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1.
Sci Rep ; 12(1): 18262, 2022 Oct 29.
Article in English | MEDLINE | ID: covidwho-2096810

ABSTRACT

Many resource-limited countries need an efficient and convenient method to assess disease progression in patients with coronavirus disease 2019 (COVID-19). This study developed and validated a complete blood count-based multivariate model for predicting the recovery of patients with moderate COVID-19. We collected the clinical data and laboratory test results of 86 patients with moderate COVID-19. These data were categorized into two subgroups depending on the laboratory test time. Univariate logistic regression and covariance diagnosis were used to screen for independent factors, and multifactorial logistic regression was used for model building. Data from 38 patients at another hospital were collected for external verification of the model. Basophils (OR 6.372; 95% CI 3.284-12.363), mean corpuscular volume (OR 1.244; 95% CI 1.088-1.422), red blood cell distribution width (OR 2.585; 95% CI 1.261-5.297), and platelet distribution width (OR 1.559; 95% CI 1.154-2.108) could be combined to predict recovery of patients with moderate COVID-19. The ROC curve showed that the model has good discrimination. The calibration curve showed that the model was well-fitted. The DCA showed that the model is clinically useful. Small increases in the above parameters within the normal range suggest an improvement in patients with moderate COVID-19.


Subject(s)
COVID-19 , Humans , Retrospective Studies , SARS-CoV-2 , Prognosis , Leukocyte Count , ROC Curve
2.
Sci Rep ; 12(1): 18126, 2022 Oct 28.
Article in English | MEDLINE | ID: covidwho-2096796

ABSTRACT

The development of tools that provide early triage of COVID-19 patients with minimal use of diagnostic tests, based on easily accessible data, can be of vital importance in reducing COVID-19 mortality rates during high-incidence scenarios. This work proposes a machine learning model to predict mortality and risk of hospitalization using both 2 simple demographic features and 19 comorbidities obtained from 86,867 electronic medical records of COVID-19 patients, and a new method (LR-IPIP) designed to deal with data imbalance problems. The model was able to predict with high accuracy (90-93%, ROC-AUC = 0.94) the patient's final status (deceased or discharged), while its accuracy was medium (71-73%, ROC-AUC = 0.75) with respect to the risk of hospitalization. The most relevant characteristics for these models were age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure. Finally, to facilitate its use by clinicians, a user-friendly website has been developed ( https://alejandrocisterna.shinyapps.io/PROVIA ).


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , Retrospective Studies , ROC Curve , Hospitalization , Triage/methods
3.
PLoS One ; 17(10): e0275761, 2022.
Article in English | MEDLINE | ID: covidwho-2089415

ABSTRACT

INTRODUCTION: Children infected with COVID-19 are susceptible to severe manifestations. We aimed to develop and validate a predictive model for severe/ critical pediatric COVID-19 infection utilizing routinely available hospital level data to ascertain the likelihood of developing severe manifestations. METHODS: The predictive model was based on an analysis of registry data from COVID-19 positive patients admitted to five tertiary pediatric hospitals across Asia [Singapore, Malaysia, Indonesia (two centers) and Pakistan]. Independent predictors of severe/critical COVID-19 infection were determined using multivariable logistic regression. A training cohort (n = 802, 70%) was used to develop the prediction model which was then validated in a test cohort (n = 345, 30%). The discriminative ability and performance of this model was assessed by calculating the Area Under the Curve (AUC) and 95% confidence interval (CI) from final Receiver Operating Characteristics Curve (ROC). RESULTS: A total of 1147 patients were included in this analysis. In the multivariable model, infant age group, presence of comorbidities, fever, vomiting, seizures and higher absolute neutrophil count were associated with an increased risk of developing severe/critical COVID-19 infection. The presence of coryza at presentation, higher hemoglobin and platelet count were associated with a decreased risk of severe/critical COVID-19 infection. The AUC (95%CI) generated for this model from the training and validation cohort were 0.96 (0.94, 0.98) and 0.92 (0.86, 0.97), respectively. CONCLUSION: This predictive model using clinical history and commonly used laboratory values was valuable in estimating the risk of developing a severe/critical COVID-19 infection in hospitalized children. Further validation is needed to provide more insights into its utility in clinical practice.


Subject(s)
COVID-19 , Infant , Humans , Child , COVID-19/epidemiology , SARS-CoV-2 , Risk Assessment , Retrospective Studies , ROC Curve , Tertiary Care Centers , Pakistan
4.
Clin Lab ; 68(10)2022 Oct 01.
Article in English | MEDLINE | ID: covidwho-2080866

ABSTRACT

BACKGROUND: In this retrospective study, we aimed to compare the laboratory and clinical results of cytokine hem-adsorption as an immunomodulation therapy in COVID-19 ICU patients with or without sepsis. METHODS: The levels of PCT, CRP, and ferritin were determined as indicators of infection/sepsis; the levels of in-terleukins (IL-6, IL-8 and IL-10, and TNF-α) were determined as indicators of cytokine storm were compared. APACHE score, SOFA score, and mortality rates were compared for the progression of the disease in 23 COVID-19 patients. RESULTS: The therapy was generally successful in reducing the levels of IL-6, IL-8, IL-10, and TNF-α but the levels measured after the procedure did not differ among the patients with or without sepsis, suggesting that the presence of sepsis did not affect the efficacy and function of the cytokine hemadsorption procedure in COVID-19 patients. All parameters were reduced after the procedure except the levels of PCT and ferritin and mortality rates of patients diagnosed with sepsis. The level of PCT was significantly higher in these patients compared with the patients without sepsis while the ferritin and mortality did not show any significant difference between the two groups, suggesting that the cytokine hemadsorption may be safe in the treatment of critical COVID-19 patients. CONCLUSIONS: As a result, the progression of sepsis in COVID-19 may be avoided with cytokine hemadsorption applied as an immunomodulator therapy. However, this therapy should be further explored and validated prior to its introduction to everyday clinical practice when the epidemic conditions end.


Subject(s)
COVID-19 , Sepsis , Cytokines , Ferritins , Hemadsorption , Humans , Immunologic Factors/therapeutic use , Interleukin-10 , Interleukin-6 , Interleukin-8 , Prognosis , ROC Curve , Retrospective Studies , Sepsis/diagnosis , Sepsis/drug therapy , Tumor Necrosis Factor-alpha
5.
Clin Lab ; 68(10)2022 Oct 01.
Article in English | MEDLINE | ID: covidwho-2080864

ABSTRACT

BACKGROUND: Inflammatory processes activated by rapid viral replication of SARS-CoV-2 can play a key role in the pathogenesis of multiple organ damage and be responsible for the COVID-19 patients' dramatic outcomes and common abnormal laboratory findings. The aim of this study was to assess the correlation between various laboratory biomarkers, ferritin/transferrin ratio (FTR) and receiver operating characteristic (ROC) analysis in monitoring COVID-19 patients. METHODS: This observational study was conducted in three groups: healthy participants, non COVID-19 patients with COVID-19-like clinical signs, and COVID-19 patients (severe and non-severe). Biochemical (CRP, ferritin, transferrin and albumin) and hematological (WBC, lymphocytes) parameters were assessed by automated methods. Moreover, FTR and NLR markers were calculated in the three groups mentioned. Statistical analyses were done using R (version 4.1.0). ROC curve was used to validate the predictive value of parameters. RESULTS: The COVID-19 positive group had significantly higher NEU, CRP, ferritin, FTR values, while it's WBC, absolute counts of lymphocytes and albumin were significantly lower compared to the non-COVID-19 patients (p < 0.001). Serum ferritin and FTR level of the severe group was significantly higher than that of the non-severe group (p = 0.006 and (p = 0.011, respectively). The strongest correlation in all subjects showed between lymphocytopenia and increased NEU (r = -0.99, p < 0.001). The AUC values of WBC (0.95), lymphocytes (0.89), NEU (0.88), and NLR (0.88) were higher than CRP (0.64) or Ferritin (0.81). CONCLUSIONS: We recommend using FTR, WBC, and NLR changes as simple, useful, and inexpensive indicators in early detection of COVID-19 patients.


Subject(s)
COVID-19 , Albumins , Biomarkers , COVID-19/diagnosis , Ferritins , Humans , Neutrophils , Prognosis , ROC Curve , Retrospective Studies , SARS-CoV-2 , Transferrin
6.
J Postgrad Med ; 68(4): 199-206, 2022.
Article in English | MEDLINE | ID: covidwho-2080671

ABSTRACT

Background: : Risk assessment with prognostic scoring, though important, is scarcely studied in emergency surgical patients with COVID-19 infection. Methods and Material: We conducted a retrospective cohort study on adult emergency surgical patients with COVID-19 infection in our institute from 1 May 2020 to 31 October 2021 to find the 30-day postoperative mortality and predictive accuracy of prognostic scores. We assessed the demographic data, prognostic risk scores (American Society of Anesthesiologists-Physical Classification (ASA-PS), Sequential Organ Failure Assessment (SOFA), Quick SOFA (qSOFA), Physiologic and Operative Severity Score for the enUmeration of Mortality and Morbidity (POSSUM) and Portsmouth-POSSUM (P-POSSUM) scores), surgical and anesthetic factors. We assessed the postoperative morbidity using the Clavien-Dindo scale and recorded the 30-day mortality. Correlation of prognostic scores and mortality was evaluated using Univariate Cox proportional hazards regression, receiver operating characteristic curve (ROC), Youden's index and Hosmer- Lemeshow goodness of fit model. Results: Emergency surgery was performed in 67 COVID-19 patients with postoperative complication and 30-day mortality rate of 33% and 19%, respectively. A positive qSOFA and ASAPS IIIE/IVE had a 9.03- and 12.7-times higher risk of mortality compared to a negative qSOFA and ASA-PS IE/IIE (P < 0.001), respectively. Every unit increase of SOFA, POSSUM and P-POSSUM scores was associated with a 50%, 18% and 17% higher risk of mortality, respectively. SOFA, POSSUM and P-POSSUM AUCROC curves showed good discrimination between survivors and non-survivors (AUC 0.8829, 0.85 and 0.86, respectively). Conclusions: SOFA score has a higher sensitivity to predict 30-day postoperative mortality as compared to POSSUM and P-POSSUM. However, in absence of a control group of non-COVID-19 patients, actual risk attributable to COVID-19 infection could not be determined.


Subject(s)
COVID-19 , Adult , Humans , Retrospective Studies , Prognosis , Postoperative Period , Risk Assessment/methods , ROC Curve , Postoperative Complications/etiology , Severity of Illness Index
7.
Int J Med Inform ; 164: 104791, 2022 08.
Article in English | MEDLINE | ID: covidwho-2076188

ABSTRACT

OBJECTIVE: COVID-19 is a novel, severely contagious disease with enormous negative impact on humanity as well as the world economy. An expeditious, feasible tool for detecting COVID-19 remains yet elusive. Recently, there has been a surge of interest in applying machine learning techniques to predict COVID-19 using non-image data. We have therefore undertaken a meta-analysis to quantify the diagnostic performance of machine learning models facilitating the prediction of COVID-19. MATERIALS AND METHODS: A comprehensive electronic database search for the period between January 1st, 2021 and December 3rd, 2021 was undertaken in order to identify eligible studies relevant to this meta-analysis. Summary sensitivity, specificity, and the area under receiver operating characteristic curves were used to assess potential diagnostic accuracy. Risk of bias was assessed by means of a revised Quality Assessment of Diagnostic Studies. RESULTS: A total of 30 studies, including 34 models, met all of the inclusion criteria. Summary sensitivity, specificity, and area under receiver operating characteristic curves were 0.86, 0.86, and 0.91, respectively. The purpose of machine learning models, class imbalance, and feature selection are significant covariates useful in explaining the between-study heterogeneity, in terms of both sensitivity and specificity. CONCLUSIONS: Our study findings show that non-image data can be used to predict COVID-19 with an acceptable performance. Further, class imbalance and feature selection are suggested to be incorporated whenever building models for the prediction of COVID-19, thus improving further diagnostic performance.


Subject(s)
COVID-19 , COVID-19/diagnosis , Humans , Machine Learning , ROC Curve , Sensitivity and Specificity
8.
Comput Math Methods Med ; 2022: 6932179, 2022.
Article in English | MEDLINE | ID: covidwho-2070619

ABSTRACT

Objective: To analyze the combination clinical value of plasma brain natriuretic peptide and serum glycated hemoglobin (HbAlc) in chronic pulmonary heart disease. Methods: A total of 200 patients with chronic pulmonary heart disease admitted to our hospital from January 2021 to January 2022 were selected as the observation group, and 200 healthy subjects were selected as the control group during the same period. All subjects were examined by an ECG vector map and plasma BNP, and HbAlc levels were detected to analyze the value and clinical significance of each index in single diagnosis and combined diagnosis. Results: Plasma BNP and HbAlc levels in the observation group were significantly higher than those in the control group (P < 0.05). There were 154 BNP positive, 146 HbAlc positive, 164 parallel combined diagnosis positive, and 132 serial combined diagnosis positive. Sensitivity of series combination diagnosis was significantly higher than other indexes (P < 0.05); especially, parallel combination diagnosis was significantly higher than other indexes (P < 0.05). Besides, area under the ROC curve of parallel combination diagnosis and series combination diagnosis was significantly higher than that of each index alone diagnosis (P < 0.05). Conclusion: In the diagnosis of chronic pulmonary heart disease, the combination of plasma BNP and HbAlc can effectively improve the diagnostic specificity and sensitivity, as well as improve the area under the ROC curve.


Subject(s)
Heart Failure , Pulmonary Heart Disease , Chronic Disease , Heart Failure/diagnosis , Humans , Natriuretic Peptide, Brain , Pulmonary Heart Disease/diagnosis , ROC Curve , Sensitivity and Specificity
9.
Arch Iran Med ; 25(7): 443-449, 2022 Jul 01.
Article in English | MEDLINE | ID: covidwho-2067651

ABSTRACT

BACKGROUND: This study aimed to investigate CURB-65, quick COVID-19 Severity Index (qCSI) and quick Sepsis Related Organ Failure Assessment (qSOFA) scores in predicting mortality and risk factors for death in patients with COVID-19. METHODS: We retrospectively analyzed a total of 1919 cases for whom the rRT-PCR assay for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was positive. For mortality risk factors, univariate and multivariate logistic regression analyses were used. Receiver operator characteristics (ROC) analysis and Kaplan-Meier survival analysis were performed for CURB-65, qCSI and qSOFA scores. RESULTS: The patients' average age was 45.7 (21.6) years. Male patients accounted for 51.7% (n=992). In univariate analysis, some clinical variables including age over 65 years and comorbid diseases such as hypertension, chronic kidney disease, malignancy, lymphopenia, troponin, lactate dehydrogenase (LDH) and fibrinogen elevation were associated with the mortality rate. In multivariate logistic regression analysis: Neutrophil lymphocyte ratio (NLR) 3.3 and above (OR, 9.1; 95% CI, 1.9-42), C-reactive protein (CRP)30 mg/L and above (OR, 4.1; 95% CI, 1.2-13.6), D-dimer 1000 ng/mL and above (OR, 4; 95% CI, 1.5-10.7) and age (OR, 1.11; 95% CI, 1.04-1.18-year increase) were identified as risk factors for mortality among COVID-19 patients. The CURB-65 and qCSI scores exhibited a high degree of discrimination in mortality prediction (AUC values were 0.928 and 0.865, respectively). Also, the qSOFA score had a moderate discriminant power (AUC value was 0.754). CONCLUSION: CURB-65 and qSCI scores had a high discriminatory power to predict mortality. Also, this study identified CURB-65, qCSI and qSOFA scores, NLR, CRP, D-dimer level, and annual age increase as important mortality risk factors.


Subject(s)
COVID-19 , Sepsis , Humans , Male , Middle Aged , Aged , Organ Dysfunction Scores , Retrospective Studies , ROC Curve , Prognosis , SARS-CoV-2 , Risk Factors
10.
J Bras Nefrol ; 44(3): 383-394, 2022.
Article in English, Portuguese | MEDLINE | ID: covidwho-2054623

ABSTRACT

BACKGROUND: the predictive ability of severity scores for mortality in patients admitted to intensive care units is not well-known among kidney transplanted (KT) patients, especially those diagnosed with coronavirus disease 2019 (COVID-19). The purpose of the present study was to evaluate the predictive ability of severity scores for mortality in KT recipients. METHODS: 51 KT recipients with COVID-19 diagnosis were enrolled. The performance of the SOFA, SAPS 3, and APACHE IV tools in predicting mortality after COVID-19 was compared by the area under the ROC curve (AUC-ROC) and univariate Cox regression analysis was performed. RESULTS: The 90-day cumulative incidence of death was 63.4%. Only APACHE IV score differed between survivors and nonsurvivors: 91.2±18.3 vs. 106.5±26.3, P = 0.03. The AUC- ROC of APACHE IV for predicting death was 0.706 (P = 0.04) and 0.656 (P = 0.06) at 7 and 90 days, respectively. Receiving a kidney from a deceased donor (HR = 3.16; P = 0.03), troponin levels at admission (HR for each ng/mL = 1.001; P = 0.03), APACHE IV score (HR for each 1 point = 1.02; P = 0.01), mechanical ventilation (MV) requirement (HR = 3.04; P = 0.002) and vasopressor use on the first day after ICU admission (HR = 3.85; P < 0.001) were associated with the 90-day mortality in the univariate analysis. CONCLUSION: KT recipients had high mortality, which was associated with type of donor, troponin levels, early use of vasopressors, and MV requirement. The other traditional severity scores investigated could not predict mortality.


Subject(s)
COVID-19 , Kidney Transplantation , Brazil/epidemiology , COVID-19 Testing , Cohort Studies , Humans , Intensive Care Units , Prognosis , ROC Curve , Retrospective Studies , Troponin
11.
Sci Rep ; 12(1): 13810, 2022 08 15.
Article in English | MEDLINE | ID: covidwho-2050439

ABSTRACT

Symptoms have been used to diagnose conditions such as frailty and mental illnesses. However, the diagnostic accuracy of the numbers of symptoms has not been well studied. This study aims to use equations and simulations to demonstrate how the factors that determine symptom incidence influence symptoms' diagnostic accuracy for disease diagnosis. Assuming a disease causing symptoms and correlated with the other disease in 10,000 simulated subjects, 40 symptoms occurred based on 3 epidemiological measures: proportions diseased, baseline symptom incidence (among those not diseased), and risk ratios. Symptoms occurred with similar correlation coefficients. The sensitivities and specificities of single symptoms for disease diagnosis were exhibited as equations using the three epidemiological measures and approximated using linear regression in simulated populations. The areas under curves (AUCs) of the receiver operating characteristic (ROC) curves was the measure to determine the diagnostic accuracy of multiple symptoms, derived by using 2 to 40 symptoms for disease diagnosis. With respect to each AUC, the best set of sensitivity and specificity, whose difference with 1 in the absolute value was maximal, was chosen. The results showed sensitivities and specificities of single symptoms for disease diagnosis were fully explained with the three epidemiological measures in simulated subjects. The AUCs increased or decreased with more symptoms used for disease diagnosis, when the risk ratios were greater or less than 1, respectively. Based on the AUCs, with risk ratios were similar to 1, symptoms did not provide diagnostic values. When risk ratios were greater or less than 1, maximal or minimal AUCs usually could be reached with less than 30 symptoms. The maximal AUCs and their best sets of sensitivities and specificities could be well approximated with the three epidemiological and interaction terms, adjusted R-squared ≥ 0.69. However, the observed overall symptom correlations, overall symptom incidence, and numbers of symptoms explained a small fraction of the AUC variances, adjusted R-squared ≤ 0.03. In conclusion, the sensitivities and specificities of single symptoms for disease diagnosis can be explained fully by the at-risk incidence and the 1 minus baseline incidence, respectively. The epidemiological measures and baseline symptom correlations can explain large fractions of the variances of the maximal AUCs and the best sets of sensitivities and specificities. These findings are important for researchers who want to assess the diagnostic accuracy of composite diagnostic criteria.


Subject(s)
Sensitivity and Specificity , Area Under Curve , Humans , ROC Curve
12.
Medicina (Kaunas) ; 58(10)2022 Sep 21.
Article in English | MEDLINE | ID: covidwho-2043859

ABSTRACT

Background: Krebs von den Lungen 6 (KL-6) is a novel biomarker for interstitial lung disease, and it reflects acute lung injury. We explored the usefulness of KL-6 to predict clinical outcomes in hospitalized coronavirus disease 2019 (COVID-19) patients. Methods: In a total of 48 hospitalized COVID-19 patients, KL-6 levels were measured using the HISCL KL-6 assay (Sysmex, Kobe, Japan) with the HISCL 5000 automated analyzer (Sysmex). Clinical outcomes (intensive care unit [ICU] admission, ventilator use, extracorporeal membrane oxygenation [ECMO] use, and 30-day mortality) were analyzed according to KL-6 percentiles. Age, initial KL-6 level, Charlson comorbidity index (CCI), and critical disease were compared using the receiver operating characteristic (ROC) curve and Kaplan-Meier methods for clinical outcomes. Results: KL-6 quartiles were associated with ICU admission, ventilator use, and ECMO use (all p < 0.05), except 30-day mortality (p = 0.187). On ROC curve analysis, initial KL-6 level predicted ICU admission, ventilator use, and ECMO use significantly better than age, CCI, and critical disease (all p < 0.05); age, initial KL-6 level, CCI, and critical disease predicted 30-day mortality comparably. On Kaplan-Meier survival analysis, hazard ratios (95% confidence interval) were 4.8 (1.2-19.3) for age, 4.7 (1.1-21.6) for initial KL-6 level, 3.9 (0.9-16.2) for CCI, and 2.1 (0.5-10.3) for critical disease. Conclusions: This study demonstrated that KL-6 could be a useful biomarker to predict clinical outcomes in hospitalized COVID-19 patients. KL-6 may contribute to identifying COVID-19 patients requiring critical care, including ICU admission and ventilator and/or ECMO use.


Subject(s)
COVID-19 , Lung Diseases, Interstitial , Humans , Child, Preschool , COVID-19/therapy , ROC Curve , Biomarkers , Japan/epidemiology
13.
Medicine (Baltimore) ; 101(38): e30759, 2022 Sep 23.
Article in English | MEDLINE | ID: covidwho-2042660

ABSTRACT

BACKGROUND: Patients with severe acute respiratory distress syndrome (ARDS) have high mortality rates; therefore, new biomarkers are necessary to predict the prognosis in the early stages. Serum lactate dehydrogenase (LDH) level is a specific marker of lung damage, but it is not sensitive because it is affected by several factors. This study aimed to determine whether the LDH/albumin ratio could be used as a prognostic biomarker in patients with severe ARDS due to COVID 19. METHODS: Tertiary intensive care unit (ICU) patients with severe ARDS and confirmed COVID-19 diagnosis between August 1, 2020, and October 31, 2021, were included. The demographic and clinical characteristics of the patients were recorded from the hospital databases, together with laboratory results on the day of admission to the ICU and the length of stay in the ICU and hospital. LDH/albumin, lactate/albumin, C-reactive protein (CRP)/albumin, and BUN/albumin ratios were calculated. Logistic regression analysis was performed to determine independent risk factors affecting mortality. RESULTS: Nine hundred and five patients hospitalized in a tertiary ICU were evaluated. Three hundred fifty-one patients with severe ARDS were included in this study. The mortality rate of the included patients was 61.8% (of 217/351). LDH/albumin, lactate/albumin, and BUN/albumin ratios were higher in the nonsurvivor group (P < .001). The area under the curve (AUC) from the receiver operating characteristic analysis that predicted in-hospital mortality was 0.627 (95% confidence intervals (CI): 0.574-0.678, P < .001) for the LDH/albumin ratio, 0.605 (95% CI: 0.551-0.656, P < .001) for lactate/albumin, and 0.638 (95% CI: 0.585-0.688, P < .001) for BUN/albumin. However, LDH/albumin ratio was independently associated with mortality in multivariate logistic regression analysis. CONCLUSION: LDH/albumin ratio can be used as an independent prognostic factor for mortality in patients with severe ARDS caused by COVID-19.


Subject(s)
COVID-19 , Respiratory Distress Syndrome , Biomarkers , C-Reactive Protein , COVID-19/complications , COVID-19/diagnosis , COVID-19 Testing , Humans , Intensive Care Units , L-Lactate Dehydrogenase , Lactates , Prognosis , ROC Curve , Respiratory Distress Syndrome/diagnosis , Respiratory Distress Syndrome/etiology , Retrospective Studies
14.
PLoS One ; 17(8): e0273842, 2022.
Article in English | MEDLINE | ID: covidwho-2021944

ABSTRACT

BACKGROUND: Due to the possibility of asymptomatic pneumonia in children with COVID-19 leading to overexposure to radiation and problems in limited-resource settings, we conducted a nationwide, multi-center study to determine the risk factors of pneumonia in children with COVID-19 in order to create a pediatric pneumonia predictive score, with score validation. METHODS: This was a retrospective cohort study done by chart review of all children aged 0-15 years admitted to 13 medical centers across Thailand during the study period. Univariate and multivariate analyses as well as backward and forward stepwise logistic regression were used to generate a final prediction model of the pneumonia score. Data during the pre-Delta era was used to create a prediction model whilst data from the Delta one was used as a validation cohort. RESULTS: The score development cohort consisted of 1,076 patients in the pre-Delta era, and the validation cohort included 2,856 patients in the Delta one. Four predictors remained after backward and forward stepwise logistic regression: age < 5 years, number of comorbidities, fever, and dyspnea symptoms. The predictive ability of the novel pneumonia score was acceptable with the area under the receiver operating characteristics curve of 0.677 and a well-calibrated goodness-of-fit test (p = 0.098). The positive likelihood ratio for pneumonia was 0.544 (95% confidence interval (CI): 0.491-0.602) in the low-risk category, 1.563 (95% CI: 1.454-1.679) in the moderate, and 4.339 (95% CI: 2.527-7.449) in the high-risk. CONCLUSION: This study created an acceptable clinical prediction model which can aid clinicians in performing an appropriate triage for children with COVID-19.


Subject(s)
COVID-19 , Pneumonia , COVID-19/epidemiology , Child , Humans , Models, Statistical , Pneumonia/diagnosis , Pneumonia/epidemiology , Pneumonia/etiology , Prognosis , ROC Curve , Retrospective Studies , Risk Assessment
15.
PLoS One ; 17(9): e0273006, 2022.
Article in English | MEDLINE | ID: covidwho-2021902

ABSTRACT

AIM: To develop an accurate lab score based on in-hospital patients' potent clinical and biological parameters for predicting COVID-19 patient severity during hospital admission. METHODS: To conduct this retrospective analysis, a derivation cohort was constructed by including all the available biological and clinical parameters of 355 COVID positive patients (recovered = 285, deceased = 70), collected in November 2020-September 2021. For identifying potent biomarkers and clinical parameters to determine hospital admitted patient severity or mortality, the receiver operating characteristics (ROC) curve and Fischer's test analysis was performed. Relative risk regression was estimated to develop laboratory scores for each clinical and routine biological parameter. Lab score was further validated by ROC curve analysis of the validation cohort which was built with 50 COVID positive hospital patients, admitted during October 2021-January 2022. RESULTS: Sensitivity vs. 1-specificity ROC curve (>0.7 Area Under the Curve, 95% CI) and univariate analysis (p<0.0001) of the derivation cohort identified five routine biomarkers (neutrophil, lymphocytes, neutrophil: lymphocytes, WBC count, ferritin) and three clinical parameters (patient age, pre-existing comorbidities, admitted with pneumonia) for the novel lab score development. Depending on the relative risk (p values and 95% CI) these clinical parameters were scored and attributed to both the derivation cohort (n = 355) and the validation cohort (n = 50). ROC curve analysis estimated the Area Under the Curve (AUC) of the derivation and validation cohort which was 0.914 (0.883-0.945, 95% CI) and 0.873 (0.778-0.969, 95% CI) respectively. CONCLUSION: The development of proper lab scores, based on patients' clinical parameters and routine biomarkers, would help physicians to predict patient risk at the time of their hospital admission and may improve hospital-admitted COVID-19 patients' survivability.


Subject(s)
COVID-19 , Pneumonia , COVID-19/diagnosis , Humans , Leukocyte Count , Prognosis , ROC Curve , Retrospective Studies
16.
BMJ Open ; 12(8): e057746, 2022 08 29.
Article in English | MEDLINE | ID: covidwho-2020034

ABSTRACT

INTRODUCTION: Increasing numbers of patients with non-haematological diseases are infected with invasive pulmonary aspergillosis (IPA), with a high mortality reported which is mainly due to delayed diagnosis. The diagnostic capability of mycological tests for IPA including galactomannan test, (1,3)-ß-D-glucan test, lateral flow assay, lateral flow device and PCR for the non-haematological patients remains unknown. This protocol aims to conduct a systematic review and meta-analysis of the diagnostic performance of mycological tests to facilitate the early diagnosis and treatments of IPA in non-haematological diseases. METHODS AND ANALYSIS: Database including PubMed, CENTRAL and EMBASE will be searched from 2002 until the publication of results. Cohort or cross-sectional studies that assessing the diagnostic capability of mycological tests for IPA in patients with non-haematological diseases will be included. The true-positive, false-positive, true-negative and false-negative of each test will be extracted and pooled in bivariate random-effects model, by which the sensitivity and specificity will be calculated with 95% CI. The second outcomes will include positive (negative) likelihood ratio, area under the receiver operating characteristic curve and diagnostic OR will also be computed in the bivariate model. When applicable, subgroup analysis will be performed with several prespecified covariates to explore potential sources of heterogeneity. Factors that may impact the diagnostic effects of mycological tests will be examined by sensitivity analysis. The risk of bias will be appraised by the Quality Assessment tool for Diagnostic Accuracy Studies (QUADAS-2). ETHICS AND DISSEMINATION: This protocol is not involved with ethics approval, and the results will be peer-reviewed and disseminated on a recognised journal. PROSPERO REGISTRATION NUMBER: CRD42021241820.


Subject(s)
Diagnostic Tests, Routine , Invasive Pulmonary Aspergillosis , Meta-Analysis as Topic , Systematic Reviews as Topic , Cross-Sectional Studies , Diagnostic Tests, Routine/standards , Hematology , Humans , Invasive Pulmonary Aspergillosis/diagnosis , Invasive Pulmonary Aspergillosis/microbiology , Likelihood Functions , Odds Ratio , ROC Curve , Sensitivity and Specificity , Systematic Reviews as Topic/methods
17.
BMC Med ; 20(1): 324, 2022 09 02.
Article in English | MEDLINE | ID: covidwho-2009398

ABSTRACT

BACKGROUND: Acute kidney injury (AKI) is frequently associated with COVID-19, and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalised COVID-19 patients, and to assess the incidence of AKI and KRT requirement. METHODS: This study is part of a multicentre cohort, the Brazilian COVID-19 Registry. A total of 5212 adult COVID-19 patients were included between March/2020 and September/2020. Variable selection was performed using generalised additive models (GAM), and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). RESULTS: The median age of the model-derivation cohort was 59 (IQR 47-70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalisation. The temporal validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. The geographic validation cohort had similar age and sex; however, this cohort had higher rates of ICU admission, AKI, need for KRT and in-hospital mortality. Four predictors of the need for KRT were identified using GAM: need for mechanical ventilation, male sex, higher creatinine at hospital presentation and diabetes. The MMCD score had excellent discrimination in derivation (AUROC 0.929, 95% CI 0.918-0.939) and validation (temporal AUROC 0.927, 95% CI 0.911-0.941; geographic AUROC 0.819, 95% CI 0.792-0.845) cohorts and good overall performance (Brier score: 0.057, 0.056 and 0.122, respectively). The score is implemented in a freely available online risk calculator ( https://www.mmcdscore.com/ ). CONCLUSIONS: The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalised COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation.


Subject(s)
Acute Kidney Injury , COVID-19 , Acute Kidney Injury/diagnosis , Acute Kidney Injury/epidemiology , Acute Kidney Injury/therapy , Adult , Aged , COVID-19/therapy , Dextrans , Female , Humans , Male , Middle Aged , Mitomycin , ROC Curve , Renal Replacement Therapy/adverse effects , Retrospective Studies , Risk Factors
18.
Int J Mol Sci ; 23(18)2022 Sep 09.
Article in English | MEDLINE | ID: covidwho-2010126

ABSTRACT

The COVID-19 pandemic poses global healthcare challenges due to its unpredictable clinical course. The aim of this study is to identify inflammatory biomarkers and other routine laboratory parameters associated with in-hospital mortality in critical COVID-19 patients. We performed a retrospective observational study on 117 critical COVID-19 patients. Following descriptive statistical analysis of the survivor and non-survivor groups, optimal cut-off levels for the statistically significant parameters were determined using the ROC method, and the corresponding Kaplan-Meier survival curves were calculated. The inflammatory parameters that present statistically significant differences between survivors and non-survivors are IL-6 (p = 0.0004, cut-off = 27.68 pg/mL), CRP (p = 0.027, cut-off = 68.15 mg/L) and IL-6/Ly ratio (p = 0.0003, cut-off = 50.39). Additionally, other statistically significant markers are creatinine (p = 0.031, cut-off = 0.83 mg/dL), urea (p = 0.0002, cut-off = 55.85 mg/dL), AST (p = 0.0209, cut-off = 44.15 U/L), INR (p = 0.0055, cut-off = 1.075), WBC (p = 0.0223, cut-off = 11.68 × 109/L) and pH (p = 0.0055, cut-off = 7.455). A survival analysis demonstrated significantly higher in-hospital mortality rates of patients with values of IL-6, IL-6/Ly, AST, INR, and pH exceeding previously mentioned thresholds. In our study, IL-6 and IL-6/Ly have a predictive value for the mortality of critically-ill patients diagnosed with COVID-19. The integration of these parameters with AST, INR and pH could contribute to a prognostic score for the risk stratification of critical patients, reducing healthcare costs and facilitating clinical decision-making.


Subject(s)
COVID-19 , Biomarkers , Creatinine , Hospital Mortality , Humans , Interleukin-6 , Pandemics , ROC Curve , Retrospective Studies , Urea
19.
PLoS One ; 17(8): e0272546, 2022.
Article in English | MEDLINE | ID: covidwho-2009688

ABSTRACT

OBJECTIVES: The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice. METHODS: This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants. RESULTS: This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day. CONCLUSIONS: This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.


Subject(s)
COVID-19 , Artificial Intelligence , Humans , Machine Learning , Pandemics , ROC Curve
20.
Medicine (Baltimore) ; 101(34): e30261, 2022 Aug 26.
Article in English | MEDLINE | ID: covidwho-2008667

ABSTRACT

The neutrophil-to-lymphocyte ratio (NLR) is used to predict the prognosis of various diseases, such as coronavirus disease 2019, community-acquired pneumonia, bacteremia, and endocarditis. However, NLR has never been reported to predict patient discharge in geriatric patients with influenza infection. This retrospective case-control study enrolled geriatric patients (≥65 years) with influenza virus infection who visited the emergency department of a medical center between January 01, 2010 and December 31, 2015. Demographic data, vital signs, past histories, influenza subtypes, outcomes, and disposition were analyzed. The optimal NLR cut-off value to predict patient discharge was determined using the Youden index. We also evaluated the accuracy of NLR in predicting patient discharge using logistic regression and receiver operating characteristic analysis. The study included 409 geriatric patients in the emergency department with a mean age of 79.5 years and an approximately equal sex ratio. NLR was significantly lower in the discharged group than in the nondischarged group (5.8 ± 3.7 vs 9.7 ± 8.4). Logistic regression revealed that patients with NLR ≤ 6.5 predicted discharge with an odds ratio of 3.62. The Hosmer-Lemeshow goodness-of-fit test was calculated as 0.36, and the adjusted area under the receiver operating characteristic was 0.75. The negative predictive value of NLR ≤ 6.5, to predict patient discharge, was 91.8%. NLR ≤ 6.5 is a simple and easy-to-obtain laboratory tool to guide the physicians to discharge geriatric patients with influenza infection in the crowded emergency department.


Subject(s)
COVID-19 , Influenza, Human , Aged , Case-Control Studies , Emergency Service, Hospital , Humans , Influenza, Human/diagnosis , Lymphocytes , Neutrophils , Patient Discharge , Prognosis , ROC Curve , Retrospective Studies
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