Nomograms Predicting the Occurrence of Sepsis in Patients following Major Hepatobiliary and Pancreatic Surgery

Purpose. Sepsis is a severe complication in patients following major hepatobiliary and pancreatic surgery. The purpose of this study was to develop and validate a nomogram based on inflammation biomarkers and clinical characteristics. Methods. Patients who underwent major hepatobiliary and pancreatic surgery between June 2015 and April 2017 were retrospectively collected. Multivariate logistic regression was used to identify the independent risk factors associated with postoperative sepsis. A training cohort of 522 patients in an earlier period was used to develop the prediction models, and a validation cohort of 136 patients thereafter was used to validate the nomograms. Results. Sepsis developed in 55 of 522 patients of the training cohort and 19 of 136 patients in the validation cohort, respectively. In the training cohort, one nomogram based on clinical characteristics was developed. The clinical independent risk factors for postoperative sepsis include perioperative blood transfusion, diabetes, operative time, direct bilirubin, and BMI. Another nomogram was based on both clinical characteristics and inflammation biomarkers. Multivariate regression analyses showed that previous clinical risk factors, PCT, and CRP were independent risk factors for postoperative sepsis. The last nomogram showed a good C-index of 0.844 (95% CI, 0.787-0.900) compared with the previous one of 0.777 (95% CI, 0.713-0.840). Patients with a total score more than 109 in the second model are at high risk. The positive predictive value and negative predictive value of the second nomogram were 27% and 97%, respectively. Conclusion. The nomogram achieved good performances for predicting postoperative sepsis in patients by combining clinical and inflammation risk factors. This model can provide the early risk estimation of sepsis for patients following major hepatobiliary and pancreatic surgery.

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ANVCFV Score System: Assessment for Probability of New Vertebral Compression Fractures after Percutaneous Vertebroplasty in Patients with Vertebral Compression Fractures

January 2018 - Pain Physician ◽

10.36076/ppj.2015/18/e1047 ◽

2015 ◽

Vol 6;18 (6;11) ◽

pp. E1047-E1057

Author(s):

Gao-Jun Teng

Keyword(s):

Risk Factors ◽

Cox Regression ◽

Validation Cohort ◽

Percutaneous Vertebroplasty ◽

Vertebral Compression Fractures ◽

Score System ◽

Training Cohort ◽

Compression Fractures ◽

Independent Risk Factors ◽

Risk Score System

Background: Percutaneous vertebroplasty (PVP) is widely used for the treatment of painful vertebral compression fractures (VCFs). However, new VCFs occur frequently after PVP. Objectives: We aim to establish an objective risk score system to assess the possibility of new vertebral fractures in patients with VCFs undergoing PVP. Study Design: This study was a retrospective study, and it was approved by the Institutional Review Board of our 2 institutions. Setting: This study consists of patients from 2 large academic centers. Methods: Patients with VCFs who underwent their first PVP and met the inclusion criteria between January 2007 and December 2013 at Hospital A (training cohort) and Hospital B (validation cohort) were included. In the training cohort, the independent risk factors for new VCFs after PVP were identified by multivariate stepwise backward Cox regression analysis from the risk factors selected by univariate analysis and Harrell’s C-statistics and used to develop the score system (assessment for new VCFs after PVP [ANVCFV]) to predict the probability of new VCFs. Results: In total, 397 patients (training cohort: n = 241; validation cohort: n = 156) were included in this study. In the training cohort, the ANVCFV score was developed based on 5 independent risk factors for the new VCFs after PVP, including lower computed tomography (CT) values, pre-existing old VCFs, intradiscal cement leakage, more than one vertebra treated, and superior or inferior marginal cement distribution in the vertebra. The patients were divided into 2 groups by the ANVCFV score of -1.5 to 8.5 vs. > 8.5 points in the probability of new VCFs (median fracture-free time: 1846 vs. 732 days; P < 0.001) in the training cohort. The accuracy of this score system was 77.4% for the training cohort and 85.3% for the validation cohort. Limitations: The main limitations of this study are that it is a retrospective study and that there is a significant difference of the treated vertebrae of PVP per session between the 2 cohorts. Conclusion: Patients who underwent their first PVP with an ANVCFV score > 8.5 points may exhibit an increased chance of suffering from new VCFs. Key words: Vertebral compression fracture, percutaneous vertebroplasty, newly developed, risk factors, risk score system, Cox regression model, accuracy, validation

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Prognostic Performance of Albumin–Bilirubin Grade With Artificial Intelligence for Hepatocellular Carcinoma Treated With Transarterial Chemoembolization Combined With Sorafenib

Frontiers in Oncology ◽

10.3389/fonc.2020.525461 ◽

2020 ◽

Vol 10 ◽

Author(s):

Bin-Yan Zhong ◽

Zhi-Ping Yan ◽

Jun-Hui Sun ◽

Lei Zhang ◽

Zhong-Heng Hou ◽

...

Keyword(s):

Risk Factors ◽

Hepatocellular Carcinoma ◽

Transarterial Chemoembolization ◽

Validation Cohort ◽

Survival Prediction ◽

Ann Model ◽

Training Cohort ◽

Prognostic Performance ◽

Independent Risk Factors ◽

Artificial Neural Network Ann

PurposeTo establish albumin-bilirubin (ALBI) grade-based and Child-Turcotte-Pugh (CTP) grade-based nomograms, as well as to develop an artificial neural network (ANN) model to compare the prognostic performance and discrimination of these two grades for hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) combined with sorafenib as an initial treatment.MethodsThis multicenter retrospective study included patients from three hospitals between January 2013 and August 2018. In the training cohort, independent risk factors associated with overall survival (OS) were identified by univariate and multivariate analyses. The nomograms and ANN were established and then validated in two validation cohorts.ResultsA total of 504 patients (319, 61, and 124 patients from hospitals A, B, and C, respectively) were included. The median OS was 15.2, 26.9, and 14.8 months in the training cohort and validation cohorts 1 and 2, respectively (P = 0.218). In the training cohort, both ALBI grade and CTP grade were identified as independent risk factors. The ALBI grade-based and CTP grade-based nomograms were established separately and showed similar prognostic performance and discrimination when validated in the validation cohorts (C-index in validation cohort 1: 0.799 vs. 0.779, P = 0.762; in validation cohort 2: 0.700 vs. 0.693, P = 0.803). The ANN model showed that the ALBI grade had higher importance in survival prediction than the CTP grade.ConclusionsThe ALBI grade and CTP grade have comparable prognostic performance for HCC patients treated with TACE combined with sorafenib. ALBI grades 1 and 2 have the potential to act as a stratification factor for clinical trials on the combination therapy of TACE and systemic therapy.

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JAK2V617F variant allele frequency >50% identifies patients with polycythemia vera at high risk for venous thrombosis

Blood Cancer Journal ◽

10.1038/s41408-021-00581-6 ◽

2021 ◽

Vol 11 (12) ◽

Author(s):

Paola Guglielmelli ◽

Giuseppe G. Loscocco ◽

Carmela Mannarelli ◽

Elena Rossi ◽

Francesco Mannelli ◽

...

Keyword(s):

Risk Factors ◽

Polycythemia Vera ◽

Allele Frequency ◽

Validation Cohort ◽

Multivariable Analysis ◽

Variant Allele ◽

Variant Allele Frequency ◽

Training Cohort ◽

Independent Risk Factors ◽

The Impact

AbstractArterial (AT) and venous (VT) thrombotic events are the most common complications in patients with polycythemia vera (PV) and are the leading causes of morbidity and mortality. In this regard, the impact of JAK2V617F variant allele frequency (VAF) is still debated. The purpose of the current study was to analyze the impact of JAK2V617F VAF in the context of other established risk factors for thrombosis in a total of 865 2016 WHO-defined PV patients utilizing two independent cohorts: University of Florence (n = 576) as a training cohort and Policlinico Gemelli, Catholic University, Rome (n = 289) as a validation cohort. In the training cohort VT free-survival was significantly shorter in the presence of a JAK2V617F VAF > 50% (HR 4; p < 0.0001), whereas no difference was found for AT (HR 0.9; p = 0.8). Multivariable analysis identified JAK2V617F VAF > 50% (HR 3.8, p = 0.001) and previous VT (HR 2.2; p = 0.04) as independent risk factors for future VT whereas diabetes (HR 2.4; p = 0.02), hyperlipidemia (HR 2.3; p = 0.01) and previous AT (HR 2; p = 0.04) were independent risk factors for future AT. Similarly, JAK2V617F VAF > 50% (HR 2.4; p = 0.01) and previous VT (HR 2.8; p = 0.005) were confirmed as independent predictors of future VT in the validation cohort. Impact of JAK2V617F VAF > 50% on VT was particularly significant in conventional low-risk patients, both in Florence (HR 10.6, p = 0.005) and Rome cohort (HR 4; p = 0.02). In conclusion, we identified JAK2V617F VAF > 50% as an independent strong predictor of VT, supporting that AT and VT are different entities which might require distinct management.

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A New Prognostic Algorithm Predicting HCC Recurrence in Patients With Barcelona Clinic Liver Cancer Stage B Who Received PA-TACE

Frontiers in Oncology ◽

10.3389/fonc.2021.742630 ◽

2021 ◽

Vol 11 ◽

Author(s):

Shuyang Hu ◽

Wei Gan ◽

Liang Qiao ◽

Cheng Ye ◽

Demin Wu ◽

...

Keyword(s):

Risk Factors ◽

Liver Cancer ◽

Evaluation System ◽

Validation Cohort ◽

Barcelona Clinic Liver Cancer ◽

Time Dependent ◽

Training Cohort ◽

Individual Probability ◽

Risk Factors For Recurrence ◽

Independent Risk Factors

BackgroundPostoperative adjuvant transcatheter arterial chemoembolization (PA-TACE) is effective in preventing the recurrence of hepatocellular carcinoma (HCC) in patients treated with surgery. However, there is a lack of reports studying the risk factors associated with recurrence in HCC patients who received PA-TACE. In this study, we identified the independent risk factors for recurrence of HCC patients who received PA-TACE. We also developed a novel, effective, and valid nomogram to predict the individual probability of recurrence, 1, 3, and 5 years after PA-TACE.MethodsA retrospective study was performed to identify the independent risk factors for recurrence of HCC in a group of 502 patients diagnosed in stage B based on the Barcelona Clinic Liver Cancer (BCLC) evaluation system for HCC that underwent curative resections. Then, subgroup analysis was performed for 184 patients who received PA-TACE, who were included in the training cohort. The other 147 HCC patients were included in a validation cohort. A recurrence-free survival (RFS)-predicting nomogram was constructed, and results were assessed using calibration and decision curves and a time-dependent AUC diagram.ResultsPA-TACE was shown to be a significant independent prognostic value for patients with BCLC stage B [p < 0.001, hazard ratio (HR) = 0.508, 95% CI = 0.375–0.689 for OS, p = 0.002; HR = 0.670, 95%CI = 0.517–0.868 for RFS]. Alpha fetoprotein (AFP), tumor number, tumor size, microvascular invasion (MVI), and differentiation were considered as independent risk factors for RFS in the training cohort, and these were further confirmed in the validation cohort. Next, a nomogram was constructed to predict RFS. The C-index for RFS in the nomogram was 0.721 (95% CI = 0.718–0.724), which was higher than SNACOR, HAP, and CHIP scores (0.587, 0.573, and 0.607, respectively). Calibration and decision curve analyses and a time-dependent AUC diagram were used. Our nomogram showed stronger performance than these other nomograms in both the training and validation cohorts.ConclusionsHCC patients diagnosed as stage B according to BCLC may benefit from PA-TACE after surgery. The RFS nomogram presented here provides an accurate and reliable prognostic model to monitor recurrence. Patients with a high recurrence score based on the nomogram should receive additional high-end imaging exams and shorter timeframes in between follow-up visits.

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Development and validation of two SCORE-based cardiovascular risk prediction models for Eastern Europe: a multicohort study

European Heart Journal ◽

10.1093/eurheartj/ehaa571 ◽

2020 ◽

Vol 41 (35) ◽

pp. 3325-3333 ◽

Cited By ~ 2

Author(s):

Taavi Tillmann ◽

Kristi Läll ◽

Oliver Dukes ◽

Giovanni Veronesi ◽

Hynek Pikhart ◽

...

Keyword(s):

Risk Factors ◽

Prediction Models ◽

Validation Cohort ◽

European Countries ◽

Eastern European ◽

Risk Prediction Models ◽

Derivation Cohort ◽

Score Model ◽

Eastern European Countries

Abstract Aims Cardiovascular disease (CVD) risk prediction models are used in Western European countries, but less so in Eastern European countries where rates of CVD can be two to four times higher. We recalibrated the SCORE prediction model for three Eastern European countries and evaluated the impact of adding seven behavioural and psychosocial risk factors to the model. Methods and results We developed and validated models using data from the prospective HAPIEE cohort study with 14 598 participants from Russia, Poland, and the Czech Republic (derivation cohort, median follow-up 7.2 years, 338 fatal CVD cases) and Estonian Biobank data with 4632 participants (validation cohort, median follow-up 8.3 years, 91 fatal CVD cases). The first model (recalibrated SCORE) used the same risk factors as in the SCORE model. The second model (HAPIEE SCORE) added education, employment, marital status, depression, body mass index, physical inactivity, and antihypertensive use. Discrimination of the original SCORE model (C-statistic 0.78 in the derivation and 0.83 in the validation cohorts) was improved in recalibrated SCORE (0.82 and 0.85) and HAPIEE SCORE (0.84 and 0.87) models. After dichotomizing risk at the clinically meaningful threshold of 5%, and when comparing the final HAPIEE SCORE model against the original SCORE model, the net reclassification improvement was 0.07 [95% confidence interval (CI) 0.02–0.11] in the derivation cohort and 0.14 (95% CI 0.04–0.25) in the validation cohort. Conclusion Our recalibrated SCORE may be more appropriate than the conventional SCORE for some Eastern European populations. The addition of seven quick, non-invasive, and cheap predictors further improved prediction accuracy.

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The Clinical Characteristics of Obese Patients with Acanthosis Nigricans and Its Independent Risk Factors

Experimental and Clinical Endocrinology & Diabetes ◽

10.1055/s-0042-123035 ◽

2017 ◽

Vol 125 (03) ◽

pp. 191-195 ◽

Cited By ~ 1

Author(s):

Yueye Huang ◽

Jiaqi Chen ◽

Xingchun Wang ◽

Yan Li ◽

Shezhen Yang ◽

...

Keyword(s):

Risk Factors ◽

Clinical Characteristics ◽

Acanthosis Nigricans ◽

Obese Patients ◽

Independent Risk Factors

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

10.21203/rs.3.rs-76134/v1 ◽

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

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Development and Validation of Nomogram to Preoperatively Predict Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Pituitary Surgery: A Retrospective Cohort Study

Frontiers in Oncology ◽

10.3389/fonc.2021.719494 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiangming Cai ◽

Junhao Zhu ◽

Jin Yang ◽

Chao Tang ◽

Feng Yuan ◽

...

Keyword(s):

Risk Factors ◽

Cerebrospinal Fluid ◽

Prediction Model ◽

Calibration Curve ◽

Validation Cohort ◽

Laboratory Data ◽

Pituitary Surgery ◽

Cerebrospinal Fluid Leakage ◽

Training Cohort ◽

Preoperative Prediction

BackgroundPituitary adenomas (PAs) are the most common tumor of the sellar region. PA resection is the preferred treatment for patients with clear indications for surgery. Intraoperative cerebrospinal fluid (iCSF) leakage is a major complication of PA resection surgery. Risk factors for iCSF leakage have been studied previously, but a predictive nomogram has not yet been developed. We constructed a nomogram for preoperative prediction of iCSF leakage in endoscopic pituitary surgery.MethodsA total of 232 patients who underwent endoscopic PA resection at the Department of Neurosurgery in Jinling Hospital between January of 2018 and October of 2020 were enrolled in this retrospective study. Patients treated by a board-certified neurosurgeon were randomly classified into a training cohort or a validation cohort 1. Patients treated by other qualified neurosurgeons were included in validation cohort 2. A range of demographic, clinical, radiological, and laboratory data were acquired from the medical records. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and uni- and multivariate logistic regression were utilized to analyze these features and develop a nomogram model. We used a receiver operating characteristic (ROC) curve and calibration curve to evaluate the predictive performance of the nomogram model.ResultsVariables were comparable between the training cohort and validation cohort 1. Tumor height and albumin were included in the final prediction model. The area under the curve (AUC) of the nomogram model was 0.733, 0.643, and 0.644 in training, validation 1, and validation 2 cohorts, respectively. The calibration curve showed satisfactory homogeneity between the predicted probability and actual observations. Nomogram performance was stable in the subgroup analysis.ConclusionsTumor height and albumin were the independent risk factors for iCSF leakage. The prediction model developed in this study is the first nomogram developed as a practical and effective tool to facilitate the preoperative prediction of iCSF leakage in endoscopic pituitary surgery, thus optimizing treatment decisions.

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A New Rapid Approach for Predicting Death in Coronavirus Patients: The Development and Validation of the COVID-19 Risk-Score in Fars Province (CRSF)

Iranian Journal of Public Health ◽

10.18502/ijph.v51i1.8310 ◽

2022 ◽

Author(s):

Mehrdad Sharifi ◽

Mohammad Hossein Khademian ◽

Razieh Sadat Mousavi-Roknabadi ◽

Vahid Ebrahimi ◽

Robab Sadegh

Keyword(s):

Risk Factors ◽

Risk Score ◽

Predictive Value ◽

Assessment Tool ◽

Fars Province ◽

Risk Of Mortality ◽

Risk Patients ◽

Independent Risk Factors ◽

Development And Validation ◽

Underlying Diseases

Background:Patients who are identified to be at a higher risk of mortality from COVID-19 should receive better treatment and monitoring. This study aimed to propose a simple yet accurate risk assessment tool to help decision-making in the management of the COVID-19 pandemic. Methods: From Jul to Nov 2020, 5454 patients from Fars Province, Iran, diagnosed with COVID-19 were enrolled. A multiple logistic regression model was trained on one dataset (training set: n=4183) and its prediction performance was assessed on another dataset (testing set: n=1271). This model was utilized to develop the COVID-19 risk-score in Fars (CRSF). Results: Five final independent risk factors including gender (male: OR=1.37), age (60-80: OR=2.67 and >80: OR=3.91), SpO2 (≤85%: OR=7.02), underlying diseases (yes: OR=1.25), and pulse rate (<60: OR=2.01 and >120: OR=1.60) were significantly associated with in-hospital mortality. The CRSF formula was obtained using the estimated regression coefficient values of the aforementioned factors. The point values for the risk factors varied from 2 to 19 and the total CRSF varied from 0 to 45. The ROC analysis showed that the CRSF values of ≥15 (high-risk patients) had a specificity of 73.5%, sensitivity of 76.5%, positive predictive value of 23.2%, and negative predictive value (NPV) of 96.8% for the prediction of death (AUC=0.824, P<0.0001). Conclusion:This simple CRSF system, which has a high NPV,can be useful for predicting the risk of mortality in COVID-19 patients. It can also be used as a disease severity indicator to determine triage level for hospitalization.

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Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development (Preprint)

10.2196/preprints.19588 ◽

2020 ◽

Author(s):

Tao Fan ◽

Bo Hao ◽

Shuo Yang ◽

Bo Shen ◽

Zhixin Huang ◽

...

Keyword(s):

Risk Factors ◽

Regression Analysis ◽

Clinical Characteristics ◽

Cox Regression ◽

Disease Onset ◽

Lasso Regression ◽

Prediction Ability ◽

Calibration Curves ◽

Independent Risk Factors ◽

Critical Patients

BACKGROUND In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. OBJECTIVE The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. METHODS In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. RESULTS A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (P<.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; P<.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; P=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; P=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, P<.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; P<.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; P=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. CONCLUSIONS This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

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