scholarly journals A novel scoring system for assessing the severity of electrolyte and acid-base disorders and predicting outcomes in hospitalized patients

2018 ◽  
Vol 67 (4) ◽  
pp. 750-760
Author(s):  
Yimei Wang ◽  
Jiachang Hu ◽  
Xuemei Geng ◽  
Xiaoyan Zhang ◽  
Xialian Xu ◽  
...  
Keyword(s):  
Risk Score ◽  
Scoring System ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Multiple Logistic Regression Analysis ◽  
Survival Rates ◽  
Hospitalized Patients ◽  
Hospital Death ◽  
Acid Base ◽  
Derivation Cohort

Electrolyte and acid-base disorders are commonly seen in critically ill and other hospitalized patients. A scoring system is needed to assess the severity of electrolyte and acid-base disorders and to predict outcome in hospital patients. Herein, we prospectively enrolled a total of 322,046 patients, including 84,700 patients in the derivation cohort and 237,346 in the validation cohort, in a large, tertiary hospital in East China from 2014 to 2017. A points-scoring system of general electrolyte and acid-base disorders with a sum of 20.8 points was generated by multiple logistic regression analysis of the derivation cohort. Receiver operating characteristic curve analysis showed that the optimal cut-off value of 2.0 was associated with 65.4% sensitivity and 88.4% specificity (area under the curve: 0.818 (95% CI 0.809 to 0.827)) to predict hospital mortality in the validation cohort. On Kaplan-Meier survival analysis, the five intervals of risk score (Q1: 0 to 2.0; Q2: 2.1 to 2.5; Q3: 2.6 to 3.3; Q4: 3.4 to 4.5; and Q5: >4.5 points) showed differences in hospital survival (p<0.001). Elevated (delta) risk score >2 during hospitalization increased the risk of hospital death, while those with a delta risk score <0 and <−2 points had higher survival rates. This novel scoring system could be used to evaluate and to dynamically monitor the severity of electrolyte and acid-base disorders in hospitalized patients.

scholarly journals Development of a practical prediction score for acute renal injury after surgery for Stanford type A aortic dissection

2020 ◽  
Vol 30 (5) ◽  
pp. 746-753
Author(s):  
Ning Dong ◽  
Hulin Piao ◽  
Yu Du ◽  
Bo Li ◽  
Jian Xu ◽  
...  
Keyword(s):  
Aortic Dissection ◽  
Risk Score ◽  
Scoring System ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Kidney Injury ◽  
Type A ◽  
Postoperative Management ◽  
Prediction Score ◽  
Improve Patient

Abstract OBJECTIVES Acute kidney injury (AKI) is a common complication of cardiovascular surgery that is associated with increased mortality, especially after surgeries involving the aorta. Early detection and prevention of AKI in patients with aortic dissection may help improve outcomes. The objective of this study was to develop a practical prediction score for AKI after surgery for Stanford type A acute aortic dissection (TAAAD). METHODS This was a retrospective cohort study that included 2 independent hospitals. A larger cohort of 326 patients from The Second Hospital of Jilin University was used to identify the risk factors for AKI and to develop a risk score. The derived risk score was externally validated in a separate cohort of 102 patients from the other hospital. RESULTS The scoring system included the following variables: (i) age &gt;45 years; (ii) body mass index &gt;25 kg/m2; (iii) white blood cell count &gt;13.5 × 109/l; and (iv) lowest perioperative haemoglobin &lt;100 g/l, cardiopulmonary bypass duration &gt;150 min and renal malperfusion. On receiver operating characteristic curve analysis, the score predicted AKI with fair accuracy in both the derivation [area under the curve 0.778, 95% confidence interval (CI) 0.726–0.83] and the validation (area under the curve 0.747, 95% CI 0.657–0.838) cohorts. CONCLUSIONS We developed a convenient scoring system to identify patients at high risk of developing AKI after surgery for TAAAD. This scoring system may help identify patients who require more intensive postoperative management and facilitate appropriate interventions to prevent AKI and improve patient outcomes.

scholarly journals Bronchopulmonary dysplasia: a predictive scoring system for very low birth weight infants. A diagnostic accuracy study with prospective data collection

2021 ◽  
Author(s):  
Ikbel El Faleh ◽  
◽  
Mohamed Faouzi ◽  
Mark Adams ◽  
Roland Gerull ◽  
...  
Keyword(s):  
Birth Weight ◽  
Low Birth Weight ◽  
Bronchopulmonary Dysplasia ◽  
Scoring System ◽  
Very Low Birth Weight ◽  
Validation Cohort ◽  
Supplementary Oxygen ◽  
Derivation Cohort ◽  
National Cohort ◽  
Predictive Risk

AbstractOur aim was to develop and validate a predictive risk score for bronchopulmonary dysplasia (BPD), according to two clinically used definitions: 1. Need for supplementary oxygen during ≥ 28 cumulative days, BPD28, 2. Need for supplementary oxygen at 36 weeks postmenstrual age (PMA), BPD36. Logistic regression was performed in a national cohort (infants born in Switzerland with a birth weight < 1501 g and/or between 23 0/7 and 31 6/7 weeks PMA in 2009 and 2010), to identify predictors of BPD. We built the score as the sum of predicting factors, weighted according to their ORs, and analysed its discriminative properties by calculating the area under the ROC (receiver operating characteristic) curves (AUCs). This score was then applied to the Swiss national cohort from the years 2014–2015 to perform external validation. The incidence of BPD28 was 21.6% in the derivation cohort (n = 1488) and 25.2% in the validation cohort (n = 2006). The corresponding numbers for BPD36 were 11.3% and 11.1%, respectively. We identified gestational age, birth weight, antenatal corticosteroids, surfactant administration, proven infection, patent ductus arteriosus and duration of mechanical ventilation as independent predictors of BPD28. The AUCs of the BPD risk scores in the derivation cohort were 0.90 and 0.89 for the BPD28 and BPD36 definitions, respectively. The corresponding AUCs in the validation cohort were 0.92 and 0.88, respectively.Conclusion: This score allows for predicting the risk of a very low birth weight infant to develop BPD early in life and may be a useful tool in clinical practice and neonatal research. What is Known:• Many studies have proposed scoring systems to predict bronchopulmonary dysplasia (BPD).• Such a risk prediction may be important to identify high-risk patients for counselling parents, research purposes and to identify candidates for specific treatment. What is New:• A predictive risk score for BPD was developed and validated in a large national multicentre cohort and its performance assessed by two indices of accuracy.• The developed scoring system allows to predict the risk of BPD development early but also at any day of life with high validity.

Abstract 17048: Dederivation and Validation of a Simple Heart Risk Score in 608,544 Thai Adults: HCUR Study

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_1) ◽  
Author(s):  
Parinya Chamnan ◽  
Weera Mahawanakul ◽  
Prasert Boongird ◽  
Wannee Nitiyanant ◽  
Wichai Aekplakorn ◽  
...  
Keyword(s):  
General Population ◽  
Risk Score ◽  
Retrospective Cohort ◽  
Validation Cohort ◽  
Information Criteria ◽  
Global Model ◽  
Derivation Cohort ◽  
Risk Equation ◽  
Risk Algorithm ◽  
Incident Cases

Introduction: Most heart risk prediction equations were developed in Western populations. These risk scores are likely to perform less well in Asian populations, who have different background risk. Hypothesis: This study aimed to develop and validate a new risk algorithm for estimating 5-year risk of developing coronary heart disease (CHD) in a large retrospective cohort of Thai general population. Methods: This retrospective cohort was derived from the linkage of 2006 health checks data with diagnostic information from electronic health records of 608,544 men and women aged 20 years and above residing in Ubon Ratchathani. It was randomly and evenly divided into the derivation and validation cohorts. An outcome of interest was first recorded diagnosis of CHD over a period of 6 years between January 2006 and December 2012. A Cox proportional hazards model was used to estimate effects of risk factors on CHD risk and to derive a risk equation in the derivation cohort. Measures of discrimination, global model fits and calibration were calculated in the validation cohort. Results: The derivation cohort comprised of 304,272 individuals, who contributed 1,757,369 person-years of follow-up and 1,272 incident cases of CHD, while the validation cohort comprised of 304,272 individuals (1,757,312 person-years), with 1,290 incident cases of stroke. The risk equation was 0.0580 x Age (years) + 0.5739 x Sex (Male=1) + 0.3850 x Hypertension (present=1) + 0.7080 x Diabetes (present=1) + 0.0386 x Body mass index (kg/m 2 ) + 0.2117 x Central obesity (present=1) - 0.1389 (if exercise 1-2 days/week) or -0.3975 (if exercise 3-5 days/week) or - 0.5598 (if exercise >5 days/week). The stroke risk equation had a reasonably good discriminatory ability in the validation cohort with the area under the receiver operating characteristic curve of 0.790 (95%CI 0.779-0.801). The risk equation had good global model fit as measured by Bayesian information criteria. The Gronnesby and Borgan test showed good calibration, with chi-square statistic of 809.45 (p<0.001). Conclusions: This simple heart risk score is the first risk algorithm to estimate the 5-year risk of CHD in a Thai general population. The risk score does not need laboratory tests and can therefore be used in clinical settings and by the public.

Application of Transthoracic Shear-Wave Ultrasound Elastography in Lung Lesions

2020 ◽  
pp. 2002347
Author(s):  
Yao-Wen Kuo ◽  
Yen-Lin Chen ◽  
Huey-Don Wu ◽  
Ying-Chun Chien ◽  
Chun-Kai Huang ◽  
...  
Keyword(s):  
Shear Wave ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Ultrasound Elastography ◽  
Youden Index ◽  
Lung Lesions ◽  
Derivation Cohort ◽  
Lung Malignancy ◽  
High Elasticity ◽  
Malignant Lesions

IntroductionThe tissue stiffness information may help in the diagnosis of lung lesions. This study aimed to investigate and validate the application of transthoracic two-dimensional shear-wave ultrasound elastography in differentiating malignant from benign subpleural lung lesions.MethodsThis study involved one retrospective observational derivation cohort from January 2016 to December 2017 and one prospective observational validation cohort from December 2017 to December 2019. The inclusion criterion was radiographic evidence of pulmonary lesions. The patients were categorised into the air-bronchogram and hypoechoic groups based on the B-mode grayscale images. The elasticity of subpleural lung lesions with acceptable shear-wave propagation was measured. Diagnoses were made on the basis of pathology, microbiological studies, or following up the clinical course for at least 6 months.ResultsA total of 354 patients were included. Among the 121 patients in the derivation cohort, a receiver operating characteristic curve was constructed and the cut-off point to differentiate benign from malignant lesions was 65 kPa with Youden index 0.60 and accuracy 84.3%. Among the 233 patients in the validation cohort, the diagnostic performance was maintained with Youden index 0.65 and accuracy 86.7%. Upon applying the cut-off point to the air-bronchogram group, Youden index was 0.70 and accuracy 85.0%.ConclusionsThis study validated the application of transthoracic shear-wave ultrasound elastography for assessing lung malignancy. A cut-off point of 65 kPa is suggested for predicting lung malignancy. Furthermore, for pulmonary air-bronchogram lesions with high elasticity, tissue proofing should be considered because of the high possibility of malignancy.

scholarly journals Development of an Automated, Real Time Surveillance Tool for Predicting Readmissions at a Community Hospital

2013 ◽  
Vol 04 (02) ◽  
pp. 153-169 ◽  
Author(s):  
R. Gildersleeve ◽  
P. Cooper
Keyword(s):  
Real Time ◽  
Community Hospital ◽  
Validation Cohort ◽  
Data Extraction ◽  
Characteristic Curve ◽  
Hospital Setting ◽  
Data Repository ◽  
Derivation Cohort ◽  
Effective Interventions ◽  
Patients At Risk

SummaryBackground: The Centers for Medicare and Medicaid Services’ Readmissions Reduction Program adjusts payments to hospitals based on 30-day readmission rates for patients with acute myocardial infarction, heart failure, and pneumonia. This holds hospitals accountable for a complex phenomenon about which there is little evidence regarding effective interventions. Further study may benefit from a method for efficiently and inexpensively identifying patients at risk of readmission. Several models have been developed to assess this risk, many of which may not translate to a U.S. community hospital setting.Objective: To develop a real-time, automated tool to stratify risk of 30-day readmission at a semi-rural community hospital.Methods: A derivation cohort was created by extracting demographic and clinical variables from the data repository for adult discharges from calendar year 2010. Multivariate logistic regression identified variables that were significantly associated with 30-day hospital readmission. Those variables were incorporated into a formula to produce a Risk of Readmission Score (RRS). A validation cohort from 2011 assessed the predictive value of the RRS. A SQL stored procedure was created to calculate the RRS for any patient and publish its value, along with an estimate of readmission risk and other factors, to a secure intranet site.Results: Eleven variables were significantly associated with readmission in the multivariate analysis of each cohort. The RRS had an area under the receiver operating characteristic curve (c-statistic) of 0.74 (95% CI 0.73-0.75) in the derivation cohort and 0.70 (95% CI 0.69-0.71) in the validation cohort.Conclusion: Clinical and administrative data available in a typical community hospital database can be used to create a validated, predictive scoring system that automatically assigns a probability of 30-day readmission to hospitalized patients. This does not require manual data extraction or manipulation and uses commonly available systems. Additional study is needed to refine and confirm the findings.Citation: Gildersleeve R, Cooper P. Development of an automated, real time surveillance tool for predicting readmissions at a community hospital. Appl Clin Inf 2013; 4: 153–169http://dx.doi.org/10.4338/ACI-2012-12-RA-0058

A New Risk Score to Predict In-Hospital Mortality in Elderly Patients With Acute Heart Failure: On Behalf of the Journey HF-TR Study Investigators

Angiology ◽  
2020 ◽  
Vol 71 (10) ◽  
pp. 948-954
Author(s):  
Gülay Gök ◽  
Mehmet Karadağ ◽  
Ümit Yaşar Sinan ◽  
Mehdi Zoghi
Keyword(s):  
Heart Failure ◽  
Elderly Patients ◽  
Hospital Mortality ◽  
Risk Score ◽  
Validation Cohort ◽  
Characteristic Curve ◽  
Univariate Analysis ◽  
Previous History ◽  
Turkish Population ◽  
Patient Journey

We aimed to predict in-hospital mortality of elderly patients with heart failure (HF) by using a risk score model which could be easily applied in routine clinical practice without using an electronic calculator. The study population (n = 1034) recruited from the Journey HF-TR (Patient Journey in Hospital with Heart Failure in Turkish Population) study was divided into a derivation and a validation cohort. The parameters related to in-hospital mortality were first analyzed by univariate analysis, then the variables found to be significant in that analysis were entered into a stepwise multivariate logistic regression (LR) analysis. Patients were classified as low, intermediate, and high risk. A risk score obtained by taking into account the regression coefficients of the significant variables as a result of the LR analysis was tested in the validation cohort using receiver operating characteristic curve analysis. In total, 6 independent variables (age, blood urea nitrogen, previous history of hemodialysis/hemofiltration, inotropic agent use, and length of intensive care stay) associated with in-hospital mortality were included in the analysis. The risk score had a good discrimination in both the derivation and validation cohorts. A new validated risk score to determine the risk of in-hospital mortality of elderly hospitalized patients with HF was developed by including 6 independent predictors.

scholarly journals A Probabilistic Model for Cushing’s Syndrome Screening in At-Risk Populations: A Prospective Multicenter Study

2016 ◽  
Vol 101 (10) ◽  
pp. 3747-3754 ◽  
Author(s):  
Antonio León-Justel ◽  
Ainara Madrazo-Atutxa ◽  
Ana I. Alvarez-Rios ◽  
Rocio Infantes-Fontán ◽  
Juan A. Garcia-Arnés ◽  
...  
Keyword(s):  
At Risk ◽  
Receiver Operating Characteristic Curve ◽  
Receiver Operating Characteristic ◽  
Risk Score ◽  
Scoring System ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Clinical Signs ◽  
At Risk Populations ◽  
Operating Characteristic Curve

Context: Cushing’s syndrome (CS) is challenging to diagnose. Increased prevalence of CS in specific patient populations has been reported, but routine screening for CS remains questionable. To decrease the diagnostic delay and improve disease outcomes, simple new screening methods for CS in at-risk populations are needed. Objective: To develop and validate a simple scoring system to predict CS based on clinical signs and an easy-to-use biochemical test. Design: Observational, prospective, multicenter. Setting: Referral hospital. Patients: A cohort of 353 patients attending endocrinology units for outpatient visits. Interventions: All patients were evaluated with late-night salivary cortisol (LNSC) and a low-dose dexamethasone suppression test for CS. Main Outcome Measures: Diagnosis or exclusion of CS. Results: Twenty-six cases of CS were diagnosed in the cohort. A risk scoring system was developed by logistic regression analysis, and cutoff values were derived from a receiver operating characteristic curve. This risk score included clinical signs and symptoms (muscular atrophy, osteoporosis, and dorsocervical fat pad) and LNSC levels. The estimated area under the receiver operating characteristic curve was 0.93, with a sensitivity of 96.2% and specificity of 82.9%. Conclusions: We developed a risk score to predict CS in an at-risk population. This score may help to identify at-risk patients in non-endocrinological settings such as primary care, but external validation is warranted.

Derivation and Validation of a Prediction Tool for Venous Thromboembolism: A VERITY Registry Study.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 697-697 ◽  
Author(s):  
Roopen Arya ◽  
Shankaranarayana Paneesha ◽  
Aidan McManus ◽  
Nick Parsons ◽  
Nicholas Scriven ◽  
...  
Keyword(s):  
Risk Factors ◽  
Venous Thromboembolism ◽  
Risk Score ◽  
Validation Cohort ◽  
Accurate Estimation ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Derivation Cohort ◽  
Hormonal Factor ◽  
Electronic Alerts

Abstract Accurate estimation of risk for venous thromboembolism (VTE) may help clinicians assess prophylaxis needs. Only empirical algorithms and risk scores have been described; an empirical risk score (‘Kucher’) based on 8 VTE risk factors (cancer, prior VTE, hypercoagulability, surgery, age>75 yrs, BMI>29, bed rest, hormonal factor) using electronic alerts improved hospitalized patient outcome (NEJM2005;352:969–77). We wished to develop a multivariate regression model for VTE risk, based on Kucher, and validate its performance. The initial derivation cohort consisted of patients enrolled in ‘VERITY’, a multicentre VTE treatment registry for whom the endpoint of VTE and all 8 risk factors were known. Initial univariate analysis (n=5928; 32.4% with diagnosis of VTE) suggested VTE risk was not accounted for by the 8 factors; an additional 3 were added (leg paralysis, smoking, IV drug use [IVD]). The final derivation cohort was 5241 patients (32.0% with VTE) with complete risk data. The validation cohort (n=915) was derived from a database of 928 consecutively enrolled patients at a single DVT clinic. Model parameters were estimated using the statistical package ‘R’ using a stepwise selection procedure to choose the optimal number of main effects and pair-wise interactions. This showed that advanced age (estimated odds ratio [OR]=2.8, p<0.001); inpatient (OR=3.0, p<0.001); surgery (OR=3.1, p<0.001); prior VTE (OR=2.9, p<0.001); leg paralysis (OR=3.8, p<0.001); cancer (OR=5.3, p<0.001); IVD (OR=14.3, p<0.001); smoking (OR=1.2, p=0.009); and thrombophilia (OR=2.8; p<0.001) increased the risk of VTE. Obesity (OR=0.7; p<0.001) increased the VTE risk only in patients with a hormonal factor (OR=2.0, p=0.007). Backward stepwise regression showed prior VTE as the most important factor followed by cancer, IVD, surgery, inpatient, age, leg paralysis, hormonal factor, obesity, thrombophilia and smoking. Expressing the parameter estimates in terms of probabilities defines a risk score model for VTE. Using the model, the receiver operating characteristic (ROC) curve (see figure) area under the curve (AUC) was estimated as 0.720 (95% CI, 0.705–0.735) for the model (dashed line), indicating a good diagnostic test significantly better (p<0.001) than Kucher (AUC=0.617, 95% CI, 0.599–0.634)(solid line). For the validation cohort, AUC was estimated as 0.678 (95% CI, 0.635–0.721) for the model, which was not significantly different from AUC for the full dataset used for model development, and was 0.587 (95% CI, 0.542–0.632) for Kucher. This model to predict individual patient risk of VTE may contribute to decision making regarding prophylaxis in clinical practice. Figure Figure

scholarly journals Dynamic Predictive Scores for Cardiac Surgery-Associated Agitated Delirium: a single-center retrospective observational study

2021 ◽  
Author(s):  
Yu Tian ◽  
Yuefu Wang ◽  
Wei Zhao ◽  
Bingyang Ji ◽  
Xiaolin Diao ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Prediction Model ◽  
Scoring System ◽  
Early Recognition ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Derivation Cohort ◽  
Preoperative Prediction ◽  
Agitated Delirium ◽  
Dynamic Scoring

Abstract Background Prevention, screening, and early treatment are the mainstays of postoperative delirium management. Score system is an objective and effective tool to stratify potential delirium risk for patients undergoing cardiac surgery Methods Patients undergoing cardiac surgery from January 1, 2012, to January 1, 2019, were enrolled in our retrospective study. The patients were divided into a derivation cohort (n = 45,744) and a validation cohort (n = 11,436). The agitated delirium (AD) predictive systems were formulated using multivariate logistic regression analysis at three time points: preoperation, ICU admittance, and 24 hours after ICU admittance. Results The prevalence of AD after cardiac surgery in the whole cohort was 3.6% (2,085/57,180). The dynamic scoring system included preoperative LVEF ≤ 45%, serum creatinine > 100 umol/L, emergency surgery, coronary artery disease, hemorrhage volume > 600 mL, intraoperative platelet or plasma use, and postoperative LVEF ≤ 45%. The area under the receiver operating characteristic curve (AUC) values for AD prediction of 0.68 (preoperative), 0.74 (on the day of ICU admission), and 0.75 (postoperative). The Hosmer-Lemeshow test indicated that the calibration of the preoperative prediction model was poor (P = 0.01), whereas that of the pre- and intraoperative prediction model (P = 0.49) and the pre-, intra- and postoperative prediction model (P = 0.35) was good. Conclusions Using perioperative data, we developed a dynamic scoring system for predicting the risk of AD following cardiac surgery. The dynamic scoring system may improve early recognition of and interventions for AD.

scholarly journals A Novel Scoring System for Early Assessment of the Risk of the COVID-19-associated Mortality in Hospitalized Patients: COVID-19 BURDEN

2022 ◽  
Author(s):  
Fatemeh Amirzadehfard ◽  
Mohammad Hossein Imanieh ◽  
Sina Zoghi ◽  
Faezeh sehatpour ◽  
Peyman Jafari ◽  
...  
Keyword(s):  
Risk Score ◽  
Scoring System ◽  
Virus Disease ◽  
Multiple Logistic Regression Analysis ◽  
Laboratory Data ◽  
Area Under The Curve ◽  
Diagnostic Efficiency ◽  
Early Assessment ◽  
Severity Of The Disease ◽  
Care Costs

Background: Corona Virus Disease 2019 (COVID-19) presentation resembles common flu or can be more severe; it can result in hospitalization with significant morbidity and/or mortality. We made an attempt to develop a predictive model and a scoring system to improve the diagnostic efficiency for COVID-19 mortality via analysis of clinical features and laboratory data on admission. Methods: We retrospectively enrolled 480 consecutive adult patients, aged 21-95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were extracted from the medical records and analyzed using multiple logistic regression analysis. Results: A novel mortality risk score (COVID-19 BURDEN) was calculated, incorporating risk factors from this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84-90%, and less than 84%), increased PT (>16.2s), diastolic blood pressure (≤75 mmHg), BUN (>23 mg/dL), and raised LDH (>731 U/L) are the features comprising the scoring system. The patients are triaged to the groups of low- (score <4) and high-risk (score ≥ 4) groups. The area under the curve, sensitivity, and specificity for predicting non-response to medical therapy with scores of ≥ 4 were 0.831, 78.12%, and 70.95%, respectively. Conclusion: Using this scoring system in COVID-19 patients, the severity of the disease will be determined in the early stages of the disease, which will help to reduce hospital care costs and improve its quality and outcome.

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