Mortality In Medical Intensive Care Unit Research Articles

Hepatic perfusion as a new predictor of prognosis and mortality in critical care patients with acute-on-chronic liver failure.

Background and aimsLiver diseases are frequent causes of morbidity and mortality worldwide. Liver diseases can lead to cirrhosis, with the risk of acute-on-chronic liver failure (ACLF). For the detection of changes in hepatic hemodynamics, Doppler ultrasonography is a well-established method. We investigated hepatic hemodynamics via serial Doppler ultrasonography to determine the predictive value of changes in hepatic perfusion for the outcome in patients with severe liver diseases compared to established prognostic models such as the MELD (Model for End-Stage Liver Disease) or CLIF-C (Chronic Liver Failure-Consortium) ACLF score.MethodsIn this prospective cohort study, hepatic perfusion was quantified at baseline before the initiation of treatment and every third day by means of serial measurements of the hepatic artery resistance index (HARI) and the maximum portal vein velocity (PVv) using Doppler ultrasonography in 50 consecutive patients with severe liver diseases admitted to a medical intensive care unit (MICU). The recorded hemodynamic parameters were compared to the MELD score, and the CLIF-C ACLF score to analyze their utility for the prediction of the outcome of patients with severe liver diseases, liver cirrhosis, and ACLF.ResultsThe changes (delta) obtained by serial measurements of the MELD score, HARI, and PVv were analyzed through scatter plots. Bivariate correlation analysis yielded a new positive linear correlation between the delta-HARI and the delta-MELD score (r = 0.469; p < 0.001). In addition, our data revealed a new negative linear correlation between delta-PVv and the delta-MELD score (r = −0.279, p = 0.001). The leading cause of MICU mortality was acute-on-chronic liver failure (ACLF). A subgroup analysis of patients with liver cirrhosis revealed a positive linear correlation between the delta-HARI and the delta-CLIF-C-ACLF score (r = 0.252, p = 0.005). Of clinical relevance, non-survivors of ACLF exhibited a significantly higher mean value for the delta-HARI (0.010 vs. −0.005; p = 0.015) and a lower mean value for the delta-PVv (−0.7 vs. 1.9 cm/s; p = 0.037) in comparison to survivors of ACLF.ConclusionThis study shows the prognostic value of the assessment of hepatic perfusion in critical care patients with severe liver diseases by bedside Doppler ultrasound examination and its utility as an accurate predictor of the outcome in patients with ACLF. Increasing HARI and a decreasing PVv are predictors of an adverse outcome. Delta-HARI and delta-PVv are new biomarkers of prognosis and ACLF-related mortality in patients with liver diseases. Delta-HARI and delta-PVv may be helpful in guiding clinical decision-making, especially in catecholamine and fluid management.

Does Vitamin D Improve All-cause Mortality in Critically Ill Adults? An Updated Systematic Review and Meta-analysis of Randomized Controlled Trials.

Vitamin D deficiency is an amendable risk factor linked to increase in mortality in critically ill patients. The aim of this systematic review was to evaluate if vitamin D supplementation reduced the mortality, and length of stay (LOS) in intensive care units (ICU) and hospitals in critically ill adults including coronavirus disease-2019 (COVID-2019) patients. We searched the literature using the PubMed, Web of Science, Cochrane and Embase databases until January 13, 2022, for RCTs comparing vitamin D administration to placebo or no treatment in ICUs. The fixed-effect model was used for the primary outcome (all-cause mortality) and the random effect model for secondary objectives (LOS in ICU, hospital, mechanical ventilation). Subgroup analysis included ICU types and high vs low risk of bias. Sensitivity analysis compared severe COVID-19 vs no COVID disease. Eleven RCTs (2,328 patients) were included in the analysis. Pooled analysis of these RCTs, showed no significant difference in all-cause mortality between the vitamin D and placebo groups [odds ratio (OR) 0.93, p = 0.47]. Inclusion of COVID-positive patients did not change the results (OR 0.91, p = 0.37). No significant difference was observed between the vitamin D and placebo groups in LOS in ICU (p = 0.34); hospital (p = 0.40) and mechanical ventilation duration (p = 0.7). In the subgroup analysis, there was no improvement in mortality in medical ICU (p = 0.36) or surgical ICU (p = 0.03). Neither low risk of bias (p = 0.41) nor high risk of bias (p = 0.39) reduced mortality. Vitamin D supplementation in the critically ill did not have statistically significant benefits on clinical outcomes in terms of overall mortality, duration of mechanical ventilation, and LOS in ICU and hospital. Kaur M, Soni KD, Trikha A. Does Vitamin D Improve All-cause Mortality in Critically Ill Adults? An Updated Systematic Review and Meta-analysis of Randomized Controlled Trials. Indian J Crit Care Med 2022;26(7):853-862.

Developing machine learning models for prediction of mortality in the medical intensive care unit

Background and objectiveAlert of patient deterioration is essential for prompt medical intervention in the Medical Intensive Care Unit (MICU). Logistic Regression (LR) has been used for the development of most conventional severity-of-illness scoring systems to anticipate the risk of mortality in the MICU. Machine Learning (ML) models such as probabilistic graphical models and Extreme Gradient Boosting (XGB) have demonstrated improved prediction accuracy in patient outcomes compared to LR. The aim was to compare three ML models to the SAPS, SAPS II, SAPS III, SOFA, serial SOFA, LODS, and OASIS for prediction of MICU mortality. MethodsA Bayesian Network (BN), Naïve Bayes network (NB), and a XGB model were developed. 9893 adult MICU-stays from the MIMIC-III database were studied. The primary outcome was MICU mortality prediction and the secondary outcome was 1-year mortality prediction. Data analyzed consisted on routine physiological measurements collected during 5 hours in the MICU, demographic and diagnoses/procedure features. The performance was evaluated by accuracy statistics, discrimination and calibration measures. Limitations of the study were discussed. ResultsThe AUROC for MICU mortality prediction was 0.919 for XGB, 0.905 for BN, and 0.864 for NB, while the conventional systems displayed much lower values with the serial SOFA having the best value (0.814). The Diagnostic Odds Ratio was ≤7.099 for all the conventional systems, reaching values of 30.115 for XGB and 22.648 for BN. The XGB achieved a sensitivity of 0.831 and specificity of 0.86 assuring an acceptable precision (0.528), whose values were much lower for the conventional systems. The Brier score was better for the ML models, except for the NB (0.119), with 0.072 for XGB and 0.081 for BN. ConclusionsThe XGB and BN substantially outperformed the conventional systems for discrimination, calibration and the accuracy statistics assessed. The NB showed inferior performance to the XGB and BN but improved the discrimination and all accuracy statistics of the conventional systems except for an inferior calibration and 1-year mortality discrimination. The XGB showed the best performance among all models. These ML models have the potential to improve the monitoring of MICU patients, which must be evaluated in future studies.

Comparison of APACHE II and Modified Charlson Index in Mortality Prediction in Patients at Medical Intensive Care Unit

Acute Physiology and Chronic Health Evaluation II (APACHE II) and Modified Charlson Index (MCI) are used to predict the fatality in intensive care units (ICU). We aimed to investigate the difference between these scores in the prediction of fatality in the medical intensive care unit. Our study is important because in our literature overview, this study is one of the rare studies that compares these scoring systems. 108 ICU patients included. In all subjects APACHE II and MCI performed. Procalcitonin, C-reactive protein(CRP) levels of patients were recorded. Patients were then grouped according to mechanically ventilated or not; mortality happened or not. Statistically significance found in age(p<0.045), mechanical ventilation, procalcitonin, CRP and MCI (p<0.001) about mortality . MCI sensitivity and specifity were higher than APACHE II in %95 confidance interval. Area under curve in ROC analysis was CRP (0.728), Procalcitonin (0.719), MCI (0.686), APACHE II (0.665) respectively. Our study demonstrates that the Modified Charlson Index combined with procalcitonin and CRP can be used for predicting mortality in medical ICU as well as APACHE II

Derivation and Validation of a Prognostic Model to Predict 6-Month Mortality in an Intensive Care Unit Population.

Identification of terminally ill patients in the intensive care unit (ICU) would facilitate decision making and timely palliative care. To develop and validate a patient-specific integrated prognostic model to predict 6-month mortality in medical ICU patients. A longitudinal prospective cohort study of temporally split samples of 1,049 consecutive medical ICU patients in a tertiary care hospital was performed. For each patient, we collected demographic data, Acute Physiology and Chronic Health Evaluation III score, Charlson comorbidity index, intensivist response to a surprise question (SQ; "Would I be surprised if this patient died in the next 6 months?") on admission, and vital status at 6 months. Between November 2013 and May 2015, derivation and validation cohorts of 500 and 549 consecutive patients were studied to develop a multivariate logistic regression model. In the multivariate logistic regression model, Charlson comorbidity index (P = 0.033), Acute Physiology and Chronic Health Evaluation III score (P < 0.001), and SQ response (P < 0.001) were predictors of vital status at 6 months. The odds of dying within 6 months were significantly higher when the SQ was answered "no" than when it was answered "yes" (odds ratio, 7.29; P < 0.001). The c-statistic for the derivation and validation cohorts were 0.832 (95% confidence interval, 0.795-0.870) and 0.84 (95% confidence interval, 0.806-0.875), respectively. Our integrated prognostic model, which includes the SQ, has strong discrimination and calibration to predict 6-month mortality in medical ICU patients. This model can aid clinicians in identifying ICU patients who may benefit from the integration of palliative care into their treatment.

Using electronic health record collected clinical variables to predict medical intensive care unit mortality.

BackgroundClinical decision support systems are used to help predict patient stability and mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians with patient management and in allocating finite resources. However, systems currently in common use have limited predictive value in the clinical setting. The increasing availability of Electronic Health Records (EHR) provides an opportunity to use medical information for more accurate patient stability and mortality prediction in the ICU.ObjectiveDevelop and evaluate an algorithm which more accurately predicts patient mortality in the ICU, using the correlations between widely available clinical variables from the EHR.MethodsWe have developed an algorithm, AutoTriage, which uses eight common clinical variables from the EHR to assign patient mortality risk scores. Each clinical variable produces a subscore, and combinations of two or three discretized clinical variables also produce subscores. A combination of weighted subscores produces the overall score. We validated the performance of this algorithm in a retrospective study on the MIMIC III medical ICU dataset.ResultsAutoTriage 12 h mortality prediction yields an Area Under Receiver Operating Characteristic value of 0.88 (95% confidence interval 0.86 to 0.88). At a sensitivity of 80%, AutoTriage maintains a specificity of 81% with a diagnostic odds ratio of 16.26.ConclusionsThrough the multidimensional analysis of the correlations between eight common clinical variables, AutoTriage provides an improvement in the specificity and sensitivity of patient mortality prediction over existing prediction methods.

A 24-Hour Postintensive Care Unit Transition-of-Care Model Shortens Hospital Stay.

Patients discharged early from the medical intensive care unit (MICU) are at risk of deterioration, MICU readmission, and increased mortality. An earlier discharge to a medical ward is desirable to reduce costs but it may adversely affect outcomes. To address this problem, a new model for the MICU transition of care was implemented at our academic center: The MICU team continued to manage all patients transferred from the MICU to the medical ward for at least 24 hours. Data were collected for all MICU patients admitted 1 year before and 1 year after the intervention. Hospital length of stay (LOS) after transfer from the MICU, readmission rate, and mortality rate were compared before and after the intervention. A nonparsimonious propensity model based on 30 factors was used to identify matched preintervention and postintervention cohorts. A total of 618 of the 848 patients admitted to the MICU were transferred to medical ward during the year prior to the implementation of the new model, and 600 of the 883 patients were transferred during the following year. Pre- and postintervention cohorts were well matched (n = 483 patients in each group). Poisson regression analysis showed a decrease in the hospital LOS after MICU transfer by 1.17 days (P < .001) without a significant change in adjusted mortality (lower by 1.9%, P = .181) and MICU readmission rates (lower by 2%, P = .264). A new model for the post-MICU transition of care, with the MICU team continuing to manage all patients transferred to the medical ward for at least 24 hours, significantly decreased duration of hospital stay after MICU transfer without affecting MICU readmission and mortality rate. The implementation of this model may lower medical costs and make transition of care safer without adverse outcomes.

Peer review report 2 on “Using electronic health record collected clinical variables to predict medical intensive care unit mortality”

Peer review report 2 on “Using electronic health record collected clinical variables to predict medical intensive care unit mortality”

Peer review report 1 “Using electronic health record collected clinical variables to predict medical intensive care unit mortality”

Peer review report 1 “Using electronic health record collected clinical variables to predict medical intensive care unit mortality”

Peer review report 3 on “Using electronic health record collected clinical variables to predict medical intensive care unit mortality”

Peer review report 3 on “Using electronic health record collected clinical variables to predict medical intensive care unit mortality”

Epidemiology of Acute Kidney Injury in Moroccan Medical Intensive Care Patients: A Regional Prospective, Observational Study

Objective: To evaluate the incidence, mortality and influencing factors for the development of Acute Kidney Injury (AKI) at admission or during Intensive Care Unit (ICU) stay. Methods: We conducted a prospective, epidemiological survey, in ICU for two years and the data of 97 patients admitted to ICU for medical illness was analyzed. Patients with AKI were categorized by serum creatinine and urine output into 3 stages. Stage 1 was defined as an absolute increase (within 48 hours) in serum creatinine of more than or equal to 0.3 mg/dl, or oliguria of less than 0.5ml/kg per hour for more than six hours. Stage 2 was defined as doubling of serum creatinine, or a urinary output lower than 0.5ml/kg /h for 12 h. Stage 3 was defined as tripling of serum creatinine or a urinary output lower than 0.3 ml/kg/h for 24 h, or anuria for 12 h. Results: Sixty patients ( 62 %) had AKI. AKI patients tended to be older and usually had antecedent of heart disease, a high Simplified Acute Physiology Score version II at admission, more use of mechanical ventilation and vasopressor treatment, more shock, more severe sepsis, more hyperosmolar hyperglycemic state (HHS) and higher mortality. In multivariate analysis, SAPS II score >30, antecedent of heart disease and shock were independent risk factors for development of AKI at admission or during ICU stay. Conclusion: AKI had a high incidence and a high mortality in medical ICU’s patients. Antecedent of severe underlying diseases, heart disease and hemodynamic failure were independent risk factors of AKI.

Ambient light levels and critical care outcomes

PurposeGuidelines for the construction of critical care units require windows in room design to ensure a contribution of natural sunlight to ambient lighting. However, few studies have been published with evidence assessing this recommendation. We investigated the association of ambient light levels with clinical outcomes and sedative/analgesic/neuroleptic use in a medical intensive care unit (MICU). MethodsThis is a retrospective, observational study at a tertiary care facility with a 29-bed MICU. First/single MICU admissions between April 19, 2006, and June 30, 2009 (N = 3577), were analyzed with respect to clinical outcomes and sedation use according to MICU room orientation and corresponding light levels. ResultsLight levels were low but varied among the 4 room orientations. There were no significant differences in MICU mortality (north, 14.0%; east, 13.5%; west, 16.2%; south, 15.6%; P = .451), hospital mortality (20.8%, 20.9%, 22.2%, 22.3%; P = .796), 28-day intensive care unit–free days (17.6 ± 10.2, 18.0 ± 10.1, 17.7 ± 10.5, 17.2 ± 10.4; P = .555), 28-day ventilator-free days (16.3 ± 11.1, 16.5 ± 11.1, 15.5 ±11.5, 15.4 ± 11.4; P = .273). No clinically significant differences in intravenous sedative/analgesic use occurred across room orientations. ConclusionsDespite differing ambient light, room orientation was not associated with critical care outcomes or differences in sedative/analgesic/neuroleptic use. Current guidelines positing that windows alone are necessary or sufficient for MICU room light management may require further investigation and consideration.

The impact of a palliative care unit on mortality rate and length of stay for medical intensive care unit patients

This study evaluates the impact of a 10-bed inpatient palliative care unit (PCU) on medical intensive care unit (MICU) mortality and length of stay (LOS) for terminally ill patients following the opening of an inpatient PCU. We hypothesized that MICU mortality and LOS would be reduced through the creation of a more appropriate location of care for critically ill MICU patients who were dying. We performed a retrospective electronic database review of all MICU discharges from January 1, 2006 through December 31, 2009 (5,035 cases). Data collected included MICU mortality, MICU LOS, and mean age. The PCU opened on January 1, 2008. We compared location of death for MICU patients during the 2-year period before and the 2-year period after the opening of the PCU. Our data showed that the mean MICU mortality and MICU LOS both significantly decreased following the opening of the PCU, from 21 to 15.8% (p = 0.003), and from 4.6 to 4.0 days (p = 0.014), respectively. The creation of an inpatient PCU resulted in a statistically significant reduction in both MICU mortality rate and MICU LOS, as terminally ill patients were transitioned out of the MICU to the PCU for end-of-life care. Our data support the hypothesis that a dedicated inpatient PCU, capable of providing care to patients requiring mechanical ventilation or vasoactive agents, can protect terminally ill patients from an ICU death, while providing more appropriate care to dying patients and their loved ones.

Admission of advanced lung cancer patients to intensive care unit: A retrospective study of 76 patients

BackgroundCriteria for admitting patients with incurable diseases to the medical intensive care unit (MICU) remain unclear and have ethical implications.MethodsWe retrospectively evaluated MICU outcomes and identified risk factors for MICU mortality in consecutive patients with advanced lung cancer admitted to two university-hospital MICUs in France between 1996 and 2006.ResultsOf 76 included patients, 49 had non-small cell lung cancer (stage IIIB n = 20; stage IV n = 29). In 60 patients, MICU admission was directly related to the lung cancer (complication of cancer management, n = 30; cancer progression, n = 14; and lung-cancer-induced diseases, n = 17). Mechanical ventilation was required during the MICU stay in 57 patients. Thirty-six (47.4%) patients died in the MICU. Three factors were independently associated with MICU mortality: use of vasoactive agents (odds ratio [OR] 6.81 95% confidence interval [95%CI] [1.77-26.26], p = 0.005), mechanical ventilation (OR 6.61 95%CI [1.44-30.5], p = 0.015) and thrombocytopenia (OR 5.13; 95%CI [1.17-22.5], p = 0.030). In contrast, mortality was lower in patients admitted for a complication of cancer management (OR 0.206; 95%CI [0.058-0.738], p = 0.015). Of the 27 patients who returned home, four received specific lung cancer treatment after the MICU stay.ConclusionsPatients with acute complications of treatment for advanced lung cancer may benefit from MCIU admission. Further studies are necessary to assess outcomes such as quality of life after MICU discharge.

Eosinopenia, an early marker of increased mortality in critically ill medical patients

Inflammatory markers may have a role in predicting severity of illness of intensive care unit (ICU) patients. The aim of this study is to determine whether low eosinophil count can predict 28-day mortality in medical ICU. A prospective study over a 4-month period. To evaluate the prognosis information provided by eosinophil count, we compared the variations in eosinophil count from ICU admission to seventh day between patients who survived and those who died. The best cutoff value was chosen using Younden's index for identification of patients with high risk of mortality. The patient outcome was 28-day mortality. A total of 200 patients were eligible. Overall 28-day ICU mortality was 28% (n=56). At ICU admission, the median eosinophil count was significantly different in survivors [30cells/mm³; interquartile range (IQR), 0-100cells/mm³] and nonsurvivors (0cells/mm³; IQR, 0-30cells/mm³; P=0.004). Absolute eosinophil counts remained significantly lower in nonsurvivors from admission to seventh day. The 28-day mortality was significantly higher in patients with eosinopenia <40cells/mm(3) (P=0.011). Multivariate analysis by Cox model with time-dependent covariates demonstrated that eosinophil count <40cells/mm(3) [hazard ratio (HR), 1.85; 95% confidence interval (CI), 1.01-3.42; P=0.046], high Acute Physiology and Chronic Health Evaluation (APACHE) II score (HR, 1.08; 95% CI, 1.01-1.14; P=0.014), high Sequential Organ Failure Assessment (SOFA) score (HR, 1.14; 95% CI, 1.03-1.25; P=0.008), and use of mechanical ventilation (HR, 27.48; 95% CI, 12.12-62.28; P<0.001) were independent predictors of 28-day all-cause mortality. This study suggests the possibility to use eosinophil cell count at admission and during the first 7days as a prognosis marker of mortality in medical ICU.

Decreased mortality resulting from a multicomponent intervention in a tertiary care medical intensive care unit

To evaluate whether a multicomponent intervention, particularly increasing staff, can achieve reductions in patient mortality in an already high-intensity, Leapfrog-compliant medical intensive care unit. Retrospective, observational study. Medical intensive care unit of a tertiary care, academic medical center. A total of 1,263 patients admitted between April 19, 2004 and April 18, 2006 (before the organizational change) were compared with 2,424 patients admitted between September 5, 2006 and September 4, 2008. A multicomponent intervention including the physical move from a 10-bed to a 29-bed medical intensive care unit with larger patient rooms, the initiation of 24-hr critical care specialist coverage in the medical intensive care unit, an increase in the respiratory therapist:patient ratio, and the addition of a clinical pharmacist to the multidisciplinary team. Measurements were made based on mortality in the intensive care unit and in-hospital. Patient comorbidity as measured by the Charlson score did not change after the intervention (2.7 ± 2.7 vs. 2.8 ± 2.6, p = .62), nor did the acuity of illness as measured by the case mix index (3.0 ± 3.7 vs. 3.1 ± 3.8, p = .69). The unadjusted medical intensive care unit mortality decreased from 18.4% to 14.9% (p = .006), as did in-hospital mortality (from 25.8% to 21.7%, p = .005). The reduction in medical intensive care unit mortality was consistent in the multivariable regression with adjustment for multiple possible confounders (odds ratio = 0.74, 95% confidence interval: 0.61-0.91, p = .003), as was the reduction in hospital mortality (odds ratio = 0.74, 95% confidence interval: 0.62-0.88, p = .001). In mechanically ventilated patients, there was an increase in median 28-day ventilator-free days (21, interquartile range 0-25 vs. 22, interquartile range 0-26, p = .04). An increase in median medical intensive care unit (2.4, interquartile range 1.1-5.2 vs. 2.7, interquartile range 1.3-5.9), p = .009) but not hospital (8.3, interquartile range 4.1-17.0 vs. 8.2, interquartile range 4.0-16.8; p = .851) length of stay in days occurred with the intervention. The mean daily dosing of fentanyl and lorazepam decreased after the intervention. A multicomponent reorganization of medical intensive care unit services was associated with important reductions in mortality for medical intensive care unit patients, as well as an increased number of ventilator-free days. Substantial and sustained changes in clinically important outcomes may be obtained from organizational changes.

Clinical Characteristics and Prognosis of Lung Cancer Patients Admitted to the Medical Intensive Care Unit at a University Hospital

Background: The management of patients with lung cancer has improved recently, and many of them will require admission to the medical intensive care unit (MICU). The aim of this study was to examine the clinical characteristics and to identify risk factors for mortality in patients with lung cancer admitted to the MICU. Methods: We conducted retrospective analysis on 88 patients with lung cancer admitted to the MICU between April 2004 and March 2008. Results: Of the 88 patients (mean age, 66 years), 71 patients (80.7%) had non-small cell lung cancer and 17 patients (19.3%) had small cell lung cancer. Distant metastasis were present in 79 patients (89.8%). The main reasons for MICU admission were acute respiratory failure (77.3%), sepsis (11.4%), and central nervous system dysfunction (4.5%). Mechanical ventilation was used in 54 patients (61.4%). Acute Physiology and Chronic Health Evaluation (APACHE) II score, length of MICU stay, need for mechanical ventilation, source of MICU admission were correlated with MICU mortality. The type of lung cancer and metastasis were not predictive factors of death in MICU. Conclusion: Most common reason for ICU admission was acute respiratory failure. Mortality rate of lung cancer patients admitted to the MICU was 65.9%. APACHE II score, length of ICU stay, need for mechanical ventilation, source of MICU admission were predicted factors of death in the MICU.

Risk-based fluconazole prophylaxis of Candida bloodstream infection in a medical intensive care unit

Candida bloodstream infection (CBSI) accounted for 50% of bloodstream infections in our medical intensive care unit (MICU) in 2004. Our objective was to evaluate a risk-based fluconazole prophylaxis program. CBSI incidence, patient demographics, and unit metrics were retrospectively reviewed for 2004. Starting on January 2005, patients meeting pre-specified criteria were placed on risk-based fluconazole prophylaxis and their outcomes, adverse events, and unit metrics were prospectively collected. The inclusion criteria were based on a clinical prediction rule and included an MICU stay greater than 72 h, broad-spectrum antibiotics, and central venous catheter, along with at least two of the following: mechanical ventilation for at least 48 h, any type of dialysis, parenteral nutrition, pancreatitis, systemic steroids, or other systemic immunosuppressive agents. For 2004, the unit had nine CBSI, corresponding to a rate of 3.4 CBSI/1,000 line-days. Four cases were caused by C. albicans, four by C. glabrata, and one by C. tropicalis. The mean +/- standard deviation (SD) APACHE II score for these patients was 25 +/- 9. In 2005, a total of 36 patients (2.6% of all unit admissions) received prophylaxis and the unit had two CBSI, corresponding to a rate of 0.79 CBSI/1,000 line-days. One patient had C. albicans and the other had C. tropicalis. The mean +/- SD APACHE II score for these patients was 21 +/- 8. The mean +/- SD duration of fluconazole prophylaxis was 8 +/- 6 days. Fluconazole was discontinued in two patients due to non-severe adverse events (acute eosinophilia, elevated transaminases). The attributable cost of CBSI in the unit in 2004 was $63,000 per episode. The total cost for the 36 courses of fluconazole was $6,000. When comparing the 2004 CBSI patients and the 2005 prophylaxis patients, we found similar acuity, demographics, and risk factors, with no differences in MICU or hospital mortality or length of stay. Risk-based fluconazole prophylaxis in an MICU with a high incidence of CBSI was safe and cost-effective when applied to a limited number of patients and produced a significant decrease in the incidence of this disease.

A RETROSPECTIVE STUDY OF OUTCOMES OF PATIENTS ADMITTED TO THE MEDICAL INTENSIVE CARE UNIT WITH A PARTIAL CODE STATUS

PURPOSE: Very little data exists on the outcome of patients with partial code status. The goal of our study was to determine the outcome of these patients admitted to the medical intensive care unit (MICU), their MICU mortality, in-hospital mortality, and the percentage that changed their code status.

Nontuberculous mycobacteria pulmonary infection in medical intensive care unit: the incidence, patient characteristics, and clinical significance

The clinical significance of nontuberculous mycobacteria (NTM) pulmonary infection in medical intensive care unit (ICU) is still unclear. We conducted a retrospective study in the medical ICUs of a medical center in Taiwan from January 1999 to June 2007. Patients with NTM isolated from respiratory specimens within 1 month before or during the ICU course were identified. Those who fulfilled the diagnostic criteria of NTM pulmonary infection were identified and compared with patients with NTM colonization and control subjects who were culture-negative for mycobacteria. Among the 5,378 patients admitted to medical ICUs, 2,866 (53.3%) had received mycobacterial culture for respiratory specimens. NTM were isolated from 169 (5.8%) patients. Of them, 47 (27.8%) were considered NTM pulmonary infection. M. avium complex and M. abscessus were the most common pathogens. Within 100 days after ICU admission, significantly more patients with NTM infection died than those with NTM colonization and control subjects (47 vs. 8 vs. 14%, P < 0.001). Twenty-one (49%) patients with NTM pulmonary infection received anti-NTM treatment, with four experiencing adverse effects. Although statistically insignificant, anti-NTM treatment was associated with prolonged survival for those who died in the ICU and shorter ICU stay for those who survived the ICU course. Our findings suggest that NTM pulmonary infection seems to associate with higher mortality in medical ICUs. Anti-NTM treatment is probably associated with a better outcome. Therefore, keeping a high suspicion when NTM is isolated and using careful consideration when starting anti-NTM treatment should be emphasized.