scholarly journals Assessment of the Validity of the Psycho-Cardiological Comorbidity Index in the Practice of a Cardiologist

2019 ◽  
Vol 4 (2) ◽  
pp. 51-54
Author(s):  
M. V. Dorofeikova ◽  
S. F. Zadvorev ◽  
N. N. Petrova ◽  
A. A. Yakovlev
Keyword(s):  
Arterial Hypertension ◽  
Roc Curve ◽  
Predictive Value ◽  
Medical Records ◽  
Validation Cohort ◽  
Mood Stabilizers ◽  
Benzodiazepine Tranquilizers ◽  
Area Under Roc Curve ◽  
Comorbidity Index ◽  
Patient Department

Background. The feasibility of creating a screening Index for the comorbidity of mental disorders and cardiovascular diseases in the practice of the cardiologist dictates the need to validate existing algorithms.Aim. To validate the previously proposed Index of psychocardiac comorbidity in prediction of outpatient recommendation of psycho-pharmacotherapy for cardiology patients.Materials and methods. Medical records of 302 consecutive patients of cardiac in-patient department were retrospectively analyzed with ROC-curve to estimate the predictive value of previously proposed Index of psychocardiac comorbidity for out-patient recommendation of psycho-pharmacotherapy.Results. The prevalence of outpatient psycho-pharmacotherapy in the examined patients was 15.2 %; this group of patients was more polymorbid and was characterized by a higher proportion of women and a higher prevalence of a labile course of arterial hypertension. The sedative antipsychotics, non-benzodiazepine tranquilizers and mood stabilizers were predominant pharmacological groups. An analysis of reproducibility of Index of psychocardiac comorbidity was provided on a cohort of cardiology in-patient department (n = 302). Index calculated by using the formula I = C + 3(6)×Q + 3×W + 8×A (C – number of Comorbid diagnoses, Q – Quake in the chest, palpitations or arrhythmia, onset before 55 (3 points) or 50 (6 points) years; W – Women; A – labile Arterial hypertension). Area under ROC-curve in validation cohort was 0.828 ± 0.035 (p < 0.001). Positive predictive value of Index was maximal for the score of ≥ 13.Conclusion. The proposed Index of psychocardiac comorbidity is a valid tool to predict the prescription of psychopharmacotherapy in cardiology patients.

scholarly journals The Role of Exhaled Hydrogen Sulfide in the Diagnosis of Colorectal Adenoma

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Nian Liu ◽  
Yuzhen Tseng ◽  
Huilu Zhang ◽  
Jian Chen
Keyword(s):  
Hydrogen Sulfide ◽  
Colorectal Adenoma ◽  
Roc Curve ◽  
Predictive Value ◽  
Diagnostic Value ◽  
Control Group ◽  
Observation Group ◽  
Area Under Roc Curve ◽  
Auc Value

Purpose. Exhaled determination can detect metabolite hydrogen sulfide in the intestine. We aim to analyze the predictive value of hydrogen sulfide in the diagnosis of colorectal adenoma. Methods. We recruited seventy patients diagnosed with colorectal adenoma as the observation group and sixty-six healthy subjects as the control group. The colorectal adenoma was diagnosed by colonoscopy at the Endoscopy Center of Huashan Hospital affiliated to Fudan University from June 2018 to November 2019. Exhaled gas was collected through the nose and mouth, respectively, and hydrogen sulfide in exhaled gas was determined according to the manufacturer’s instructions. Results. Receiver operating characteristic (ROC) curve was analyzed based on the exhaled data of the observation group and the control group. The ROC curve showed an area under ROC curve (AUC) 0.724 for nasal exhaled H2S, which had a diagnostic value. When nasal exhaled H2S was >13.3 part per billion (ppb), the sensitivity and the specificity of predicting colorectal adenoma were 57% and 78%, respectively. The exhaled H2S of the observation group was significantly different from that of the control group. The AUC value was 0.716 as a prognostic factor of colorectal adenoma. As exhaled H2S was >28.8 ppb, the sensitivity and the specificity of predicting colorectal adenoma were 63% and 77%, respectively. Conclusion. Exhaled and nasal H2S determination has a predictive value for colorectal adenoma as a novel and noninvasive method. Therefore, it is worth conducting more research to analyze exhaled and nasal H2S.

VALUE OF COMBINATION OF HAP SCORE AND BISAP SCORE IN PREDICTING SEVERITY IN PATIENTS OF ACUTE PANCREATITIS

2019 ◽  
pp. 96-100
Author(s):  
Thi Ngoc Suong Le ◽  
Pham Chi Tran ◽  
Van Huy Tran
Keyword(s):  
Acute Pancreatitis ◽  
Positive Predictive Value ◽  
Roc Curve ◽  
Predictive Value ◽  
Prognostic Value ◽  
Acute Inflammation ◽  
Clinical Symptoms ◽  
Mortality Rates ◽  
Multiple Organ ◽  
Central Hospital

Acute pancreatitis (AP) is an acute inflammation of the pancreas, usually occurs suddenly with a variety of clinical symptoms, complications of multiple organ failure and high mortality rates. Objectives: To determine the value of combination of HAP score and BISAP score in predicting the severity of acute pancreatitis of the Atlanta 2012 Classification. Patients and Methods: 75 patients of acute pancreatitis hospitalized at Hue Central Hospital between March 2017 and July 2018; HAP and BISHAP score is calculated within the first 24 hours. The severity of AP was classified by the revised Atlanta criteria 2012. Results: When combining the HAP and BISAP scores in predicting the severity of acute pancreatitis, the area under the ROC curve was 0,923 with sensitivity value was 66.7%, specificity value was 97.1%; positive predictive value was 66.7%, negative predictive value was 97.1%. Conclusion: The combination of HAP and BISAP scores increased the sensitivity, predictive value, and prognostic value in predicting the severity of acute pancreatitis of the revised Atlanta 2012 classification in compare to each single scores. Key words: HAPscore, BiSAP score, acute pancreatitis, predicting severity

scholarly journals Development and validation of a prognostic COVID-19 severity assessment (COSA) score and machine learning models for patient triage at a tertiary hospital

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Verena Schöning ◽  
Evangelia Liakoni ◽  
Christine Baumgartner ◽  
Aristomenis K. Exadaktylos ◽  
Wolf E. Hautz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Clinical Outcomes ◽  
Predictive Value ◽  
Validation Cohort ◽  
Tertiary Hospital ◽  
Learning Models ◽  
Clinical Risk ◽  
Machine Learning Models ◽  
Risk Stratification Score

Abstract Background Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcomes. They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes. Methods In- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st (‘first wave’, n = 198) and September 1st through November 16th 2020 (‘second wave’, n = 459) were used as training and prospective validation cohort, respectively. A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause). Test accuracy was assessed using the area under the receiver operating characteristic curve (AUROC). Results Sex, C-reactive protein, sodium, hemoglobin, glomerular filtration rate, glucose, and leucocytes around the time of first positive testing (− 3 to + 1 days) were the most predictive parameters. AUROC of the risk stratification score on training data (AUROC = 0.94, positive predictive value (PPV) = 0.97, negative predictive value (NPV) = 0.80) were comparable to the prospective validation cohort (AUROC = 0.85, PPV = 0.91, NPV = 0.81). The most successful ML algorithm with respect to AUROC was support vector machines (median = 0.96, interquartile range = 0.85–0.99, PPV = 0.90, NPV = 0.58). Conclusion With a small set of easily obtainable parameters, both the clinical risk stratification score and the ML models were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation.

Echocardiography for the 10-year prediction of cardiomyopathy in long-term survivors of childhood cancer

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J.M Leerink ◽  
H.J.H Van Der Pal ◽  
E.A.M Feijen ◽  
P.G Meregalli ◽  
M.S Pourier ◽  
...  
Keyword(s):  
Childhood Cancer ◽  
Median Time ◽  
Cancer Diagnosis ◽  
Predictive Value ◽  
Validation Cohort ◽  
Funding Source ◽  
Derivation Cohort ◽  
After Cancer ◽  
Radiotherapy Dose

Abstract Background Childhood cancer survivors (CCS) treated with anthracyclines and/or chest-directed radiotherapy receive life-long echocardiographic surveillance to detect cardiomyopathy early. Current risk stratification and surveillance frequency recommendations are based on anthracycline- and chest-directed radiotherapy dose. We assessed the added prognostic value of an initial left ventricular ejection fraction (EF) measurement at &gt;5 years after cancer diagnosis. Patients and methods Echocardiographic follow-up was performed in asymptomatic CCS from the Emma Children's Hospital (derivation; n=299; median time after diagnosis, 16.7 years [inter quartile range (IQR) 11.8–23.15]) and from the Radboud University Medical Center (validation; n=218, median time after diagnosis, 17.0 years [IQR 13.0–21.7]) in the Netherlands. CCS with cardiomyopathy at baseline were excluded (n=16). The endpoint was cardiomyopathy, defined as a clinically significant decreased EF (EF&lt;40%). The predictive value of the initial EF at &gt;5 years after cancer diagnosis was analyzed with multivariable Cox regression models in the derivation cohort and the model was validated in the validation cohort. Results The median follow-up after the initial EF was 10.9 years and 8.9 years in the derivation and validation cohort, respectively, with cardiomyopathy developing in 11/299 (3.7%) and 7/218 (3.2%), respectively. Addition of the initial EF on top of anthracycline and chest radiotherapy dose increased the C-index from 0.75 to 0.85 in the derivation cohort and from 0.71 to 0.92 in the validation cohort (p&lt;0.01). The model was well calibrated at 10-year predicted probabilities up to 5%. An initial EF between 40–49% was associated with a hazard ratio of 6.8 (95% CI 1.8–25) for development of cardiomyopathy during follow-up. For those with a predicted 10-year cardiomyopathy probability &lt;3% (76.9% of the derivation cohort and 74.3% of validation cohort) the negative predictive value was &gt;99% in both cohorts. Conclusion The addition of the initial EF &gt;5 years after cancer diagnosis to anthracycline- and chest-directed radiotherapy dose improves the 10-year cardiomyopathy prediction in CCS. Our validated prediction model identifies low-risk survivors in whom the surveillance frequency may be reduced to every 10 years. Calibration in both cohorts Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Dutch Heart Foundation

FC 051PREDICTIVE VALUE OF PLASMA NGAL:HEPCIDIN-25 FOR MAJOR ADVERSE KIDNEY EVENTS AFTER CARDIAC SURGERY WITH CARDIOPULMONARY BYPASS: A PILOT STUDY

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Saban Elitok ◽  
Anja Haase-Fielitz ◽  
Martin Ernst ◽  
Michael Haase
Keyword(s):  
Cardiac Surgery ◽  
Cardiopulmonary Bypass ◽  
Roc Curve ◽  
Predictive Value ◽  
Kidney Injury ◽  
Urinary Output ◽  
Icu Admission ◽  
Plasma Ngal ◽  
Net Reclassification Improvement ◽  
Major Adverse Kidney Events

Abstract Background and Aims Neutrophil gelatinase-associated lipocalin (NGAL) and hepcidin-25 appear to be involved in catalytic iron-related kidney injury after cardiac surgery with cardiopulmonary bypass. We aimed to explore the predictive value of plasma NGAL, plasma hepcidin-25, and the plasma NGAL:hepcidin-25 ratio for major adverse kidney events after cardiac surgery. Method We compared the predictive value of plasma NGAL, hepcidin-25, and NGAL:hepcidin-25 with those of serum creatinine (Cr), and urinary output and urinary protein for primary endpoint major adverse kidney events (MAKE; acute kidney injury [AKI] stages 2 and 3, persistent AKI &gt; 48 hrs, acute dialysis, and in-hospital mortality) and secondary-endpoint AKI in 100 cardiac surgery patients at intensive care unit (ICU) admission. We performed ROC curve, logistic regression, and reclassification analyses. Results At ICU admission, plasma NGAL, plasma NGAL:hepcidin-25, and Cr predicted MAKE (area under the ROC curve [AUC]: 0.77 [95% confidence interval (CI) 0.60–0.94], 0.79 [0.63–0.95], 0.74 [0.51–0.97]) and AKI (0.73 [0.53–0.93], 0.89 [0.81–0.98], 0.70 [0.48–0.93]). For AKI prediction, NGAL:hepcidin-25 had a higher discriminatory power than Cr (AUC difference 0.26 [95% CI 0.00–0.53]). Urinary output and protein, plasma lactate, C-reactive protein, creatine kinase myocardial band, and brain natriuretic peptide did not predict MAKE or AKI (AUC &lt; 0.70). Only plasma NGAL:hepcidin-25 correctly reclassified patients for MAKE or AKI (category-free net reclassification improvement: 0.82 [95% CI 0.12–1.52], 1.03 [0.29–1.77]). After adjustment to the Cleveland risk score, plasma NGAL:hepcidin-25 ≥ 0.9 independently predicted MAKE (adjusted odds ratio 16.34 [95% CI 1.77–150.49], P = 0.014), whereas Cr did not. Conclusion NGAL:hepcidin-25 is a promising plasma marker for predicting postoperative MAKE.

scholarly journals Polimorfismos Genéticos Associados ao Aparecimento de Hipertensão Arterial Numa População Portuguesa

2018 ◽  
Vol 31 (10) ◽  
pp. 542 ◽  
Author(s):  
Ana Célia Sousa ◽  
Roberto Palma dos Reis ◽  
Andreia Pereira ◽  
Sofia Borges ◽  
Ana Isabel Freitas ◽  
...  
Keyword(s):  
Water Management ◽  
Risk Factors ◽  
Arterial Hypertension ◽  
Roc Curve ◽  
Genetic Variants ◽  
Multifactorial Disease ◽  
Renin Angiotensin Aldosterone System ◽  
Renin Angiotensin ◽  
Traditional Risk Factors ◽  
Gnb3 C825t

Introduction: Arterial hypertension is a complex, multifactorial disease, controlled by genetic and environmental factors.Objective: Evaluate the genetic susceptibility for developing arterial hypertension and its association with the traditional risk factors in the outbreak of this pathology.Material and Methods: Case-control study with 1712 individuals, mean age of 51.0 ± 7.9 years (860 hypertensive patients and 852 controls). Biochemical and traditional risk factors, and genetic variants were evaluated: ACE I/D rs4340, ACE A2350G rs4343, AGT T174M rs4762, AGT M235T rs699 AGTR1 A1166C rs5186, CYP11B2 -344 C/T rs1799998, ADRB1 R389G rs1801253, ADRB2 R16G rs1042713, ADD1 G460W rs4961, SCNN1G G173A rs5718, GNB3 C825T rs5443, ATP2B1 A/G rs2681472, CYP17A1 T/C rs11191548, SLC4A2 C/T rs2303934. The risk of each gene for hypertension was estimated by the dominant, recessive, co-dominant and multiplicative models. By logistic regression, variables associated with hypertension were evaluated. ROC curves were first performed with traditional risk factors and then adding the genetic variants associated with hypertension. Data were analyzed by SPSS for Windows 19.0 and MedCalc v. 13.3.3.0.Results: The genetic variants ADD1 G460W, GNB3 C825T, ACE I/D, ACE A2350G were associated with hypertension. ROC curve with traditional risk factors and these variants showed an increase in the predictive capacity of hypertension (p = 0.018).Discussion: According to the results of our study, the genetic variants found to be associated with hypertension were: ACE I/D rs4340, ACE A2350G rs4343, ADD1 G460W rs4961 and GNB3 C825T rs5443. The first two variants are associated with hypertension by interfering with the renin-angiotensin-aldosterone system, which plays an important role in regulating blood pressure. It should be noted that genes encoding the components of renin-angiotensin-aldosterone system are natural candidates for the development and progression of hypertension. In our population alpha-aducin polymorphism (ADD1 G460W rs4961) was also associated with hypertension. In a Portuguese population, known to have high salt intake, it makes sense that this polymorphism which is relevant in salt and water management may consequently be relevant in the onset of hypertension. The genetic variant GNB3 C825T rs5443 that affects intracellular signalling was also found to be a strong risk candidate for hypertension. Initially, with the elaboration of the ROC curve and calculation of the AUC using only with traditional risk factors and later by adding the variants ADD1 G460W, GNB3 C825T, ACE I/D and ACE A2350G to the traditional risk factors, we verified that genetic polymorphisms increased the predictive risk of hypertension, when compared to the risk given only by traditional risk factors, with statistical significance (p = 0.018). This suggests that hypertension is a multifactorial disease that results from the interaction of environmental, genetic and lifestyle factors that interact with each other and lead to the advent of this important pathology.Conclusion: In our study, the hypertension-associated polymorphisms are linked to the renin-angiotensin-aldosterone axis (ACE I/D, ACE A2350G), as well as to salt and water management (ADD1 G460W, GNB3 C825T). Through a multivariate analysis, it was concluded that these two last genetic variants together with four of the traditional risk factors (smoking, alcohol consumption, obesity and diabetes) are associated in a significant and independent way with essential hypertension. In a predictive model of hypertension, the introduction of genetic variants slightly increases the predictive value of the model.

scholarly journals Parsing Expression Grammars and Their Induction Algorithm

2020 ◽  
Vol 10 (23) ◽  
pp. 8747
Author(s):  
Wojciech Wieczorek ◽  
Olgierd Unold ◽  
Łukasz Strąk
Keyword(s):  
Genetic Programming ◽  
Roc Curve ◽  
Classification Performance ◽  
Grammatical Inference ◽  
Inference Algorithm ◽  
Finite Sample ◽  
Induction Method ◽  
Grammar Learning ◽  
Area Under Roc Curve ◽  
Inference Methods

Grammatical inference (GI), i.e., the task of finding a rule that lies behind given words, can be used in the analyses of amyloidogenic sequence fragments, which are essential in studies of neurodegenerative diseases. In this paper, we developed a new method that generates non-circular parsing expression grammars (PEGs) and compares it with other GI algorithms on the sequences from a real dataset. The main contribution of this paper is a genetic programming-based algorithm for the induction of parsing expression grammars from a finite sample. The induction method has been tested on a real bioinformatics dataset and its classification performance has been compared to the achievements of existing grammatical inference methods. The evaluation of the generated PEG on an amyloidogenic dataset revealed its accuracy when predicting amyloid segments. We show that the new grammatical inference algorithm achieves the best ACC (Accuracy), AUC (Area under ROC curve), and MCC (Mathew’s correlation coefficient) scores in comparison to five other automata or grammar learning methods.

scholarly journals Differences in controlled versus uncontrolled arterial hypertension in a primary health care centre—a quality improvement cycle study

2021 ◽  
Vol 31 (Supplement_2) ◽  
Author(s):  
M T Reis ◽  
A C Roque ◽  
M Pinto ◽  
L M Santiago
Keyword(s):  
Arterial Hypertension ◽  
Medical Records ◽  
Best Practice ◽  
Growth Dynamics ◽  
Primary Health Care Centre ◽  
Population Based ◽  
Significant Rise ◽  
Clinical Activity ◽  
Health Care Centre ◽  
Scientific Meetings

Abstract Background Cardiovascular diseases are the leading cause of death worldwide and uncontrolled Arterial Hypertension (AH) is the most important involved risk factor. An observational study published in 2019 found differences between controlled and uncontrolled AH patients, namely less frequent chronotherapy and family history health records. We aimed to study medical designed population based interventions to reach an increase in controlled AH defining a set of growth dynamics (Δ) ≥ +5 in AH prevalence of control defined for a value of 140/90 mmHg. Methods 2019 results were presented in small medical teams weekly periodic scientific meetings and upon such awareness strategies were developed for improving AH control, according to the best practice evidence available. Data were collected, 6 months later, in a randomized representative sample of AH patients in each Primary Care Health Unit for a confidence interval 95%, error margin of 5%. Descriptive and inferential analysis, for a P &lt; 0.001 and growth dynamics (Δ) were calculated. Results In a n = 148 sample there was a significant improvement in the rate of controlled hypertension (43.9% to 67.9%, P &lt; 0.001, Δ = +54.0). Chronotherapy improved from 29.0% to 66.2% (P &lt; 0.001), Δ = +128.3) and family medical records also improved (88.1% to 100%, Δ + 11.9). Conclusions Significant rise in the prevalence of AH control was achieved after informative and formative intervention. Chronotherapy was significantly improved, allowing a 67.9% AH control. Family medical records also significantly improved. Healthcare professional’s knowledge and reflection about their clinical activity seem to be important tools to achieve better results and hopefully health outcomes, in Arterial Hypertension.

scholarly journals Clinical Characteristics, Risk Factors and Predictive Value of COVID-19 Pneumonia: A Retrospective Study of 173 Patients in Wuhan, China

2020 ◽  
Author(s):  
Yang Zhang ◽  
Jun Xue ◽  
Mi Yan ◽  
Jing Chen ◽  
Hai Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Predictive Model ◽  
Disease Severity ◽  
Roc Curve ◽  
Predictive Value ◽  
Older Patients ◽  
Clinical Characteristics ◽  
Lactic Dehydrogenase ◽  
Older Age ◽  
Hospital Outcome

Abstract Background: COVID-19 is a globally emerging infectious disease. As the global epidemic continues to spread, the risk of COVID-19 transmission and diffusion in the world will also remain. Currently, several studies describing its clinical characteristics have focused on the initial outbreak, but rarely to the later stage. Here we described clinical characteristics, risk factors for disease severity and in-hospital outcome in patients with COVID-19 pneumonia from Wuhan. Methods: Patients with COVID-19 pneumonia admitted to Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology from February 13 to March 8, 2020, were retrospectively enrolled. Multivariable logistic regression analysis was used to identify risk factors for disease severity and in-hospital outcome and establish predictive models. Receiver operating characteristic (ROC) curve was used to assess the predictive value of above models.Results: 106 (61.3%) of the patients were female. The mean age of study populations was 62.0 years, of whom 73 (42.2%) had underlying comorbidities mainly including hypertension (24.9%). The most common symptoms on admission were fever (67.6%) and cough (60.1%), digestive symptoms (22.0%) was also very common. Older age (OR: 3.420; 95%Cl: 1.415-8.266; P=0.006), diarrhea (OR: 0.143; 95%Cl: 0.033-0.611; P=0.009) and lymphopenia (OR: 4.769; 95%Cl: 2.019-11.266; P=0.000) were associated with severe illness on admission; the area under the ROC curve (AUC) of predictive model were 0.860 (95%CI: 0.802-0.918; P=0.000). Older age (OR: 0.309; 95%Cl: 0.142-0.674; P=0.003), leucopenia (OR: 0.165; 95%Cl: 0.034-0.793; P=0.025), increased lactic dehydrogenase (OR: 0.257; 95%Cl: 0.100-0.659; P=0.005) and interleukins-6 levels (OR: 0.294; 95%Cl: 0.099-0.872; P=0.027) were associated with poor in-hospital outcome; AUC of predictive model were 0.752 (95%CI: 0.681-0.824; P=0.000).Conclusion: Older patients with diarrhea and lymphopenia need early identification and timely intervention to prevent the progression to severe COVID-19 pneumonia. However, older patients with leucopenia, increased lactic dehydrogenase and interleukins-6 levels are at a high risk for poor in-hospital outcome.Trial registration: ChiCTR2000029549

scholarly journals Cardiac biomarkers as prognosticators among SARS-CoV-19 patients in a tertiary hospital in Philippines

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
R Ong ◽  
C Chacon ◽  
S Javier
Keyword(s):  
Creatine Kinase ◽  
Logistic Regression ◽  
Hospital Mortality ◽  
Roc Curve ◽  
Predictive Value ◽  
Troponin I ◽  
Binary Logistic Regression ◽  
High Sensitivity ◽  
Cardiac Biomarkers ◽  
Optimal Cutoff

Abstract Background There is overwhelming volume of confirmed cases of COVID-19, despite this numerous knowledge gaps remain in the diagnosis, management, and prognostication of this novel coronavirus infection, making prevention and control a challenge. Methods This retrospective cohort study included patients with real-time reverse transcriptase polymerase chain reaction (rRT-PCR)-confirmed COVID-19. Binary logistic regression was used to determine the association between the cardiac biomarkers and in-hospital mortality. ROC, AUC, and cutoff analyses were used to determine optimal cutoff values for the cardiac biomarkers. Results A total of 90 subjects with a complete panel of cardiac biomarkers out of the 224 rRT-PCR confirmed cases were included. The median age was 57 years (IQR, 47–67 years), majority were males. Sixty-six (77.6%) subjects survived while 19 (22.4%) expired. The most common presenting symptom was fever (75.6%), and the most common comorbidity was hypertension (67.8%). Spearman rho correlation analysis showed moderate positive association of high sensitivity troponin I (hsTnI) with in-hospital mortality (R, 0.434, p = &lt;0.001). Multivariate binary logistic regression analysis showed that creatine kinase and hsTnI were independently associated with in-hospital mortality (OR, 4.103 [95% CI, 1.241–13.563], p=0.021; and OR, 7.899 [95% CI, 2.430–25.675], p=0.001, respectively). ROC curve analysis showed that hsTnI was a good predictor for in-hospital mortality (AUC, 0.829 [95% CI, 0.735–0.923], p = &lt;0.001) and that creatine kinase was a poor predictor (AUC, 0.677 [95% CI, 0.531–0.823], p=0.018). Optimal cutoff point derived from the ROC curve for hsTnI was 0.010 ng/ml (J, 0.574) with a sensitivity of 84% (TPR, 0.842 [95% CI, 0.604–0.966]), specificity of 73% (TNR, 0.732 [95% CI, 0.614–0.386]), and an adjusted negative predictive value of 99% (Known prevalence*adjusted NPV, 0.989), a positive likelihood ratio of 20% (LR+, 3.147 [95% CI, 2.044–4.844]) and a negative likelihood ratio of 30% (LR−, 0.216 [95% CI, 0.076–0.615]). Conclusion High sensitivity troponin I level was a good tool with a very high negative predictive value in significantly predicting in-hospital mortality among rRT-PCR positive COVID-19 patients. FUNDunding Acknowledgement Type of funding sources: None. ROC Curve

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