From immuno-ecology to conservation: how deciphering physiological mechanisms can help conservation

Trabajo presentado al 3rd World Seabird Conference, celebrado on-line el 5 de octubre de 2021.Peer reviewe

Hanging out at the club: Breeding status and territoriality affect individual space use, multi‐species overlap and pathogen transmission risk at a seabird colony

1. Wildlife movement ecology often focuses on breeders, whose territorial attachments facilitate trapping and following individuals over time. This leads to incomplete understanding of movements of individuals not actively breeding due to age, breeding failure, subordinance, and other factors. These individuals are often present in breeding populations and contribute to processes such as competition and pathogen spread. Therefore, excluding them from movement ecology studies could bias or mask important spatial dynamics. 2. Loafing areas offer an alternative to breeding sites for capturing and tracking individuals. Such sites may allow for sampling individuals regardless of breeding status, while also avoiding disturbance of sensitive breeding areas. However, little is known about the breeding status of individuals attending loafing sites, or how their movements compare to those of breeders captured at nests. 3. We captured a seabird, the brown skua, attending either nests or loafing areas (‘clubs’) at a multi-species seabird breeding site on Amsterdam Island (southern Indian Ocean). We outfitted skuas with GPS-UHF transmitters and inferred breeding statuses of individuals captured at clubs using movement patterns of breeders captured at nests. We then compared space use and activity patterns between breeders and nonbreeders. 4. Both breeding and nonbreeding skuas attended clubs. Nonbreeders ranged more widely, were more active, and overlapped more with other seabirds and marine mammals than did breeders. Moreover, some nonbreeders occupied fixed territories and displayed more restricted movements than those without territories. Nonbreeders became less active over the breeding season, while activity of breeders remained stable. Nonbreeding skuas were exposed to the agent of avian cholera at similar rates to breeders but were more likely to forage in breeding areas of the endangered endemic Amsterdam albatross, increasing opportunities for interspecific pathogen transmission. 5. Our results show that inference based only on breeders fails to capture important aspects of population-wide movement patterns. Capturing nonbreeders as well as breeders would help to improve population-level representation of movement patterns, elucidate and predict effects of external changes and conservation interventions (e.g. rat eradication) on movement patterns and pathogen spread, and develop strategies to manage outbreaks of diseases such as highly pathogenic avian influenza

The AKIpredictor an online calculator to predict acute kidney injury

status: publishe

Informatics in neurocritical care: new ideas for Big Data

Big data is the new hype in business and healthcare. Data storage and processing has become cheap, fast, and easy. Business analysts and scientists are trying to design methods to mine these data for hidden knowledge. Neurocritical care is a field that typically produces large amounts of patient-related data, and these data are increasingly being digitized and stored. This review will try to look beyond the hype, and focus on possible applications in neurointensive care amenable to Big Data research that can potentially improve patient care.status: publishe

Clinical prediction models for acute kidney injury.

OBJECTIVE: To report on the currently available prediction models for the development of acute kidney injury in heterogeneous adult intensive care units. METHODS: A systematic review of clinical prediction models for acute kidney injury in adult intensive care unit populations was carried out. PubMed® was searched for publications reporting on the development of a novel prediction model, validation of an established model, or impact of an existing prediction model for early acute kidney injury diagnosis in intensive care units. RESULTS: We screened 583 potentially relevant articles. Among the 32 remaining articles in the first selection, only 5 met the inclusion criteria. The nonstandardized adaptations that were made to define baseline serum creatinine when the preadmission value was missing led to heterogeneous definitions of the outcome. Commonly included predictors were sepsis, age, and serum creatinine level. The final models included between 5 and 19 risk factors. The areas under the Receiver Operating Characteristic curves to predict acute kidney injury development in the internal validation cohorts ranged from 0.78 to 0.88. Only two studies were externally validated. CONCLUSION: Clinical prediction models for acute kidney injury can help in applying more timely preventive interventions to the right patients. However, in intensive care unit populations, few models have been externally validated. In addition, heterogeneous definitions for acute kidney injury and evaluation criteria and the lack of impact analysis hamper a thorough comparison of existing models. Future research is needed to validate the established models and to analyze their clinical impact before they can be applied in clinical practice.status: publishe

Machine learning versus physicians' prediction of acute kidney injury in critically ill adults: a prospective evaluation of the AKIpredictor

BACKGROUND: Early diagnosis of acute kidney injury (AKI) is a major challenge in the intensive care unit (ICU). The AKIpredictor is a set of machine-learning-based prediction models for AKI using routinely collected patient information, and accessible online. In order to evaluate its clinical value, the AKIpredictor was compared to physicians' predictions. METHODS: Prospective observational study in five ICUs of a tertiary academic center. Critically ill adults without end-stage renal disease or AKI upon admission were considered for enrollment. Using structured questionnaires, physicians were asked upon admission, on the first morning, and after 24 h to predict the development of AKI stages 2 or 3 (AKI-23) during the first week of ICU stay. Discrimination, calibration, and net benefit of physicians' predictions were compared against the ones by the AKIpredictor. RESULTS: Two hundred fifty-two patients were included, 30 (12%) developed AKI-23. In the cohort of patients with predictions by physicians and AKIpredictor, the performance of physicians and AKIpredictor were respectively upon ICU admission, area under the receiver operating characteristic curve (AUROC) 0.80 [0.69-0.92] versus 0.75 [0.62-0.88] (n = 120, P = 0.25) with net benefit in ranges 0-26% versus 0-74%; on the first morning, AUROC 0.94 [0.89-0.98] versus 0.89 [0.82-0.97] (n = 187, P = 0.27) with main net benefit in ranges 0-10% versus 0-48%; after 24 h, AUROC 0.95 [0.89-1.00] versus 0.89 [0.79-0.99] (n = 89, P = 0.09) with main net benefit in ranges 0-67% versus 0-50%. CONCLUSIONS: The machine-learning-based AKIpredictor achieved similar discriminative performance as physicians for prediction of AKI-23, and higher net benefit overall, because physicians overestimated the risk of AKI. This suggests an added value of the systematic risk stratification by the AKIpredictor to physicians' predictions, in particular to select high-risk patients or reduce false positives in studies evaluating new and potentially harmful therapies. Due to the low event rate, future studies are needed to validate these findings. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03574896 registration date: July 2nd, 2018.status: publishe

Near-Infrared-Based Cerebral Oximetry for Prediction of Severe Acute Kidney Injury in Critically Ill Children After Cardiac Surgery.

Cerebral oximetry by near-infrared spectroscopy is used frequently in critically ill children but guidelines on its use for decision making in the PICU are lacking. We investigated cerebral near-infrared spectroscopy oximetry in its ability to predict severe acute kidney injury after pediatric cardiac surgery and assessed its additional predictive value to routinely collected data. Design: Prospective observational study. The cerebral oximeter was blinded to clinicians. Setting: Twelve-bed tertiary PICU, University Hospitals Leuven, Belgium, between October 2012 and November 2015. Patients: Critically ill children with congenital heart disease, younger than 12 years old, were monitored with cerebral near-infrared spectroscopy oximetry from PICU admission until they were successfully weaned off mechanical ventilation. Interventions: None. Measurements and Main Results: The primary outcome was prediction of severe acute kidney injury 6 hours before its occurrence during the first week of intensive care. Near-infrared spectroscopy-derived predictors and routinely collected clinical data were compared and combined to assess added predictive value. Of the 156 children included in the analysis, 55 (35%) developed severe acute kidney injury. The most discriminant near-infrared spectroscopy-derived predictor was near-infrared spectroscopy variability (area under the receiver operating characteristic curve, 0.68; 95% CI, 0.67-0.68), but was outperformed by a clinical model including baseline serum creatinine, cyanotic cardiopathy pre-surgery, blood pressure, and heart frequency (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.75-0.75; p < 0.001). Combining clinical and near-infrared spectroscopy information improved model performance (area under the receiver operating characteristic curve, 0.79; 95% CI, 0.79-0.80; p < 0.001). Conclusions: After pediatric cardiac surgery, near-infrared spectroscopy variability combined with clinical information improved discrimination for acute kidney injury. Future studies are required to identify whether supplementary, timely clinical interventions at the bedside, based on near-infrared spectroscopy variability analysis, could improve outcome.status: Published onlin

Continuous monitoring of urine output and hemodynamic disturbances improves early detection of acute kidney injury during first week of ICU stay

status: publishe

Near-Infrared Cerebral Oximetry to Predict Outcome After Pediatric Cardiac Surgery: A Prospective Observational Study

Objectives: To assess whether near-infrared cerebral tissue oxygen saturation, measured with the FORESIGHT cerebral oximeter (CAS Medical Systems, Branford, CT) predicts PICU length of stay, duration of invasive mechanical ventilation, and mortality in critically ill children after pediatric cardiac surgery. Design: Single-center prospective, observational study. Setting: Twelve-bed PICU of a tertiary academic hospital. Patients: Critically ill children and infants with congenital heart disease, younger than 12 years old, admitted to the PICU between October 2012 and November 2015. Children were monitored with the FORESIGHT cerebral oximeter from PICU admission until they were weaned off mechanical ventilation. Clinicians were blinded to cerebral tissue oxygen saturation data. Interventions: None. Measurements and Main Results: Primary outcome was the predictive value of the first 24 hours of postoperative cerebral tissue oxygen saturation for duration of PICU stay (median [95% CI], 4 d [3–8 d]) and duration of mechanical ventilation (median [95% CI], 111.3 hr (69.3–190.4 hr]). We calculated predictors on the first 24 hours of cerebral tissue oxygen saturation monitoring. The association of each individual cerebral tissue oxygen saturation predictor and of a combination of predictors were assessed using univariable and multivariable bootstrap analyses, adjusting for age, weight, gender, Pediatric Index of Mortality 2, Risk Adjustment in Congenital Heart Surgery 1, cyanotic heart defect, and time prior to cerebral tissue oxygen saturation monitoring. The most important risk factors associated with worst outcomes were an increased SD of a smoothed cerebral tissue oxygen saturation signal and an elevated cerebral tissue oxygen saturation desaturation score. Conclusions: Increased SD of a smoothed cerebral tissue oxygen saturation signal and increased depth and duration of desaturation below the 50% saturation threshold were associated with longer PICU and hospital stays and with longer duration of mechanical ventilation after pediatric cardiac surgery.status: publishe

Development and External Validation of an Online Clinical Prediction Model for Augmented Renal Clearance in Adult Mixed Critically Ill Patients: The Augmented Renal Clearance Predictor.

OBJECTIVES: Augmented renal clearance might lead to subtherapeutic plasma levels of drugs with predominant renal clearance. Early identification of augmented renal clearance remains challenging for the ICU physician. We developed and validated our augmented renal clearance predictor, a clinical prediction model for augmented renal clearance on the next day during ICU stay, and made it available via an online calculator. We compared its predictive performance with that of two existing models for augmented renal clearance. DESIGN: Multicenter retrospective registry-based cohort study. SETTING: Three Belgian tertiary care academic hospitals. PATIENTS: Adult medical, surgical, and cardiac surgery ICU patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Development of the prediction model was based on clinical information available during ICU stay. Out of 33,258 ICU days, we found augmented renal clearance on 19.6% of all ICU days in the development cohort. We retained six clinical variables in our augmented renal clearance predictor: day from ICU admission, age, sex, serum creatinine, trauma, and cardiac surgery. We assessed performance by measuring discrimination, calibration, and net benefit. We externally validated the final model in a single-center population (n = 10,259 ICU days). External validation confirmed good performance with an area under the curve of 0.88 (95% CI 0.87-0.88) and a sensitivity and specificity of 84.1 (95% CI 82.5-85.7) and 76.3 (95% CI 75.4-77.2) at the default threshold probability of 0.2, respectively. CONCLUSIONS: Augmented renal clearance on the next day can be predicted with good performance during ICU stay, using routinely collected clinical information that is readily available at bedside. Our augmented renal clearance predictor is available at www.arcpredictor.com.status: publishe