Predictors of COVID-19 Hospital Treatment Outcome

Tomasiuk, Ryszard; Dąbrowski, Jan; Smykiewicz, Jolanta; Wiącek, Magdalena

doi:10.2147/ijgm.s334544

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…The extracted probabilistic relationships inform evidence-based knowledge on the course of COVID-19 pneumonia with inpatient treatment. Some of these relationships matched with the results of other researchers [ 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. However, other extracted results are new, such as the influence of monocytes from the third time interval (10–17 days) to lethality.…”

Section: Discussionsupporting

confidence: 87%

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Derevitskii

Mramorov

Usoltsev

et al. 2022

JPM

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The primary goal of this paper is to develop an approach for predicting important clinical indicators, which can be used to improve treatment. Using mathematical predictive modeling algorithms, we examined the course of COVID-19-based pneumonia (CP) with inpatient treatment. Algorithms used include dynamic and ordinary Bayesian networks (OBN and DBN), popular ML algorithms, the state-of-the-art auto ML approach and our new hybrid method based on DBN and auto ML approaches. Predictive targets include treatment outcomes, length of stay, dynamics of disease severity indicators, and facts of prescribed drugs for different time intervals of observation. Models are validated using expert knowledge, current clinical recommendations, preceding research and classic predictive metrics. The characteristics of the best models are as follows: MAE of 3.6 days of predicting LOS (DBN plus FEDOT auto ML framework), 0.87 accuracy of predicting treatment outcome (OBN); 0.98 F1 score for predicting facts of prescribed drug (DBN). Moreover, the advantage of the proposed approach is Bayesian network-based interpretability, which is very important in the medical field. After the validation of other CP datasets for other hospitals, the proposed models can be used as part of the decision support systems for improving COVID-19-based pneumonia treatment. Another important finding is the significant differences between COVID-19 and non-COVID-19 pneumonia.

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Section: Discussionsupporting

confidence: 87%

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Derevitskii

Mramorov

Usoltsev

et al. 2022

JPM

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show abstract

“…This model is reported to have shown the sensitivity of psychological therapy and certain sociodemographic factors as predictors of mental health [ 23 , 24 ]. One study on the Bayesian model explored the capacity of several parameters of laboratory tests (e.g., C-reactive protein, procalcitonin, and fibrinogen) to predict the effect of drug treatments on the recovery of patients with COVID-19 [ 25 ]. Furthermore, the model has been employed to examine the distribution of dengue transmission [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis Used to Identify Clinical and Laboratory Variables Capable of Predicting Progression to Severe Dengue among Infected Pediatric Patients

Corzo-Gómez,

Guzmán-Aquino,

Vargas-De-León

et al. 2023

Children

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The current contribution aimed to evaluate the capacity of the naive Bayes classifier to predict the progression of dengue fever to severe infection in children based on a defined set of clinical conditions and laboratory parameters. This case-control study was conducted by reviewing patient files in two public hospitals in an endemic area in Mexico. All 99 qualifying files showed a confirmed diagnosis of dengue. The 32 cases consisted of patients who entered the intensive care unit, while the 67 control patients did not require intensive care. The naive Bayes classifier could identify factors predictive of severe dengue, evidenced by 78% sensitivity, 91% specificity, a positive predictive value of 8.7, a negative predictive value of 0.24, and a global yield of 0.69. The factors that exhibited the greatest predictive capacity in the model were seven clinical conditions (tachycardia, respiratory failure, cold hands and feet, capillary leak leading to the escape of blood plasma, dyspnea, and alterations in consciousness) and three laboratory parameters (hypoalbuminemia, hypoproteinemia, and leukocytosis). Thus, the present model showed a predictive and adaptive capacity in a small pediatric population. It also identified attributes (i.e., hypoalbuminemia and hypoproteinemia) that may strengthen the WHO criteria for predicting progression to severe dengue.

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“…1-3 Since then, it has resulted in a worldwide pandemic, with more than 228 million confirmed cases and 4.6 million confirmed deaths. 4…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] Since then, it has resulted in a worldwide pandemic, with more than 228 million confirmed cases and 4.6 million confirmed deaths. 4 Covid-19 infection usually starts with flu like symptoms 5 and can be asymptomatic or may have a mild to severe course. 6 Admitted Patients have variable symptoms.…”

Section: Introductionmentioning

confidence: 99%

Serum ACE2 Level is Associated With Severe SARS-CoV-2 Infection: A Cross-Sectional Observational Study

et al. 2022

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Objectives: Angiotensin-converting enzyme 2 (ACE2) represents the primary receptor for SARS-CoV-2 to enter endothelial cells, causing coronavirus disease of 2019 (COVID-19). In this study, we investigate the association between circulating ACE2 levels with the severity of COVID-19. Methods: Serum ACE2 levels were measured in 144 COVID-19-positive subjects at hospital admission, and 123 COVID-19-negative control subjects. The association between ACE2 and clinical outcomes was analyzed. Results: About 144 COVID-19 patients and 123 healthy controls data were analyzed, the mean age of patients was 62 years and 50% of them were males. The mean age of the control group was 55 years and 63% were males. ACE-II level was measured and compared between COVID-19 patients and controls and revealed no significant differences ( P > .05). ACE-II level was measured in COVID-19 patients and compared between different patient’s subgroups, ACE II level was not dependent on gender, smoking, ACE intake, or comorbidities ( P > .05), and was significantly correlated with cardiovascular diseases (CVS) ( P-value = .046) ICU admission ( P-value = .0007) and Death ( P-value = .0082). Conclusion: There was no significant difference between the COVID-19 and Control group, however, ACE2 serum level was significantly higher in patients with COVID-19 who were critically ill or non-survivors, its increased level is also associated with length of stay. Elevated ACE2 level is associated with the severity of COVID-19 disease, and it has the potential to be a predictor of the severity of the disease.

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Predictors of COVID-19 Hospital Treatment Outcome

Cited by 5 publications

References 40 publications

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

Bayesian Analysis Used to Identify Clinical and Laboratory Variables Capable of Predicting Progression to Severe Dengue among Infected Pediatric Patients

Serum ACE2 Level is Associated With Severe SARS-CoV-2 Infection: A Cross-Sectional Observational Study

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