Personalized 5-Year Prostate Cancer Risk Prediction Model in Korea Based on Nationwide Representative Data.
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ABSTRACT:
SUBMITTER: Yeo Y
PROVIDER: S-EPMC8780119 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Early detection of lung cancer by screening has contributed to reduce lung cancer mortality. Identifying high risk subjects for lung cancer is necessary to maximize the benefits and minimize the harms followed by lung cancer screening. In the present study, individual lung cancer risk in Korea was presented using a risk prediction model. Participants who completed health examinations in 2009 based on the Korean National Health Insurance (KNHI) database (DB) were eligible for the present study. Risk scores were assigned based on the adjusted hazard ratio (HR), and the standardized points for each risk factor were calculated to be proportional to the b coefficients. Model discrimination was assessed using the concordance statistic (c-statistic), and calibration ability assessed by plotting the mean predicted probability against the mean observed probability of lung cancer. Among candidate predictors, age, sex, smoking intensity, body mass index (BMI), presence of chronic obstructive pulmonary disease (COPD), pulmonary tuberculosis (TB), and type 2 diabetes mellitus (DM) were finally included. Our risk prediction model showed good discrimination (c-statistic, 0.810; 95% CI: 0.801-0.819). The relationship between model-predicted and actual lung cancer development correlated well in the calibration plot. When using easily accessible and modifiable risk factors, this model can help individuals make decisions regarding lung cancer screening or lifestyle modification, including smoking cessation.
| S-EPMC8307783 | biostudies-literature
Project description:AbstractTo explore the influencing factors of prostate cancer occurrence, set up risk prediction model, require reference for the preliminary diagnosis of clinical doctors, this model searched database through the data of prostate cancer patients and prostate hyperplasia patients National Clinical Medical Science Data Center.With the help of Stata SE 12.0 and SPSS 25.0 software, the biases between groups were balanced by propensity score matching. Based on the matched data, the relevant factors were further screened by stepwise logistic regression analysis, the key variable and artificial neural network model are established. The prediction accuracy of the model is evaluated by combining the probability of test set with the area under receiver operating characteristic curve (ROC).After 1:2 PSM, 339 pairs were matched successfully. There are 159 cases in testing groups and 407 cases in training groups. And the regression model was P = 1 / (1 + e (0.122 ∗ age + 0.083 ∗ Apo lipoprotein C3 + 0.371 ∗ total prostate specific antigen (tPSA) -0.227 ∗ Apo lipoprotein C2-6.093 ∗ free calcium (iCa) + 0.428 ∗ Apo lipoprotein E-1.246 ∗ triglyceride-1.919 ∗ HDL cholesterol + 0.083 ∗ creatine kinase isoenzyme [CKMB])). The logistic regression model performed very well (ROC, 0.963; 95% confidence interval, 0.951 to 0.978) and artificial neural network model (ROC, 0.983; 95% confidence interval, 0.964 to 0.997). High degree of Apo lipoprotein E (Apo E) (Odds Ratio, [OR], 1.535) in blood test is a risk factor and high triglyceride (TG) (OR, 0.288) is a protective factor.It takes the biochemical examination of the case as variables to establish a risk prediction model, which can initially reflect the risk of prostate cancer and bring some references for diagnosis and treatment.
| S-EPMC8084031 | biostudies-literature
Project description:Preoperative risk assessment is essential for shared decision-making and adequate perioperative care. Common scores provide limited predictive quality and lack personalized information. The aim of this study was to create an interpretable machine-learning-based model to assess the patient's individual risk of postoperative mortality based on preoperative data to allow analysis of personal risk factors. After ethical approval, a model for prediction of postoperative in-hospital mortality based on preoperative data of 66,846 patients undergoing elective non-cardiac surgery between June 2014 and March 2020 was created with extreme gradient boosting. Model performance and the most relevant parameters were shown using receiver operating characteristic (ROC-) and precision-recall (PR-) curves and importance plots. Individual risks of index patients were presented in waterfall diagrams. The model included 201 features and showed good predictive abilities with an area under receiver operating characteristic (AUROC) curve of 0.95 and an area under precision-recall curve (AUPRC) of 0.109. The feature with the highest information gain was the preoperative order for red packed cell concentrates followed by age and c-reactive protein. Individual risk factors could be identified on patient level. We created a highly accurate and interpretable machine learning model to preoperatively predict the risk of postoperative in-hospital mortality. The algorithm can be used to identify factors susceptible to preoperative optimization measures and to identify risk factors influencing individual patient risk.
| S-EPMC10153050 | biostudies-literature
Project description:Intracranial hemorrhage (ICH) is a rare and life-threatening hemorrhagic event in patients with immune thrombocytopenia (ITP). However, its mortality and related risk factors remain unclear. Herein, we conducted a nationwide multicenter real-world study of ICH in adult ITP patients. According to data from 27 centers in China from 2005 to 2020, the mortality rate from ICH was 33.80% (48/142) in ITP adults. We identified risk factors by logistic univariate and multivariate logistic regression for 30-day mortality in a training cohort of 107 patients as follows: intraparenchymal hemorrhage (IPH), platelet count ≤10 × 109/L at ICH, a combination of serious infections, grade of preceding bleeding events, and Glasgow coma scale (GCS) level on admission. Accordingly, a prognostic model of 30-day mortality was developed based on the regression equation. Then, we evaluated the performance of the prognostic model through a bootstrap procedure for internal validation. Furthermore, an external validation with data from a test cohort with 35 patients from 11 other centers was conducted. The areas under the receiver operating characteristic (ROC) curves for the internal and external validation were 0.954 (95% confidence interval [CI], 0.910-0.998) and 0.942 (95% CI, 0.871-1.014), respectively. Both calibration plots illustrated a high degree of consistency in the estimated and observed risk. In addition, the decision curve analysis showed a considerable net benefit for patients. Thus, an application (47.94.162.105:8080/ich/) was established for users to predict 30-day mortality when ICH occurred in adult patients with ITP.
| S-EPMC9327537 | biostudies-literature
Project description:BACKGROUND:In 2002, a voluntary diagnosis-related groups (DRGs) payment system was introduced in South Korea for seven disease groups, and participation in the DRGs was mandated for all hospitals beginning in 2013. The primary aim of this study was to compare results reflective of patient care between voluntary participation hospitals (VPHs) and mandatory participation hospitals (MPHs) governed by either the DRGs or fee-for-service (FFS) payment system. METHODS:We collected DRGs and FFS inpatient records (n=3,038,006) from the Health Insurance Review and Assessment for the period of July 2011 to July 2014 and compared length-of-stay, total medical costs, shifting services to an outpatient setting, and readmission rates according to payment system, time of DRGs implementation, and hospital type. We analyzed the effects of mandatory introduction in DRGs payment system on results for patient care and used generalized estimating equations with difference-in-difference methodology. RESULTS:Most notably, patients at MPHs had significantly shorter LOS and lower readmission rates than VPH patients after mandatory introduction of the DRGs. Shifting services to an outpatient setting was similar between the groups. CONCLUSIONS:Our findings suggest that the DRGs payment policy in Korea has decreased LOS and readmission rates. These findings support the continued implementation and enlargement of the DRGs payment system for other diseases in South Korea, given its potential for curbing unnecessary resource usage encouraged by FFS. If the Korean government deliberates on expansion of the DRGs to include other diseases with higher rates of complications, policymakers need to monitor deterioration of health care quality caused by fixed pricing.
| S-EPMC6822472 | biostudies-literature
Project description:ObjectiveThis study aimed to promote an understanding of spontaneous pneumothorax by analysing the prevalence rate and medical service use by patients with spontaneous pneumothorax according to sociodemographic characteristics.DesignA 12-year nationwide study.SettingData obtained from the Korean National Health Insurance Service Sharing Service.ParticipantsA total of 4658 participants who used medical services due to spontaneous pneumothorax between 2002 and 2013 in Korea.Outcome measuresFor those diagnosed with spontaneous pneumothorax, use of medical services, hospitalisation data, sociodemographics, comorbidity, treatment administered and medication prescribed were recorded.ResultsThe annual prevalence of spontaneous pneumothorax ranged from 39 to 66 per 100?000 individuals, while the prevalence of hospitalisation due to spontaneous pneumothorax ranged from 18 to 36 per 100?000 individuals. The prevalence rate of spontaneous pneumothorax in Korea has increased since 2002. The male to female ratio was approximately 4-10:1, with a higher prevalence rate in men. By age, the 15-34?years old group, and particularly those aged 15-19?years old, showed the highest prevalence rate; the rate then declined before increasing again for those aged 65 years or older. In total, 47%-57% of patients with spontaneous pneumothorax underwent hospitalisation. The average number of rehospitalisations due to pneumothorax was 1.56 per person, and more than 70% of recurrences occurred within 1?year. Chronic obstructive pulmonary disease was the most common comorbidity. The average treatment period was 11 days as an outpatient and 14 days in-hospital. The average medical costs were $94.50 for outpatients and $2523 for hospital admissions. The most common treatment for spontaneous pneumothorax was oxygen inhalation and thoracostomy, and the most commonly prescribed medications were analgesics, antitussives and antibiotics.ConclusionsWe here detailed the epidemiology and treatments for spontaneous pneumothorax in Korea. This information can contribute to the understanding of spontaneous pneumothorax.
| S-EPMC6830684 | biostudies-literature
Project description:BackgroundWe compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user.MethodsTen North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach.ResultsAmong 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history.ConclusionDevelopers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.
| S-EPMC9306143 | biostudies-literature
Project description:PurposeBlack breast cancer (BC) survivors have a higher risk of developing contralateral breast cancer (CBC) than Whites. Existing CBC risk prediction tools are developed based on mostly White women. To address this racial disparity, it is crucial to develop tools tailored for Black women to help them inform about their actual risk of CBC.MethodsWe propose an absolute risk prediction model, CBCRisk-Black, specifically for Black BC patients. It uses data on Black women from two sources: Breast Cancer Surveillance Consortium (BCSC) and Surveillance, Epidemiology, and End Results (SEER). First, a matched lasso logistic regression model for estimating relative risks (RR) is developed. Then, it is combined with relevant hazard rates and attributable risks to obtain absolute risks. Six-fold cross-validation is used to internally validate CBCRisk-Black. We also compare CBCRisk-Black with CBCRisk, an existing CBC risk prediction model.ResultsThe RR model uses data from BCSC on 744 Black women (186 cases). CBCRisk-Black has four risk factors (RR compared to baseline): breast density (2.13 for heterogeneous/extremely dense), family history of BC (2.28 for yes), first BC tumor size (2.14 for T3/T4, 1.56 for TIS), and age at first diagnosis of BC (1.41 for < 40). The area under the receiver operating characteristic curve (AUC) for 3- and 5-year predictions are 0.72 and 0.65 for CBCRisk-Black while those are 0.65 and 0.60 for CBCRisk.ConclusionCBCRisk-Black may serve as a useful tool to clinicians in counseling Black BC patients by providing a more accurate and personalized CBC risk estimate.
| S-EPMC9637387 | biostudies-literature
Project description:In this study using national health insurance data, we investigated the risk of ischemic heart disease (IHD) and stroke among prostate cancer (PC) survivors compared with the general population, as well as the risk of cardiovascular disease (CVD) according to primary treatment. A total of 48,298 PC patients diagnosed from 2007 to 2013 were included and matched to non-cancer controls. Compared to the general population, PC survivors had a slightly lower risk of IHD (adjusted hazard ratio [aHR] = 0.89, 95% confidence interval [CI] 0.83-0.96) or stroke (aHR 0.90, 95% CI 0.87-0.95). Especially, survivors who underwent surgery had lower risks of IHD (aHR 0.70, 95% CI 0.61-0.80) or stroke (aHR 0.73, 95% CI 0.67-0.81). Compared to survivors in the active surveillance/watchful waiting group, the androgen deprivation therapy (ADT) group had a significantly greater risk of stroke (aHR 1.16, 95% CI 1.02-1.32), but the IHD risk was not significantly elevated (aHR 1.06, 95% CI 0.88-1.29). In conclusion, PC survivors had a slightly lower risk of CVD compared to the general population, which was attributable to self-selection for PSA screening, specifically in the surgery-only group. CVD risk was dependent on treatment received, and attention should be given to patients who receive ADT.
| S-EPMC7316755 | biostudies-literature
Project description:BackgroundOut-of-hospital cardiac arrest (OHCA) is a serious public health issue, and predicting the prognosis of OHCA patients can assist clinicians in making decisions about the treatment of patients, use of hospital resources, or termination of resuscitation.ObjectiveThis study aimed to develop a time-adaptive conditional prediction model (TACOM) to predict clinical outcomes every minute.MethodsWe performed a retrospective observational study using data from the Korea OHCA Registry in South Korea. In this study, we excluded patients with trauma, those who experienced return of spontaneous circulation before arriving in the emergency department (ED), and those who did not receive cardiopulmonary resuscitation (CPR) in the ED. We selected patients who received CPR in the ED. To develop the time-adaptive prediction model, we organized the training data set as ongoing CPR patients by the minute. A total of 49,669 patients were divided into 39,602 subjects for training and 10,067 subjects for validation. We compared random forest, LightGBM, and artificial neural networks as the prediction model methods. Model performance was quantified using the prediction probability of the model, area under the receiver operating characteristic curve (AUROC), and area under the precision recall curve.ResultsAmong the three algorithms, LightGBM showed the best performance. From 0 to 30 min, the AUROC of the TACOM for predicting good neurological outcomes ranged from 0.910 (95% CI 0.910-0.911) to 0.869 (95% CI 0.865-0.871), whereas that for survival to hospital discharge ranged from 0.800 (95% CI 0.797-0.800) to 0.734 (95% CI 0.736-0.740). The prediction probability of the TACOM showed similar flow with cohort data based on a comparison with the conventional model's prediction probability.ConclusionsThe TACOM predicted the clinical outcome of OHCA patients per minute. This model for predicting patient outcomes by the minute can assist clinicians in making rational decisions for OHCA patients.
| S-EPMC8406108 | biostudies-literature