INTRODUCTION: The main objective of this study was to identify the best combination of admission day parameters for predicting COVID-19 mortality in hospitalized patients. Furthermore, we sought to compare the predictive capacity of pulmonary parameters to that of renal parameters for mortality from COVID-19. METHODS: In this retrospective study, all patients admitted to a tertiary hospital between September 1st, 2020, and December 31st, 2020, who were clinically symptomatic and tested positive for COVID-19, were included. We gathered extensive data on patient admissions, including laboratory results, comorbidities, chest X-ray (CXR) images, and SpO2 levels, to determine their role in predicting mortality. Experienced radiologists evaluated the CXR images and assigned a score from 0 to 18 based on the severity of COVID-19 pneumonia. Further, we categorized patients into two independent groups based on their renal function using the RIFLE and KDIGO criteria to define the acute kidney injury (AKI) and chronic kidney disease (CKD) groups. The first group ("AKI&CKD") was subdivided into six subgroups: normal renal function (A); CKD grade 2+3a (B); AKI-DROP (C); CKD grade 3b (D); AKI-RISE (E); and grade 4 + 5 CKD (F). The second group was based only on estimated glomerular filtration rate (eGFR) at the admission, and thus it was divided into four grades: grade 1, grade 2+3a, grade 3b, and grade 4 + 5. RESULTS: The cohort comprised 619 patients. Patients who died during hospitalization had a significantly higher mean radiological score compared to those who survived, with a p value <0.01. Moreover, we observed that the risk for mortality was significantly increased as renal function deteriorated, as evidenced by the AKI&CKD and eGFR groups (p < 0.001 for each group). Regarding mortality prediction, the area under the curve (AUC) for renal parameters (AKI&CKD group, eGFR group, and age) was found to be superior to that of pulmonary parameters (age, radiological score, SpO2, CRP, and D-dimer) with an AUC of 0.8068 versus 0.7667. However, when renal and pulmonary parameters were combined, the AUC increased to 0.8813. Optimal parameter combinations for predicting mortality from COVID-19 were identified for three medical settings: Emergency Medical Service (EMS), the Emergency Department, and the Internal Medicine Floor. The AUC for these settings was 0.7874, 0.8614, and 0.8813, respectively. CONCLUSIONS: Our study demonstrated that selected renal parameters are superior to pulmonary parameters in predicting COVID-19 mortality for patients requiring hospitalization. When combining both renal and pulmonary factors, the predictive ability of mortality significantly improved. Additionally, we identified the optimal combination of factors for mortality prediction in three distinct settings: EMS, Emergency Department, and Internal Medicine Floor.
Robust quantitative structure-activity relationships (QSARs) for hBACE-1 inhibitors (pIC50) for a large database (n = 1706) are established. New statistical criteria of the predictive potential of models are suggested and tested. These criteria are the index of ideality of correlation (IIC) and the correlation intensity index (CII). The system of self-consistent models is a new approach to validate the predictive potential of QSAR-models. The statistical quality of models obtained using the CORAL software (http://www.insilico.eu/coral) for the validation sets is characterized by the average determination coefficient R2v= 0.923, and RMSE = 0.345. Three new promising molecular structures which can become inhibitors hBACE-1 are suggested.
The algorithm of building up a model for the biological activity of peptides as a mathematical function of a sequence of amino acids is suggested. The general scheme is the following: The total set of available data is distributed into the active training set, passive training set, calibration set, and validation set. The training (both active and passive) and calibration sets are a system of generation of a model of biological activity where each amino acid obtains special correlation weight. The numerical data on the correlation weights calculated by the Monte Carlo method using the CORAL software (http://www.insilico.eu/coral). The target function aimed to give the best result for the calibration set (not for the training set). The final checkup of the model is carried out with data on the validation set (peptides, which are not visible during the creation of the model). Described computational experiments confirm the ability of the approach to be a tool for the design of predictive models for the biological activity of peptides (expressed by pIC50).
- Publikační typ
- časopisecké články MeSH
Type 2 diabetes mellitus (T2DM) is associated with increased fracture risk; the underlying mechanism remains unexplained. This study aimed to investigate the relationships between body composition and bone and glucose metabolism in postmenopausal women with T2DM. Dual-energy X-ray absorptiometry was used to measure bone mineral density (BMD) and body composition. A total of 68 postmenopausal women with T2DM and 71 controls were eligible for the study. In contrast to normal BMD in T2DM, a similar prevalence of low-trauma fractures was observed in both groups. T2DM women had significantly higher Trunk fat% and A/G ratio and significantly lower Legs LM% and Legs FM%. Legs LM% was significantly lower in fractured T2DM group and negatively correlated with glycaemia and HbA1c (p<0.01). Serum osteocalcin was significantly lower in T2DM and inversely correlated with FM%, Trunk FM% and A/G ratio (p<0.01) and positively correlated with Legs FM% and total LM% (p<0.05). In conclusion, abdominal obesity and decrease in muscle mass may contribute to low bone formation in T2DM women. Further research is needed to unravel underlying pathophysiological mechanisms and to determine whether maintenance of muscle mass, especially in the lower extremities and/or reduction of central fat mass can prevent fractures.
- MeSH
- diabetes mellitus 2. typu diagnóza metabolismus MeSH
- glukosa metabolismus MeSH
- kostní denzita fyziologie MeSH
- lidé středního věku MeSH
- lidé MeSH
- postmenopauza metabolismus MeSH
- senioři MeSH
- složení těla fyziologie MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- pozorovací studie MeSH
BACKGROUNDS: The CORAL software has been developed as a tool to build up quantitative structure- activity relationships (QSAR) for various endpoints. OBJECTIVE: The task of the present work was to estimate and to compare QSAR models for biochemical activity of various therapeutic agents, which are built up by the CORAL software. METHOD: The Monte Carlo technique gives possibility to build up predictive model of an endpoint by means of selection of so-called correlation weights of various molecular features extracted from simplified molecular input-line entry system (SMILES). Descriptors calculated with these weights are basis for building up correlations "structure - endpoint". RESULTS: Optimal descriptors, which are aimed to predict values of endpoints with apparent influence upon metabolism are crytically compared in aspect of their robustness and heuristic potential. Arguments which are confirming the necessity of reformulation of basics of QSARs are listed: (i) each QSAR model is stochastic experiment. The result of this experiment is defined by distribution into the training set and validation set; (ii) predictive potential of a model should be checked up with a group of different splits; and (iii) only model stochastically stable for a group of splits can be estimated as a reliable tool for the prediction. Examples of the improvement of the models previously suggested are demonstrated. CONCLUSION: The current version of the CORAL software remains a convenient tool to build up predictive models. The Monte Carlo technique involved for the software confirms the principle "QSAR is a random event" is important paradigm for the QSPR/QSAR analyses.
- MeSH
- kvantitativní vztahy mezi strukturou a aktivitou * MeSH
- léčivé přípravky metabolismus MeSH
- lidé MeSH
- metoda Monte Carlo MeSH
- molekulární modely * MeSH
- software * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
