Survival of critically ill patients hospitalized in and out of intensive care* (original) (raw)
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Objective: The demand for intensive care beds far exceeds their availability in many European countries. Consequently, many critically ill patients occupy hospital beds outside intensive care units, throughout the hospital. The outcome of patients who fit intensive care unit admission criteria but are hospitalized in regular wards needs to be assessed for policy implications. The object was to screen entire hospital patient populations for critically ill patients and compare their 30-day survival in and out of the intensive care unit. Design: Screening teams visited every hospital ward on four selected days in five acute care Israeli hospitals. The teams listed all patients fitting a priori developed study criteria. One-month data for each patient were abstracted from the medical records. Setting: Five acute care Israeli hospitals. Patients: All patients fitting a priori developed study criteria. Interventions:None. Measurements and Main Results: Survival in and out of the intensive care unit was compared for screened patients from the day a patient first met study criteria. Cox multivariate models were constructed to adjust survival comparisons for various confounding factors. The effect of intensive care unit vs. other departments was estimated separately for the first 3 days after deterioration and for the remaining follow-up time. Results showed that 5.5% of adult hospitalized patients were critically ill (736 of 13,415). Of these, 27% were admitted to intensive care units, 24% to specialized care units, and 49% to regular departments. Admission to an intensive care unit was associated with better survival during the first 3 days of deterioration, after we adjusted for age and severity of illness (p .018). There was no additional survival advantage for intensive care unit patients (p .9) during the remaining follow-up time. Conclusions: The early survival advantage in the intensive care unit suggests a window of critical opportunity for these patients. Under economic constraints and dearth of intensive care unit beds, increasing the turnover of patients in the intensive care unit, thus exposing more needy patients to the early benefit of treatment in the intensive care unit, may be advantageous.
The effect of intensive care on in-hospital survival
Health Services and Outcomes Research Methodology, 2004
Intensive Care Units (ICU) are one of the most powerful and expensive technologies within inpatient care. However, its effect on survival is still an issue under discussion. The objective of this paper is to assess the effect of General ICU on in-hospital survival. We assessed the effect of ICU on survival using Linear and Probit regressions. Since admission to IC is not random and depends on unobserved (to the researcher) heterogeneity, we reassessed the IC effect by Instrumental Variables (IV) and Bivariate Probit techniques, using crowding in the IC unit as an instrument. The results show that a simple Probit of the IC effect on survival is 7-10 percentage-points (pts). The IV estimate of the IC effect on survival is 21-34 pts, and the Bivariate Probit estimate is 17-21 pts. We conclude that although admitted patients are at lower risk of death, as determined by their observable (to the researcher) characteristics, controlling for observable differences, those with a higher unobserved risk of mortality are more likely to be admitted. The implications for an optimal admission policy are discussed.
Variations in Mortality and Length of Stay in Intensive Care Units
Annals of Internal Medicine, 1993
Objective: To evaluate the amount of variation in in-hospital mortality and length of intensive care unit (ICU) stay that can be accounted for by clinical data available at ICU admission. Design: Inception cohort study. Setting: Forty-two ICUs in 40 hospitals, including 26 hospitals that were randomly selected and 14 large tertiary care hospitals that volunteered for the study. Participants: A consecutive sample of 16 622 patients and 17 440 ICU admissions. Measurements and Main Outcomes: Data on selected demographic characteristics, comorbidity, and specific physiologic variables were recorded during the first ICU day for an average of 415 admissions at each ICU; hospital discharge status (dead or alive) and length of ICU stay were recorded for individual patients; and the ratio of actual to predicted in-hospital mortality, standardized mortality ratios, and the ratio of actual to predicted length of ICU stay were recorded for individual ICUs. Results: Unadjusted in-hospital mortality rates for the 42 units varied from 6.4% to 40%, and 90% (R 2 = 0.90) of this variation was attributable to patient characteristics at admission. The standard mortality ratio varied from 0.67 to 1.25. The mean unadjusted length of ICU stay varied from 3.3 to 7.3 days, and 78% of the variation (R 2 = 0.78) was attributed to patient and selected institutional characteristics. The best performing unit had a length of stay ratio of 0.88, whereas the poorest performing unit had a ratio of 1.21. Conclusions: Clinicians can use readily available admission data to adjust for considerable variations in patient severity and type in different ICUs. Such data should permit precise evaluation and comparison of ICU effectiveness and efficiency, which varied substantially in this study, and result in improved methods of risk prediction and evaluation of new medical practices. Intensive care units (ICUs), first introduced in the 1960s, now account for approximately 7% of total U.S. hospital beds, 20% to 30% of hospital costs, and 1% of the U.S. gross domestic product [1,2,3]. These economic and institutional consequences have increased the need for outcome evaluation and guidance regarding efficient utilization. Mortality rates, an insensitive measure for an entire hospital [4,5,6], are high enough in ICUs to serve as one reliable performance indicator. Substantial progress has also been made in identifying clinical risk factors for death and resource utilization for patients in ICUs [7,8,9,10,11]. The objective of this study was to explore the ability to evaluate ICU performance using risk-adjusted in-hospital mortality rates and length of ICU stay. In this report, we focus on the amount of variation that can be accounted for after adjusting for patient characteristics present at admission. We describe the nature and relative importance of these factors and the extent of the remaining variation in outcome performance after such adjustment.
The comparison of the survival rates of intensive and palliative care units
2020
Introduction Palliative care is a multidisciplinary therapy formed by physical, social, psychological, cultural and spiritual support of patients and families. The aim of the present study is to compare the survival rates of the intensive care unit (ICU) and palliative care unit (PCU). Materials and Methods A retrospective observational cohort study was performed using the database of an intensive care unit. Patients with terminal illness admitted to the intensive care unit or palliative care unit were included in the study. Demographic data, comorbidities, time of admission, discharge and death were recorded. The survival estimation was completed using Kaplan Meier survival analysis. Result A total of 112 patients were included in the study. Patients were divided into two groups where 60 patients (53.6%) were in Group ICU and 52 (46.4%) were in Group PCU. The Kaplan-Meier estimation of survival curves showed that the overall median time was 29 days. This result demonstrated that 50...
Modeling in-hospital patient survival during the first 28 days after intensive care unit admission
Journal of Critical Care, 2008
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 = 17 138) 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. ☆ Authors' contribution and competing interests. Rui Moreno and Philipp Metnitz actively organized and chaired the SAPS 3 project (see electronic supplementary material for a complete list of participants in the project) and actively participated in all steps of data collection, analysis, and model development. Barbara Metnitz and Peter Bauer were responsible for data management and statistical analysis of the SAPS 3 project. Manuscript preparation was done by Rui Moreno, Philipp Metnitz, and Barbara Metnitz.
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 admis-sion 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 points Conclusions: 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.
Pharmacy World & Science, 2004
Introduction: The length of stay (LOS) in patients admitted to intensive care units (ICUs) is influenced by the clinical history of the patient, so the main factors affecting clinical outcome are logical candidates to be predictors of LOS. Since there is still limited information about which factors can influence LOS in these patients, we undertook this observational study in Italian hospitals. Materials and methods: From 1 August to 31 October 2001 we enrolled a maximum of 10 consecutive patients admitted to ICUs in 16 Italian hospitals. The following information was recorded from each patient: date of admission; APACHE II score on admission; active sepsis and/or septic shock on admission; sepsis and/or septic shock developed during the stay in ICU; Glasgow coma scale on the third day; date and clinical outcome upon discharge from the hospital (alive or dead). Results: In the study 131 patients were enrolled; 31 (23.7%) had active sepsis upon admission to ICU and 10 (7.6%) had septic shock; 12 (9.2%) developed sepsis during hospitalization and 12 (9.2%) developed septic shock. At the end of the study, 101 patients were alive and 30 had died. The overall mean LOS was 12 days. The mean LOS was 18.3 days for the subgroup with sepsis and 8.3 days in the subgroup without sepsis. Sepsis was the only factor that significantly influenced the LOS (P ϭ 0.016). Conclusions: Our study was aimed to analyse the factors that influence the LOS in ICU patients and found that among the variables that affected LOS, sepsis had the greatest impact. Other studies had evaluated the impact of some variables on LOS and identified sepsis and infection as a determinant prolonging LOS.
Introduction: The aim of the study was to assess whether adults admitted to hospitals with both Intensive Care Units (ICU) and Intermediate Care Units (IMCU) have lower in-hospital mortality than those admitted to ICUs without an IMCU. Methods: An observational multinational cohort study performed on patients admitted to participating ICUs during a four-week period. IMCU was defined as any physically and administratively independent unit open 24 hours a day, seven days a week providing a level of care lower than an ICU but higher than a ward. Characteristics of hospitals, ICUs and patients admitted to study ICUs were recorded. The main outcome was all-cause in-hospital mortality until hospital discharge (censored at 90 days). Results: One hundred and sixty-seven ICUs from 17 European countries enrolled 5,834 patients. Overall, 1,113 (19.1%) patients died in the ICU and 1,397 died in hospital, with a total of 1,397 (23.9%) deaths. The illness severity was higher for patients in ICUs with an IMCU (median Simplified Acute Physiology Score (SAPS) II: 37) than for patients in ICUs without an IMCU (median SAPS II: 29, P <0.001). After adjustment for patient characteristics at admission such as illness severity, and ICU and hospital characteristics, the odds ratio of mortality was 0.63 (95% CI 0.45 to 0.88, P = 0.007) in favour of the presence of IMCU. The protective effect of the IMCU was absent in patients who were admitted for basic observation, for example, after surgery (odds ratio 1.15, 95% CI 0.65 to 2.03, P = 0.630) but was strong in patients admitted to an ICU for other reasons (odds ratio 0.54, 95% CI 0.37 to 0.80, P = 0.002). Conclusions: The presence of an IMCU in the hospital is associated with significantly reduced adjusted hospital mortality for adults admitted to the ICU. This effect is relevant for the patients requiring full intensive treatment.
Indian Journal of Critical Care Medicine
Objectives: Emergency department (ED) length of stay (LOS) is defined as the time a patient is registered to the time the patient is shifted to a hospital bed or discharged. Increasing demand for quality emergency care has resulted in increased wait times due to demand and supply mismatch. It is perceived that longer LOS in the ED of critical patients leads to poor outcomes. Our goal was to study the impact of LOS in the ED on the patients who required critical care admissions. Methods: This was a retrospective study conducted in the ED of a tertiary center. Data were collected using electronic health records (EHR) for patients admitted to the intensive care units (ICUs). Patient's LOS in ED was divided into 0-4, 4-8, 8-12, 12-24, and >24 hours. ED LOS was calculated from the registration time to the time patient was handed over in the ICU. Patients were divided into four categories (1-4) based on their criticality. LOS in ED, mortality, and total hospital LOS were analyzed in the study. Results: Three thousand four hundred and twenty-nine patients were enrolled in the study. Mean age was 62.69 years (95% CI 62.11-63.26).