Sylvia Brinkman

Influence of mild therapeutic hypothermia after cardiac arrest on hospital mortality

Objective:Following two randomized controlled trials that demonstrated reduced mortality and better neurological outcome in cardiac arrest patients, mild therapeutic hypothermia was implemented in many intensive care units. Up to now, no large observational studies have confirmed the beneficial effects of mild therapeutic hypothermia. Design:Internet-based survey combined with a retrospective, observational study. Patients:All patients admitted to an intensive care unit in The Netherlands after cardiac arrest from January 1, 1999 until January 1, 2009. Data Source:Dutch National Intensive Care Evaluation database. Methods:The moment of implementation of mild therapeutic hypothermia for each hospital participating in the Dutch National Intensive Care Evaluation database was determined with an Internet survey. To compare mortality before and after implementation of mild therapeutic hypothermia, the odds ratio adjusted for Simplified Acute Physiology Score II score, age, gender, propensity score, and in- or out-of-hospital cardiac arrest was calculated. Patients were excluded if 1) they were admitted to an intensive care unit that did not respond to the survey, 2) they were admitted within 3 months after implementation of mild therapeutic hypothermia, 3) they had a Glasgow Coma Scale score of >8, or 4) they did not satisfy the Simplified Acute Physiology Score II inclusion criteria. Interventions:None. Measurements and Main Results:A total of 13,962 patients were admitted to an intensive care unit following cardiac arrest. In total 8,645 patients were excluded, 5,544 because of a Glasgow Coma Scale score of >8. Of the resultant 5,317 patients, 1,547 patients were treated before and 3,770 patients after implementation of mild therapeutic hypothermia. Patients admitted after implementation of mild therapeutic hypothermia had lower minimal and maximal temperatures (p < .0001) during the first 24 hrs on the intensive care unit compared to patients admitted before implementation of mild therapeutic hypothermia. The adjusted odds ratio of the hospital mortality of patients treated after implementation of mild therapeutic hypothermia was 0.80 (95% confidence interval of 0.65–0.98, p = .029). Conclusion:The results of this retrospective, observational survey suggest that implementation of mild therapeutic hypothermia in Dutch intensive care units is associated with a 20% relative reduction of hospital mortality in cardiac arrest patients.

Mortality After Hospital Discharge in ICU Patients

Objectives:To assess the mortality risk of ICU patients after hospital discharge and compare it to mortality of the general Dutch population. Design:Cohort study of ICU admissions from a national ICU registry linked to administrative records from an insurance claims database. Setting:Eighty-one Dutch ICUs. Patients:ICU patients (n = 91,203) who were discharged alive from the hospital between January 1, 2007, and October 1, 2010. Interventions:None. Measurements and Main Results:The unadjusted observed survival was inspected by Kaplan-Meier curves. Mortality risk at 1, 2, and 3 years after hospital discharge was 12.5%, 19.3%, and 27.5%, respectively. The 3-year mortality after hospital discharge in ICU patients was higher than the weighted average of the gender and age-specific death risks of the general Dutch population (27.5% versus 8.2%). The 1-year mortality after hospital discharge was adjusted for case-mix differences by a set of determinants which showed a statistically significant influence on the outcome in a 10-fold cross validation. The elective and cardiac surgical patients have statistically significantly better mortality outcomes (adjusted hazard ratio, 0.73 and 0.28, respectively), whereas medical patients and patients admitted for cancer have statistically significantly worse mortality outcomes (adjusted hazard ratio, 1.41, 1.94, respectively) compared with other ICU patients. Urgent surgery patients and patients with a subarachnoid hemorrhage, trauma, acute renal failure, or severe community-acquired pneumonia did not differ statistically from the other ICU patients after adjustment for case-mix differences. Conclusions:In-hospital mortality underestimates the true mortality of ICU patients as the mortality in the first months after hospital discharge is substantial. Most ICU patients still have an increased mortality risk in the subsequent years after hospital discharge compared with the general Dutch population. The mortality after hospital discharge differs widely between ICU subgroups. Future studies should focus on the analysis of mortality after hospital discharge that is attributable to the former ICU admission.

Guideline Bundles Adherence and Mortality in Severe Sepsis and Septic Shock

Objective:Surviving Sepsis Campaign bundles have been associated with reduced mortality in severe sepsis and septic shock patients. Case-mix adjusted mortality evaluations have not been performed to compare hospitals participating in sepsis bundle programs with those not participating. We aimed to achieve an individual bundle target adherence more than 80% and a relative mortality reduction of at least 15% (absolute mortality reduction 5.2%) at the end of 2012. Design:Prospective multicenter cohort study in participating and nonparticipating centers. Setting:Eighty-two ICUs in The Netherlands. Patients:In total, 213,677 adult ICU patients admitted to all ICUs among which 8,387 severe sepsis patients at 52 participating ICUs and 8,031 severe sepsis patients at 30 nonparticipating ICUs. Interventions:A national program to screen patients for severe sepsis and septic shock and implement Surviving Sepsis Campaign bundles to complete within 6 and 24 hours after ICU admission. Measurements and Main Results:Bundle target adherence and case-mix adjusted in-hospital mortality were evaluated through odds ratios of time since program initiation by logistic generalized estimating equation analyses (July 2009 through January 2013). Outcomes were adjusted for age, gender, admission type, severity of illness, and sepsis diagnosis location. Participation duration was associated with improved bundle target adherence (adjusted odds ratio per month = 1.024 [1.016–1.031]) and decreased in-hospital mortality (adjusted odds ratio per month = 0.992 [0.986–0.997]) equivalent to 5.8% adjusted absolute mortality reduction over 3.5 years. Mortality reduced in screened patients with other diagnoses (1.9% over 3.5 yr, adjusted odds ratio per month = 0.995 [0.9906–0.9996]) and did not change in nonscreened patients in participating ICUs, nor in patients with sepsis or other diagnoses in nonparticipating ICUs. Conclusions:Implementation of a national sepsis program resulted in improved adherence to sepsis bundles in severe sepsis and septic shock patients and was associated with reduced adjusted in-hospital mortality only in participating ICUs, suggesting direct impact of sepsis screening and bundle application on in-hospital mortality.

External validation of Acute Physiology and Chronic Health Evaluation IV in Dutch intensive care units and comparison with Acute Physiology and Chronic Health Evaluation II and Simplified Acute Physiology Score II

PURPOSE The aim of this study was to validate and compare the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) IV in the Dutch intensive care unit (ICU) population to the APACHE II and Simplified Acute Physiology Score (SAPS) II. MATERIALS AND METHODS This is a prospective study based on data from a national quality registry between 2006 and 2009 from 59 Dutch ICUs. The validation set consisted of 62,737 patients; the 3 models were compared using 44,112 patients. Measures of discrimination, accuracy, and calibration (area under the receiver operating characteristic curve (AUC), Brier score, R(2), and Ĉ-statistic) were calculated using bootstrapping. In addition, the standardized mortality ratios were calculated. RESULTS The original APACHE IV showed good discrimination and accuracy (AUC = 0.87, Brier score = 0.10, R(2) = 0.29) but poor calibration (Ĉ-statistic = 822.67). Customization significantly improved the performance of the APACHE IV. The overall discrimination and accuracy of the customized APACHE IV were statistically better, and the overall Ĉ-statistic was inferior to those of the customized APACHE II and SAPS II, but these differences were small in perspective of clinical use. CONCLUSIONS The 3 models have comparable capabilities for benchmarking purposes after customization. Main advantage of APACHE IV is the large number of diagnoses that enable subgroup analysis. The APACHE IV coronary artery bypass grafting (CABG) model has a good performance in the Dutch ICU population and can be used to complement the 3 models.

Determinants of mortality after hospital discharge in ICU patients: literature review and Dutch cohort study.

Objectives:First, to conduct a literature review on the long-term mortality of ICU patients and its determinants. Second, to assess the influence of the found determinants at 3, 6, and 12 months mortality after hospital discharge in the Dutch ICU population. Design:Combination of a literature review to evaluate determinants of long-term mortality and a Dutch cohort study in which the found determinants are applied. Setting:PubMed and EMBASE were searched on English written articles published between 1966 and 2011. The cohort study was conducted in ICU patients from 81 Dutch mixed ICUs. Data:A total of 24 articles with a main focus on describing or predicting the case-mix adjusted long-term mortality of the general ICU population were identified. The cohort study consisted of 48,107 ICU patients who were discharged alive from the hospital between January 1, 2007, and October 1, 2010. Interventions:None. Measurements and Main Results:The included articles are summarized on patient and study characteristics, methods, results, and determinants used for case-mix adjustment. Additionally, the quality of the included articles was assessed using a checklist for studies on long-term survival. The median mortality rate of the general ICU population 1 year after ICU admission was 24% (range 16% to 44%). The determinants used for case-mix adjustment differed widely between the studies. In the cohort study, we found that age, reason for ICU admission, and comorbidities were associated with all long-term mortality endpoints. However, the magnitude and direction of the influence by these determinants differed for the different endpoints (i.e., 3, 6, and 12 mo after hospital discharge). Conclusions:The long-term mortality found in the included articles was difficult to compare due to low quality, variation in case-mix, study design, and differences in case-mix adjustment. The most commonly used determinants in the literature were comparable to the most important determinants found in the Dutch cohort study.

In-hospital mortality and long-term survival of patients with acute intoxication admitted to the ICU

Objective:To assess in-hospital and long-term mortality of Dutch ICU patients admitted with an acute intoxication. Design:Cohort of ICU admissions from a national ICU registry linked to records from an insurance claims database. Setting:Eighty-one ICUs (85% of all Dutch ICUs). Patients:Seven thousand three hundred thirty-one admissions between January 1, 2008, and October 1, 2011. Interventions:None. Measurements and Main Results:Kaplan-Meier curves were used to compare the unadjusted mortality of the total intoxicated population and for specific intoxication subgroups based on the Acute Physiology and Chronic Health Evaluation IV reasons for admission: 1) alcohol(s), 2) analgesics, 3) antidepressants, 4) street drugs, 5) sedatives, 6) poisoning (carbon monoxide, arsenic, or cyanide), 7) other toxins, and 8) combinations. The case-mix adjusted mortality was assessed by the odds ratio adjusted for age, gender, severity of illness, intubation status, recurrent intoxication, and several comorbidities. The ICU mortality was 1.2%, and the in-hospital mortality was 2.1%. The mortality 1, 3, 6, 12, and 24 months after ICU admission was 2.8%, 4.1%, 5.2%, 6.5%, and 9.3%, respectively. Street drugs had the highest mortality 2 years after ICU admission (12.3%); a combination of different intoxications had the lowest (6.3%). The adjusted observed mortality showed that intoxications with street drugs and “other toxins” have a significant higher mortality 1 month after ICU admission (odds ratioadj = 1.63 and odds ratioadj= 1.73, respectively). Intoxications with alcohol or antidepressants have a significant lower mortality 1 month after ICU admission (odds ratioadj = 0.50 and odds ratioadj = 0.46, respectively). These differences were not found in the adjusted mortality 3 months upward of ICU admission. Conclusions:Overall, the mortality 2 years after ICU admission is relatively low compared with other ICU admissions. The first 3 months after ICU admission there is a difference in mortality between the subgroups, not thereafter. Still, the difference between the in-hospital mortality and the mortality after 2 years is substantial.

The need for ICU admission in intoxicated patients: a prediction model

Abstract Context: Intoxicated patients are frequently admitted from the emergency room to the ICU for observational reasons. The question is whether these admissions are indeed necessary. Objective: The aim of this study was to develop a model that predicts the need of ICU treatment (receiving mechanical ventilation and/or vasopressors <24 h of the ICU admission and/or in-hospital mortality). Materials and methods: We performed a retrospective cohort study from a national ICU-registry, including 86 Dutch ICUs. We aimed to include only observational admissions and therefore excluded admissions with treatment, at the start of the admission that can only be applied on the ICU (mechanical ventilation or CPR before admission). First, a generalized linear mixed-effects model with binominal link function and a random intercept per hospital was developed, based on covariates available in the first hour of ICU admission. Second, the selected covariates were used to develop a prediction model based on a practical point system. To determine the performance of the prediction model, the sensitivity, specificity, positive, and negative predictive value of several cut-off points based on the assigned number of points were assessed. Results: 9679 admissions between January 2010 until January 2015 were included for analysis. In total, 632 (6.5%) of the patients admitted to the ICU eventually turned out to actually need ICU treatment. The strongest predictors for ICU treatment were respiratory insufficiency, age >55 and a GCS <6. Alcohol and “other poisonings” (e.g., carbonmonoxide, arsenic, cyanide) as intoxication type and a systolic blood pressure ≥130 mmHg were indicators that ICU treatment was likely unnecessary. The prediction model had high sensitivity (93.4%) and a high negative predictive value (98.7%). Discussion and conclusion: Clinical use of the prediction model, with a high negative predictive value (98.7%), would result in 34.3% less observational admissions.

The authors reply, Why Did Poisoned Patients Eventually Die Long After Their ICU Stay?

To the Editor—We have read with great interest the comments from dr Ajit Pai regarding our recently published article. in his letter, dr Pai stressed several points regarding our results. First, dr Pai stated that our study only confirms the well-known fact that the majority of leaks will heal spontaneously. We would like to answer this first comment with 2 points. 1) Although spontaneous healing of leakage seems logical and straightforward, the literature focusing on this specific situation is extremely poor, as is discussed in our article, and, most importantly, 2) the present study was driven by a very pragmatic question: “Can the diverting stoma be reversed at 6 months in patients with persisting leakage?” our study was prompted by the absence of a clear answer to this question in the available literature. We strongly believe that our policy of stoma reversal at 6 months, even in the case of persisting leakage tract, is supported by the results of our study for most of the patients. however, we completely agree with dr Pai on his point regarding the anatomy of the leakage. As we stated in our article, we select our patients based on the results previously published by Zhuo et al and do not perform stoma closure in patients with a large pelvic cavity, complex tracts, or associated anastomosis stenosis. Second, dr Pai stressed the impact of anastomotic leakage on the risk of cancer recurrence and the role of chemotherapy and, especially antiangiogenic therapy, motic complications. information on use of this molecule would be useful. in conclusion, although there is a wealth of data available, it could have been further mined to provide a better algorithm for management of the persistent anastomotic leak.