Cost-sensitive ordinal classification methods to predict SARS-CoV-2 pneumonia severity

Objective: To study the suitability of cost-sensitive ordinal artificial intelligence-machine learning (AI-ML) strategies in the prognosis of SARS-CoV-2 pneumonia severity. Materials & methods: Observational, retrospective, longitudinal, cohort study in 4 hospitals in Spain. Information regarding demographic and clinical status was supplemented by socioeconomic data and air pollution exposures. We proposed AI-ML algorithms for ordinal classification via ordinal decomposition and for cost-sensitive learning via resampling techniques. For performance-based model selection, we defined a custom score including per-class sensitivities and asymmetric misprognosis costs. 260 distinct AI-ML models were evaluated via 10 repetitions of 5×5 nested cross-validation with hyperparameter tuning. Model selection was followed by the calibration of predicted probabilities. Final overall performance was compared against five well-established clinical severity scores and against a ‘standard’ (non-cost sensitive, non-ordinal) AI-ML baseline. In our best model, we also evaluated its explainability with respect to each of the input variables. Results: The study enrolled $n$=1548 patients: 712 experienced low, 238 medium, and 598 high clinical severity. $d$=131 variables were collected, becoming $d′$=148 features after categorical encoding. Model selection resulted in our best-performing AI-ML pipeline having: a) no imputation of missing data, b) no feature selection (i.e. using the full set of $d′$ features), c) ‘Ordered Partitions’ ordinal decomposition, d) cost-based reimbalance, and e) a Histogram-based Gradient Boosting classifier. This best model (calibrated) obtained a median accuracy of 68.1% [67.3%, 68.8%] (95% confidence interval), a balanced accuracy of 57.0% [55.6%, 57.9%], and an overall area under the curve (AUC) 0.802 [0.795, 0.808]. In our dataset, it outperformed all five clinical severity scores and the ‘standard’ AI-ML baseline. Discussion & conclusion: We conducted an exhaustive exploration of AI-ML methods designed for both ordinal and cost-sensitive classification, motivated by a real-world application domain (clinical severity prognosis) in which these topics arise naturally. Our model with the best classification performance exploited successfully the ordering information of ground truth classes, coping with imbalance and asymmetric costs. However, these ordinal and cost-sensitive aspects are seldom explored in the literature

Extracting relevant predictive variables for COVID-19 severity prognosis: An exhaustive comparison of feature selection techniques

With the COVID-19 pandemic having caused unprecedented numbers of infections and deaths, large research efforts have been undertaken to increase our understanding of the disease and the factors which determine diverse clinical evolutions. Here we focused on a fully data-driven exploration regarding which factors (clinical or otherwise) were most informative for SARS-CoV-2 pneumonia severity prediction via machine learning (ML). In particular, feature selection techniques (FS), designed to reduce the dimensionality of data, allowed us to characterize which of our variables were the most useful for ML prognosis. We conducted a multi-centre clinical study, enrolling n=1548 patients hospitalized due to SARS-CoV-2 pneumonia: where 792, 238, and 598 patients experienced low, medium and high-severity evolutions, respectively. Up to 106 patient-specific clinical variables were collected at admission, although 14 of them had to be discarded for containing ⩾60% missing values. Alongside 7 socioeconomic attributes and 32 exposures to air pollution (chronic and acute), these became d=148 features after variable encoding. We addressed this ordinal classification problem both as a ML classification and regression task. Two imputation techniques for missing data were explored, along with a total of 166 unique FS algorithm configurations: 46 filters, 100 wrappers and 20 embeddeds. Of these, 21 setups achieved satisfactory bootstrap stability (⩾0.70) with reasonable computation times: 16 filters, 2 wrappers, and 3 embeddeds. The subsets of features selected by each technique showed modest Jaccard similarities across them. However, they consistently pointed out the importance of certain explanatory variables. Namely: patient’s C-reactive protein (CRP), pneumonia severity index (PSI), respiratory rate (RR) and oxygen levels –saturation SpO2, quotients SpO2/RR and arterial SatO2/FiO2 –, the neutrophil-to-lymphocyte ratio (NLR) –to certain extent, also neutrophil and lymphocyte counts separately–, lactate dehydrogenase (LDH), and procalcitonin (PCT) levels in blood. A remarkable agreement has been found a posteriori between our strategy and independent clinical research works investigating risk factors for COVID-19 severity. Hence, these findings stress the suitability of this type of fully data-driven approaches for knowledge extraction, as a complementary to clinical perspectives

Impact of outdoor air pollution on severity and mortality in COVID-19 pneumonia

The relationship between exposure to air pollution and the severity of coronavirus disease 2019 (COVID-19) pneumonia and other outcomes is poorly understood. Beyond age and comorbidity, risk factors for adverse outcomes including death have been poorly studied. The main objective of our study was to examine the relationship between exposure to outdoor air pollution and the risk of death in patients with COVID-19 pneumonia using individual-level data. The secondary objective was to investigate the impact of air pollutants on gas exchange and systemic inflammation in this disease. This cohort study included 1548 patients hospitalised for COVID-19 pneumonia between February and May 2020 in one of four hospitals. Local agencies supplied daily data on environmental air pollutants ($PM_{10}$, $PM_{2.5}$, $O_3$, $NO_2$, $NO$ and $NO_X$) and meteorological conditions (temperature and humidity) in the year before hospital admission (from January 2019 to December 2019). Daily exposure to pollution and meteorological conditions by individual postcode of residence was estimated using geospatial Bayesian generalised additive models. The influence of air pollution on pneumonia severity was studied using generalised additive models which included: age, sex, Charlson comorbidity index, hospital, average income, air temperature and humidity, and exposure to each pollutant. Additionally, generalised additive models were generated for exploring the effect of air pollution on C-reactive protein (CRP) level and Sp$O_2$/Fi$O_2$ at admission. According to our results, both risk of COVID-19 death and CRP level increased significantly with median exposure to $PM_{10}$, $NO_2$, $NO$ and $NO_X$, while higher exposure to $NO_2$, $NO$ and $NO_X$ was associated with lower Sp$O_2$/Fi$O_2$ ratios. In conclusion, after controlling for socioeconomic, demographic and health-related variables, we found evidence of a significant positive relationship between air pollution and mortality in patients hospitalised for COVID-19 pneumonia. Additionally, inflammation (CRP) and gas exchange (Sp$O_2$/Fi$O_2$) in these patients were significantly related to exposure to air pollution

Effects of Benzopyrene-7,8-Diol-9,10-Epoxide (BPDE) In Vitro and of Maternal Smoking In Vivo on Micronuclei Frequencies in Fetal Cord Blood

Up to 20% of pregnant women smoke and there is indirect evidence that certain tobacco-specific metabolites can cross the placental barrier and are genotoxic to the fetus. The presence of micronuclei results from chromosome damage and reflects the degree of underlying genetic instability. Fetal blood was obtained from the cord blood of 143 newborns (102 from nonsmoking mothers and 41 from mothers smoking >10 cigarettes/d during pregnancy). The micronucleus assay was performed following the guidelines established by the Human MicroNucleus project with modifications. To test the micronucleus assay, we evaluated the effect of a range of benzopyrene-7,8-diol-9,10-epoxide concentrations (from 3.125 nM to 4 microM) on cord blood from nonsmoking mothers. This validation showed that the number of micronuclei and apoptotic cells increased with benzopyrene-7,8-diol-9,10-epoxide dose (p < 0.0001 and p = 0.001, respectively); the minimal detectable effect was induced by 12.5 nM benzopyrene-7,8-diol-9,10-epoxide. In our sample, the number of MN was significantly higher in the 41 cord blood samples from mothers who smoked during pregnancy [smokers: 4 (1; 10.5); nonsmokers: 3 (0; 8); p = 0.016]. Therefore, the data reported herein support the hypothesis that tobacco compounds are able to induce chromosomal losses and breaks that are detectable as an increased number of micronuclei

SAR Studies Leading to the Identification of a Novel Series of Metallo-β-lactamase Inhibitors for the Treatment of Carbapenem-Resistant Enterobacteriaceae Infections That Display Efficacy in an Animal Infection Model

The clinical effectiveness of carbapenem antibiotics such as meropenem is becoming increasingly compromised by the spread of both metallo-β-lactamase (MBL) and serine-β-lactamase (SBL) enzymes on mobile genetic elements, stimulating research to find new β-lactamase inhibitors to be used in conjunction with carbapenems and other β-lactam antibiotics. Herein, we describe our initial exploration of a novel chemical series of metallo-β-lactamase inhibitors, from concept to efficacy, in a survival model using an advanced tool compound (ANT431) in conjunction with meropenem

Influence of socioeconomic status on community-acquired pneumonia outcomes in elderly patients requiring hospitalization: a multicenter observational study

The associations between socioeconomic status and community-acquired pneumonia outcomes in adults have been studied although studies did not always document a relationship. The aim of this multicenter observational study was to determine the association between socioeconomic status and community-acquired pneumonia outcomes in the elderly, in the context of a public health system providing universal free care to the whole population

Expression of Toll-like receptor 2 is up-regulated in monocytes from patients with chronic obstructive pulmonary disease

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is characterised by pulmonary and systemic inflammation which flare-up during episodes of acute exacerbation (AECOPD). Given the role of Toll-like receptors (TLRs) in the induction of inflammatory responses we investigated the involvement of TLRs in COPD pathogenesis. METHODS: The expression of TLR-2, TLR-4 and CD14 in monocytes was analyzed by flow cytometry. To study the functional responses of these receptors, monocytes were stimulated with peptidoglycan or lipopolysaccharide and the amounts of TNFα and IL-6 secreted were determined by ELISA. RESULTS: We found that the expression of TLR-2 was up-regulated in peripheral blood monocytes from COPD patients, either clinically stable or during AECOPD, as compared to never smokers or smokers with normal lung function. Upon stimulation with TLR-2 ligand monocytes from COPD patients secreted increased amounts of cytokines than similarly stimulated monocytes from never smokers and smokers. In contrast, the expressions of TLR-4 and CD14 were not significantly different between groups, and the response to lipopolysaccharide (a TLR-4 ligand) stimulation was not significantly different either. At discharge from hospital TLR-2 expression was down-regulated in peripheral blood monocytes from AECOPD patients. This could be due to the treatment with systemic steroids because, in vitro, steroids down-regulated TLR-2 expression in a dose-dependent manner. Finally, we demonstrated that IL-6, whose plasma levels are elevated in patients, up-regulated in vitro TLR-2 expression in monocytes from never smokers. CONCLUSION: Our results reveal abnormalities in TLRs expression in COPD patients and highlight its potential relationship with systemic inflammation in these patients

Determinants of short and long term functional recovery after hospitalization for community-acquired pneumonia in the elderly: role of inflammatory markers

BACKGROUND: Hospitalization for older patients with community-acquired pneumonia (CAP) is associated with functional decline. Little is know about the relationship between inflammatory markers and determinants of functional status in this population. The aim of the study is to investigate the association between tumor necrosis factor (TNF)-α, C-reactive protein (CRP) and Activities of Daily Living, and to identify risk factors associated with one year mortality or hospital readmission. METHODS: 301 consecutive patients hospitalized for CAP (mean age 73.9 ± 5.3 years) in a University affiliated hospital over 18 month period were included. All patients were evaluated on admission to identify baseline demographic, microbiological, cognitive and functional characteristics. Serum levels for TNF-α and CRP were collected at the same time. Reassessment of functional status at discharge, and monthly thereafter till 3 months post discharge was obtained and compared with preadmission level to document loss or recovery of functionality. Outcome was assessed by the composite endpoint of hospital readmission or death from any cause up to one year post hospital discharge. RESULTS: 36% of patients developed functional decline at discharge and 11% had persistent functional impairment at 3 months. Serum TNF-α (odds ratio [OR] 1.12, 95% CI 1.08–1.15; p < 0.001) and the Charlson Index (OR = 1.39, 95% CI 1.14 to 1.71; p = 0.001) but not age, CRP, or cognitive status were independently associated with loss of functionality at the time of hospital discharge. Lack of recovery in functional status at 3 months was associated with impaired cognitive ability and preadmission comorbidities. In Cox regression analysis, persistent functional impairment at 3 months, impaired cognitive function, and the Charlson Index were highly predictive of one year hospital readmission or death. CONCLUSION: Serum TNF-α levels can be useful in determining patients at risk for functional impairment following hospitalization from CAP. Old patients with impaired cognitive function and preexisting comorbidities who exhibit delay in functional recovery at 3 months post discharge may be at high risk for hospital readmission and death. With the scarcity of resources, a future risk stratification system based on these findings might be proven helpful to target older patients who are likely to benefit from interventional strategies

Predicción de la gravedad de neumonías por SARS-CoV-2 a partir de información clínica y contaminación, mediante inteligencia artificial

Introducción La contaminación del aire exterior se ha relacionado con mayor gravedad de las infecciones respiratorias. Por tanto, su inclusión en algoritmos predictivos podrían añadir información para pronosticar la gravedad de neumonías SARS-CoV-2. Material y métodos Estudio observacional longitudinal retrospectivo de cohortes, multicéntrico en 4 hospitales. Se incluyeron ingresos por neumonía SARS-CoV-2 en el primer pico epidémico de COVID-19 (febrero-mayo 2020). Se recogieron hasta 93 variables clínicas, analíticas y radiológicas por cada paciente (sexo, edad, peso, comorbilidades, síntomas, variables fisiológicas en urgencias, sangre, gasometría, etc.). Además, se calcularon los niveles exposición a contaminación por PM$_{10}$, PM$_{2.5}$, O$_{3}$, NO$_{2}$, NO, NO$_{X}$, SO$_{2}$ y CO en su código postal. En función de la evolución clínica de la neumonía, se definieron 3 niveles de gravedad [Tabla 1]. Para predecir dicha gravedad, se desarrolló un algoritmo de inteligencia artificial (IA), tipo ‘Random Forest’ con balanceo y ajuste automático de sus parámetros internos. El algoritmo se entrenó y evaluó mediante 20 repeticiones de validación cruzada 10-fold (90% entrenamiento, 10% validación), estratificando aleatoriamente por hospital y gravedad. Resultados En los conjuntos de validación, el algoritmo alcanzó una capacidad predictiva (área bajo la curva ROC) promedio AUC=0.834 para gravedad nivel 0, AUC=0.724 para 1 y AUC=0.850 para 2 [Figura 1]. Sin la información de contaminantes, su capacidad predictiva se degradó ligeramente (AUCs = 0.829, 0.722, 0.844; respectivamente). Conclusiones Nuestro algoritmo IA es capaz de predecir de manera satisfactoria la evolución de la gravedad en la neumonía; en particular para los casos más leves y más severos. El algoritmo IA extrae las reglas más relevantes a partir principalmente de la información clínica, analítica y radiológica de cada individuo; no obstante, la incorporación de la exposición a contaminantes mejora ligeramente la capacidad predictiva. El impacto de la contaminación podría estar ya reflejado en las analíticas de sangre, a través de su efecto en los niveles de inflamación del paciente (PCT, PCR, LDH, etc.)

Occupational exposure of workers to pesticides: Toxicogenetics and susceptibility gene polymorphisms

Farm workers are often exposed to pesticides, which are products belonging to a specific chemical group that affects the health of agricultural workers and is mostly recognized as genotoxic and carcinogenic. The exposure of workers from Piauí, Brazil, to these hazardous chemicals was assessed and cytogenetic alterations were evaluated using the buccal micronucleus assay, hematological and lipid parameters, butyrylcholinesterase (BChE) activity and genetic polymorphisms of enzymes involved in the metabolism of pesticides, such as PON1, as well as of the DNA repair system (OGG1, XRCC1 and XRCC4). Two groups of farm workers exposed to different types of pesticides were evaluated and compared to matched non-exposed control groups. A significant increase was observed in the frequencies of micronuclei, kariorrhexis, karyolysis and binucleated cells in the exposed groups (n = 100) compared to controls (n = 100). No differences were detected regarding the hematological parameters, lipid profile and BChE activity. No significant difference was observed either regarding DNA damage or nuclear fragmentation when specific metabolizing and DNA repair genotypes were investigated in the exposed groups