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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

A comparison of the performance of a model based on administrative data and a model based on clinical data: effect of severity of illness on standardized mortality ratios of intensive care units.

OBJECTIVES: It has been postulated that prognostic models based on administrative data can provide valid adjusted mortality rates in specific patient populations. In this study we compared the performance and robustness of a model based on administrative data (customized hospital standardized mortality ratio) and a model based on clinical data (customized Simplified Acute Physiology Score II) in the Dutch intensive care unit population. DESIGN: Cohort study of intensive care unit records from a national intensive care unit quality registry linked to administrative records from the Dutch National Medical Registration. The hospital standardized mortality ratio and Simplified Acute Physiology Score II models were first-level customized on the intensive care unit population. SETTING: Fifty-five Dutch intensive care units. PATIENTS: A total of 66,564 intensive care unit patients admitted from 2005 to 2008. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Performance expressed by measures of discrimination, accuracy, and calibration (area under the receiver operating characteristic curve, Brier score, Hosmer-Lemeshow C-statistic, and calibration plots). Additionally, the robustness of the models was assessed by simulating changes in the population's severity of illness and analyzing the effect on the intensive care units' standardized mortality ratios.The area under the receiver operating characteristic curve and Brier score of the customized Simplified Acute Physiology Score II were significantly superior to that of the customized hospital standardized mortality ratio (0.85 and 0.11 vs. 0.77 and 0.13, respectively). Calibration plots showed good agreement between observed and predicted mortality for low-risk patients in both models, with more discrepancy in the high-risk patients when using the customized hospital standardized mortality ratio. Severity of illness had influence on the intensive care units' standardized mortality ratios in both models, but the customized Simplified Acute Physiology Score II showed more robustness. CONCLUSIONS: The customized Simplified Acute Physiology Score II outperforms the customized hospital standardized mortality ratio in the Dutch intensive care unit population. Comparing institutions based on standardized mortality ratios can be unfavorable for those with a more severely ill intensive care unit population, especially when using the customized hospital standardized mortality ratio.

The use of linked registries to assess long-term mortality of ICU patients.

Clinical registries are frequently used to monitor and analyze the quality of health care by assessing the in-hospital mortality. However, long-term mortality is often ignored as it is rarely recorded in such clinical registries. In this study linkage of a clinical registry and administrative database is used to assess the longterm mortality of a large ICU sample. Information about long-term mortality may be used to inform patients about their prognosis, to get insight in factors that influence long-term mortality, and to adjust admission policy to the ICU. This study showed that the observed mortality in the total ICU population at 3, 6, and 12 months after ICU admission was 20.3%, 22.9%, and 26.6% respectively. Medical and urgent surgery patients showed a higher long-term mortality risk and planned surgery patients showed a lower long-term mortality risk compared to the other ICU patients. In this study we have focused on the general ICU population, though linkage of clinical and administrative databases can also be used to perform analyses in specific diagnostic ICU populations or for non-ICU patients. In this study 71.4% of the clinical records could be linked with the administrative database. Future studies should focus on improving linkage of different registries.

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.

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.

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.

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.