Platform trials

Platform trials focus on the perpetual testing of many interventions in a disease or a setting. These trials have lasting organizational, administrative, data, analytic, and operational frameworks making them highly efficient. The use of adaptation often increases the probabilities of allocating participants to better interventions and obtaining conclusive results. The COVID-19 pandemic showed the potential of platform trials as a fast and valid way to improved treatments. This review gives an overview of key concepts and elements using the Intensive Care Platform Trial (INCEPT) as an example.</p

Using machine learning for predicting intensive care unit resource use during the COVID-19 pandemic in Denmark

The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, …, 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R(2)) between 0.334 and 0.989 and use of ventilation with an R(2) between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R(2) 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R(2) 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large

Using Machine Learning to Identify Patients at High Risk of Inappropriate Drug Dosing in Periods with Renal Dysfunction

PURPOSE: Dosing of renally cleared drugs in patients with kidney failure often deviates from clinical guidelines, so we sought to elicit predictors of receiving inappropriate doses of renal risk drugs. PATIENTS AND METHODS: We combined data from the Danish National Patient Register and in-hospital data on drug administrations and estimated glomerular filtration rates for admissions between 1 October 2009 and 1 June 2016, from a pool of about 2.6 million persons. We trained artificial neural network and linear logistic ridge regression models to predict the risk of five outcomes (>0, ≥1, ≥2, ≥3 and ≥5 inappropriate doses daily) with index set 24 hours after admission. We used time-series validation for evaluating discrimination, calibration, clinical utility and explanations. RESULTS: Of 52,451 admissions included, 42,250 (81%) were used for model development. The median age was 77 years; 50% of admissions were of women. ≥5 drugs were used between admission start and index in 23,124 admissions (44%); the most common drug classes were analgesics, systemic antibacterials, diuretics, antithrombotics, and antacids. The neural network models had better discriminative power (all AUROCs between 0.77 and 0.81) and were better calibrated than their linear counterparts. The main prediction drivers were use of anti-inflammatory, antidiabetic and anti-Parkinson's drugs as well as having a diagnosis of chronic kidney failure. Sex and age affected predictions but slightly. CONCLUSION: Our models can flag patients at high risk of receiving at least one inappropriate dose daily in a controlled in-silico setting. A prospective clinical study may confirm that this holds in real-life settings and translates into benefits in hard endpoints

Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records

We sought to craft a drug safety signalling pipeline associating latent information in clinical free text with exposures to single drugs and drug pairs. Data arose from 12 secondary and tertiary public hospitals in two Danish regions, comprising approximately half the Danish population. Notes were operationalised with a fastText embedding, based on which we trained 10 270 neural‐network models (one for each distinct single‐drug/drug‐pair exposure) predicting the risk of exposure given an embedding vector. We included 2 905 251 admissions between May 2008 and June 2016, with 13 740 564 distinct drug prescriptions; the median number of prescriptions was 5 (IQR: 3–9) and in 1 184 340 (41%) admissions patients used ≥5 drugs concomitantly. A total of 10 788 259 clinical notes were included, with 179 441 739 tokens retained after pruning. Of 345 single‐drug signals reviewed, 28 (8.1%) represented possibly undescribed relationships; 186 (54%) signals were clinically meaningful. Sixteen (14%) of the 115 drug‐pair signals were possible interactions, and two (1.7%) were known. In conclusion, we built a language‐agnostic pipeline for mining associations between free‐text information and medication exposure without manual curation, predicting not the likely outcome of a range of exposures but also the likely exposures for outcomes of interest. Our approach may help overcome limitations of text mining methods relying on curated data in English and can help leverage non‐English free text for pharmacovigilance

Heterogeneity of treatment effect of stress ulcer prophylaxis in ICU patients:A secondary analysis protocol

Background In the Stress Ulcer Prophylaxis in the Intensive Care Unit (SUP-ICU) trial, 3291 adult ICU patients at risk for gastrointestinal (GI) bleeding were randomly allocated to intravenous pantoprazole 40 mg or placebo once daily in the ICU. No difference was observed between the groups in the primary outcome 90-day mortality or the secondary outcomes, except for clinically important gastrointestinal bleeding. However, heterogeneity of treatment effect (HTE) not detected by conventional subgroup analyses could be present. Methods This is a protocol and statistical analysis plan for a secondary, post hoc, exploratory analysis of the SUP-ICU trial. We will explore HTE in one set of subgroups based on severity of illness (using the Simplified Acute Physiology Score [SAPS] II) and another set of subgroups based on the total number of risk factors for GI bleeding in each patient using Bayesian hierarchical models. We will summarise posterior probability distributions using medians and 95% credible intervals and present probabilities for different levels of benefit and harm of the intervention in each subgroup. Finally, we will assess if the treatment effect interacts with SAPS II and the number of risk factors separately on the continuous scale using marginal effects plots. Conclusions The outlined post hoc analysis will explore whether HTE was present in the SUP-ICU trial and may help answer some of the remaining questions regarding the balance between benefits and harms of pantoprazole in ICU patients at risk of GI bleeding. ClinicalTrials.gov registration NCT02467621