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A prognostic model for clinical stage I melanoma of the upper extremity. The importance of anatomic subsites in predicting recurrent disease.

Thirteen variables were studied for their relative usefulness in predicting recurrent disease in 107 patients with clinical Stage I melanoma of the upper extremity. After a mean follow-up period of 54 months, the only patents who have had recurrent disease to date are those who primary lesions were located either on the hand or posterior upper arm. The five-year disease-free survival role for 44 patients with melanoma at these sites was 68%. None of 63 patients with melanoma located on the forearm of anterior upper arm have had recurrent disease (i.e., the five-year, disease-free survival rate was 100% (p = 0.00004), compared with the hand or posterior arm group). A Cox proportional hazards (multivariate) analysis demonstrated that two primary tumor histologic variable, thickness in millimeters and ulceration, interacted to produce the best prognostic model for those 44 patients with melanoma of the hand or posterior upper arm. Twenty-one patients with primary lesions at these sites had primary tumors less than 2.25 mm in thickness and no evidence of ulceration histologically. Their five-year, disease-free survival role was 95%. For the remaining 23 patients with primary tumors on the hand or posterior upper arm who had either histologic evidence of ulceration or primary tumors greater than or equal to 2.25 mm, the five-year disease-free survival rate was 37% (p = 0.002, compared with group nonulcerated, thin lesions). The excellent survival rate for patients with melanomas on the forearm or anterior upper arm was not completely explained by pathologic stage, by primary tumor thickness, or by histologic ulceration of the primary tumor.

Arm↗

Peripheral T-cell lymphoma unspecified (PTCL-U): a new prognostic model from a retrospective multicentric clinical study.

To assess the prognosis of peripheral T-cell lymphoma unspecified, we retrospectively analyzed 385 cases fulfilling the criteria defined by the World Health Organization classification. Factors associated with a worse overall survival (OS) in a univariate analysis were age older than 60 years (P=.0002), equal to or more than 2 extranodal sites (P=.0002), lactic dehydrogenase (LDH) value at normal levels or above (P<.0001), performance status (PS) equal to or more than 2 (P< or =.0001), stage III or higher (P=.0001), and bone marrow involvement (P=.0001). Multivariate analysis showed that age (relative risk, 1.732; 95% CI, 1.300-2.309; P<.0001), PS (relative risk, 1.719; 95% CI, 1.269-2.327, P<.0001), LDH level (relative risk, 1.905; 95% CI, 1.415-2.564; P<.0001), and bone marrow involvement (relative risk, 1.454; 95% CI, 1.045-2.023; P=.026) were factors independently predictive for survival. Using these 4 variables we constructed a new prognostic model that singled out 4 groups at different risk: group 1, no adverse factors, with 5-year and 10-year OS of 62.3% and 54.9%, respectively; group 2, one factor, with a 5-year and 10-year OS of 52.9% and 38.8%, respectively; group 3, 2 factors, with 5-year and 10-year OS of 32.9% and 18.0%, respectively; group 4, 3 or 4 factors, with a 5-year and 10-year OS of 18.3 and 12.6%, respectively (P< or =.0001; log-rank, 66.79).

Antineoplastic Agents↗

Development of a prognostic model for grading chronic graft-versus-host disease.

The disease-specific survival (DSS) of 151 patients with chronic graft-versus-host disease (cGVHD) was studied in an attempt to stratify patients into risk groups and to form a basis for a new grading of cGVHD. The data included the outcome and 23 variables at the diagnosis of cGVHD and at the primary treatment failure (PTF). Eighty-nine patients (58%) failed primary therapy for cGVHD. Nonrelapse mortality was 44% after a median follow-up of 7.8 years. The probability of DSS at 10 years after diagnosis of cGVHD (DSS1) and after PTF (DSS2) was 51% (95% confidence interval [CI] = 39%, 60%) and 38% (95% CI = 28%, 49%), respectively. According to multivariate analysis, extensive skin involvement (ESI) more than 50% of body surface area; hazard ratio (HR) of 7.0 (95% CI = 3.6-13.4), thrombocytopenia (TP) (< 100 000/microL; HR, 3.6; 95% CI = 1.9-6.8), and progressive-type onset (PTO) (HR, 1.7; 95% CI = 0.9-3.0) significantly influenced DSS1. These 3 factors and Karnofsky Performance Score of less than 50% at PTF were significant predictors for DSS2. The DSS1 at 10 years for patients with prognostic factor score (PFS) at diagnosis of 0 (none), 1.9 and below [corrected] (ESI only or TP and/or PTO), above 1.9 and not above 3.5 [corrected] (ESI plus either TP or PTO), and more than 3.5 (all 3 factors) was 82%, 68%, 34%, and 3% (P =.05, <.001, <.001), respectively. The DSS2 at 5 years for patients with PFS at PTF of 0, 2 or less, 2 to 3.5, and more than 3.5 were 91%, 71%, 22%, and 4% (P =.2,.005, and <.001), respectively. It was concluded that these prognostic models might be useful in grouping the patients with similar outcome.

Actuarial Analysis↗

A prognostic model for the presence of neurogenic lesions in atypical idiopathic scoliosis.

STUDY DESIGN: Consecutive series of patients with idiopathic scoliosis with atypical features. OBJECTIVES: The purpose of this study is to define a specific yet sensitive set of signs and symptoms to indicate the use of MRI in patients with atypical idiopathic scoliosis. Specifically, this study empirically defines a new diagnostic test for the presence of neurogenic lesions based on clinical and radiologic data and then reports the properties of this test in relation to MRI as the gold standard. SUMMARY OF BACKGROUND DATA: The reported prevalence of brain stem and spinal cord abnormalities in patients with idiopathic scoliosis associated with atypical features varies from 0% to 60%. This wide range most likely results from the fact that the samples studied are either not well defined or are heterogeneous across studies. Because of these issues, the likelihood of neurogenic lesions in atypical idiopathic scoliosis is not known; consequently, the decision to order an MRI is controversial.METHODS A total of 1,206 patients coded as having idiopathic scoliosis were identified from our institutional database. Of these, 72 patients had one or more atypical features: early-onset scoliosis, atypical curve, severe curve despite immaturity (>45 degrees ), rapidly progressive curve (>1 degrees per month), back pain, headache, or neurologic abnormalities on clinical examination. All 72 patients underwent brain and spinal cord MRI. Logistic regression was used to determine significant predictors of positive MRI and to define the prognostic model. RESULTS: Eleven patients (15%) had abnormal findings on MRI. Eight had an Arnold-Chiari type I malformation associated with a syrinx; 1 had an Arnold-Chiari type I malformation; 1 a syrinx; and 1 a cervical syrinx with a conus lipoma. MRI was positive in 5 of 9 patients (55%) with severe curves despite immaturity. Twenty patients had one or more abnormal neurologic signs. Of these, 8 (40%) had a positive MRI, while only 3 of the 52 patients (6%) with a normal neurologic examination (but other atypical features) had a positive MRI. The most predictive model included the variables neurologic abnormalities (yes or no) and severe curve despite immaturity (yes or no). Using this model, patients with atypical characteristics other than severe curvatures or abnormal neurologic abnormalities(s) had a 3% probability (95% confidence interval [CI], 1-12%) of having a positive MRI; patients with abnormal neurologic change(s), but a nonsevere curve, had a 29% probability of a positive MRI (95% CI, 12-53%) and patients with severe curves and no neurologic change(s) had a 32% probability of positive MRI (95% CI, 8-71%). Patients with both a severe curve and abnormal neurologic change(s) had an 86% probability of positive MRI (95% CI, 46-98%). Agreement between this test and the MRI was 75%, with a sensitivity of 82% (95% CI, 48-97%) and a specificity of 74% (95% CI, 61-83%). CONCLUSIONS: The model derived in this study indicates that the probability of neurogenic lesions is extremely low in most patients with idiopathic scoliosis with atypical features. However, patients with severe curves despite skeletal immaturity and an abnormal neurologic examination have a significant probability of neurogenic lesions. Therefore, clinical efficiency will be enhanced by narrowing the indications for MRI to those patients with these risk factors.

Age of Onset↗

Immunological surrogate parameters in a prognostic model for multi-organ failure and death.

OBJECTIVE: To assess the ability of clinical or biochemical parameters to predict outcome (survival or non-survival; severe or moderate/no complication) using multiple regression analyses. DESIGN: Prospective, descriptive cohort study with no interventions SETTING: 12 surgical intensive care units of university hospitals and large community hospitals; four medical school research laboratories in eight European countries PATIENTS: 128 surgical patients with major intra-abdominal surgery admitted for at least two days to an intensive care unit MAIN OUTCOME MEASURES: Prediction of complications or survival based on analysis of clinical (Multiple Organ Dysfunction Score, Multi-Organ-Failure Score, Acute Physiology and Chronic Health Evaluation II scores) and immunological (plasma levels of endotoxin, endotoxin neutralizing capacity, IL-6, IL-8, cell associated IL-8, Fc-receptor polymorphism, soluble CD-14) parameters, with comparison of predicted and actual outcomes. RESULTS: APACHE II, MODS score, MOF score, platelets, IL-6, IL-8, ENC, cell ass. IL-8 were significantly different between survivors and non-survivors and patients with/without severe complications by univariate analysis. By multivariate analysis only MOF, MODS score, IL-6, platelets, comorbidity predicted complications with a sensitivity of 82% and a specificity of 87%. Multivariate analysis demonstrated that only APACHE II score, plasma IL-8 and complications predicted death (sensitivity 84%; specificity 90%). CONCLUSION: Immunological surrogate parameters may predict complications and death of surgical ICU patients. The use of several parameters may add to increase sensitivity and specificity in a prognostic model.

APACHE↗

A 2-step comprehensive high-dose chemoradiotherapy second-line program for relapsed and refractory Hodgkin disease: analysis by intent to treat and development of a prognostic model.

Salvage of patients with relapsed and refractory Hodgkin disease (HD) with high-dose chemoradiotherapy (HDT) and autologous stem cell transplantation (ASCT) results in event-free survival (EFS) rates from 30% to 50%. Unfortunately, the reduction in toxicity associated with modern supportive care has improved EFS by only 5% to 10% and has not reduced the relapse rate. Results of a comprehensive 2-step protocol encompassing dose-dense and dose-intense second-line chemotherapy, followed by HDT and ASCT, are reported. Sixty-five consecutive patients, 22 with primary refractory HD and 43 with relapsed HD, were treated with 2 biweekly cycles of ifosfamide, carboplatin, and etoposide (ICE). Peripheral blood progenitor cells from responding patients were collected, and the patients were given accelerated fractionation involved field radiotherapy (IFRT) followed by cyclophosphamide-etoposide and either intensive accelerated fractionation total lymphoid irradiation or carmustine and ASCT. The EFS rate at a median follow-up of 43 months, as analyzed by intent to treat, was 58%. The response rate to ICE was 88%, and the EFS rate for patients who underwent transplantation was 68%. Cox regression analysis identified 3 factors before the initiation of ICE that predicted for outcome: B symptoms, extranodal disease, and complete remission duration of less than 1 year. EFS rates were 83% for patients with 0 to 1 adverse factors, 27% for patients with 2 factors, and 10% for patients with 3 factors (P <.001). These results compare favorably with other series and document the feasibility and efficacy of giving uniform dose-dense and dose-intense cytoreductive chemotherapy and integrating accelerated fractionation radiotherapy into an ASCT treatment program. This prognostic model provides a basis for risk-adapted HDT.

Adolescent↗

Prognostic models and the propensity score.

Subjects in observational studies of exposure effects have not been randomized to exposure groups and may therefore differ systematically with regard to variables related to exposure and/or outcome. To obtain unbiased estimates and tests of exposure effects one needs to adjust for these variables. A common method is adjustment via a parametric model incorporating all known prognostic variables. Rosenbaum and Rubin propose adjustment by the conditional exposure probability given a set of covariates which they call the propensity score. They show that, at any value of the propensity score, covariates are on average balanced between exposure groups. Thus matching on the propensity score leads to unbiased estimators and tests of exposure effect. However, the validity of the method depends on knowing the exposure probability. This quantity is usually not known in observational studies and needs to be estimated.

Bias↗

[Prognostic models in severe head injury].

Severe head injury (SHI) is one of the most important health, social, and economic problems in industrialized countries. Most of the recent studies related to this entity still show pessimistic results, with percentages of mortality and unfavourable outcomes very similar than those reported in the last quarter of century. In order to make predictions for patients with SHI, different "prognostic formulas or models" reviewed in this manuscript, have been developed with the main objective of performing reliable predictions for patients with this pathology. These models are constructed by using a group of "prognostic indicators or factors" and different "prognostic scales" useful for measuring the final outcome. The different "statistical techniques or methods" necessary to develop these prognostic models are also analyzed in this paper.

Bayes Theorem↗

[Risk predictors, scoring systems and prognostic models in anesthesia and intensive care. Part I: anesthesia].

Risk predictors and scoring systems are commonly used in medicine to provide a reliable and objective estimation of disease prognoses, probability of adverse events and outcome. Furthermore, they were designed to classify severity of illness or the course of diagnostic and therapeutic interventions and to perform a risk stratification for scientific studies in a standardized way. In quality management and cost control, scoring systems and predictors are used for risk adjustment and evaluation of care performance. The aim of this review article was to describe common risk indices and scoring systems in anesthesia (part I) and intensive care (part II), and to point out their possible benefits and limitations. Different scoring systems and classifications are available to stratify perioperative risk and adverse events in anesthesia. Especially in cardiac surgery, an increasing interest in risk-adjusted outcome studies led to the modeling and validation of different prognostic systems for postoperative morbidity, mortality and length of stay. Furthermore, there are scoring-systems for special events, such as difficult laryngoscopy or postoperative nausea and vomiting (PONV). Risk check lists and risk indices are superior to the ASA classification of physical status in providing more exact results and the possibility of statistic risk calculation. Nevertheless, they are not frequently used in clinical routine. Because of its simplicity and easy handling the ASA classification has worldwide popularity and recent studies demonstrated at least equal prognostic performance.

Anesthesia↗

Development and validation of a machine learning prognostic model based on an epigenomic signature in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: In Pancreatic Ductal Adenocarcinoma (PDAC), current prognostic scores are unable to fully capture the biological heterogeneity of the disease. While some approaches investigating the role of multi-omics in PDAC are emerging, the analysis of methylation data is under exploited. MATERIALS AND METHODS: We analyzed CpG sites from two publicly available datasets, the TCGA-PAAD used as discovery set and the CPTAC-PDA as external test set. Single mutations and co-mutation of KRAS and TP53 genes were identified as targets, and differentially methylated CpG sites (DMC) were detected accordingly. We trained and validated Random Forest (RF) models to predict each target. Area Under the Receiver Operating Characteristic curve (AUROC) and Area Under the Precision-Recall curve (AUPRC) were used as performance metrics. Then, we performed consensus clustering from the DMCs to identify novel patients' profiles. Finally, we trained and validated a combination of eXtreme Gradient Boosting (XGB) and tree models to select an epigenomic prognostic determinant. RESULTS: From 598 DMCs extracted, an RF model predicted KRAS and TP53 co-mutation on the external test set with AUROC of 0.77 and AUPRC of 0.87. The consensus clustering allowed us to identify 4 clusters (C1, C2, C3, and C4) of patients. The C4 cluster captured a subgroup of patients with favorable Overall Survival (OS) with respect to others. The XGB model perfectly predicted C4 vs other clusters on the discovery set. In both cohorts, patients were stratified into two risk groups according to methylation levels of cg16854533, individuated as the most important CpG site. CONCLUSION: We analyzed methylation data to develop a classifier for the TP53 and KRAS mutational status. Four prognostic clusters were pointed out and a prognostic model using a CpG site was validated in an independent cohort. Our results evidence that the proposed use of methylation data facilitates risk stratification for PDAC.

Humans↗

A multifactorial prognostic model for adult soft tissue sarcoma considering clinical, histopathological and molecular data.

Soft tissue sarcomas (STS) are malignant mesenchymal lesions with a high degree of prognostic variability. Different prognostic markers such as grading, staging, tumour type and localisation are known. The establishment of these markers was based on the evaluation at results of extensive cohorts of patients. Therefore, only the established markers provide us with information about probabilities in relation to other qualities. Considering as many different markers as possible in one prognostic statement should increase the value of the resultant information. Therefore, we developed a model involving known prognostic markers to formulate an individual prognostic index. In a retrospective analysis, different prognostic factors of 198 adult STS patients with histological tumour free resection margins were evaluated using a multifactorial analysis. On the basis of a Cox-Regression-Model with proportional hazards, the prognostic factors (tumour type, staging, localisation and type of surgical resection) were selected using previous knowledge and a statistical step backward selection procedure adjusting the immunohistochemical status of p53/Mdm2 expression. On the basis of the baseline survival function of our cohort (S0 (t)), the cumulative probability of survival for two S (2) and five S (5) years was estimated. As a result of our analysis the equations S (2) = (e-00393)P and S (5) = (e-00869)P can be used to estimate the individual two and five-year probability of survival in our cohort. Here p is the result of the amount of the estimated regression-coefficients of the exact variables of the respective individual patient. This model makes it possible to include all the evaluated prognostic factors which, in turn, increases the accuracy of the prognostic information for individual patients underlining the proportional hazards assumption.

Abdominal Neoplasms↗

Time and PSA threshold model prognosticates long-term overall and disease-specific survival in prostate cancer patients as early as 3 months after external beam radiation therapy.

The specific aim of this analysis was to evaluate the capability of a time and prostate-specific antigen (PSA) threshold model to prognosticate overall survival (OS) and disease-specific survival (DSS) based on early PSA kinetics after radiotherapy for prostate cancer by retrospective review of outcomes in 918 patients. Crossing below analyzed PSA thresholds at specific defined time points reduced disease-specific death hazard ratios to relative to the cohort above threshold. The time and PSA threshold model demonstrates the ability to prognosticate OS and DSS as early as 3 months post-radiotherapy for prostate cancer.

Aged↗

Prognostic model for patients treated for colorectal adenomas with regard to development of recurrent adenomas and carcinoma.

OBJECTIVE: To quantify the risk of developing recurrent adenomas or colorectal cancer for patients who had already had colorectal adenomas removed. DESIGN: Retrospective study. SETTING: University hospital, Denmark. SUBJECTS: 479 patients who had colorectal adenomas removed between 1958-80. INTERVENTIONS: All patients were followed up by rectoscopy and double contrast barium enema. The survival data were analysed by Cox's proportional hazards model. MAIN OUTCOME MEASURES: Variables of significant prognostic importance for recurrence of adenomas and the development of cancer were identified. Results. For the long term risk of recurrence of the adenoma (more than 1.5 years after removal of the first adenoma), two variables were of prognostic significance: The occurrence of synchronous adenomas or recurrent adenomas, and the sex of the patient. The model for development of colorectal cancer identified two variables of prognostic significance: the grade of dysplasia and the size of the first adenoma. CONCLUSION: We suggest that these variables can be used in the management of patients with adenomas, particularly in developing individual follow-up regimens.

Adenoma↗

Primary central nervous system lymphoma: the Memorial Sloan-Kettering Cancer Center prognostic model.

PURPOSE: The purpose of this study was to analyze prognostic factors for patients with newly diagnosed primary CNS lymphoma (PCNSL) in order to establish a predictive model that could be applied to the care of patients and the design of prospective clinical trials. PATIENTS AND METHODS: Three hundred thirty-eight consecutive patients with newly diagnosed PCNSL seen at Memorial Sloan-Kettering Cancer Center (MSKCC; New York, NY) between 1983 and 2003 were analyzed. Standard univariate and multivariate analyses were performed. In addition, a formal cut point analysis was used to determine the most statistically significant cut point for age. Recursive partitioning analysis (RPA) was used to create independent prognostic classes. An external validation set obtained from three prospective Radiation Therapy Oncology Group (RTOG) PCNSL clinical trials was used to test the RPA classification. RESULTS: Age and performance status were the only variables identified on standard multivariate analysis. Cut point analysis of age determined that patients age < or = 50 years had significantly improved outcome compared with older patients. RPA of 282 patients identified three distinct prognostic classes: class 1 (patients < 50 years), class 2 (patients > or =50; Karnofsky performance score [KPS] > or = 70) and class 3 (patients > or = 50; KPS < 70). These three classes significantly distinguished outcome with regard to both overall and failure-free survival. Analysis of the RTOG data set confirmed the validity of this classification. CONCLUSION The MSKCC prognostic score is a simple, statistically powerful model with universal applicability to patients with newly diagnosed PCNSL. We recommend that it be adopted for the management of newly diagnosed patients and incorporated into the design of prospective clinical trials.

Adult↗

[Simplified prognostic model of overall intrahospital mortality of children in central Africa].

OBJECTIVES: To find a simple mortality prediction model based on nutritional and infection indicators for the assessment of the care of children admitted to hospital in central Africa. METHOD: Cohort study of 414 children admitted at Goma Hospital between 1.4.2003 and 31.3.2004. We conducted univariate analysis and logistic regression, computed adjusted odds ratios and constructed a prognostic score from the coefficients of logistic regression. The performance of logistic model and score were evaluated by the calculation of areas under the ROC curves. RESULTS: The intrahospital mortality rate reached 15.9%. In univariate analysis, age, WAZ, arm circumference, neurological status (Blantyre coma score), stiff neck, subcostal indrawning, and infection were significantly associated with mortality. Logistic regression model analysis and adjusted odds ratios (AOR) confirmed higher risks of death for young (AOR 3.4 (1.4-8.8) and underweight children (WAZ -2->-3 and WAZ < or = -3, AOR 3.2 (1.4-7.6) and AOR 4.4 (1.7-11.2)), for children with arm circumference under 115 mm (AOR 3.4 (1.5-7.3)), impaired consciousness (AOR 9.6 (3.1-29.9)) and bloodstream infections (AOR 6.6 (2.1-21.1)). The area under the ROC curve of the prognostic model is 0.83 (0.78-0.88), that of the prognostic score, 0.80 (0.75-0.86). CONCLUSION: This study provides a simple mortality prediction model for hospitalised children in central Africa, based on age, weight for age or arm circumference, neurological status (Blantyre coma score), and infection. This model and scoring system can be used to evaluate programs set up to reduce intrahospital mortality in this region.

Age Factors↗

A new prognostic model comprising p53, EGFR, and tumor grade in early stage epithelial ovarian carcinoma and avoiding the problem of inaccurate surgical staging.

Epithelial ovarian carcinoma rarely occurs because of a single event. Therefore, no single biological tumor factor will give accurate prognostic information for all ovarian cancer patients. On the other hand, a combination of two or more independent factors may yield an improved overall prognostic index. Because FIGO stage is included in most of the previously presented models, inaccurate surgical staging in patients with apparently early disease has been a problem. In a series of 226 patients with epithelial ovarian carcinomas in FIGO stages IA-IIC, a number of clinicopathological factors (age, FIGO stage, histopathologic type, and tumor grade) were studied in relation to the biological factors p53 and epidermal growth factor receptor (EGFR), important regulators of the apoptosis and mitosis. Immunohistochemical techniques were used. All patients received adjuvant radiotherapy or chemotherapy after the primary surgery. Expression of p53 was significantly associated with the tumor grade and disease-free survival (DFS). EGFR expression was also associated with DFS. In a Cox multivariate analysis, tumor grade, p53 status, and EGFR status were all independent and significant prognostic factors with regard to DFS. A prognostic model was proposed using these factors. A low-risk group, an intermediate-risk group, and a high-risk group were defined. DFS amounted to 89% in the low-risk group (grades 1-2, p53-negative, and EGFR-negative), 66% in the intermediate-risk group (grade 3, p53-negative, and EGFR-negative or grades 1-2, p53-positive or EGFR-positive) and 39% in the high-risk group (grade 3, p53-positive, and EGFR-positive).

Adenocarcinoma, Mucinous↗

Construction and accuracy assessment of an efferocytosis-related prognostic model for ovarian cancer: A diagnostic accuracy study.

The study aimed to investigate the prognostic significance of efferocytosis-related genes in ovarian cancer (OC) with regard to cancer development, progression, invasion, and metastasis. OC cohorts were assembled from bioinformatics repositories. Utilizing consensus clustering analysis, distinct clusters were delineated based on the intersection of OC-related genes and efferocytosis-related genes. A prognostic signature specific to efferocytosis in OC was developed using data from The Cancer Genome Atlas, validated against the gene expression omnibus database, and subjected to independent prognostic analysis. Subsequently, a nomogram model was formulated. Moreover, investigations encompassed the immune microenvironment, immunotherapy, mutation profiling, drug sensitivity assessments, drug prediction models, and molecular docking analyses. Finally, quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays were employed to ascertain the mRNA expression levels of key genes. Five key genes, FCGBP, BTN3A3, WDR91, SLC25A45, and BTNL3, were identified as significantly associated with OC. Both datasets and qRT-PCR demonstrated elevated expression levels of FCGBP and WDR91 in OC. Notably, AFLATOXIN B1 exhibited strong binding affinity to SLC25A45, ciclopirox to BTN3A3, and irinotecan to WDR91. The risk score, age, and stage were identified as independent prognostic factors, with the nomogram displaying efficacy in predicting OC patient survival. Variations in the immune cell infiltration profiles, including naive B cells, and expression levels of 6 immune checkpoint genes, such as CTLA4, were notable. High tumor mutation burden scores were associated with improved survival outcomes. Additionally, significant differences in the IC50 values of 123 anticancer drugs were observed between the 2 risk groups. This findings of this study highlight the efficacy of the efferocytosis-associated risk model in predicting the survival outcomes of OC patients, thus providing a novel reference for prognostic prediction in OC patients.

Humans↗

Prognosis in glomerulonephritis. II. Regression analyses of prognostic factors affecting the course of renal function and the mortality in 395 patients. Calculation of a prognostic model. Report from a Copenhagen study group of renal diseases.

The course of the renal function and mortality were analysed in 395 patients with biopsy-proven glomerulonephritis (GN), using Cox's proportional hazards model. Seventeen clinical, biochemical and histopathological parameters were analysed for prognostic information. The patients were grouped according to their serum creatinine levels. Increase in serum creatinine, decrease in serum creatinine, cure and death were used as endpoints for the analysis. Caplan Meyer curves were made for 7 transitions between different groups and the variables were reduced by a step-wise procedure to a final model. Thirteen of the variables considered offered significant prognostic information (p less than 0.05) for at least one of the transitions. Short duration of disease, young age, non-nephritic urinary sediment and preceding streptococcal infection were predictors of cure. Extracapillary, membranoproliferative and unclassifiable GN, old age and arterial hypertension predicted increase in serum creatinine in patients with low serum creatinine, while male sex, short duration of disease and pathological electrocardiogram favoured a further increase in patients with high serum creatinine. A later decrease in serum creatinine was signified by a preceding streptococcal infection, short duration of disease, absence of arterial hypertension and low urinary protein excretion. Death without uremia was predicted by high age, connective tissue disease and extracapillary GN. Using these parameters and the models, it is possible to make a prognostic forecast for the individual GN patient. Examples of such a forecast are described.

Adult↗