Predicting acute kidney injury with an artificial intelligence-driven model in a pediatric cardiac intensive care unit.

BACKGROUND: Acute kidney injury (AKI) is among the most common complications following cardiac surgery in adult and pediatric patients, significantly affecting morbidity and mortality. Artificial Intelligence (AI) with Machine Learning (ML) can be used to predict outcomes. AKI diagnosis anticipation may be an ideal target of these methods. The scope of the study is building a Machine Learning (ML) train model with Random Forest (RF) algorithm, based on electronic health record (EHR) data, able to forecast AKI continuously after 48 h in post-cardiac surgery children, and to test its performance. Four hundred nineteen consecutive patients out of 1115 hospital admissions were enrolled in a single-center retrospective study. Patients were younger than 18 years and admitted from August 2018 to February 2020 in a pediatric cardiac intensive care unit (PCICU) undergoing cardiac surgery, invasive procedure (hemodynamic studies), and medical conditions with complete EHR records and discharged after 48 h or more. RESULTS: Thirty-six variables were selected to build the algorithm according to commonly described cardiac surgery-associated AKI clinical predictors. We evaluated different models for different outcomes: binary AKI (no AKI vs. AKI), severe AKI (no-mild vs severe AKI), and multiclass classification (maximum AKI and the most frequent level of AKI, mode AKI). The algorithm performance was assessed with the area under the curve receiver operating characteristics (AUC ROC) for binary classification, with accuracy and K for multiclass classification. AUC ROC for binary AKI was 0.93 (95% CI 0.92-0.94), and for severe AKI was 0.99 (95% CI 0.98-1). Mode AKI accuracy was 0.95, and K was 0.80 (95% CI 0.94-0.96); maximum AKI accuracy was 0.92, and K was 0.71 (95% CI 0.91-0.93). The importance matrix plot demonstrated creatinine, basal creatinine, platelets count, adrenaline support, and lactate dehydrogenase for binary AKI with the addition of cardiopulmonary bypass duration for severe AKI as the most relevant variables of the model. CONCLUSIONS: We validated a ML model to detect AKI occurring after 48 h in a retrospective observational study that could help clinicians in individuating patients at risk of AKI, in which a preventive strategy can be determinant to improve the occurrence of renal dysfunction.

Spatio-temporal modelling of COVID-19 incident cases using Richards' curve: An application to the Italian regions.

We introduce an extended generalised logistic growth model for discrete outcomes, in which spatial and temporal dependence are dealt with the specification of a network structure within an Auto-Regressive approach. A major challenge concerns the specification of the network structure, crucial to consistently estimate the canonical parameters of the generalised logistic curve, e.g. peak time and height. We compared a network based on geographic proximity and one built on historical data of transport exchanges between regions. Parameters are estimated under the Bayesian framework, using Stan probabilistic programming language. The proposed approach is motivated by the analysis of both the first and the second wave of COVID-19 in Italy, i.e. from February 2020 to July 2020 and from July 2020 to December 2020, respectively. We analyse data at the regional level and, interestingly enough, prove that substantial spatial and temporal dependence occurred in both waves, although strong restrictive measures were implemented during the first wave. Accurate predictions are obtained, improving those of the model where independence across regions is assumed.

Stratification of hospitalized COVID-19 patients into clinical severity progression groups by immuno-phenotyping and machine learning.

Quantitative or qualitative differences in immunity may drive clinical severity in COVID-19. Although longitudinal studies to record the course of immunological changes are ample, they do not necessarily predict clinical progression at the time of hospital admission. Here we show, by a machine learning approach using serum pro-inflammatory, anti-inflammatory and anti-viral cytokine and anti-SARS-CoV-2 antibody measurements as input data, that COVID-19 patients cluster into three distinct immune phenotype groups. These immune-types, determined by unsupervised hierarchical clustering that is agnostic to severity, predict clinical course. The identified immune-types do not associate with disease duration at hospital admittance, but rather reflect variations in the nature and kinetics of individual patient's immune response. Thus, our work provides an immune-type based scheme to stratify COVID-19 patients at hospital admittance into high and low risk clinical categories with distinct cytokine and antibody profiles that may guide personalized therapy.

Seagrass restoration monitoring and shallow-water benthic habitat mapping through a photogrammetry-based protocol.

Seagrasses rank among the most productive yet highly threatened ecosystems on Earth. Loss of seagrass habitat because of anthropogenic disturbances and evidence of their limited resilience have provided the impetus for investigating and monitoring habitat restoration through transplantation programmes. Although Structure from Motion (SfM) photogrammetry is becoming a more and more relevant technique for mapping underwater environments, no standardised methods currently exist to provide 3-dimensional high spatial resolution and accuracy cartographic products for monitoring seagrass transplantation areas. By synthesizing various remote sensing applications, we provide an underwater SfM-based protocol for monitoring large seagrass restoration areas. The data obtained from consumer-grade red-green-blue (RGB) imagery allowed the fine characterization of the seabed by using 3D dense point clouds and raster layers, including orthophoto mosaics and Digital Surface Models (DSM). The integration of high spatial resolution underwater imagery with object-based image classification (OBIA) technique provided a new tool to count transplanted Posidonia oceanica fragments and estimate the bottom coverage expressed as a percentage of seabed covered by such fragments. Finally, the resulting digital maps were integrated into Geographic Information Systems (GIS) to run topographic change detection analysis and evaluate the mean height of transplanted fragments and detect fine-scale changes in seabed vector ruggedness measure (VRM). Our study provides a guide for creating large-scale, replicable and ready-to-use products for a broad range of applications aimed at standardizing monitoring protocols in future seagrass restoration actions.

Predicting the geographical distribution of two invasive termite species from occurrence data.

Predicting the potential habitat of species under both current and future climate change scenarios is crucial for monitoring invasive species and understanding a species' response to different environmental conditions. Frequently, the only data available on a species is the location of its occurrence (presence-only data). Using occurrence records only, two models were used to predict the geographical distribution of two destructive invasive termite species, Coptotermes gestroi (Wasmann) and Coptotermes formosanus Shiraki. The first model uses a Bayesian linear logistic regression approach adjusted for presence-only data while the second one is the widely used maximum entropy approach (Maxent). Results show that the predicted distributions of both C. gestroi and C. formosanus are strongly linked to urban development. The impact of future scenarios such as climate warming and population growth on the biotic distribution of both termite species was also assessed. Future climate warming seems to affect their projected probability of presence to a lesser extent than population growth. The Bayesian logistic approach outperformed Maxent consistently in all models according to evaluation criteria such as model sensitivity and ecological realism. The importance of further studies for an explicit treatment of residual spatial autocorrelation and a more comprehensive comparison between both statistical approaches is suggested.

Development of a numerical index quantitating small bowel damage as detected by ultrasonography in Crohn's disease.

Small intestine contrast ultrasonography (SICUS) has emerged as a valuable tool in the detection of intestinal damage in Crohn's disease (CD). Our aim was to develop a numerical index quantitating small bowel damage as detected by SICUS in patients with an established diagnosis of CD. One hundred and ten patients with ileal or ileocolonic CD were prospectively enrolled and followed up for one year. Disease activity was assessed by CDAI and CRP levels. Study variables included bowel wall thickness, lumen diameter, lesion length and number of lesion site. Fistula, mesenteric adipose tissue alteration, abscess and lymphnodes were also considered. Bowel segments were considered as a hollow cylinder. Standardized variations of variables were combined into a statistical and mathematical model to create an algorithm scoring an index value ranging from 0 to 200. Index was subdivided into a severity scale with 5 classes from the lower (A) to the higher score (E). Median lesion index value was significantly higher (p<0.005) in patients with a CDAI>150 and in patients with CRP>5 mg/l (p=0.003). Patients classified in class E and D at SICUS underwent surgery within one year follow up more frequently than those in class C, B and A (p<0.0001). We propose a new index for assessment of small bowel lesions in CD (SLIC: sonographic lesion index for CD) developed by using SICUS. This index may turn ultrasonography in CD from a descriptive qualitative assessment to a quantitative numerical index suitable for comparison studies.

Metabolic profiling of Burkholderia cenocepacia, Burkholderia ambifaria, and Burkholderia pyrrocinia isolates from maize rhizosphere.

Burkholderia cenocepacia, Burkholderia ambifaria, and Burkholderia pyrrocinia are the Burkholderia cepacia complex (Bcc) species most frequently associated with roots of crop plants. To investigate the ecophysiological diversity of these species, metabolic profiling of maize rhizosphere isolates was carried out by means of the Biolog system, using GN2 and SFN2 plates and different parameters related to optical density (OD). The metabolic profiles produced by the SFN2 and GN2 plates were identical, but the SFN2's narrower range of OD values and significantly longer reaction times made these plates less suitable for differentiation of isolates. Principal component analysis of maximum OD (ODM) and maximum substrate oxidation rate (muM) data generated by GN2 plates allowed the selection of a reduced number of carbon sources. Statistical analysis of ODM values highlighted marked differences between the metabolic profiles of B. cenocepacia and B. ambifaria, whereas metabolic profiles of B. pyrrocinia clustered very often with those of B. cenocepacia. Analysis of the mu(M) parameter resulted in a slightly lower differentiation among the three Bcc species and a higher metabolic diversity within the single species, in particular within B. cenocepacia. Finally, B. cenocepacia and B. pyrrocinia showed generally higher oxidation rates than B. ambifaria on those GN2 substrates that commonly occur in maize root exudates.