Characteristics and outcome of COVID-19 patients admitted to the ICU: a nationwide cohort study on the comparison between the consecutive stages of the COVID-19 pandemic in the Netherlands, an update.

BACKGROUND: Previously, we reported a decreased mortality rate among patients with COVID-19 who were admitted at the ICU during the final upsurge of the second wave (February-June 2021) in the Netherlands. We examined whether this decrease persisted during the third wave and the phases with decreasing incidence of COVID-19 thereafter and brought up to date the information on patient characteristics. METHODS: Data from the National Intensive Care Evaluation (NICE)-registry of all COVID-19 patients admitted to an ICU in the Netherlands were used. Patient characteristics and rates of in-hospital mortality (the primary outcome) during the consecutive periods after the first wave (periods 2-9, May 25, 2020-January 31, 2023) were compared with those during the first wave (period 1, February-May 24, 2020). RESULTS: After adjustment for patient characteristics and ICU occupancy rate, the mortality risk during the initial upsurge of the third wave (period 6, October 5, 2021-January, 31, 2022) was similar to that of the first wave (ORadj = 1.01, 95%-CI [0.88-1.16]). The mortality rates thereafter decreased again (e.g., period 9, October 5, 2022-January, 31, 2023: ORadj = 0.52, 95%-CI [0.41-0.66]). Among the SARS-CoV-2 positive patients, there was a huge drop in the proportion of patients with COVID-19 as main reason for ICU admission: from 88.2% during the initial upsurge of the third wave to 51.7%, 37.3%, and 41.9% for the periods thereafter. Restricting the analysis to these patients did not modify the results on mortality. CONCLUSIONS: The results show variation in mortality rates among critically ill COVID-19 patients across the calendar time periods that is not explained by differences in case-mix and ICU occupancy rates or by varying proportions of patients with COVID-19 as main reason for ICU admission. The consistent increase in mortality during the initial, rising phase of each separate wave might be caused by the increased virulence of the contemporary virus strain and lacking immunity to the new strain, besides unmeasured patient-, treatment- and healthcare system characteristics.

Strain on Scarce Intensive Care Beds Drives Reduced Patient Volumes, Patient Selection, and Worse Outcome: A National Cohort Study.

OBJECTIVES: Strain on ICUs during the COVID-19 pandemic required stringent triage at the ICU to distribute resources appropriately. This could have resulted in reduced patient volumes, patient selection, and worse outcome of non-COVID-19 patients, especially during the pandemic peaks when the strain on ICUs was extreme. We analyzed this potential impact on the non-COVID-19 patients. DESIGN: A national cohort study. SETTING: Data of 71 Dutch ICUs. PARTICIPANTS: A total of 120,393 patients in the pandemic non-COVID-19 cohort (from March 1, 2020 to February 28, 2022) and 164,737 patients in the prepandemic cohort (from January 1, 2018 to December 31, 2019). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Volume, patient characteristics, and mortality were compared between the pandemic non-COVID-19 cohort and the prepandemic cohort, focusing on the pandemic period and its peaks, with attention to strata of specific admission types, diagnoses, and severity. The number of admitted non-COVID-19 patients during the pandemic period and its peaks were, respectively, 26.9% and 34.2% lower compared with the prepandemic cohort. The pandemic non-COVID-19 cohort consisted of fewer medical patients (48.1% vs. 50.7%), fewer patients with comorbidities (36.5% vs. 40.6%), and more patients on mechanical ventilation (45.3% vs. 42.4%) and vasoactive medication (44.7% vs. 38.4%) compared with the prepandemic cohort. Case-mix adjusted mortality during the pandemic period and its peaks was higher compared with the prepandemic period, odds ratios were, respectively, 1.08 (95% CI, 1.05-1.11) and 1.10 (95% CI, 1.07-1.13). CONCLUSIONS: In non-COVID-19 patients the strain on healthcare has driven lower patient volume, selection of fewer comorbid patients who required more intensive support, and a modest increase in the case-mix adjusted mortality.

Predicting 30-day mortality in intensive care unit patients with ischaemic stroke or intracerebral haemorrhage.

BACKGROUND: Stroke patients admitted to an intensive care unit (ICU) follow a particular survival pattern with a high short-term mortality, but if they survive the first 30 days, a relatively favourable subsequent survival is observed. OBJECTIVES: The development and validation of two prognostic models predicting 30-day mortality for ICU patients with ischaemic stroke and for ICU patients with intracerebral haemorrhage (ICH), analysed separately, based on parameters readily available within 24 h after ICU admission, and with comparison with the existing Acute Physiology and Chronic Health Evaluation IV (APACHE-IV) model. DESIGN: Observational cohort study. SETTING: All 85 ICUs participating in the Dutch National Intensive Care Evaluation database. PATIENTS: All adult patients with ischaemic stroke or ICH admitted to these ICUs between 2010 and 2019. MAIN OUTCOME MEASURES: Models were developed using logistic regressions and compared with the existing APACHE-IV model. Predictive performance was assessed using ROC curves, calibration plots and Brier scores. RESULTS: We enrolled 14 303 patients with stroke admitted to ICU: 8422 with ischaemic stroke and 5881 with ICH. Thirty-day mortality was 27% in patients with ischaemic stroke and 41% in patients with ICH. Important factors predicting 30-day mortality in both ischaemic stroke and ICH were age, lowest Glasgow Coma Scale (GCS) score in the first 24 h, acute physiological disturbance (measured using the Acute Physiology Score) and the application of mechanical ventilation. Both prognostic models showed high discrimination with an AUC 0.85 [95% confidence interval (CI), 0.84 to 0.87] for patients with ischaemic stroke and 0.85 (0.83 to 0.86) in ICH. Calibration plots and Brier scores indicated an overall good fit and good predictive performance. The APACHE-IV model predicting 30-day mortality showed similar performance with an AUC of 0.86 (95% CI, 0.85 to 0.87) in ischaemic stroke and 0.87 (0.86 to 0.89) in ICH. CONCLUSION: We developed and validated two prognostic models for patients with ischaemic stroke and ICH separately with a high discrimination and good calibration to predict 30-day mortality within 24 h after ICU admission. TRIAL REGISTRATION: Trial registration: Dutch Trial Registry ( https://www.trialregister.nl/ ); identifier: NTR7438.

Quality improvement of Dutch ICUs from 2009 to 2021: A registry based observational study.

PURPOSE: To investigate the development in quality of ICU care over time using the Dutch National Intensive Care Evaluation (NICE) registry. MATERIALS AND METHODS: We included data from all ICU admissions in the Netherlands from those ICUs that submitted complete data between 2009 and 2021 to the NICE registry. We determined median and interquartile range for eight quality indicators. To evaluate changes over time on the indicators, we performed multilevel regression analyses, once without and once with the COVID-19 years 2020 and 2021 included. Additionally we explored between-ICU heterogeneity by calculating intraclass correlation coefficients (ICC). RESULTS: 705,822 ICU admissions from 55 (65%) ICUs were included in the analyses. ICU length of stay (LOS), duration of mechanical ventilation (MV), readmissions, in-hospital mortality, hypoglycemia, and pressure ulcers decreased significantly between 2009 and 2019 (OR <1). After including the COVID-19 pandemic years, the significant change in MV duration, ICU LOS, and pressure ulcers disappeared. We found an ICC ≤0.07 on the quality indicators for all years, except for pressure ulcers with an ICC of 0.27 for 2009 to 2021. CONCLUSIONS: Quality of Dutch ICU care based on seven indicators significantly improved from 2009 to 2019 and between-ICU heterogeneity is medium to small, except for pressure ulcers. The COVID-19 pandemic disturbed the trend in quality improvement, but unaltered the between-ICU heterogeneity.

Characteristics and outcome of COVID-19 patients admitted to the ICU: a nationwide cohort study on the comparison between the first and the consecutive upsurges of the second wave of the COVID-19 pandemic in the Netherlands.

BACKGROUND: To assess trends in the quality of care for COVID-19 patients at the ICU over the course of time in the Netherlands. METHODS: Data from the National Intensive Care Evaluation (NICE)-registry of all COVID-19 patients admitted to an ICU in the Netherlands were used. Patient characteristics and indicators of quality of care during the first two upsurges (N = 4215: October 5, 2020-January 31, 2021) and the final upsurge of the second wave, called the 'third wave' (N = 4602: February 1, 2021-June 30, 2021) were compared with those during the first wave (N = 2733, February-May 24, 2020). RESULTS: During the second and third wave, there were less patients treated with mechanical ventilation (58.1 and 58.2%) and vasoactive drugs (48.0 and 44.7%) compared to the first wave (79.1% and 67.2%, respectively). The occupancy rates as fraction of occupancy in 2019 (1.68 and 1.55 vs. 1.83), the numbers of ICU relocations (23.8 and 27.6 vs. 32.3%) and the mean length of stay at the ICU (HRs of ICU discharge = 1.26 and 1.42) were lower during the second and third wave. No difference in adjusted hospital mortality between the second wave and the first wave was found, whereas the mortality during the third wave was considerably lower (OR = 0.80, 95% CI [0.71-0.90]). CONCLUSIONS: These data show favorable shifts in the treatment of COVID-19 patients at the ICU over time. The adjusted mortality decreased in the third wave. The high ICU occupancy rate early in the pandemic does probably not explain the high mortality associated with COVID-19.

Body Mass Index and Mortality in Coronavirus Disease 2019 and Other Diseases: A Cohort Study in 35,506 ICU Patients.

OBJECTIVES: Obesity is a risk factor for severe coronavirus disease 2019 and might play a role in its pathophysiology. It is unknown whether body mass index is related to clinical outcome following ICU admission, as observed in various other categories of critically ill patients. We investigated the relationship between body mass index and inhospital mortality in critically ill coronavirus disease 2019 patients and in cohorts of ICU patients with non-severe acute respiratory syndrome coronavirus 2 viral pneumonia, bacterial pneumonia, and multiple trauma. DESIGN: Multicenter observational cohort study. SETTING: Eighty-two Dutch ICUs participating in the Dutch National Intensive Care Evaluation quality registry. PATIENTS: Thirty-five-thousand five-hundred six critically ill patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patient characteristics and clinical outcomes were compared between four cohorts (coronavirus disease 2019, nonsevere acute respiratory syndrome coronavirus 2 viral pneumonia, bacterial pneumonia, and multiple trauma patients) and between body mass index categories within cohorts. Adjusted analyses of the relationship between body mass index and inhospital mortality within each cohort were performed using multivariable logistic regression. Coronavirus disease 2019 patients were more likely male, had a higher body mass index, lower Pao2/Fio2 ratio, and were more likely mechanically ventilated during the first 24 hours in the ICU compared with the other cohorts. Coronavirus disease 2019 patients had longer ICU and hospital length of stay, and higher inhospital mortality. Odds ratios for inhospital mortality for patients with body mass index greater than or equal to 35 kg/m2 compared with normal weight in the coronavirus disease 2019, nonsevere acute respiratory syndrome coronavirus 2 viral pneumonia, bacterial pneumonia, and trauma cohorts were 1.15 (0.79-1.67), 0.64 (0.43-0.95), 0.73 (0.61-0.87), and 0.81 (0.57-1.15), respectively. CONCLUSIONS: The obesity paradox, which is the inverse association between body mass index and mortality in critically ill patients, is not present in ICU patients with coronavirus disease 2019-related respiratory failure, in contrast to nonsevere acute respiratory syndrome coronavirus 2 viral and bacterial respiratory infections.

Nurse Operation Workload (NOW), a new nursing workload model for intensive care units based on time measurements: An observational study.

BACKGROUND: Several instruments have been developed to measure nursing workload. The commonly used Nursing Activities Score (NAS) and Therapeutic Intervention Scoring System (TISS) are applied to all types of ICU patients. Former research showed that NAS explained 59 to 81% of actual nursing time, whereas the Therapeutic Intervention Scoring System (TISS) described only 43% of the actual nursing time. In both models the development was not based on time measurements. OBJECTIVES: The aim of this study was to develop a time-based model which can assess patient related nursing workload more accurately and to evaluate whether patient characteristics influence nursing time and therefore should be included in the model. DESIGN: Observational study design. SETTING: All 82 Dutch ICUs participate in the National Intensive Care Evaluation (NICE) quality registry. Fifteen of these ICUs are participating in the newly implemented voluntary nursing capacity module. Seven of these ICUs voluntarily participated in this study. PARTICIPANTS: The patient(s) that were under the responsibility of a chosen nurse were followed by the observer during the entire shift. METHODS: Time spent per nursing activity per patient was measured in different shifts in seven Dutch ICUs. Nursing activities were measured using an in-house developed web application. Three different models of varying complexity (1. nursing activities only; 2. nursing activities and case-mix correction; 3. complex model with case-mix correction per nursing activity) were developed to explain the total amount of nursing time per patient. The performance of the three models was assessed in 1000 bootstrap samples using the squared Pearson correlation coefficient (R2), Root Mean Squared Prediction Error (RMSPE), Mean Absolute Prediction Error (MAPE), and prediction bias. RESULTS: In total 287 unique patients have been observed in 371 shifts. Model one's Pearson's R was 0.89 (95%CI 0.86-0.92), model two with case-mix correction 0.90 (95%CI 0.88-0.93), and the third complex model 0.64 (95%CI 0.56-0.72) compared with the actual patient related nursing workload. CONCLUSION: The newly developed Nurse Operation Workload (NOW) model outperforms existing models in measuring nursing workload, while it includes a lower number of activities and therewith lowers the registration burden. Case-mix correction does not further improve the performance of this model. The patient related nursing workload measured by the NOW gives insight in the actual nursing time needed by patients and can therefore be used to evaluate the average workload per patient per nurse.

Long-Term Mortality Among ICU Patients With Stroke Compared With Other Critically Ill Patients.

OBJECTIVES: Assessment of all-cause mortality of intracerebral hemorrhage and ischemic stroke patients admitted to the ICU and comparison to the mortality of other critically ill ICU patients classified into six other diagnostic subgroups and the general Dutch population. DESIGN: Observational cohort study. SETTING: All ICUs participating in the Dutch National Intensive Care Evaluation database. PATIENTS: All adult patients admitted to these ICUs between 2010 and 2015; patients were followed until February 2017. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of all 370,386 included ICU patients, 7,046 (1.9%) were stroke patients, 4,072 with ischemic stroke, and 2,974 with intracerebral hemorrhage. Short-term mortality in ICU-admitted stroke patients was high with 30 days mortality of 31% in ischemic stroke and 42% in intracerebral hemorrhage. In the longer term, the survival curve gradient among ischemic stroke and intracerebral hemorrhage patients stabilized. The gradual alteration of mortality risk after ICU admission was assessed using left-truncation with increasing minimum survival period. ICU-admitted stroke patients who survive the first 30 days after suffering from a stroke had a favorable subsequent survival compared with other diseases necessitating ICU admission such as patients admitted due to sepsis or severe community-acquired pneumonia. After having survived the first 3 months after ICU admission, multivariable Cox regression analyses showed that case-mix adjusted hazard ratios during the follow-up period of up to 3 years were lower in ischemic stroke compared with sepsis (adjusted hazard ratio, 1.21; 95% CI, 1.06-1.36) and severe community-acquired pneumonia (adjusted hazard ratio, 1.57; 95% CI, 1.39-1.77) and in intracerebral hemorrhage patients compared with these groups (adjusted hazard ratio, 1.14; 95% CI, 0.98-1.33 and adjusted hazard ratio, 1.49; 95% CI, 1.28-1.73). CONCLUSIONS: Stroke patients who need intensive care treatment have a high short-term mortality risk, but this alters favorably with increasing duration of survival time after ICU admission in patients with both ischemic stroke and intracerebral hemorrhage, especially compared with other populations of critically ill patients such as sepsis or severe community-acquired pneumonia patients.

The performance of acute versus antecedent patient characteristics for 1-year mortality prediction during intensive care unit admission: a national cohort study.

BACKGROUND: Multiple factors contribute to mortality after ICU, but it is unclear how the predictive value of these factors changes during ICU admission. We aimed to compare the changing performance over time of the acute illness component, antecedent patient characteristics, and ICU length of stay (LOS) in predicting 1-year mortality. METHODS: In this retrospective observational cohort study, the discriminative value of four generalized mixed-effects models was compared for 1-year and hospital mortality. Among patients with increasing ICU LOS, the models included (a) acute illness factors and antecedent patient characteristics combined, (b) acute component only, (c) antecedent patient characteristics only, and (d) ICU LOS. For each analysis, discrimination was measured by area under the receiver operating characteristics curve (AUC), calculated using the bootstrap method. Statistical significance between the models was assessed using the DeLong method (p value < 0.05). RESULTS: In 400,248 ICU patients observed, hospital mortality was 11.8% and 1-year mortality 21.8%. At ICU admission, the combined model predicted 1-year mortality with an AUC of 0.84 (95% CI 0.84-0.84). When analyzed separately, the acute component progressively lost predictive power. From an ICU admission of at least 3 days, antecedent characteristics significantly exceeded the predictive value of the acute component for 1-year mortality, AUC 0.68 (95% CI 0.68-0.69) versus 0.67 (95% CI 0.67-0.68) (p value < 0.001). For hospital mortality, antecedent characteristics outperformed the acute component from a LOS of at least 7 days, comprising 7.8% of patients and accounting for 52.4% of all bed days. ICU LOS predicted 1-year mortality with an AUC of 0.52 (95% CI 0.51-0.53) and hospital mortality with an AUC of 0.54 (95% CI 0.53-0.55) for patients with a LOS of at least 7 days. CONCLUSIONS: Comparing the predictive value of factors influencing 1-year mortality for patients with increasing ICU LOS, antecedent patient characteristics are more predictive than the acute component for patients with an ICU LOS of at least 3 days. For hospital mortality, antecedent patient characteristics outperform the acute component for patients with an ICU LOS of at least 7 days. After the first week of ICU admission, LOS itself is not predictive of hospital nor 1-year mortality.

Workload scoring systems in the Intensive Care and their ability to quantify the need for nursing time: A systematic literature review.

INTRODUCTION: The Intensive Care Unit is a resource intense service with a high nursing workload per patient resulting in a low ratio of patients per nurse. This review aims to identify existing scoring systems for measuring nursing workload on the Intensive Care and assess their validity and reliability to quantify the needed nursing time. METHODS: We conducted a systematic review of the literature indexed before 01/Mar/2018 in the bibliographic databases MEDLINE, Embase, and Cinahl. Full-text articles were selected and data on systems measuring nursing workload on the Intensive Care and translation of this workload into the amount of nursing time needed was extracted. RESULTS: We included 71 articles identifying 34 different scoring systems of which 27 were included for further analysis as these described a translation of workload into nursing time needed. Almost all systems were developed with nurses. The validity of most scoring systems was evaluated by comparing them with another system (59%) or by using time measurements (26%). The most common way to translate workload-scores into nursing time needed was by categorizing the Nurse:Patient-ratios. Validation of the Nurse:Patient-ratios was mostly evaluated by comparing the results with other systems or with the actual planning and not with objective time measurements. CONCLUSION: Despite the large attention given to nursing workload systems for Intensive Care, only a few systems objectively evaluated the validity and reliability of measuring nursing workload with moderate results. The Nursing Activity Score system performed best. Poor methodology for the translation of workload scores into Nurse:Patient-ratio weakens the value of nursing workload scoring systems in daily Intensive Care practice.

The Nursing Activities Score Per Nurse Ratio Is Associated With In-Hospital Mortality, Whereas the Patients Per Nurse Ratio Is Not.

OBJECTIVES: Studies have shown contradicting results on the association of nursing workload and mortality. Most of these studies expressed workload as patients per nurse ratios; however, this does not take into account that some patients require more nursing time than others. Nursing time can be quantified by tools like the Nursing Activities Score. We investigated the association of the Nursing Activities Score per nurse ratio, respectively, the patients per nurse ratio with in-hospital mortality in ICUs. DESIGN: Retrospective analysis of the National Intensive Care Evaluation database. SETTING: Fifteen Dutch ICUs. PATIENTS: All ICU patients admitted to and registered ICU nurses working at 15 Dutch ICUs between January 1, 2016, and January 1, 2018, were included. The association of mean or day 1 patients per nurse ratio and Nursing Activities Score per nurse ratio with in-hospital mortality was analyzed using logistic regression models. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Nursing Activities Score per nurse ratio greater than 41 for both mean Nursing Activities Score per nurse ratio as well as Nursing Activities Score per nurse ratio on day 1 were associated with a higher in-hospital mortality (odds ratios, 1.19 and 1.17, respectively). After case-mix adjustment the association between a Nursing Activities Score per nurse ratio greater than 61 for both mean Nursing Activities Score per nurse ratio as well as Nursing Activities Score per nurse ratio on day 1 and in-hospital mortality remained significant (odds ratios, 1.29 and 1.26, respectively). Patients per nurse ratio was not associated with in-hospital mortality. CONCLUSIONS: A higher Nursing Activities Score per nurse ratio was associated with higher in-hospital mortality. In contrast, no association was found between patients per nurse ratios and in-hospital mortality in The Netherlands. Therefore, we conclude that it is more important to focus on the nursing workload that the patients generate rather than on the number of patients the nurse has to take care of in the ICU.

Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU.

OBJECTIVES: To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs). DESIGN: Prospective cohort study. SETTING: A total of 306 ICUs from 24 European countries. PARTICIPANTS: Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81-87 y]; 51.8% male). MEASUREMENTS: Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30-day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves. RESULTS: The 30-day-mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was .80, and the Brier score was .18. At a cut point of 10 or higher (75% of all patients), the model predicts 30-day mortality in 91.1% of all patients who die. CONCLUSION: A predictive model of cumulative events predicts 30-day mortality in patients older than 80 years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision-making capacity.

Incorporation of Daily Goals in Daily Care Planning Does Not Shorten Length of Stay in the Intensive Care Unit.

A strategy of defining and checking explicitly formulated patient-specific treatments targets or "daily goals" in the intensive care unit has been associated with improved communication. We investigated the effect of incorporation of daily goals into daily care planning on length of stay in the intensive care unit. Furthermore, the type of daily goals and deviations from daily goals in daily care with or without documented reason were evaluated. Four university hospitals in the Netherlands, of which 2 study "daily goal" hospitals and 2 control hospitals, participated in a prospective before-after study. During the before phase of the study, daily goals were formulated by the attending physician but kept blinded from doctors and nurses caring for the patient. During the after phase of the study, daily goals were integrated in the care plan for patients admitted to the 2 study hospitals but not for patients admitted to the control hospitals. The implementation of daily goals was, after case-mix correction, not associated with a change in intensive care unit length of stay. However, this study showed that an improved administrative discipline, that is, the recording of the reason why a daily goal or standard protocol was not accomplished, is in favor of the daily goal implementation.

Trends in short-term and 1-year mortality in very elderly intensive care patients in the Netherlands: a retrospective study from 2008 to 2014.

PURPOSE: To describe the trends in short-term and long-term mortality in very elderly intensive care unit (ICU) patients between 2008 and 2014. METHODS: A retrospective cohort study was conducted using data from the National Intensive Care Evaluation Foundation from 31 Dutch ICUs. Generalized linear mixed-effects models were used to determine the change in adjusted short-term mortality (ICU/hospital deaths) and long-term mortality (3, 6, and 12 months after ICU admission) over the period 2008-2014 in very elderly patients and in patients less than 80 years old admitted to the ICU. RESULTS: A total of 216,196 patients admitted to 31 ICUs in the period from 2008 to 2014 were included in the study, including 28,284 (13.1%) very elderly patients (80 years or older). Follow-up data for determination of 3-, 6-, and 12-month mortality were available for, respectively, 210,005 (97.1%), 202,551 (93.7%), and 176,847 (81.8%) ICU admissions. The crude ICU and in-hospital mortality decreased, respectively, from 17.6% to 13.0% and from 30.7% to 21.0%. The annual risk-adjusted ICU and in-hospital mortality of very elderly patients (adjusted for APACHE III score, comorbidities, and admission type) decreased significantly during the study period [adjusted odds ratio 0.97 (0.95-0.99) and 0.92 (0.91-0.93), respectively]. Additionally, the annual risk-adjusted 3-, 6-, and 12-month mortality decreased significantly from 2008 to 2014 [adjusted odds ratio 0.96 (0.95-0.97), 0.96 (0.94-0.97), and 0.97 (0.95-0.98), respectively]. A similar significant annual decrease in risk-adjusted short-term and long-term mortality was observed in patients aged less than 80 years. CONCLUSIONS: Both short-term and long-term risk-adjusted mortality decreased significantly during the study period in both very elderly ICU patients and patients aged less than 80 years in the Netherlands. This study clearly shows that in our setting very elderly patients benefit almost as much as their younger counterparts from improvement in quality of care over time.

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

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 association between ICU level of care and mortality in the Netherlands.

PURPOSE: The relationship between the number of patients admitted to an intensive care unit (ICU) volume and mortality is currently the subject of debate. After implementation of a national guideline in 2006, all Dutch ICUs have been classified into three levels based on ICU size, patient volume, ventilation days, and staffing. The goal of this study is to investigate the association between ICU level and mortality of ICU patients in the Netherlands. METHODS: We analyzed data from 132,159 patients admitted to 87 ICUs between January 1, 2009 and October 1, 2011. Logistic GEE analyses were performed to assess the influence of ICU level on in-hospital mortality and 90-day mortality in the total ICU population and in different ICU subgroups while adjusting for severity of illness by APACHE IV. RESULTS: No significant differences were found in the adjusted in-hospital mortality of the total ICU population and in different subgroups admitted to level 1, 2 and 3 ICUs. In-hospital mortality in level 2 and 3 ICUs as opposed to level 1 ICUs was 1.06 (0.93-1.22) and 1.10 (0.94-1.29), respectively, and 90-day mortality was 0.92 (0.80-1.06) and 1.01 (0.88-1.17). CONCLUSION: We demonstrated that ICU level was not associated with significant differences in the case-mix adjusted in-hospital and long-term mortality of ICU patients. This finding is in contrast with some earlier studies suggesting a volume-outcome relationship. Our results may be explained by the successful implementation of nationwide mandatory quality requirements and adequate staffing in all three levels of ICUs over the last years.

[Potential lowering of sepsis-related mortality via screening and implementation of guidelines]. / Sepsissterfte kan lager door screening en richtlijnen.

The incidence of sepsis continues to increase. However, over the past decade marked reductions in sepsis-related in-hospital mortality have been reported. Large variations in the presentation and severity of illness may be encountered in ICU patients with severe sepsis, which might preclude the success of screening and guideline programmes. However, the authors of this article were able to prove that a national programme involving screening and a package of interventions did lower relative in-hospital mortality by 16.7% over 3.5 years in 52 participating hospitals in the Netherlands. In-hospital mortality did not change in 30 non-participating hospitals. Therefore, the authors recommend implementing updated guidelines, sepsis quality indicators and programmes with a package of interventions to further reduce sepsis mortality. Furthermore, additional research on long term consequences in sepsis survivors is warranted.

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.