Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

Variation in Structure and Process of Care in Traumatic Brain Injury: Provider Profiles of European Neurotrauma Centers Participating in the CENTER-TBI Study.

INTRODUCTION: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. METHODS: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. RESULTS: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. CONCLUSION: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches

EPIdemiology of Surgery-Associated Acute Kidney Injury (EPIS-AKI) : Study protocol for a multicentre, observational trial

More than 300 million surgical procedures are performed each year. Acute kidney injury (AKI) is a common complication after major surgery and is associated with adverse short-term and long-term outcomes. However, there is a large variation in the incidence of reported AKI rates. The establishment of an accurate epidemiology of surgery-associated AKI is important for healthcare policy, quality initiatives, clinical trials, as well as for improving guidelines. The objective of the Epidemiology of Surgery-associated Acute Kidney Injury (EPIS-AKI) trial is to prospectively evaluate the epidemiology of AKI after major surgery using the latest Kidney Disease: Improving Global Outcomes (KDIGO) consensus definition of AKI. EPIS-AKI is an international prospective, observational, multicentre cohort study including 10 000 patients undergoing major surgery who are subsequently admitted to the ICU or a similar high dependency unit. The primary endpoint is the incidence of AKI within 72 hours after surgery according to the KDIGO criteria. Secondary endpoints include use of renal replacement therapy (RRT), mortality during ICU and hospital stay, length of ICU and hospital stay and major adverse kidney events (combined endpoint consisting of persistent renal dysfunction, RRT and mortality) at day 90. Further, we will evaluate preoperative and intraoperative risk factors affecting the incidence of postoperative AKI. In an add-on analysis, we will assess urinary biomarkers for early detection of AKI. EPIS-AKI has been approved by the leading Ethics Committee of the Medical Council North Rhine-Westphalia, of the Westphalian Wilhelms-University Münster and the corresponding Ethics Committee at each participating site. Results will be disseminated widely and published in peer-reviewed journals, presented at conferences and used to design further AKI-related trials. Trial registration number NCT04165369

Does counting species count as taxonomy? On misrepresenting systematics, yet again

Recent commentary by Costello and collaborators on the current state of the global taxonomic enterprise attempts to demonstrate that taxonomy is not in decline as feared by taxonomists, but rather is increasing by virtue of the rate at which new species are formally named. Having supported their views with data that clearly indicate as much, Costello et al. make recommendations to increase the rate of new species descriptions even more. However, their views appear to rely on the perception of species as static and numerically if not historically equivalent entities whose value lie in their roles as "metrics". As such, their one-dimensional portrayal of the discipline, as concerned solely with the creation of new species names, fails to take into account both the conceptual and epistemological foundations of systematics. We refute the end-user view that taxonomy is on the rise simply because more new species are being described compared with earlier decades, and that, by implication, taxonomic practice is a formality whose pace can be streamlined without considerable resources, intellectual or otherwise. Rather, we defend the opposite viewpoint that professional taxonomy is in decline relative to the immediacy of the extinction crisis, and that this decline threatens not just the empirical science of phylogenetic systematics, but also the foundations of comparative biology on which other fields rely. The allocation of space in top-ranked journals to propagate views such as those of Costello et al. lends superficial credence to the unsupportive mindset of many of those in charge of the institutional fate of taxonomy. We emphasize that taxonomy and the description of new species are dependent upon, and only make sense in light of, empirically based classifications that reflect evolutionary history; homology assessments are at the centre of these endeavours, such that the biological sciences cannot afford to have professional taxonomists sacrifice the comparative and historical depth of their hypotheses in order to accelerate new species descriptions.Fil: de Carvalho, María Rosa. Universidade de Sao Paulo; BrasilFil: Ebach, Malte C.. University of New South Wales; AustraliaFil: Williams, David M.. Natural History Museum; Reino UnidoFil: Nihei, Silvio S.. Universidade de Sao Paulo; BrasilFil: Rodrigues, Miguel Trefaut. Universidade de Sao Paulo; BrasilFil: Grant, Taran. Universidade de Sao Paulo; BrasilFil: Silveira, Luís F.. Universidade de Sao Paulo; BrasilFil: Zaher, Hussam. Universidade de Sao Paulo; BrasilFil: Gill, Anthony C.. University of Sydney; AustraliaFil: Schelly, Robert C.. American Museum of Natural History; Estados UnidosFil: Sparks, John S.. American Museum of Natural History; Estados UnidosFil: Bockmann, Flávio. Universidade de Sao Paulo; BrasilFil: Séret, Bernard. Muséum National d'Histoire Naturelle; FranciaFil: Ho, Hsuan-Ching. National Museum of Marine Biology and Aquarium; ChinaFil: Grande, Lance. Field Museum Of Natural History; Estados UnidosFil: Rieppel, Olivier. Field Museum Of Natural History; Estados UnidosFil: Dubois, Alain. Muséum National d'Histoire Naturelle; FranciaFil: Ohler, Annemarie. Muséum National d'Histoire Naturelle; FranciaFil: Faivovich, Julián. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales “Bernardino Rivadavia”; ArgentinaFil: Assis, Leandro C.S.. Universidade Federal de Minas Gerais; BrasilFil: Wheeler, Quentin D.. Arizona State University; Estados UnidosFil: Goldstein, Paul Z.. University of Maryland; Estados UnidosFil: De Almeida, Eduardo A.B.. Universidade de Sao Paulo; BrasilFil: Valdecasa, A.G.. Museo Nacional de Ciencias Naturales; EspañaFil: Nelson, Gareth. University of Melbourne; Australi

Balancing financial incentives during COVID-19: a comparison of provider payment adjustments across 20 countries.

Objective Provider payment mechanisms were adjusted in many countries in response to the COVID-19 pandemic in 2020. Our objective was to review adjustments for hospitals and healthcare professionals across 20 countries. Method We developed an analytical framework distinguishing between payment adjustments compensating income loss and those covering extra costs related to COVID-19. Information was extracted from the Covid-19 Health System Response Monitor (HSRM) and classified according to the framework. Findings We found that income loss was not a problem in countries where professionals were paid by salary or capitation and hospitals received global budgets. In countries where payment was based on activity, income loss was compensated through budgets and higher fees. New FFS payments were introduced to incentivize remote services. Payments for COVID-19 related costs included new fees for out- and inpatient services but also new PD and DRG tariffs for hospitals. Budgets covered the costs of adjusting wards, creating new (ICU) beds, and hiring staff. Conclusions We conclude that public payers assumed most of the COVID-19-related financial risk. In view of future pandemics policymakers should work to increase resilience of payment systems by: (1) having systems in place to rapidly adjust payment systems; (2) being aware of the economic incentives created by these adjustments such as cost-containment or increasing the number of patients or services, that can result in unintended consequences such as risk selection or overprovision of care; and (3) periodically evaluating the effects of payment adjustments on access and quality of care