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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.

PurposeHalf of the patients experience pain during their ICU stay which is known to influence their outcomes. Nurses and physicians encounter organizational barriers towards pain assessment and treatment. We aimed to evaluate the association between adequate pain management and nurse to patient ratio, bed occupancy rate, and fulltime presence of an intensivist.Materials and methodsWe performed unadjusted and case-mix adjusted mixed-effect logistic regression modeling on data from thirteen Dutch ICUs to investigate the association between ICU organizational characteristics and adequate pain management, i.e. patient-shift observations in which patients' pain was measured and acceptable, or unacceptable and normalized within 1 h.All ICU patients admitted between December 2017 and June 2018 were included, excluding patients who were delirious, comatose or had a Glasgow coma score < 8 at the first day of ICU admission.ResultsCase-mix adjusted nurse to patient ratios of 0.70 to 0.80 and over 0.80 were significantly associated with adequate pain management (OR [95% confidence interval] of respectively 1.14 [1.07–1.21] and 1.16 [1.08–1.24]). Bed occupancy rate and intensivist presence showed no association.ConclusionHigher nurse to patient ratios increase the percentage of patients with adequate pain management especially in medical and mechanically ventilated patients.

Purpose: To compare categorical and continuous combinations of the standardized mortality ratio (SMR) and the standardized resource use (SRU) to evaluate ICU performance. Materials and methods: We analysed data from adult patients admitted to 128 ICUs in Brazil and Uruguay (BR/UY) and 83 ICUs in The Netherlands between 2016 and 2018. SMR and SRU were calculated using SAPS-3 (BR/UY) or APACHE-IV (The Netherlands). Performance was defined as a combination of metrics. The categorical combination was the efficiency matrix, whereas the continuous combination was the average SMR and SRU (average standardized ratio, ASER). Association among metrics in each dataset was evaluated using Spearman's rho and R2. Results: We included 277,459 BR/UY and 164,399 Dutch admissions. Median [interquartile range] ASER = 0.99[0.83–1.21] in BR/UY and 0.99[0.92–1.09] in Dutch datasets. The SMR and SRU were more correlated in BR/UY ICUs than in Dutch ICUs (Spearman's Rho: 0.54vs.0.24). The highest and lowest ASER values were concentrated in the least and most efficient groups. An expert focus group listed potential advantages and limitations of both combinations. Conclusions: The categorical combination of metrics is easy to interpret but limits statistical inference for benchmarking. The continuous combination offers appropriate statistical properties for evaluating performance when metrics are positively correlated.

PURPOSE: Critically ill COVID-19 patients have an increased risk of developing pulmonary embolism (PE). Diagnosis of PE by point-of-care ultrasound (POCUS) might reduce the need for computed tomography pulmonary angiography (CTPA), while decreasing time-to-diagnosis.MATERIALS & METHODS: This prospective, observational study included adult ICU patients with COVID-19. Multi-organ (lungs, deep vein, cardiac) POCUS was performed within 24 h of CTPA, looking for subpleural consolidations, deep venous thrombosis (DVT), and right ventricular strain (RVS). We reported the scan time, and calculated diagnostic accuracy measures for these signs separately and in combination.RESULTS: 70 consecutive patients were included. 23 patients (32.8%) had a PE. Median scan time was 14 min (IQR 11-17). Subpleural consolidations' diagnostic accuracy was: 42.9% (95%CI [34.1-52.0]). DVT's and RVS' diagnostic accuracy was: 75.6% (95%CI [67.1-82.9]) and 74.4% (95%CI [65.8-81.8]). Their sensitivity was: 24.0% (95%CI [9.4-45.1]), and 40.0% (95%CI [21.3-61.3]), while their specificity was: 88.8% (95%CI [80.8-94.3]), and: 83.0% (95%CI [74.2-89.8]), respectively. Multi-organ POCUS sensitivity was: 87.5% (95%CI [67.6-97.3]), and specificity was: 25% (95%CI [16.9-34.7]).CONCLUSIONS: Multi-organ rather than single-organ POCUS can be of aid in ruling out PE in critically ill COVID-19 and help select patients for CTPA. In addition, finding RVS can make PE more likely, while a DVT would preclude the need for a CTPA.REGISTRATION: www.trialregister.nl: NL8540.

Purpose: We described the association between Intensive care units (ICU) characteristics and ICU Length of stay (LoS), after correcting for patient characteristics. We also compared the predictive performances of models including either patient and ICU characteristics or only patient characteristics. Materials and methods: We included all admissions of 38 ICUs participating in the Dutch National Intensive Care Evaluation registry (NICE) between 2014 and 2016. We performed mixed effect regression including, one ICU characteristic in each model and a random intercept per ICU. Furthermore, we developed a prediction model containing multiple ICU characteristics and patients characteristics. Results: We found negative associations for the number of hospital beds; number of ICU beds; availability of fellows in training for intensivist; full-time equivalent ICU nurses; and discharged in a shift with 100% bed occupancy. Furthermore, we found a U-shaped association with the nurses to patient ratio as spline function. The performance based on R-2 was between 0.30 and 0.32 for both the model containing only patient characteristics and the model also containing ICU characteristics. Conclusion: After correcting for patient characteristics, we found statistically significant associations between ICU LoS and six ICU characteristics, mainly describing staff availability. Furthermore, we conclude that including ICU characteristics did not significantly improve ICU LoS prediction. (C) 2017 Elsevier Inc. All rights reserved

Purpose: To determine the utility of APACHE II in a low-and middle-income (LMIC) setting and the implications of missing data. Materials and methods: Patients meeting APACHE II inclusion criteria admitted to 18 ICUs in Sri Lanka over three consecutive months had data necessary for the calculation of APACHE II, probabilities prospectively extracted from case notes. APACHE II physiology score (APS), probabilities, Standardised (ICU) Mortality Ratio (SMR), discrimination (AUROC), and calibration (C-statistic) were calculated, both by imputing missing measurements with normal values and by Multiple Imputation using Chained Equations (MICE). Results: From a total of 995 patients admitted during the study period, 736 had APACHE II probabilities calculated. Data availability for APS calculation ranged from 70.6% to 88.4% for bedside observations and 18.7% to 63.4% for invasive measurements. SMR (95% CI) was 1.27 (1.17, 1.40) and 0.46 (0.44, 0.49), AUROC (95% CI) was 0.70 (0.65, 0.76) and 0.74 (0.68, 0.80), and C-statistic was 68.8 and 156.6 for normal value imputation and MICE, respectively. Conclusions: An incomplete dataset confounds interpretation of prognostic model performance in LMICs, wherein imputation using normal values is not a suitable strategy. Improving data availability, researching imputation methods and developing setting-adapted and simpler prognostic models are warranted. (c) 2017 Elsevier Inc. All rights reserved

Purpose: Drug-drug interactions (DDIs) may cause adverse outcomes in patients admitted to the Intensive Care Unit (ICU). Computerized decision support systems (CDSSs) may help prevent DDIs by timely showing relevant warning alerts, but knowledge on which DDIs are clinically relevant in the ICU setting is limited. Therefore, the purpose of this study was to identify DDIs relevant for the ICU. Materials and methods: We conducted a modified Delphi procedure with a Dutch multidisciplinary expert panel consisting of intensivists and hospital pharmacists to assess the clinical relevance of DDIs for the ICU. The procedure consisted of two rounds, each included a questionnaire followed by a live consensus meeting. Results: In total the clinical relevance of 148 DDIs was assessed, of which agreement regarding the relevance was reached for 139 DDIs (94%). Of these 139 DDIs, 53 (38%) were considered not clinically relevant for the ICU setting. Conclusions: A list of clinically relevant DDIs for the ICU setting was established on a national level. The clinical value of CDSSs for medication safety could be improved by focusing on the identified clinically relevant DDIs, thereby avoiding alert fatigue. (c) 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

PURPOSE: To assess the association of clinical variables and the development of specified chronic conditions in ICU survivors.MATERIALS AND METHODS: A retrospective cohort study, combining a national health insurance claims database and a national quality registry for ICUs. Claims data from 2012 to 2014 were combined with clinical data of patients admitted to an ICU during 2013. To assess the association of clinical variables (ICU length of stay, mechanical ventilation, acute physiology score, reason for ICU admission, mean arterial pressure score and glucose score) and the development of chronic conditions (i.e. heart diseases, COPD or asthma, Diabetes mellitus type II, depression and kidney diseases), logistic regression was used.RESULTS: 49,004 ICU patients were included. ICU length of stay was associated with the development of heart diseases, asthma or COPD and depression. The reason for ICU admission was an important risk factor for the development of all chronic conditions with adjusted ORs ranging from 2.05 (CI 1.56; 2.69) for kidney diseases to 5.14 (CI 3.99; 6.62) for depression.CONCLUSIONS: Clinical variables, especially the reason for ICU admission, are associated with the development of chronic conditions after ICU discharge. Therefore, these clinical variables should be considered when organizing follow-up care for ICU survivors.