Applying machine learning to international drug monitoring: classifying cannabis resin collected in Europe using cannabinoid concentrations.

In Europe, concentrations of ∆9-tetrahydrocannabinol (THC) in cannabis resin (also known as hash) have risen markedly in the past decade, potentially increasing risks of mental health disorders. Current approaches to international drug monitoring cannot distinguish between different types of cannabis resin which may have contrasting health effects due to THC and cannabidiol (CBD) content. Here, we compared concentrations of THC and CBD in different types of cannabis resin collected in Europe (either Moroccan-type, or Dutch-type). We then tested the ability of machine learning algorithms to classify the type of cannabis resin (either Moroccan-type, or Dutch-type) using routinely collected monitoring data on THC and CBD. Finally, we applied the optimal algorithm to new samples collected in countries where the type of cannabis resin was unknown, the UK and Denmark. Results showed that overall, Dutch-type samples had higher THC (Hedges' g = 2.39) and lower CBD (Hedges' g = 0.81) than Moroccan-type samples. A Support Vector Machine algorithm achieved classification accuracy exceeding 95%, with little variation in this estimate, good interpretability, and plausibility. It made contrasting predictions about the type of cannabis resin collected in the UK (94% Moroccan-type; 6% Dutch-type) and Denmark (36% Moroccan-type; 64% Dutch-type). In conclusion, we provide proof-of-concept evidence for the potential of machine learning to inform international drug monitoring. Our findings should not be interpreted as objective confirmatory evidence but suggest that Dutch-type cannabis resin has higher THC concentrations than Moroccan-type cannabis resin, which may contribute to variation in drug markets and health outcomes for people who use cannabis in Europe.

How to Use Lean Thinking for the Optimization of Clinical Pathways: A Systematic Review and a Proposed Framework to Analyze Pathways on a System Level.

Lean Thinking and clinical pathways are commonly used concepts to improve healthcare. However, little is known on how to use Lean Thinking for the optimization of pathways or the quantification of both concepts. This study aims to create a framework to analyze pathways with Lean Thinking on a system level, by quantifying the seven wastes, flow and pull. A systematic literature review was performed. Inclusion criteria were the focus of the article on a well-defined group of patients and studied a pathway optimization with Lean Thinking. Data were extracted on measured outcomes, type of intervention and type of researched pathway. Thirty-six articles were included. No articles described the implementation of the Lean Thinking philosophy or studied the development of their people and partners ("4 P" model). Most articles used process optimization tools or problem-solving tools. The majority of the studies focused on process measures. The measures found in the review were used as input for our suggested framework to identify and quantify wastes, flow, and pull in a clinical pathway. The proposed framework can be used to create an overview of the improvement potential of a pathway or to analyze the level of improvement after an enhancement is introduced to a pathway. Further research is needed to study the use of the suggested quantifications.

Prolonged use of intravenous administration sets on central line associated bloodstream infection, nursing workload and material use: A before-after study.

OBJECTIVES: One of the interventions to reduce risk of central line associated bloodstream infection (CLABSI) is routine replacement of the intravenous administration sets. Guidelines advises a time interval that ranges between four and seven days. However many hospitals replace intravenous administration sets every four days to prevent CLABSI. RESEARCH METHODOLOGY: In this single centre retrospective study we analysed whether the extension of the time interval from four to seven days for routine replacement of intravenous administration sets had impact on the incidence of CLABSI and colonization of the central venous catheter. Secondary outcomes were the effects on nursing workload, material use and costs. RESULTS: In total, 1,409 patients with 1,679 central lines were included. During the pre-intervention period 2.8 CLABSI cases per 1,000 catheter days were found in comparison with 1.3 CLABSI cases per 1,000 catheter days during the post-intervention period. The rate difference between the groups was 1.52 CLABSI cases per 1,000 catheter days (95% CI: -0.50 to +4.13, p = 0.138). The intervention resulted in a saving of 345 intravenous single use plastic administration sets and 260 hours nursing time, and reduced cost with an estimate of at least 17.250 Euros. CONCLUSION: Extension of the time interval from four to seven days for routine replacement of intravenous administration sets did not negatively affect the incidence of CLABSI. IMPLICATIONS FOR CLINICAL PRACTICE: Additional benefits of the prolonged time interval were saving of nursing time by avoiding unnecessary routine procedures, the reducing of waste because of reducing the use of disposable materials and healthcare costs.

Quality of life after extra corporeal life support therapy.

BACKGROUND: Extra Corporeal Life Support (ECLS) may be a life-saving treatment for patients with reversible cardiac and/or respiratory failure. ECLS is associated with a high risk of complications and mortality. Because only a small number of studies have been conducted into the long-term effects of ECLS, we investigated the difference in quality of life, anxiety and depressive complaints and PTSD 3 months after ICU discharge. METHOD: It is a retrospective case-control study covering the period January 2012 to December 2017. The ECLS patient group was compared to a matched similar patient group in the Intensive Care (IC) that did not have ECLS therapy. Quality of life was measured with the Short-Form-36 (SF-36) questionnaire, anxiety and depression was measured with the Hospital Anxiety and Depression Scale (HADS) questionnaire and for PTSD the Impact of Events Scale (IES) questionnaire was used, comparing sum scores and cut-off points of scores from both groups. RESULTS: Included were 19 patients in the ECLS group and 38 in the control group. The mean sum scores on the sub scales of the SF36 questionnaire were the same for both groups. Only the mean score of 66.2 (scale 0-100) on the domain 'general health experience' was statistically significantly different in the ECLS group than in the control group (56.8, p = .02). There was no significant difference between the sum scores of both groups on anxiety and depressive complaints. In the ECLS group 32% of the patients may have a depressive disorder versus 18% from the control group (p = .32). And 26% of the patients from the ECLS group may have an anxiety disorder versus 7% from the control group (p = .51). The incidence of PTSD was 42% in the ECLS group and 24% in the control group (p = .22). CONCLUSION: We found no statistically significant difference in quality of life, anxiety and depressive symptoms and PTSD symptoms between ECLS patients and the matched control group - 3 months after the ICU discharge. The incidence of anxiety and depressive symptoms and PTSD in the ECLS group is higher than in the control group, however, this difference is not significant.

Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning.

BACKGROUND: For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor resources. METHODS: From the multi-center Dutch Data Warehouse of COVID-19 ICU patients from 25 hospitals, we selected all 3619 episodes of prone positioning in 1142 invasively mechanically ventilated patients. We excluded episodes longer than 24 h. Berlin ARDS criteria were not formally documented. We used supervised machine learning algorithms Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machine and Extreme Gradient Boosting on readily available and clinically relevant features to predict success of prone positioning after 4 h (window of 1 to 7 h) based on various possible outcomes. These outcomes were defined as improvements of at least 10% in PaO2/FiO2 ratio, ventilatory ratio, respiratory system compliance, or mechanical power. Separate models were created for each of these outcomes. Re-supination within 4 h after pronation was labeled as failure. We also developed models using a 20 mmHg improvement cut-off for PaO2/FiO2 ratio and using a combined outcome parameter. For all models, we evaluated feature importance expressed as contribution to predictive performance based on their relative ranking. RESULTS: The median duration of prone episodes was 17 h (11-20, median and IQR, N = 2632). Despite extensive modeling using a plethora of machine learning techniques and a large number of potentially clinically relevant features, discrimination between responders and non-responders remained poor with an area under the receiver operator characteristic curve of 0.62 for PaO2/FiO2 ratio using Logistic Regression, Random Forest and XGBoost. Feature importance was inconsistent between models for different outcomes. Notably, not even being a previous responder to prone positioning, or PEEP-levels before prone positioning, provided any meaningful contribution to predicting a successful next proning episode. CONCLUSIONS: In mechanically ventilated COVID-19 patients, predicting the success of prone positioning using clinically relevant and readily available parameters from electronic health records is currently not feasible. Given the current evidence base, a liberal approach to proning in all patients with severe COVID-19 ARDS is therefore justified and in particular regardless of previous results of proning.

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.

INCIDENCE, RISK FACTORS, AND OUTCOME OF SUSPECTED CENTRAL VENOUS CATHETER-RELATED INFECTIONS IN CRITICALLY ILL COVID-19 PATIENTS: A MULTICENTER RETROSPECTIVE COHORT STUDY.

ABSTRACT: Background: Aims of this study were to investigate the prevalence and incidence of catheter-related infection, identify risk factors, and determine the relation of catheter-related infection with mortality in critically ill COVID-19 patients. Methods: This was a retrospective cohort study of central venous catheters (CVCs) in critically ill COVID-19 patients. Eligible CVC insertions required an indwelling time of at least 48 hours and were identified using a full-admission electronic health record database. Risk factors were identified using logistic regression. Differences in survival rates at day 28 of follow-up were assessed using a log-rank test and proportional hazard model. Results: In 538 patients, a total of 914 CVCs were included. Prevalence and incidence of suspected catheter-related infection were 7.9% and 9.4 infections per 1,000 catheter indwelling days, respectively. Prone ventilation for more than 5 days was associated with increased risk of suspected catheter-related infection; odds ratio, 5.05 (95% confidence interval 2.12-11.0). Risk of death was significantly higher in patients with suspected catheter-related infection (hazard ratio, 1.78; 95% confidence interval, 1.25-2.53). Conclusions: This study shows that in critically ill patients with COVID-19, prevalence and incidence of suspected catheter-related infection are high, prone ventilation is a risk factor, and mortality is higher in case of catheter-related infection.

Rapid Evaluation of Coronavirus Illness Severity (RECOILS) in intensive care: Development and validation of a prognostic tool for in-hospital mortality.

BACKGROUND: The prediction of in-hospital mortality for ICU patients with COVID-19 is fundamental to treatment and resource allocation. The main purpose was to develop an easily implemented score for such prediction. METHODS: This was an observational, multicenter, development, and validation study on a national critical care dataset of COVID-19 patients. A systematic literature review was performed to determine variables possibly important for COVID-19 mortality prediction. Using a logistic multivariable model with a LASSO penalty, we developed the Rapid Evaluation of Coronavirus Illness Severity (RECOILS) score and compared its performance against published scores. RESULTS: Our development (validation) cohort consisted of 1480 (937) adult patients from 14 (11) Dutch ICUs admitted between March 2020 and April 2021. Median age was 65 (65) years, 31% (26%) died in hospital, 74% (72%) were males, average length of ICU stay was 7.83 (10.25) days and average length of hospital stay was 15.90 (19.92) days. Age, platelets, PaO2/FiO2 ratio, pH, blood urea nitrogen, temperature, PaCO2, Glasgow Coma Scale (GCS) score measured within +/-24 h of ICU admission were used to develop the score. The AUROC of RECOILS score was 0.75 (CI 0.71-0.78) which was higher than that of any previously reported predictive scores (0.68 [CI 0.64-0.71], 0.61 [CI 0.58-0.66], 0.67 [CI 0.63-0.70], 0.70 [CI 0.67-0.74] for ISARIC 4C Mortality Score, SOFA, SAPS-III, and age, respectively). CONCLUSIONS: Using a large dataset from multiple Dutch ICUs, we developed a predictive score for mortality of COVID-19 patients admitted to ICU, which outperformed other predictive scores reported so far.

Predictors for extubation failure in COVID-19 patients using a machine learning approach.

INTRODUCTION: Determining the optimal timing for extubation can be challenging in the intensive care. In this study, we aim to identify predictors for extubation failure in critically ill patients with COVID-19. METHODS: We used highly granular data from 3464 adult critically ill COVID patients in the multicenter Dutch Data Warehouse, including demographics, clinical observations, medications, fluid balance, laboratory values, vital signs, and data from life support devices. All intubated patients with at least one extubation attempt were eligible for analysis. Transferred patients, patients admitted for less than 24 h, and patients still admitted at the time of data extraction were excluded. Potential predictors were selected by a team of intensive care physicians. The primary and secondary outcomes were extubation without reintubation or death within the next 7 days and within 48 h, respectively. We trained and validated multiple machine learning algorithms using fivefold nested cross-validation. Predictor importance was estimated using Shapley additive explanations, while cutoff values for the relative probability of failed extubation were estimated through partial dependence plots. RESULTS: A total of 883 patients were included in the model derivation. The reintubation rate was 13.4% within 48 h and 18.9% at day 7, with a mortality rate of 0.6% and 1.0% respectively. The grandient-boost model performed best (area under the curve of 0.70) and was used to calculate predictor importance. Ventilatory characteristics and settings were the most important predictors. More specifically, a controlled mode duration longer than 4 days, a last fraction of inspired oxygen higher than 35%, a mean tidal volume per kg ideal body weight above 8 ml/kg in the day before extubation, and a shorter duration in assisted mode (< 2 days) compared to their median values. Additionally, a higher C-reactive protein and leukocyte count, a lower thrombocyte count, a lower Glasgow coma scale and a lower body mass index compared to their medians were associated with extubation failure. CONCLUSION: The most important predictors for extubation failure in critically ill COVID-19 patients include ventilatory settings, inflammatory parameters, neurological status, and body mass index. These predictors should therefore be routinely captured in electronic health records.

Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome.

OBJECTIVES: As coronavirus disease 2019 is a novel disease, treatment strategies continue to be debated. This provides the intensive care community with a unique opportunity as the population of coronavirus disease 2019 patients requiring invasive mechanical ventilation is relatively homogeneous compared with other ICU populations. We hypothesize that the novelty of coronavirus disease 2019 and the uncertainty over its similarity with noncoronavirus disease 2019 acute respiratory distress syndrome resulted in substantial practice variation between hospitals during the first and second waves of coronavirus disease 2019 patients. DESIGN: Multicenter retrospective cohort study. SETTING: Twenty-five hospitals in the Netherlands from February 2020 to July 2020, and 14 hospitals from August 2020 to December 2020. PATIENTS: One thousand two hundred ninety-four critically ill intubated adult ICU patients with coronavirus disease 2019 were selected from the Dutch Data Warehouse. Patients intubated for less than 24 hours, transferred patients, and patients still admitted at the time of data extraction were excluded. MEASUREMENTS AND MAIN RESULTS: We aimed to estimate between-ICU practice variation in selected ventilation parameters (positive end-expiratory pressure, Fio2, set respiratory rate, tidal volume, minute volume, and percentage of time spent in a prone position) on days 1, 2, 3, and 7 of intubation, adjusted for patient characteristics as well as severity of illness based on Pao2/Fio2 ratio, pH, ventilatory ratio, and dynamic respiratory system compliance during controlled ventilation. Using multilevel linear mixed-effects modeling, we found significant (p ≤ 0.001) variation between ICUs in all ventilation parameters on days 1, 2, 3, and 7 of intubation for both waves. CONCLUSIONS: This is the first study to clearly demonstrate significant practice variation between ICUs related to mechanical ventilation parameters that are under direct control by intensivists. Their effect on clinical outcomes for both coronavirus disease 2019 and other critically ill mechanically ventilated patients could have widespread implications for the practice of intensive care medicine and should be investigated further by causal inference models and clinical trials.

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients.

BACKGROUND: The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients. METHODS: A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers. RESULTS: Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive. CONCLUSIONS: In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.

Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse.

BACKGROUND: The identification of risk factors for adverse outcomes and prolonged intensive care unit (ICU) stay in COVID-19 patients is essential for prognostication, determining treatment intensity, and resource allocation. Previous studies have determined risk factors on admission only, and included a limited number of predictors. Therefore, using data from the highly granular and multicenter Dutch Data Warehouse, we developed machine learning models to identify risk factors for ICU mortality, ventilator-free days and ICU-free days during the course of invasive mechanical ventilation (IMV) in COVID-19 patients. METHODS: The DDW is a growing electronic health record database of critically ill COVID-19 patients in the Netherlands. All adult ICU patients on IMV were eligible for inclusion. Transfers, patients admitted for less than 24 h, and patients still admitted at time of data extraction were excluded. Predictors were selected based on the literature, and included medication dosage and fluid balance. Multiple algorithms were trained and validated on up to three sets of observations per patient on day 1, 7, and 14 using fivefold nested cross-validation, keeping observations from an individual patient in the same split. RESULTS: A total of 1152 patients were included in the model. XGBoost models performed best for all outcomes and were used to calculate predictor importance. Using Shapley additive explanations (SHAP), age was the most important demographic risk factor for the outcomes upon start of IMV and throughout its course. The relative probability of death across age values is visualized in Partial Dependence Plots (PDPs), with an increase starting at 54 years. Besides age, acidaemia, low P/F-ratios and high driving pressures demonstrated a higher probability of death. The PDP for driving pressure showed a relative probability increase starting at 12 cmH2O. CONCLUSION: Age is the most important demographic risk factor of ICU mortality, ICU-free days and ventilator-free days throughout the course of invasive mechanical ventilation in critically ill COVID-19 patients. pH, P/F ratio, and driving pressure should be monitored closely over the course of mechanical ventilation as risk factors predictive of these outcomes.

[Clinical course of COVID-19 in the Netherlands: an overview of 2607 patients in hospital during the first wave]. / Klinisch beloop van covid-19 in Nederland.

OBJECTIVE: To systematically collect clinical data from patients with a proven COVID-19 infection in the Netherlands. DESIGN: Data from 2579 patients with COVID-19 admitted to 10 Dutch centers in the period February to July 2020 are described. The clinical data are based on the WHO COVID case record form (CRF) and supplemented with patient characteristics of which recently an association disease severity has been reported. METHODS: Survival analyses were performed as primary statistical analysis. These Kaplan-Meier curves for time to (early) death (3 weeks) have been determined for pre-morbid patient characteristics and clinical, radiological and laboratory data at hospital admission. RESULTS: Total in-hospital mortality after 3 weeks was 22.2% (95% CI: 20.7% - 23.9%), hospital mortality within 21 days was significantly higher for elderly patients (> 70 years; 35, 0% (95% CI: 32.4% - 37.8%) and patients who died during the 21 days and were admitted to the intensive care (36.5% (95% CI: 32.1% - 41.3%)). Apart from that, in this Dutch population we also see a risk of early death in patients with co-morbidities (such as chronic neurological, nephrological and cardiac disorders and hypertension), and in patients with more home medication and / or with increased urea and creatinine levels. CONCLUSION: Early death due to a COVID-19 infection in the Netherlands appears to be associated with demographic variables (e.g. age), comorbidity (e.g. cardiovascular disease) but also disease char-acteristics at admission.

Automated and cost-efficient early detection of hemolysis in patients with extracorporeal life support: Use of the hemolysis-index of routine clinical chemistry platforms.

PURPOSE: Patients with extracorporeal life support (ECLS) are at risk for hemolysis-related complications. Therefore, monitoring of free hemoglobin (fHb) levels is indicated. Conventional methods for fHb are laborious and not always available. Here we evaluated the suitability of the hemolysis-index (H-index), an internal quality control parameter of clinical chemistry platforms, as a clinical parameter for ECLS patients. MATERIALS AND METHODS: The performance of the H-index assay was evaluated using standard procedures. Furthermore, H-index data from ECLS patients (n = 56) was analyzed retrospectively. RESULTS: The H-index significantly correlated with fHb and showed good analytical performance. During ECLS 19.6% of the patients had an H-index above 20 in at least 2 consecutive blood draws, indicating significant hemolysis. In the patients with clot formation in the pumphead the H-index peaked above 100. Visible clots at other locations did not always coincide with hemolysis. H-index peaks were more prevalent in patients that died during ECLS support. CONCLUSIONS: We conclude that the H-index is a suitable and cost-efficient alternative for the conventional fHb analysis with good analytic performance. The H-index aids in the early detection of hemolysis in patients with ECLS. A repeated H-index>20 was a predictor of mortality.

Survival and neurological outcome with extracorporeal cardiopulmonary resuscitation for refractory cardiac arrest caused by massive pulmonary embolism: A two center observational study.

BACKGROUND: Cardiac arrest (CA) due to pulmonary embolism (PE) is associated with low survival rates and poor neurological outcomes. We examined whether Extracorporeal Cardiopulmonary Resuscitation (ECPR) improves the outcomes of patients who suffer from CA due to massive PE. METHODS: We retrospectively included 39 CA patients with proven or strongly suspected PE in two hospitals in the Netherlands, in a 'before/after'-design. 20 of these patients were treated with Conventional Cardiopulmonary Resuscitation (CCPR) and 19 patients with ECPR. RESULTS: The main outcomes of this study were ICU survival and favourable neurological outcome, defined as Cerebral Performance Category (CPC) score 1-2. The ICU survival rate in CCPR patients was 5% compared to 26% in ECPR patients (p<0.01). Survival with favourable neurological outcome was present in 0/20 (0%) CCPR patients compared to 4/19 (21%) of the ECPR patients (p<0.05). CONCLUSION: ECPR seems a promising treatment for cardiac arrest patients due to (suspected) massive pulmonary embolism compared to conventional CPR, though outcomes remain poor.

Changes in cannabis potency and first-time admissions to drug treatment: a 16-year study in the Netherlands.

BACKGROUND: The number of people entering specialist drug treatment for cannabis problems has increased considerably in recent years. The reasons for this are unclear, but rising cannabis potency could be a contributing factor. METHODS: Cannabis potency data were obtained from an ongoing monitoring programme in the Netherlands. We analysed concentrations of δ-9-tetrahydrocannabinol (THC) from the most popular variety of domestic herbal cannabis sold in each retail outlet (2000-2015). Mixed effects linear regression models examined time-dependent associations between THC and first-time cannabis admissions to specialist drug treatment. Candidate time lags were 0-10 years, based on normative European drug treatment data. RESULTS: THC increased from a mean (95% CI) of 8.62 (7.97-9.27) to 20.38 (19.09-21.67) from 2000 to 2004 and then decreased to 15.31 (14.24-16.38) in 2015. First-time cannabis admissions (per 100 000 inhabitants) rose from 7.08 to 26.36 from 2000 to 2010, and then decreased to 19.82 in 2015. THC was positively associated with treatment entry at lags of 0-9 years, with the strongest association at 5 years, b = 0.370 (0.317-0.424), p < 0.0001. After adjusting for age, sex and non-cannabis drug treatment admissions, these positive associations were attenuated but remained statistically significant at lags of 5-7 years and were again strongest at 5 years, b = 0.082 (0.052-0.111), p < 0.0001. CONCLUSIONS: In this 16-year observational study, we found positive time-dependent associations between changes in cannabis potency and first-time cannabis admissions to drug treatment. These associations are biologically plausible, but their strength after adjustment suggests that other factors are also important.

Potency trends of Δ9-tetrahydrocannabinol, cannabidiol and cannabinol in cannabis in the Netherlands: 2005-15.

BACKGROUND AND AIMS: Between 2000 and 2005 the average percentage of Δ(9) -tetrahydrocannabinol (THC) in marijuana as sold in Dutch coffeeshops has increased substantially; the potency of domestic products (Nederwiet and Nederhasj) has particularly increased. In contrast with imported marijuana, Nederwiet hardly contained any cannabidiol (CBD), a cannabinoid that is thought to offset some of the adverse effects of THC. In 2005, the THC content in Nederwiet was significantly lower than in 2004. This study investigates the further decrease or increase of cannabinoids in these cannabis products. METHODS: From 2005 to 2015 five different cannabis products were bought anonymously in 50 coffeeshops that were selected randomly each year from all coffeeshops in the Netherlands. A total of 2126 cannabis samples were bought, consisting of 664 Nederwiet samples (most popular), 537 Nederwiet samples (supposed strongest varieties), 183 imported herbal cannabis samples, 140 samples of cannabis resin made of Nederwiet and 602 samples of imported cannabis resin. All samples were analysed chemically for their THC, CBD and cannabinol (CBN) content. RESULTS: Between 2005 and 2015, the mean potencies of the most popular and the strongest Nederwiet and of imported cannabis resin were 16.0±4.0%, 17.0±3,9% and 16.5±6.3%, respectively. Imported herbal cannabis (6.5±3.5%) and cannabis resin made from Nederwiet (30.2±16.4%) contained, respectively, less (ß=-10.0, P<0.001) and more (ß=13.7, P<0.001) THC than imported cannabis resin. Linear regression models were used to study the trends in THC of the different cannabis products over time. A marginal, but significant (P<0.001), overall decline of THC per year of 0.22% was found in all cannabis products. However, no significant difference was found between the five products in the THC linear trajectories across time. Of all the cannabis products, only imported cannabis resin contained a relatively high CBD/THC ratio (median 0.42). CONCLUSION: The average tetrahydrocannabinol (THC) content of the most popular herbal cannabis products in the Netherlands has decreased slightly since 2005. The popular Nederwiet type still has a relatively high THC to cannabidiol ratio.