Role of medical regulators in physician wellness:leading or lagging? A brief report on physician wellness practices

Background Physician wellness remains a growing concern, not only affecting the physicians’ quality of life but also the quality of care delivered. One of the core tasks of medical regulatory authorities (MRAs) is to supervise the quality and safety of care. This brief report aimed to evaluate the practices of MRAs regarding physician wellness and their views on residents as a high-risk group for decreased physician wellness. Methods A questionnaire was sent to MRAs worldwide, related to four topics: the identification of physician wellness as a risk factor for quality of care, data collection, interventions and the identification of residents as high risk for poor physician wellness. 26 responses were included. Results 23 MRAs consider poor physician wellness a risk factor for quality of care, 10 collect data and 13 have instruments to improve physician wellness. Nine MRAs identify residents as a high-risk group for poor physician wellness. Seven MRAs feel no responsibility for physician wellness. Conclusion Although almost all MRAs see poor physician wellness as a risk factor, actively countering this risk does not yet appear to be common practice. Given their unique position within the healthcare regulatory framework, MRAs could help improve physician wellness.</p

Causal prediction models for medication safety monitoring: The diagnosis of vancomycin-induced acute kidney injury

The current best practice approach for the retrospective diagnosis of adverse drug events (ADEs) in hospitalized patients relies on a full patient chart review and a formal causality assessment by multiple medical experts. This evaluation serves to qualitatively estimate the probability of causation (PC); the probability that a drug was a necessary cause of an adverse event. This practice is manual, resource intensive and prone to human biases, and may thus benefit from data-driven decision support. Here, we pioneer a causal modeling approach using observational data to estimate a lower bound of the PC (PC$_{low}$). This method includes two key causal inference components: (1) the target trial emulation framework and (2) estimation of individualized treatment effects using machine learning. We apply our method to the clinically relevant use-case of vancomycin-induced acute kidney injury in intensive care patients, and compare our causal model-based PC$_{low}$ estimates to qualitative estimates of the PC provided by a medical expert. Important limitations and potential improvements are discussed, and we conclude that future improved causal models could provide essential data-driven support for medication safety monitoring in hospitalized patients.Comment: Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 14 page

Tracheotomy does not affect reducing sedation requirements of patients in intensive care – a retrospective study

INTRODUCTION: Translaryngeal intubated and ventilated patients often need sedation to treat anxiety, agitation and/or pain. Current opinion is that tracheotomy reduces sedation requirements. We determined sedation needs before and after tracheotomy of intubated and mechanically ventilated patients. METHODS: We performed a retrospective analysis of the use of morphine, midazolam and propofol in patients before and after tracheotomy. RESULTS: Of 1,788 patients admitted to our intensive care unit during the study period, 129 (7%) were tracheotomized. After the exclusion of patients who received a tracheotomy before or at the day of admittance, 117 patients were left for analysis. The daily dose (DD; the amount of sedatives for each day) divided by the mean daily dose (MDD; the mean amount of sedatives per day for the study period) in the week before and the week after tracheotomy was 1.07 ± 0.93 DD/MDD versus 0.30 ± 0.65 for morphine, 0.84 ± 1.03 versus 0.11 ± 0.46 for midazolam, and 0.62 ± 1.05 versus 0.15 ± 0.45 for propofol (p < 0.01). However, when we focused on a shorter time interval (two days before and after tracheotomy), there were no differences in prescribed doses of morphine and midazolam. Studying the course in DD/MDD from seven days before the placement of tracheotomy, we found a significant decline in dosage. From day -7 to day -1, morphine dosage (DD/MDD) declined by 3.34 (95% confidence interval -1.61 to -6.24), midazolam dosage by 2.95 (-1.49 to -5.29) and propofol dosage by 1.05 (-0.41 to -2.01). After tracheotomy, no further decrease in DD/MDD was observed and the dosage remained stable for all sedatives. Patients in the non-surgical and acute surgical groups received higher dosages of midazolam than patients in the elective surgical group. Time until tracheotomy did not influence sedation requirements. In addition, there was no significant difference in sedation between different patient groups. CONCLUSION: In our intensive care unit, sedation requirements were not further reduced after tracheotomy. Sedation requirements were already sharply declining before tracheotomy was performed

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

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

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

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

Clinicians’ response to hyperoxia in ventilated patients in a Dutch ICU depends on the level of FiO2

Hyperoxia may induce pulmonary injury and may increase oxidative stress. In this retrospective database study we aimed to evaluate the response to hyperoxia by intensivists in a Dutch academic intensive care unit. All arterial blood gas (ABG) data from mechanically ventilated patients from 2005 until 2009 were extracted from an electronic storage database of a mixed 32-bed intensive care unit in a university hospital in Amsterdam. Mechanical ventilation settings at the time of the ABG tests were retrieved. The results of 126,778 ABG tests from 5,498 mechanically ventilated patients were retrieved including corresponding ventilator settings. In 28,222 (22%) of the ABG tests the arterial oxygen tension (PaO2) was > 16 kPa (120 mmHg). In only 25% of the tests with PaO2 > 16 kPa (120 mmHg) was the fraction of inspired oxygen (FiO(2)) decreased. Hyperoxia was accepted without adjustment in ventilator settings if FiO(2) was 0.4 or lower. Hyperoxia is frequently seen but in most cases does not lead to adjustment of ventilator settings if FiO(2) <0.41. Implementation of guidelines concerning oxygen therapy should be improved and further research is needed concerning the effects of frequently encountered hyperoxi

The effect of ICU-tailored drug-drug interaction alerts on medication prescribing and monitoring: Protocol for a cluster randomized stepped-wedge trial

Background: Drug-drug interactions (DDIs) can cause patient harm. Between 46 and 90% of patients admitted to the Intensive Care Unit (ICU) are exposed to potential DDIs (pDDIs). This rate is twice as high as patients on general wards. Clinical decision support systems (CDSSs) have shown their potential to prevent pDDIs. However, the literature shows that there is considerable room for improvement of CDSSs, in particular by increasing the clinical relevance of the pDDI alerts they generate and thereby reducing alert fatigue. However, consensus on which pDDIs are clinically relevant in the ICU setting is lacking. The primary aim of this study is to evaluate the effect of alerts based on only clinically relevant interactions for the ICU setting on the prevention of pDDIs among Dutch ICUs. Methods: To define the clinically relevant pDDIs, we will follow a rigorous two-step Delphi procedure in which a national expert panel will assess which pDDIs are perceived clinically relevant for the Dutch ICU setting. The intervention is the CDSS that generates alerts based on the clinically relevant pDDIs. The intervention will be evaluated in a stepped-wedge trial. A total of 12 Dutch adult ICUs using the same patient data management system, in which the CDSS will operate, were invited to participate in the trial. Of the 12 ICUs, 9 agreed to participate and will be enrolled in the trial. Our primary outcome measure is the incidence of clinically relevant pDDIs per 1000 medication administrations. Discussion: This study will identify pDDIs relevant for the ICU setting. It will also enhance our understanding of the effectiveness of alerts confined to clinically relevant pDDIs. Both of these contributions can facilitate the successful implementation of CDSSs in the ICU and in other domains as well. Trial registration: Nederlands Trial register Identifier: NL6762. Registered November 26, 2018

Emergency Department to ICU Time Is Associated With Hospital Mortality: A Registry Analysis of 14,788 Patients From Six University Hospitals in The Netherlands*

Objectives: Prolonged emergency department to ICU waiting time may delay intensive care treatment, which could negatively affect patient outcomes. The aim of this study was to investigate whether emergency department to ICU time is associated with hospital mortality. Design, Setting, and Patients: We conducted a retrospective observational cohort study using data from the Dutch quality registry National Intensive Care Evaluation. Adult patients admitted to the ICU directly from the emergency department in six university hospitals, between 2009 and 2016, were included. Using a logistic regression model, we investigated the crude and adjusted (for disease severity; Acute Physiology and Chronic Health Evaluation IV probability) odds ratios of emergency department to ICU time on mortality. In addition, we assessed whether the Acute Physiology and Chronic Health Evaluation IV probability modified the effect of emergency department to ICU time on mortality. Secondary outcomes were ICU, 30-day, and 90-day mortality. Interventions: None. Measurements and Main Results: A total of 14,788 patients were included. The median emergency department to ICU time was 2.0 hours (interquartile range, 1.3–3.3hr). Emergency department to ICU time was correlated to adjusted hospital mortality (p < 0.002), in particular in patients with the highest Acute Physiology and Chronic Health Evaluation IV probability and long emergency department to ICU time quintiles: odds ratio, 1.29; 95% CI, 1.02–1.64 (2.4–3.7hr) and odds ratio, 1.54; 95% CI, 1.11–2.14 (> 3.7hr), both compared with the reference category (< 1.2hr). For 30-day and 90-day mortality, we found similar results. However, emergency department to ICU time was not correlated to adjusted ICU mortality (p = 0.20). Conclusions: Prolonged emergency department to ICU time (> 2.4hr) is ass