A nationwide overview of 1-year mortality in cardiac arrest patients admitted to intensive care units in the Netherlands between 2010 and 2016

Mandigers, Loes; Termorshuizen, Fabian; Keizer, Nicolette F. de; Gommers, Diederik; Miranda, Dinís Dos Reis; Rietdijk, Wim; Uil, Corstiaan A. den

doi:10.1016/j.resuscitation.2019.12.029

Cited by 15 publications

(19 citation statements)

References 40 publications

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“…Patients with a malignancy who received a written TLD during ICU stay were significantly older than patients without a malignancy who received a written TLD, but SOFA scores on 3 [1][2][3][4][5][6][7][8][9][10] the day of the TLD were not significantly different in patients with and without a malignancy ( Table 2). The univariate binary logistic regression analysis showed a higher incidence of written TLDs during ICU stay in patients with an active malignancy than in patients without a malignancy (OR 1.97; 95% CI 1.29-3.01; Table 3).…”

Section: Resultsmentioning

confidence: 99%

“…1,5 Approximately 30% of patients requiring cardiopulmonary resuscitation (CPR) after an in-hospital cardiac arrest (IHCA) survived the hospital admission, 6 the survival of patients with return of spontaneous circulation admitted to the ICU is up to 50%. 7 In contrast, hospital survival of patients with a malignancy requiring CPR after IHCA was significantly lower (5-10%). 8,9 Therefore, CPR can in certain circumstances be seen as a traumatic, undignified and inappropriate medical intervention, 10,11 often with prolonged hospitalization and invasive treatment as result.…”

Section: Introductionmentioning

confidence: 93%

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Treatment Limitation Decisions in Critically Ill Patients With a Malignancy on the Intensive Care Unit

Zee

Epker

Bakker

et al. 2020

J Intensive Care Med

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Background: Treatment limitation decisions (TLDs) on the ICU can be challenging, especially in patients with a malignancy. Up-to-date literature regarding TLDs in critically ill patients with a malignancy admitted to the ICU is scarce. The aim was to compare the incidence of written TLDs between patients with an active malignancy, patients with a malignancy in their medical history (complete remission, CR) and patients without a malignancy admitted unplanned to the ICU. Methods: We conducted a retrospective cohort study in a large university hospital in the Netherlands. We identified all unplanned admissions to the ICU in 2017 and categorized the patients in 3 groups: patients with an active malignancy (study population), with CR and without a malignancy. A TLD was defined as a written instruction not to perform life-saving treatments, such as CPR in case of cardiac arrest. A multivariate binary logistic regression analysis was used to identify whether having a malignancy was associated with TLDs. Results: Of the 1046 unplanned admissions, 125 patients (12%) had an active malignancy and 76 (7.3%) patients had CR. The incidence of written TLDs in these subgroups were 37 (29.6%) and 20 (26.3%). Age (OR 1.03; 95% CI 1.01 -1.04), SOFA score at ICU admission (OR 1.11; 95% CI 1.05 -1.18) and having an active malignancy (OR 1.75; 95% CI 1.04-2.96) compared to no malignancy were independently associated with written TLDs. SOFA scores on the day of the TLD were not significantly different in patients with and without a malignancy. Conclusions: This study shows that the presence of an underlying malignancy is independently associated with written TLDs during ICU stay. Patients with CR were not at risk of more written TLDs. Whether this higher incidence of TLDs in patients with a malignancy is justified, is at least questionable and should be evaluated in future research.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Treatment Limitation Decisions in Critically Ill Patients With a Malignancy on the Intensive Care Unit

Zee

Epker

Bakker

et al. 2020

J Intensive Care Med

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“…A bad prognosis of ICU patients with cancer and after CPR, even independent of COVID-19, is expected and known. [24] , [25] …”

Section: As For Finding Strong Prognostic Factors Similar To Other Studies We Found Age Sex and Patient History (Comorbidities) To Be Prementioning

confidence: 99%

Can we reliably automate clinical prognostic modelling? A retrospective cohort study for ICU triage prediction of in-hospital mortality of COVID-19 patients in the Netherlands

Vagliano

Brinkman

Abu‐Hanna

et al. 2022

International Journal of Medical Informatics

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Background Building Machine Learning (ML) models in healthcare may suffer from time-consuming and potentially biased pre-selection of predictors by hand that can result in limited or trivial selection of suitable models. We aimed to assess the predictive performance of automating the process of building ML models (AutoML) in-hospital mortality prediction modelling of triage COVID-19 patients at ICU admission versus expert-based predictor pre-selection followed by logistic regression. Methods We conducted an observational study of all COVID-19 patients admitted to Dutch ICUs between February and July 2020. We included 2,690 COVID-19 patients from 70 ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry. The main outcome measure was in-hospital mortality. We asessed model performance (at admission and after 24 hours, respectively) of AutoML compared to the more traditional approach of predictor pre-selection and logistic regression. Findings : Predictive performance of the autoML models with variables available at admission shows fair discrimination (average AUROC = 0·75-0·76 (sdev = 0·03), PPV = 0·70-0·76 (sdev = 0·1) at cut-off = 0·3 (the observed mortality rate), and good calibration. This performance is on par with a logistic regression model with selection of patient variables by three experts (average AUROC = 0·78 (sdev = 0·03) and PPV = 0·79 (sdev = 0·2)). Extending the models with variables that are available at 24 hours after admission resulted in models with higher predictive performance (average AUROC = 0·77-0·79 (sdev = 0·03) and PPV = 0·79-0·80 (sdev = 0·10-0·17)). Conclusions AutoML delivers prediction models with fair discriminatory performance, and good calibration and accuracy, which is as good as regression models with expert-based predictor pre-selection. In the context of the restricted availability of data in an ICU quality registry, extending the models with variables that are available at 24 hours after admission showed small (but significantly) performance increase.

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“…The National Intensive Care Evaluation (NICE) registry was used to identify all adult patients with an unplanned admission to ICUs in the Netherlands from 2008 to 2017. This registry contains data of patients from 85% of the Dutch ICUs in 2008 to 100% of the Dutch ICUs in 2017 (24), and focuses on quality improvement of ICU care in the Netherlands. It contains whether the ICU admission was planned or unplanned, demographics, physiological and diagnostic data, ICU characteristics, and ICU and hospital mortality of patients.…”

Section: Patient Datamentioning

confidence: 99%

One-Year Mortality of Cancer Patients with an Unplanned ICU Admission: A Cohort Analysis Between 2008 and 2017 in the Netherlands

Zee

Termorshuizen

Benoît

et al. 2020

Preprint

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BackgroundOver the last decades, a decrease in short-term mortality in cancer patients admitted to the ICU has been described in literature. However, it is unclear whether this decrease also results in a decrease in long-term mortality. Therefore, we examined the 1-year mortality of cancer patients (either haematological or solid) with an unplanned ICU admission.Methods and dataAll adult patients registered in the National Intensive Care Evaluation registry with an unplanned ICU admission in the Netherlands between 2008 and 2017 were included. The primary outcome was 1-year mortality, analysed with a mixed-effects cox-proportional hazard regression. We examined the trend in mortality rates over the inclusion period. Furthermore, we compared the 1-year mortality of cancer patients to that of patients without cancer. ResultsWe included 470,305 patients: 10,401 with haematological cancer, 35,920 with solid tumours, and 423,984 without cancer. The 1-year mortality rates were 60.1%, 46.2%, and 28.3% (p<0.01), respectively. While no statistically significant trend was found (p=0.58), a visual inspection of the graph showed a slightly decreasing trend in 1-year mortality over the inclusion period. Although we found a statistical significant difference in 1-year mortality in patients with a solid tumour (p<0.01), visual inspection showed a wide variety per year. We found a decreasing trend in 1-year mortality in ICU patients without a malignancy (p<0.01). Cancer patients surviving their critical illness and hospital admission had a 30% mortality at 1 year. ConclusionThe 1-year mortality in cancer patients with an unplanned ICU admission (either haematological or solid) was significantly higher than that of patients without cancer. A visual inspection showed a slight decrease in 1-year mortality in haematological patients, while the 1-year mortality in patients with a solid tumour varied per year. After hospital discharge, a considerable part of the patients with a malignancy died within 1 year. Physicians should discuss the long-term prognosis after an ICU admission with patients and relatives in order to manage treatments and expectations. Future research should focus on identifying cancer patients who will benefit from an ICU admission.

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A nationwide overview of 1-year mortality in cardiac arrest patients admitted to intensive care units in the Netherlands between 2010 and 2016

Cited by 15 publications

References 40 publications

Treatment Limitation Decisions in Critically Ill Patients With a Malignancy on the Intensive Care Unit

Treatment Limitation Decisions in Critically Ill Patients With a Malignancy on the Intensive Care Unit

Can we reliably automate clinical prognostic modelling? A retrospective cohort study for ICU triage prediction of in-hospital mortality of COVID-19 patients in the Netherlands

One-Year Mortality of Cancer Patients with an Unplanned ICU Admission: A Cohort Analysis Between 2008 and 2017 in the Netherlands

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