Austrian validation and customization of the SAPS 3 Admission Score.

OBJECTIVE: To test the prognostic performance of the SAPS 3 Admission Score in a regional cohort and to empirically test the need and feasibility of regional customization. DESIGN: Prospective multicenter cohort study. PATIENTS AND SETTING: Data on a total of 2,060 patients consecutively admitted to 22 intensive care units in Austria from October 2, 2006 to February 28, 2007. MEASUREMENTS AND RESULTS: The database includes basic variables, SAPS 3, length-of-stay and outcome data. The original SAPS 3 Admission Score overestimated hospital mortality in Austrian intensive care patients through all strata of the severity-of-illness. This was true for both available equations, the General and the Central and Western Europe equation. For this reason a customized country-specific model was developed, using cross-validation techniques. This model showed excellent calibration and discrimination in the whole cohort (Hosmer-Lemeshow goodness-of-fit: H = 4.50, P = 0.922; C = 5.61, P = 0.847, aROC, 0.82) as well as in the various tested subgroups. CONCLUSIONS: The SAPS 3 Admission Score's general equation can be seen as a framework for addressing the problem of outcome prediction in the general population of adult ICU patients. For benchmarking purposes, region-specific or country-specific equations seem to be necessary in order to compare ICUs on a similar level.

Errors in administration of parenteral drugs in intensive care units: multinational prospective study.

OBJECTIVE: To assess on a multinational level the frequency, characteristics, contributing factors, and preventive measures of administration errors in parenteral medication in intensive care units. DESIGN: Observational, prospective, 24 hour cross sectional study with self reporting by staff. SETTING: 113 intensive care units in 27 countries. PARTICIPANTS: 1328 adults in intensive care. MAIN OUTCOME MEASURES: Number of errors; impact of errors; distribution of error characteristics; distribution of contributing and preventive factors. RESULTS: 861 errors affecting 441 patients were reported: 74.5 (95% confidence interval 69.5 to 79.4) events per 100 patient days. Three quarters of the errors were classified as errors of omission. Twelve patients (0.9% of the study population) experienced permanent harm or died because of medication errors at the administration stage. In a multiple logistic regression with patients as the unit of analysis, odds ratios for the occurrence of at least one parenteral medication error were raised for number of organ failures (odds ratio per increase of one organ failure: 1.19, 95% confidence interval 1.05 to 1.34); use of any intravenous medication (yes v no: 2.73, 1.39 to 5.36); number of parenteral administrations (per increase of one parenteral administration: 1.06, 1.04 to 1.08); typical interventions in patients in intensive care (yes v no: 1.50, 1.14 to 1.96); larger intensive care unit (per increase of one bed: 1.01, 1.00 to 1.02); number of patients per nurse (per increase of one patient: 1.30, 1.03 to 1.64); and occupancy rate (per 10% increase: 1.03, 1.00 to 1.05). Odds ratios for the occurrence of parenteral medication errors were decreased for presence of basic monitoring (yes v no: 0.19, 0.07 to 0.49); an existing critical incident reporting system (yes v no: 0.69, 0.53 to 0.90); an established routine of checks at nurses' shift change (yes v no: 0.68, 0.52 to 0.90); and an increased ratio of patient turnover to the size of the unit (per increase of one patient: 0.73, 0.57 to 0.93). CONCLUSIONS: Parenteral medication errors at the administration stage are common and a serious safety problem in intensive care units. With the increasing complexity of care in critically ill patients, organisational factors such as error reporting systems and routine checks can reduce the risk for such errors.

Acute kidney injury in critically ill patients classified by AKIN versus RIFLE using the SAPS 3 database.

OBJECTIVE: Acute kidney injury (AKI) is associated with significantly increased morbidity and mortality. To provide a uniformly accepted definition, the RIFLE classification was introduced by the Acute Dialysis Quality Initiative, recently modified by the Acute Kidney Injury Network (AKIN), suggesting staging of AKI based on dynamic changes within 48 h. This study compares these two classification systems with regard to outcome. DESIGN: Cohort analysis of SAPS 3 database. MEASUREMENTS: Sixteen thousand seven hundred and eighty-four ICU patients from 303 ICUs were analysed. Classification was performed according to RIFLE (Risk, Injury, Failure) or according to AKIN (stage 1, 2, 3) without including a requirement of renal replacement therapy in the analysis. Changes of serum creatinine as well as urinary output were assessed for both AKIN and RIFLE during the first 48 h of ICU admission. Primary endpoint was hospital mortality. RESULTS: Incidence of AKI in our population of critically ill patients was found to range between 28.5 and 35.5% when applying AKIN and RIFLE criteria, respectively, associated with increased hospital mortality averaging 36.4%. Observed-to-expected mortality ratios revealed excess mortality conferred by any degree of AKI increasing from 0.81 for patients classified as non-AKI up to 1.31 and 1.23 with AKIN stage 3 or RIFLE Failure, respectively. AKIN misclassified 1,504 patients as non-AKI compared to RIFLE which misclassified 504 patients. CONCLUSION: Acute kidney injury classified by either RIFLE or AKIN is associated with increased hospital mortality. Despite presumed increased sensitivity by the AKIN classification, RIFLE shows better robustness and a higher detection rate of AKI during the first 48 h of ICU admission.

End-of-life practices in 282 intensive care units: data from the SAPS 3 database.

OBJECTIVE: To report incidence and characteristics of decisions to forgo life-sustaining therapies (DFLSTs) in the 282 ICUs who contributed to the SAPS3 database. METHODS: We reviewed data on DFLSTs in 14,488 patients. Independent predictors of DFLSTs have been identified by stepwise logistic regression. RESULTS: DFLSTs occurred in 1,239 (8.6%) patients [677 (54.6%) withholding and 562 (45.4%) withdrawal decisions]. Hospital mortality was 21% (3,050/14,488); 36.2% (1,105) deaths occurred after DFLSTs. Across the participating ICUs, hospital mortality in patients with DFLSTs ranged from 80.3 to 95.4% and time from admission to decisions ranged from 2 to 4 days. Independent predictors of decisions to forgo LSTs included 13 variables associated with increased incidence of DFLSTs and 7 variables associated with decrease incidence of DFLST. Among hospital and ICU-related variables, a higher number of nurses per bed was associated with increased incidence of DFLST, while availability of an emergency department in the same hospital, presence of a full time ICU-specialist and doctors presence during nights and week-ends were associated with a decreased incidence of DFLST. CONCLUSION: This large study identifies structural variables that are associated with substantial variations in the incidence and the characteristics of decisions to forgo life-sustaining therapies.

Epidemiology of mechanical ventilation: analysis of the SAPS 3 database.

OBJECTIVE: 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.

Modeling in-hospital patient survival during the first 28 days after intensive care unit admission: a prognostic model for clinical trials in general critically ill patients.

OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. CONCLUSIONS: Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.