Comparison of four different threshold values of shock index in predicting mortality of COVID-19 patients

2021 ◽  
pp. 1-15
Author(s):  
Rohat Ak ◽  
Fatih Doğanay
Keyword(s):  
Operating Characteristic ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Threshold Value ◽  
Shock Index ◽  
Threshold Values ◽  
Healthcare Resources ◽  
Icu Admission ◽  
Mortality Outcome ◽  
Study Population

Abstract Objective: The object of this study was to examine the accuracy in pre-hospital shock index (SI) for predicting intensive care unit (ICU) requirement and 30-day mortality among from COVID-19 patients transported to the hospital by ambulance. Method: All consecutive patients who were the age ≥18 years, transported to the emergency department (ED) by ambulance with a suspected or confirmed COVID-19 in the pre-hospital frame were included in the study. Four different cut-off points were compared (0.7, 0.8, 0.9, and 1.0) to examine the predictive performance of both the mortality and ICU requirement of the SI. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) was employed to evaluate each cut-off value discriminatory for predicting 30-day mortality and ICU admission. Results: The total of 364 patients was included in this study. The median age in the study population was 69 (55-80), of which 196 were men and 168 were women. AUC values for 30-day mortality outcome were calculated as 0.672, 0.674, 0.755, and 0.626, respectively, for threshold values of 0.7, 0.8, 0.9 and 1.0. ICU admission was more likely for the patients with pre-hospital SI> 0.9. Similarly, the mortality rate was higher in patients with pre-hospital SI> 0.9. Conclusion: Early triage of COVID-19 patients will ensure efficient use of healthcare resources. The SI could be a helpful, fast and powerful tool for predicting mortality status and ICU requirements of adult COVID-19 patients. It was concluded that the most useful threshold value for the shock index in predicting the prognosis of COVID-19 patients is 0.9.

scholarly journals Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Espen Jimenez-Solem ◽  
Tonny S. Petersen ◽  
Casper Hansen ◽  
Christian Hansen ◽  
Christina Lioma ◽  
...  
Keyword(s):  
Prediction Models ◽  
Severe Disease ◽  
External Validation ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Mortality Prediction ◽  
Icu Admission ◽  
Risk Of Death ◽  
Online Risk ◽  
The United Kingdom

AbstractPatients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

scholarly journals Upregulation of the RNF8 gene can predict the presence of sperm in azoospermic individuals

2020 ◽  
Vol 47 (1) ◽  
pp. 61-67
Author(s):  
Majid Nazari ◽  
Emad Babakhanzadeh ◽  
S. Mohsen Aghaei Zarch ◽  
Mehrdad Talebi ◽  
Nima Narimani ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Testicular Biopsy ◽  
Negative Group ◽  
Nonobstructive Azoospermia ◽  
Entire Study ◽  
Biopsy Specimens ◽  
Study Population ◽  
Polymerase Chain

Objective: In this study, specimens from testicular biopsies of men with nonobstructive azoospermia (NOA) were used to investigate whether <i>RNF8</i> gene could serve as a biomarker to predict the presence of sperm in these patients.Methods: Testicular biopsy specimens from 47 patients were classified according to the presence of sperm (positive vs. negative groups) and investigated for the expression of <i>RNF8</i>. The level of <i>RNF8</i> gene expression in the testes was compared between these groups using reverse-transcription polymerase chain reaction.Results: The expression level of <i>RNF8</i> was significantly higher in testicular samples from the positive group than in those from the negative group. Moreover, the area under the curve of <i>RNF8</i> expression for the entire study population was 0.84, showing the discriminatory power of <i>RNF8</i> expression in differentiating between the positive and negative groups of men with NOA. A receiver operating characteristic curve analysis showed that <i>RNF8</i> expression had a sensitivity of 81% and a specificity of 84%, with a cutoff level of 1.76.Conclusion: This study points out a significant association between the expression of <i>RNF8</i> and the presence of sperm in NOA patients, which suggests that quantified <i>RNF8</i> expression in testicular biopsy samples may be a valuable biomarker for predicting the presence of spermatozoa in biopsy samples.

Ambulant erworbene Pneumonie: Die Mortalität nachhaltig senken

2020 ◽  
Vol 8 (5) ◽  
pp. 252-253
Author(s):  
Stefan Krüger
Keyword(s):  
Operating Characteristic ◽  
Distress Syndrome ◽  
Area Under The Curve ◽  
Scoring Systems ◽  
Roc Curves ◽  
Community Acquired Pneumonia ◽  
Ambulant Erworbene Pneumonie ◽  
Icu Admission ◽  
Failure Assessment

Background: The study aimed to investigate the predictive value of the quick sequential organ failure assessment (qSOFA) for clinical outcomes in emergency patients with community-acquired pneumonia (CAP). Methods: A total of 742 CAP cases from the emergency department (ED) were enrolled in this study. The scoring systems including the qSOFA, SOFA and CURB-65 (confusion, urea, respiratory rate, blood pressure and age) were used to predict the prognostic outcomes of CAP in ICU-admission, acute respiratory distress syndrome (ARDS) and 28-day mortality. According to the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, the accuracies of prediction of the scoring systems were analyzed among CAP patients. Results: The AUC values of the qSOFA, SOFA and CURB-65 scores for ICU-admission among CAP patients were 0.712 (95%CI: 0.678–0.745, P < 0.001), 0.744 (95%CI: 0.711–0.775, P < 0.001) and 0.705 (95%CI: 0.671–0.738, P < 0.001), respectively. For ARDS, the AUC values of the qSOFA, SOFA and CURB-65 scores were 0.730 (95%CI: 0.697–0.762, P < 0.001), 0.724 (95%CI: 0.690–0.756, P < 0.001) and 0.749 (95%CI: 0.716–0.780, P < 0.001), respectively. After 28 days of follow-up, the AUC values of the qSOFA, SOFA and CURB-65 scores for 28-day mortality were 0.602 (95%CI: 0.566–0.638, P < 0.001), 0.587 (95%CI: 0.551–0.623, P < 0.001) and 0.614 (95%CI: 0.577–0.649, P < 0.001) in turn. There were no statistical differences between qSOFA and SOFA scores for predicting ICU-admission (Z = 1.482, P = 0.138), ARDS (Z = 0.321, P = 0.748) and 28-day mortality (Z = 0.573, P = 0.567). Moreover, we found no differences to predict the ICU-admission (Z = 0.370, P = 0.712), ARDS (Z = 0.900, P = 0.368) and 28-day mortality (Z = 0.768, P = 0.442) using qSOFA or CURB-65 scores. Conclusion: qSOFA was not inferior to SOFA or CURB-65 scores in predicting the ICU-admission, ARDS and 28-day mortality of patients presenting in the ED with CAP.

scholarly journals Comparison of predictive performance for toxicity by accumulative dose of DVH parameter addition and DIR addition for cervical cancer patients

2020 ◽  
Vol 62 (1) ◽  
pp. 155-162
Author(s):  
Yuya Miyasaka ◽  
Noriyuki Kadoya ◽  
Rei Umezawa ◽  
Yoshiki Takayama ◽  
Kengo Ito ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cancer Patients ◽  
Operating Characteristic ◽  
External Beam Radiotherapy ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Roc Curves ◽  
Dose Volume ◽  
Gi Toxicity ◽  
The Difference

Abstract We compared predictive performance between dose volume histogram (DVH) parameter addition and deformable image registration (DIR) addition for gastrointestinal (GI) toxicity in cervical cancer patients. A total of 59 patients receiving brachytherapy and external beam radiotherapy were analyzed retrospectively. The accumulative dose was calculated by three methods: conventional DVH parameter addition, full DIR addition and partial DIR addition. ${D}_{2{cm}^3}$, ${D}_{1{cm}^3}$ and ${D}_{0.1{cm}^3}$ (minimum doses to the most exposed 2 cm3, 1cm3 and 0.1 cm3 of tissue, respectively) of the rectum and sigmoid were calculated by each method. V50, V60 and V70 Gy (volume irradiated over 50, 60 and 70 Gy, respectively) were calculated in full DIR addition. The DVH parameters were compared between toxicity (≥grade1) and non-toxicity groups. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves were compared to evaluate the predictive performance of each method. The differences between toxicity and non-toxicity groups in ${D}_{2{cm}^3}$ were 0.2, 5.7 and 3.1 Gy for the DVH parameter addition, full DIR addition and partial DIR addition, respectively. The AUCs of ${D}_{2{cm}^3}$ were 0.51, 0.67 and 0.57 for DVH parameter addition, full DIR addition and partial DIR addition, respectively. In full DIR addition, the difference in dose between toxicity and non-toxicity was the largest and AUC was the highest. AUCs of V50, V60 and V70 Gy were 0.51, 0.63 and 0.62, respectively, and V60 and V70 were high values close to the value of ${D}_{2{cm}^3}$ of the full DIR addition. Our results suggested that the full DIR addition may have the potential to predict toxicity more accurately than the conventional DVH parameter addition, and that it could be more effective to accumulate to all pelvic irradiation by DIR.

scholarly journals The usefulness of Shock Index and Modified Shock Index in predicting outcome of COVID-19 patients

2021 ◽  
pp. 1-16
Author(s):  
Erdem KURT ◽  
Suphi BAHADIRLI
Keyword(s):  
Hospital Mortality ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Male Gender ◽  
Chronic Obstructive ◽  
Shock Index ◽  
Obstructive Pulmonary Disease ◽  
Predicting Outcome ◽  
The Mean ◽  
Artery Disease

Abstract Objective: The aim of this study is to investigate the accuracy of shock index (SI) and modified shock index (mSI) in predicting intensive care unit (ICU) requirement and in-hospital mortality among COVID-19 patients who admitted to the emergency department (ED). Likewise, the effects of patients’ conditions such as age, gender and comorbidity on prognosis will be analyzed. Methods: The files were retrospectively scanned for all COVID-19 patients over the age of 18 who were admitted to the ED and hospitalized between January 1, 2021 and March 15, 2021. The area under the receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used to assess each scoring system discriminatory for predicting in-hospital mortality and ICU admission. Results: There were 464 patients included in this study. The mean age of the patients was 62.4±16.7, of which 245 were men and 219 were women. The most common comorbidity in patients was hypertension 200 (43.1%), followed by chronic obstructive pulmonary disease 174 (37.5%) and coronary artery disease 154 (33.2%). In terms of in-hospital mortality, the AUC of SI, and mSI were 0.719, and 0.739, respectively. In terms of ICU requirement, the AUC of SI, and mSI were 0.704, and 0.729, respectively. Conclusions: In this study, it was concluded that SI and mSI are useful in predicting in-hospital mortality and ICU requirement in COVID-19 patients. In addition, it is another important result of the study that advanced age, male gender and hypertension may be associated with poor prognosis.

Classical (adiponectin, leptin, resistin) and new (chemerin, vaspin, omentin) adipocytokines in patients with prediabetes

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Antoaneta Gateva ◽  
Yavor Assyov ◽  
Adelina Tsakova ◽  
Zdravko Kamenov
Keyword(s):  
Insulin Resistance ◽  
Predictive Value ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Serum Adiponectin ◽  
Endocrine Organ ◽  
Serum Leptin ◽  
Discriminating Power ◽  
Study Population ◽  
Obese Subjects

Abstract Background In the last decade, there has been an increased interest toward fat tissue as an endocrine organ that secretes many cytokines and bioactive mediators that play a role in insulin sensitivity, inflammation, coagulation and the pathogenesis of atherosclerosis. The aim of this study was to investigate classical (adiponectin, leptin, resistin) and new (chemerin, vaspin, omentin) adipocytokine levels in subjects with prediabetes [impaired fasting glucose (IFG) and/or impaired glucose tolerance (IGT)] and obese subjects with normoglycemia. Methods In this study, 80 patients with a mean age of 50.4 ± 10.6 years were recruited, divided into two groups with similar age and body mass index (BMI) – with obesity and normoglycemia (n = 41) and with obesity and prediabetes (n = 39). Results Serum adiponectin levels were significantly higher in subjects with normoglycemia compared to patients with prediabetes. Adiponectin has a good discriminating power to distinguish between patients with and without insulin resistance in our study population [area under the curve (AUC) = 0.728, p = 0.002]. Other adipocytokine levels were not significantly different between the two groups. The patients with metabolic syndrome (MetS) had significantly lower levels of leptin compared to those without MetS (33.03 ± 14.94 vs. 40.24 ± 12.23 ng/mL) and this difference persisted after adjustment for weight and BMI. Receiver operating characteristic (ROC) analysis showed that low serum leptin can predict the presence of MetS (p = 0.03), AUC = 0.645. Conclusion Serum adiponectin is statistically higher in patients with normoglycemia compared to those with prediabetes and has a predictive value for distinguishing between patients with and without insulin resistance in the studied population. Serum leptin has a good predictive value for distinguishing between patients with and without MetS in the studied population.

scholarly journals A Preliminary Study on the Ability of the Trypsin-Like Peptidase Activity Assay Kit to Detect Periodontitis

2020 ◽  
Vol 8 (3) ◽  
pp. 98
Author(s):  
Masanori Iwasaki ◽  
Michihiko Usui ◽  
Wataru Ariyoshi ◽  
Keisuke Nakashima ◽  
Yoshie Nagai-Yoshioka ◽  
...  
Keyword(s):  
Diagnostic Accuracy ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Population Based ◽  
Case Definition ◽  
Peptidase Activity ◽  
Activity Assay ◽  
Severe Periodontitis ◽  
Study Population ◽  
Preliminary Study

This study aimed to explore whether the Trypsin-Like Peptidase Activity Assay Kit (TLP-AA-Kit), which measures the activity of N-benzoyl-dl-arginine peptidase (trypsin-like peptidase), can be used as a reliable tool for periodontitis detection in population-based surveillance. In total, 105 individuals underwent a full-mouth periodontal examination and provided tongue swabs as specimens for further analyses. The results of the TLP-AA-Kit were scored between 1 and 5; higher scores indicated higher trypsin concentrations. Receiver operating characteristic analyses were used to evaluate the predictive validity of the TLP-AA-Kit, where the periodontitis case definition provided by the Centers for Disease Control/American Academy of Periodontology served as the reference. Severe and moderate periodontitis were identified in 4.8% and 16.2% of the study population, respectively. The TLP-AA-Kit showed high diagnostic accuracy for severe periodontitis, with an area under the curve of 0.93 (95% confidence interval = 0.88–0.99). However, the diagnostic accuracy of the TLP-AA-Kit for moderate/severe periodontitis was not reliable. While further studies are necessary to validate our results, the results provided herein highlight the potential of the TLP-AA-Kit as a useful tool for the detection of periodontitis, particularly in severe cases, for population-based surveillance.

scholarly journals The Clinical Utility of Serial Procalcitonin and Procalcitonin Clearance in Predicting the Outcome of COVID-19 Patients

2021 ◽  
Author(s):  
Sara I. Taha ◽  
Aalaa K. Shata ◽  
Shereen A. Baioumy ◽  
Shaimaa H. Fouad ◽  
Mariam K. Youssef
Keyword(s):  
Hospital Mortality ◽  
Prospective Observational Study ◽  
Operating Characteristic ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Roc Curve Analysis ◽  
Laboratory Tool ◽  
Serial Measurements ◽  
Better Than

Background: The pandemic of coronavirus disease 2019 (COVID‐19) represents a great threat to global health. Sensitive tests that effectively predict the disease outcome are essentially required to guide proper intervention. Objectives: To evaluate the prognostic ability of serial procalcitonin (PCT) measurement to predict the outcome of COVID-19 patients, using PCT clearance (PCT-c) as a tool to reflect its dynamic changes. Methods: A prospective observational study of inpatients diagnosed with COVID-19 at the Quarantine Hospitals of Ain-Shams University, Cairo, Egypt. During the first five days of hospitalization, serial PCT and PCT-c values were obtained and compared between survivors and non-survivors. Patients were followed up to hospital discharge or in-hospital mortality. Results: Compared to survivors, serial PCT levels of non-survivors were significantly higher (p<0.001) and progressively increased during follow-up, in contrast, PCT-c values were significantly lower (p<0.01) and progressively decreased. Receiver operating characteristic (ROC) curve analysis showed that by using the initial PCT value alone, at a cut off value of 0.80 ng/ml, the area under the curve for predicting in-hospital mortality was 0.81 with 61.1% sensitivity and 87.3% accuracy. Serial measurements showed better predictive performance and the combined prediction value was better than the single prediction by the initial PCT. Conclusions: Serial PCT measurement could be a useful laboratory tool to predict the prognosis and outcome of COVID-19 patients. Moreover, PCT-c could be a reliable tool to assess PCT progressive kinetics.

scholarly journals Neural network analysis of clinical variables predicts escalated care in COVID-19 patients: a retrospective study

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11205
Author(s):  
Joyce Q. Lu ◽  
Benjamin Musheyev ◽  
Qi Peng ◽  
Tim Q. Duong
Keyword(s):  
Operating Characteristic ◽  
Vital Signs ◽  
Area Under The Curve ◽  
Blood Gases ◽  
Characteristic Analysis ◽  
Test Dataset ◽  
Clinical Variables ◽  
Icu Admission ◽  
Reactive Protein ◽  
Laboratory Variables

This study sought to identify the most important clinical variables that can be used to determine which COVID-19 patients hospitalized in the general floor will need escalated care early on using neural networks (NNs). Analysis was performed on hospitalized COVID-19 patients between 7 February 2020 and 4 May 2020 in Stony Brook Hospital. Demographics, comorbidities, laboratory tests, vital signs and blood gases were collected. We compared those data obtained at the time in emergency department and the time of intensive care unit (ICU) upgrade of: (i) COVID-19 patients admitted to the general floor (N = 1203) vs. those directly admitted to ICU (N = 104), and (ii) patients not upgraded to ICU (N = 979) vs. those upgraded to the ICU (N = 224) from the general floor. A NN algorithm was used to predict ICU admission, with 80% training and 20% testing. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis (ROC). We found that C-reactive protein, lactate dehydrogenase, creatinine, white-blood cell count, D-dimer and lymphocyte count showed temporal divergence between COVID-19 patients hospitalized in the general floor that were upgraded to ICU compared to those that were not. The NN predictive model essentially ranked the same laboratory variables to be important predictors of needing ICU care. The AUC for predicting ICU admission was 0.782 ± 0.013 for the test dataset. Adding vital sign and blood-gas data improved AUC (0.822 ± 0.018). This work could help frontline physicians to anticipate downstream ICU need to more effectively allocate healthcare resources.

scholarly journals Healthcare Expenditure Prediction with Neighbourhood Variables – A Random Forest Model

2020 ◽  
Vol 11 (2) ◽  
pp. 111-138
Author(s):  
Sigrid M. Mohnen ◽  
Adriënne H. Rotteveel ◽  
Gerda Doornbos ◽  
Johan J. Polder
Keyword(s):  
Random Forest ◽  
Healthcare Costs ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Full Model ◽  
Random Forest Models ◽  
Study Population ◽  
Explained Variance

AbstractWe investigated the additional predictive value of an individual’s neighbourhood (quality and location), and of changes therein on his/her healthcare costs. To this end, we combined several Dutch nationwide data sources from 2003 to 2014, and selected inhabitants who moved in 2010. We used random forest models to predict the area under the curve of the regular healthcare costs of individuals in the years 2011–2014. In our analyses, the quality of the neighbourhood before the move appeared to be quite important in predicting healthcare costs (i.e. importance rank 11 out of 126 socio-demographic and neighbourhood variables; rank 73 out of 261 in the full model with prior expenditure and medication). The predictive performance of the models was evaluated in terms of R2 (or proportion of explained variance) and MAE (mean absolute (prediction) error). The model containing only socio-demographic information improved marginally when neighbourhood was added (R2 +0.8%, MAE −€5). The full model remained the same for the study population (R2 = 48.8%, MAE of €1556) and for subpopulations. These results indicate that only in prediction models in which prior expenditure and utilization cannot or ought not to be used neighbourhood might be an interesting source of information to improve predictive performance.

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