Clinical subtypes in critically ill patients with sepsis: validation and parsimonious classifier model development

BACKGROUND

The application of sepsis subtypes to enhance personalized medicine in critically ill patients is hindered by the lack of validation across diverse cohorts and the absence of a simple classification model. We aimed to validate the previously identified SENECA clinical sepsis subtypes in multiple large ICU cohorts, and to develop parsimonious classifier models for δ-type adjudication in clinical practice.

METHODS

Data from four cohorts between 2008 and 2023 were used to assign α, β, γ and δ-type in patients fulfilling the Sepsis-3 criteria using clinical variables: (I) The Molecular diAgnosis and Risk stratification of Sepsis (MARS, n = 2449), (II) a contemporary continuation of the MARS study (MARS2, n = 2445) (III) the Dutch National Intensive Care Evaluation registry (NICE, n = 28,621) and (IV) the Medical Information Mart for Intensive Care including (MIMIC-IV, n = 18,661). K-means clustering using clinical variables was conducted to assess the optimal number of classes and compared to the SENECA subtypes. Parsimonious models were built in the SENECA derivation cohort to predict subtype membership using logistic regression, and validated in MARS and MIMIC-IV.

RESULTS

Among 52.226 patients with sepsis, the subtype distribution in MARS, MARS2 and NICE was 2-6% for the α-type, 1-5% for the β-type, 49-65% for the γ-type and 26-48% for the δ-type compared to 33%, 27%, 27% and 13% in the original SENECA derivation cohort, whereas subtype distribution in MIMIC-IV was more similar at 25%, 24%, 27% and 25%, respectively. In-hospital mortality rates were significantly different between the four cohorts for α, γ and δ-type (p < 0.001). Method-based validation showed moderate overlap with the original subtypes in both MARS and MIMIC-IV. A parsimonious model for all four subtypes had moderate to low accuracy (accuracy 62.2%), while a parsimonious classifier model with 3 variables (aspartate aminotransferase, serum lactate, and bicarbonate) had excellent accuracy in predicting the δ-type patients from all other types in the derivation cohort and moderate accuracy in the validation cohorts (MARS: area under the receiver operator characteristic curve (AUC) 0.93, 95% CI [0.92-0.94], accuracy 85.5% [84.0-86.8%]; MIMIC-IV: AUC 0.86 [0.85-0.87], accuracy 82.9% [82.4-83.4%]).

CONCLUSIONS

The distribution and mortality rates of clinical sepsis subtypes varied between US and European cohorts. A three-variable model could accurately identify the δ-type sepsis patients.

Three-year mortality of ICU survivors with sepsis, an infection or an inflammatory illness: an individually matched cohort study of ICU patients in the Netherlands from 2007 to 2019

BACKGROUND

Sepsis is a frequent reason for ICU admission and a leading cause of death. Its incidence has been increasing over the past decades. While hospital mortality is decreasing, it is recognized that the sequelae of sepsis extend well beyond hospitalization and are associated with a high mortality rate that persists years after hospitalization. The aim of this study was to disentangle the relative contribution of sepsis (infection with multi-organ failure), of infection and of inflammation, as reasons for ICU admission to long-term survival. This was done as infection and inflammation are both cardinal features of sepsis. We assessed the 3-year mortality of ICU patients admitted with sepsis, with individually matched ICU patients with an infection but not sepsis, and with an inflammatory illness not caused by infection, discharged alive from hospital.

METHODS

A multicenter cohort study of adult ICU survivors admitted between January 1st 2007 and January 1st 2019, with sepsis, an infection or an inflammatory illness. Patients were classified within the first 24 h of ICU admission according to APACHE IV admission diagnoses. Dutch ICUs (n = 78) prospectively recorded demographic and clinical data of all admissions in the NICE registry. These data were linked to a health care insurance claims database to obtain 3-year mortality data. To better understand and distinct the sepsis cohort from the non-sepsis infection and inflammatory condition cohorts, we performed several sensitivity analyses with varying definitions of the infection and inflammatory illness cohort.

RESULTS

Three-year mortality after discharge was 32.7% in the sepsis (N = 10,000), 33.6% in the infectious (N = 10,000), and 23.8% in the inflammatory illness cohort (N = 9997). Compared with sepsis patients, the adjusted HR for death within 3 years after hospital discharge was 1.00 (95% CI 0.95-1.05) for patients with an infection and 0.88 (95% CI 0.83-0.94) for patients with an inflammatory illness.

CONCLUSIONS

Both sepsis and non-sepsis infection patients had a significantly increased hazard rate of death in the 3 years after hospital discharge compared with patients with an inflammatory illness. Among sepsis and infection patients, one third died in the next 3 years, approximately 10% more than patients with an inflammatory illness. The fact that we did not find a difference between patients with sepsis or an infection suggests that the necessity for an ICU admission with an infection increases the risk of long-term mortality. This result emphasizes the need for greater attention to the post-ICU management of sepsis, infection, and severe inflammatory illness survivors.

Creatinine clearance/eGFR ratio: a simple index for muscle mass related to mortality in ICU patients

INTRODUCTION

In patients admitted to the intensive care unit (ICU), muscle mass is inversely associated with mortality. Although muscle mass can be estimated with 24-h urinary creatinine excretion (UCE), its use for risk prediction in individual patients is limited because age-, sex-, weight- and length-specific reference values for UCE are lacking. The ratio between measured creatinine clearance (mCC) and estimated glomerular filtration rate (eGFR) might circumvent this constraint. The main goal was to assess the association of the mCC/eGFR ratio in ICU patients with all-cause hospital and long-term mortality.

METHODS

The mCC/eGFR ratio was determined in patients admitted to our ICU between 2005 and 2021 with KDIGO acute kidney injury (AKI) stage 0-2 and an ICU stay ≥ 24 h. mCC was calculated from UCE and plasma creatinine and indexed to 1.73 m2. mCC/eGFR was analyzed by categorizing patients in mCC/eGFR quartiles and as continuous variable.

RESULTS

Seven thousand five hundred nine patients (mean age 61 ± 15 years; 38% female) were included. In-hospital mortality was 27% in the lowest mCC/eGFR quartile compared to 11% in the highest quartile (P < 0.001). Five-year post-hospital discharge actuarial mortality was 37% in the lowest mCC/eGFR quartile compared to 19% in the highest quartile (P < 0.001). mCC/eGFR ratio as continuous variable was independently associated with in-hospital mortality in multivariable logistic regression (odds ratio: 0.578 (95% CI: 0.465-0.719); P < 0.001). mCC/eGFR ratio as continuous variable was also significantly associated with 5-year post-hospital discharge mortality in Cox regression (hazard ratio: 0.27 (95% CI: 0.22-0.32); P < 0.001).

CONCLUSIONS

The mCC/eGFR ratio is associated with both in-hospital and long-term mortality and may be an easily available index of muscle mass in ICU patients.

Phosphodiesterase 3 inhibitors do not influence lactate kinetics and clinical outcomes in patients with septic shock: A multicentre cohort study

PURPOSE

We investigated the association between the administration of phosphodiesterase 3 inhibitors (PDE3i) and lactate kinetics, resolution of organ failure, ICU and hospital length of stay (LOS) and hospital mortality in a retrospective cohort of patients with septic shock and persistently elevated lactate concentrations.

MATERIAL AND METHODS

Patients with septic shock and two arterial lactate concentrations ≥4 mmol/L with at least 4 h between measurements were eligible. Clinical data of the first four days of admission were collected in an online database. For each patient, the area between the actual lactate concentrations and 2.2 mmol/L (AUClact2.2), was calculated for three days.

RESULTS

Data on 229 patients from 10 hospitals were collected, of whom 123 received PDE3i (54%). First, a linear multivariate model was developed to predict AUClact2.2 (R2 = 0.57). Adding PDE3i as a cofactor did not affect R2. Second, 60 patients receiving PDE3i at any time between days 0 and 2 were compared to 60 propensity matched no-PDE3i patients. Third, 30 patients who received PDE3i from ICU admission to day 3 were compared to 30 propensity-matched no-PDE3i patients. These analyses showed no differences in AUClact2.2, SOFA scores, ICU or hospital LOS or hospital mortality between treatment groups.

CONCLUSIONS

No association was found between the administration of PDE3i and lactate kinetics, resolution of organ failure, ICU or hospital LOS or hospital mortality.

Estimation of renal function immediately after cessation of continuous renal replacement therapy at the ICU

Estimating glomerular filtration (eGFR) after Continuous Renal Replacement Therapy (CRRT) is important to guide drug dosing and to assess the need to re-initiate CRRT. Standard eGFR equations cannot be applied as these patients neither have steady-state serum creatinine concentration nor average muscle mass. In this study we evaluate the combination of dynamic renal function with CT-scan based correction for aberrant muscle mass to estimate renal function immediately after CRRT cessation. We prospectively included 31 patients admitted to an academic intensive care unit (ICU) with a total of 37 CRRT cessations and measured serum creatinine before cessation (T1), directly (T2) and 5 h (T3) after cessation and the following two days when eGFR stabilized (T4, T5). We used the dynamic creatinine clearance calculation (D3C) equation to calculate eGFR (D3CGFR) and creatinine clearance (D3Ccreat) between T2-T3. D3Ccreat was corrected for aberrant muscle mass when a CT-scan was available using the CRAFT equation. We compared D3CGFR to stabilized CKD-EPI at T5 and D3CCreat to 4-h urinary creatinine clearance (4-h uCrCl) between T2-T3. We retrospectively validated these results in a larger retrospective cohort (NICE database; 1856 patients, 2064 cessations). The D3CGFR was comparable to observed stabilized CKD-EPI at T5 in the prospective cohort (MPE = - 1.6 ml/min/1.73 m2, p30 = 76%) and in the retrospective NICE-database (MPE = 3.2 ml/min/1.73 m2, p30 = 80%). In the prospective cohort, the D3CCreat had poor accuracy compared to 4-h uCrCl (MPE = 17 ml/min/1.73 m2, p30 = 24%). In a subset of patients (n = 13) where CT-scans were available, combination of CRAFT and D3CCreat improved bias and accuracy (MPE = 8 ml/min/1.73 m2, RMSE = 18 ml/min/1.73 m2) versus D3CCreat alone (MPE = 18 ml/min/1.73 m2, RMSE = 32 ml/min/1.73 m2). The D3CGFR improves assessment of eGFR in ICU patients immediately after CRRT cessation. Although the D3CCreat had poor association with underlying creatinine clearance, inclusion of CT derived biometric parameters in the dynamic renal function algorithm further improved the performance, stressing the role of muscle mass integration into renal function equations in critically ill patients.

Changes in quality of life 1 year after intensive care: a multicenter prospective cohort of ICU survivors

BACKGROUND

With survival rates of critical illness increasing, quality of life measures are becoming an important outcome of ICU treatment. Therefore, to study the impact of critical illness on quality of life, we explored quality of life before and 1 year after ICU admission in different subgroups of ICU survivors.

METHODS

Data from an ongoing prospective multicenter cohort study, the MONITOR-IC, were used. Patients admitted to the ICU in one of eleven participating hospitals between July 2016 and June 2021 were included. Outcome was defined as change in quality of life, measured using the EuroQol five-dimensional (EQ-5D-5L) questionnaire, and calculated by subtracting the EQ-5D-5L score 1 day before hospital admission from the EQ-5D-5L score 1 year post-ICU. Based on the minimal clinically important difference, a change in quality of life was defined as a change in EQ-5D-5L score of ≥ 0.08. Subgroups of patients were based on admission diagnosis.

RESULTS

A total of 3913 (50.6%) included patients completed both baseline and follow-up questionnaires. 1 year post-ICU, patients admitted after a cerebrovascular accident, intracerebral hemorrhage, or (neuro)trauma, on average experienced a significant decrease in quality of life. Conversely, 11 other subgroups of ICU survivors reported improvements in quality of life. The largest average increase in quality of life was seen in patients admitted due to respiratory disease (mean 0.17, SD 0.38), whereas the largest average decrease was observed in trauma patients (mean -0.13, SD 0.28). However, in each of the studied 22 subgroups there were survivors who reported a significant increase in QoL and survivors who reported a significant decrease in QoL.

CONCLUSIONS

This large prospective multicenter cohort study demonstrated the diversity in long-term quality of life between, and even within, subgroups of ICU survivors. These findings emphasize the need for personalized information and post-ICU care.

TRIAL REGISTRATION

The MONITOR-IC study was registered at ClinicalTrials.gov: NCT03246334 on August 2nd 2017.

Implementing an ICU registry in Ethiopia-Implications for critical care quality improvement

PURPOSE

Intensive care units (ICUs) in low- and middle-income countries have high mortality rates, and clinical data are needed to guide quality improvement (QI) efforts. This study utilizes data from a validated ICU registry specially developed for resource-limited settings to identify evidence-based QI priorities for ICUs in Ethiopia.

MATERIALS AND METHODS

A retrospective cohort analysis of data from two tertiary referral hospital ICUs in Addis Ababa, Ethiopia from July 2021-June 2022 was conducted to describe casemix, complications and outcomes and identify features associated with ICU mortality.

RESULTS

Among 496 patients, ICU mortality was 35.3%. The most common reasons for ICU admission were respiratory failure (24.0%), major head injury (17.5%) and sepsis/septic shock (13.3%). Complications occurred in 41.0% of patients. ICU mortality was higher among patients with respiratory failure (46.2%), sepsis (66.7%) and vasopressor requirements (70.5%), those admitted from the hospital ward (64.7%), and those experiencing major complications in the ICU (62.3%).

CONCLUSIONS

In this study, ICU mortality was high, and complications were common and associated with increased mortality. ICU registries are invaluable tools to understand local casemix and clinical outcomes, especially in resource-limited settings. These findings provide a foundation for QI efforts and a baseline to evaluate their impact.

The Influence of Potential Organ Donors on Standardized Mortality Ratios and ICU Benchmarking

OBJECTIVES

The standardized mortality ratio (SMR) is a common metric to benchmark ICUs. However, SMR may be artificially distorted by the admission of potential organ donors (POD), who have nearly 100% mortality, although risk prediction models may not identify them as high-risk patients. We aimed to evaluate the impact of PODs on SMR.

DESIGN

Retrospective registry-based multicenter study.

SETTING

Twenty ICUs in Finland, Estonia, and Switzerland in 2015-2017.

PATIENTS

Sixty thousand forty-seven ICU patients.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We used a previously validated mortality risk model to calculate the SMRs. We investigated the impact of PODs on the overall SMR, individual ICU SMR and ICU benchmarking. Of the 60,047 patients admitted to the ICUs, 514 (0.9%) were PODs, and 477 (93%) of them died. POD deaths accounted for 7% of the total 6738 in-hospital deaths. POD admission rates varied from 0.5 to 18.3 per 1000 admissions across ICUs. The risk prediction model predicted a 39% in-hospital mortality for PODs, but the observed mortality was 93%. The ratio of the SMR of the cohort without PODs to the SMR of the cohort with PODs was 0.96 (95% CI, 0.93-0.99). Benchmarking results changed in 70% of ICUs after excluding PODs.

CONCLUSIONS

Despite their relatively small overall number, PODs make up a large proportion of ICU patients who die. PODs cause bias in SMRs and in ICU benchmarking. We suggest excluding PODs when benchmarking ICUs with SMR.

Consensus-based indicators for evaluating and improving the quality of regional collaborative networks of intensive care units: Results of a nationwide Delphi study

PURPOSE

To select a consensus-based set of relevant and feasible indicators for monitoring and improving the quality of regional ICU network collaboratives.

METHODS

A three-round Delphi study was conducted in the Netherlands between April and July 2022. A multidisciplinary expert panel prioritized potentially relevant and feasible indicators in two questionnaire rounds with two consensus meetings between both rounds. The RAND/UCLA appropriateness method was used to categorize indicators and synthesize results. A core set of highest ranked indicators with consensus-based levels of relevance and feasibility were finally tested in two ICU networks to assess their measurability.

RESULTS

Twenty-four indicators were deemed as relevant and feasible. Seven indicators were selected for the core set measuring the standardized mortality rate in the region (n = 1) and evaluating the presence, content and/or follow-up of a formal plan describing network structures and policy agreements (n = 3), a long-term network vision statement (n = 1), and network meetings to reflect on and learn from outcome data (n = 2). The practice tests led to minor reformulations.

CONCLUSIONS

This study generated relevant and feasible indicators for monitoring and improving the quality of ICU network collaboratives based on the collective opinion of various experts. The indicators may help to effectively govern such networks.

Opportunities and barriers for prehospital emergency medical services research in the Netherlands; results of a mixed-methods consensus study

INTRODUCTION

Quality improvement in prehospital emergency medical services (EMS) can only be achieved by high-quality research and critical appraisal of current practices. This study examines current opportunities and barriers in EMS research in the Netherlands.

METHODS

This mixed-methods consensus study consisted of three phases. The first phase consisted of semi-structured interviews with relevant stakeholders. Thematic analysis of qualitative data derived from these interviews was used to identify main themes, which were subsequently discussed in several online focus groups in the second phase. Output from these discussions was used to shape statements for an online Delphi consensus study among relevant stakeholders in EMS research. Consensus was met if 80% of respondents agreed or disagreed on a particular statement.

RESULTS

Forty-nine stakeholders participated in the study; qualitative thematic analysis of the interviews and focus group discussions identified four main themes: (1) data registration and data sharing, (2) laws and regulations, (3) financial aspects and funding, and (4) organization and culture. Qualitative data from the first two phases of the study were used to construct 33 statements for an online Delphi study. Consensus was reached on 21 (64%) statements. Eleven (52%) of these statements pertained to the storage and use of EMS patient data.

CONCLUSION

Barriers for prehospital EMS research in the Netherlands include issues regarding the use of patient data, privacy and legislation, funding and research culture in EMS organizations. Opportunities to increase scientific productivity in EMS research include the development of a national strategy for EMS data and the incorporation of EMS topics in research agendas of national medical professional associations.

Prediction of Long-Term Physical, Mental, and Cognitive Problems Following Critical Illness: Development and External Validation of the PROSPECT Prediction Model

OBJECTIVES

ICU survivors often suffer from long-lasting physical, mental, and cognitive health problems after hospital discharge. As several interventions that treat or prevent these problems already start during ICU stay, patients at high risk should be identified early. This study aimed to develop a model for early prediction of post-ICU health problems within 48 hours after ICU admission.

DESIGN

Prospective cohort study in seven Dutch ICUs.

SETTING/PATIENTS

ICU patients older than 16 years and admitted for greater than or equal to 12 hours between July 2016 and March 2020.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Outcomes were physical problems (fatigue or ≥ 3 new physical symptoms), mental problems (anxiety, depression, or post-traumatic stress disorder), and cognitive impairment. Patient record data and questionnaire data were collected at ICU admission, and after 3 and 12 months, of 2,476 patients. Several models predicting physical, mental, or cognitive problems and a composite score at 3 and 12 months were developed using variables collected within 48 hours after ICU admission. Based on performance and clinical feasibility, a model, PROSPECT, predicting post-ICU health problems at 3 months was chosen, including the predictors of chronic obstructive pulmonary disease, admission type, expected length of ICU stay greater than or equal to 2 days, and preadmission anxiety and fatigue. Internal validation using bootstrapping on data of the largest hospital ( n = 1,244) yielded a C -statistic of 0.73 (95% CI, 0.70-0.76). External validation was performed on data ( n = 864) from the other six hospitals with a C -statistic of 0.77 (95% CI, 0.73-0.80).

CONCLUSIONS

The developed and externally validated PROSPECT model can be used within 48 hours after ICU admission for identifying patients with an increased risk of post-ICU problems 3 months after ICU admission. Timely preventive interventions starting during ICU admission and follow-up care can prevent or mitigate post-ICU problems in these high-risk patients.

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 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.

Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges

BACKGROUND

Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data.

METHODS

Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status.

RESULTS

A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71-0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74-0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables.

CONCLUSIONS

We developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making.

Impact of the COVID-19 pandemic on prevalence of highly resistant microorganisms in hospitalised patients in the Netherlands, March 2020 to August 2022

BackgroundThe COVID-19 pandemic resulted in adaptation in infection control measures, increased patient transfer, high occupancy of intensive cares, downscaling of non-urgent medical procedures and decreased travelling.AimTo gain insight in the influence of these changes on antimicrobial resistance (AMR) prevalence in the Netherlands, a country with a low AMR prevalence, we estimated changes in demographics and prevalence of six highly resistant microorganisms (HRMO) in hospitalised patients in the Netherlands during COVID-19 waves (March-June 2020, October 2020-June 2021, October 2021-May 2022 and June-August 2022) and interwaves (July-September 2020 and July-September 2021) compared with pre-COVID-19 (March 2019-February 2020).MethodsWe investigated data on routine bacteriology cultures of hospitalised patients, obtained from 37 clinical microbiological laboratories participating in the national AMR surveillance. Demographic characteristics and HRMO prevalence were calculated as proportions and rates per 10,000 hospital admissions.ResultsAlthough no significant persistent changes in HRMO prevalence were detected, some relevant non-significant patterns were recognised in intensive care units. Compared with pre-COVID-19 we found a tendency towards higher prevalence of meticillin-resistant Staphylococcus aureus during waves and lower prevalence of multidrug-resistant Pseudomonas aeruginosa during interwaves. Additionally, during the first three waves, we observed significantly higher proportions and rates of cultures with Enterococcus faecium (pooled 10% vs 6% and 240 vs 120 per 10,000 admissions) and coagulase-negative Staphylococci (pooled 21% vs 14% and 500 vs 252 per 10,000 admissions) compared with pre-COVID-19.ConclusionWe observed no substantial changes in HRMO prevalence in hospitalised patients during the COVID-19 pandemic.

Influenza Season and Outcome After Elective Cardiac Surgery: An Observational Cohort Study

BACKGROUND

An asymptomatic respiratory viral infection during cardiac surgery could lead to pulmonary complications and increased mortality. For elective surgery, testing for respiratory viral infection before surgery or vaccination could reduce the number of these pulmonary complications. The aim of this study was to investigate the association between influenzalike illness (ILI) seasons and prolonged mechanical ventilation and inhospital mortality in a Dutch cohort of adult elective cardiac surgery patients.

METHODS

Cardiac surgery patients who were admitted to the intensive care unit between January 1, 2014, and February 1, 2020, were included. The primary endpoint was the duration of invasive mechanical ventilation in the ILI season compared with baseline season. Secondary endpoints were the median Pao2 to fraction of inspired oxygen ratio on days 1, 3, and 7 and postoperative inhospital mortality.

RESULTS

A total of 42,277 patients underwent cardiac surgery, 12,994 (30.7%) in the ILI season, 15,843 (37.5%) in the intermediate season, and 13,440 (31.8%) in the baseline season. No hazard rates indicative of a longer duration of invasive mechanical ventilation during the ILI season were found. No differences were found for the median Pao2 to fraction of inspired oxygen ratio between seasons. However, inhospital mortality was higher in the ILI season compared with baseline season (odds ratio 1.67; 95% CI, 1.14-2.46).

CONCLUSIONS

Patients undergoing cardiac surgery during the ILI season were at increased risk of inhospital mortality compared with patients in the baseline season. No evidence was found that this difference is caused by direct postoperative pulmonary complications.

Acute kidney injury associated with nephrotoxic drugs in critically ill patients: a multicenter cohort study using electronic health record data

Background

Nephrotoxic drugs frequently cause acute kidney injury (AKI) in adult intensive care unit (ICU) patients. However, there is a lack of large pharmaco-epidemiological studies investigating the associations between drugs and AKI. Importantly, AKI risk factors may also be indications or contraindications for drugs and thereby confound the associations. Here, we aimed to estimate the associations between commonly administered (potentially) nephrotoxic drug groups and AKI in adult ICU patients whilst adjusting for confounding.

Methods

In this multicenter retrospective observational study, we included adult ICU admissions to 13 Dutch ICUs. We measured exposure to 44 predefined (potentially) nephrotoxic drug groups. The outcome was AKI during ICU admission. The association between each drug group and AKI was estimated using etiological cause-specific Cox proportional hazard models and adjusted for confounding. To facilitate an (independent) informed assessment of residual confounding, we manually identified drug group-specific confounders using a large drug knowledge database and existing literature.

Results

We included 92 616 ICU admissions, of which 13 492 developed AKI (15%). We found 14 drug groups to be associated with a higher hazard of AKI after adjustment for confounding. These groups included established (e.g. aminoglycosides), less well established (e.g. opioids) and controversial (e.g. sympathomimetics with α- and β-effect) drugs.

Conclusions

The results confirm existing insights and provide new ones regarding drug associated AKI in adult ICU patients. These insights warrant caution and extra monitoring when prescribing nephrotoxic drugs in the ICU and indicate which drug groups require further investigation.

Conservative versus Liberal Oxygenation Targets in Intensive Care Unit Patients (ICONIC): A Randomized Clinical Trial

Rationale: Supplemental oxygen is widely administered to ICU patients, but appropriate oxygenation targets remain unclear. Objectives: This study aimed to determine whether a low-oxygenation strategy would lower 28-day mortality compared with a high-oxygenation strategy. Methods: This randomized multicenter trial included mechanically ventilated ICU patients with an expected ventilation duration of at least 24 hours. Patients were randomized 1:1 to a low-oxygenation (PaO2, 55-80 mm Hg; or oxygen saturation as measured by pulse oximetry, 91-94%) or high-oxygenation (PaO2, 110-150 mm Hg; or oxygen saturation as measured by pulse oximetry, 96-100%) target until ICU discharge or 28 days after randomization, whichever came first. The primary outcome was 28-day mortality. The study was stopped prematurely because of the COVID-19 pandemic when 664 of the planned 1,512 patients were included. Measurements and Main Results: Between November 2018 and November 2021, a total of 664 patients were included in the trial: 335 in the low-oxygenation group and 329 in the high-oxygenation group. The median achieved PaO2 was 75 mm Hg (interquartile range, 70-84) and 115 mm Hg (interquartile range, 100-129) in the low- and high-oxygenation groups, respectively. At Day 28, 129 (38.5%) and 114 (34.7%) patients had died in the low- and high-oxygenation groups, respectively (risk ratio, 1.11; 95% confidence interval, 0.9-1.4; P = 0.30). At least one serious adverse event was reported in 12 (3.6%) and 17 (5.2%) patients in the low- and high-oxygenation groups, respectively. Conclusions: Among mechanically ventilated ICU patients with an expected mechanical ventilation duration of at least 24 hours, using a low-oxygenation strategy did not result in a reduction of 28-day mortality compared with a high-oxygenation strategy. Clinical trial registered with the National Trial Register and the International Clinical Trials Registry Platform (NTR7376).

Automated identification of patient subgroups: A case-study on mortality of COVID-19 patients admitted to the ICU

BACKGROUND

- Subgroup discovery (SGD) is the automated splitting of the data into complex subgroups. Various SGD methods have been applied to the medical domain, but none have been extensively evaluated. We assess the numerical and clinical quality of SGD methods.

METHOD

- We applied the improved Subgroup Set Discovery (SSD++), Patient Rule Induction Method (PRIM) and APRIORI - Subgroup Discovery (APRIORI-SD) algorithms to obtain patient subgroups on observational data of 14,548 COVID-19 patients admitted to 73 Dutch intensive care units. Hospital mortality was the clinical outcome. Numerical significance of the subgroups was assessed with information-theoretic measures. Clinical significance of the subgroups was assessed by comparing variable importance on population and subgroup levels and by expert evaluation.

RESULTS

- The tested algorithms varied widely in the total number of discovered subgroups (5-62), the number of selected variables, and the predictive value of the subgroups. Qualitative assessment showed that the found subgroups make clinical sense. SSD++ found most subgroups (n = 62), which added predictive value and generally showed high potential for clinical use. APRIORI-SD and PRIM found fewer subgroups (n = 5 and 6), which did not add predictive value and were clinically less relevant.

CONCLUSION

- Automated SGD methods find clinical subgroups that are relevant when assessed quantitatively (yield added predictive value) and qualitatively (intensivists consider the subgroups significant). Different methods yield different subgroups with varying degrees of predictive performance and clinical quality. External validation is needed to generalize the results to other populations and future research should explore which algorithm performs best in other settings.

The daily updated Dutch national database on COVID-19 epidemiology, vaccination and sewage surveillance

The Dutch national open database on COVID-19 has been incrementally expanded since its start on 30 April 2020 and now includes datasets on symptoms, tests performed, individual-level positive cases and deaths, cases and deaths among vulnerable populations, settings of transmission, hospital and ICU admissions, SARS-CoV-2 variants, viral loads in sewage, vaccinations and the effective reproduction number. This data is collected by municipal health services, laboratories, hospitals, sewage treatment plants, vaccination providers and citizens and is cleaned, analysed and published, mostly daily, by the National Institute for Public Health and the Environment (RIVM) in the Netherlands, using automated scripts. Because these datasets cover the key aspects of the pandemic and are available at detailed geographical level, they are essential to gain a thorough understanding of the past and current COVID-19 epidemiology in the Netherlands. Future purposes of these datasets include country-level comparative analysis on the effect of non-pharmaceutical interventions against COVID-19 in different contexts, such as different cultural values or levels of socio-economic disparity, and studies on COVID-19 and weather factors.

Assessing the FAIRness of databases on the EHDEN portal: A case study on two Dutch ICU databases

OBJECTIVE

To address the growing need for effective data reuse in health research, healthcare institutions need to make their data Findable, Accessible, Interoperable, and Reusable (FAIR). A prevailing method to model databases for interoperability is the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), developed by the Observational Health Data Sciences and Informatics (OHDSI) initiative. A European repository for OMOP CDM-converted databases called the "European Health Data & Evidence Network (EHDEN) portal" was developed, aiming to make these databases Findable and Accessible. This paper aims to assess the FAIRness of databases on the EHDEN portal.

MATERIALS AND METHODS

Two researchers involved in the OMOP CDM conversion of separate Dutch Intensive Care Unit (ICU) research databases each manually assessed their own database using seventeen metrics. These were defined by the FAIRsFAIR project as a list of minimum requirements for a database to be FAIR. Each metric is given a score from zero to four based on how well the database adheres to the metric. The maximum score for each metric varies from one to four based on the importance of the metric.

RESULTS

Fourteen out of the seventeen metrics were unanimously rated: seven were rated the highest score, one was rated half of the highest score, and five were rated the lowest score. The remaining three metrics were assessed differently for the two use cases. The total scores achieved were 15.5 and 12 out of a maximum of 25.

CONCLUSION

The main omissions in supporting FAIRness were the lack of globally unique identifiers such as Uniform Resource Identifiers (URIs) in the OMOP CDM and the lack of metadata standardization and linkage in the EHDEN portal. By implementing these in future updates, the EHDEN portal can be more FAIR.

Development and External Validation of a Prediction Model for Quality of Life of ICU Survivors: A Subanalysis of the MONITOR-IC Prospective Cohort Study

OBJECTIVES

To develop and externally validate a prediction model for ICU survivors' change in quality of life 1 year after ICU admission that can support ICU physicians in preparing patients for life after ICU and managing their expectations.

DESIGN

Data from a prospective multicenter cohort study (MONITOR-IC) were used.

SETTING

Seven hospitals in the Netherlands.

PATIENTS

ICU survivors greater than or equal to 16 years old.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Outcome was defined as change in quality of life, measured using the EuroQol 5D questionnaire. The developed model was based on data from an academic hospital, using multivariable linear regression analysis. To assist usability, variables were selected using the least absolute shrinkage and selection operator method. External validation was executed using data of six nonacademic hospitals. Of 1,804 patients included in analysis, 1,057 patients (58.6%) were admitted to the academic hospital, and 747 patients (41.4%) were admitted to a nonacademic hospital. Forty-nine variables were entered into a linear regression model, resulting in an explained variance ( R2 ) of 56.6%. Only three variables, baseline quality of life, admission type, and Glasgow Coma Scale, were selected for the final model ( R2 = 52.5%). External validation showed good predictive power ( R2 = 53.2%).

CONCLUSIONS

This study developed and externally validated a prediction model for change in quality of life 1 year after ICU admission. Due to the small number of predictors, the model is appealing for use in clinical practice, where it can be implemented to prepare patients for life after ICU. The next step is to evaluate the impact of this prediction model on outcomes and experiences of patients.

Drug-related causes attributed to acute kidney injury and their documentation in intensive care patients

PURPOSE

To investigate drug-related causes attributed to acute kidney injury (DAKI) and their documentation in patients admitted to the Intensive Care Unit (ICU).

METHODS

This study was conducted in an academic hospital in the Netherlands by reusing electronic health record (EHR) data of adult ICU admissions between November 2015 to January 2020. First, ICU admissions with acute kidney injury (AKI) stage 2 or 3 were identified. Subsequently, three modes of DAKI documentation in EHR were examined: diagnosis codes (structured data), allergy module (semi-structured data), and clinical notes (unstructured data).

RESULTS

n total 8124 ICU admissions were included, with 542 (6.7%) ICU admissions experiencing AKI stage 2 or 3. The ICU physicians deemed 102 of these AKI cases (18.8%) to be drug-related. These DAKI cases were all documented in the clinical notes (100%), one in allergy module (1%) and none via diagnosis codes. The clinical notes required the highest time investment to analyze.

CONCLUSIONS

Drug-related causes comprise a substantial part of AKI in the ICU patients. However, current unstructured DAKI documentation practice via clinical notes hampers our ability to gain better insights about DAKI occurrence. Therefore, both automating DAKI identification from the clinical notes and increasing structured DAKI documentation should be encouraged.

Predicting Readmission or Death After Discharge From the ICU: External Validation and Retraining of a Machine Learning Model

OBJECTIVES

Many machine learning (ML) models have been developed for application in the ICU, but few models have been subjected to external validation. The performance of these models in new settings therefore remains unknown. The objective of this study was to assess the performance of an existing decision support tool based on a ML model predicting readmission or death within 7 days after ICU discharge before, during, and after retraining and recalibration.

DESIGN

A gradient boosted ML model was developed and validated on electronic health record data from 2004 to 2021. We performed an independent validation of this model on electronic health record data from 2011 to 2019 from a different tertiary care center.

SETTING

Two ICUs in tertiary care centers in The Netherlands.

PATIENTS

Adult patients who were admitted to the ICU and stayed for longer than 12 hours.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We assessed discrimination by area under the receiver operating characteristic curve (AUC) and calibration (slope and intercept). We retrained and recalibrated the original model and assessed performance via a temporal validation design. The final retrained model was cross-validated on all data from the new site. Readmission or death within 7 days after ICU discharge occurred in 577 of 10,052 ICU admissions (5.7%) at the new site. External validation revealed moderate discrimination with an AUC of 0.72 (95% CI 0.67-0.76). Retrained models showed improved discrimination with AUC 0.79 (95% CI 0.75-0.82) for the final validation model. Calibration was poor initially and good after recalibration via isotonic regression.

CONCLUSIONS

In this era of expanding availability of ML models, external validation and retraining are key steps to consider before applying ML models to new settings. Clinicians and decision-makers should take this into account when considering applying new ML models to their local settings.

Real-world Evidence of the Effects of Novel Treatments for COVID-19 on Mortality: A Nationwide Comparative Cohort Study of Hospitalized Patients in the First, Second, Third, and Fourth Waves in the Netherlands

Background

Large clinical trials on drugs for hospitalized coronavirus disease 2019 (COVID-19) patients have shown significant effects on mortality. There may be a discrepancy with the observed real-world effect. We describe the clinical characteristics and outcomes of hospitalized COVID-19 patients in the Netherlands during 4 pandemic waves and analyze the association of the newly introduced treatments with mortality, intensive care unit (ICU) admission, and discharge alive.

Methods

We conducted a nationwide retrospective analysis of hospitalized COVID-19 patients between February 27, 2020, and December 31, 2021. Patients were categorized into waves and into treatment groups (hydroxychloroquine, remdesivir, neutralizing severe acute respiratory syndrome coronavirus 2 monoclonal antibodies, corticosteroids, and interleukin [IL]-6 antagonists). Four types of Cox regression analyses were used: unadjusted, adjusted, propensity matched, and propensity weighted.

Results

Among 5643 patients from 11 hospitals, we observed a changing epidemiology during 4 pandemic waves, with a decrease in median age (67-64 years; P < .001), in in-hospital mortality on the ward (21%-15%; P < .001), and a trend in the ICU (24%-16%; P = .148). In ward patients, hydroxychloroquine was associated with increased mortality (1.54; 95% CI, 1.22-1.96), and remdesivir was associated with a higher rate of discharge alive within 29 days (1.16; 95% CI, 1.03-1.31). Corticosteroids were associated with a decrease in mortality (0.82; 95% CI, 0.69-0.96); the results of IL-6 antagonists were inconclusive. In patients directly admitted to the ICU, hydroxychloroquine, corticosteroids, and IL-6 antagonists were not associated with decreased mortality.

Conclusions

Both remdesivir and corticosteroids were associated with better outcomes in ward patients with COVID-19. Continuous evaluation of real-world treatment effects is needed.

Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records

PURPOSE

To assess, validate and compare the predictive performance of models for in-hospital mortality of COVID-19 patients admitted to the intensive care unit (ICU) over two different waves of infections. Our models were built with high-granular Electronic Health Records (EHR) data versus less-granular registry data.

METHODS

Observational study of all COVID-19 patients admitted to 19 Dutch ICUs participating in both the national quality registry National Intensive Care Evaluation (NICE) and the EHR-based Dutch Data Warehouse (hereafter EHR). Multiple models were developed on data from the first 24 h of ICU admissions from February to June 2020 (first COVID-19 wave) and validated on prospective patients admitted to the same ICUs between July and December 2020 (second COVID-19 wave). We assessed model discrimination, calibration, and the degree of relatedness between development and validation population. Coefficients were used to identify relevant risk factors.

RESULTS

A total of 1533 patients from the EHR and 1563 from the registry were included. With high granular EHR data, the average AUROC was 0.69 (standard deviation of 0.05) for the internal validation, and the AUROC was 0.75 for the temporal validation. The registry model achieved an average AUROC of 0.76 (standard deviation of 0.05) in the internal validation and 0.77 in the temporal validation. In the EHR data, age, and respiratory-system related variables were the most important risk factors identified. In the NICE registry data, age and chronic respiratory insufficiency were the most important risk factors.

CONCLUSION

In our study, prognostic models built on less-granular but readily-available registry data had similar performance to models built on high-granular EHR data and showed similar transportability to a prospective COVID-19 population. Future research is needed to verify whether this finding can be confirmed for upcoming waves.

Comparison of patient characteristics and long‐term mortality between transferred and non‐transferred COVID‐19 patients in Dutch Intensive Care Units; A national cohort study

Background

COVID‐19 patients were often transferred to other intensive care units (ICUs) to prevent that ICUs would reach their maximum capacity. However, transferring ICU patients is not free of risk. We aim to compare the characteristics and outcomes of transferred versus non‐transferred COVID‐19 ICU patients in the Netherlands.

Methods

We included adult COVID‐19 patients admitted to Dutch ICUs between March 1, 2020 and July 1, 2021. We compared the patient characteristics and outcomes of non‐transferred and transferred patients and used a Directed Acyclic Graph to identify potential confounders in the relationship between transfer and mortality. We used these confounders in a Cox regression model with left truncation at the day of transfer to analyze the effect of transfers on mortality during the 180 days after ICU admission.

Results

We included 10,209 patients: 7395 non‐transferred and 2814 (27.6%) transferred patients. In both groups, the median age was 64 years. Transferred patients were mostly ventilated at ICU admission (83.7% vs. 56.2%) and included a larger proportion of low‐risk patients (70.3% vs. 66.5% with mortality risk <30%). After adjusting for age, APACHE IV mortality probability, BMI, mechanical ventilation, and vasoactive medication use, the hazard of mortality during the first 180 days was similar for transferred patients compared to non‐transferred patients (HR [95% CI] = 0.99 [0.91–1.08]).

Conclusions

Transferred COVID‐19 patients are more often mechanically ventilated and are less severely ill compared to non‐transferred patients. Furthermore, transferring critically ill COVID‐19 patients in the Netherlands is not associated with mortality during the first 180 days after ICU admission.

A Quality Improvement Project to Support Post-Intensive Care Unit Patients with COVID-19: Structured Telephone Support

BACKGROUND

More than 50% of intensive care unit (ICU) survivors suffer from long-lasting physical, psychosocial, and cognitive health impairments, also called "post-intensive care syndrome" (PICS). Intensive care admission during the COVID-19 pandemic was especially uncertain and stressful, both for patients and for their family. An additional risk of developing symptoms of PICS was feared in the absence of structural aftercare for the patient and family shortly after discharge from the hospital. The purpose of this quality improvement study was to identify PICS symptoms and to support post-intensive care patients and families in the transition from the hospital to the home. Therefore, we offered post-ICU patients and families structured telephone support (STS).

METHODS

This was a quality improvement study during the 2019 COVID-19 pandemic. A project team developed and implemented a tool to structure telephone calls to identify and order symptoms according to the PICS framework and to give individual support based on this information. We supported post-ICU patients diagnosed with COVID-19 pneumonia and their family caregivers within four weeks after hospital discharge. The reported findings were both quantitative and qualitative.

RESULTS

Forty-six post-ICU patients received structured telephone support and reported symptoms in at least one of the three domains of the PICS framework. More than half of the patients experienced a loss of strength or condition and fatigue. Cognitive and psychological impairments were reported less frequently. Family caregivers reported fewer impairments concerning fatigue and sleeping problems and expressed a need for a continuity of care. Based on the obtained information, the ICU nurse practitioners were able to check if individual care plans were optimal and clear and, if indicated, initiated disciplines to optimize further follow-up.

CONCLUSIONS

The implementation of the STS tool gave insight in the impairments of post-ICU patients. Surprisingly, family caregivers expressed fewer impairments. Giving support early after hospital discharge in a structured way may contribute to providing guidance in the individual care plans and treatment of the early symptoms of PICS (-F).

Trends in Early and Late Mortality in Patients With Severe Acute Pancreatitis Admitted to ICUs: A Nationwide Cohort Study

OBJECTIVES

To investigate national mortality trends over a 12-year period for patients with severe acute pancreatitis (SAP) admitted to Dutch ICUs. Additionally, an assessment of outcome in SAP was undertaken to differentiate between early (< 14 d of ICU admission) and late (> 14 d of ICU admission) mortality.

DESIGN

Data from the Dutch National Intensive Care Evaluation and health insurance companies' databases were extracted. Outcomes included 14-day, ICU, hospital, and 1-year mortality. Mortality before and after 2010 was compared using mixed logistic regression and mixed Cox proportional-hazards models. Sensitivity analyses, excluding early mortality, were performed to assess trends in late mortality.

SETTING

Not applicable.

PATIENTS

Consecutive adult patients with SAP admitted to all 81 Dutch ICUs between 2007 and 2018.

INTERVENTIONS

Not applicable.

MEASUREMENTS AND MAIN RESULTS

Among 4,160 patients treated in 81 ICUs, 14-day mortality was 17%, ICU mortality 17%, hospital mortality 23%, and 1-year mortality 33%. After 2010 in-hospital mortality adjusted for age, sex, modified Marshall, and Acute Physiology and Chronic Health Evaluation III scores were lower (odds ratio [OR], 0.76; 95% CI, 0.61-0.94) than before 2010. There was no change in ICU and 1-year mortality. Sensitivity analyses excluding patients with early mortality demonstrated a decreased ICU mortality (OR, 0.45; 95% CI, 0.32-0.64), decreased in-hospital (OR, 0.48; 95% CI, 0.36-0.63), and decreased 1-year mortality (hazard ratio, 0.81; 95% CI, 0.68-0.96) after 2010 compared with 2007-2010.

CONCLUSIONS

Over the 12-year period examined, mortality in patients with SAP admitted to Dutch ICUs did not change, although after 2010 late mortality decreased. Novel therapies should focus on preventing early mortality in SAP.

The effect of treatment and clinical course during Emergency Department stay on severity scoring and predicted mortality risk in Intensive Care patients

Background

Treatment and the clinical course during Emergency Department (ED) stay before Intensive Care Unit (ICU) admission may affect predicted mortality risk calculated by the Acute Physiology and Chronic Health Evaluation (APACHE)-IV, causing lead-time bias. As a result, comparing standardized mortality ratios (SMRs) among hospitals may be difficult if they differ in the location where initial stabilization takes place. The aim of this study was to assess to what extent predicted mortality risk would be affected if the APACHE-IV score was recalculated with the initial physiological variables from the ED. Secondly, to evaluate whether ED Length of Stay (LOS) was associated with a change (delta) in these APACHE-IV scores.

Methods

An observational multicenter cohort study including ICU patients admitted from the ED. Data from two Dutch quality registries were linked: the Netherlands Emergency department Evaluation Database (NEED) and the National Intensive Care Evaluation (NICE) registry. The ICU APACHE-IV, predicted mortality, and SMR based on data of the first 24 h of ICU admission were compared with an ED APACHE-IV model, using the most deviating physiological variables from the ED or ICU.

Results

A total of 1398 patients were included. The predicted mortality from the ICU APACHE-IV (median 0.10; IQR 0.03–0.30) was significantly lower compared to the ED APACHE-IV model (median 0.13; 0.04–0.36; p < 0.01). The SMR changed from 0.63 (95%CI 0.54–0.72) to 0.55 (95%CI 0.47–0.63) based on ED APACHE-IV. Predicted mortality risk changed more than 5% in 321 (23.2%) patients by using the ED APACHE-IV. ED LOS > 3.9 h was associated with a slight increase in delta APACHE-IV of 1.6 (95% CI 0.4–2.8) compared to ED LOS < 1.7 h.

Conclusion

Predicted mortality risks and SMRs calculated by the APACHE IV scores are not directly comparable in patients admitted from the ED if hospitals differ in their policy to stabilize patients in the ED before ICU admission. Future research should focus on developing models to adjust for these differences.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-03986-2.

Using real-world data to monitor and improve quality of care in coronary artery disease: results from the Netherlands Heart Registration

Worldwide, quality registries for cardiovascular diseases enable the use of real-world data to monitor and improve the quality of cardiac care. In the Netherlands Heart Registration (NHR), cardiologists and cardiothoracic surgeons register baseline, procedural and outcome data across all invasive cardiac interventional, electrophysiological and surgical procedures. This paper provides insight into the governance and processes as organised by the NHR in collaboration with the hospitals. To clarify the processes, examples are given from the percutaneous coronary intervention and coronary artery bypass grafting registries. Physicians who are mandated by their hospital to instruct the NHR to process their data are united in registration committees. The committees determine standard sets of variables and periodically discuss the completeness and quality of data and patient-relevant outcomes. In the case of significant variation in outcomes, processes of healthcare delivery are discussed and good practices are shared in a non-competitive and safe setting. To create new insights for further improvement in patient-relevant outcomes, quality projects are initiated on, for example, multivessel disease treatment, cardiogenic shock and diagnostic intracoronary procedures. Moreover, possibilities are explored to expand the quality registries through additional relevant indicators, such as resource use before and after the procedure, by enriching NHR data with other existing data resources.

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

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 24h, 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 24h 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 24h after admission showed small (but significantly) performance increase.

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge

OBJECTIVES

The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective.

METHODS

A 1-year, 7-state Markov model reflecting the ICU care pathway and incorporating the PC decision tool was developed. A hypothetical cohort of 1000 adult Dutch patients admitted in the ICU was entered in the model. We used the literature, expert opinion, and data from Amsterdam University Medical Center for model parameters. The uncertainty surrounding the incremental cost-effectiveness ratio was assessed using deterministic and probabilistic sensitivity analyses and scenario analyses.

RESULTS

PC was a cost-effective strategy with an incremental cost-effectiveness ratio of €18 507 per quality-adjusted life-year. PC remained cost-effective over standard care in multiple scenarios and sensitivity analyses. The likelihood that PC will be cost-effective was 71% at a willingness-to-pay threshold of €30 000 per quality-adjusted life-year. The key driver of the results was the parameter "reduction in ICU length of stay."

CONCLUSIONS

We showed that PC has the potential to be cost-effective for Dutch ICUs in a time horizon of 1 year. This study is one of the first cost-effectiveness analyses of a machine learning device. Further research is needed to validate the effectiveness of PC, thereby focusing on the key parameter "reduction in ICU length of stay" and potential spill-over effects.

ICONIC study—conservative versus conventional oxygenation targets in intensive care patients: study protocol for a randomized clinical trial

Background

Oxygen therapy is a widely used intervention in acutely ill patients in the intensive care unit (ICU). It is established that not only hypoxia, but also prolonged hyperoxia is associated with poor patient-centered outcomes. Nevertheless, a fundamental knowledge gap remains regarding optimal oxygenation for critically ill patients. In this randomized clinical trial, we aim to compare ventilation that uses conservative oxygenation targets with ventilation that uses conventional oxygen targets with respect to mortality in ICU patients.

Methods

The “ConservatIve versus CONventional oxygenation targets in Intensive Care patients” trial (ICONIC) is an investigator-initiated, international, multicenter, randomized clinical two-arm trial in ventilated adult ICU patients. The ICONIC trial will run in multiple ICUs in The Netherlands and Italy to enroll 1512 ventilated patients. ICU patients with an expected mechanical ventilation time of more than 24 h are randomized to a ventilation strategy that uses conservative (PaO₂ 55–80 mmHg (7.3–10.7 kPa)) or conventional (PaO₂ 110–150 mmHg (14.7–20 kPa)) oxygenation targets. The primary endpoint is 28-day mortality. Secondary endpoints are ventilator-free days at day 28, ICU mortality, in-hospital mortality, 90-day mortality, ICU- and hospital length of stay, ischemic events, quality of life, and patient opinion of research and consent in the emergency setting.

Discussion

The ICONIC trial is expected to provide evidence on the effects of conservative versus conventional oxygenation targets in the ICU population. This study may guide targeted oxygen therapy in the future.

Trial registration

Trialregister.nl NTR7376. Registered on 20 July, 2018.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13613-021-00978-3.

Comparison of outcome and characteristics between 6343 COVID-19 patients and 2256 other community-acquired viral pneumonia patients admitted to Dutch ICUs

Purpose

Describe the differences in characteristics and outcomes between COVID-19 and other viral pneumonia patients admitted to Dutch ICUs.

Materials and methods

Data from the National-Intensive-Care-Evaluation-registry of COVID-19 patients admitted between February 15th and January 1th 2021 and other viral pneumonia patients admitted between January 1st 2017 and January 1st 2020 were used. Patients' characteristics, the unadjusted, and adjusted in-hospital mortality were compared.

Results

6343 COVID-19 and 2256 other viral pneumonia patients from 79 ICUs were included. The COVID-19 patients included more male (71.3 vs 49.8%), had a higher Body-Mass-Index (28.1 vs 25.5), less comorbidities (42.2 vs 72.7%), and a prolonged hospital length of stay (19 vs 9 days). The COVID-19 patients had a significantly higher crude in-hospital mortality rate (Odds ratio (OR) = 1.80), after adjustment for patient characteristics and ICU occupancy rate the OR was respectively 3.62 and 3.58.

Conclusion

Higher mortality among COVID-19 patients could not be explained by patient characteristics and higher ICU occupancy rates, indicating that COVID-19 is more severe compared to other viral pneumonia. Our findings confirm earlier warnings of a high need of ICU capacity and high mortality rates among relatively healthy COVID-19 patients as this may lead to a higher mental workload for the staff.

Incorrect application of the KDIGO acute kidney injury staging criteria

Background

Recent research demonstrated substantial heterogeneity in the Kidney Disease: Improving Global Outcomes (KDIGO) acute kidney injury (AKI) diagnosis and staging criteria implementations in clinical research. Here we report an additional issue in the implementation of the criteria: the incorrect description and application of a stage 3 serum creatinine (SCr) criterion. Instead of an increase in SCr to or beyond 4.0 mg/dL, studies apparently interpreted this criterion as an increase in SCr by 4.0 mg/dL.

Methods

Using a sample of 8124 consecutive intensive care unit (ICU) admissions, we illustrate the implications of such incorrect application. The AKI stage distributions associated with the correct and incorrect stage 3 SCr criterion implementations were compared, both with and without the stage 3 renal replacement therapy (RRT) criterion. In addition, we compared chronic kidney disease presence, ICU mortality rates and hospital mortality rates associated with each of the AKI stages and the misclassified cases.

Results

Where incorrect implementation of the SCr stage 3 criterion showed a stage 3 AKI rate of 29%, correct implementation revealed a rate of 34%, mainly due to shifts from stage 1 to stage 3. Without the stage 3 RRT criterion, the stage 3 AKI rates were 9% and 19% after incorrect and correct implementation, respectively. The ICU and hospital mortality rates in cases misclassified as stage 1 or 2 were similar to those in cases correctly classified as stage 1 instead of stage 3.

Conclusions

While incorrect implementation of the SCr stage 3 criterion has significant consequences for AKI severity epidemiology, consequences for clinical decision making may be less severe. We urge researchers and clinicians to verify their implementation of the AKI staging criteria.

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

Introduction: A decrease in short-term mortality of critically ill cancer patients with an unplanned intensive care unit (ICU) admission has been described. Few studies describe a change over time of 1-year mortality. Therefore, we examined the 1-year mortality of cancer patients (hematological or solid) with an unplanned ICU admission and we described whether the mortality changed over time. Methods: We used the National Intensive Care Evaluation (NICE) registry and extracted all patients with an unplanned ICU admission in the Netherlands between 2008 and 2017. The primary outcome was 1-year mortality, analyzed with a mixed-effects Cox proportional hazard regression. We compared the 1-year mortality of cancer patients to that of patients without cancer. Furthermore, we examined changes in mortality over the study period. Results: We included 470,305 patients: 10,401 with hematological cancer, 35,920 with solid cancer, and 423,984 without cancer. The 1-year mortality rates were 60.1%, 46.2%, and 28.3% respectively (P< .01). Approximately 30% of the cancer patients surviving their hospital admission died within 1 year, this was 12% in patients without cancer. In hematological patients, 1-year mortality decreased between 2008 and 2011, after which it stabilized. In solid cancer patients, inspection showed neither an increasing nor decreasing trend over the inclusion period. For patients without cancer, 1-year mortality decreased between 2008 and 2013, after which it stabilized. A clear decrease in hospital mortality was seen within all three groups. Conclusion: The 1-year mortality of cancer patients with an unplanned ICU admission (hematological and solid) was higher than that of patients without cancer. About one-third of the cancer patients surviving their hospital admission died within 1 year after ICU admission. We found a decrease in 1-year mortality until 2011 in hematology patients and no decrease in solid cancer patients. Our results suggest that for many cancer patients, an unplanned ICU admission is still a way to recover from critical illness, and it does not necessarily lead to success in long-term survival. The underlying type of malignancy is an important factor for long-term outcomes in patients recovering from critical illness.

What intensive care registries can teach us about outcomes

PURPOSE OF REVIEW

Critical care registries are synonymous with measurement of outcomes following critical illness. Their ability to provide longitudinal data to enable benchmarking of outcomes for comparison within units over time, and between units, both regionally and nationally is a key part of the evaluation of quality of care and ICU performance as well as a better understanding of case-mix. This review aims to summarize literature on outcome measures currently being reported in registries internationally, describe the current strengths and challenges with interpreting existing outcomes and highlight areas where registries may help improve implementation and interpretation of both existing and new outcome measures.

RECENT FINDINGS

Outcomes being widely reported through ICU registries include measures of survival, events of interest, patient-reported outcomes and measures of resource utilization (including cost). Despite its increasing adoption, challenges with quality of reporting of outcomes measures remain. Measures of short-term survival are feasible but those requiring longer follow-ups are increasingly difficult to interpret given the evolving nature of critical care in the context of acute and chronic disease management. Furthermore, heterogeneity in patient populations and in healthcare organisations in different settings makes use of outcome measures for international benchmarking at best complex, requiring substantial advances in their definitions and implementation to support those seeking to improve patient care.

SUMMARY

Digital registries could help overcome some of the current challenges with implementing and interpreting ICU outcome data through standardization of reporting and harmonization of data. In addition, ICU registries could be instrumental in enabling data for feedback as part of improvement in both patient-centred outcomes and in service outcomes; notably resource utilization and efficiency.

The impact of COVID-19 on nursing workload and planning of nursing staff on the Intensive Care: A prospective descriptive multicenter study

INTRODUCTION

The impact of the care for COVID-19 patients on nursing workload and planning nursing staff on the Intensive Care Unit has been huge. Nurses were confronted with a high workload and an increase in the number of patients per nurse they had to take care of.

OBJECTIVE

The primary aim of this study is to describe differences in the planning of nursing staff on the Intensive Care in the COVID period versus a recent non-COVID period. The secondary aim was to describe differences in nursing workload in COVID-19 patients, pneumonia patients and other patients on the Intensive Care. We finally wanted to assess the cause of possible differences in Nursing Activities Scores between the different groups.

METHODS

We analyzed data on nursing staff and nursing workload as measured by the Nursing Activities Score of 3,994 patients and 36,827 different shifts in 6 different hospitals in the Netherlands. We compared data from the COVID-19 period, March 1st 2020 till July 1st 2020, with data in a non-COVID period, March 1st 2019 till July 1st 2019. We analyzed the Nursing Activities Score per patient, the number of patients per nurse and the Nursing Activities Score per nurse in the different cohorts and time periods. Differences were tested by a Chi-square, non-parametric Wilcoxon or Student's t-test dependent on the distribution of the data.

RESULTS

Our results showed both a significant higher number of patients per nurse (1.1 versus 1.0, p<0.001) and a significant higher Nursing Activities Score per Intensive Care nurse (76.5 versus 50.0, p<0.001) in the COVID-19 period compared to the non-COVID period. The Nursing Activities Score was significantly higher in COVID-19 patients compared to both the pneumonia patients (55.2 versus 50.0, p<0.001) and the non-COVID patients (55.2 versus 42.6, p<0.001), mainly due to more intense hygienic procedures, mobilization and positioning, support and care for relatives and respiratory care.

CONCLUSION

With this study we showed the impact of COVID-19 patients on the planning of nursing care on the Intensive Care. The COVID-19 patients caused a high nursing workload, both in number of patients per nurse and in Nursing Activities Score per nurse.

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

Supplemental Digital Content is available in the text.

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.

Association Between an Increase in Serum Sodium and In-Hospital Mortality in Critically Ill Patients

Supplemental Digital Content is available in the text.

OBJECTIVES:

In critically ill patients, dysnatremia is common, and in these patients, in-hospital mortality is higher. It remains unknown whether changes of serum sodium after ICU admission affect mortality, especially whether normalization of mild hyponatremia improves survival.

DESIGN:

Retrospective cohort study.

SETTING:

Ten Dutch ICUs between January 2011 and April 2017.

Patients:

Adult patients were included if at least one serum sodium measurement within 24 hours of ICU admission and at least one serum sodium measurement 24–48 hours after ICU admission were available.

INTERVENTIONS:

None.

MEASUREMENTS AND MAIN RESULTS:

A logistic regression model adjusted for age, sex, and Acute Physiology and Chronic Health Evaluation-IV–predicted mortality was used to assess the difference between mean of sodium measurements 24–48 hours after ICU admission and first serum sodium measurement at ICU admission (Δ48 hr-[Na]) and in-hospital mortality. In total, 36,660 patients were included for analysis. An increase in serum sodium was independently associated with a higher risk of in-hospital mortality in patients admitted with normonatremia (Δ48 hr-[Na] 5–10 mmol/L odds ratio: 1.61 [1.44–1.79], Δ48 hr-[Na] > 10 mmol/L odds ratio: 4.10 [3.20–5.24]) and hypernatremia (Δ48 hr-[Na] 5–10 mmol/L odds ratio: 1.47 [1.02–2.14], Δ48 hr-[Na] > 10 mmol/L odds ratio: 8.46 [3.31–21.64]). In patients admitted with mild hyponatremia and Δ48 hr-[Na] greater than 5 mmol/L, no significant difference in hospital mortality was found (odds ratio, 1.11 [0.99–1.25]).

CONCLUSIONS:

An increase in serum sodium in the first 48 hours of ICU admission was associated with higher in-hospital mortality in patients admitted with normonatremia and in patients admitted with hypernatremia.

Physical, Mental, and Cognitive Health Status of ICU Survivors Before ICU Admission: A Cohort Study

OBJECTIVES

Although patient's health status before ICU admission is the most important predictor for long-term outcomes, it is often not taken into account, potentially overestimating the attributable effects of critical illness. Studies that did assess the pre-ICU health status often included specific patient groups or assessed one specific health domain. Our aim was to explore patient's physical, mental, and cognitive functioning, as well as their quality of life before ICU admission.

DESIGN

Baseline data were used from the longitudinal prospective MONITOR-IC cohort study.

SETTING

ICUs of four Dutch hospitals.

PATIENTS

Adult ICU survivors (n = 2,467) admitted between July 2016 and December 2018.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Patients, or their proxy, rated their level of frailty (Clinical Frailty Scale), fatigue (Checklist Individual Strength-8), anxiety and depression (Hospital Anxiety and Depression Scale), cognitive functioning (Cognitive Failure Questionnaire-14), and quality of life (Short Form-36) before ICU admission. Unplanned patients rated their pre-ICU health status retrospectively after ICU admission. Before ICU admission, 13% of all patients was frail, 65% suffered from fatigue, 28% and 26% from symptoms of anxiety and depression, respectively, and 6% from cognitive problems. Unplanned patients were significantly more frail and depressed. Patients with a poor pre-ICU health status were more often likely to be female, older, lower educated, divorced or widowed, living in a healthcare facility, and suffering from a chronic condition.

CONCLUSIONS

In an era with increasing attention for health problems after ICU admission, the results of this study indicate that a part of the ICU survivors already experience serious impairments in their physical, mental, and cognitive functioning before ICU admission. Substantial differences were seen between patient subgroups. These findings underline the importance of accounting for pre-ICU health status when studying long-term outcomes.

Higher 1-year mortality in women admitted to intensive care units after cardiac arrest: A nationwide overview from the Netherlands between 2010 and 2018

PURPOSE

We study sex differences in 1-year mortality of out-of-hospital cardiac arrest (OHCA) and in-hospital cardiac arrest (IHCA) patients admitted to the intensive care unit (ICU).

DATA

A retrospective cohort analysis of OHCA and IHCA patients registered in the NICE registry in the Netherlands. The primary and secondary outcomes were 1-year and hospital mortality, respectively.

RESULTS

We included 19,440 OHCA patients (5977 women, 30.7%) and 13,461 IHCA patients (4889 women, 36.3%). For OHCA, 1-year mortality was 63.9% in women and 52.6% in men (Hazard Ratio [HR] 1.28, 95% Confidence Interval [95% CI] 1.23-1.34). For IHCA, 1-year mortality was 60.0% in women and 57.0% in men (HR 1.09, 95% CI 1.04-1.14). In OHCA, hospital mortality was 57.4% in women and 46.5% in men (Odds Ratio [OR] 1.42, 95% CI 1.33-1.52). In IHCA, hospital mortality was 52.0% in women and 48.2% in men (OR 1.11, 95% CI 1.03-1.20).

CONCLUSION

Women admitted to the ICU after cardiac arrest have a higher mortality rate than men. After left-truncation, we found that this sex difference persisted for OHCA. For IHCA we found that the effect of sex was mainly present in the initial phase after the cardiac arrest.

SURvival PRediction In SEverely Ill Patients Study-The Prediction of Survival in Critically Ill Patients by ICU Physicians

The surprise question, "Would I be surprised if this patient died in the next 12 months?" is a tool to identify patients at high risk of death in the next year. Especially in the situation of an ICU admission, it is important to recognize patients who could and could not have the benefits of an intensive treatment in the ICU department.

Design and Setting

A single-center, prospective, observational cohort study was conducted between April 2013 and April 2018, in ICU Gelre hospitals, location Apeldoorn.

Patients

A total of 3,140 patients were included (57% male) with a mean age of 63.5 years. Seven-hundred thirteen patients (23%) died within 1 year.

Interventions

The physician answered three different surprise question's with either "yes" or "no": "I expect that the patient is going to survive the ICU admission" (surprise question 1), "I expect that the patient is going to survive the hospital stay" (surprise question 2), and "I expect that the patient is going to survive one year after ICU admission" (surprise question 3). We tested positive and negative predicted values of the surprise questions, the mean accuracy of the surprise questions, and kappa statistics.

Measurements and Main Results

The positive and negative predictive values of the surprise questions for ICU admission, hospital admission, and 1-year survival were, respectively, 64%/94%, 59%/92%, and 60%/86%. Accordingly, the mean accuracy and kappa statistics were 93% (95% CI, 92-94%), κ equals to 0.43, 89% (95% CI, 88-90%), κ equals to 0.40, and 81% (95% CI, 80-82%), κ equals to 0.43.

Conclusions

The frequently overlooked simple and cheap surprise question is probably an useful tool to evaluate the prognosis of acutely admitted critically ill patients.

Number of intensivists per bed is associated with efficiency of Dutch intensive care units

PURPOSE

To measure efficiency in Intensive Care Units (ICUs) and to determine which organizational factors are associated with ICU efficiency, taking confounding factors into account.

MATERIALS AND METHODS

We used data of all consecutive admissions to Dutch ICUs between January 1, 2016 and January 1, 2019 and recorded ICU organizational factors. We calculated efficiency for each ICU by averaging the Standardized Mortality Ratio (SMR) and Standardized Resource Use (SRU) and examined the relationship between various organizational factors and ICU efficiency. We thereby compared the results of linear regression models before and after covariate adjustment using propensity scores.

RESULTS

We included 164,399 admissions from 83 ICUs. ICU efficiency ranged from 0.51-1.42 (average 0.99, 0.15 SD). The unadjusted model as well as the propensity score adjusted model showed a significant association between the ratio of employed intensivists per ICU bed and ICU efficiency. Other organizational factors had no statistically significant association with ICU efficiency after adjustment.

CONCLUSIONS

We found marked variability in efficiency in Dutch ICUs. After applying covariate adjustment using propensity scores, we identified one organizational factor, ratio intensivists per bed, having an association with ICU efficiency.

The objective nursing workload and perceived nursing workload in Intensive Care Units: Analysis of association

BACKGROUND

A range of classification systems are in use for the measurement of nursing workload in Intensive Care Units. However, it is unknown to what extent the measured (objective) nursing workload, usually in terms of the amount of nursing activities, is related to the workload actually experienced (perceived) by nurses.

OBJECTIVES

The aim of this study was to assess the association between the objective nursing workload and the perceived nursing workload and to identify other factors associated with the perceived nursing workload.

METHODS

We measured the objective nursing workload with the Nursing Activities Score and the perceived nursing workload with the NASA-Task Load Index during 228 shifts in eight different Intensive Care Units. We used linear mixed-effect regression models to analyze the association between the objective and perceived nursing workload. Furthermore, we investigated the association of patient characteristics (severity of illness, comorbidities, age, body mass index, and planned or unplanned admission), education level of the nurse, and contextual factors (numbers of patients per nurse, the type of shift (day, evening, night) and day of admission or discharge) with perceived nursing workload. We adjusted for confounders.

RESULTS

We did not find a significant association between the observed workload per nurse and perceived nursing workload (p=0.06). The APACHE-IV Acute Physiology Score of a patient was significantly associated with the perceived nursing workload, also after adjustment for confounders (p=0.02). None of the other patient characteristics was significantly associated with perceived nursing workload. Being a certified nurse or a student nurse was the only nursing or contextual factor significantly associated with the perceived nursing workload, also after adjustment for confounders (p=0.03).

CONCLUSION

Workload is perceived differently by nurses compared to the objectively measured workload by the Nursing Activities Score. Both the severity of illness of the patient and being a student nurse are factors that increase the perceived nursing workload. To keep the workload of nurses in balance, planning nursing capacity should be based on the Nursing Activities Score, on the severity of patient illness and the graduation level of the nurse.

Clinically relevant potential drug-drug interactions in intensive care patients: A large retrospective observational multicenter study

PURPOSE

Potential drug-drug interactions (pDDIs) may harm patients admitted to the Intensive Care Unit (ICU). Due to the patient's critical condition and continuous monitoring on the ICU, not all pDDIs are clinically relevant. Clinical decision support systems (CDSSs) warning for irrelevant pDDIs could result in alert fatigue and overlooking important signals. Therefore, our aim was to describe the frequency of clinically relevant pDDIs (crpDDIs) to enable tailoring of CDSSs to the ICU setting.

MATERIALS & METHODS

In this multicenter retrospective observational study, we used medication administration data to identify pDDIs in ICU admissions from 13 ICUs. Clinical relevance was based on a Delphi study in which intensivists and hospital pharmacists assessed the clinical relevance of pDDIs for the ICU setting.

RESULTS

The mean number of pDDIs per 1000 medication administrations was 70.1, dropping to 31.0 when considering only crpDDIs. Of 103,871 ICU patients, 38% was exposed to a crpDDI. The most frequently occurring crpDDIs involve QT-prolonging agents, digoxin, or NSAIDs.

CONCLUSIONS

Considering clinical relevance of pDDIs in the ICU setting is important, as only half of the detected pDDIs were crpDDIs. Therefore, tailoring CDSSs to the ICU may reduce alert fatigue and improve medication safety in ICU patients.

Automatic generation of minimum dataset and quality indicators from data collected routinely by the clinical information system in an intensive care unit

BACKGROUND

Quality indicators (QIs) are being increasingly used in medicine to compare and improve the quality of care delivered. The feasibility of data collection is an important prerequisite for QIs. Information technology can improve efforts to measure processes and outcomes. In intensive care units (ICU), QIs can be automatically measured by exploiting data from clinical information systems (CIS).

OBJECTIVE

To describe the development and application of a tool to automatically generate a minimum dataset (MDS) and a set of ICU quality metrics from CIS data.

METHODS

We used the definitions for MDS and QIs proposed by the Spanish Society of Critical Care Medicine and Coronary Units. Our tool uses an extraction, transform, and load process implemented with Python to extract data stored in various tables in the CIS database and create a new associative database. This new database is uploaded to Qlik Sense, which constructs the MDS and calculates the QIs by applying the required metrics. The tool was tested using data from patients attended in a 30-bed polyvalent ICU during a six-year period.

RESULTS

We describe the definitions and metrics, and we report the MDS and QI measurements obtained through the analysis of 4546 admissions. The results show that our ICU's performance on the QIs analyzed meets the standards proposed by our national scientific society.

CONCLUSIONS

This is the first step toward using a tool to automatically obtain a set of actionable QIs to monitor and improve the quality of care in ICUs, eliminating the need for professionals to enter data manually, thus saving time and ensuring data quality.