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ObjectiveCoronavirus disease 2019 (COVID-19) may induce short- and long-term cognitive failures after recovery, but the underlying risk factors are still a matter of debate. Identifying patients at the highest risk is now a research priority to prevent persistent symptoms after recovery. In this study, we investigated whether: (i) the odds of experiencing persistent cognitive failures may differ based on the patients disease course severity and sex; (ii) the patients electrolytic profile at the acute stage may represent a risk factor for persistent cognitive failures. MethodsWe analysed data from 204 patients suffering from COVID-19 and hospitalised during the first pandemic wave. According to the 7-point WHO-OS Scale, their disease course was classified as severe (if the patient needed ventilation) or mild (if they did not). We investigated the presence of persistent cognitive failures using a modified version of the Cognitive Failures Questionnaire, collected after hospital discharge, while electrolyte profiles were collected during hospitalisation. We explored our hypotheses via logistic regression models. ResultsFemales who suffered from mild COVID-19 were more likely to report mental fatigue than those with severe COVID-19 ({beta}= 0.29, 95%CI [0.06; 0.53], p= 0.01). Furthermore, they present a statistically significant risk effect of Na+ alteration at the acute phase on the odds of presenting persistent mental fatigue ({beta}= 0.37, 95%CI [0.09; 0.64], p= 0.01). InterpretationThese findings have important implications for the clinical management of COVID-19 hospitalised patients. Attention should be paid to potential electrolyte imbalances, mainly in females suffering from mild COVID-19. Key PointsO_ST_ABSQuestionC_ST_ABSDo disease severity and sex predict the risk of persistent cognitive failures in COVID-19 hospitalised survivors? Does electrolytic imbalance at the acute phase represent a risk factor for persistent cognitive failures after recovery? FindingsFemales who suffered from mild compared to severe COVID-19 had a higher risk of presenting persistent mental fatigue. In this group, dysnatraemia at the acute stage represented a significant risk factor on the odds of showing such a persistent cognitive failure after recovery. MeaningSodium levels must be monitored and balanced during hospitalisation of females affected by mild COVID-19 to prevent mental fatigue among the possible short- and long-term effects.
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BackgroundAn urgent need exists for an early detection of cases with a high-risk of SARS-CoV-2 infection, particularly in high-flow and -risk settings, such as emergency departments (EDs). The aim of this work is to develop and validate a predictive model for the evaluation of SARS-CoV-2 infection risk, with the rationale of using this tool to manage ED patients. MethodsA retrospective study was performed by cross-sectionally reviewing the electronical case records of patients admitted to Niguarda Hospital or referred to its ED in the period 15 March to 24 April 2020. Derivation sample was composed of non-random inpatients hospitalized on 24 April and admitted before 22 April 2020. Validation sample was composed of consecutive patients who visited the ED between 15 and 25 March 2020. The association between the dichotomic outcome and each predictor was explored by univariate analysis with logistic regression models. ResultsA total of 113 patients in the derivation sample and 419 in the validation sample were analyzed. History of fever, elder age and low oxygen saturation showed to be significant predictors of SARS-CoV-2 infection. The neutrophil count improves the discriminative ability of the model, even if its calibration and usefulness in terms of diagnosis is unclear. ConclusionThe discriminatory ability of the identified models makes the overall performance suboptimal; their implementation to calculate the individual risk of infection should not be used without additional investigations. However, they could be useful to evaluate the spatial allocation of patients while awaiting the result of the nasopharyngeal swab. Key Messages boxO_ST_ABSWhat is already known on this topicC_ST_ABS1 year after the onset of the coronavirus disease 2019 (COVID-19) pandemic, the trend of its spread has not shown a substantial global reduction. An urgent need exists for efficient early detection of cases with a high risk of SARS-CoV-2 infection and a number of diagnostic prediction models have been developed, but a few models were externally validated in high-flow and -risk settings, such as emergency departments (EDs). What this study addsThis study develops and validate predictive models for the evaluation of SARS-CoV-2 infection risk, with the rationale of using these tools to promptly manage patients who are afferent to the ED, allocating them accordingly to the risk of infection while awaiting swab result. History of fever, older age and low oxygen saturation showed to be significant predictors of the presence of SARS-CoV-2 infection. The use of laboratory findings, such as neutrophil count, showed to improve the discriminative ability of the model, even if its calibration and usefulness in terms of diagnosis is unclear.
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The paper proposes a new approach to heart activity diagnosis based on Gram polynomials and probabilistic neural networks (PNN). Heart disease recognition is based on the analysis of phonocardiogram (PCG) digital sequences. The PNN provides a powerful tool for proper classification of the input data set. The novelty of the proposed approach lies in a powerful feature extraction based on Gram polynomials and the Fourier transform. The proposed system presents good performance obtaining overall sensitivity of 93%, specificity of 91% and accuracy of 94%, using a public database of over 3000 heart beat sound recordings, classified as normal and abnormal heart sounds. Thus, it can be concluded that Gram polynomials and PNN prove to be a very efficient technique using the PCG signal for characterizing heart diseases.