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Results for “Predictive modeling”

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PubMed2006

BRCA1 and BRCA2 genetic testing in Italian breast and/or ovarian cancer families: mutation spectrum and prevalence and analysis of mutation prediction models.

BACKGROUND: Breast cancer is an extremely complex disease, characterized by a progressive multistep process caused by interactions of both genetic and non-genetic factors. A combination of BRCA1 and BRCA2 gene mutations appears responsible for about 20%-30% of the cases with breast cancer familial history. The prevalence of BRCA1/2 pathogenic mutations largely varies within different populations; in particular, the rate of mutations in Italian breast and/or ovarian cancer families is rather controversial and ranges from 8% to 37%. PATIENTS AND METHODS: Of the 152 breast/ovarian cancer families counseled in our centre, 99 were selected for BRCA1/2 mutation screening according to our minimal criteria. The entire coding sequences and each intron/exon boundary of BRCA1/2 genes were screened by direct sequencing (PTT limited to BRCA1 exon 11). For each proband, the a priori probability of carrying a pathogenic BRCA1/2 germline mutation was calculated by means of different mutation prediction models (BRCApro, IC and Myriad Table) in order to evaluate their performances. RESULTS: Our analysis resulted in the identification of 25 and 52 variants in the BRCA1 and BRCA2 genes, respectively. Seventeen of them represent novel variants, including four deleterious truncating mutations in the BRCA2 gene (472insA, E33X, C1630X and IVS6+1G>C). Twenty-seven of the 99 probands harbored BRCA1 (n = 15) and BRCA2 (n = 12) pathogenic germline mutations, indicating an overall detection rate of 27.3% and increasing by more than 15% the spectrum of mutations in the Italian population. Furthermore, we found the lowest detection rate (19.4%) in pure hereditary breast cancer family subset. All of the prediction models showed praises and faults, with the IC software being extremely sensitive but poorly specific, compared to BRCApro. In particular all models accumulated most false-negative prediction in the HBC subset. Interestingly preliminary results of a study addressing the presence of genomic rearrangements in HBC probands with BRCApro or IC prediction scores >/=95%, provided evidence for additional mutations undetectable with our conventional screening for point mutations. CONCLUSIONS: Altogether our results suggest that HBC families, the largest pool in our series, represent an heterogeneous group where the apparently faulty performances of the prediction models might be at least partially explained by the presence of additional kinds of BRCA1/2 alteration (such as genomic rearrangements) or by mutations on different breast cancer related genes.

Adult↗

PubMed2004

A simple and accurate prediction model to estimate the intrahospital mortality risk of hospitalised cancer patients.

We aimed to form a risk prediction model to assess the probability of intrahospital death in cancer patients at the time of hospitalisation. The medical records and the relevant clinical parameters of cancer patients who died in or who were discharged from a teaching hospital between 1997 and 2000 (n = 334) were reviewed to explore the determinants of intrahospital death, which later were verified prospectively (n = 131). Eastern Cooperative Oncology Group (ECOG) performance status of four, short duration of disease (on a logarithmic scale), emergency admission, low haemoglobin (Hb) value (on a linear scale) and lactate dehydrogenase (LDH) value greater than 378 micro/ml were significantly and independently associated with the risk of intrahospital death. This model had a receiver operating characteristic area of 0.88 in the derivation cohort and 0.82 in the validation cohort. Using readily available clinical parameters, it is possible to devise an accurate and applicable risk prediction model for the hospitalised cancer patients.

Adolescent↗

PubMed1986

Oxygen concentration gradient across the ovarian follicular epithelium: model, predictions and implications.

A mathematical model has been devised for predicting the oxygen concentration gradient across the epithelium of ovarian follicles at pre-antral stages. Most dissolved oxygen entering the follicle by diffusion is evidently consumed in the outer layer of cells; little reaches the oocyte. Even when the rate of consumption or the diffusion coefficient of oxygen was adjusted by an order of magnitude to favour oxygen penetration, the concentration gradient into the follicle remained steep. On the basis of measurements of ovine granulosa cell respiration in vitro, the model predicts that a large pre-antral follicle with a radius of 0.15 mm consumes oxygen at the rate of 0.22 nmol min-1.

Animals↗

PubMed2026

Construction of a new predictive model in head and neck squamous cell carcinoma based on the investigation of extracellular matrix-associated genes.

A key aspect influencing immune cell infiltration is the composition of the extracellular matrix (ECM). Therefore, investigating the association between ECM-associated proteins and immune cell infiltration is key for the identification of new biomarkers to distinguish 'immune-hot' solid tumors and predict patient prognosis. A total of 513 head and neck squamous cell carcinoma (HNSCC) cases as training samples from The Cancer Genome Atlas and an additional 270 as testing samples from the Gene Expression Omnibus were obtained for use in the present study. Using a single-sample Gene Set Enrichment Analysis method, the 513 training samples were divided into Cluster 1 and Cluster 2. Subsequently, the present analysis uncovered 1,573 differentially expressed genes distinguishing the two clusters. After performing an intersection analysis with 751 ECM-associated genes, 103 differentially expressed ECM-associated genes were identified. Least absolute shrinkage and selection operator-Cox and multivariate Cox regression analyses were employed to identify candidate ECM risk genes (P<0.05) and to construct a predictive model. Finally, a nomogram and a three gene (cerebellin 2, galectin-10 and cathepsin G) predictive model were developed. Therefore, the present prognostic risk score model can evaluate the immune infiltration, predict the prognosis of HNSCC, and potentially guide more personalized immunotherapy interventions.

extracellular matrix↗

PubMed2002

Risk assessment in localized primary cutaneous melanoma: a Southwest Oncology Group study evaluating nine factors and a test of the Clark logistic regression prediction model.

We studied 9 clinical and pathologic factors in 259 patients using Cox model regression analysis to determine which factors have independent predictive value. Median follow-up time in all patients still alive was 12.3 years (range, 1.7 to 16.7 years). Tumor-infiltrating lymphocytes (P = .005), primary site (P = .006), and thickness (P = .02) had independent predictive value. Ulceration (P = .06) and age (P = .07) had marginal value. We used 6 of those factors to test the Clark logistic regression prediction model, which accurately predicted 8-year survival in 121 (72.9%) of 166 patients and accurately predicted melanoma-specific mortality in 32 (43%) of 74 patients. The combined or overall accuracy of the Clark model was only 64%.

Female↗

PubMed2002

Comparison of predictive models for postoperative nausea and vomiting.

BACKGROUND: In order to identify patients who would benefit from prophylactic amtiemetics, six predictive models have been described for the risk assessment of postoperative nausea and vomiting (PONV). This study compared the validity and practicability of these models in patients undergoing general anaesthesia. METHODS: Data were analysed from 1566 patients who underwent balanced anaesthesia without prophylactic antiemetic treatment for various types of surgery. A systematic literature search identified six predictive models for PONV. These models were compared with respect to validity (discriminating power and calibration characteristics) and practicability. Discriminating power was measured by the area under the receiver operating characteristic curve (AUC) and calibration was assessed by weighted linear regression analysis between predicted and actual incidences of PONV. Practicability was assessed according to the number of factors to be considered for the model (the fewer factors the better), and whether the score could be used in combination with a previously applied cost-effective concept. RESULTS: The incidence of PONV was 600/1566 (38.1%). The discriminating power (AUC) obtained by the models (named according to the first author) using the risk classes from the recommended prophylactic concept were as follows: Apfel, 0.68; Koivuranta, 0.66; Sinclair, 0.66; Palazzo, 0.63; Gan, 0.61; Scholz, 0.61. For four models, the following calibration curves (expressed as the slope and the offset) were plotted: Apfel, y=0.82x+0.01, r2=0.995; Koivuranta, y=1.13x-0.10, r2=0.999; Sinclair, y=0.49x+0.29, r2=0.789; Palazzo, y=0.30x+0.30, r2=0.763. The numbers of parameters to be considered were as follows: Apfel, 4; Koivuranta, 5; Palazzo, 5; Scholz, 9; Sinclair, 12; Gan, 14. CONCLUSION: The simplified risk scores provided better discrimination and calibration properties compared with the more complex risk scores. Therefore, simplified risk scores can be recommended for antiemetic strategies in clinical practice as well as for group comparisons in randomized controlled antiemetic trials.

Adult↗

PubMed1995

Using prediction models and cost-effectiveness analysis to improve clinical decisions: emergency department patients with acute chest pain.

Prediction models and cost-effectiveness analysis are two of the methodologies included in the broad definition of outcomes research. These methodologies are designed to improve physicians' abilities to identify clinical risks and to choose appropriate management strategies based on these risks. For the evaluation and management of patients with acute chest pain, prediction models have markedly improved our ability to estimate risk, and cost-effectiveness analyses have helped guide the development of new paradigms and the incorporation of new technologies. In the past decade, the management of patients who come to emergency departments with acute chest pain has fundamentally changed, with far fewer patients being admitted to coronary intensive care units and an increasing majority being admitted to nonintensive, observation units for shorter and shorter periods of time. These changes in management approaches actually allow more patients to be admitted, hence reducing the risk of inappropriate discharge, while still reducing the utilization of resources.

Chest Pain↗

PubMed2005

Prospective validation of two models predicting pregnancy leading to live birth among untreated subfertile couples.

BACKGROUND: Models predicting clinical outcome need external validation before they can be applied safely in daily practice. This study aimed to validate two models for the prediction of the chance of treatment-independent pregnancy leading to live birth among subfertile couples. METHODS: The first model uses the woman's age, duration and type of subfertility, percentage of progressive sperm motility and referral status. The second model in addition uses the result of the post-coital test (PCT). For validation, these characteristics were collected prospectively in two University hospitals for 302 couples consulting for subfertility. The models' ability to distinguish between women who became pregnant and women who did not (discrimination) and the agreement between predicted and observed probabilities of treatment-independent pregnancy (calibration) were assessed. RESULTS: The discrimination of both models was slightly lower in the validation sample than in the original sample which provided the model. Calibration was good: the observed and predicted probabilities of treatment-independent pregnancy leading to live birth did not differ for both models. CONCLUSIONS: The chance of pregnancy leading to live birth was reliably estimated in the validation sample by both models. The use of PCT improved the discrimination of the models. These models can be useful in counselling subfertile couples.

Birth Rate↗

PubMed2005

Improving predictive modeling in pediatric drug development: pharmacokinetics, pharmacodynamics, and mechanistic modeling.

A workshop was conducted on November 18-19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adult-like in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

Adult↗

PubMed1996

A predictive model to estimate the risk of serious bacterial infections in febrile infants.

UNLABELLED: Low risk criteria have been defined to identify febrile infants unlikely to have serious bacterial infection (SBI). Using these criteria approximately 40% of all febrile infants can be defined as being at low risk. Of the remaining infants (60%) only 10%-20% have an SBI. No adequate criteria exist to identify these infants. All infants aged 2 weeks-1 year, presenting during a 1-year-period with rectal temperature > or = 38.0 degrees C to the Sophia Children's Hospital were included in a prospective study. Infants with a history of prematurity, perinatal complications, known underlying disease, antibiotic treatment or vaccination during the preceding 48 h were excluded. Clinical and laboratory variables at presentation were evaluated by a multivariate logistic regression model using SBI as the dependent variable. By using likelihood ratios a predictive model was derived, providing a post test probability of SBI for every individual patient. Of the 138 infants included in the study, 33 (24%) had SBI. Logistic regression analysis defined C-reactive protein (CRP), duration of fever, standardized clinical impression score, a history of diarrhoea and focal signs of infection as independent predictors of SBI. CONCLUSION: CRP, duration of fever, the "standardized clinical impression score", a history of diarrhoea and focal signs of infection were the independent, most powerful predictors of SBI in febrile infants, identified by logistic regression analysis. Although the predictive model is not validated for direct clinical use, it illustrates the clinical potential of the used technique. This technique offers the advantage of assess the probability of SBI in every individual infant. This probability will form the best basis for well-founded decisions in the management of the individual febrile infant.

Bacterial Infections↗

PubMed2025

Proteomic signatures and predictive modeling of cadmium-associated anxiety in middle-aged and elderly populations: an environmental exposure association study.

BACKGROUND: Emerging evidence implicates environmental contaminants such as cadmium (Cd) as modifiable risk factors for anxiety. Despite growing recognition of heavy metal toxicity in neuropsychiatric disorders, the molecular mechanisms linking environmental exposure to anxiety pathogenesis remain poorly understood. METHODS: Based on the established cohort of individuals with cognitive impairment in cadmium-contaminated areas, this cross-sectional association study enrolled 50 middle-aged and elderly hospitalized patients from these regions, adhering to the STROBE guidelines. Blood concentrations of cadmium (Cd), lead (Pb), and mercury (Hg) were analyzed in relation to anxiety severity assessed via the Hamilton Anxiety Rating Scale (HAMA). Plasma proteomic profiling was performed using data-independent acquisition (DIA) quantitative technology with an LC-MS/MS platform (timsTOF Pro, Bruker Daltonics), systematically characterizing 2,531 proteins across all samples. Machine learning techniques, specifically XGBoost and LASSO, were employed to identify biomarkers that were subsequently validated through mediation analysis and animal experiments, allowing for the screening of key protein signatures. Finally, clinical variables were integrated to construct a comprehensive model, which was then thoroughly evaluated. RESULTS: Anxious individuals exhibited significantly higher blood Cd levels than controls (β = 0.50, 95% CI: 0.07-0.93, p < 0.01), with anxiety positively correlating with depression (r = 0.62, p = 0.003) and inversely with ApoE3 genotype prevalence. Proteomics identified 120 differentially expressed proteins in anxious patients, enriched in oxidative phosphorylation and neurodegenerative pathways. CCDC126 emerged as a cadmium-associated biomarker, validated in rat models exposed to Cd. Combining CCDC126, blood Cd, Pb, and hypertension, a clinical prediction model achieved robust discrimination (AUC = 0.80, validation cohort). CONCLUSIONS: This first integrative environmental-proteomic study highlights cadmium's synergistic role in anxiety pathophysiology and psychiatric comorbidity. The predictive model offers translatable potential for early risk stratification, while CCDC126 provides mechanistic insights for targeted interventions in populations exposed to environmental pollutants.

Cadmium↗

PubMed1995

Predictive Models of Lumbar Loadings When Handling Boxes.

Back problems resulting from the compression forces on the intervertebral disks during manual material-handling tasks are an important problem affecting workers in various industries. The quantification of these forces using intradiscal pressure or biomechanical modeling is complex, time consuming, and costly, and these methods cannot be readily used in the workplace to estimate loadings on the lower back. The objective of this study was to develop a predictive model that would allow the estimation of lumbar loadings for lifting and lowering boxes using easily measured anthropometric variables and variables related to the task. A dynamic and planar segmental model and a model of internal forces at L5/S1 were used to determine the compression forces on the lower back. Two predictive models, a field model and a laboratory model, were developed to estimate the compression forces when lifting or lowering 3.3 kg to 22.0 kg boxes between heights of 15 cm and 185 cm. Both models were validated by an examination of the residuals. Their predictive performance was also compared, with the laboratory model offering a slightly better prediction than the field model. Thus, these equations represent a practical tool for a better planning of handling tasks in the working environment with the purpose of reducing the back injuries of workers.

biomechanics↗

PubMed2000

A predictive model for neutropenia associated with cancer chemotherapy.

Studies of primary prophylaxis of febrile neutropenia (FN) with recombinant human granulocyte colony-stimulating factor (rHu-G-CSF, filgrastim) administered to all patients starting their initial course of chemotherapy have demonstrated clinical effectiveness and an economic advantage in a wide range of settings. A recent meta-analysis confirmed the ability of filgrastim to reduce the risk of FN and documented infection in a variety of malignancies in both adults and children. The threshold risk for FN at which a cost saving is achieved by using filgrastim is inversely related to the daily cost of the drug and duration of hospitalization. Clinical practice guidelines for the use of filgrastim were developed based on these observations. Recent studies incorporating indirect institutional costs demonstrated that a cost saving can be achieved at substantially lower FN risk thresholds than previously estimated. Despite the demonstrated efficacy of filgrastim in primary prophylaxis, its value may be further increased by appropriately selecting patients and better understanding the importance of sustaining dose intensity in specific malignancies. Clinical prediction models capable of identifying individuals at high risk for neutropenic complications yield further reductions in FN risk thresholds and treatment costs in patients receiving cancer chemotherapy. These models also may be used to evaluate the cost-effectiveness or cost-efficiency of filgrastim. A clinical prediction model recently was presented and validated incorporating both baseline clinical characteristics as well as the results of the first cycle of chemotherapy in patients with early-stage breast cancer. A cost-effectiveness ratio of $34,297/year of life saved was estimated based on dose-response assumptions derived from a previously reported adjuvant breast cancer trial studying the impact of dose reduction on disease-free survival. The cost-effectiveness of filgrastim was evident over a wide range of clinical and cost assumptions. Clinical prediction models permit the rational and cost-effective identification of patients for filgrastim support. Existing clinical practice guidelines should be reevaluated in light of new information available on both the total costs associated with FN as well as the cost-effectiveness of these agents in patients receiving chemotherapy for sensitive and potentially curable malignancies.

Antineoplastic Agents↗

PubMed1993

Predicting survival from in-hospital CPR: meta-analysis and validation of a prediction model.

OBJECTIVE: To better clarify patient factors that predict survival from in-hospital cardiopulmonary resuscitation (CPR), using two methods: 1) meta-analysis and 2) validation of a prediction model, the pre-arrest morbidity (PAM) index. DESIGN: Meta-analysis of previously published studies by standard techniques. Retrospective chart review of validation sample. SETTING: University-affiliated teaching hospital. PATIENTS/PARTICIPANTS: Meta-analytic sample of 21 previous studies from 1965-1989. The validation sample consisted of all patients surviving resuscitation from the authors' hospital during the period September 1986 to January 1991. A matched sample of patients who did not survive from the same time period was used as the comparison group. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The strongest negative predictors of survival, by meta-analysis, were renal failure (r = 0.088, p < 0.0002), cancer (r = 0.08, p < 0.0002), and age more than 60 years (r = 0.063, p < 0.006). Sepsis (r = 0.046, p < 0.02), recent cerebrovascular accident (CVA) (r = 0.038, p < 0.04), and congestive heart failure (CHF) class III/IV (r = 0.036, p < 0.05) were weaker negative predictors. Presence of acute myocardial infarction (AMI) was a significant positive predictor of survival (r = 0.15, p < 0.0001). The PAM score was highly predictive of survival in a logistic regression model (p < 0.0003, R2 = 9.6%). No patient who survived to discharge had a PAM score higher than 8. CONCLUSION: Meta-analysis reveals that the most significant negative predictors of survival from CPR are renal failure, cancer, and age more than 60 years, while AMI is a significant positive predictor. The PAM index is a useful method of stratifying probability of survival from CPR, especially for those patients with high PAM scores, who have essentially no chance of survival.

Age Factors↗

PubMed2006

Evaluation of a predictive model for air/surface adsorption equilibrium constants and enthalpies.

A model used to predict equilibrium adsorption to surfaces using a poly-parameter linear free-energy relationship as well as an empirical model used to predict enthalpies of adsorption of volatile compounds were evaluated with new experimental data to cover semivolatile compounds and a larger variability of compound classes. Equilibrium adsorption constants on a quartz surface ranging over seven orders of magnitude were measured for 142 compounds, and enthalpies of adsorption on a quartz surface from -33.7 to -99.8 kJ/mol were measured for 76 compounds. Agreement between experimental and predicted data was within a factor of two (82.1%) or three (100.0%) for the equilibrium adsorption constants and within 20% for the enthalpy of adsorption values. Thus, the scatter in the validation data sets reported here were practically the same as that for the calibration data sets used to derive the models. The few outliers that we identified in the prediction of equilibrium adsorption constants likely are caused by either shortcomings of the reported sorbate parameters or the occurrence of chemical speciation in the water layer on the surface of the quartz.

Adsorption↗

PubMed1996

Prognostic factors and a predictive model of follicular lymphoma: a 25-year study at a single institution in Japan.

The incidence of follicular lymphoma in Japan is far lower than that in western countries, and no large-scale clinicopathologic studies on this neoplasm have been conducted in Japan. We reviewed histopathological specimens from 118 of 135 patients who had been diagnosed as having follicular lymphoma between 1968 and 1993. Prognostic factors influencing survival were analyzed using univariate and multivariate analyses. Factors that were independently significant upon multivariate analysis were incorporated into a predictive model. Ninety-three patients (78.8%) had a confirmed diagnosis of follicular lymphoma. Twenty-one of the remaining 25 patients were categorized as having other lymphoma subtypes, and four patients showed indefinite findings or those suggesting diseases other than lymphoma. Major characteristics of the 93 patients with follicular lymphoma were a median age of 53 years (20-85); 59 males (63%) and 34 females (37%); small cleaved cell type in 33 (35%), mixed cell type in 41 (44%) and large cell type in 19 (20%); stage I/II in 41 (44%) and stage III/IV in 50 (54%). Overall survival was 71% at 5 years, 58% at 10 years, and 43% at 15 years with a median survival of 13.3 years. Multivariate analysis revealed that two variables, age (>60) (P=0.001) and the serum LDH level (>1 x normal value) (P=0.026), were unfavorably significant prognostic factors influencing survival. The predictive model using these two variables identified three risk groups with estimated five-year survival rates of 88.5%, 56.8%, and 31.5%. Age and serum LDH were significant predictors of survival in Japanese patients with follicular lymphoma. Our predictive model may provide a basis for future therapeutic trials against follicular lymphoma in Japan.

Adult↗

PubMed2003

A predictive model for outcome after conservative decompression surgery for lumbar spinal stenosis.

This study was designed to develop predictive models for surgical outcome based on information available prior to lumbar stenosis surgery. Forty patients underwent decompressive laminarthrectomy. Preop and 1-year postop evaluation included Waddell's nonorganic signs, CT scan, Waddell disability index, Oswestry low back pain disability questionnaire, low back outcome score (LBOS), visual analog scale (VAS) for pain intensity, and trunk strength testing. Statistical comparisons of data used adjusted error rates within families of predictors. Mathematical models were developed to predict outcome success using stepwise logistic regression and decision-tree methodologies (chi-squared automatic interaction detection, or CHAID). Successful outcome was defined as improvement in at least three of four criteria: VAS, LBOS, and reductions in claudication and leg pain. Exact logistic regression analysis resulted in a three-predictor model. This model was more accurate in predicting unsuccessful outcome (negative predictive value 75.0%) than in successful outcome (positive predictive value 69.6%). A CHAID model correctly classified 90.1% of successful outcomes (positive predictive value 85.7%, negative predictive value 100%). The use of conservative surgical decompression for lumbar stenosis can be recommended, as it demonstrated a success rate similar to that of more invasive techniques. Given its physiologic and biomechanical advantages, it can be recommended as the surgical method of choice in this indication. Underlying subclinical vascular factors may be involved in the complaints of spinal stenosis patients. Those factors should be investigated more thoroughly, as they may account for some of the failures of surgical relief. The CHAID decision tree appears to be a novel and useful tool for predicting the results of spinal stenosis surgery

Adolescent↗

PubMed2006

A prediction model for patient classification according to nursing need: Using data mining techniques.

The purpose of this study was to construct a prediction model for patient classification according to nursing need. The results were assessed from the classification of the hospitalized cancer patients by three different data mining techniques: logistic regression, decision tree and neural network. Among these three techniques, neural network showed the best prediction power in ROC curve verification. The prediction model for patient classification developed by neural network based on nurse needs produced a prediction accuracy of 84.06%.

Adult↗