Intensive care unit length of stay: recent changes and future challenges

Reintubation Rate and Mortality After Emergent Airway Management Outside the Operating Room

BACKGROUND

Little is known about reintubations outside of the operating room. The objective of this study was to evaluate the reintubation rate and mortality after emergent airway management outside operating room (OR), including intensive care unit (ICU) and nonICU settings.

METHODS

A retrospective cohort study. The primary outcome measures were reintubation rate and mortality. Secondary outcome measures were location and indication for intubation, time until reintubation, total intubated days, ICU-stay, hospital-stay, 30-day in-hospital mortality, and overall in-hospital mortality.

RESULTS

A total of 336 outside-OR intubations were performed in 275 patients. Of those 275 patients, 51 (18.5%) were reintubated during the same hospital admission. (41%) of the reintubations occurred in a non-ICU setting. Reintubations occurred after up to 30-days after extubation. Most frequently between 7 and 30 days (32.8%, n = 20). Most of the reintubated patients were reintubated just once (56.9%; n = 29), but some were reintubated 2 times (29.4%; n = 15) or over 3 times (13.7%; n = 7). Reintubated patients had significant longer total ICU-stay (24 ± 3 days vs 12 ± 1 day, p < .001), hospital stay (37 ± 3 vs18 ± 1, p < .001), and total intubation days (8 ± 1 vs 7 ± 0.6, P < .02). The 30-day in-hospital mortality in reintubated patients was 13.7% (n = 7) compared to nonreintubated patients 35.9% (n = 80; P = .002).

CONCLUSION

Reintubation was associated with a significant increase in hospital and ICU stay. The higher mortality rate among nonreintubated patients may indicate survival bias, in that severely sick patients did not survive long enough to attempt extubation.

Evaluating the Impact of Point-of-Care Electroencephalography on Length of Stay in the Intensive Care Unit: Subanalysis of the SAFER-EEG Trial

BACKGROUND

Electroencephalography (EEG) is needed to diagnose nonconvulsive seizures. Prolonged nonconvulsive seizures are associated with neuronal injuries and deleterious clinical outcomes. However, it is uncertain whether the rapid identification of these seizures using point-of-care EEG (POC-EEG) can have a positive impact on clinical outcomes.

METHODS

In a retrospective subanalysis of the recently completed multicenter Seizure Assessment and Forecasting with Efficient Rapid-EEG (SAFER-EEG) trial, we compared intensive care unit (ICU) length of stay (LOS), unfavorable functional outcome (modified Rankin Scale score ≥ 4), and time to EEG between adult patients receiving a US Food and Drug Administration-cleared POC-EEG (Ceribell, Inc.) and those receiving conventional EEG (conv-EEG). Patient records from January 2018 to June 2022 at three different academic centers were reviewed, focusing on EEG timing and clinical outcomes. Propensity score matching was applied using key clinical covariates to control for confounders. Medians and interquartile ranges (IQRs) were calculated for descriptive statistics. Nonparametric tests (Mann-Whitney U-test) were used for the continuous variables, and the χ2 test was used for the proportions.

RESULTS

A total of 283 ICU patients (62 conv-EEG, 221 POC-EEG) were included. The two populations were matched using demographic and clinical characteristics. We found that the ICU LOS was significantly shorter in the POC-EEG cohort compared to the conv-EEG cohort (3.9 [IQR 1.9-8.8] vs. 8.0 [IQR 3.0-16.0] days, p = 0.003). Moreover, modified Rankin Scale functional outcomes were also different between the two EEG cohorts (p = 0.047).

CONCLUSIONS

This study reveals a significant association between early POC-EEG detection of nonconvulsive seizures and decreased ICU LOS. The POC-EEG differed from conv-EEG, demonstrating better functional outcomes compared with the latter in a matched analysis. These findings corroborate previous research advocating the benefit of early diagnosis of nonconvulsive seizure. The causal relationship between the type of EEG and metrics of interest, such as ICU LOS and functional/clinical outcomes, needs to be confirmed in future prospective randomized studies.

ICU-Managed Patients' Epidemiology, Characteristics, and Outcomes: A Retrospective Single-Center Study

Background

Resources are limited, and it is exceedingly difficult to provide intensive care in developing nations. In Somalia, intensive care unit (ICU) care was introduced only a few years ago.

Purpose

In this study, we aimed to determine the epidemiology, characteristics, and outcome of ICU-managed patients in a tertiary hospital in Mogadishu.

Methods

We retrospectively evaluated the files of 1082 patients admitted to our ICU during the year 2021.

Results

The majority (39.7%) of the patients were adults (aged between 20 and 39 years), and 67.8% were male patients. The median ICU length of stay was three days (IQR = 5 days), and nonsurvivors had shorter stays, one day. The mortality rate was 45.1%. The demand for critical care services in low-income countries is high.

Conclusion

The country has a very low ICU bed capacity. Critical care remains a neglected area of health service delivery in this setting, with large numbers of patients with potentially treatable conditions not having access to such services.

Epidemiological trends of surgical admissions to the intensive care unit in the United States

BACKGROUND

Epidemiologic assessment of surgical admissions into intensive care units (ICUs) provides a framework to evaluate health care system efficiency and project future health care needs.

METHODS

We performed a 9-year (2008-2016), retrospective, cohort analysis of all adult admissions to 88 surgical ICUs using the prospectively and manually abstracted Cerner Acute Physiology and Chronic Health Evaluation Outcomes database. We stratified patients into 13 surgical cohorts and modeled temporal trends in admission, mortality, surgical ICU length of stay (LOS), and change in functional status (FS) using generalized mixed-effects and Quasi-Poisson models to obtain risk-adjusted outcomes.

RESULTS

We evaluated 78,053 ICU admissions and observed a significant decrease in admissions after transplant and thoracic surgery, with a concomitant increase in admissions after otolaryngological and facial reconstructive procedures (all p < 0.05). While overall risk-adjusted mortality remained stable over the study period; mortality significantly declined in orthopedic, cardiac, urologic, and neurosurgical patients (all p < 0.05). Cardiac, urologic, gastrointestinal, neurosurgical, and orthopedic admissions showed significant reductions in LOS (all p < 0.05). The overall rate of FS deterioration increased per year, suggesting ICU-related disability increased over the study period.

CONCLUSION

Temporal analysis demonstrates a significant change in the type of surgical patients admitted to the ICU over the last decade, with decreasing mortality and LOS in selected cohorts, but an increasing rate of FS deterioration. Improvement in ICU outcomes may highlight the success of health care advancements within certain surgical cohorts, while simultaneously identifying cohorts that may benefit from future intervention. Our findings have significant implications in health care systems planning, including resource and personnel allocation, education, and surgical training.

LEVEL OF EVIDENCE

Economic/decision, level IV.Epidemiologic, level IV.

Health problems among family caregivers of former intensive care unit (ICU) patients: an interview study

Background

Family caregivers of former intensive care unit (ICU) patients may suffer from physical and mental problems owing to ICU hospitalisation of their loved ones. These problems can have a major impact on their daily lives. Little is known about experienced consequences of ICU hospitalisation on caregivers in general practice.

Aim

To explore health problems in family caregivers of former ICU patients and the consequences in their daily lives.

Design & setting

Semi-structured interviews with family caregivers of former critically ill patients treated in a Dutch ICU.

Method

Purposively sampled relatives of former ICU patients were interviewed between April and May 2019. Interviews were conducted until data saturation was reached. Interviews were then thematically analysed.

Results

In total, 13 family caregivers were interviewed. The interviews took place 3 months to 3 years after ICU discharge. Expressed problems were categorised into six themes: (1) physical functioning (for example, tiredness, headache, and feeling sick more often); (2) mental health (for example, anxiety, more stress and difficulty in expressing emotions); (3) existential dimension and future (for example, uncertainty about the future); (4) quality of life (for example, losing freedom in life); (5) relationship and social participation (for example, experiencing a lack of understanding); and (6) daily functioning (for example, stopping working).

Conclusion

Caregivers experience several health problems, even years after their relative's ICU episode. Healthcare providers should be focused not only on former ICU patients’ health, but also on their caregivers’, and need to signal and identify caregivers' health problems earlier in order to give them the appropriate care and support they need.

Emergent airway management outside of the operating room - a retrospective review of patient characteristics, complications and ICU stay

Background

Emergent airway management outside of the operating room is a high-risk procedure. Limited data exists about the indication and physiologic state of the patient at the time of intubation, the location in which it occurs, or patient outcomes afterward.

Methods

We retrospectively collected data on all emergent airway management interventions performed outside of the operating room over a 6-month period. Documentation included intubation performance, and intubation related complications and mortality. Additional information including demographics, ASA-classification, comorbidities, hospital-stay, ICU-stay, and 30-day in-hospital mortality was obtained.

Results

336 intubations were performed in 275 patients during the six-month period. The majority of intubations (n = 196, 58%) occurred in an ICU setting, and the rest 140 (42%) occurred on a normal floor or in a remote location. The mean admission ASA status was 3.6 ± 0.5, age 60 ± 16 years, and BMI 30 ± 9 kg/m². Chest X-rays performed immediately after intubation showed main stem intubation in 3.3% (n = 9). Two immediate (within 20 min after intubation) intubation related cardiac arrest/mortality events were identified. The 30-day in-hospital mortality was 31.6% (n = 87), the overall in-hospital mortality was 37.1% (n = 102), the mean hospital stay was 22 ± 20 days, and the mean ICU-stay was 14 days (13.9 ± 0.9, CI 12.1–15.8) with a 7.3% ICU-readmission rate.

Conclusion

Patients requiring emergent airway management are a high-risk patient population with multiple comorbidities and high ASA scores on admission. Only a small number of intubation-related complications were reported but ICU length of stay was high.

Survival and Quality of Life after Prolonged Intensive Care Unit Stay

There are few data on long-term outcomes in mixed groups of intensive care unit (ICU) patients with prolonged stays. We evaluated the relationship between length of stay in the ICU and long-term outcome in all patients admitted to our 31-bed department of medico-surgical intensive care over a one-year period who stayed in the department for more than 10 days (n=189, 7% of all ICU admissions). Mortality increased with length of stay from 1 to 10 days (1 day 5%, 5 days 15%, 9 days 24%, 10 days 33%) but remained stable at about 35% for longer ICU stays. In the long-stay patients, the most common reasons for ICU admission were intracranial bleeding (23%), polytrauma (14%), respiratory failure (13%) and septic shock (11%). The main reasons for prolonged ICU stay were ventilator dependency (40%), infectious complications (23%) and coma (16%). Long-stay patients had a 65% ICU survival, 55% hospital survival and 37% one-year survival. At one-year follow-up, 73% of surviving patients reported no or minor persistent physical complaints compared to before the acute illness; 27% had a major functional impairment, including 8% who required daily assistance. In conclusion, in ICU patients, mortality increases with length of stay up to 10 days. Patients staying in the ICU for more than 10 days have a relatively good long-term survival. Most survivors have an acceptable quality of life.

Effect of diagnosis related groups implementation on the intensive care unit of a Swiss tertiary hospital: a cohort study

BACKGROUND

In 2013 the Swiss Diagnosis Related Groups ((Swiss)-DRG) was implemented in Intensive Care Units (ICU). Its impact on hospitalizations has not yet been examined. We compared the number of ICU admissions, according to clinical severity and referring institution, and screened whether implementation of Swiss-DRG affected admission policy, ICU length-of-stay (ICU-LOS) or ICU mortality.

METHODS

Retrospective, single centre, cohort study conducted at the University Hospital Zurich, Switzerland between January 2009 and end of September 2013. Demographic and clinical data was retrieved from a quality assurance database.

RESULTS

Admissions (n = 17,231) before the introduction of Swiss-DRG were used to model expected admissions after DRG, and then compared to the observed admissions. Forecasting matched observations in patients with a high clinical severity admitted from internal units and external hospitals (admitted / predicted: 709 / 703, [95% Confidence Interval (CI), 658-748] and 302 / 332, [95% CI, 269-365] respectively). In patients with low severity of disease, in-house admissions became more frequent than expected and external admission were less frequent (admitted / predicted: 1972 / 1910, [95% CI, 1898-1940] and 436 / 518, [95% CI, 482-554] respectively). Various mechanisms related to Swiss-DRG may have led to these changes. DRG could not be linked to significant changes in regard to ICU-LOS and ICU mortality.

CONCLUSIONS

DRG introduction had not affected ICU admissions policy, except for an increase of in-house patients with a low clinical severity of disease. DRG had neither affected ICU mortality nor ICU-LOS.

Clinical Characteristics and Outcomes of Surgical Patients with Intensive Care Unit Lengths of Stay of 90 Days and Greater

BACKGROUND

The aim of this study was to evaluate the influence of prolonged length of stay in an intensive care unit (ICU) on the mortality and morbidity of surgical patients.

METHODS

We performed a monocentric and retrospective observational study in the surgical critical care unit of the department of surgery at the Medical Center of the University of Freiburg, Germany. Clinical data was collected from patients assigned to the ICU with a length of stay (LOS) of 90 days and greater.

RESULTS

From the total of the 19 patients with ICU LOS over 90 days, ten patients died in the ICU whereas nine patients were discharged to the normal ward. The ICU mortality rate was 52%. The overall survival one year after ICU discharge was 32%. Regarding factors affecting mortality of the patients, significantly higher mortality was associated with age of the patients at the time point of the ICU admission and with postoperative need of renal replacement therapy.

CONCLUSIONS

We found a high but in our opinion acceptable mortality rate in surgical patients with ICU LOS of 90 days and greater. We identified age and the need of renal replacement therapy as risk factors for mortality.

Which Models Can I Use to Predict Adult ICU Length of Stay? A Systematic Review

OBJECTIVE

We systematically reviewed models to predict adult ICU length of stay.

DATA SOURCES

We searched the Ovid EMBASE and MEDLINE databases for studies on the development or validation of ICU length of stay prediction models.

STUDY SELECTION

We identified 11 studies describing the development of 31 prediction models and three describing external validation of one of these models.

DATA EXTRACTION

Clinicians use ICU length of stay predictions for planning ICU capacity, identifying unexpectedly long ICU length of stay, and benchmarking ICUs. We required the model variables to have been published and for the models to be free of organizational characteristics and to produce accurate predictions, as assessed by R across patients for planning and identifying unexpectedly long ICU length of stay and across ICUs for benchmarking, with low calibration bias. We assessed the reporting quality using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.

DATA SYNTHESIS

The number of admissions ranged from 253 to 178,503. Median ICU length of stay was between 2 and 6.9 days. Two studies had not published model variables and three included organizational characteristics. None of the models produced predictions with low bias. The R was 0.05-0.28 across patients and 0.01-0.64 across ICUs. The reporting scores ranged from 49 of 78 to 60 of 78 and the methodologic scores from 12 of 22 to 16 of 22.

CONCLUSION

No models completely satisfy our requirements for planning, identifying unexpectedly long ICU length of stay, or for benchmarking purposes. Physicians using these models to predict ICU length of stay should interpret them with reservation.

Difficulties in access and estimates of public beds in intensive care units in the state of Rio de Janeiro

OBJECTIVE

To estimate the required number of public beds for adults in intensive care units in the state of Rio de Janeiro to meet the existing demand and compare results with recommendations by the Brazilian Ministry of Health.

METHODS

The study uses a hybrid model combining time series and queuing theory to predict the demand and estimate the number of required beds. Four patient flow scenarios were considered according to bed requests, percentage of abandonments and average length of stay in intensive care unit beds. The results were plotted against Ministry of Health parameters. Data were obtained from the State Regulation Center from 2010 to 2011.

RESULTS

There were 33,101 medical requests for 268 regulated intensive care unit beds in Rio de Janeiro. With an average length of stay in regulated ICUs of 11.3 days, there would be a need for 595 active beds to ensure system stability and 628 beds to ensure a maximum waiting time of six hours. Deducting current abandonment rates due to clinical improvement (25.8%), these figures fall to 441 and 417. With an average length of stay of 6.5 days, the number of required beds would be 342 and 366, respectively; deducting abandonment rates, 254 and 275. The Brazilian Ministry of Health establishes a parameter of 118 to 353 beds. Although the number of regulated beds is within the recommended range, an increase in beds of 122.0% is required to guarantee system stability and of 134.0% for a maximum waiting time of six hours.

CONCLUSIONS

Adequate bed estimation must consider reasons for limited timely access and patient flow management in a scenario that associates prioritization of requests with the lowest average length of stay.

Contributions of tele-intensive care unit (Tele-ICU) technology to quality of care and patient safety

The deployment of remote monitoring of intensive care unit (ICU) patients, known as tele-ICU technology, promotes the efficient use of critical care resources. Although tele-ICU use has spread rapidly, the benefits vary widely among studies, and little is known about the specific characteristics of tele-ICU that provide benefits to patient care. The purpose of this study was to identify aspects of tele-ICU that contribute, whether positively or negatively, to care processes and patient outcomes. This study was not aimed at evaluating the impact of tele-ICU on care outcomes. We conducted a qualitative study using semistructured interviews. Sixty-one tele-ICU staff from 5 tele-ICUs participated in the study. We performed inductive content analysis and coded 722 pieces of text into 19 positive and 9 negative/neutral tele-ICU contributions to care processes and patient outcomes. We found that availability of extra resources can reduce on mortality and length of stay, that a tele-ICU could serve as a quality trigger to improve evidence-based medicine compliance, that tele-ICU can support medication management and improve medication safety, and that tele-ICU software alerts and monitoring by camera can help reduce the risk of patient falls and extubations. We also found that tele-ICU physicians can make poor care decisions leading to medication errors if they lack patient-related information. Moreover, the tele-ICU has no impact on patient care processes and outcomes when the technology is not accepted and used by ICU staff.

Epidemiological trends in the neurological intensive care unit from 2000 to 2008

Intensive care units (ICU) specializing in the treatment of patients with neurological diseases (Neuro-ICU) have become increasingly common. However, there are few data on the longitudinal demographics of this patient population. Identifying admission trends may provide targets for improving resource utilization. We performed a retrospective analysis of admission logs for primary diagnosis, age, sex, and length of stay, for all patients admitted to the Neuro-ICU at Columbia University Medical Center (CUMC) between 2000 and 2008. From 2000 to 2008, inclusive, the total number of Neuro-ICU admissions increased by 49.9%. Overall mean patient age (54.6 ± 17.4 to 56.2 ± 18.0 years, p=0.041) and gender (55.9-50.3% female, p=0.005) changed significantly, while median length of stay (2 days) did not. When comparing the time period prior to construction of a larger Neuro-ICU (2000-2004) to that after completion (2005-2008), patient age (56.0 ± 17.6 compared to 56.9 ± 17.5 years, p=0.012) and median length of stay (1 compared to 2 days, p<0.001) both significantly increased. Construction of a newer, larger Neuro-ICU at CUMC led to a substantial increase in admissions and changes in diagnoses from 2000 to 2008. Advances in neurocritical care, neurosurgical practices, and the local and global expansion and utilization of ICU resources likely led to differences in lengths of stay.

A review of statistical estimators for risk-adjusted length of stay: analysis of the Australian and new Zealand Intensive Care Adult Patient Data-Base, 2008-2009

BACKGROUND

For the analysis of length-of-stay (LOS) data, which is characteristically right-skewed, a number of statistical estimators have been proposed as alternatives to the traditional ordinary least squares (OLS) regression with log dependent variable.

METHODS

Using a cohort of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 2008-2009, 12 different methods were used for estimation of intensive care (ICU) length of stay. These encompassed risk-adjusted regression analysis of firstly: log LOS using OLS, linear mixed model [LMM], treatment effects, skew-normal and skew-t models; and secondly: unmodified (raw) LOS via OLS, generalised linear models [GLMs] with log-link and 4 different distributions [Poisson, gamma, negative binomial and inverse-Gaussian], extended estimating equations [EEE] and a finite mixture model including a gamma distribution. A fixed covariate list and ICU-site clustering with robust variance were utilised for model fitting with split-sample determination (80%) and validation (20%) data sets, and model simulation was undertaken to establish over-fitting (Copas test). Indices of model specification using Bayesian information criterion [BIC: lower values preferred] and residual analysis as well as predictive performance (R2, concordance correlation coefficient (CCC), mean absolute error [MAE]) were established for each estimator.

RESULTS

The data-set consisted of 111663 patients from 131 ICUs; with mean(SD) age 60.6(18.8) years, 43.0% were female, 40.7% were mechanically ventilated and ICU mortality was 7.8%. ICU length-of-stay was 3.4(5.1) (median 1.8, range (0.17-60)) days and demonstrated marked kurtosis and right skew (29.4 and 4.4 respectively). BIC showed considerable spread, from a maximum of 509801 (OLS-raw scale) to a minimum of 210286 (LMM). R2 ranged from 0.22 (LMM) to 0.17 and the CCC from 0.334 (LMM) to 0.149, with MAE 2.2-2.4. Superior residual behaviour was established for the log-scale estimators. There was a general tendency for over-prediction (negative residuals) and for over-fitting, the exception being the GLM negative binomial estimator. The mean-variance function was best approximated by a quadratic function, consistent with log-scale estimation; the link function was estimated (EEE) as 0.152(0.019, 0.285), consistent with a fractional-root function.

CONCLUSIONS

For ICU length of stay, log-scale estimation, in particular the LMM, appeared to be the most consistently performing estimator(s). Neither the GLM variants nor the skew-regression estimators dominated.

Mortality and associated factors in a thoracic surgery ICU

OBJECTIVE

To assess mortality and identify mortality risk factors in patients admitted to a thoracic surgery ICU.

METHODS

We retrospectively evaluated 141 patients admitted to the thoracic surgery ICU of the Denizli State Hospital, located in the city of Denizli, Turkey, between January of 2006 and August of 2008. We collected data regarding gender, age, reason for admission, invasive interventions and operations, invasive mechanical ventilation, infections, and length of ICU stay.

RESULTS

Of the 141 patients, 103 (73.0%) were male, and 38 (23.0%) were female. The mean age was 52.1 years (range, 12-92 years), and the mortality rate was 16.3%. The most common reason for admission was trauma. Mortality was found to correlate with advanced age (p < 0.05), requiring invasive mechanical ventilation (OR = 42.375; p < 0.05), prolonged ICU stay (p < 0.05), and specific reasons for admission-trauma, gunshot wound, stab wound, and malignancy (p < 0.05 for all).

CONCLUSIONS

Among patients in a thoracic surgery ICU, the rates of morbidity and mortality are high. Increased awareness of mortality risk factors can improve the effectiveness of treatment, which should reduce the rates of morbidity and mortality, thereby providing time savings and minimizing costs.

Chronic critical illness: prevalence, profile, and pathophysiology

The syndrome of chronic critical illness has well-documented emotional, social, and financial burdens for individuals, caregivers, and the health care system. The purpose of this article is to provide experienced acute and critical care clinicians with essential information about the prevalence and profile of the chronically critically ill patient needed for comprehensive care. In addition, pathophysiology contributing to chronic critical illness is addressed, though the exact mechanism underlying the conversion of acute critical illness to chronic critical illness is unknown. Clinicians can use this information to identify at-risk intensive care unit patients and to institute proactive care to minimize burden and distress experienced by patients and their caregivers.

A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay

BACKGROUND

Patients with a prolonged intensive care unit (ICU) length of stay account for a disproportionate amount of resource use. Early identification of patients at risk for a prolonged length of stay can lead to quality enhancements that reduce ICU stay. This study developed and validated a model that identifies patients at risk for a prolonged ICU stay.

METHODS

We performed a retrospective cohort study of 343,555 admissions to 83 ICUs in 31 U.S. hospitals from 2002-2007. We examined the distribution of ICU length of stay to identify a threshold where clinicians might be concerned about a prolonged stay; this resulted in choosing a 5-day cut-point. From patients remaining in the ICU on day 5 we developed a multivariable regression model that predicted remaining ICU stay. Predictor variables included information gathered at admission, day 1, and ICU day 5. Data from 12,640 admissions during 2002-2005 were used to develop the model, and the remaining 12,904 admissions to internally validate the model. Finally, we used data on 11,903 admissions during 2006-2007 to externally validate the model.

RESULTS

The variables that had the greatest impact on remaining ICU length of stay were those measured on day 5, not at admission or during day 1. Mechanical ventilation, PaO2: FiO2 ratio, other physiologic components, and sedation on day 5 accounted for 81.6% of the variation in predicted remaining ICU stay. In the external validation set observed ICU stay was 11.99 days and predicted total ICU stay (5 days + day 5 predicted remaining stay) was 11.62 days, a difference of 8.7 hours. For the same patients, the difference between mean observed and mean predicted ICU stay using the APACHE day 1 model was 149.3 hours. The new model's r2 was 20.2% across individuals and 44.3% across units.

CONCLUSIONS

A model that uses patient data from ICU days 1 and 5 accurately predicts a prolonged ICU stay. These predictions are more accurate than those based on ICU day 1 data alone. The model can be used to benchmark ICU performance and to alert physicians to explore care alternatives aimed at reducing ICU stay.

Outcome prediction in critical care: the Acute Physiology and Chronic Health Evaluation models

PURPOSE OF REVIEW

A new generation of predictive models for critically ill patients was described between 2005 and 2008. This review will give details of the latest version of the Acute Physiology and Chronic Health Evaluation (APACHE) predictive models, and discuss it in relation to recent critical care outcome studies. We also compare APACHE IV with other systems and address the issue of model complexity.

RECENT FINDINGS

APACHE IV required the remodeling of over 40 equations. These new models calibrate better to contemporary data than older versions of APACHE and there is good predictive accuracy within diagnostic subgroups. Physiology accounts for 66% and diagnosis for 17% of the APACHE IV mortality model's predictive power. Thus, physiology and diagnosis account for 83% of the accuracy of APACHE IV.

SUMMARY

Predictive models have a modest window of applicability, and therefore must be revalidated frequently. This was shown to be true for APACHE III, and hence a major reestimation of models was carried out to generate APACHE IV. Although overall model accuracy is important, it is also imperative that predictive models work well within diagnostic subgroups.

Predicting Outcomes for Cardiac Surgery Patients After Intensive Care Unit Admission

Most performance assessments of cardiac surgery programs use models based on preoperative risk factors. Models that were primarily developed to assess performance in general intensive care unit (ICU) populations have also been used to evaluate the quality of surgical, anesthetic, and ICU management after cardiac surgery. Although there are currently 5 models for evaluating general ICU populations, only the Acute Physiology and Chronic Health Evaluation (APACHE) system has been independently validated for cardiac surgery patients. This review describes the evolution, rationale, and accuracy of APACHE models that are specific for cardiac surgery patients as well as for patients who have had vascular and thoracic procedures. In addition to performance comparisons based on observed and predicted mortality, APACHE provides similar comparisons of ICU and hospital lengths of stay and duration of mechanical ventilation. However, the low mortality incidence of many cardiac outcomes means that very large numbers of patients must be obtained to get good predictive models. Thus, the equations are not designed for predicting individual patients' outcome but have proven useful in performance comparisons and for quality improvement initiatives.

Mortality and length-of-stay outcomes, 1993-2003, in the binational Australian and New Zealand intensive care adult patient database

OBJECTIVE

Intensive care unit (ICU) outcomes have been the subject of controversy. The objective was to model hospital mortality and ICU length-of-stay time-change of patients recorded in the Australian and New Zealand Intensive Care Society adult patient database.

DESIGN

Retrospective, cohort study of prospectively collected data on index patient admissions.

SETTING

Australian and New Zealand ICUs, 1993-2003.

PATIENTS

The Australian and New Zealand Intensive Care Society adult patient database, which contains data for 223,129 patients.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Hospital mortality and ICU length of stay were modeled using logistic and linear regression, respectively, with determination (80%) and validation (20%) data sets. Model adequacy was assessed by discrimination (receiver operating characteristic curve area, AZ) and calibration (Hosmer-Lemeshow C) for mortality and R2 for length of stay. Predictor variables included patient demographics, severity score, surgical and ventilation status, ICU categories, and geographical locality. The data set comprised 223,129 patients: Their mean (SD) age was 59.2 (18.9) yrs, 41.7% were female, their mean (SD) Acute Physiology and Chronic Health Evaluation (APACHE) III score was 53 (31), they had 16.1% overall mortality rate, and 45.7% were mechanically ventilated. ICU length of stay was 3.6 (5.6) days. A(Z), C statistic, and R2 for developmental and validation model data sets were 0.88, 17.64 (p = .02), and 0.18; and 0.88, 12.32 (p = .26), and 0.18, respectively. Variables with mortality impact (p < or = .001) were age (odds ratio [OR] 1.023), gender (OR 1.16; males vs. females), APACHE III score (OR 1.06), mechanical ventilation (OR 1.66), and surgical status (elective, OR 0.17; emergency, OR 0.47; compared with nonsurgical). ICU level and locality had significant mortality-time effects. Similar variables were found to predict length of stay. Risk-adjusted mortality declined, during 1993-2003, from 0.19 (95% confidence interval 0.17-0.21) to 0.15 (0.13-0.16) and similarly for ventilated patients: 0.26 (0.24-0.29) to 0.23 (0.21-0.25). Predicted mean ICU length of stay (days) demonstrated minimal overall time-change: 3.4 (2.2) in 1993 to 3.5 (2.7) in 2003, peaking at 3.7 (2.4) in 2000.

CONCLUSIONS

Overall hospital mortality rate in patients admitted to Australian and New Zealand ICUs decreased 4% over 11 yrs. A similar trend occurred for mechanically ventilated patients. Length of stay changed minimally over this period.

Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients

OBJECTIVE

To improve the accuracy of the Acute Physiology and Chronic Health Evaluation (APACHE) method for predicting hospital mortality among critically ill adults and to evaluate changes in the accuracy of earlier APACHE models.

DESIGN

: Observational cohort study.

SETTING

A total of 104 intensive care units (ICUs) in 45 U.S. hospitals.

PATIENTS

A total of 131,618 consecutive ICU admissions during 2002 and 2003, of which 110,558 met inclusion criteria and had complete data.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We developed APACHE IV using ICU day 1 information and a multivariate logistic regression procedure to estimate the probability of hospital death for randomly selected patients who comprised 60% of the database. Predictor variables were similar to those in APACHE III, but new variables were added and different statistical modeling used. We assessed the accuracy of APACHE IV predictions by comparing observed and predicted hospital mortality for the excluded patients (validation set). We tested discrimination and used multiple tests of calibration in aggregate and for patient subgroups. APACHE IV had good discrimination (area under the receiver operating characteristic curve = 0.88) and calibration (Hosmer-Lemeshow C statistic = 16.9, p = .08). For 90% of 116 ICU admission diagnoses, the ratio of observed to predicted mortality was not significantly different from 1.0. We also used the validation data set to compare the accuracy of APACHE IV predictions to those using APACHE III versions developed 7 and 14 yrs previously. There was little change in discrimination, but aggregate mortality was systematically overestimated as model age increased. When examined across disease, predictive accuracy was maintained for some diagnoses but for others seemed to reflect changes in practice or therapy.

CONCLUSIONS

APACHE IV predictions of hospital mortality have good discrimination and calibration and should be useful for benchmarking performance in U.S. ICUs. The accuracy of predictive models is dynamic and should be periodically retested. When accuracy deteriorates they should be revised and updated.

Characteristics and outcomes for critically ill patients with prolonged intensive care unit stays*

OBJECTIVE

Prolonged stay in the intensive care unit (ICU) is associated with high mortality, morbidity, and costs. Identifying those patients who are most likely to benefit from an extended ICU stay would be helpful in guiding clinical decisions. We sought to describe the characteristics and outcomes for a heterogeneous group of patients who required a prolonged ICU stay.

DESIGN

Observational study.

SETTING

Adult ICUs of three teaching and five community hospitals.

PATIENTS

The study group comprised 5,881 patients consecutively admitted to the ICUs during a 10-month period.

MEASUREMENTS AND MAIN RESULTS

A prolonged stay was defined as one >21 days at teaching hospitals and >10 days at community hospitals. For patients meeting the criteria of prolonged stay, Therapeutic Intervention Scoring System (TISS) score and Multiple Organ Dysfunction Score (MODS) were measured prospectively from days 10 and 21 in community and teaching hospitals, respectively, and retrospectively before this. Prolonged-stay patients represented 5.6% of ICU admissions and 39.7% of ICU bed-days. Compared with short-stay patients, they were significantly older and had higher admission Acute Physiology and Chronic Health Evaluation (APACHE) II scores (p < .01). ICU and hospital mortality for prolonged-stay patients were 24.4% and 35.2%, respectively, compared with 11% and 15.9% for short-stay patients (p < .001). Mean admission TISS and MODS scores for prolonged-stay patients were 30.8 (sd, 11.1) and 4.8 (sd, 3.3) respectively. For prolonged-stay patients the dominant reason for ICU care was multiple organ failure (37.8%), ventilator support (30.7%), or nonventilated single organ failure (31.5%). Hospital mortality was highest in the group with multiple organ failure (53%).

CONCLUSIONS

We developed a method to broadly classify a heterogeneous population of prolonged-stay ICU patients on the basis of MODS and the ICU interventions received. Mortality among prolonged-stay patients was highest for those with multiple organ failure. Future research should evaluate whether the proposed classification system can be used to influence the delivery of ICU care.

Critical care use during the course of serious illness

Despite its expense and importance, it is unknown how common critical care use is. We describe longitudinal patterns of critical care use among a nationally representative cohort of elderly patients monitored from the onset of common serious illnesses. A retrospective population-based cohort study of elderly patients in fee-for-service Medicare is used, with 1,108,060 Medicare beneficiaries at least 68 years of age and newly diagnosed with serious illnesses: 1 of 9 malignancies, stroke, congestive heart failure, hip fracture, or myocardial infarction. Medicare inpatient hospital claims from diagnosis until death (65.1%) or fixed-right censoring (more than 4 years) were reviewed. Distinct hospitalizations involving critical care use (intensive care unit or critical care unit) were counted and associated reimbursements were assessed; repeated use was defined as five or more such hospitalizations. Of the cohort, 54.9% used critical care at some time after diagnosis. Older patients were much less likely to ever use critical care (odds ratio, 0.31; comparing patients more than 90 years old with those 68-70 years old), even after adjustment. A total of 31,348 patients (2.8%) were repeated users of critical care; they accounted for 3.6 billion dollars in hospital charges and 1.4 billion dollars in Medicare reimbursement. We conclude that critical care use is common in serious chronic illness and is not associated solely with preterminal hospitalizations. Use is uneven, and a minority of patients who repeatedly use critical care account for disproportionate costs.

Mortality rate and length of stay of patients admitted to the intensive care unit in July*

OBJECTIVE

At the beginning of each academic year in July, inexperienced residents and fellows begin to care for patients. This inexperience can lead to poor patient outcome, especially in patients admitted to the intensive care unit (ICU). The objective of this study was to determine the impact of July ICU admission on patient outcome.

DESIGN

Retrospective, cohort study.

SETTING

Academic, tertiary medical center.

PATIENTS

Patients admitted to the ICU from October 1994 through September 2002.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Demographics, Acute Physiology and Chronic Health Evaluation (APACHE) III score and predicted mortality, admission source, admission date, intensity of treatment, ICU length of stay (LOS), and hospital mortality of 29,084 patients were obtained. The actual and predicted weighted ICU LOS and their ratio were calculated. Logistic regression analysis was used to compare the hospital mortality rate of patients admitted to the ICU in July with those admitted during the rest of the year, with adjustment for potentially confounding variables. The patients' mean age was 62.3 +/- 17.6 yrs; 57.3% were male and 95.5% white. Both the customized predicted and observed hospital mortality rates of the entire cohort were 8.2%. The majority (76.7%) of the patients were discharged home, and 15.1% were discharged to other facilities. When adjusted for potentially confounding variables, ICU admission in July was not associated with higher hospital mortality rate compared with any other month. There were no significant differences in the discharge location of patients between July and any one of the other months. There were no statistically significant differences in the weighted ICU LOS ratio between July and any of the other months.

CONCLUSIONS

ICU admission in July is not associated with increased hospital mortality rate or ICU length of stay.

Projected Benefits of Active Surveillance for Vancomycin‐Resistant Enterococci in Intensive Care Units

Hospitals use many strategies to control nosocomial transmission of vancomycin-resistant enterococci (VRE). Strategies include "passive surveillance," with isolation of patients with known previous or current VRE colonization or infection, and "active surveillance," which uses admission cultures, with subsequent isolation of patients who are found to be colonized with VRE. We created a mathematical model of VRE transmission in an intensive care unit (ICU) using data from an existing active surveillance program; we used the model to generate the estimated benefits associated with active surveillance. Simulations predicted that active surveillance in a 10-bed ICU would result in a 39% reduction in the annual incidence of VRE colonization when compared with no surveillance. Initial isolation of all patients, with withdrawal of isolation if the results of surveillance cultures are negative, was predicted to result in a 65% reduction. Passive surveillance was minimally effective. Using the best available data, active surveillance is projected to be effective for reducing VRE transmission in ICU settings.

Neurologic intensive care resource use after brain tumor surgery: An analysis of indications and alternative strategies

OBJECTIVE

Greater demand and limited resources for intensive care monitoring for patients with neurologic disease may change patterns of intensive care unit utilization. The necessity and duration of intensive care unit management for all neurosurgical patients after brain tumor resection are not clear. This study evaluates a) the preoperative and perioperative variables predictive of extended need for intensive care unit monitoring (>1 day); and b) the type and timing of intensive care unit resources in patients for whom less intensive postoperative monitoring may be feasible.

DESIGN

Retrospective chart review.

SETTING

A neurocritical care unit of a university teaching hospital.

PATIENTS

Patients were 158 consecutive postoperative brain tumor resection patients admitted to a neurocritical care unit within a 1-yr period (1998-1999).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Twenty-three patients (15%) admitted to the neurocritical care unit for >24 hrs were compared with 135 (85%) patients admitted for <24 hrs. Predictors of >1-day stay in the neurocritical care unit in a logistic regression model were a tumor severity index comprising radiologic characteristics of tumor location, mass effect, and midline shift on the preoperative magnetic resonance imaging scan (odds ratio, 12.5; 95% confidence interval, 3.1-50.5); an intraoperative fluid score comprising estimated blood loss, total volume of crystalloid, and other colloid/hypertonic solutions administered (odds ratio, 1.8; 95% confidence interval, 1.2-2.6); and postoperative intubation (odds ratio, 67.5; 95% confidence interval, 6.5-702.0). Area under the receiver operating characteristic curve for the model of independent predictors for staying >1 day in the neurocritical care unit was 0.91. Neurocritical care unit resource use was reviewed in detail for 134 of 135 patients who stayed in the neurocritical care unit for <1 day. Sixty-five (49%) patients required no interventions beyond postanesthetic care and frequent neurologic exams. A total of 226 intensive care unit interventions were performed (mean +/- sd, 1.7 +/- 2.6) in 69 (51%) patients. Ninety (67%) patients had no further interventions after the first 4 hrs. Neurocritical care unit resource use beyond 4 hrs, largely consisting of intravenous analgesic use (72% of orders), was significantly associated with female gender, benign tumor on frozen section biopsy, and postoperative intubation (chi-square, p <.05).

CONCLUSIONS

A small fraction of patients require prolonged intensive care unit stay after craniotomy for tumor resection. A patient's risk of prolonged stay can be well predicted by certain radiologic findings, large intraoperative blood loss, fluid requirements, and the decision to keep the patient intubated at the end of surgery. Of those patients requiring intensive care unit resources beyond the first 4 hrs, the interventions may not be critical in nature. A prospective outcome study is required to determine feasibility, cost, and outcome of patients cared for in extended recovery and then transferred to a skilled nursing ward.

The use of benchmarking to identify top performing critical care units: a preliminary assessment of their policies and practices

PURPOSE

To describe the policies and practices of intensive care units (ICUs) with good patient survival and highly efficient resource use and to identify relevant variables for future investigation.

MATERIALS AND METHODS

We used clinical data for 359,715 patients from 108 ICUs to compare the ratios of actual with Acute Physiology and Chronic Health Evaluation (APACHE) III predicted hospital mortality, ICU and hospital stay, and the proportion of low-risk monitor patients. The best performing ICUs (top 10%) were defined by a mortality ratio of 1.0 or less, and either the lowest ratio for ICU stay, hospital stay, or percentage of low-risk monitor patients. The medical and nursing directors of top performing ICUs completed a questionnaire to describe their unit's structure policies and practices.

RESULTS

Among the 108 ICUs, 61 (56%) had a ratio of actual to predicted hospital mortality of 1.0 or less and the best performing units had ICU stay ratios of 0.62 to 0.79, hospital stay ratios of 0.73 to 0.77, and admitted 10% to 38% low-risk monitor patients. ICU structure varied among the best performing ICUs. Units with the shortest ICU and hospital stay had alternatives to intensive care, methods to facilitate patient throughput, used multiple protocols for high-volume diagnoses and care processes, and continuously monitored resource use. Units with the fewest low-risk monitor patients screened potential admissions, had intermediate care areas, extended-stay recovery rooms, and care pathways for high-volume diagnoses.

CONCLUSIONS

Benchmarking can be used to identify ICUs with good patient survival and highly efficient resource use. The combination of policies and practices used by these units might improve resource use in other ICUs.

The role of the critical care nurse in the assessment and management of the patient with severe sepsis

Sepsis with acute organ dysfunction is common, frequently fatal, and expensive. The critical care nurse is involved in the continuous bedside care of the critically ill patient; consequently, he or she has the opportunity to prevent sepsis through infection control practices and general nursing care, to identify patients at risk for the disease, to monitor these patients for the clinical signs of sepsis, and to detect developing organ dysfunction as a manifestation of severe sepsis. In addition, the nurse is responsible for monitoring the patient's response to organ support measures and specific antisepsis interventions. The role of the critical care nurse in the assessment and management of severe sepsis is significant and can greatly improve outcomes for the patient with this disease. Drotrecogin alfa (activated) is a promising new therapy in the treatment of severe sepsis. Nurses caring for patients with this disease need to understand the issues related to the administration of drotrecogin alfa (activated) and the monitoring of patients receiving this drug to promote optimal and appropriate use of this innovative therapy.

Early indicators of prolonged intensive care unit stay: impact of illness severity, physician staffing, and pre-intensive care unit length of stay

OBJECTIVE

Scoring systems that predict mortality do not necessarily predict prolonged length of stay or costs in the intensive care unit (ICU). Knowledge of characteristics predicting prolonged ICU stay would be helpful, particularly if some factors could be modified. Such factors might include process of care, including active involvement of full-time ICU physicians and length of hospital stay before ICU admission.

DESIGN

Demographic data, clinical diagnosis at ICU admission, Simplified Acute Physiology Score, and organizational characteristics were examined by logistic regression for their effect on ICU and hospital length of stay and weighted hospital days (WHD), a proxy for high cost of care.

SETTING

A total of 34 ICUs at 27 hospitals participating in Project IMPACT during 1998.

PATIENTS

A total of 10,900 critically ill medical, surgical, and trauma patients qualifying for Simplified Acute Physiology Score assessment.

INTERVENTIONS

None.

RESULTS

Overall, 9.8% of patients had excess WHD, but the percentage varied by diagnosis. Factors predicting high WHD include Simplified Acute Physiology Score survival probability, age of 40 to 80 yrs, presence of infection or mechanical ventilation 24 hrs after admission, male sex, emergency surgery, trauma, presence of critical care fellows, and prolonged pre-ICU hospital stay. Mechanical ventilation at 24 hrs predicts high WHD across diagnostic categories, with a relative risk of between 2.4 and 12.9. Factors protecting against high WHD include do-not-resuscitate order at admission, presence of coma 24 hrs after admission, and active involvement of full-time ICU physicians.

CONCLUSIONS

Patients with high WHD, and thus high costs, can be identified early. Severity of illness only partially explains high WHD. Age is less important as a predictor of high WHD than presence of infection or ventilator dependency at 24 hrs. Both long ward stays before ICU admission and lack of full-time ICU physician involvement in care increase the probability of long ICU stays. These latter two factors are potentially modifiable and deserve prospective study.

Proactive error prevention in the intensive care unit

Care provided in the ICU accounts for nearly 30% of acute care hospital costs and, with the aging of Americans, there is an increased demand for critical care services [1]. Critical illness reduces an individual's physical resilience. Minute-to-minute care decisions and interventions mean life or death during this acute disease phase. Critically ill patients have limited ability to defend themselves from the consequences of health care error. This patient population has the least ability to communicate symptoms to health care providers. The risk of adverse events caused by medications or equipment malfunction is higher because patients in the ICU receive twice as many medications as patients in general care units [2] and often require mechanical support of normal body functions, such as breathing, eating, and eliminating body waste. Consequently, the patient in the ICU has a higher exposure to medical error than patients in other areas of the hospital.

Recent innovations in intensive care unit risk-prediction models

During the past 20 years, ICU risk-prediction models have undergone significant development, validation, and refinement. Among the general ICU severity of illness scoring systems, the Acute Physiology and Chronic Health Evaluation (APACHE), Mortality Prediction Model (MPM), and the Simplified Acute Physiology Score (SAPS) have become the most accepted and used. To risk-adjust patients with longer, more severe illnesses like sepsis and acute respiratory distress syndrome, several models of organ dysfunction or failure have become available, including the Multiple Organ Dysfunction Score (MODS), the Sequential Organ Failure Assessment (SOFA), and the Logistic Organ Dysfunction Score (LODS). Recent innovations in risk adjustment include automatic physiology and diagnostic variable retrieval and the use of artificial intelligence. These innovations have the potential of extending the uses of case-mix and severity-of-illness adjustment in the areas of clinical research, patient care, and administration. The challenges facing intensivists in the next few years are to further develop these models so that they can be used throughout the IUC stay to assess quality of care and to extend them to more specific patient groups such as the elderly and patients with chronic ICU courses.

A structure of care for the chronically critically ill

The chronically critically ill (CCI) are complicated, labor-intensive, and costly patients to care for. A defined structure of care with different focuses at the beginning, middle, and end of a care episode may improve their outcomes and resource utilization. This article reviews the prediction of CCI, outlines some unifying processes of care during an episode of chronic critical illness, and explores some of the difficulties in defining consistent goals of care for this patient population.

A multicenter description of intermediate-care patients: comparison with ICU low-risk monitor patients

STUDY OBJECTIVES

To describe the characteristics and outcomes of patients admitted to intermediate-care areas (ICAs) and to compare them with those of ICU patients who receive monitoring only on day 1 and are at a low risk (i.e., < 10%) for receiving subsequent active life-supporting therapy (i.e., low-risk monitor patients).

DESIGN

Nonrandomized, retrospective, cohort study.

SETTING

Thirteen US teaching hospitals and 19 nonteaching hospitals.

PATIENTS

A consecutive sample of 8,971 patients at 37 ICAs and 5,116 low-risk (i.e., < 10%) monitor patients at 59 ICUs in 32 US hospitals.

INTERVENTIONS

None.

MEASUREMENTS AND RESULTS

We recorded demographic and clinical characteristics, resource use, and outcomes for the ICA and ICU low-risk monitor patients. Patient data and outcomes for this study were collected concurrently or retrospectively. ICA and ICU low-risk monitor patients were similar in regard to gender, race, and frequency of comorbitities, but ICA patients were significantly (p < 0.001) older, had fewer physiologic abnormalities (mean acute physiology score, 16.7 vs 19.8, respectively), and were more frequently admitted due to nonoperative diagnoses. The mean length of stay for ICA patients was significantly longer (3.9 days) than for ICU low-risk monitor patients (2.6 days; p < 0.001). The hospital mortality rate was significantly higher for ICA patients (3.1%) compared to ICU low-risk monitor patients (2.3%; p = 0.002).

CONCLUSIONS

The clinical features of ICA patients are similar, but not identical to, those of less severely ill ICU monitor patients. Comparisons of hospital death rates and lengths of stay for these patients should be adjusted for characteristics that previously have been shown to influence these outcomes.